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%.

To solve the problem that the existing distributed database based on Log Structured Merge-Tree (LSM-Tree) only supports efficient primary key query and cannot allow users to quickly apply it in their own clusters, a light-weight distributed index implementation method based on LSM-Tree, called SIBL (Secondary Index Based LSM-Tree), was proposed. Firstly, the query efficiency of the non-primary key attributes was improved by indexing the primary key attribute columns. Then, a distributed index construction algorithm and an index interval division algorithm based on equidistant sampling were proposed to ensure the even distribution of indexes in the system. And the query algorithm of the traditional index was optimized, and the index file was regarded as a special data file and stored in the system in a distributed manner, ensuring the load balance and scalability of the system. Finally, experiments of the proposed method with Huawei's secondary index scheme HIndex were carried out on the HBase database to compare performances such as time and space overhead of index construction, index query performance and system load balance, verifying that the proposed method improves query performance by 50 to 200 times.

With substantial attention being received from industry and academia over last decade, salient object detection has become an important fundamental research in computer vision. The solution of salient object detection will be helpful to make breakthroughs in various visual tasks. Although various works have achieved remarkable success for saliency detection tasks in visible light scenes, there still remain a challenging issue on how to extract salient objects with clear boundary and accurate internal structure in weak light images with low signal-to-noise ratios and limited effective information. For that fuzzy boundary and incomplete internal structure cause low accuracy of salient object detection in weak light scenes, an Ant Colony Optimization (ACO) algorithm based saliency detection framework was proposed. Firstly, the input image was transformed into an undirected graph with different nodes by multi-scale superpixel segmentation. Secondly, the optimal feature selection strategy was adopted to capture the useful information contained in the salient object and eliminate the redundant noise information from weak light image with low contrast. Then, the spatial contrast strategy was introduced to explore the global saliency cues with relatively high contrast in the weak light image. To acquire more accurate saliency estimation at low signal-to-noise ratio, the ACO algorithm was used to optimize the saliency map. Through the experiments on three public datasets (MSRA, CSSD and PASCAL-S) and the Nighttime Image (NI) dataset, it can be seen that the Area Under the Curve (AUC) value of the proposed model reached 87.47%, 84.27% and 81.58% on three public datasets respectively, and the AUC value of the model was increased by 2.17 percentage points compared to that of the Low Rank Matrix Recovery (LR) model (which ranked the second) on the NI dataset. The results demonstrate that the proposed model has the detection effect with more accurate structure and clearer boundary compared to 11 mainstream saliency detection models and effectively suppresses the interference of weak light scenes on the detection performance of salient objects.

The traditional neural network model cannot reflect the macro discourse structure information between different sections in scientific paper, which leads to the incomplete structure and incoherent content of the generated scientific paper summarization. In order to solve the problem, a scientific paper summarization model using macro discourse structure was proposed. Firstly, a hierarchical encoder based on macro discourse structure was built. Graph convolution neural network was used to encode the macro discourse structure information between sections, so as to construct the hierarchical semantic representation of sections. Then, an information fusion module was proposed to effectively fuse macro discourse structure information and word-level information, so as to assist the decoder to generate the summarization. Finally, the attention mechanism optimization unit was used to update and optimize the context vector. Experimental results show that the proposed model is 3.53, 1.15 and 4.29 percetage points higher than the baseline model in ROUGE (Recall-Oriented Understudy for Gisting Evaluation)-1, ROUGE-2 and ROUGE-L respectively. Through the analysis and comparison of the generated summarization content, it can be further proved that the proposed model can effectively improve the quality of the generated summarization.

Since the semi-closed and semi-opened spaces such as high-speed railway station have the indoor thermal comfort level difficult to predict, a Deep Forest (DF)-based deep learning method was proposed to realize the scientific prediction of thermal comfort level. Firstly, the heat exchange environment of high-speed railway station was modeled based on field survey and Energy Plus platform. Secondly, 8 influence factors, such as passenger density, operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, were presented, and 424 operating conditions were designed to obtain massive data. Finally, DF was used to obtain the relationship between thermal comfort and influence factors in order to predict the indoor thermal comfort level of high-speed rail station. Deep Neural Network (DNN) and Support Vector Machine (SVM) were provided as comparison algorithms for the verification. Experimental results show that, among the three models, DF performs best in terms of the prediction accuracy and weighted- F ₁, and has the best prediction accuracy of 99.76% and the worst of 98.11%. Therefore, DF can effectively predict the indoor thermal comfort level of high-speed railway stations.

As many factors affect the thermal comfort of semi-enclosed buildings such as high-speed railway stations in a complex way and there exists contradiction between thermal comfort and energy consumption, a comprehensive prediction method for thermal comfort and energy consumption of high-speed railway station based on machine learning was proposed. Firstly, with sensor data capturing and Energy Plus platform, the indoor and outdoor status, the control units like multi-evaporator air conditioners and heat exchangers as well as the thermal energy transmission environment of high-speed railway station were modeled. Secondly, eight factors influencing the thermal comfort of high-speed railway station, such as the operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, the operating number of heat exchangers, passenger density, outdoor temperature, indoor temperature, indoor humidity, and indoor carbon dioxide concentration, were proposed, 424 model operating conditions and 3 714 240 instances were designed. Finally, in order to effectively predict indoor thermal comfort and energy consumption of high-speed railway station, six machine learning methods, which are deep neural network, support vector regression, decision tree regression, linear regression, ridge regression and Bayesian ridge regression, were designed. Experimental results show that decision tree regression has the best prediction performance in a short time with average mean squared error of 0.002 2. The obtained research results can directly provide actively predicted environmental parameters and realize real-time decision-making for the temperature control strategy in the next stage.

In order to solve the problem of low accuracy of video-based person re-identification caused by factors such as occlusion, background interference, and person appearance and posture similarity in video surveillance, a video-based person re-identification method of Evenly Sampling-random Erasing (ESE) and global temporal feature pooling was proposed. Firstly, aiming at the situation where the object person is disturbed or partially occluded, a data enhancement method of evenly sampling-random erasing was adopted to effectively alleviate the occlusion problem, improving the generalization ability of the model, so as to more accurately match the person. Secondly, to further improve the accuracy of video-based person re-identification, and learn more discriminative feature representations, a 3D Convolutional Neural Network (3DCNN) was used to extract temporal and spatial features. And a Global Temporal Feature Pooling (GTFP) layer was added to the network before the output of person feature representations, so as to ensure the obtaining of spatial information of the context, and refine the intra-frame temporal information. Lots of experiments conducted on three public video datasets, MARS, DukeMTMC-VideoReID and PRID-201l, prove that the method of jointing evenly sampling-random erasing and global temporal feature pooling is competitive compared with some state-of-the-art video-based person re-identification methods.

Aiming at the problem that the existing liveness detection algorithms based on deep learning are mostly based on large convolutional neural network,a liveness detection algorithm based on lightweight network MobileNetV2 and feature fusion was proposed. Firstly,the improved MobileNetV2 was used as the basic network to extract features from RGB,HSV and LBP images respectively. Then,the obtained feature maps were stacked together to perform the feature layer fusion. Finally,the features were extracted from the merged feature maps,and the Softmax layer was used to make the judgment whether the face was real or fake. Simulation results show that the Equal Error Rate(EER)of the proposed algorithm on NUAA dataset was 0. 02%,the Average Classification Error Rate(ACER)on Siw dataset was 0. 75%,and the time to test single image costed 6 ms. Experimental results verify that:the fusion of different information can obtain a lower error rate, and the improved lightweight network guarantees the efficiency of the algorithm and meets the real-time requirement.

In order to accurately diagnose the synovitis patient's condition, doctors mainly rely on manual labeling and outlining method to extract synovial hyperplasia areas in the Magnetic Resonance Image (MRI). This method is time-consuming and inefficient, has certain subjectivity and is of low utilization rate of image information. To solve this problem, a new articular synovium segmentation algorithm, named 2D ResU-net segmentation algorithm was proposed. Firstly, the two-layer residual block in the Residual Network (ResNet) was integrated into the U-Net to construct the 2D ResU-net. Secondly, the sample dataset was divided into training set and testing set, and data augmentation was performed to the training set. Finally, all the training samples after augmentation were applied to the training of the network model. In order to test the segmentation effect of the model, the tomographic images containing synovitis in the testing set were selected for segmentation test. The final average segmentation accuracy indexes are as follow:Dice Similarity Coefficient (DSC) of 69.98%, IOU (Intersection over Union) index of 79.90% and Volumetric Overlap Error (VOE)of 12.11%. Compared with U-Net algorithm, 2D ResU-net algorithm has the DSC increased by 10.72%, IOU index increased by 4.24% and VOE decreased by 11.57%. Experimental results show that this algorithm can achieve better segmentation effect of synovial hyperplasia areas in MRI images, and can assist doctors to make diagnosis of the disease condition in time.

To address the problem that the RANdom SAmple Consensus (RANSAC) algorithm in the ORB-SLAM2 system has a low efficiency due to the randomness of the algorithm when eliminating mismatches and fails to build dense point cloud map in ORB-SLAM2 system, a PROgressive SAmple Consensus (PROSAC) algorithm was adopted to improve the mismatch elimination in the ORB-SLAM2 system and the dense point cloud map and the octree map building threads were added in this system. Firstly, compared with RANSAC algorithm, in PROSAC algorithm, the feature points were preordered according to the evaluation function, and the feature points with high evaluation quality were selected to solve the homography matrix. According to the solution of the homography matrix and the matching error threshold, the mismatches were eliminated. Secondly, the pose estimation and relocation of the camera were carried out according to the ORB-SLAM2 system. Finally, the dense point cloud map and the octree map were constructed according to the selected key frames. According to the experimental results on TUM dataset, PROSAC algorithm took about 50% time to perform the mismatch elimination of the same images compared to RANSAC algorithm, and the proposed system had the absolute trajectory error and relative pose error basically consistent with the ORB-SLAM2 system, showing good robustness. Besides, compared with the sparse point cloud map, the proposed new maps could be directly used for robot navigation and path planning.

For the problem of time, effort and money consuming to obtain a large number of samples by conventional means faced by Artificial Intelligence (AI) application research in different fields, a variety of sample augmentation methods have been proposed in many AI research fields. Firstly, the research background and significance of data augmentation were introduced. Then, the methods of data augmentation in several common fields (including natural image recognition, character recognition and discourse parsing) were summarized, and on this basis, a detailed overview of sample acquisition or augmentation methods in the field of medical image assisted diagnosis was provided, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) images. Finally, the key issues of data augmentation methods in AI application fields were summarized and the future development trends were prospected. It can be concluded that obtaining a sufficient number of broadly representative training samples is the key to the research and development of all AI fields. Both the common fields and the professional fields have conducted sample augmentation, and different fields or even different research directions in the same field have different sample acquisition or augmentation methods. In addition, sample augmentation is not simply to increase the number of samples, but to reproduce the existence of real samples that cannot be completely covered by small sample size as far as possible, so as to improve sample diversity and enhance AI system performance.

In view of the different constraints on the moving objects between road network environment and Euclidean space environment, a virtual trajectory filling algorithm was proposed, which was applicable to both constraints. The interaction between the user and the provider of Location-Based Services (LBS) was taken over by the algorithm, and virtual user trajectory was constructed to confuse and fill the real trajectory, realizing the hiding and protection of the real trajectory. Firstly, the target region was partitioned and the points of convergence were extracted. Then, the trajectory segmentation and virtual trajectory were generated based on the convergence points. Finally, the reasonable distribution of the virtual trajectory was achieved by constructing the timing preset algorithm and the trajectory confusion filling algorithm, which increased the difficulty of associating the trajectory information with a specific target object. Experimental results show that after less than 15 virtual trajectories per user being filled, the probability of the location privacy disclosure of the target object is dropped from 60% to and stabilizes at around 10%, and the trajectory privacy disclosure probability is decreased from 50% to and stabilizes at about 6%, achieving good effect of location privacy protection.

To solve the problem that Markov model has poor prediction accuracy and sparse matching in location prediction, a mobile user location prediction method based on Adaboost-Markov model was proposed. Firstly, the original trajectory data was preprocessed by a trajectory division method based on angle offset and distance offset to extract feature points, and density clustering algorithm was used to cluster the feature points into interest regions of the user, then the original trajectory data was discretized into a trajectory sequence composed of interest regions. Secondly, according to the matching degree of prefix trajectory sequence and historical trajectory pattern tree, the model order k was adaptively determined. Finally, Adaboost algorithm was used to assign the corresponding weight coefficients according to the importance degree of 1 to k order Markov models to form a multi-order fusion Markov model, realizing the prediction of future interest regions of the mobile user. The experimental results on a large-scale real user trajectory dataset show that the average prediction accuracy of Adaboost-Markov model is improved by 20.83%, 11.3%, and 5.38% respectively compared with the first-order Markov model, the second-order Markov model, and the multi-order fusion Markov model with average weight coefficient, and the proposed model has good universality and multi-step prediction performance.

Aiming at the problem of poor pedestrian detection effect in complex scenes, a pedestrian detection algorithm based on improved Mask R-CNN framework was proposed with the use of the leading research results in deep learning-based object detection. Firstly, K-means algorithm was used to cluster the object frames of the pedestrian datasets to obtain the appropriate aspect ratio. By adding the set of aspect ratio (2:5), 12 anchors were able to be adapted to the size of the pedestrian in the image. Secondly, combined with the technology of fine-grained image recognition, the high accuracy of pedestrian positioning was realized. Thirdly, the foreground object was segmented by the Full Convolutional Network (FCN), and pixel prediction was performed to obtain the local mask (upper body, lower body) of the pedestrian, so as to achieve the fine-grained detection of pedestrians. Finally, the overall mask of the pedestrian was obtained by learning the local features of the pedestrian. In order to verify the effectiveness of the improved algorithm, the proposed algorithm was compared with the current representative object detection methods (such as Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv2 and R-FCN (Region-based Fully Convolutional Network)) on the same dataset. The experimental results show that the improved algorithm increases the speed and accuracy of pedestrian detection and reduces the false positive rate.

To address the problem that dynamic objects affect pose estimation in indoor Simultaneous Localization And Mapping (SLAM) systems, a semantic segmentation based SLAM system in dynamic scenes was proposed. Firstly, an image was semantically segmented by the Pyramid Scene Parsing Network (PSPNet) after being captured by the camera. Then image feature points were extracted, feature points distributed in the dynamic object were removed, and camera pose was estimated by using static feature points. Finally, the semantic point cloud map and semantic octree map were constructed. Results of multiple comparison tests on five dynamic sequences of public datasets show that compared with the SLAM system using SegNet network, the proposed system has the standard deviation of absolute trajectory error improved by 6.9%-89.8%, and has the standard deviation of translation and rotation drift improved by 73.61% and 72.90% respectively in the best case in high dynamic scenes. The results show that the improved method can significantly reduce the error of pose estimation in dynamic scenes, and can correctly estimate the camera pose in dynamic scenes.

In recent years, the tendency of depression tends to occur at a younger age and affects more people. Although research on the topic has achieved some results, it still lacks a more objective and accurate method for identifying depressive tendencies, and research on depressive tendencies from multiple perspectives is lacking. Therefore, the combination of mental health self-check table and eye-tracking was proposed as a method for identifying depressive tendencies and was studied from multiple perspectives. The innovative features of eye movement, memory, cognitive style, and network behaviors were incorporated. In order to address complex feature relationship and extract more useful information, a scanning process with combining a stacking method was proposed to form a proposed recognition model for depressive tendencies called scanning stacking model. To comprehensively and objectively evaluate the performance of scanning and stacking model, the independent contributions of both scanning process and stacking method were evaluated in the experiment. The experimental results show that the independent contribution of scanning process is 0.03, and the independent contribution of stacking method is 0.02. In addition, the scanning stacking model was compared with several models from parameter R-squared, Mean Square Error (MSE) and average absolute error, and the results show that the scanning stacking model has better prediction effect.

A cloud model is not suitable for non-normal distribution. In order to solve the problem, a new one-dimensional backward cloud algorithm based on uniform distribution was proposed and applied to the credibility evaluation system of simulation system. Firstly, the importance of simulation credibility was expounded, and the credibility evaluation index of the evaluation results for a type of equipment concerning anti-jamming capability was established based on the actual project background. Secondly, the system was evaluated by using the evaluation method for simulation credibility based on cloud model, and the evaluation method was improved. Finally, in order to improve the evaluation method, a one-dimensional backward cloud algorithm based on uniform distribution was derived, and the experiment was designed for verifying the validity of the algorithm. The simulation experimental results show that, the average absolute error of the proposed backward cloud algorithm is less than 5% for large data, which has high applicability and provides a way of thinking for the perfection of cloud model theory. In addition, the simulation credibility evaluation results show that, the proposed method has high accuracy and contains the data information of dispersion and agglomeration, which can provides more comprehensive evaluation and the prediction of error data.

Although the relevant research on Web advertisement effect has achieved good results, there are still a lack of thorough research on the interaction between advertisement and each blue link in a Web page, as well as a lack of thorough analysis of the impact of user characteristics and advertising features, and advertising metrics are also inappropriate. Therefore, a method based on multi-modal feature fusion was proposed to study the effectiveness of Internet advertising and user behavior patterns under the influence of multiple factors. Through the quantitative analysis of multi-modal features, the attractiveness of advertising was verified, and the attention effects under different conditions were summarized. By mining frequent patterns of user behavior information and combining with the characteristics of the data, the Directional Frequent Browsing Patterns (DFBP) algorithm was proposed to directionally mine the most common browsing patterns of users with fixed-length. Memory was used as a new index to measure the quality of advertising, and the random forest algorithm was improved by frequent pattern, then a new advertising memory model was built by fusing multimodal features. Experimental results show that the memory model has an accuracy of 91.64%, and it has good robustness.

Focusing on the person re-identification in non-overlapping camera views and the high dimensional feature extracted from the images, a person re-identification method based on block sparse representation was proposed. The Canonical Correlation Analysis (CCA) was taken to carry out the feature projection transformation, and the curse of dimensionality caused by high dimensional feature operation was avoided by improving the feature matching ability, and the feature vectors in a probe image were made to be probably linear with the corresponding gallery feature vectors in the learned projected space of CCA transformation. A person re-identification model was also built with block structure feature of pedestrian dataset, and the associated optimization problem was solved by utilizing the alternating direction framework. Finally, the residues were used to deal with the person in the probe set to be identified and the index of the minimum value in the residues was regarded as the identity of the person. Several experiments were conducted on public datasets such as PRID 2011, iLIDS-VID and VIPeR. The experimental results show that the Rank1 value of the proposed method on three experimental datasets reaches 40.4%, 38.11% and 23.68%, respectively, which is significantly higher than that of Large Margin Nearest Neighbor (LMNN) method, and the matching rate of it on Rank-1 is also much bigger than that of LMNN method; besides, the overall performance of it is better than the classical algorithms based on feature representation and metric learning. The experimental results verify the effectiveness of the proposed method on person re-identification.

Most of the existing digit recognition algorithms recognize single-type digits, and can not recognize multi-source digits. Aiming at the character recognition scenarios with handwritten digits and digital tube digits, a multi-source digit recognition algorithm based on improved Convolutional Neural Network (CNN) was proposed. Firstly, a mixed data set consisting of handwritten and digital tube digits was established by using the samples collected from the field of digital display instrument manufacturer and MINIST data set. Then, considering better robustness, an improved CNN was proposed, which was trained by the above mixed data set, and a network was realized to recognize multi-type digits. Finally, the trained neural network model was successfully applied to the multi-source digit recognition scene of RoboMaster robotics competition. The test results show that, the overall recognition accuracy of the proposed algorithm is stable and high, and it has good robustness and generalization ability.

Person Re-Identification (Re-ID) under non-overlapping multi-camera is easily affected by illumination, posture, and occlusion, and there are image mismatches in the experimental process. A Re-ID method based on siamese network and reranking was proposed. Firstly, a pair of pedestrian training images were given, a discriminative Convolutional Neural Network (CNN) feature and similarity measure could be simultaneously learned by the siamese network to predict the pedestrian identity of the two input images and determine whether they belonged to the same pedestrian. Then, the k-reciprocal neighbor method was used to reduce the image mismatches. Finally, Euclidean distance and Jaccard distance were weighted to rerank the sorted list. Several experiments were performed on the datasets Market1501 and CUHK03. The experimental results show that the Rank1 (the probability of matching successfully for the first time) reaches 83.44% and mAP (mean Average Precision) is 68.75% under Single Query on Market1501. In the case of single-shot on CUHK03, the Rank1 reaches 85.56% and mAP is 88.32%, which are significantly higher than those of the traditional methods based on feature representation and metric learning.

Clustering algorithm is used to preprocess personal privacy information in order to achieve differential privacy protection, which can reduce the reconstruction error caused by directly distributing histogram data, and the reconstruction error caused by different combining methods of histogram. Aiming at the problem of sensitivity to input data parameters in DP-DBSCAN (Differential Privacy-Density-Based Spatial Clustering of Applications with Noise) differential privacy algorithm, the OPTICS (Ordering Points To Identify Clustering Structure) algorithm based on density clustering was applied to differential privacy protection. And an improved differential privacy protection algorithm, called DP-OPTICS (Differential Privacy-Ordering Points To Identify Clustering Structure) was introduced, the sparse dataset was compressed, the same variance noise and different variance noise were used as two noise-adding ways by comparison, considering the probability of privacy information's being broken by the attacker, the upper bound of privacy parameter ε was determined, which effectively balanced the relationship between the privacy of sensitive information and the usability of data. The DP-OPTICS algorithm was compared with the differential privacy protection algorithm based on OPTICS clustering and DP-DBSCAN algorithm. The DP-OPTICS algorithm is between the other two in time consumption. However, in the case of having the same parameters, the stability of the DP-OPTICS algorithm is the best among them, so the improved OP-OPTICS differential privacy protection algorithm is generally feasible.

The static gesture recognition speed is higher than that of dynamic gesture recognition for RGB-D (RGB-Depth) data, but redundancy gestures and repeated gestures lead to low recognition accuracy. In order to solve the problem, a static gesture recognition method based on locking mechanism was proposed. First, RGB data flow and the Depth data stream were obtained through Kinect equipment, then two kinds of data flow were integrated into human body skeleton data flow. Second, the locking mechanism was used to identify static gestures, and comparison and calculation were done with the established bone point feature model gesture library before. Finally, an "advanced programmers road" brain-training Web game was designed for application and experiment. In the experiments of six different movement gestures, compared with the static gesture recognition method, the average recognition accuracy of the proposed method was increased by 14.4%; compared with the dynamic gesture recognition method, the gesture recognition speed of the proposed method was improved by 14%. The experimental results show that the proposed method keeps the high speed of static recognition method, realizes the real-time recognition; and also improves the identification accuracy through eliminating redundant repeated gestures.

The data link capacity of 1090ES (1090 MHz Extended Squitter) could be expended by modulating 1090 MHz signal with phase information, thus RS (Reed-Solomon) calibration technology of 1090ES expansion system based on 8PSK (8 Phase Shift Keying) phase modulation was studied. Firstly, the total length of the RS code symbols was designed as 54 according to the characteristics of RS code and the data link structure of 1090ES expansion system. Secondly, error performance with different RS code coding efficiency was discussed, and its influence on performance of the 1090ES expansion system was analyzed, thereby, the optimum selection of RS code coding efficiency range was determined as 0.6-0.7. Finally, the concrete analysis of the error performance in the selected encoding efficiency range was given, and then the experimental results show that the length of information symbols could be chosen as 32, 34 or 36. Furthermore, Matlab simulation analysis shows that the designed RS code can effectively improve the error performance of 1090ES expansion system with RS(54, 32) as an example.

In this paper, the military Petrol-Oil and Lubricants (POL) allotment and transportation problem was studied by introducing the concept of support time window. Considering the complicated restrictions of POL support time and transportation capability, the POL allotment and transportation model based on multiple time windows was proposed by using Constraint Satisfaction Problem (CSP) modelling approach. Firstly, the formalized description of the problem elements was presented, such as POL support station, demand unit, support time window, support demand, and support task. Based on the formalized description, the CSP model for POL support was constructed. The multi-objective model was transformed into single-objective one by using perfect point method. Finally, the solving procedure and its steps were designed based on Particle Swarm Optimization (PSO) algorithm, and an arithmetic example was followed to demonstrate the application of the method. In the example, the two optimization schemes obtained by the model given in this paper and got by the model in which the objective is maximizing the quantity supported were compared. In the two schemes, the transportation capacity both reached a maximum utilization, but the start supporting time of each POL demand in the scheme of the proposed method was no later than the one in the scheme of the single-objective model. By comparing different optimization schemes, it is shown that the proposed model and algorithm can effectively solve the multi-objective POL support optimization problem.

A Novel Quantum Differential Evolutionary (NQDE) algorithm was proposed for the Blocking Flowshop Scheduling Problem (BFSP) to minimize the makespan. The NQDE algorithm combined Quantum Evolutionary Algorithm (QEA) with Differential Evolution (DE) algorithm, and a novel quantum rotating gate was designed to control the evolutionary trend and increase the diversity of population. An effective Quantum-inspired Evolutionary Algorithm-Variable Neighborhood Search (QEA-VNS) co-evolutionary strategy was also developed to enhance the global search ability of the algorithm and to further improve the solution quality. The proposed algorithm was tested on the Taillard's benchmark instances, and the results show that the number of optimal solutions obtained by NQDE is bigger than the current better heuristic algorithm-Improved Nawaz-Enscore-Ham Heuristic (INEH) evidently. Specifically, the optimal solutions of 64 instances in the 110 instances are improved by NQDE. Moreover, the performance of NQDE is superior to the valid meta-heuristic algorithm-New Modified Shuffled Frog Leaping Algorithm (NMSFLA) and Hybrid Quantum DE (HQDE), and the Average Relative Percentage Deviation (ARPD) of NQDE algorithm decreases by 6% contrasted with the latter ones. So it is proved that NQDE algorithm is suitable for the large scale BFSP.

Aiming at the problems of too long operation time and complex modeling of the three-dimensional roadway intersection modeling in geotechnical engineering, a method about cylinder-Axis Aligned Bounding Box (AABB) two-level bounding box detection was proposed according to the characteristics of tunnel's shape. The proposed method could quickly find out the possible intersected triangular elements and established a new approach to solve the Irregular Triangular Network (TIN) modeling problem of tunnel intersection by combining the three-Dimensional (3D) Boolean operation. The basic principles of the cylinder-AABB double bounding box collision detection and the key technologies about Boolean operation to implement intersection modeling in 3D were described, and an optimization scheme for generated entity mesh was proposed. Through the engineering examples, it is proved that, compared with the Oriented Bounding Box (OBB) hierarchical bounding box method, the modeling method by cylinder-AABB detection increases nearly 50% on the bounding box production efficiency in the roadway surface intersection modeling. The proposed method has the advantages of simple modeling, short detection time, high top detection accuracy, and so on.

To deal with the combinatorial optimization problem in the era of big data, a parallel Ant Colony Optimization (ACO) algorithm based on Spark, a framework for the distributed memory computing, was presented. To achieve the parallelization of the phase of solution construction in ant colony optimization, a class of ants was encapsulated to a resilient distributed dataset and the corresponding transformation operators were given. The simulation results in solving the Traveling Salesman Problem (TSP) prove the feasibility of the proposed parallel algorithm. Under the same experimental environment, the comparison results between MapReduce based ant colony algorithm and the proposed algorithm show that the proposed algorithm significantly improves the optimization speed at least ten times than the MapReduce one.

To effectively control large-scale outbreak, the propagation properties of the leeching P2P (Peer-to-Peer) botnet was studied using dynamics theory. Firstly, a delayed differential-equation model was proposed according to the formation of the botnet. Secondly, the threshold expression of controlling botnet was obtained by the explicit mathematical analysis. Finally, the numerical simulations verified the correctness of theoretical analysis. The theoretical analysis and experimental results show that the botnet can be completely eliminated if the basic reproduction number is less than 1. Otherwise, the defense measures can only reduce the scale of botnet. The simulation results show that decreasing the infection rate of bot programs or increasing the immune rate of nodes in the network can effectively inhibit the outbreak of botnet. In practice, the propagation of bot programs can be controlled by some measures, such as uneven distribution of nodes in the network, timely downloading patch and so on.

Accurate background model is the paramount base for object extracting and tracing. In response to swing objects which part quasi-periodically changed in intricate scene, based on multi-Gaussian background model, a new Quasi-Periodic Background Algorithm (QPBA) was proposed to suppress the swing objects and establish an accurate and stable background model. The specific process included: According to multi-Gaussian background model, the object classification in scene was set up, and the effect on Gaussian model's parameters caused by swing objects was analyzed. By using color distribution values as samples to establish Gaussian model to keep swing pixels, the swing model in swing pixels was integrated into background model with weight factors of occurrence frequency and time interval. Comparison among QPBA and the classical background modeling algorithms such as GMM (Gaussian Mixture Model), ViBe (Visual Background extractor) and CodeBook was put forward, and the results were assessed in aspects of quality, quantity and efficiency. It shows that QPBA has a more obvious suppression on swing objects, and its fall-out ratio is less than 1%, so that it can handle the scene with swing objects. At the same time, its correct detection number is consistent with other algorithms, thus the moving objects can be reserved perfectly. In addition, the efficiency of QPBA is high, and its resolving time is approximate to CodeBook, which can satisfy the requirements of real-time computation.