Table of Content

10 August 2018, Volume 38 Issue 8

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Deep reinforcement learning method based on weighted densely connected convolutional network

XIA Min, SONG Wenzhu, SHI Bicheng, LIU Jia

2018, 38(8):  2141-2147.  DOI: 10.11772/j.issn.1001-9081.2018010268

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To solve the problem of gradient vanishing caused by too many layers of Convolutional Neural Network (CNN) in deep reinforcement learning, a deep reinforcement learning method based on weighted densely connected convolutional network was proposed. Firstly, image features were extracted by skip-connection structure in densely connected convolutional network. Secondly, weight coefficients were added into densely connected convolutional neural network, and each layer in a weighted densely connected convolutional network received all the feature maps generated by its previous layers and was initialized the weight in the skip-connection with different value. Finally, the weight of each layer was dynamically adjusted during training to extract features more effectively. Compared with conventional deep reinforcement learning, in GridWorld simulation experiment, the average reward value of the proposed method was increased by 85.67% under the same number of training steps; in FlappyBird simulation experiment, the average reward value was increased by 55.05%. The experimental results show that the proposed method can achieve better performance in game simulation experiments with different difficulty levels.

Hybrid two-norm particle swarm optimization algorithm with crossover term

ZHANG Xin, ZOU Dexuan, SHEN Xin

2018, 38(8):  2148-2156.  DOI: 10.11772/j.issn.1001-9081.2018010257

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To reduce the possibility of falling into the local optima during the search process of the original Particle Swarm Optimization (PSO) and avoid destroying the population diversity, a hybrid two-norm particle swarm optimization algorithm with crossover term, namely HTPSO, was proposed. Firstly, the two-norm was employed to measure the Euclidean distance between current particle and its individual history best one. Then, the Euclidean distance was incorporated into the velocity updating formula in order to affect the influence of social term on particles' velocity, and inertia weight was randomly distributed in accordance with certain rules. Based on these operations, what's more, HTPSO was simplified and the crossover operator in the Differential Evolution (DE) algorithm was incorporated into the algorithm, which enables each particle to intersect with its individual history best one under a certain probability. In order to verify the excellent performance of HTPSO, four improved PSOs were introduced, including Particle Swarm Optimization algorithm for improved weight using Sine function (SinPSO), Self-adjusted Particle Swarm Optimization algorithm (SelPSO), Mean Particle Swarm Optimization algorithm based on Adaptive inertia Weight (MAWPSO) and Simple Particle Swarm Optimization algorithm (SPSO). The optima of eight commonly used benchmark functions in different dimensions were compared, the results of five algorithms were analyzed by T-test, success rate and average iteration times. Compared with the contrast algorithms, HTPSO has strong convergence, and the particles' movements are very flexible.

Multi-objective differential evolution algorithm with improved ranking-based mutation

LIU Bao, DONG Minggang, JING Chao

2018, 38(8):  2157-2163.  DOI: 10.11772/j.issn.1001-9081.2018010260

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Focusing on the slow convergence and the poor uniformity of multi-objective differential evolution algorithms when solving multi-objective optimization problems, a Multi-Objective Differential Evolution algorithm with Improved Ranking-based Mutation (MODE-IRM) was proposed. The optimal individual involved in the mutation was used as the base vector, which accelerated the resolving speed of the ranking-based mutation operator. In addition, a strategy of opposition-based parameter was adopted to dynamically adjust the values of parameters in different optimization stages, so the convergence rate was further accelerated. Finally, an improved crowding distance calculation formula was introduced in the sort operation, which improved the uniformity of solutions. Simulation experiments were conducted on the standard multi-objective optimization problems including ZDTl-ZDT4, ZDT6 and DTLZ6-DTLZ7. MODE-IRM's overall performance was much better than MODE-RMO and other three algorithms of the PlatEMO including MOEA/D-DE (Multiobjective Evolutionary Algorithm based on Decomposition with Differential Evolution), RM-MEDA (Regularity Model-based Multi-objective Estimation of Distribution Algorithm) and IM-MOEA (Inverse Modeling Multi-objective Evolutionary Algorithm). Moreover, in terms of the performance metrics including GD (Generational Distance), IGD (Inverted Generational Distance) and SP (Spacing), the mean and variance of MODE-IRM on all problems were significantly less than those of MODE-RMO. The simulation results show that MODE-IRM has better performance in convergence and uniformity.

Crowd evacuation algorithm based on human-robot social force model

HU Xuemin, XU Shanshan, KANG Meiyu, WEI Jieling, BAI Liyun

2018, 38(8):  2164-2169.  DOI: 10.11772/j.issn.1001-9081.2018010173

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To deal with the difficulty and low performance of emergency crowd evacuation in public spaces, a crowd evacuation method using robots based on the social force model was proposed. A new human-robot social force model based on the original social force model was first developed, where the human-robot interaction from robots to pedestrians was added to the original social force model. And then, a new method using robot based on the human-robot social force model was presented to evacuate the crowd. After joining the crowd evacuation scenes, the robots can influence the motion of the surrounding pedestrians and reduce the pressure among the pedestrians by moving in the crowd, thus increasing the crowd motion speed and improving the efficiency of crowd evacuation. Two classical scenarios, including a group of crowd escaping from a closed environment and two groups of crowd moving to each other by crossing, were designed and simulated to test the proposed method, and the crowd evacuation method without robots was used for comparison. The experimental results demonstrate that the proposed method based on human-robot social force model can obviously increase the crowd motion speed and improve the efficiency of crowd evacuation.

Identification method of user's medical intention in chatting robot

YU Hui, FENG Xupeng, LIU Lijun, HUANG Qingsong

2018, 38(8):  2170-2174.  DOI: 10.11772/j.issn.1001-9081.2018010190

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Traditional user intention recognition methods in chatting robot are usually based on template matching or artificial feature sets. To address the problem that those methods are difficult, time-consuming but have a week extension, an intention recognition model based on Biterm Topic Model (BTM) and Bidirectional Gated Recurrent Unit (BiGRU) was proposed with considering the features of the chatting texts about health. The identification of user's medical intention was regarded as a classification problem and topic features were used in the hybrid model. Firstly, the topic of user's every chatting sentence was mined by BTM with quantification. Then last step's results were fed into BiGRU to do context-based learning for getting the final representation of user's continuous statements. At last, the task was finished by making classification. In the comparison experiments on crawling corpus, the BTM-BiGRU model obviously outperforms to other traditional methods such as Support Vector Machine (SVM), even the F value approximately increses by 1.5 percentage points compared to the state-of-the-art model combining Convolution Neural Network and Long-Short Term Memory Network (CNN-LSTM). Experimental results show that the proposed method can effectively improve the accuracy of the intention recognition focusing on characteristics of the study.

Real-time visual tracking algorithm based on correlation filters and sparse convolutional features

XIONG Changzhen, CHE Manqiang, WANG Runling

2018, 38(8):  2175-2179.  DOI: 10.11772/j.issn.1001-9081.2017123030

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Concerning the real-time performance of the hierarchical convolutional features for visual tracking, a real-time object tracking algorithm based on sparse convolution features was proposed. By analyzing the characteristics of different convolution layers, the equidistant interval sampling method was adopted to extract the sparse convolutional features of each layer. Then the correlation filter response values of each convolutional feature were combined to estimate the location of target object. Finally, a sparse update strategy was applied to improve the computing speed. Experimental results on benchmark dataset OTB-2015 show that the average distance precision of the proposed algorithm is 82.2%, which is 5.25 percentage points higher than that of the original hierarchical convolutional feature tracking algorithm; furthermore, it has better robustness to appearance changes and occlusions. The average tracking speed of the proposed algorithm is 32.6 frames per second, which is nearly 2 times faster than before, which can achieve real-time performance.

Short text clustering algorithm based on weighted kernel nonnegative matrix factorization

CAO Dawei, HE Chaobo, CHEN Qimai, LIU Hai

2018, 38(8):  2180-2184.  DOI: 10.11772/j.issn.1001-9081.2018020356

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Clustering analysis of a large number of short texts generated by the Internet is of great application value. Because the characteristics of short texts such as sparse features and difficulty of extracting features, the traditional text clustering algorithm faces many challenges in short text clustering. To solve the problem, a short text clustering algorithm based on Weighted Kernel Nonnegative Matrix Factorization (WKNMF) was proposed by using Nonnegative Matrix Factorization (NMF) model. To make full use of hidden semantic features in short texts for clustering, sparse feature space was mapped to high-dimensional implicit vectors by using kernel method. In addition, kernel trick was used to simplify the complex operation of high-dimensional data, and the weight vectors of short texts were dynamically adjusted through iterative optimization updating rules, so that the importance of different short texts to clustering can be distinguished. Experiments were conducted on real Micro-blog data sets and WKNMF algorithm was compared with K-means, Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF) and Self-Organization Map (SOM). The experimental results show that the proposed WKNMF algorithm has a better clustering performance than the contrast algorithms, its accuracy and Normalized Mutual Information (NMI) reach 66.38% and 66.91% respectively.

Batch process monitoring based on k nearest neighbors in discriminated kernel principle component space

ZHANG Cheng, GUO Qingxiu, LI Yuan

2018, 38(8):  2185-2191.  DOI: 10.11772/j.issn.1001-9081.2018020345

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Aiming at the nonlinear and multi-mode features of batch processes, a fault detection method for batch process based on k Nearest Neighbors (kNN) rule in Discriminated kernel Principle Component space, namely Dis-kPCkNN, was proposed. Firstly, in kernel Principal Component Analysis (kPCA), according to discriminating category labels, the kernel window width parameter was selected between within-class width and between-class width, thus the kernel matrix can effectively extract data correlation features and keep accurate category information. Then kNN rule was used to replace the conventional T² statistical method in the kernel principal component space, which can deal with fault detection of process with nonlinear and multi-mode features. Finally, the proposed method was validated in the numerical simulation and the semiconductor etching process. The experimental results show that the kNN rule in discriminated kernel principle component space can effectively deal with the nonlinear and multi-mode conditions, improve the computational efficiency and reduce memory consumption, in addition, the fault detection rate is significantly better than the comparative methods.

Rapid stable detection of human faces in image sequence based on MS-KCF model

YE Yuanzheng, LI Xiaoxia, LI Minze

2018, 38(8):  2192-2197.  DOI: 10.11772/j.issn.1001-9081.2018020363

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In order to quickly and stably detect the faces with large change of angle and serious occlusion in image sequence, a new automatic Detection-Tracking-Detection (DTD) model was proposed by combining the fast and accurate target detection model MobileNet-SSD (MS) and the fast tracking model Kernel Correlation Filtering (KCF), namely MS-KCF face detection model. Firstly, the face was detected quickly and accurately by using MS model, and the tracking model was updated. Secondly, the detected face coordinate information was input into the KCF tracking model to track steadily, and the overall detection speed was accelerated. Finally, to prevent tracking loss, the detection model was updated again after tracking several frames, then the face was detected again. The recall of MS-KCF model in the FDDB face detection benchmark was 93.60%; the recall in Easy, Medium and Hard data sets of WIDER FACE benchmark were 93.11%, 92.18% and 82.97%, respectively; the average speed was 193 frames per second. Experimental results show that the MS-KCF model is stable and fast, which has a good detection effect on the faces with serious shadows and large angle changes.

Fine-grained image classification method based on deep model transfer

LIU Shangwang, GAO Xiang

2018, 38(8):  2198-2204.  DOI: 10.11772/j.issn.1001-9081.2018020301

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To solve the problems of fine-grained image classification methods, such as highly complex methods and difficulty of using deeper models, a Deep Model Transfer (DMT) method was proposed. Firstly, the deep model was pre-trained on the coarse-grained image dataset. Secondly, the pre-trained deep model classification layer was trained based on inexact supervised learning by using fine-grained image dataset and transferred to the feature distribution direction of the novel dataset. Finally, the trained model was exported and tested on the corresponding test sets. The experimental results show that the classification accuracy rates on the STANFORD DOGS, CUB-200-2011 and OXFORD FLOWER-102 fine-grained image datasets are 72.23%, 73.33%, and 96.27%, respectively, which proves the effectiveness of DMT method on fine-grained image classification.

Palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain

LI Xinchun, CAO Zhiqiang, LIN Sen, ZHANG Chunhua

2018, 38(8):  2205-2210.  DOI: 10.11772/j.issn.1001-9081.2018010183

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Single biometric identification technology can be easily affected by various external factors, thus the recognition rate and stability are poor. A palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain, namely NSCT-NBP, was proposed. Firstly, palmprint and palmvein images were decomposed by using Non-Subsampled Contourlet Transform (NSCT), then the obtained low-frequency and high-frequency sub-images were respectively merged by using the regional energy and image self-similarity principle. Secondly, the texture features were extracted from the fused images by using Neighbor based Binary Pattern (NBP), thus the eigenvector was got. Finally, the similarity of the fused images was calculated by Hamming distance of the feature vectors, to get Equal Error Rate (EER). The experiments were conducted on PolyU and a self-built database, the experimental results show that the lowest EER of NSCT-NBP algorithm were 0.72% and 0.96%, the identification time were only 0.0530 s and 0.0871 s. Compared with the current best palmprint-palmvein fusion method based on wavelet transform and Gabor filter, the EER of the two databases were reduced by 4% and 36.8%, respectively. The NSCT-NBP algorithm can effectively fuse the texture features of the palmprint-palmvein images and has good recognition performance. The fusion of palmprint-palmvein features can enhance the security of the recognition system.

Garment image recognition based on adaptive pooling neural network

HU Cong, QU Jinjin, XU Chuanpei, ZHU Aijun

2018, 38(8):  2211-2217.  DOI: 10.11772/j.issn.1001-9081.2018010223

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Focusing on the issue that traditional pooling methods cannot extract valid eigenvalues, an adaptive pooling method was proposed to adjust the pooling results based on the size of the pooling domain, the element values in the pooling domain and the network training rounds. A function based on the interpolation principle and the maximum pooling model was constructed, and the specific function value was used as the pooling result. Then the cross-validation method was used for the comparative experiment of the model. Focusing on the issue that the hyperparameter selection based on empirical values to verify on all datasets is inefficient, a small sample tuning method was proposed. On the original data set, small samples were extracted according to the rule of stratified sampling, and the encoded hyperparameter combinations were cyclically trained and tested based on the small data set. The optimal hyperparameters were identified by decoding the combination with the highest recognition. Using DeepFashion dataset for the related experiments, the results show that the recognition rate of adaptive pooling model is about 83%, which is about 2.5% higher than that of the maximum pooling model. Hyperparameters selected by small samples were compared though experiments with random combinations of hyperparameters on the original dataset. The results show that the hyperparameters selected by the small sample tuning method are optimal within the empirical value range. The recognition result is 86.98%, which is about 41.4% higher than the average recognition rate of the hyperparameters random combinations. The adaptive pooling method can be extended to other neural networks, and the small sample tuning method provides a basis for efficient selection of hyperparameters of deep neural networks.

Cloud/Snow classification based on multi-dimensional multi-grained cascade forest in plateau region

WENG Liguo, LIU Wan'an, SHI Bicheng, XIA Min

2018, 38(8):  2218-2223.  DOI: 10.11772/j.issn.1001-9081.2018010218

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To solve the problem that the traditional algorithms, such as Support Vector Machine (SVM) and random forest, cannot make full use of the texture features and optical parameters of satellite images, a method of cloud/snow recognition based on Multi-dimensional multi-grained cascade Forest (M-gcForest) was proposed. Firstly, according to the difference between single-spectral and multi-spectral images, SVM, random forest, Convolution Neural Network (CNN), and gcForest (multi-grained cascade Forest) were selected to recognize cloud and snow on single-spectral satellite images, by quantitatively analyzing the performance of each algorithm on single-spectral images, CNN and M-gcForest were selected for multi-spectral cloud/snow recognition. Finally, improved M-gcForest was used to predict on HJ-1A/1B multi-spectral satellite images. The experimental results show that compared with CNN, the test accuracy of the M-gcForest on the multi-spectral dataset is increased by 0.32%, the training time is reduced by 91.2%, and the testing time is reduced by 53.7%. Therefore, the proposed algorithm has practicability in real-time and accurate snow disaster monitoring tasks.

Rolling bearing sub-health recognition algorithm based on fusion deep learning

ZHANG Li, SUN Jun, LI Dawei, NIU Minghang, GAO Yidan

2018, 38(8):  2224-2229.  DOI: 10.11772/j.issn.1001-9081.2017112702

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The deep learning model increases the number of hidden layers, which makes the model have a good effect on speech recognition, image video classification and so on. However, to establish a model suitable for a specific object, a large number of data sets are required to train it for a long time to get the appropriate weights and biases. To resolve the above problems, a sub-health diagnosis method for rolling bearing was proposed based on depth autoencoder-relevance vector machine network model. Firstly, the bearing vibration signal was collected and transformed by Fourier transform and normalization. Secondly, the improved automatic encoder, named sparse edge noise reduction autoencoder, was designed, which combined the features of sparse automatic encoder and edge noise reduction automatic encoder. Then the depth autoencoder-relevance vector machine network model was designed, in which the supervised function was used to finely tune the parameters of each hidden layer, and it was trained by Relevance Vector Machine (RVM). Finally, the final classification results were obtained according to D-S (Dempster-Shafer) evidence fusion theory. The experimental results show that the proposed algorithm can effectively improve the recognition precision of the "sub-health" state of the rolling bearing and correct the error classification.

Bloom filter-based wear-leveling scheme for novel hybrid memory architecture

ZHANG Zhen, FU Yinjin, HU Guyu

2018, 38(8):  2230-2235.  DOI: 10.11772/j.issn.1001-9081.2018020419

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Phase Change Memory (PCM) is a promising candidate for next-generation main memory due to its low power consumption, but its endurance has limited its further application. The existed DRAM buffering and wear-leveling technologies were proposed to overcome the PCM endurance problem from two design issues:write count reduction and uniform write count distribution. However, the former fails to consider the access tendency when writing back pages. The latter can be further enhanced in terms of operation granularity, space overhead, and random access, especially in data streams of strong spatial locality. Therefore, the authors designed a novel hybrid memory architecture based on DRAM and PCM, proposed a replacement policy according to LRU (Least Recently Used) and LFU-Aging (Least Frequenctly Used with Aging) for the prediction of read and write access tendency, and finally presented a Bloom Filter (BF) based dynamic wear-leveling algorithm for strong-locality applications. The scheme can effectively decrease redundant write operations and realize inter-group wear-leveling with low space-overhead. The experimental results indicate that our scheme can reduce 13.4%-38.6% write operations in PCM and effectively homogenize the distribution of write counts for up to 90% groups.

Data imputation using matrix factorization based on session-based temporal similarity

QIAO Yongwei, ZHANG Yuxiang, XIAO Chunjing

2018, 38(8):  2236-2242.  DOI: 10.11772/j.issn.1001-9081.2018010264

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The actual relationship between users cannot be captured by the existing data imputation methods because they only consider the rating information and traditional similarity. To alleviate data sparsity and improve recommendation accuracy, a data imputation method was proposed. Firstly, the defects of traditional similarity were analyzed and a new session-based temporal similarity based on tempoaral similarity and dissimilarity was defined, which integrated time context into rating patterns to better identify neighbors for active user. Additionally, the rating sub-matrix of key item set was extracted from similar users and their consumption items which can mine the potential influence factors of users and items, and it was imputed by using matrix factorization. Then the user probabilistic topic distribution for each stage was obtained by using Latent Dirichlet Allocation (LDA) and the user dynamic profile was built with the temporal penalty weights. Finally, the items were recommended based on the correlation of probabilistic topic distribution between users and user-based collaborative filtering. Experimental results show that compared with other imputation-based methods, the proposed method can reduce the Mean Absolute Error (MAE) and improve the recommendation performance under different sparsity.

New post quantum authenticated key exchange protocol based on ring learning with errors problem

LI Zichen, XIE Ting, CAI Juliang, ZHANG Xiaowei

2018, 38(8):  2243-2248.  DOI: 10.11772/j.issn.1001-9081.2018020387

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In view of the fact that the rapid development of quantum computer technology poses serious threat to the security of the traditional public-key cryptosystem, a new authenticated key exchange protocol scheme based on Ring Learning With Errors (RLWE) problem was proposed. By using Peikert error reconciliation mechanism, both parties of communication can directly obtain the shared bit value of the uniform distribution and get the same session key. The encoding bases of lattice was used to analyze the error tolerance, and reasonable parameters were selected to ensure that both parties can get the same session key with significant probability. The security of the protocol was proved in the BR (Bellare-Rogaway) model with weak perfect forward secrecy. The security of the protocol was attributed to the difficult RLWE problem of lattice, so that the protocol can resist quantum attacks. Compared with the existing authenticated key exchange protocols based on RLWE, the size of the parameter value modulus decreases from sub-exponential to polynomial magnitude, thus the corresponding amount of computation and communication are also significantly reduced. The results show that the proposed scheme is a more concise and efficient post quantum authenticated key exchange protocol.

Traceable and fully verifiable for outsourced decryption for CP-ABE

LI Cong, YANG Xiaoyuan, BAI Ping, WANG Xu'an

2018, 38(8):  2249-2255.  DOI: 10.11772/j.issn.1001-9081.2018020305

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In Ciphertext-Policy Attribute-Based Encryption (CP-ABE) schemes, the private key is defined on attributes shared by multiple users. For any private key that can not be traced back to the owner of the original key, the malicious users may sell their decryption privileges to the third parties for economic benefit and will not be discoverable. In addition, most of the existing ABE schemes have a linear increase in decryption cost and ciphertext size with the complexity of access structure. These problems severely limit the applications of CP-ABE. By defining a traceable table to trace the users who intentionally disclosed the key, the cost of the decryption operation was reduced through the outsourcing operation, and a CP-ABE scheme with traceable and fully verifiable outsourced decryption was proposed. The scheme can simultaneously check the correctness for transformed ciphertexts of authorized users and unauthorized users, and supports any monotonous access structure, which traceability will not have any impact on its security. Finally, the proposed scheme is proved to be CPA (Chosen Plaintext Attack)-secure in the standard model.

Cloud outsourcing multiparty private set intersection protocol based on homomorphic encryption and Bloom filter

ZHANG En, JIN Ganggang

2018, 38(8):  2256-2260.  DOI: 10.11772/j.issn.1001-9081.2018010075

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Considering the low computing efficiency of current multiparty Private Set Intersection (PSI) protocol and the leakage of user private information when it is applied in the cloud environment, a cloud outsourcing multiparty PSI protocol based on Bloom Filter (BF) and homomorphic encryption was proposed. Firstly, the NTRU Cryptosystems-based proxy re-encryption algorithm was used in the protocol to convert ciphertexts encrypted with different public keys into ciphertexts encrypted with the same public keys, and a large amount of complicated computing was outsourced to a cloud server. Secondly, Bloom filter, characterized by its low computing complexity, high space utilization rate and great query efficiency, was used to improve the efficiency of information encrypting, decrypting and querying when the protocol was operated. The user only needs a small amount of computing during the operation of the protocol instead of taking interactions and staying online in real time. Theoretical analysis and experimental results show that the proposed protocol has linear computation and communication complexity, it can work out intersection results without leaking user private information, which meets the requirement of practical application.

Data transmission protocol based on short signature scheme for railway bridge monitoring

ZUO Liming, HU Kaiyu, ZHANG Mengli, CHEN Lanlan

2018, 38(8):  2261-2266.  DOI: 10.11772/j.issn.1001-9081.2018010272

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Aiming at the problems of network security such as information disclosure and tampering in the process of information exchange for railway bridge monitoring systems under open Internet environment, a data transmission protocol based on short signature scheme was proposed for railway bridge monitoring. Firstly, an identity-based short signature scheme was designed on the basis of Boneh's short signature. Then the scheme was proved to be safe under the random oracle model and the Inverse Computational Diffie-Hellman Problem (Inv-CDHP), and it was further applied to the data transmission protocol for railway bridge monitoring. Finally, the key code of the scheme was given and compared with several schemes. The experimental results and analysis show that the average time consumption of the proposed scheme is close to the classical Boneh's scheme, but 6% and 22% lower than that of Fangguo Zhang's scheme and Leyou Zhang's scheme. Therefore, the proposed scheme has more advantages in terms of signature length and efficiency, and can effectively solve the problem of lack of integrity protection and identity reliability authentication of monitoring data.

Trojan implantation method based on information hiding

ZHANG Ru, HUANG Fuhong, LIU Jianyi, ZHU Feng

2018, 38(8):  2267-2273.  DOI: 10.11772/j.issn.1001-9081.2018020558

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Since a large number of Trojans are easily tracable on the Internet, a new Trojan attack scheme based on multimedia document was proposed. Firstly, the Trojan program was embedded into a carrier image as secret data by steganography. After the Trojan program was successfully injected, the encrypted user information was also hidden into the carrier image by steganography. Then the host automatically uploaded pictures to a social network. Finally, the attacker downloaded images from the social network and extracted secret data from images. The theoretical analysis and simulation results show that the proposed JPEG image steganography algorithm has good performance, and the Trojan scheme based on it outperfoms some existing algorithms in concealment, anti-forensics, anti-tracking and penetrating auditing. Such Trojans in social networks can cause user privacy leaks, so some precautions are given at last.

Multi-purpose watermark algorithm for color image based on multiple transform domains

CHEN Shanxue, QI Ruolan, TANG Yiyuan

2018, 38(8):  2274-2279.  DOI: 10.11772/j.issn.1001-9081.2018010158

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Concerning the single function of single watermark algorithm, a multi-purpose watermarking algorithm for color image combined with Discrete Wavelet Transform (DWT) and Quaternion Discrete Cosine Transform (QDCT) was proposed. Firstly, the DWT was applied to the three scrambled channels of a color image, and QDCT was applied to their low frequency subbands and medium frequency subbands, then some of the real coefficients were used to construct a coefficient matrix, and the robust watermark was embedded into the singular value of it by adding principle. Secondly, the image was divided into 2×2 sub-blocks and preprocessed with QDCT. The characteristic fragile watermark was generated by the low frequency modulus coefficients of QDCT and embedded into the Least Significant Bits (LSB) of the space domain. The experimental results show that the robust watermark has good robustness against JPEG compression, noise, contrast adjustment, cropping, rotating and hybrid attacks, and the fragile watermark is sensitive to tampering and has accurate tamper localization.

Cloud database oriented attribute based encryption and query translation middleware

JIANG Bingcheng, HE Qian, CHEN Yiting, LIU Peng

2018, 38(8):  2280-2286.  DOI: 10.11772/j.issn.1001-9081.2018010279

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Focusing on the problem of encryption and querying of tenant private data on cloud database, a cloud database oriented Attribute Based Encryption (ABE) and query transform service middleware was proposed and realized. Firstly, the tenant symmetric keys were encrypted in the encryption and decryption component of the service middleware through attribute based encryption, and the ciphertext was generated and saved. Secondly, the query statements were translated in the query translation component so that they can be correctly executed on the encrypted database. Finally, the tenant privacy data was stored in the cloud database after symmetric encryption. The experimental results show that compared with the unencrypted database, the write time of the encrypted database is equivalent, while the querying time is increased by 10% to 150% according to the complexity of the query statement. The theoretical analysis shows that the proposed proxy decryption method is secure, and it has superiority over traditional Key Policy Attribute Based Encryption (KP-ABE) algorithm in time complexity.

LASSO based image reversible watermarking

ZHENG Hongchang, WANG Chuntao, WANG Junxiang

2018, 38(8):  2287-2292.  DOI: 10.11772/j.issn.1001-9081.2018020471

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For the Difference Expansion-Histogram Shifting (DE-HS) based reversible watermarking, improving the prediction accuracy helps to decrease the prediction errors, resulting in higher embedding capacity at the same embedding distortion. To predict image pixels more accurately, an LASSO (Least Absolute Shrinkage and Selection Operator) based local predictor was proposed. Specifically, by taking into account the fact that there exist edges and textures in natural images, the problem of image pixel prediction was formulated as the optimization problem of LASSO, then the prediction coefficients were obtained by solving the optimization problem, generating prediction errors accordingly. By applying the technique of DE-HS on the yielded prediction errors, an LASSO-based reversible watermarking scheme was designed. The experimental results show that compared with the least-square-based predictor, the proposed scheme has higher Peak Signal-to-Noise Ratio (PSNR) when embedding the same data.

Reversible data hiding method based on texture partition for medical images

CAI Xue, YANG Yang, XIAO Xingxing

2018, 38(8):  2293-2300.  DOI: 10.11772/j.issn.1001-9081.2017122885

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To solve the problem that contrast enhancement effect is affected by the embedding rate in most existing Reversible Data Hiding (RDH) algorithms, a new RDH method based on texture partition for medical images was proposed. Firstly, the contrast of an image was stretched to enhance image contrast, and then according to the characteristics of medical image texture, the medical image was divided into high and low texture levels. The key partion of the medical image mainly had high texture level. To enhance the contrast of high texture level further and guarantee the infomation embedding capacity, different embedding processes were adopted for high and low texture levels. In order to compare the effect of contrast enhancement between the proposed method and other RDH algorithms for medical images, No-Reference Contrast-Distorted Images Quality Assessment (NR-CDIQA) was adopted as the evaluation standards. The experimental results show that the marked images processed by the proposed method can get better NR-CDIQA and contrst enhancement in different embedding rate.

Audio watermarking algorithm in MP3 compressed domain based on low frequency energy ratio of channels

LI Chen, WANG Kexin, TIAN Lihua

2018, 38(8):  2301-2305.  DOI: 10.11772/j.issn.1001-9081.2018020298

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For the inefficiency and imbalance between robustness and imperceptibility of most of the current audio watermarking algorithms when applied to MP3 audio, a watermarking algorithm in compressed domain based on low frequency energy of channels of MP3 frames was proposed. The watermarking can be embedded and extracted during MP3 compression and decompression processes, which greatly enhances the efficiency. Considering the good stability of low frequency energy, the low frequency energy of channels was calculated by using Modified Discrete Cosine Transform (MDCT) coefficients produced in MP3 encoding and decoding processes, then the ratio between the energy of left and right channels was quantized with fixed step, and the watermarking was embedded by modifying some MDCT coefficients according to quantified results. Meanwhile, with the proportion of energy in different scalefactor bands, the embedding bands were selected before calculating low frequency energy of channels, which ensured a good balance between robustness and imperceptibility. The experimental results show that the proposed algorithm has a good robustness against various types of attacks while maintaining the original audio quality, especially against MP3 recompression attacks.

Information hiding algorithm based on fractal graph

BAI Sen, ZHOU Longhu, YANG Yi, LI Jing, JI Xiaoyong

2018, 38(8):  2306-2310.  DOI: 10.11772/j.issn.1001-9081.2018020420

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For the existing steganography, it is hard to extract secret information without original cover-image and easy to be detected by steganalysts when the hiding capacity is high. To solve this problem, a new scheme of steganography based on fractal graph was proposed. In this scheme, firstly, Black-and-White Fractal Graph (BWFG) was created by utilizing affine transformation and fractal iterated function system. Then the BWFG was transformed to Black-and-White Pixel Image (BWPI) based on the idea of coordinate transformation. At last, the BWPI was divided into several non-overlapping blocks and the positions of black and white pixels in each block were altered to hide the secret information, generating stego-image. The receiver could create the cover-image by utilizing the parameters of affine transformation and times of iteration, and extract secret information by comparing the difference of pixels in corresponding blocks. Theoretical analysis and simulation experiments show that, compared with the information hiding algorithm in frequency domain, the proposed scheme has good imperceptibility and high hiding capacity, and can resist steganalysis based on image features and transform domain coefficient change.

Reversible data hiding algorithm based on pixel value order

LI Tianxue, ZHANG Minqing, WANG Jianping, MA Shuangpeng

2018, 38(8):  2311-2315.  DOI: 10.11772/j.issn.1001-9081.2018020297

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For the distortion of the image after embedding secret is excessively obvious, a new Reversible Data Hiding (RDH) based on Pixel Value Order (PVO) was proposed. Firstly, the pixels of a carrier image were divided into gray and white layers, the pixels of a gray layer were selected as the target pixels, and the four white pixels in the cross positions of the target pixels were sorted. Secondly, according to the sorting result, the mean value of the two end pixels and the mean value of the median pixels were calculated, and the reversible constraint was used to achieve dynamic prediction of pixels. Finally, a Prediction Error Histogram (PEH) was constructed according to the prediction result. Six images in the USC-SIPI standard image library were used for simulation experiments. The experimental results show that, when the Embedding Capacity (EC) is 10000 b and the average Peak Signal-to-Noise Ratio (PSNR) is 61.89 dB, the proposed algorithm can effectively reduce the distortion of the image with ciphertext.

Ignorant-Lurker-Disseminator-Removed propagation model of spam information on Internet

CAI Xiumei, LIU Chao, HUANG Xianying, LIU Xiaoyang, YANG Hongyu

2018, 38(8):  2316-2322.  DOI: 10.11772/j.issn.1001-9081.2018010259

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For the problem that qualitative analysis methods are mostly adopted for the study of spam information propagation and it is difficult to reveal intrinsic propagation rules of spam information propagation, an ILDR (Ignorant-Lurker-Disseminator-Removed) model of spam information was proposed based on the idea of virus propagation modeling by considering the actual factors such as different input rates and removal rates. Firstly, the equilibrium point and the propagation threshold were calculated, and the stability conditions of the equilibrium point were given. Secondly, the local stability of non-spam information and spam information was proved by the Routh-Hurwitz criterion, then the global stability of non-spam information was certified via the invariance principle of LaSlle, which was proved based on the Bendixson criterion. Theoretical research shows that the non-spam information equilibrium is global asymptotically stable when propagation threshold is less than 1; the spam information equilibrium is global asymptotically stable when propagation threshold is greater than 1. The numerical simulation validates that the value of the propagation threshold can be decreased when decreasing the transfer rate from the lurker to the disseminator, increasing the transfer rate from the ignorant to the remover and the transfer rate from the lurker to the remover; the value of the disseminator can be decreased via increasing the proportionality coefficient from the ignorant to the lurker, or increasing the transfer rate of the disseminator to the remover and the system removal rate.

Non-unified pricing power control based on Stackelberg game in ultra-dense network

XU Changbiao, WU Jie

2018, 38(8):  2323-2329.  DOI: 10.11772/j.issn.1001-9081.2018020321

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Aiming at the problem of inter-zone interference in co-frequency deployment of ultra-dense network cells, a non-uniform pricing power control scheme based on Stackelberg game was proposed. Firstly, a non-uniform pricing power control model based on Stackelberg game was established. Then the optimal price based transmission power was obtained by this model, which is a function of interference price. Secondly, the Lagrange function was introduced to solve the problem of the optimal interference price of the corresponding base station, and the controller sent the interference price to the corresponding base station, and the base station adjusted its own transmission power value to weaken the interference to the current users. The simulation results show that compared with the unified pricing power control scheme based on Stackelberg game, the proposed scheme reduces the average outage probability of the system by an average of 3 percentage points. Compared with the power control scheme based on the weight of the base station, when the number of base stations is less than 105, the proposed scheme increases the average outage probability by an average of 1.4 percentage points; when the number of base stations exceeds 105, the proposed scheme reduces the average outage probability by an average of 1.6 percentage points. In addition, compared with the two scheme mentioned above, the proposed scheme increases the average throughput of the system by 12 percentage points and 10.5 percentage points respectively, the average spectrum efficiency of the system is increased by 9 percentage points and 8.5 percentage points respectively, and the average power efficiency of the system is improved by 13 percentage points and 12 percentage points respectively. The experimental results show that the proposed solution can better improve the performance of the cellular system when more base stations are deployed.

Secondary user bandwidth and power allocation in imperfect channel for Internet of things sensor networks

WEN Jinyi, TANG Lun, CHEN Qianbin

2018, 38(8):  2330-2336.  DOI: 10.11772/j.issn.1001-9081.2018010133

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Aiming at the problem of wireless resource scarcity and errors caused by imperfect channel in Internet of Things (IoT) sensor network, a method for allocating bandwidth and power to Secondary IoT Device (SID), under the condition of imperfect Channel State Information (CSI), was proposed. Firstly, considering the secondary and primary situations, the imperfect CSI of links between secondary transmitter side and primary receiving end, secondary transmitter side and secondary receiving end were modeled. Secondly, a mechanism for allocating bandwidth and power to secondary IoT devices was proposed. Meanwhile, the measurement and corresponding penalty policy were considered when current bandwidth is not enough to allocate. The idea is to maximize the secondary Energy Efficiency (EE) while fully using resources in extent. Finally, Particle Swarm Optimization (PSO) algorithm and weighted Chebyshev method were implemented to solve the problem, which made it possible to obtain the optimal allocation policy while lowering the problem complexity. The simulation results show that compared with equal allocation method and random allocation method, the proposed method improves the overall system transmission rate and minimizes the average secondary IoT base station transmit power by 75%, which can effectively enhance the energy efficiency of the entire network.

Fast online distributed dual average optimization algorithm

LI Dequan, WANG Junya, MA Chi, ZHOU Yuejin

2018, 38(8):  2337-2342.  DOI: 10.11772/j.issn.1001-9081.2018010189

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To improve the convergence speed of distributed online optimization algorithms, a fast first-order Online Distributed Dual Average optimization (FODD) algorithm was proposed by sequentially adding edges to the underlying network topology. Firstly, aiming at solving the problem of the online distributed optimization to make the selected edge and network model mix quickly by using the method of edge addition, a mathematical model was established and solved by FODD. Secondly, the relationship between network topology designed and the convergence rate of the online distributed dual average algorithm was revealed, which clearly showed that, by improving the algebraic connectivity of the underlying topology network, the Regret bound could also be greatly improved. The Online Distributed Dual Average (ODDA) algorithm was extended from static networks to time-varying networks. Meanwhile, the proposed FODD algorithm was proved to be convergent and the convergence rate was specified. Finally, the results of numerical simulations show that, compared with existing algorithms such as ODDA, the proposed FODD algorithm has better convergence performance.

Data plane fast forwarding of collaborative caching for software defined networking

ZHU Xiaodong, WANG Jinlin, WANG Lingfang

2018, 38(8):  2343-2347.  DOI: 10.11772/j.issn.1001-9081.2018010088

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When using the in-network nodes with cache ability for collaborative caching, the packets need to be forward quickly according to the surrounding caching status. A new data-plane-fast-forwarding method was proposed for this problem. Two bloom filters were kept for each port in the switch to maintain the surrounding caching status at the data plane. Meanwhile, the action of protocol oblivious forwarding was also extended. The extended action searched the bloom filters directly, and the optimized forwarding process was used to forward packets according to the searching results, then the packets were forwarded quickly based on the surrounding caching status. The evaluation results show that the caching status maintained by the controller reaches the forwarding performance bottleneck when the input rate is 80 Kb/s. The packets can be forwarded at line speed when the input rate is 111 Mb/s by using the data-plane-fast-forwarding method, which efficiency of forwarding is superior to the output action of protocol oblivious forwarding. The memory overhead of maintaining caching status by using the bloom filter is up to 20% of that by using the flow table. In Software Defined Networking (SDN) with cache ability, the proposed method can maintain the surrounding caching status at the data plane and promote the efficiency of forwarding packets by the surrounding caching status for collaborative caching.

Fault diagnosis algorithm of WSN based on precondition of neighbor nodes

MA Mengying, ZENG Yali, WEI Tiantian, CHEN Zhide

2018, 38(8):  2348-2352.  DOI: 10.11772/j.issn.1001-9081.2018010110

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To address the problem of low detection accuracy when the fault node rate was higher than 50% in Wireless Sensor Network (WSN), a wireless sensor fault diagnosis algorithm based on the precondition of neighbor nodes and neighbor node data was proposed. Firstly, the historical data of nodes were used to pre-calculate the states of sensor nodes initially. Then the final state of each node was judged by taking advantage of similarity of nodes and pre-states of neighbor nodes. Finally, the fault node information was sent to the base station by mobile sensors through the optimal path, which effectively reduced the number of communications. A WSN was simulated in an area of 100 m*100 m. The experimental results show that compared with the traditional Distributed Fault Detection (DFD) algorithm, the diagnosis accuracy of the proposed algorithm is improved by 9.84 percentage points. Moreover, the proposed algorithm even achieves more than 95% fault diagnosis accuracy when the node failure rate is as high as 50% in the network. In practical application, the proposed algorithm improves the fault diagnosis accuracy, reduces the energy consumption effectively, and prolongs the network lifetime as well.

Data gathering optimization based on dynamic data compression in energy harvesting wireless sensor network

XIE Xiaojun, YU Hao, TAO Lei, ZHANG Xinming

2018, 38(8):  2353-2358.  DOI: 10.11772/j.issn.1001-9081.2018020360

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Aiming at the data gathering optimization problem in energy harvesting Wireless Sensor Network (WSN), a scheme based on dynamic sensor node sampling rate and data compression was proposed, where the spatial-temporal characteristics of energy harvested by individual sensor node was considered. To maximize the total amount of sampling data in the network, first, according to the neighbor information of the nodes, a local compression algorithm was proposed to determine the optimal compression strategy. Considering the data receiving and forwarding energy consumption of the node based on its topological position in the data aggregation tree, its sampling rate was gradually increased until its total energy consumption reached the collection energy consumption threshold. After that, a global optimization problem of network performance was constructed, and a heuristic algorithm was proposed. By iteratively solving linear programming problems, the optimal sampling rate and compression scheme were obtained. The experimental results show that compared with the existing adaptive sensing and compression rate selection scheme, the proposed two data collection optimization algorithms can maintain more stable sensor node battery levels and achieve higher network performance.

Bluetooth location algorithm based on feature matching and distance weighting

LU Mingchi, WANG Shouhua, LI Yunke, JI Yuanfa, SUN Xiyan, DENG Guihui

2018, 38(8):  2359-2364.  DOI: 10.11772/j.issn.1001-9081.2018020295

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Focusing on the issues that large fluctuation of Received Signal Strength Indication (RSSI), complex clustering of fingerprint database and large positioning error in traditional iBeacon fingerprinting, a new Bluetooth localization algorithm based on sort feature matching and distance weighting was proposed. In the off-line stage, the RSSI vector size was used to generate the sorting characteristic code. The generated code combined with the information of the position coordinates constituted the fingerprint information, to form the fingerprint library. While in the online positioning stage, the RSSI was firstly weighted by sliding window. Then, indoor pedestrian positioning was achieved by using the sort eigenvector fingerprint matching positioning algorithm and distance-based optimal Weighted K Nearest Neighbors (WKNN). In the localization simulation experiments, the feature codes were used for automatical clustering to reduce the complexity of clustering with maximum error of 0.952 m of indoor pedestrian localization.

Hybrid precoding scheme based on improved particle swarm optimization algorithm in mmWave massive MIMO system

LI Renmin, HUANG Jinsong, CHEN Chen, WU Junqin

2018, 38(8):  2365-2369.  DOI: 10.11772/j.issn.1001-9081.2017123026

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To address the problem that the hybrid precoding scheme based on traditional Particle Swarm Optimization (PSO) algorithm in millimeter Wave (mmWave) massive Multi-Input Multi-Output (MIMO) systems has a low convergence speed and is easy to fall into the local optimal value in the later iteration, a hybrid precoding scheme based on improved PSO algorithm was proposed. Firstly, the particles' position vector and velocity vector were initialized randomly, and the initial swarm optimal position vector was given by maximizing the system sum rate. Secondly, the position vector and velocity vector were updated, and two updated particles' individual-historical-best position vectors were randomly selected to get their weighted sum as the new individual-historical-best position vector, and then some of particles that maximized the system sum rate were picked out. The weighted average value of the individual-historical-best position vectors of these particles was taken as the new swarm optimal position vector and compared with the previous one. After many iterations, the final swarm optimal position vector was formed, which was the desired best hybrid precoding vector. The simulation results show that compared with the hybrid precoding scheme based on traditional PSO algorithm, the proposed scheme is optimized both in terms of convergence speed and sum rate. The convergence speed of the proposed scheme is improved by 100%, and its performance can reach 90% of the full digital precoding scheme. Therefore, the proposed scheme can effectively improve system performance and accelerate convergence.

Weight adjustable interference alignment algorithm with imperfect channel state information

XU Dong, LI Yong, LIU Dongdong, LU Yakai

2018, 38(8):  2370-2374.  DOI: 10.11772/j.issn.1001-9081.2018010138

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For the problem of estimation error and feedback delay in the process of acquiring channel information in Multiple-Input Multiple-Output (MIMO) systems, a robust interference alignment algorithm based on weight adjustment was proposed to improve the system performance at low Signal-to-Noise Ratio (SNR). Firstly, the system model was reconstructed by considering the influence of channel error on the basis of the ideal channel; secondly, the signal space at the receiving terminal was decomposed by matrix projection and divided into two parts, including desired signal subspace and interference signal subspace; thirdly, considering the interaction between the desired signal and the interference signal, the weighted sum of the power leaked into the corresponding subspaces was used as the objective function, and the iterative idea was used to calculate the precoding and interference suppression matrix. Finally, the calculated pre-coding and interference suppression matrix was used to derive the sum-rate expression of the channel error. Simulation results show that compared with the robust minimum interference leakage algorithm, when the SNR is 10 dB and the channel error variance value is 0.05, the spectrum efficiency of the system is increased by 25% and the energy efficiency is increased by 38%. Therefore, the proposed algorithm can effectively improve system performance at low SNR.

Compression method based on bit extraction of independent rule sets for packet classification

WANG Xiaolong, LIU Qinrang, LIN Senjie, Huang Yajing

2018, 38(8):  2375-2380.  DOI: 10.11772/j.issn.1001-9081.2018010069

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The continuous expansion in scale of multi-field entries and the growing increase in bit-width bring heavy storage pressure in hardware on the Internet. In order to solve this problem, a compression method based on Bit Extraction of Independent rule Subsets (BEIS) was proposed. Firstly, some fields were merged based on the logical relationships among multiple match fields, thus reducing the number of match fields and the width of flow tables. Secondly, with the division of independent rule subsets for the merged rule set, some differentiate bits in the divided subsets were extracted to achieve the matching and searching function, further reducing the used Ternary Content Addressable Memory (TCAM) space. Finally, the lookup hardware architecture of this method was put forward. Simulation results show that, with certain time complexity, the storage space of the proposed method can be reduced by 20% compared with Field Trimmer (FT) in OpenFlow flow table; in addition, for common packet classification rule sets such as access control list and firewall in practical application, the compression ratio of 20%-40% can be achieved.

Hierarchical approach for 3D non-rigid shape registration

WANG Xupeng, LEI Hang, LIU Yan, SANG Nan

2018, 38(8):  2381-2385.  DOI: 10.11772/j.issn.1001-9081.2018020374

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Shape registration is a common task in non-rigid 3D shape analysis. In order to solve the problems of high complexity, large computation cost and low accuracy of the traditional algorithms, a new hierarchical shape registration method was proposed. Firstly, the heat kernel signature function was defined as the scalar field for a model, and persistence-based clustering was used to extract feature points and salient regions of the model. Then, a novel tree-based shape representation was proposed, whose root node, internal nodes and leaf nodes were defined as the model, the salient regions and the corresponding feature points, respectively. Finally, a new hierarchical shape registration method was designed to make full use of the tree-based shape representation. The hierarchical shape registration algorithm was tested on the SHREC 2010 correspondence dataset and compared with the Generalized Multi-Dimensional Scaling (GMDS) and game theory algorithms. Experimental results show that the proposed hierarchical shape registration method achieves higher accuracy than GMDS and game theory under various shape transformations, including isometric transformation, holes, micro holes, scaling, local scaling, resampling, noise, shot noise and topological transformation; in addition, the computational complexity is reduced significantly.

Image inpainting model based on structure-texture decomposition and local total variation minimization

YANG Wenxia, ZHANG Liang

2018, 38(8):  2386-2392.  DOI: 10.11772/j.issn.1001-9081.2018010231

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Exemplar-based image inpainting methods may cause local mosaic effects and visual incoherence, since the interference of image tiny texture and noise often result in invalid priority terms that makes the inpainting order abnormal. Besides, when searching for the best matching patch, the inter structure information of patches are ignored, which leads to non-unique best matching patches. To tackle these aforementioned issues, a new image inpainting model based on structure-texture decomposition and local total variation minimization was proposed. Three improvements were presented and detailed. Firstly, for an given image to be inpainted, the structure image was extracted by using the logarithm total variation minimization model, then the inpainting priority was calculated on this auxiliary image. In this way, a more robust filling mechanism can be achieved, since the isophote direction of the structure image is less noisy than the original image. Secondly, the priority term was redefined as the weighted summation of data term and confidence term to eliminate the product effect and ensure that the data term was always effective. As a result, the image mismatching rate caused by unreasonable inpainting order was reduced. Finally, the problem of choosing the best matching patch was converted into a 0-1 optimization problem aiming to reach a minimal local total variation. Comprehensive comparisons with the state-of-the-art three inpainting methods show that the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm is improved by 1.12-3.56 dB, and the Structural Similarity Index Measure (SSIM) is improved by 0.02-0.04. The proposed model can ensure a better selection of pixel candidates to fill in, and achieve a better global coherence of the reconstruction; therefore, the results are more visually appealing and with less block artifacts for inpainting large damaged images.

Embedded real-time compression for hyper-spectral images based on KLT and HEVC

LI Zhuo, XU Zhe, CHEN Xin, LI Shuqin

2018, 38(8):  2393-2397.  DOI: 10.11772/j.issn.1001-9081.2018010241

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The existing hyperspectral image compression algorithms that aim at high compression quality generally have problems such as high computational complexity, off-line processing, and difficulty in implementing an embedded platform. They are difficult to be implemented in practical applications at present. To resolve the above problems, a real-time compression method for embedded hyperspectral images based on Karhunen-Loeve Transform (KLT) and HEVC (High Efficiency Video Coding) was designed. Firstly, the inter-spectral correlation was reduced by KLT. Then, the spatial correlation was removed by HEVC. Finally, the process of quantization and entropy coding was accomplished by HEVC. Based on NVIDIA Jetson TX1 platform, a heterogeneous parallel compression system which utilizes both the CPU and GPU was designed and implemented. Using real data sets, the performance of the designed algorithm and the practicability of the implemented platform were verified. The experimental results show that compared with the Discrete Wavelet Transform (DWT)+JPEG2000 algorithm, the reconstruction accuracy is improved significantly under the same compression ratio. The Peak Signal-to-Noise Ratio (PSNR) is increased by 1.36 dB on average; at the same time, compared with CPU, performing KLT calculations on GPU can also reduce the runtime by 33% at most.

Microscopic 3D reconstruction method based on improved iterative shrinkage thresholding algorithm

WU Qiuyu, ZHANG Mingxin, LIU Yongjun, ZHENG Jinlong

2018, 38(8):  2398-2404.  DOI: 10.11772/j.issn.1001-9081.2018010271

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Iterative Shrinkage Thresholding Algorithm (ISTA) often uses fixed iteration step to solve the dynamic optimization problem of depth from defocus, which leads to poor convergence efficiency and low accuracy of reconstructed microscopic 3D shape. A method based on gradient estimation of acceleration operator and secant linear search, called Fast Linear Iterative Shrinkage Thresholding Algorithm (FL-ISTA), was proposed to optimize ISTA. Firstly, the acceleration operator, which consists of the linear combination of the current and previous points, was introduced to reestimate the gradient and update the iteration point during each iteration process. Secondly, in order to change the restriction of the fixed iteration step, secant linear search was used to determine the optimal iteration step dynamically. Finally, the improved algorithm was applied to solve the dynamic optimization problem of depth from defocus, which accelerated the convergence of the algorithm and improved the accuracy of reconstructed microscopic 3D shape. Experimental results of reconstructed standard 500 nm grid show that compared with ISTA, FISTA (Fast ISTA) and MFISTA (Monotohy FISTA), the efficiency of FL-ISTA was improved and the depth from defocus decreased by 10 percentage points, which is closer to the scale of standard 500 nm grid. Compared with ISTA, the Mean Square Error (MSE) and average error of microscopic 3D shape reconstructed by FL-ISTA were decreased by 18 percentage points and 40 percentage points respectively. The experimental results indicate that FL-ISTA can effectively improve the convergence rate of solving the dynamic optimization problem of depth from defocus and elevate the accuracy of the reconstructed microscopic 3D shape.

New aesthetic QR code algorithm based on region of interest and RS code

XU Xiaoyu, LU Jianfeng, LI Li, ZHANG Shanqing

2018, 38(8):  2405-2410.  DOI: 10.11772/j.issn.1001-9081.2018020317

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The existing aesthetic QR code algorithms do not consider the region of interest of a background image, which affects the beautification effect. A new aesthetic QR code algorithm based on region of interest and RS code mechanism was proposed. Firstly, an improved region of interest detection algorithm based on multiple features was proposed to get the salient binary image of the background image. Secondly, an intermediate QR code was obtained by performing an XOR operation on the original RS code by using the RS coding matrix, which was completely consistent with the salient binary image of the background image. Then the background image and the intermediate QR code were fused according to a specific fusion method. To further expand the aesthetic area, the RS error correction mechanism was performed on the fusion map, getting the final aesthetic QR code image. Experimental results on the established test sample set show that the proposed aesthetic algorithm can achieve complete background replacement, save more image information, and has a better visual effect and a higher decoding rate.

Improved pitch contour creation and selection algorithm for melody extraction

LI Qiang, YU Fengqin

2018, 38(8):  2411-2415.  DOI: 10.11772/j.issn.1001-9081.2018020311

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Aiming at the problem that the discontinuity of the pitch sequence of the same sound source was caused by the interference of different sound sources in polyphonic music which reduced the accuracy of pitch estimation, an improved pitch contour creation and selection algorithm for melody extraction was proposed. Firstly, a method based on auditory streaming cues and the continuity of pitch salience was proposed to create pitch contour by calculating the pitch salience of each point in the time-frequency spectrum. In order to further select the melody pitch contour, the non-melodic pitch contours were removed according to the repetitive characteristics of the accompaniment, and dynamic time warping algorithm was used to calculate the similarity between the melodic and non-melodic pitch contours. Finally, the octave errors in the melodic pitch contours was detected based on the long term relationship of the adjacent pitch contours. Simulation experiments on the data set ORCHSET show that the pitch estimation accuracy and the overall accuracy of the proposed algorithm are improved by 2.86% and 3.32% respectively compared with the oringinal algorithm, which can effectively solve the pitch estimation problem.

Review of model and optimization of vehicle scheduling for emergency material distribution

CAO Qi, CAO Yang

2018, 38(8):  2416-2422.  DOI: 10.11772/j.issn.1001-9081.2018010202

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The effective planning and scheduling of the rescue operation plays an important role in saving lives and reducing property losses. Relying on mathematical modeling methods and computer simulation technology to assist decision-makers to complete the vehicle scheduling for emergency material distribution has become a consensus in the academic world. Focused on two key issues, i.e., model and optimization, the recent research status of the vehicle scheduling problem for emergency material distribution were analyzed. The main optimization objects and influence factors in the model of vehicle scheduling problem for emergency material distribution were reduced. The application effects of different optimization algorithms were compared and analyzed. And the existing problems of current research were brought forward. Finally, the future research trends of the vehicle scheduling for emergency material distribution were discussed.

Auction based vehicle resource allocation and pricing mechanism for car rental

LIU Xudong, ZHANG Xuejie, ZHANG Jixian, LI Weidong, ZHANG Jing

2018, 38(8):  2423-2430.  DOI: 10.11772/j.issn.1001-9081.2018010234

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Since the vehicles provided by current online car rental platforms are in the fixed price, there are some issues coming up such as unreasonable allocation of the vehicle resources, unreliable price that could not indicates the real market supply and demand timely, and generally low social welfare. Therefore, an auction based vehicle allocation and pricing mechanism for car rental was proposed. Firstly, a mathematical model and a social welfare maximization objective function were established by studying the model of online car rental issues. Secondly, based on the minimum cost and maximum flow algorithm, the optimal vehicle resource allocation algorithm was adopted among the rental vehicle allocation algorithms. Finally, in terms of the price calculation algorithms, a truthful VCG (Vickrey-Clarke-Groves) price algorithm was used to calculate the final price. As a result, compared with the traditional first-come-first-serving algorithms, the order success rate of the proposed scheme was increased by 20% to 30%, and the revenue was increased by about 30%. Theoretical analysis and experiment results show that the proposed mechanism has the advantages of optimizing vehicle allocation and flexible price strategy.

Consensus of heterogeneous multi-agent systems with input and velocity saturation

LIU Chenchen, YIN Yanyan, LIU Fei

2018, 38(8):  2431-2436.  DOI: 10.11772/j.issn.1001-9081.2018020293

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Since the heterogeneous multi-agent systems composed of first-order and second-order agents have input and velocity saturation characteristics, the systems cannot achieve consensus. A leaderless distributed controller and a leader-following distributed controller of the heterogeneous multi-agent systems were constructed, and the sufficient conditions ensuring the successful consensus were given. The range of communication gain was calculated by using Lyapunov stability theory and Lasalle invariant set principle. The result of numerical simulation of leaderless heterogeneous multi-agent systems with input and velocity saturation characteristics shows that when the communication gain is not in a proper range, consensus of the leaderless heterogeneous multi-agent systems can not be achieved. However, when the proposed gain selection method is used to select the communication gain, the heterogeneous multi-agent systems can overcome input and velocity saturation characteristics, then consensus of the leaderless heterogeneous multi-agent systems can be achieved. The result of numerical simulation of leader following heterogeneous multi-agent systems with input and velocity saturation characteristics proves that the calculation method of the communication gain is also applicable to the semi-global leader-following consensus control of the heterogeneous multi-agent systems.

Short-term bus load forecasting based on hierarchical clustering method and extreme learning machine

YAN Hongwen, SHENG Chenggong

2018, 38(8):  2437-2441.  DOI: 10.11772/j.issn.1001-9081.2018010017

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Traditionally, days before the forecast day are usually selected as historical similar days for training the forecasting model to forecast bus load, without considering the effects of weather situation, weekday and vacation information. Therefore, traditional methods will cause differences of daily characteristics between historical similar days and the forecast day. To solve the problem, a new bus load forecasting method based on Hierarchical Clustering (HC) and Extreme Learning Machine (ELM) was proposed. Firstly, HC method was used for clustering the historical daily bus load. Secondly, a decision tree based on the clustering results was constructed. Thirdly, according to the properties of the forecast day, such as temperature, humidity, weekday and vacation information, historical daily bus load was obtained to train the forecasting model of extreme learning machine through the decision tree. Finally, the forecasting model was established to predict the bus load. When forecasting load of two different buses, compared with traditional single ELM, the proposed algorithm decreases the Mean Absolute Percentage Error (MAPE) by 1.4 percentage points and 0.8 percentage points. The experimental results show that the proposed method has higher accuracy and better stability for forecasting short-term bus load.

Object manipulation system with multiple stereo cameras for logistics applications

ZHANG Zekun, TANG Bing, CHEN Xiaoping

2018, 38(8):  2442-2448.  DOI: 10.11772/j.issn.1001-9081.2018020312

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To meet the low cost and real-time requirements of logistics sorting, a systematic method was proposed to extract complete stereo information of typical objects by using multiple stereo cameras. Combining the cameras with an arm and other hardware, a validation and experiment platform was constructed to test the performance of this method. Two Microsoft Kinect cameras were used to measure the locations of objects in horizontal plane with accuracy of 3 millimeters. The stereo features and models of objects were calculated from the complete stereo information at processing rate of about 1 second per frame. Utilizing these features, the arm continuously picked 100 objects without failure. The experimental results demonstrate that the proposed method can extract the stereo features of objects with various sizes and shapes in real-time without off-line training, and based on which the arm can operate on objects with high accuracy.