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Multi-objective robust optimization design of blood supply chain network based on improved whale optimization algorithm

DONG Hai, WU Yao, QI Xinna

Journal of Computer Applications 2021, 41 (10): 3063-3069. DOI: 10.11772/j.issn.1001-9081.2020111729

Abstract （231）

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In order to solve the uncertainty problem of blood supply chain network design, a multi-objective robust optimization design model of blood supply chain network was established. Firstly, for the blood supply chain network with five nodes, an optimization function considering safe stock, minimum cost and shortest storage time was established, and the ε-constraint, Pareto optimization and robust optimization method were used to deal with the established model, so that the multi-objective problem was transformed into a single objective robust problem. Secondly, by improving the original Whale Optimization Algorithm (WOA), the concept of crossover and mutation of the differential algorithm was introduced to WOA to enhance the search ability and improve the limitations, so as to obtain the Differential WOA (DWOA), which was used to solve the processed model. Finally, a numerical example verified that the shortage of the robust model is 76% less than that of the deterministic model when the test problems are the same. Therefore, the optimization robust model has more advantages in dealing with demand shortage. Compared with WOA, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), DWOA has shorter interruption time and lower cost.

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Prediction of organic reaction based on gated graph convolutional neural network

LAI Zicheng, ZHANG Yuping, MA Yan

Journal of Computer Applications 2021, 41 (10): 3070-3074. DOI: 10.11772/j.issn.1001-9081.2020111752

Abstract （249）

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Under the development of modern pharmaceutical and computer technologies, using artificial intelligence technology to accelerate drug development progress has become a research hotspot. And efficient prediction of organic reaction products is a key issue in drug retrosynthesis path planning. Concerning the problem of uneven distribution of chemical reaction types in the sample dataset, an Active Sampling-training Gated Graph Convolutional Neural-network (ASGGCN) model was proposed. Firstly, the SMILES (Simplified Molecular Input Line Entry Specification) codes of the chemical reactants were input into the model, and the location of the reaction center was predicted through Gated Graph Convolutional Neural-network (GGCN) and attention mechanism. Then, according to chemical constraint conditions and the candidate reaction centers, the possible chemical bond combinations were enumerated to generate candidate reaction products. After that, the gated graph convolutional difference network was used to rank the candidate products and obtain the final reaction product. Compared with the traditional graph convolutional network, the gated graph convolutional network has three weight parameter matrices and fuse the information through gating, so it can obtain more abundant atom hidden feature information. At the same time, the gated graph convolutional network is trained by active sampling, which can take into account both the analysis abilities of poor samples and ordinary samples. Experimental results show that the Top-1 prediction accuracy of the reaction product of the proposed model reaches 87.2%, which is increased by 1.6 percentage points compared to the accuracy of WLDN (Weisfeiler-Lehman Difference Network) model, illustrating that the organic reaction products can be predicted more accurately by the proposed model.

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Automatic segmentation method of microwave ablation region based on Nakagami parameters images of ultrasonic harmonic envelope

ZHUO Yuxin, HAN Suya, ZHANG Yufeng, LI Zhiyao, DONG Yifeng

Journal of Computer Applications 2021, 41 (10): 3089-3096. DOI: 10.11772/j.issn.1001-9081.2020121948

Abstract （274）

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The existing Nakagami parametric imaging of ultrasonic harmonic envelope signals can realize non-invasive monitoring of the ablation process, but it cannot estimate the ablation area accurately. In order to solve the problem, a Gaussian Approximation adaptive Threshold Segmentation (GATS) method based on ultrasonic harmonic envelope Nakagami parameter images was proposed to monitor microwave ablation areas accurately and effectively. Firstly, a high-pass filter was used to obtain the harmonic components of the ultrasound echo Radio Frequency (RF) signal. Then, the Nakagami shape parameters of the harmonic signal envelope were estimated, and Nakagami parameter image was generated by composite window imaging. Finally, Gaussian approximation of Nakagami parameter image was applied to present the ablation area, the anisotropic smoothing preprocessing was performed to the approximated image, and the threshold segmentation of the smoothed image was used to accurately estimate the ablation area. The results of microwave ablation experiments show that, the long and short axis errors of the threshold segmentation ablation area after anisotropic smoothing based on Perona-Malik (P-M) algorithm and the actual ablation area are reduced by 3.15 percentage points and 2.21 percentage points respectively compared with the errors obtained by using Catte algorithm, and decreased by 7.87 percentage points and 5.74 percentage points compared with the errors obtained by using Median algorithm. It can be seen that GATS using P-M algorithm for ultrasonic harmonic envelope Nakagami parameter images can estimate the ablation area more accurately and provide effective monitoring for clinical ablation surgery.

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Task allocation optimization for automated guided vehicles based on variable neighborhood simulated annealing algorithm

YANG Wei, LI Ran, ZHANG Kun

Journal of Computer Applications 2021, 41 (10): 3056-3062. DOI: 10.11772/j.issn.1001-9081.2020121919

Abstract （300）

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In order to solve the task allocation problem of multi-Automated Guided Vehicle (AGV) storage system, a Variable Neighborhood_Simulated Annealing (VN_SA) algorithm was proposed. Firstly, according to the system operation process and operating characteristics of AGV, with the path cost, time cost and task equilibrium value cost of AGV during the task execution as the goals, and adding the power consumption situations of AGV driving with and without load to the constraints, a more practical multi-objective optimization model of task allocation for multi-AGV storage system was built. Then, aiming at the characteristics of the problem, a VN_SA algorithm was designed. The search range of the simulated annealing algorithm was expanded by the neighborhood perturbation operation in the algorithm, and the local optimum was jumped out by the algorithm and the global development effect was obtained by combining the probability mutation characteristics. The simulation experiments were carried out on works with the number of tasks of 20, 50, 100 respectively. Experimental results show that, the optimized total cost of the proposed algorithm is reduced by 6.4, 7.5 and 13.2 percentage points respectively compared with Genetic Algorithm (GA), which verifies the effectiveness of the proposed algorithm under different task sizes. It can be seen that the proposed algorithm has better convergence and search efficiency.

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Optimization algorithm of ship dispatching in container terminals with two-way channel

ZHENG Hongxing, ZHU Xutao, LI Zhenfei

Journal of Computer Applications 2021, 41 (10): 3049-3055. DOI: 10.11772/j.issn.1001-9081.2020121973

Abstract （302）

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For the problems of encountering and overtaking in the process of in-and-out port of ships in the container terminals with two-way channel, a new ship dispatching optimization algorithm focusing on the service rules was proposed. Firstly, the realistic constraints of two-way channel and the safety regulations of port night sailing were considered at the same time. Then, a mixed integer programming model with the goal of minimizing the total waiting time of ships in the terminal was constructed to obtain the optimal in-and-out port sequence of ships. Finally, the branch-cut algorithm with embedded polymerization strategy was designed to solve the model. The numerical experimental results show that, the average relative deviation between the result of the branch-cut algorithm using embedded polymerization strategy and the lower bound is 2.59%. At the same time, compared with the objective function values obtained by the simulated annealing algorithm and quantum differential evolution algorithm, the objective function values obtained by the proposed branch-cut algorithm are reduced by 23.56% and 17.17% respectively, which verifies the effectiveness of the proposed algorithm. The influences of different safe time intervals of ship arriving the port and ship type proportions were compared in the sensitivity analysis of the scheme obtained by the proposed algorithm, providing the decision and support for ship dispatching optimization in container terminals with two-way channel.

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Element component content dynamic monitoring system based on time sequence characteristics of solution images

LU Rongxiu, CHEN Mingming, YANG Hui, ZHU Jianyong

Journal of Computer Applications 2021, 41 (10): 3075-3081. DOI: 10.11772/j.issn.1001-9081.2020101682

Abstract （409）

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In view of the difficulties in real-time monitoring of component contents in rare earth extraction process and the high time consumption and memory consumption of existing component content detection methods, a dynamic monitoring system for element component content based on time sequence characteristics of solution images was designed. Firstly, the image acquisition device was used to obtain the time sequence image of the extraction tank solution. Considering the color characteristics of the extracted liquid and the incompleteness of single color space, the time sequence characteristics of the image were extracted in the color space of the fusion of HSI (Hue, Saturation, Intensity) and YUV (Luminance-Bandwidth-Chrominance) by using Principal Component Analysis (PCA) method, and combined with the production index, the Whale Optimization Algorithm (WOA) based Least Squares Support Vector Machine (LSSVM) classifier was constructed to judge the status of the working condition. Secondly, when the working condition was not optimal, the color histogram and color moment features of the image were extracted in HSV (Hue, Saturation, Value) color space, and an image retrieval system was developed with the linear weighted value of the mixed feature difference between solution images as the similarity measurement to obtain the value of component content. Finally, the test of the mixed solution of the praseodymium/neodymium extraction tank was carried out, and the results show that this system can realize the dynamic monitoring of element component content.

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Early identification and prediction of abnormal carotid arteries based on variational autoencoder

HUANG Xiaoxiang, HU Yongmei, WU Dan, REN Lijie

Journal of Computer Applications 2021, 41 (10): 3082-3088. DOI: 10.11772/j.issn.1001-9081.2020101695

Abstract （323）

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Carotid artery stenosis, Carotid Intima Media Thickness (CIMT) or carotid artery plaque may lead to stroke. For large-scale preliminary screening of stroke, an improved Variational AutoEncoder (VAE) based on medical data was proposed to predict and identify abnormal carotid arteries. Firstly, for the missing values in medical data, K-Nearest Neighbor ( KNN), Mixture of mean, mode and KNN (M KNN) method and improved VAE were respectively used to impute the missed values to obtain the complete dataset, improving the application range of the data. Secondly, the feature attributes were analyzed and the features were ranked in order of importance. Thirdly, four supervised algorithms, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting Tree (XGBT), were combined with Genetic Algorithm (GA) to build the abnormal carotid artery identification models. Finally, based on the improved VAE, a semi-supervised abnormal carotid artery prediction model was built. Compared to the performance of baseline model, the performance of the semi-supervised model based on the improved VAE improves significantly with sensitivity of 0.893 8, specificity of 0.927 2, F1-measure of 0.910 5 and classification accuracy of 0.910 5. Experimental results show that this semi-supervised model can be used to identify the abnormal carotid arteries and thus serves as a tool to recognize high-risk groups of stroke, preventing and reducing the occurrence of stroke.

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Inventory routing optimization model with heterogeneous vehicles based on horizontal collaboration strategy

YANG Hualong, WANG Meiyu, XIN Yuchen

Journal of Computer Applications 2021, 41 (10): 3040-3048. DOI: 10.11772/j.issn.1001-9081.2020101577

Abstract （290）

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In order to minimize the expected logistics cost of the supplier alliance, the Inventory Routing Problem (IRP) of multiple suppliers and multiple products under random fluctuations of demand was studied. Based on the horizontal collaboration strategy, a reasonable share method of vehicle distribution costs among the members of the supplier alliance was designed. By considering the retailer's distribution soft and hard time windows and inventory service level requirements, a heterogeneous vehicle inventory routing mixed-integer stochastic programming model of multiple suppliers and multiple products was established, and the inverse function of demand cumulative distribution was employed to transform this model into a deterministic programming model. Then an improved genetic algorithm was designed to solve the programming model. The results of example analysis show that the use of heterogeneous vehicles for distribution can reduce the total cost of supplier alliance by 8.3% and 11.92% respectively and increase the loading rate of distribution vehicles by 24% and 17% respectively, compared with the use of homogeneous heavy-duty and light-duty vehicles. The sensitivity analysis results indicate that no matter how the proportion of suppliers' supply to the total supply of the alliance and the variation coefficient of retailers' commodity demand change, the total cost of the supplier alliance can be effectively reduced by using heterogeneous vehicles for distribution; and the greater the demand variation coefficient is, the more obvious the advantage of using heterogeneous vehicles for distribution has.

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Cattle eye image feature extraction method based on improved DenseNet

ZHENG Zhiqiang, HU Xin, WENG Zhi, WANG Yuhe, CHENG Xi

Journal of Computer Applications 2021, 41 (9): 2780-2784. DOI: 10.11772/j.issn.1001-9081.2020101533

Abstract （415）

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To address the problem of low recognition accuracy caused by vanishing gradient and overfitting in the cattle eye image feature extraction process, an improved DenseNet based cattle eye image feature extraction method was proposed. Firstly, the Scaled exponential Linear Unit (SeLU) activation function was used to prevent the vanishing gradient of the network. Secondly, the feature blocks of cattle eye images were randomly discarded by DropBlock, so as to prevent overfitting and strengthen the generalization ability of the network. Finally, the improved dense layers were superimposed to form an improved Dense convolutional Network (DenseNet). Feature information extraction recognition experiments were conducted on the self-built cattle eyes image dataset. Experimental results show that the recognition accuracy, precision and recall of the improved DenseNet are 97.47%, 98.11% and 97.90% respectively, and compared to the network without improvement, the above recognition accuracy rate, precision rate, recall rate are improved by 2.52 percentage points, 3.32 percentage points, 2.94 percentage points respectively. It can be seen that the improved network has higher precision and robustness.

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Real-time fall detection method based on threshold and extremely randomized tree

LIU Xiaoguang, JIN Shaokang, WEI Zihui, LIANG Tie, WANG Hongrui, LIU Xiuling

Journal of Computer Applications 2021, 41 (9): 2761-2766. DOI: 10.11772/j.issn.1001-9081.2020111816

Abstract （278）

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Aiming at the problem that wearable device-based fall detection cannot have good accuracy real-timely, a real-time fall detection method based on the fusion of threshold and extremely randomized tree was proposed. In this method, the wearable devices only needed to calculate the threshold value and did not need to ensure the accuracy of fall detection, which reduced the amount of calculation; at the same time, the host computer used the extremely randomized tree algorithm to ensure the accuracy of fall detection. Most of the daily actions were filtered by the wearable devices through the threshold method, so as to reduce the amount of action data detected by the host computer. In this way, the proposed method had high accuracy of fall detection in real time. In addition, in order to reduce the false positive rate of fall detection, the attitude angle sensor and the pressure sensor were integrated into the wearable devices, and the feedback mechanism was added to the host computer. When the detection result was false positive, the wrong detected sample was added to the non-fall dataset for retraining through the host computer. Through this kind of continuous learning, the model would generate an alarm model suitable for the individual. And this feedback mechanism provided a new idea for reducing the false positive rate of fall detection. Experimental results show that in 1 259 test samples, the proposed method has an average accuracy of 99.7% and the lowest false positive rate of 0.08%.

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De novo peptide sequencing by tandem mass spectrometry based on graph convolutional neural network

MOU Changning, WANG Haipeng, ZHOU Piyu, HOU Xinhang

Journal of Computer Applications 2021, 41 (9): 2773-2779. DOI: 10.11772/j.issn.1001-9081.2020111875

Abstract （397）

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In proteomics, de novo sequencing is one of the most important methods for peptide sequencing by tandem mass spectrometry. It has the advantage of being independent on any protein databases and plays a key role in the determination of protein sequences of unknown species, monoclonal antibodies sequencing and other fields. However, due to its complexity, the accuracy of de novo sequencing is much lower than that of the database search methods, therefore the wide application of de novo sequencing is limited. Focused on the issue of low accuracy of de novo sequencing, denovo-GCN, a de novo sequencing method based on Graph Convolutional neural Network (GCN) was proposed. In this method, the relationships between peaks in mass spectrometry were expressed by using graph structure, and the peak features were extracted from each corresponding peptide cleavage site. Then the amino acid type at the current cleavage site was predicted by GCN, and finally a complete sequence was formed step by step. Three significant parameters affecting the model were experimentally determined, including the GCN model layer number, the combination of ion types and the number of spectral peaks used for sequencing, and datasets of a wide variety of species were used for experimental comparison. Experimental results show that, the peptide-level recall of denovo-GCN is 4.0 percentage points to 21.1 percentage points higher than those of the graph theory-based methods Novor and pNovo, and is 2.1 percentage points to 10.7 percentage points higher than that of DeepNovo, which adopts Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network.

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Consensus of time-varying multi-agent systems based on event-triggered impulsive control

CHAI Jie, GUO Liuxiao, SHEN Wanqiang, CHEN Jing

Journal of Computer Applications 2021, 41 (9): 2748-2753. DOI: 10.11772/j.issn.1001-9081.2020111843

Abstract （280）

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For the consensus problem of time-varying multi-agent systems under time-varying topology connection, an event-triggered impulsive control protocol was proposed. In this protocol, for each agent, the controller would be updated only when the related state error exceeded a threshold, and the control inputs would be carried out only at the event triggering instants, and continuous communication between agents was avoided. This protocol would greatly reduce the cost of communication and control for network consensus. The sufficient conditions for the multi-agent systems with time-varying characteristics to achieve consensus under event-triggered impulsive control were analyzed based on the algebraic graph theory, Lyapunov stability and impulsive differential equation. At the same time, it was proved theoretically that there was no Zeno behavior in the event-triggered time sequences. Finally, the effectiveness of the obtained theoretical conclusion was verified through several numerical simulations.

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Degree centrality based method for cognitive feature selection

ZHANG Xiaofei, YANG Yang, HUANG Jiajin, ZHONG Ning

Journal of Computer Applications 2021, 41 (9): 2767-2772. DOI: 10.11772/j.issn.1001-9081.2020111794

Abstract （237）

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To address the uncertainty of cognitive feature selection in brain atlas, a Degree Centrality based Cognitive Feature Selection Method (DC-CFSM) was proposed. First, the Functional Brain Network (FBN) of the subjects in the cognitive experiment tasks was constructed based on the brain atlas, and the Degree Centrality (DC) of each Region Of Interest (ROI) of the FBN was calculated. Next, the difference significances of the subjects' same cortical ROI under different cognitive states during executing cognitive task were statistically compared and ranked. Finally, the Human Brain Cognitive Architecture-Area Under Curve (HBCA-AUC) values were calculated for the ranked regions of interest, and the performances of several cognitive feature selection methods were evaluated. In the experiments on functional Magnetic Resonance Imaging (fMRI) data of mental arithmetic cognitive tasks, the values of HBCA-AUC obtained by DC-CFSM on the Task Positive System (TPS), Task Negative System (TNS), and Task Support System (TSS) of the human brain cognitive architecture were 0.669 2, 0.304 0 and 0.468 5 respectively. Compared with Extremely randomized Trees (Extra Trees), Adaptive Boosting (AdaBoost), random forest, and eXtreme Gradient Boosting (XGB), the recognition rate for TPS of DC-CFSM was increased by 22.17%, 13.90%, 24.32% and 37.19% respectively, while its misrecognition rate for TNS was reduced by 20.46%, 29.70%, 44.96% and 33.39% respectively. DC-CFSM can better reflect the categories and functions of the human brain cognitive system in the selection of cognitive features of brain atlas.

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Co-evolutionary simulation regarding emergency logistics in major public health risk governance

GONG Ying, HE Yanting, CAO Cejun

Journal of Computer Applications 2021, 41 (9): 2754-2760. DOI: 10.11772/j.issn.1001-9081.2020111728

Abstract （330）

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To enhance the efficiency of emergency logistics during the process of major public health risk governance, an efficient emergency logistics collaboration mechanism was designed based on the analysis of the behavioral characteristics of government and logistics enterprise. An evolutionary game model between local government and logistics enterprise was established to investigate the evolutional laws and paths of local government's supervision and logistics enterprise's collaboration. Then, the feasibility and effectiveness of the proposed model were verified based on the numerical simulation. The results indicate that the emergency logistics collaboration mechanism in major public health risk governance significantly depends on local government's supervision compared with the collaboration mechanism of commercial logistics and it makes the collaboration level of logistics enterprise fluctuate between 0.25 and 0.9 repeatedly. After the establishment of a dynamic reward and punishment mechanism for local government, the obtained collaboration level of logistics enterprise stabilizes at 0.46 when the number of games reaches 30. It can be seen that this dynamic reward and punishment mechanism improves the stability of emergency logistics collaboration mechanism significantly.

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Real-time remaining life prediction method of Web software system based on self-attention-long short-term memory network

DANG Weichao, LI Tao, BAI Shangwang, GAO Gaimei, LIU Chunxia

Journal of Computer Applications 2021, 41 (8): 2346-2351. DOI: 10.11772/j.issn.1001-9081.2020091486

Abstract （307）

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In order to predict the Remaining Useful Life (RUL) of Web software system in real time and accurately, taking into consideration the time sequence characteristics of the Web system health status performance indicators and the interdependence between the indicators, a real-time remaining life prediction method of Web software system based on Self-Attention-Long Short-Term Memory (Self-Attention-LSTM) network was proposed. Firstly, an accelerated life test platform was built to collect the performance indicators data reflecting the aging trend of the Web software system. Then, according to the time sequence characteristics of the performance indicators data, a Long Short-Term Memory (LSTM) recurrent neural network was constructed to extract the hidden layer characteristics of the performance indicators, and the self-attention mechanism was used to model the dependency relationship between the characteristics. Finally, the real-time RUL prediction value of the Web system was obtained. On three test sets, the proposed model was compared with the Back Propagation (BP) network and the conventional Recurrent Neural Network (RNN). Experimental results show that the Mean Absolute Error (MAE) of the model is 16.92% lower than that of LSTM on average, and the relative accuracy (Accuracy) of the model is 5.53% higher than that of LSTM on average, which verify the effectiveness of the RUL model of Self-Attention-LSTM network. It can be seen that the proposed method can provide technical support for optimizing the software rejuvenation decision of the Web system.

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Hydraulic tunnel defect recognition method based on dynamic feature distillation

HUANG Jishuang, ZHANG Hua, LI Yonglong, ZHAO Hao, WANG Haoran, FENG Chuncheng

Journal of Computer Applications 2021, 41 (8): 2358-2365. DOI: 10.11772/j.issn.1001-9081.2020101596

Abstract （285）

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Aiming at the problems that the existing Deep Convolutional Neural Network (DCNN) have insufficient defect image feature extraction ability, few recognition types and long reasoning time in hydraulic tunnel defect recognition tasks, an autonomous defect recognition method based on dynamic feature distillation was proposed. Firstly, the deep curve estimation network was used to optimize the image to improve the image quality in low illumination environment. Secondly, the dynamic convolution module with attention mechanism was constructed to replace the traditional static convolution, and the obtained dynamic features were used to train the teacher network to obtain better model feature extraction ability. Finally, a dynamic feature distillation loss was constructed by fusing the discriminator structure in the knowledge distillation framework, and the dynamic feature knowledge was transferred from the teacher network to the student network through the discriminator, so as to achieve the high-precision recognition of six types of defects while significantly reducing the model reasoning time. In the experiments, the proposed method was compared with the original residual network on a hydraulic tunnel defect dataset of a hydropower station in Sichuan Province. The results show that this method has the recognition accuracy reached 96.15%, and the model parameter amount and reasoning time reduced to 1/2 and 1/6 of the original ones respectively. It can be seen from the experimental results that fusing the dynamic feature distillation information of the defect image into the recognition network can improve the efficiency of hydraulic tunnel defect recognition.

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Dam defect object detection method based on improved single shot multibox detector

CHEN Jing, MAO Yingchi, CHEN Hao, WANG Longbao, WANG Zicheng

Journal of Computer Applications 2021, 41 (8): 2366-2372. DOI: 10.11772/j.issn.1001-9081.2020101603

Abstract （301）

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In order to improve the efficiency of dam safety operation and maintenance, the dam defect object detection models can help to assist inspectors in defect detection. There is variability of the geometric shapes of dam defects, and the Single Shot MultiBox Detector (SSD) model using traditional convolution methods for feature extraction cannot adapt to the geometric transformation of defects. Focusing on the above problem, a DeFormable convolution Single Shot multi-box Detector (DFSSD) was proposed. Firstly, in the backbone network of the original SSD:Visual Geometry Group (VGG16), the standard convolution was replaced by the deformable convolution, which was used to deal with the geometric transformation of defects, and the model's spatial information modeling ability was increased by learning the convolution offset. Secondly, according to the sizes of different features, the ratio of the prior bounding box was improved to prompt the detection accuracy of the model to the bar feature and the model's generalization ability. Finally, in order to solve the problem of unbalanced positive and negative samples in the training set, an improved Non-Maximum Suppression (NMS) algorithm was adopted to optimize the learning effect. Experimental results show that the average detection accuracy of DFSSD is improved by 5.98% compared to the benchmark model SSD on dam defect images. By comparing with Faster Region-based Convolutional Neural Network (Faster R-CNN) and SSD models, it can be seen that DFSSD model has a better effect in improving the detection accuracy of dam defect objects.

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Prediction method of capacity data in telecom industry based on recurrent neural network

DING Yin, SANG Nan, LI Xiaoyu, WU Feizhou

Journal of Computer Applications 2021, 41 (8): 2373-2378. DOI: 10.11772/j.issn.1001-9081.2020101677

Abstract （507）

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In the capacity prediction process of telecom operation and maintenance, there are problems of too many capacity indicators and deployed business classes. Most of the existing researches do not consider the difference of indicator data types, and use the same prediction method for all types of data, which results in both good and bad prediction effects. In order to improve the efficiency of indicator prediction, a classification method of data type was proposed, and the data types were divided into trend type, periodic type and irregular type. Aiming at the prediction of periodical data, a periodic capacity indicator prediction model based on Bi-directional Recurrent Neural Network (BiRNN), called BiRNN-BiLSTM-BI, was proposed. Firstly, In order to analyze the periodic characteristics of capacity data, a busy and idle distribution analysis algorithm was proposed. Secondly, a Recurrent Neural Network (RNN) model was built, which included a layer of BiRNN and a layer of Bi-directional Long Short-Term Memory network (BiLSTM). Finally, the output of BiRNN was optimized by the system's busy and idle distribution information. Experimental results compared with the best one among Holt-Winters, AutoRregressive Integrated Moving Average (ARIMA) model and Back Propagation (BP) neural network model show that, the proposed BiRNN-BiLSTM-BI model has the Mean Square Error (MSE) reduced by 15.16% and 45.67% on the unified log dataset and the distributed cache service dataset respectively, showing that the prediction accuracy is greatly improved.

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Research progress on driver distracted driving detection

QIN Binbin, PENG Liangkang, LU Xiangming, QIAN Jiangbo

Journal of Computer Applications 2021, 41 (8): 2330-2337. DOI: 10.11772/j.issn.1001-9081.2020101691

Abstract （672）

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With the rapid development of the vehicle industry and world economy, the number of private cars continues to increase, which results in more and more traffic accidents, and traffic safety problem has become a global hotpot. The research of driver distracted driving detection is mainly divided into two types:traditional Computer Vision (CV) algorithms and deep learning algorithms. In the driver distraction detection based on traditional CV algorithm, image features are extracted by the feature operators such as Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG), then Support Vector Machine (SVM) is combined to build model and classify the images. However, the traditional CV algorithms have disadvantages of high requirements for the environment, narrow application range, large amount of parameters and high computational complexity. In recent years, deep learning has shown excellent performance such as fast speed and high precision in extracting data features. Therefore, the researchers began to introduce deep learning into driver distracted driving detection. The methods based on deep learning can realize the end-to-end distracted driving detection network with high accuracy. The research status of the traditional CV algorithms and deep learning algorithms in driver distracted driving detection was introduced. Firstly, the situations of the traditional CV algorithms used in the image field and the research of driver distracted driving detection were elaborated. Secondly, the research of driver distracted driving based on deep learning was introduced. Thirdly, the accuracies and model parameters of different driver distracted driving detection methods were compared and analyzed. Finally, the existing research was summarized and three problems that driver distracted driving detection need to solve in the future were put forward:the driver's distraction state and the distraction degree division standards need to be further improved, three aspects of person-car-road need to be considered comprehensively, and how to reduce neural network parameters more effectively.

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Multi-person collaborative creation system of building information modeling drawings based on blockchain

SHEN Yumin, WANG Jinlong, HU Diankai, LIU Xingyu

Journal of Computer Applications 2021, 41 (8): 2338-2345. DOI: 10.11772/j.issn.1001-9081.2020101549

Abstract （436）

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Multi-person collaborative creation of Building Information Modeling (BIM) drawings is very important in large building projects. However, the existing methods of multi-person collaborative creation of BIM drawings based on Revit and other modeling software or cloud service have the confusion of BIM drawing version, difficulty of traceability, data security risks and other problems. To solve these problems, a blockchain-based multi-person collaborative creation system for BIM drawings was designed. By using the on-chain and off-chain collaborative storage method, the blockchain and database were used to store BIM drawings information after each creation in the BIM drawing creation process and the complete BIM drawings separately. The decentralization, traceability and anti-tampering characteristics of the blockchain were used to ensure that the version of the BIM drawings is clear, and provide a basis for the future copyright division. These characteristics were also used to enhance the data security of BIM drawings information. Experimental results show that the average block generation time of the proposed system in the multi-user concurrent case is 0.467 85 s, and the maximum processing rate of the system is 1 568 transactions per second, which prove the reliability of the system and that the system can meet the needs of actual application scenarios.

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Binary classification to multiple classification progressive detection network for aero-engine damage images

FAN Wei, LI Chenxuan, XING Yan, HUANG Rui, PENG Hongjian

Journal of Computer Applications 2021, 41 (8): 2352-2357. DOI: 10.11772/j.issn.1001-9081.2020101575

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Aero-engine damage is an important factor affecting flight safety. There are two main problems in the current computer vision-based damage detection of engine borescope image:one is that the complex background of borescope image makes the model detect the damage with low accuracy; the other one is that the data source of borescope image is limited, which leads to fewer detectable classes for the model. In order to solve these two problems, a Mask R-CNN (Mask Region-based Convolutional Neural Network) based progressive detection network from binary classification to multiple classification was proposed for aero-engine damage images. By adding a binary classification detection branch to the Mask R-CNN, firstly, the damage in the image was detected in binary way and regression optimization was performed to the localization coordinates. Secondly, the original detection branch was used to progressively perform multiple classification detection, so as to further optimize the damage detection results by regression and determine the damage class. Finally, instance segmentation was performed to the damage through the Mask branch according to the results of multiple classification detection. In order to increase the detection classes of the model and verify the effectiveness of the method, a dataset of 1 315 borescope images with 8 damage classes was constructed. The training and testing results on this set show that the Average Precision (AP) and AP75 (Average Precision under IoU (Intersection over Union) of 75%) of multiple classification detection are improved by 3.34% and 9.71% respectively, compared with those of Mask R-CNN. It can be seen that the proposed method can effectively improve the multiple classification detection accuracy for damages in borescope images.

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Raw sugar demand forecasting model for sugar manufacturing enterprise based on modified Elman neural network

LI Yangying, CHEN Zhijun, ZHANG Zihao, YOU Lan

Journal of Computer Applications 2021, 41 (7): 2113-2120. DOI: 10.11772/j.issn.1001-9081.2020061000

Abstract （228）

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The sugar manufacturing enterprises use traditional algorithm to forcast the raw sugar demand, which ignors the influence of time factors and the industry characteristics, resulting in low accuracy. To address this problem, combining with the periodic characteristics of the supply and demand of raw materials of refining sugar,a temporal feature-correlated raw sugar demand forecast model based on improved Elman Neural Network with Modified Cuckoo Search(MCS) optimization was proposed, namely TMCS-ENN. Firstly, an adaptive learning rate formula was proposed to optimize Elman Neural Network (ENN). Secondly, the adaptive parasitic failure probability and adaptive step-length control variable formula were introduced to obtain MCS algorithm to optimize the weight and threshold of ENN, which effectively improved the local search ability of the model and avoided local optimum. Finally, combining time correlation and hysteresis of raw material purchase of sugar manufacturing enterprise, the data slices were designed based on week granularity, and the ENN was trained with festivals and holidays as important features to obtain TMCS-ENN. Experimental results show that, with week as time granularity, the forecasting accuracy of the proposed TMCS-ENN forecasting model reaches 93. 89%. It can be seen that TMCS-ENN can meet the forecast accuracy demand of sugar manufacturing enterprises and effectively improve their production efficiency.

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Multiple ring scan chains using the same test pin in round robin manner

ZHANG Ling, KUANG Jishun

Journal of Computer Applications 2021, 41 (7): 2156-2160. DOI: 10.11772/j.issn.1001-9081.2020081665

Abstract （353）

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Test architecture design is the basic and key issue of Integrated Circuit (IC) test, and the design of effective test architecture that meet the needs of IC is of great importance to reduce chip cost, improve product quality and increase product competitiveness. Therefore, a test architecture with several ring scan chains using the same test pin in the round robin manner was proposed, namely RRR Scan. In RRR Scan, the scan flip-flops were designed as multiple ring scan chains, which can work in stealth scan mode, ring shift scan mode and linear scan mode. The ring shift scan mode enables the reuse of test data, thus reducing the size of the test set; the stealth scan mode can shorten the test data shifting path, thus significantly reduing the test shifting power consumption, so that the architecture is a general test architecture with the characteristics of data reuse and low power consumption. In addition, in the architecture, the physically adjacent scan cells can be set into the same ring scan chain with little wiring cost. With stealth scan mode, both the shifting length and the delay of test data can be reduced. Experimental results show that the shifting power consumption can be reduced greatly by RRR Scan, and for S13207 circuit, the shifting power consumption is only 0.42% of that of the linear scan.

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Simultaneous measurement of range and speed based on pulse position and amplitude modulation

HUANG Shaowei, HUANG Wanlin, LEI Runlong, MAO Xuesong

Journal of Computer Applications 2021, 41 (7): 2145-2149. DOI: 10.11772/j.issn.1001-9081.2020081666

Abstract （273）

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For dealing with the problem that Doppler laser radar employing traditional wave methods cannot obtain high resolution for both parameters when it is used in range and speed measurement applications, a new measurement signal waveform modulated by both position and amplitude was proposed, which can solve the contradiction between measurement precision of range and speed, and make the two parameters independent in measurement process. In addition, the feasibility of applying the method for range and speed measurement in road environments for intelligent driving vehicles was analyzed. Firstly, the difficulties of classical modulation methods in range and speed measurement simultaneously were discussed, based on which a solution was designed for simultaneous modulation of the transmit signal waveform in position and amplitude, and the physical realizability of the proposed method was introduced by combing the amplifying properties of the in-line optical fiber amplifier. Then, the frequency calculation method for output heterodyne signal of laser radar employing position and amplitude modulation method and the data accumulation method for laser radar receiver output echo signal were discussed, so as to measure the range and speed independently. Finally, within the range of Doppler frequency that can be generated by moving targets in road environments, simulations were performed to verify the feasibility of the proposed method and the independence of the two measurement parameters, meanwhile, the measurement precision was analyzed. Simulation results show that the method of simultaneous position and amplitude modulation scheme can effectively measure the range and speed of targets even when the Signal-to-Noise Ratio (SNR) of laser radar receiver output signal is below 0 dB, and the measurement process of these two parameters to be measured are totally independent.

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Synthetic aperture radar ship detection method based on self-adaptive and optimal features

HOU Xiaohan, JIN Guodong, TAN Lining, XUE Yuanliang

Journal of Computer Applications 2021, 41 (7): 2150-2155. DOI: 10.11772/j.issn.1001-9081.2020081187

Abstract （332）

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In order to solve the problem of poor small target detection effect in Synthetic Aperture Radar (SAR) target ship detection, a self-adaptive anchor single-stage ship detection method was proposed. Firstly, on the basis of Feature Selective Anchor-Free (FSAF) algorithm, the optimal feature fusion method was obtained by using the Neural Architecture Search (NAS) to make full use of the image feature information. Secondly, a new loss function was proposed to solve the imbalance of positive and negative samples while enabling the network to regress the position more accurately. Finally, the final detection results were obtained by combining the Soft-NMS filtering detection box which is more suitable for ship detection. Several groups of comparison experiments were conducted on the open SAR ship detection dataset. Experimental results show that, compared with the original target detection algorithm, the proposed method significantly reduces the missed detections and false positives of small targets, and improves the detection performance for inshore ships to a certain extent.

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Channel structure choice of closed-loop supply chain under uncertain demand and recovery

ZHANG Meng, GUO Jianquan

Journal of Computer Applications 2021, 41 (7): 2100-2107. DOI: 10.11772/j.issn.1001-9081.2020101617

Abstract （291）

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Aiming at the optimal choice of sales channel structure in the closed-loop supply chain, considering the uncertainty of market demand and quality level of recycled products, four average gross profit models for the closed-loop supply chain system with four sales channel structures under the government differentially weighted subsidy were constructed with the objective of maximizing the gross profit. Firstly, Fuzzy Chance Constrained Programming (FCCP) method was used to transform the fuzzy constraints into clear corresponding expressions equivalently. Then, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) were used to solve numerical examples of the model comparatively. Finally, sensitivity analysis was performed on the parameters. The results show that the maximum difference ratio of the above two algorithms is 0.018%, indicating that both algorithms do not fall into the local optimal solution, which verifies the validity of the algorithms and the confidence of the models. Enterprises can formulate optimal recycling, production and sales strategies according to different confidence levels of the potential demands, choose the optimal channel structure and increase the gross profit gradually.

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Two-dimensional mapping of swarm robot based on random walk

LU Guoqing, SUN Hao

Journal of Computer Applications 2021, 41 (7): 2121-2127. DOI: 10.11772/j.issn.1001-9081.2020081239

Abstract （275）

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Robots need to quickly and accurately obtain environmental map information when exploring unknown environments autonomously. For the problems of efficient exploration and map construction of unknown environments, the random walk algorithm was applied to the exploration of swarm robots, which simulate Brownian motion and build maps of the searched area. Then, the Brownian motion algorithm was improved to avoid the robot to search a region repeatedly by setting the maximum rotation angle when the robot walks randomly, so that the robot was able to explore more areas in the same time and the search efficiency of the robot was improved. Finally, simulation experiments were carried out through a group of mobile robots equipped with lidar, the influences of maximum rotation angle increment, the number of robots and movement steps of robot on the search area were analyzed.

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Attention-based object detection with millimeter wave radar-lidar fusion

LI Chao, LAN Hai, WEI Xian

Journal of Computer Applications 2021, 41 (7): 2137-2144. DOI: 10.11772/j.issn.1001-9081.2020081334

Abstract （675）

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To address problems of missing occluded objects, distant objects and objects in extreme weather scenarios when using lidar for object detection in autonomous driving, an attention-based object detection method with millimeter wave radar-lidar feature fusion was proposed. Firstly, the scan frame data of millimeter wave radar and lidar were aggregated into their respective labeled frames, and the points of millimeter wave radar and lidar were spatially aligned, then PointPillar was employed to encode both the millimeter wave radar and lidar data into pseudo images. Finally, the features of both millimeter wave radar and lidar sensors were extracted by the middle convolution layer, and the features maps of them were fused by attention mechanism, and the fused feature map was passed through a single-stage detector to obtain detection results. Experimental results on nuScenes dataset show that compared to the basic PointPillar network, the mean Average Precision (mAP) of the proposed attention fusion algorithm is higher, which performs better than concatenation fusion, multiply fusion and add fusion methods. The visualization results show that the proposed method is effective and can improve the robustness of the network for detecting occluded objects, distant objects and objects surrounded by rain and fog.

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Signal timing optimization model of dual-ring phase under condition of setting waiting area

YANG Zhen, MA Jianxiao, WANG Baojie

Journal of Computer Applications 2021, 41 (7): 2108-2112. DOI: 10.11772/j.issn.1001-9081.2020081332

Abstract （265）

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In order to improve the driving efficiency of intersections with waiting areas, the effect of setting waiting area was firstly equal to the increase of lane green ratio. Then a signal timing optimization model for intersection was developed based on National Electronic Manufacturers Association (NEMA) standard dual-ring phase with the objective of minimizing the average vehicular delay. Next, a genetic algorithm for solving the model was designed by considering the ring-barrier constraint in phase structure. Finally, the model and algorithm were applied to the example intersection. The results show that compared to the signal timing scheme obtained by Synchro software, the model can obtain the scheme with shorter cycle and lower average vehicular delay. The delay reduction of the proposed model ranges from 12.9% to 17.4% when only left-turn waiting areas are provided at the intersections, and from 17.5% to 25.5% when both left-turn and through-movement waiting areas are provided. Besides, the model is not sensitive to the value of queue clearance rate, and can obtain almost the same signal timing scheme at the minimum and maximum vehicular speeds.

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Path planning method of unmanned aerial vehicle based on chaos sparrow search algorithm

TANG Andi, HAN Tong, XU Dengwu, XIE Lei

Journal of Computer Applications 2021, 41 (7): 2128-2136. DOI: 10.11772/j.issn.1001-9081.2020091513

Abstract （1038）

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Focusing on the issues of large alculation amount and difficult convergence of Unmanned Aerial Vehicle (UAV) path planning, a path planning method based on Chaos Sparrow Search Algorithm (CSSA) was proposed. Firstly, a two-dimensional task space model and a path cost model were established, and the path planning problem was transformed into a multi-dimensional function optimization problem. Secondly, the cubic mapping was used to initialize the population, and the Opposition-Based Learning (OBL) strategy was used to introduce elite particles, so as to enhance the diversity of the population and expand the scope of the search area. Then, the Sine Cosine Algorithm (SCA) was introduced, and the linearly decreasing strategy was adopted to balance the exploitation and exploration abilities of the algorithm. When the algorithm fell into stagnation, the Gaussian walk strategy was adopted to make the algorithm jump out of the local optimum. Finally, the performance of the proposed improved algorithm was verified on 15 benchmark test functions and applied to solve the path planning problem. Simulation results show that CSSA has better optimization performance than Particle Swarm Optimization (PSO) algorithm, Beetle Swarm Optimization (BSO) algorithm, Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO) algorithm and Sparrow Search Algorithm (SSA), and can quickly obtain a safe and feasible path with optimal cost and satisfying constraints, which proves the effectiveness of the proposed method.

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