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    Research on Fine-Grained Fault Diagnosis of Rolling Bearings
    RUAN Hui, HUANG Xixia, LI Dengfeng, WANG Le
    Computer Engineering and Applications    2024, 60 (6): 312-322.   DOI: 10.3778/j.issn.1002-8331.2210-0341
    Abstract25)      PDF(pc) (895KB)(26)       Save
    Aiming at the current situation that supervised deep learning is mainly used to extract fault features and detect coarse-grained types of faults in rolling bearing fault diagnosis, a fine-grained fault diagnosis method for rolling bearings integrated with Gaussian mixture models (GMM) and deep residual shrinkage networks (DRSN) is proposed. The GMM model integrates multiple Gaussian distribution functions to fit the distribution of fine-grained fault data and realize the clustering of bearing vibration signals without labels. The attention mechanism in DRSN model focused on the more critical information for the current task from a large number of fault feature information. Soft threshold is designed to set different thresholds for bearing samples in different health states. The method is validated by collecting 30 bearing health states from Case Western Reserve University (CWRU) data. The results show that integrate the unsupervised model with the deep learning model, which can process the bearing fault data without labels, achieve the purpose of fine-grained classification of bearing faults, provide a basis for subsequent equipment maintenance, and have good practical engineering significance and popularization.
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    Human Respiratory Rate Detection on UAV Platform
    LIANG Shuai, YANG Xuezhi, ZANG Zongdi
    Computer Engineering and Applications    2024, 60 (6): 323-329.   DOI: 10.3778/j.issn.1002-8331.2211-0159
    Abstract17)      PDF(pc) (698KB)(19)       Save
    It is a means of injury assessment to detect respiratory rate using an unmanned aerial camera. However, the existing video-based respiratory rate detection algorithms are only applicable to fixed cameras. On the basis of spatial phase based respiratory signal extraction technology, a non-contact measurement method of human respiratory rate with videos recorded by unmanned aerial vehicle (UAV) is proposed. The complex steerable pyramid is used to extract the spatial phase of each frame image, and the phase sequence is obtained in chronological order. Secondly, the empirical mode decomposition (EMD) is used to decompose multiple modal components from the phase sequence, and the frequency variability analysis model is designed to select the components with stable frequencies, or the target respiratory signal. The peak value detection method is used to detect the human respiratory rate. The experimental results show that the average accuracy of the method can reach more than 98%, which is superior to the existing detection methods.
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    Hybrid LightGBM Model for Breast Cancer Diagnosis
    XING Changzheng, XU Jiayu
    Computer Engineering and Applications    2024, 60 (6): 330-338.   DOI: 10.3778/j.issn.1002-8331.2211-0218
    Abstract26)      PDF(pc) (692KB)(36)       Save
    Breast cancer is one of the most common types of cancer, and its prevalence continues to rise every year. Without surgical biopsy, it can effectively provide auxiliary diagnosis and treatment for doctors and reduce the pain of patients by analyzing various indicators of the nucleus to predict whether the mass is benign or not. Therefore, a breast cancer diagnosis model based on LightGBM algorithm is proposed. Firstly, the borderline-synthetic minority oversampling technique (Borderline-SMOTE)  is used to improve the problem of imbalanced breast cancer diagnosis data. Secondly, the PWLCM chaotic map, the new inertia weight and the criss-cross algorithm are introduced into the sparrow search algorithm (SSA)  to improve it, and then the improved SSA algorithm is used to automatically optimize the parameters of LightGBM. Then, because LightGBM is sensitive to noise, an OVR-Jacobian regularization method is proposed to reduce the noise of LightGBM. Finally, the improved LightGBM hybrid model is used to diagnose breast cancer. The experimental results show that the proposed hybrid model is superior to the common models in the three indicators of mean square error, coefficient of determination and cross-validation score, showing its better diagnostic effect.
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    Path Planning for Blood Transportation by Unmanned Aerial Vehicles with Safety of Taking-Off and Landing
    XU Weihua, ZHANG Genrui, QIU Longlong, ZHAO Caimei, XIONG Jian
    Computer Engineering and Applications    2024, 60 (6): 339-348.   DOI: 10.3778/j.issn.1002-8331.2211-0296
    Abstract37)      PDF(pc) (692KB)(40)       Save
    To solve the problem of path planning in blood transportation by unmanned aerial vehicles (UAVs), a path planning model for blood transportation by UAVs is established with the goal of minimizing total transportation distance of UAVs. Considering the safe time interval of continuous take-off and landing of UAVs under the condition that the number of UAVs take-off and landing platforms is limited, a scheduling strategy of UAVs take-off sequence is designed to reduce the total time consumption for UAVs to complete transportation. And an imperialist competitive algorithm based on the imperialist reform is proposed. A sinusoidal disturbance strategy and an imperialist reform are introduced to improve the search accuracy of the algorithm. The acceptance criteria related to solution quality is designed to increase the diversity of the population. The benchmark example and the UAVs transporting blood example are used for verification. The results show that the proposed algorithm can provide transport tasks for the blood transportation by UAVs that meets various constraints and without UAVs take-off and landing conflicts. In addition, using the scheduling strategy of UAVs take-off sequence can reduce the total time spent by UAVs to actually complete the tasks effectively.
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    Path Tracking Control Strategy for 4WD/4WS Autonomous Vehicle with Considering Lateral Stability
    XIANG Jingyan, ZHOU Kui, FU Yongzhi, XU Yang, PENG Xufeng
    Computer Engineering and Applications    2024, 60 (6): 349-358.   DOI: 10.3778/j.issn.1002-8331.2211-0154
    Abstract28)      PDF(pc) (884KB)(48)       Save
    According to the characteristics of single-wheel solo controllable of unmanned vehicle with distributed four-wheel drive/steering, a path tracking control strategy considering stability is proposed to improve the vehicle stability of unmanned vehicle under high-speed and low-attachment working conditions. Specifically, the path tracking controller is built by pre-targeting-following theory to achieve real-time tracking of the planned path of the unmanned vehicle; and the integrated control of the rear wheel turning angle and direct transverse moment is carried out by sliding mode control theory, and the torque distribution is realized by considering the front and rear axle loads and road adhesion coefficients to improve the body stability. The simulation results show that compared with the path tracking control strategy without considering stability, the path tracking strategy with considering stability decreases the average lateral tracking error by 5.2%, the peak mass lateral declination by 57.4% and the peak transverse pendulum velocity by 23% in the high-speed double shift line condition; in the low attachment mountain road condition, the average lateral tracking error decreases by 9.1%, the peak mass lateral declination by 54.3% and the peak transverse pendulum velocity by 1.4%. On the premise of ensuring the vehicle path tracking accuracy, the driving stability of the vehicle is improved.
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    Correctness Detection of Smart Contract Based on Fuzzing
    WANG Jiacheng, JIANG Jiajia, ZHAO Jiahao, ZHANG Yushu, WANG Liangmin
    Computer Engineering and Applications    2024, 60 (5): 307-320.   DOI: 10.3778/j.issn.1002-8331.2211-0142
    Abstract33)      PDF(pc) (668KB)(28)       Save
    The development of smart contracts is in its early stages. Different underlying programming languages and application platforms make the design of smart contracts lack specifications, which is prone to loopholes and losses. For the security vulnerability of smart contracts on Ethereum, it proposes a method for correctness detection of smart contracts based on fuzzing. This method generates fuzzy inputs based on the content and specifications of the smart contract, executes the smart contract in Ethereum virtual machine according to the fuzzy inputs, monitors the behavior of the contract in the execution process, generates multiple log files, extracts key information from the log files, triggers the test cases to get the vulnerabilities contained in the smart contract, and achieves the correctness detection. During the experiment, it detects 416 smart contracts for seven common vulnerability types and identifies 19 smart contracts as vulnerabilities. According to the analysis of artificial auditing, 18 of the 19 marked incorrect contracts do have security vulnerabilities. The experimental results show that the proposes method can identify the vulnerabilities contained in the smart contract with high accuracy, to detect the correctness of the smart contract.
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    Improved YOLOv5 Model for Surface Defect Detection of Automotive Gear Components
    ZHU Deping, CHENG Guang, YAO Jingli
    Computer Engineering and Applications    2024, 60 (5): 321-327.   DOI: 10.3778/j.issn.1002-8331.2306-0425
    Abstract54)      PDF(pc) (633KB)(48)       Save
    Aiming at the problems of low efficiency and poor precision in surface defect detection of automotive gear components, an improved defect detection method YOLO-CNF based on YOLOv5 is proposed. Firstly, add the CBAM attention module to the backbone network to make the model pay more attention to the defect areas of gear components and improve the ability to identify small defects. Secondly, the F2C module is designed to fuse shallow features, which alleviates the problem of the loss of small defect location information to a certain extent. Finally, NWD is used to optimize the regression loss to reduce the sensitivity to small target position deviations, and further improving the accuracy and precision of target positions. The experimental results show that the average precision of the improved algorithm reaches 86.7%, which is 3.2 percentage points higher than the original algorithm, and the detection speed is 43 frames per second. The improved algorithm basically meets the needs of the surface defect detection of automotive gear components.
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    Improved Miner Chin Strap Detection and Personnel Tracking with YOLOv8s and DeepSORT
    DING Ling, MIAO Xiaoran, HU Jianfeng, ZHAO Zuopeng, ZHANG Xinjian
    Computer Engineering and Applications    2024, 60 (5): 328-335.   DOI: 10.3778/j.issn.1002-8331.2310-0280
    Abstract44)      PDF(pc) (695KB)(44)       Save
    Ensuring proper safety helmet usage is of utmost importance in underground mining inspections to protect workers. However, challenging conditions, such as high temperatures, often lead to non-compliant helmet wearing behavior. Existing detection methods are insufficient for underground environments, resulting in low recognition accuracy and inadequate detection of correctly worn helmets. To address these issues, this paper proposes an improved version of the CM-YOLOv8s algorithm that focuses on the chin strap as a small target for safety helmet detection and compliance assessment. The DeepSORT algorithm is then employed to track workers who fail to comply with helmet-wearing regulations. To begin, a comprehensive dataset is curated utilizing underground surveillance cameras. The CM-YOLOv8s algorithm is leveraged for safety helmet detection by incorporating higher-resolution feature maps and introducing a cascaded query mechanism. This approach enables precise detection of small targets without significantly increasing computational costs. Furthermore, the enhanced DeepSORT algorithm is employed for person tracking by replacing the small residual network in DeepSORT with deeper convolutional layers, thereby enhancing the extraction of appearance information. The proposed algorithm is validated using a self-made dataset for underground safety helmet detection and tracking. Experimental results demonstrate that CM-YOLOv8s achieves an average precision of 92.3% for safety helmet recognition, which is a 4.2 percentage points improvement over YOLOv8s. Additionally, the average accuracy of the safety helmet compliance recognition system, based on CM-YOLOv8s and DeepSORT, is 85.37%, with a detection speed of 59 FPS. The proposed algorithm effectively addresses compliance detection in safety helmet wearing by accurately assessing the position of the chin strap in proximity to the individual’s jaw. It strikes an optimal balance between detection speed and accuracy while exhibiting robust adaptability to the complex underground environments. The successful implementation of this algorithm at the Chensilou Coal Mine over an extended period has demonstrated its efficacy in monitoring and providing early warnings for abnormal safety helmet wearing, thereby bolstering regulatory oversight and promoting the compliant use of safety helmets among miners. The algorithm holds great potential for enhancing safety measures in underground mining inspections and can be applied to similar industrial scenarios. Further research and development in this direction are warranted to expand its applicability and impact.
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    CIEFRNet:Abandoned Objects Detection Algorithm for Highway
    LI Xu, SONG Huansheng, SHI Qin, ZHANG Zhaoyang, LIU Zedong, SUN Shijie
    Computer Engineering and Applications    2024, 60 (5): 336-346.   DOI: 10.3778/j.issn.1002-8331.2306-0395
    Abstract41)      PDF(pc) (928KB)(36)       Save
    Highway abandoned objects endanger traffic safety, easily cause traffic accidents, so it is critical to recognize and clean them up in time. Due to the small area of highway abandoned objects in the image and complex image background, the existing detection methods often have the problems of missed and false detection. To address the above problems, an abandoned objects detection algorithm based on contextual information enhancement and feature refinement is proposed, which is called CIEFRNet. Firstly, a backbone feature extraction module (CSP-COT) incorporating contextual Transformer is designed to fully mine local static and global dynamic context information, and enhance the feature representation of small abandoned objects. In addition, the proposes improved spatial pyramid pooling (ISPP) is used in the backbone, multi-scale downsampling of features is realized by cascade dilated convolution, which reduces the loss of object detail information; in order to improve the feature fusion ability, a feature refine module (CNAB) is designed, in which a proposed mixed attention mechanism (ECSA) is embedded, which can suppress image background noise, and enhances the features of tiny abandoned objects. Finally, it uses the WIoU loss function based on dynamic non-monotonic focus mechanism to improve the learning ability of small abandoned objects and accelerate the network convergence. The experi-
    mental results demonstrate that the proposed method achieves 96.5%, 81.6%, 88.1% and 46.5% of accuracy, recall, AP0.5 and AP0.5:0.95 on the self-made highway abandoned objects dataset, respectively, which is better than the currently prevailing object detection methods, and its algorithm complexity is also lower to meet the needs of practical scene applications.
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    Multi-Resource Cooperative Optimization of Container Terminals Considering Inter-Ship Transshipment Service
    WANG Xiaoguang, WANG Hongyu, LIU Boyu
    Computer Engineering and Applications    2024, 60 (5): 347-356.   DOI: 10.3778/j.issn.1002-8331.2210-0107
    Abstract42)      PDF(pc) (606KB)(35)       Save
    To make efficient use of terminal resources and improve the operation efficiency of container terminals, based on the background of large-scale transshipment ports, aiming at the tactical decision-making problem of terminal resource allocation, considering the interaction of ship schedule stability, inter-ship container transshipment flow, berth scheduling and yard allocation, and based on the terminal active management strategy, a terminal multi-resource collaborative optimization model is established with the objective of minimizing the completion time of all container transshipment tasks. Based on this, an artificial immune algorithm based on tabu search is designed to solve the model. The effectiveness of the model and algorithm is verified by six numerical experiments of different scales and the comparison experiments with genetic algorithm and artificial immune algorithm. The comprehensiveness and superiority of the collaborative scheduling model are tested by case comparison experiments in different scenarios. The results show that the minimum transit time can be shortened by 4.33% and the maximum can be shortened by 20.19%. It provides an effective decision-making idea for the design of multi-resource allocation template of container terminal.
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    Multi-Objective Shop Floor Scheduling Combining NSGA-II and CSA
    YANG Qing, XI Zhenzhen, GE Liang, LIN Xingyu, XING Zhichao
    Computer Engineering and Applications    2024, 60 (4): 315-323.   DOI: 10.3778/j.issn.1002-8331.2210-0486
    Abstract41)      PDF(pc) (2494KB)(23)       Save
    Aiming at the simultaneous scheduling problem of scheduling jobs and automated guide vehicles (AGVs) in the flexible workshop system, consider building the objective function to minimize maximum processing machine duration, single AGV handling time, and total AGV handling time in the case of a finite number of AGVs and processing machines. Design an improved algorithm that combines NSGA-II (non-dominated sorting genetic algorithms) and clonal selection algorithm (CSA) to solve such problems. Firstly, the workpiece, the processing machine and the AGV are used for three-part coding. Secondly, the total score of non-dominated ranking and objective function value size sorting is introduced to stratify the population, so as to effectively retain excellent individuals. Thirdly, for the cloned population, different probabilities of variation are adopted for different levels of populations, and the genetic recombination of internal exchange and uniform cross-mixing exchange of chromosomes is carried out to effectively improve the diversity of the population and prevent it from falling into local optimum. Finally, three sets of comparative experiments verify that the algorithm has the advantages of short running time, high stability and good convergence when exploring the optimal solution.
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    Predication Method of Continuous Non-Invasive Arterial Blood Pressure Using Fusion U-net Model
    WANG Jun’ang, ZHANG Lixin, WANG Sai, WU Kaifeng, KAN Xi, CHEN Naiyuan
    Computer Engineering and Applications    2024, 60 (4): 324-330.   DOI: 10.3778/j.issn.1002-8331.2211-0031
    Abstract46)      PDF(pc) (2180KB)(40)       Save
    Continuous blood pressure monitoring is helpful to the diagnosis and treatment of cardiovascular diseases. At present, machine learning and deep learning are used to predict blood pressure by manually extracting feature parameters. This method cannot reconstruct complete blood pressure signals. Therefore, a continuous non-invasive arterial blood pressure measurement method based on the fused U-net model is proposed. Firstly, the original photoplethysmogram (PPG) signal is used as the input to reduce the error of manually extracting feature parameters. Secondly, the U-net network is used to reconstruct the arterial blood pressure signal. In order to further improve the accuracy of the predicted blood pressure waveform, the reconstructed blood pressure signal is used as the input of the MultiResUnet network. The MultiRes module is used to learn different features from the data. The Res Path module alleviates the semantic differences between the encoder and the decoder, making the model learning easier. The arterial blood pressure (ABP) waveform predicted by the fused U-net network in the subject evaluation of MIMIC -Ⅲ dataset is highly correlated with the actual waveform. The calculated mean absolute errors of systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean pressure (MAP) are 2.20 ± 4.30 mmHg, 1.82 ± 3.146 mmHg and 2.25 ± 2.86 mmHg. The method satisfies the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and reaches Grade A in the British High Pressure Society (BHS) standard.
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    Dialogue Symptom Inference Based on Structured Self-Attention Network
    PAN Dinghao, YANG Zhihao, LIN Hongfei, WANG Jian
    Computer Engineering and Applications    2024, 60 (4): 331-337.   DOI: 10.3778/j.issn.1002-8331.2211-0071
    Abstract25)      PDF(pc) (2099KB)(14)       Save
    Symptom inference is a critical component of the medical dialogue system for automatic diagnosis. With the improvement of online diagnosis in recent years, the number of doctor-patient dialogue texts has continued to increase. The initial research on symptom inference based on electronic health records has gradually shifted to doctor-patient dialogue texts. However, most existing studies ignore the special role and symptom entity structure prior knowledge in dialogue, which can help the model learn contextual associations better. Therefore, this paper proposes an improved self-attention network based on the prior knowledge of role and entity structure and combines it with a pre-trained language model. The proposed model integrates the prior knowledge of role and entity structure into the encoding stage of the text, and can more accurately infer the attributes of symptom entities. Experimental results on the CHIP-MDCFNPC dataset of the CBLUE2.0 show that the proposed model outperforms the baseline model, which verifies the effectiveness of prior knowledge and model structure.
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    Financial Fraud Recognition Method for Listed Companies Based on Deep Learning and Textual Emotion
    CAO Ce, CHEN Yan, ZHOU Lanjiang
    Computer Engineering and Applications    2024, 60 (4): 338-346.   DOI: 10.3778/j.issn.1002-8331.2305-0281
    Abstract54)      PDF(pc) (2187KB)(43)       Save
    Financial fraud of listed companies refers to the untrustworthy behavior of distorting accounting information by improper means, which has a negative impact on company operations, economic development, and social interests. At present, more research focuses on financial digital data, and less research on text information and deep learning algorithms. Therefore, a financial fraud recognition method for listed companies based on deep learning and textual emotional feature is proposed. Firstly, the method selects and preprocesses the financial indicators, and uses Bi-LSTM to extract the emotional features of the stock review text. Then, the method uses the RCC (residual-cross-convolutional) parallel network to recognize financial fraud. The network uses residual network, cross network, convolutional network and long short-term memory network to extract financial fraud features in parallel, and uses batch normalization and full connection to obtain the final recognition result. The experiment results show that this method achieves better results than other models in recognizing financial fraud for listed companies, with a recall rate and AUC of 88.46% and 82.06% respectively.
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    End-to-End Robotic Arm Vision Servo Research Combined with Bottleneck Attention Mechanism
    LIU Bingkun, PI Jiatian, XU Jin
    Computer Engineering and Applications    2024, 60 (4): 347-354.   DOI: 10.3778/j.issn.1002-8331.2207-0459
    Abstract26)      PDF(pc) (2333KB)(19)       Save
    Aiming at the cumbersome feature extraction steps and poor real-time performance of traditional visual servoing algorithms, an end-to-end direct visual servoing algorithm based on convolutional neural network is proposed. By directly predicting the instantaneous linear velocity and instantaneous angular velocity of the camera installed at the end of the robotic arm, servo positioning works without the need to label handcrafted features, camera intrinsics, or depth information. Firstly, the image observed by the camera is input into the GhostNet feature extraction network for information extraction, and the bottleneck attention module (BAM) is integrated into the network to enhance the spatial information and channel information of the target object. Then, the fully connected layer is used as the speed regression function, the linear velocity and angular velocity are decoupled and regressed. Finally, a real-time capture method is used to create the dataset required for training in the real environment, and the velocity labels are generated according to the position-based visual servo control law algorithm. Efficient and accurate localization and tracking tasks are accomplished under the trained initial pose. The experimental test results in a large number of real scenes verify the effectiveness of the algorithm, and it is also robust to scene background information.
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    Solving Time Dependent Vehicle Routing Problem Based on Improved Aquila Optimizer Algorithm
    SHI Xiaojuan, ZHAO Xingfang, YAN Long, TANG Yuan, ZHAO Huimin
    Computer Engineering and Applications    2024, 60 (4): 355-365.   DOI: 10.3778/j.issn.1002-8331.2211-0048
    Abstract60)      PDF(pc) (2678KB)(36)       Save
    An improved Aquila optimizer (IAO) algorithm is proposed for the time dependent vehicle routing problem with time-varying speed, and the travel time calculation method of the time dependent road network is analyzed. An Aquila-customer (A-C) coding and decoding method is designed based on the characteristics of vehicle routing problem. Combining the four hunting methods of Aquila: expanding exploration, narrowing exploration range, expanding exploitation range and narrowing exploitation range, its intelligent search behavior is redefined, adaptive large neighborhood search (ALNS) strategy is introduced, and various neighborhood destroy operators and repair operators are designed. The inferior solution acceptance criterion is added to the algorithm, and two stagnation perturbation strategies, circular heuristic perturbation mechanism and elite perturbation mechanism, are proposed. Solomon benchmark and simulations based on Figliozzi test cases with genetic algorithms, particle swarm algorithms and ant colony algorithms demonstrate the optimization performance of the IAO algorithm, while the experimental results of real cases verify the superiority of the IAO algorithm in terms of convergence speed and solution quality. It is shown that IAO algorithm has the application value for solving time dependent vehicle routing problem.
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    Path Planning of Robotic Arm Based on Improved RRT Algorithm Combined with A*
    LONG Houyun, LI Guang, TAN Xinxing, XUE Chenkang, YI Jing
    Computer Engineering and Applications    2024, 60 (4): 366-374.   DOI: 10.3778/j.issn.1002-8331.2303-0157
    Abstract54)      PDF(pc) (2566KB)(35)       Save
    For the problem that the RRT (rapidly-exploring random tree) path planning algorithm generates a huge number of nodes when planning the obstacle avoidance path of robotic arm in high-dimensional space, resulting in a large burden of algorithm operation, poor obstacle avoidance performance, and easy to fall into local extremes, an improved RRT algorithm combining A* judgment function is proposed. The sampling method of RRT is changed to generate a sequence of multiple randomly sampled points each time, and the improved A* judgment function is used for sorting. Distance judgment is performed on each generated node to prevent it from falling into local search. Finally, a repetitive greedy strategy is used to remove redundant nodes, and cubic B-spline is used to make path smooth. The performance of the algorithm is analyzed in 2D and 3D maps and robot arm simulations and prototype experiments. The improved RRT algorithm can effectively reduce the number of nodes for the robotic arm to reach the target poses, alleviate the local extremes, and avoid obstacles to reach the target poses quickly and stably, which proves the effectiveness and superiority of the improved RRT algorithm.
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    Optimization of Location-Routing for Collection Center Under Smart Waste Collection
    MA Yanfang, JIA Jiapeng, LI Zongmin, YAN Fang
    Computer Engineering and Applications    2024, 60 (3): 309-320.   DOI: 10.3778/j.issn.1002-8331.2210-0197
    Abstract38)      PDF(pc) (794KB)(40)       Save
    With the rapid development of Internet of things technology and the improvement of public awareness of environmental protection, smart dustbins are gradually popular, making the waste recycling work face new challenges. Aiming at the location-routing problem with capacity constraints, a multi-agent optimization model of waste collection location-routing is constructed by introducing the two-commodity flow formulation. Smart recycling enterprise locates the collection center and aims to minimize the total cost in the upper level. Outsourcing transportation company selects the smart bins to be visited based on the recycling threshold, plans the recycling path and ensures that transportation costs are minimized in the lower level. An improved genetic algorithm is used to solve the problem: the upper layer uses clustering algorithm to determine the location of the collection center; the lower layer uses random generation and Clarke and Wright savings method to generate the initial population. And best-cost route crossover operator and inversion mutation operator are introduced. Based on Prins and Barreto benchmarks and compared with BKS, GAPSO and BSA, the average gap between the results and BKS is 0.419%. The smart waste collection routing method can effectively reduce the total cost, tested by simulating real cases, which provides decision support to solve the location-routing problem with capacity constraints in the context of smart waste collection.
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    Clinical Feature Recalibration Attention Network for Cataract Recognition
    ZHANG Xiaoqing, XIAO Zunjie, ZHAO Yuhang, WU Xiao, Risa Higashita, LIU Jiang
    Computer Engineering and Applications    2024, 60 (3): 321-330.   DOI: 10.3778/j.issn.1002-8331.2210-0158
    Abstract31)      PDF(pc) (791KB)(24)       Save
    In recent years, convolutional neural networks (CNNs) have been widely used for automatic age-related cataract classification. However, incorporating clinical prior knowledge of age-related cataracts into CNN design improves the classification performance and the interpretability of the decision-making process of CNNs, which has been less studied. To this problem, this paper proposes a clinical-feature recalibration attention network (CFANet) to classify age-related cataract severity levels automatically. In the CFANet, a simple yet effective clinical feature recalibration attention (CFA) block is designed to fuse clinical features adaptively by setting relative weights, aiming to highlight significant channels and suppress redundant ones. This paper conducts extensive experiments on a clinical AS-OCT image dataset of nuclear cataract and a public eye image dataset to verify the effectiveness of CFANet. The results show that CFANet outperforms advanced baselines by above 3.54 percentage points of accuracy on the clinical AS-OCT image dataset, such as squeeze-and-excitation network (SENet), efficient channel network (ECANet), style-based recalibration module (SRM). And the results on the public eye dataset also show that compared with strong attention-based CNNs and published works, proposed method obtains over 1 percentage point improvement. Moreover, this paper also uses visualization methods to analyze clinical feature weights and channel attention weights to enhance the interpretability of the decision-making process for proposed method.
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    Dataset Enhancement Quality Evaluation Method for Chinese Error Correction Task as Example
    SONG Cheng, XIE Zhenping
    Computer Engineering and Applications    2024, 60 (3): 331-339.   DOI: 10.3778/j.issn.1002-8331.2210-0253
    Abstract30)      PDF(pc) (615KB)(19)       Save
    Data augmentation is considered to be an effective solution to improve model performance. However, when selecting the generated data, it is necessary to consider the inherent data characteristics and the corresponding task relevance. Aiming at this problem, taking the Chinese error correction task scenario as an example, an evaluation method that can be used for dataset enhancement quality is proposed. The method uses the pre-training model optimized by contrastive learning to extract the feature vector of the dataset, and then proposes three basic evaluation indicators such as mutual coverage, total dispersion, and self-support, and gives a comprehensive dataset quality fusion indicator. The experimental analysis results on four data enhancement methods, two Chinese error correction data sets and three Chinese error correction models show that the above evaluation method can be independent of the test set performance inspection method, providing an important basis for the selection of different enhanced datasets.
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    Defect Detection of Photovoltaic Modules Based on Multi-Scale Feature Fusion
    TIAN Hao, ZHOU Qiang, HE Chenlong
    Computer Engineering and Applications    2024, 60 (3): 340-347.   DOI: 10.3778/j.issn.1002-8331.2304-0390
    Abstract53)      PDF(pc) (661KB)(34)       Save
    A photovoltaic modules defect detection algorithm based on multi-scale feature fusion is proposed to address the challenges of complex defect backgrounds, large differences in defect scales, and a high number of small target defects that traditional object detection algorithms cannot solve. The algorithm is based on the YOLOv5s framework. Firstly, a coordinate attention mechanism is embedded in the backbone network to extract important defect shapes and enhance the network’s feature extraction ability. Secondly, a bidirectional feature pyramid network is used in the Neck network to adaptively fuse image features of different scales using adaptive weights. Finally, a tiny target detection layer is added to the prediction layer, and the ASFF detection head is used to adaptively fuse different output layers to reduce the loss of target feature information. The improved algorithm is validated on a photovoltaic component dataset, and the experimental results show that it can quickly and accurately identify defects, with an mAP of 91.9% and a recall rate of 90.8%, which represents a 3.2 and 4.5 percentage points improvement in mAP and recall rate, respectively, compared to the YOLOv5s network.
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    Application of ADASVM-CSLINEX Model Considering Misclassification Cost
    YANG Yuanyuan, LU Tongyu, CUI Jun, XU Wenfu
    Computer Engineering and Applications    2024, 60 (3): 348-356.   DOI: 10.3778/j.issn.1002-8331.2210-0379
    Abstract28)      PDF(pc) (659KB)(15)       Save
    In binary classification prediction,there are two types of errors that inevitably occur in the prediction of stocks,but the misclassification costs of these two types of errors are often different in practical applications, and this article focuses on this issue. This paper introduces an asymmetric LINEX (Linear-exponential) loss function, which uses LINEX loss to achieve cost sensitive learning by penalizing the low cost of misclassification class linearly and punishing the high cost of misclassification class exponentially.The model uses SVM as the base classifier for AdaBoost, embedding LINEX loss function into AdaBoost-SVM weight update equation and updating the samples weights according to the different misclassification costs of positive and negative samples and whether the samples are misclassified or not. This paper takes the components of HS300 from January 2011 to December 2020 as samples for empirical research, and the proposed model is used to predict the rise and fall. The result shows that the ADASVM-CSLINEX model can obtain higher investment performance.
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    Surface Defect Detection of Polarizer Based on Improved YOLOX-S Algorithm
    CHEN Le, ZHOU Yongxia, ZU Jiazhen
    Computer Engineering and Applications    2024, 60 (2): 295-303.   DOI: 10.3778/j.issn.1002-8331.2209-0375
    Abstract43)      PDF(pc) (711KB)(49)       Save
    Polarizers are an essential part of liquid crystal displays, whose surface defects not only reduce the display quality of liquid crystal displays, but also cause the scrapping of the entire liquid crystal panel. Aiming at the problems that the surface defects of polarizers have large scale differences and various shapes, an improved YOLOX-S polarizer surface defect detection algorithm is proposed in this paper. The adaptive balanced feature pyramid (ABFP) module is proposed to sufficiently incorporate the multi-level features extracted by the backbone network, while increasing the detection branch through a single convolution to further enhance the multi-scale detection capability of the model. Then, an attention module CBAM is introduced in ABFP to focus on important features. In addition, the Mish activation function is used instead of the SiLU activation function while the CIoU loss function is adopted. The experimental results show that the improved algorithm achieves 92.97% and 55.16% of mAP50 and mAP50:95 on the polarizer surface defect dataset, which are 1.86 and 1.34 percentage points higher than that of YOLOX-S (FPN). Frames per second reaches 50, which basically meets the needs of industrial real-time detection.
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    Weed Identification Method in Corn Fields Applied to Embedded Weeding Robots
    HE Quanling, YANG Jingwen, LIANG Jinxin, FU Leiyang, TENG Jie, LI Shaowen
    Computer Engineering and Applications    2024, 60 (2): 304-313.   DOI: 10.3778/j.issn.1002-8331.2211-0282
    Abstract54)      PDF(pc) (786KB)(41)       Save
    In order to ensure the accuracy and rapidity of the embedded weeding robot in the corn field, a real-time target detection algorithm based on GBC-Yolov5s is proposed. First, the combination of the 1×1 convolution and depth-separable convolution is used to replace the traditional convolution, which reduces the redundant features generated by the backbone network without changing the size of the output feature map. Secondly, a bidirectional feature fusion network (S-BiFPN) network is designed to enhance the ability of feature extraction, which can make full use of different scale features to improve the speed of weed detection and combine the multi-channel structure with the self-attention mechanism to enhance the attention of small targets by compressing and reweighting the input features. Finally, MWeed data sets are built for different environments to test the proposed algorithm. The results show that compared with the Yolov5s and Faster RCNN model algorithms, the size of the GBC-Yolov5s model after lightweight is only 3.3 MB, the detection time of the input image (GPU) reaches 15.6 ms, and the average accuracy (mAP) reaches 96.3%, which can effectively improve the target detection speed and recognition accuracy, and provide a theoretical basis for the field of intelligent agricultural weeding.
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    Optimization of Container Multimodal Transport Network Based on Underground Logistics System
    HOU Yujie, LIANG Chengji
    Computer Engineering and Applications    2024, 60 (2): 314-325.   DOI: 10.3778/j.issn.1002-8331.2212-0379
    Abstract49)      PDF(pc) (947KB)(43)       Save
    In order to reduce the adverse impact of road transportation on urban traffic and environment, a new sustainable transportation mode——underground logistics system is gradually proposed to solve the common problems faced by the current development of port cities. Aiming at the layout scheme of underground logistics system in the Yangtze River Delta city agglomeration, a comprehensive transportation network optimization model is established from three perspectives:carbon emissions, time and cost, so as to analyze the rationality of implementing underground logistics system in port city agglomeration. The NSGA-III algorithm is used to solve the results that the introduction of underground logistics can reduce costs, and achieve the purpose of energy saving, emission reduction and alleviation of traffic congestion. Furthermore, NSGA-III algorithm is based on NSGA-II to optimize the crowding ranking, by introducing widely distributed reference points to maintain the diversity of the population. However, NSGA-III always deals with conflicting goals by prioritizing satisfying constraints, thus neglecting to maintain population diversity. Aiming at the problem that the population is trapped in the local optimal solution of the high-dimensional objective space, an improved NSGA-III algorithm is proposed to help the population span the large and discrete infeasible region by simultaneously dealing with the optimization objective and constraints. For this problem, “minimizing the shipping costs” “minimizing the vehicle waiting time” and “minimizing the carbon emissions” are selected as the objective functions, and the improved dynamic constrained NSGA-III is used for simulation analysis. The optimization results of the improved NSGA-III are compared with NSGA-III to verify the effectiveness of the algorithm. At the same time, it is proved that it can be applied to the practical logistics scheduling scheme.
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    Fuzzy Dispersion Entropy and Its Application
    HU Baohua, ZHU Zongjun, JIN Feixiang, LU Cuiping, XIU Lei, WANG Yong
    Computer Engineering and Applications    2024, 60 (2): 326-336.   DOI: 10.3778/j.issn.1002-8331.2208-0360
    Abstract50)      PDF(pc) (846KB)(21)       Save
    Dispersion entropy (DispEn) is a new dynamic index to measure the degree of signal irregularity. Compared with sample entropy, dispersion entropy can detect both amplitude and frequency changes at the same time and shorten the calculation time greatly. However, DispEn is sensitive to parameter selection, especially the number of classes (quantization level). Since the dispersion entropy is set based on the round function (step function), in some cases a small change in signal amplitude due to noise will alter the quantization sequence, thus changing the entropy value. In order to solve these limitations, fuzzy dispersion entropy (FuzzyDispEn) is proposed by combining fuzzy membership function and dispersion entropy. In FuzzyDispEn, fuzzy membership between embedding vector and quantization level is realized based on Euclidean distance. The advantages of FuzzyDispEn are tested using different synthetic time series signals. The results show that compared with DispEn, FuzzyDispEn has lower sensitivity to signal length and parameter selection, and better anti-noise performance. FuzzyDispEn is also used in completely analysis of EEG and bearing signal. Experimental results show that FuzzyDispEn also performs better than DispEn in real physical signal analysis. The results show that fuzzy dispersion entropy can provide a new method for signal complexity measurement.
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    Improved Semantic Segmentation Model and Its Application
    WANG Yaowen, CHENG Junsheng, YANG Yu
    Computer Engineering and Applications    2024, 60 (2): 337-343.   DOI: 10.3778/j.issn.1002-8331.2210-0032
    Abstract55)      PDF(pc) (626KB)(37)       Save
    The training of semantic segmentation model requires complicated manual labeling, and there are also some problems in the construction and operation of semantic segmentation model, such as determining its hyperparameters and becoming bloated. To solve these problems, this paper proposes a label generation method based on heat map generated by ground truth box, which simplifies the manual labeling process of semantic segmentation training labels. A neural architecture search method with lower hardware requirements is proposed, which based on the differentiable neural architecture search method. By this method, the improved semantic segmentation model which contains a new feature pyramid is constructed. Tested on the helmet and mask detection datasets, compared with U-NET, FPN and other models, the new model takes the advantages in the number of parameters, calculation speed and accuracy.
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    Lane Change Intention Recognition Based on Multi-Model Fusion
    FANG Yijie, LIAO Zhuhua, HUANG Haokai, LI Yanjun
    Computer Engineering and Applications    2024, 60 (2): 344-352.   DOI: 10.3778/j.issn.1002-8331.2210-0058
    Abstract40)      PDF(pc) (752KB)(38)       Save
    Fast and accurate identification of lane-changing intentions of surrounding vehicles is of great significance for decision support and safety prevention of advanced autonomous driving assistance systems. Aiming at the problems that the existing methods fail to fully consider the interaction between vehicles and the front-to-back dependence of trajectory data, this paper proposes a lane-changing intention recognition framework based on multi-model fusion. The framework mainly includes input processing and lane-changing intention recognition. The input processing part cleans, labels, slices and one-hot codes the vehicle track data. The BiLSTM-F(BiLSTM-fusion) model is specifically proposed for lane change intention recognition. In this model, the attention mechanism is introduced into BiLSTM, and the weight of the output information in the input processing part is divided. Finally, conditional random field is introduced to fully learn the dependency of input data and quickly find the global optimal lane-changing intention. In the experiment, NGSIM is used for training and evaluation. The validation results show that the model can achieve the highest accuracy of 97.19%, and can identify the vehicle’s lane changing intention 2 s before the vehicle arrives at the lane-changing point, with an accuracy of 94.16%. Compared with the baseline lane-change intention recognition model, the accuracy, loss, F1 value and stability of the proposed model are better than those of the baseline model.
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    Design of Embedded Real-Time Large Size SAR Image Ship Detection System
    LU Tianyu, XU Zhan, CUI Hongyuan, GONG Hao, WANG Cheng
    Computer Engineering and Applications    2024, 60 (1): 301-309.   DOI: 10.3778/j.issn.1002-8331.2209-0287
    Abstract47)      PDF(pc) (833KB)(40)       Save
    In applications of real-time ship detection in large size synthetic aperture radar (SAR) images after real-time imaging of spaceborne or airborne high-resolution SAR, it is difficult for traditional FPGA+DSP embedded system to realize both of the SAR imaging process and the artificial intelligence-based ship detection in real-time for large size SAR images. In this paper, a large size SAR images oriented real-time ship detection system on 3U VPX FPGA+GPU is proposed and a YOLOv5s based ship detection model is proposed as well, which applies L2-norm sparsity penalty scaling factor control method for lightweight. The average detection accuracy of the proposed lightweight ship detection model is 0.968, where the number of the parameters of the model is reduced by 47.39%, and the computational cost is reduced by 18.67%. The lightweight ship detection model is applied to the embedded ship real-time detection system for large size SAR images. For the typical large size images application scenario of 10 km×10 km, the embedded real-time detection system is designed and implemented by utilizing multi-threading and GPU-based many-core parallel programming. Performance evaluation are conducted on a public SAR image data set. Experimental results verify that the proposed system is able to meet the requirements of the real-time ship detection for large size SAR images under different resolutions.
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    Study on Optimization of Cooperative Distribution Path Between UAVs and Vehicles Under Rural E-Commerce Logistics
    XU Ling, YANG Linchao, ZHU Wenxing, ZHONG Shaojun
    Computer Engineering and Applications    2024, 60 (1): 310-318.   DOI: 10.3778/j.issn.1002-8331.2306-0115
    Abstract66)      PDF(pc) (666KB)(46)       Save
    Drone delivery has emerged as a significant solution to address the challenges of last-mile logistics. The collaborative delivery model between drones and vehicles overcomes the limitations of insufficient drone delivery capacity and enhances safety, making it a vital approach for drone involvement in the delivery process. To tackle the difficulties and high costs associated with “last-mile” delivery in rural e-commerce logistics, this study constructs a mixed-integer programming model. The objective is to minimize delivery costs while considering constraints such as the collaborative drone-vehicle mode and multi drone multi-parcel delivery. A two-stage algorithm is proposed to optimize the paths for drone-vehicle collaborative delivery. In the first stage, a constrained adaptive K-means algorithm is utilized to determine the range of vehicle docking points. In the second stage, an improved genetic algorithm that incorporates hill climbing and splitting operators is employed to identify the optimal delivery paths for drones and vehicles. Subsequently, a case study experiment is conducted to validate the feasibility and effectiveness of the model and algorithm. The research findings are expected to offer novel insights and valuable references for cost reduction and efficiency improvement in last-mile delivery for rural e-commerce logistics.
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    Application of Random Matrix Theory in Critical Path Identification of Expressway
    ZHANG Fang, WANG Fei, SUN Baoshuo
    Computer Engineering and Applications    2024, 60 (1): 319-326.   DOI: 10.3778/j.issn.1002-8331.2209-0103
    Abstract27)      PDF(pc) (857KB)(29)       Save
    Expressway network connects all regions in China, and critical path identification is of great significance to ensure the reliable operation of expressway network. The traditional critical path analysis method is based on topology and does not consider the traffic volume characteristics of the traffic network. The existing analysis methods based on traffic volume data only consider the characteristics of some routes, which is difficult to reflect the actual operation of the expressway network. Using the route traffic volume data to build a random matrix model of traffic volume, according to the change characteristics of traffic volume after the expressway network is abnormal, the critical path evaluation index is defined to realize the quantitative evaluation of the degree of abnormal impact. On this basis, a data-driven critical path identification method for expressway network is proposed. Finally, the rationality and effectiveness of the proposed method are verified by analyzing the expressway network in Liaoning Province, and the method is applied to the urban road network case, which further proves the universality of the method.
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    Multi-Scale Transmission Line Component Detection Incorporating Efficient Attention
    CHEN Siyu, FU Zhangjie
    Computer Engineering and Applications    2024, 60 (1): 327-336.   DOI: 10.3778/j.issn.1002-8331.2209-0125
    Abstract43)      PDF(pc) (835KB)(63)       Save
    For the problem that different types of components span large scales and are difficult to be detected in a balanced manner in high-resolution transmission line images, a multi-scale transmission line component detection algorithm incorporating efficient attention is proposed. Firstly, an efficient attention module ECBAM is designed and added to the YOLO v5 object detection algorithm to improve the feature extraction capability of the algorithm. Secondly, according to the feature distribution statistics of transmission line components, the high-resolution transmission line images are sliced by sliding window, and the improved YOLO v5 algorithm is used separately to train the models for the images before and after slicing. Finally, the detection results of the two models are integrated to obtain the detection results of multi-scale transmission line components. On the publicly available PLAD overhead transmission line image dataset, the detection performance of the proposed model far exceeds existing object detection models, with Precision up to 83.2% and Recall up to 92.8%. Compared with the model proposed by the original authors of the dataset, the mAP value improves by 1.6 percentage points to 90.8% and it can detect hidden objects that are not labeled on the original dataset, which verifies the effectiveness of detecting multi-scale transmission line components in high-resolution images.
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    Low Carbon Path Optimization of Two-Level Hybrid Cold Chain for Group Purchase Consi-dering Satisfaction
    QI Chunhao, ZHU Lin
    Computer Engineering and Applications    2024, 60 (1): 337-347.   DOI: 10.3778/j.issn.1002-8331.2209-0193
    Abstract33)      PDF(pc) (685KB)(29)       Save
    Considering the current community group purchase fresh products cold chain transportation process, due to quality decay leading to customer satisfaction is reduced, while the demand blowout causes insufficient capacity, a two-level cold chain collaborative optimization distribution strategy based on the crowdsourcing model is proposed, that is, the enterprise refrigerated truck completes the first-level cold chain transportation from the city warehouse to transit warehouse, and the crowdsourced refrigerated truck completes the secondary cold chain transportation from the transfer warehouse to the head of the regiment, and the total cost including service delay cost, carbon emission cost and fixed cost is minimized. And the customer’s satisfaction with the product quality is the optimization goal, and a two-stage open-close hybrid cold chain vehicle low-carbon path planning model with crowdsourcing is established. According to the characteristics of the model, an improved adaptive large neighborhood search (IALNS) algorithm is constructed, a new destruction-repair solution strategy is designed, and the idea of simulated annealing (SA) is added in the operator selection stage to speed up the convergence speed and improve the global search capability of the algorithm. By comparing the results with the example optimization results of adaptive large neighborhood search (ALNS) , simulated annealing (SA) , genetic algorithm (GA) , and particle swarm optimization (PSO) , it is proved that this method is feasible and effective. The strategy takes into account corporate profits and customer needs, and compares the experimental results in different distribution modes, which verifies that the model has positive significance in solving the problem of cold chain logistics of community group purchase fresh products.
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    Trajectory Planning Method of Intelligent Vehicle Based on Convex Approximate Obstacle Avoidance Principle and Sampling Area Optimization
    ZHANG Yixu, TIAN Guofu, WANG Haitao
    Computer Engineering and Applications    2024, 60 (1): 348-358.   DOI: 10.3778/j.issn.1002-8331.2209-0003
    Abstract51)      PDF(pc) (689KB)(30)       Save
    Aiming at the problem of obstacle avoidance trajectory tracking for intelligent vehicles moving at constant speed on structured roads, a trajectory tracking method of intelligent vehicles based on convex approximate obstacle avoidance principle and sampling area optimization is proposed. The convex approximate obstacle avoidance principle is introduced to obtain the feasible range of trajectory. The sampling area is divided into static sampling area and dynamic sampling area, according to the motion state of the obstacle, the dynamic obstacle and static obstacle sampling area are also divided. The idea of “dynamic programming (DP) + quadratic programming (QP)” is used to solve the trajectory. The sampling points are connected successively by the quintic polynomial, and the dynamic programming cost function is established and the rough trajectory is obtained by searching. Through the quadratic programming and the construction of constraints, the rough trajectory is smoothed and the optimal trajectory is obtained. The simulation results show that: the vehicle can effectively obtain smooth trajectories and avoid obstacles for static, low-speed and dynamic obstacles.
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    Surface Defect Detection Algorithm of Micro-Channel Aluminum Flat Tube Based on Improved FCOS Model
    GUI Penghui, SONG Tao, TANG Jianbin, XU Zhipeng, CAO Songxiao, JIANG Qing
    Computer Engineering and Applications    2023, 59 (24): 298-308.   DOI: 10.3778/j.issn.1002-8331.2208-0204
    Abstract36)      PDF(pc) (1233KB)(42)       Save
    Aiming at the task of detecting surface defects of micro-channel aluminum flat tubes, an improved FCOS(fully convolutional one-stage object detection) algorithm is proposed. Firstly, a feature convolution pyramid network is designed to enable the model to adaptively mix feature maps from different layers for detection. Secondly, through analyzing the limitations of the original FCOS algorithm in the detection of long and narrow defects, and the positive sample deployment strategy of the model is improved, the missed detection of long and narrow defects is reduced. Then, a more suitable mapping function and center-ness function are designed to solve the regression problem and center-ness calculation problem of positive sample points outside the labeled frame. Finally, the EIoU(efficient IoU) loss is used to replace the IoU loss in the original model to further improve the regression ability of the model. The experimental results show that in the surface defect detection task of micro-channel aluminum flat tubes, the improved FCOS model achieves the mAP(mean average precision) of 76.4%, which is 7.7 percentage points higher than the original model.
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    Research on Construction and Application of Knowledge Graph for Industrial Equipment Fault Disposal
    QU Zhihao, HU Jianpeng, HUANG Ziqi, ZHANG Geng
    Computer Engineering and Applications    2023, 59 (24): 309-318.   DOI: 10.3778/j.issn.1002-8331.2208-0186
    Abstract69)      PDF(pc) (712KB)(64)       Save
    The use of knowledge graph to assist industrial equipment fault disposal can effectively improve the fault disposal efficiency. Addressing the problem that the annotation of entities in the field of industrial equipment fault mainly relies on human resources, which is time-consuming and labor-intensive, a semi-automatic annotation method of entities for equipment fault disposal based on external knowledge base is proposed, which achieves semi-automatic annotation of entities in the field using crawled equipment information and external knowledge such as sememe, saving nearly half of the manual annotation cost. Aiming at the problem that the entity types and entity labels are incorrectly identified by using the existing entity extraction methods. The method incorporates the lexical information and word boundary information of the word in the word embedding based on the BERT pre-trained word vector to obtain more semantic information than other word embedding methods, and combines BiLSTM and CRF to form the entity extraction model in this paper. The experimental results show that the recognition performance of the proposed model has been improved by 3.8 percentage points compared with BERT-BiLSTM-CRF. At the same time, better results can be obtained with fewer iterations. On the application of knowledge graph, a multi-modal information fusion method for equipment fault disposal solution recommendation is proposed, which uses deep learning models and sensor information to determine the occurrence of faults, and recommends maintenance personnel and maintenance methods based on the knowledge graph.
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    Complete Coverage Path Planning for Multiple Robots for Facade Maintenance Operations
    XIE Bicheng, ZHANG Xiaojun
    Computer Engineering and Applications    2023, 59 (24): 319-327.   DOI: 10.3778/j.issn.1002-8331.2208-0246
    Abstract41)      PDF(pc) (749KB)(49)       Save
    The current multi-robot complete coverage algorithm is aimed at the problems of uneven task distribution, inadequate consideration of flexibility in the selection of robot starting and ending positions, single objective of algorithm solution, and inflexible coverage of complex infeasible domain maps. Combined with a method for solving the multiple traveling salesman problems, the improved immune genetic algorithm is applied to multi-robot complete coverage path planning for facade maintenance operations. The method utilizes a region decomposition method that relies on infeasible domain distribution to decompose the region to be covered into several sub-regions. Minimizing the maximum work duration and the total workload of multiple robots, an adaptation calculation method is designed based on the premise that the coverage task is equally divided using a dynamic equalization operator and considering the characteristics of arbitrary starting and ending positions of robots for facade maintenance operations. The global approximate optimal solution is also found by using a stepped clone selection operator combined with a dynamic programming algorithm. Then, the transposition operator with heuristically superior chromosome fragments is used to speed up the convergence of the algorithm. Finally, the effectiveness and stability of the algorithm as well as the computational quality of the algorithm are verified through simulation and comparison experiments of complex maps.
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    Research on Location Allocation of Four-Way Shuttle Storage and Retrieval System
    LI Jia, HE Fei, XIE Gangwei, YANG Yang, FANG Yihe
    Computer Engineering and Applications    2023, 59 (24): 328-335.   DOI: 10.3778/j.issn.1002-8331.2208-0265
    Abstract45)      PDF(pc) (752KB)(41)       Save
    Aiming at the problems of the dense distribution and the dynamic changes of cargo spaces in the location allocation of four-way shuttle storage and retrieval system, a multi-objective cargo space allocation method aiming at improving warehouse work efficiency and equipment stability is established, and a new method is proposed. An improved shuffled frog leading algorithm is proposed to simulate and optimize the model. First of all, according to the distribution characteristics of cargo spaces in intensive warehousing, a strategy of cargo space and cargo lanes is proposed, and a cargo space allocation principle for goods stored in cargo lanes by category is established. Then, a mathematical model of cargo space allocation is established with the goal of shortest path, balanced distribution of goods and lower center of shelves gravity. Finally, an improved shuffled frog leading algorithm is proposed to solve the model by using dynamic adaptive crossover to improve the local search strategy of the standard shuffled frog leading algorithm. The simulation results show that the cargo space allocation model is reasonable, and compared with the standard genetic algorithm and the standard shuffled frog leading algorithm, the improved shuffled frog leading algorithm has faster convergence speed, more reasonable optimization of cargo space, and can effectively solve the problem of location allocation of four-way shuttle storage and retrieval system.
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    Adaptive Trajectory Tracking Control Based on Trajectory Evaluation Model
    XU Wan, ZHOU Hang
    Computer Engineering and Applications    2023, 59 (24): 336-344.   DOI: 10.3778/j.issn.1002-8331.2308-0105
    Abstract38)      PDF(pc) (773KB)(33)       Save
    In addressing the trajectory tracking control for existing mobile robots, the predominant has been focused on mitigating inherent self-pose errors, while the influence of trajectory curvature on tracking control has been overlooked. In order to further enhance the tracking accuracy of intelligent mobile wire-launching robots, a control approach is introduced herein, denoted as the trajectory evaluation model controller(TEMC), which is predicated upon a trajectory evaluation model. Firstly, the establishment of kinematic models pertinent to mobile robots is undertaken. Secondly, to articulate the geometric interrelation between the reference trajectory and the robot, a trajectory evaluation model(TEM) is formulated. The model incorporates curvature and its rate of change to quantify trajectory complexity. A comprehensive trajectory evaluation function is devised, amalgamating trajectory complexity with factors of self-pose errors. Additionally, utilizing a back propagation neural network, a controller founded upon the trajectory evaluation model is introduced, accompanied by a presentation of the stability proof. Finally, the efficacy of the trajectory evaluation model is substantiated through rigorous simulation, and the essential parameter range within the model is determined empirically via experimental methods. It is demonstrated that the TEMC yields a tracking precision enhancement of over 48% compared to conventional adaptive backstepping controllers.
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    Multi-Layer Equipment Scheduling and Simulation Analysis of Automated Container Terminal
    WANG Panlong, LIANG Chengji, WANG Yu
    Computer Engineering and Applications    2023, 59 (24): 345-359.   DOI: 10.3778/j.issn.1002-8331.2207-0208
    Abstract38)      PDF(pc) (756KB)(38)       Save
    In order to improve the efficiency of container turnovers within the automated terminals,  a coordination problem of multi-layer equipment operation is considered. Starting from the containers departing from the yard, the conditions and operational rules of containers transferring among yard crane, AGV, quay crane gantry trolley, transfer platform and quay crane main trolley to the ship are analyzed in detail for the multi-layer equipment joint operation problem. A time-space flow diagram of the above process is proposed. Considering the  mutual waiting time of equipment, the order of containers entering and leaving the transfer platform, and the capacity of the transfer platform as the constraints, a multi-layer equipment integration scheduling model is formulated with the goal of minimizing the maximum completion time. The multi-layer equipment joint scheduling problem is solved by adaptive genetic algorithm(AGA), Tabu search algorithm (TS) and simulated annealing algorithm(SA), respectively. By implementing the algorithms in a plant-simulation scene and results comparison, the probability of the AGA algorithm outperforming the other two algorithms in all experiments is 81%. The probability that the running time of the AGA algorithm is better than that of the TS algorithm and that of the SA algorithm is 100% and 69%, respectively. Based on the above research, three AGV operation strategies are designed in the simulation scene, namely, random route, fixed route and group random route. The completion time, equipment waiting time and congestion time of the three strategies are compared, and the algorithm achieves the best results under the strategy of group random route.
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