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Full-Text Articles in Systems Science
Object Detection Networks And Their Interpretability In Rain, Fog, And Snow Scenarios, Yanji Jiang, Jiayu Cui, Hao Dong, Daqian Liu, Bowen Fei, Miao Yu, Jinshan Huang
Object Detection Networks And Their Interpretability In Rain, Fog, And Snow Scenarios, Yanji Jiang, Jiayu Cui, Hao Dong, Daqian Liu, Bowen Fei, Miao Yu, Jinshan Huang
Journal of System Simulation
To address the severe degradation of object detection performance under extreme weather conditions, a detection framework based on the Kolmogorov-Arnold theorem, termed KADet, is proposed. A dynamic Kolmogorov-Arnold Transformer is designed, which leverages learnable nonlinear activation functions to enhance the modeling capability for complex distortions introduced by weather degradation. A Kolmogorov-Arnold spatial-channel network is developed by integrating KAT convolution with spatial-channel convolution to strengthen feature learning of relationships between targets and backgrounds in degraded scenes. An improved loss function is introduced to guide the optimization of the activation functions, and interpretability is analyzed through visualization of their curves. …
Research And Analysis Of Algorithm For Detecting Surface Defects On Automotive Wheel Hubs Based On Ccl-Yolov8, Yanjun Chen, Min Zhou, Meng Zha, Meizhou Zhang
Research And Analysis Of Algorithm For Detecting Surface Defects On Automotive Wheel Hubs Based On Ccl-Yolov8, Yanjun Chen, Min Zhou, Meng Zha, Meizhou Zhang
Journal of System Simulation
Abstract: To address the challenges such as low detection efficiency, difficulties in identifying small defects, and poor accuracy in detecting surface defects on automotive wheel hubs, a lightweight neural network called CCL-YOLOv8 was proposed based on an improved YOLOv8n architecture. A synergistic improvement in both detection accuracy and efficiency was achieved through a three-stage model optimization strategy. A convolutional attention fusion module was introduced, which integrated convolution operations with self-attention mechanisms, thereby enhancing the model's ability to capture local features of small defects while perceiving global context under low signal-to-noise ratio conditions.A C2f-Star module was constructed to reduce computational overhead …
Reinforcement Learning Based Method For Uav Team Orienteering Optimization Under Multi-Constraint Condition, Can Yang, Kai Chen, Feng Zhu
Reinforcement Learning Based Method For Uav Team Orienteering Optimization Under Multi-Constraint Condition, Can Yang, Kai Chen, Feng Zhu
Journal of System Simulation
Abstract: Traditional optimization methods struggle with efficiency, while reinforcement learning approaches often yield low solution quality and high training costs. In response, this paper proposes an attention mechanism-based reinforcement learning method. A dynamic attention strategy network with multi-information fusion is designed to improve solution quality. A visibility-graph approach is employed to simplify threat zone constraints and speed up convergence, and a decoding sequence reordering mechanism is introduced for further performance optimization of the solution. The simulation results show that the method generates high-quality solutions within milliseconds, achieving total rewards that approach or even surpass those obtained by traditional solvers …
Lightweight Assembly Workpiece Detection Algorithm Based On Improved Yolov8, Shuheng Wu, Yongkui Liu, Lin Zhang, Yingying Xiao, Lihui Wang
Lightweight Assembly Workpiece Detection Algorithm Based On Improved Yolov8, Shuheng Wu, Yongkui Liu, Lin Zhang, Yingying Xiao, Lihui Wang
Journal of System Simulation
Abstract: To address the issues of low recognition accuracy and slow detection speed with existing deep learning-based object detection algorithms for robotic automatic assembly tasks, a lightweight assembly workpiece object detection algorithm based on YOLOv8 was proposed. The PConv was introduced to improve the C2f module, and a new Faster_C2f module was designed to enhance the detection speed of the model. The SIoU loss function was employed to optimize the location prediction accuracy of the CIoU loss function and improve the localization accuracy of small targets. The high-level screening-feature fusion pyramid networks (HS-FPN) structure was used to improve the Neck …
Image Feature Point Matching Algorithm Based On Attention And Hierarchical Features, Na Chen, Jiajia Bai, Qiyin Zhou, Jialin Li
Image Feature Point Matching Algorithm Based On Attention And Hierarchical Features, Na Chen, Jiajia Bai, Qiyin Zhou, Jialin Li
Journal of System Simulation
Abstract: Feature point detection and matching is one of the core technologies in the field of intelligent driving. Aiming at the lack of consistency and continuity of feature points extracted by the existing algorithms, as well as the problem of easily ignoring the contextual semantic information when matching, this paper proposes an image feature point matching algorithm based on attention and hierarchical features (AHMF). In the feature point detection stage, differential interaction attention module (DIAM) is proposed to enhance the model's attention to the salient regions so as to improve the robustness of the feature points; further introduction of hierarchical …
Lane Detection In Dark Light Based On Instance Association, Yanji Jiang, Yingyang Zhang, Hao Dong, Xiaoguang Zhang, Meihui Wang
Lane Detection In Dark Light Based On Instance Association, Yanji Jiang, Yingyang Zhang, Hao Dong, Xiaoguang Zhang, Meihui Wang
Journal of System Simulation
Abstract: In current research on lane detection, existing algorithms can efficiently detect lane lines under good lighting conditions. However, lane detection in low light still faces the challenge of a high false negative rate. A detection algorithm called Instance Association Net(IANet) is proposed to address this issue by utilizing the structural relationships between lane lines, which is helpful for low light conditions. The algorithm first generates unique masks for different lane lines using features at the starting points of the lane lines and a global feature map, achieving instance-level feature separation of the lane lines. It employs an instance-level attention …
A Drl⁃Based Approach For Distributed Equipment Nodes Selection, Ziyi Wang, Kai Zhang, Dianwei Qian, Yuzhen Liu
A Drl⁃Based Approach For Distributed Equipment Nodes Selection, Ziyi Wang, Kai Zhang, Dianwei Qian, Yuzhen Liu
Journal of System Simulation
Abstract: Aiming at the problem of insufficient solution speed and poor generalization of traditional algorithms in large-scale scenarios, this paper intelligently solves the large-scale distributed equipment system preference problem based on deep reinforcement learning. According to the characteristics of distributed equipment system combat, using the complex network to its graph form modeling, and based on the attention mechanism to the equipment between the connecting edge relationship for the characterization, in order to build a distributed equipment system digital simulation environment. Simulation results show that compared with the genetic evolutionary algorithm, the obtained model has obvious advantages in terms of solution …
A Quadrotor Trajectory Tracking Control Method Based On Deep Reinforcement Learning, Guohua Wu, Jiaheng Zeng, Dezhi Wang, Long Zheng, Wei Zou
A Quadrotor Trajectory Tracking Control Method Based On Deep Reinforcement Learning, Guohua Wu, Jiaheng Zeng, Dezhi Wang, Long Zheng, Wei Zou
Journal of System Simulation
Abstract: Traditional quadrotor controllers, constrained by fixed model equation structures, encounter challenges in addressing control errors stemming from variations in parameters and environmental disturbances. This paper proposes a deep reinforcement learning solution for the quadrotor trajectory following control problem. We present the PPO-SAG algorithm incorporated into the PPO framework, utilizing adaptive mechanisms and PID expert knowledge to enhance training convergence and stability. Target functions incorporating distance constraint penalties and entropy policies are designed in alignment with the characteristics of the given problem. We also devise innovative disturbance-adaptive structures and trajectory feature selection mechanisms to augment control error information and extract …
Research On Decision-Making Of Autonomous Driving In Highway Environment Based On Knowledge And Large Language Model, Xiang Wang, Guozhen Tan
Research On Decision-Making Of Autonomous Driving In Highway Environment Based On Knowledge And Large Language Model, Xiang Wang, Guozhen Tan
Journal of System Simulation
Abstract: Aiming at the lack of continuous learning and interpretability of current autonomous driving system, a decision model with cognition, generalization and learning ability is proposed. The model utilizes large language model (LLM) and attention mechanisms to understand and explain driving scenes. the system can accumulate and learn from driving experiences, continuously improving its decisionmaking ability. In a simulation environment, the closed-loop test decision model is applied in high-speed scenarios.The simulation results show that the success rate of the knowledge-driven model is 7% and 4% higher than those of the rule-based and data-driven methods. Additionally, the model exhibits generalization and …
Traffic Signal Detection Based On Improved Yolov7, Lanyue Zheng, Yujie Zhang
Traffic Signal Detection Based On Improved Yolov7, Lanyue Zheng, Yujie Zhang
Journal of System Simulation
Abstract: An improved YOLOv7 is proposed to address the problem of low recognition accuracy in general object detection algorithms for traffic signal detection. The algorithm removes the 20×20 detection scale and adds a 160×160 detection scale to increase shallow features while making the model lightweight. It combines the bi-level routing attention (BRA) proposed in BiFormer with axial attention, and innovatively proposes axially-guided BRA (ABRA). This module is specifically designed for the characteristics of traffic signal positions. To address the issue of object size sensitivity to the IoU metric, the normalized wasserstein distance (NWD) measurement is introduced to improve object location …
Remote Sensing Small Object Detection Based On Cross-Stage Two-Branch Feature Aggregation, Jie Li, Yang Liu, Liang Li, Bengan Su, Jialong Wei, Guangda Zhou, Yanmin Shi, Zhen Zhao
Remote Sensing Small Object Detection Based On Cross-Stage Two-Branch Feature Aggregation, Jie Li, Yang Liu, Liang Li, Bengan Su, Jialong Wei, Guangda Zhou, Yanmin Shi, Zhen Zhao
Journal of System Simulation
Abstract: Aiming at YOLOv8's leakage and false detection problems caused by target scale difference and complex background in remote sensing small target detection, this paper proposes a remote sensing image small target detection method based on cross-stage two-branch feature aggregation. The global shared weights in the convolution operator and the context-aware weights of specific tokens in the attention are fused to obtain high-frequency local information and low-frequency global information; the global remote dependencies are captured using a lightweight MLP, and the parallel cross-stage learnable vision center mechanism is designed to capture the information of the local corner regions of the …
Research On The Target Allocation Method For Air Defense And Anti-Missile Defense Of Naval Ships, Shuaidi Fei, Changlong Cai, Fei Liu, Minghui Chen, Xiaoming Liu
Research On The Target Allocation Method For Air Defense And Anti-Missile Defense Of Naval Ships, Shuaidi Fei, Changlong Cai, Fei Liu, Minghui Chen, Xiaoming Liu
Journal of System Simulation
Abstract: To solve the problems of multiple types of state information and correlation of time-series state information encountered in the dynamic weapon target assignment problem, a dynamic weapon target assignment method based on an improved deep reinforcement learning algorithm is proposed. A multiinput assignment model of target missile-interceptor unit, interceptor unit, and defense unit under multiwave target and multi-phase is constructed. A multi-input state space is designed, and a Markov decision process is established in conjunction with the problem model. A feature extraction network combining multi-input information processing and gated recurrent network is designed, which improves the ability to extract …
Traffic Sign Recognition Model With Long-Tail Distribution Based On Yolox-Tiny, Yunpeng Wu, Yingxiong Fu, Lijun Shen, Feng Cui
Traffic Sign Recognition Model With Long-Tail Distribution Based On Yolox-Tiny, Yunpeng Wu, Yingxiong Fu, Lijun Shen, Feng Cui
Journal of System Simulation
Abstract: Accurate recognition of traffic signs plays an important role in the field of intelligent driving. Traffic sign training datasets with long-tail distribution increase the difficulty of traffic sign recognition. A traffic sign recognition model with long-tail distribution based on YOLOX-Tiny was proposed to improve the poor performance of the model trained on long-tail distribution datasets. A long-tail traffic sign dataset was created based on the TT100K_2021 (tsinghua-tencent 100K 2021) dataset. YOLOX-Tiny was chosen as the underlying model by considering picture numbers in datasets, sample distribution, and model size. Equalization loss v2 (EQL v2) was used as classification loss to …
Global-Local Fusion For Efficient 3d Object Detection, Bin Lu, Minghan Wang, Yang Sun, Zhenyu Yang
Global-Local Fusion For Efficient 3d Object Detection, Bin Lu, Minghan Wang, Yang Sun, Zhenyu Yang
Journal of System Simulation
Abstract: As the 3D object detection based on point clouds shows an incapacity of feature extraction and incongruity between classification and regression, this research introduces a novel ResCST architecture based on the SECOND network. It incorporates residual connections into the 3D sparse convolutional layer, with the advantages of capturing long-distance dependent relation by SwinTransformer and obtaining local features by convolutional neural network integrated, proposing the CNN-SwinTransformer hybrid model for enhanced feature extraction. It introduces the RCIoU method for the joint optimization of classification and regression tasks. The experimental results show that the model achieves a 3D detection accuracy of 91.21%, …
Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo
Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo
Journal of System Simulation
Abstract: For the resource limitation of intensive surveillance tasks in edge computing, a surveillance task offloading and resource allocation algorithm based on DRL is proposed. With the optimization objectives of surveillance task delay and recognition accuracy, the joint decision objective optimization solution of task offloading, wireless channel allocation, and image compression rate was modeled as a Markov decision process. To address the problem of slow and unstable algorithm convergence due to the high volatility of training samples caused by the dynamic nature of wireless channels and the randomness of surveillance tasks, an attention mechanism is used to jointly encode channel …
Simulation Of Rice Disease Recognition Based On Improved Attention Mechanism Embedded In Pr-Net Model, Yang Lu, Pengfei Liu, Siyuan Xu, Qiwang Liu, Fuqian Gu, Peng Wang
Simulation Of Rice Disease Recognition Based On Improved Attention Mechanism Embedded In Pr-Net Model, Yang Lu, Pengfei Liu, Siyuan Xu, Qiwang Liu, Fuqian Gu, Peng Wang
Journal of System Simulation
Abstract: Aiming at the low accuracy of existing CNN models in identifying rice leaf diseases, a hybrid convolutional neural network model PRC-Net (parallel residual with coordinate attention network) combining parallel structure and residual structure is proposed. A parallel structure is introduced to improve the receptive field of convolution, and the residual structure is combined to achieve the complete and continuous transmission of feature information. An improved spatial attention mechanism is embedded into the backbone model PR-Net to enhance the degree of aggregation of lesion feature information at different scales. In order to further improve the accuracy of disease identification and …
Image Semantic Segmentation Algorithm Based On Improved Deeplabv3+, Weiping Zhao, Yu Chen, Song Xiang, Yuanqiang Liu, Chaoyue Wang
Image Semantic Segmentation Algorithm Based On Improved Deeplabv3+, Weiping Zhao, Yu Chen, Song Xiang, Yuanqiang Liu, Chaoyue Wang
Journal of System Simulation
Abstract: Mainstream image semantic segmentation networks currently face problems such as incorrec segmentation, discontinuous segmentation, and high model complexity, which cannot be flexibly and efficiently deployed in practical scenarios. To this end, an image semantic segmentation network that optimizes the DeepLabv3+ model is designed by comprehensively considering the network parameters, prediction time, and accuracy. The lightweight EfficientNetv2 is adopted to extract backbone network features and improve parameter utilization. In the atrous spatial pyramid pooling module, the mixed strip pooling is utilized to replace the global average pooling, and a depthwise separable dilated convolution is introduced to reduce parameters and improve …
Rgb-D Saliency Object Detection Based On Cross-Refinement And Circular Attention, Qingqing Dong, Hao Wu, Wenhua Qian, Fengling Kong
Rgb-D Saliency Object Detection Based On Cross-Refinement And Circular Attention, Qingqing Dong, Hao Wu, Wenhua Qian, Fengling Kong
Journal of System Simulation
Abstract: In order to solve the problems that the boundary of the saliency object detection area is vague, and the detection area is incomplete or inaccurate, an RGB-D saliency object detection method based on cross-refinement and circular attention is proposed. A cross-refinement module is designed at the stage of extracting features using encoders, which is used to supplement feature information of each other and improve the feature quality before fusion. It also suppresses the negative impact of poor-quality depth maps and addresses the issue that the edges of the saliency object are blurred. For the features after fusion, the circular …
Surface Defect Detection Of Power Equipment Using Adaptive Receptive Field Network, Hao Yu, Jinxia Jiang, Xiaohan Lai, Feng Mei
Surface Defect Detection Of Power Equipment Using Adaptive Receptive Field Network, Hao Yu, Jinxia Jiang, Xiaohan Lai, Feng Mei
Journal of System Simulation
Abstract: For the detection of defects such as icing, rust, and contamination of power equipment in substations, a novel adaptive receptive field network (ARFN) is proposed, in which an adaptive receptive field module (ARFM) combined with the attention mechanism can effectively fuse multi-scale features. Considering the small sample learning attribute of defect detection, a power equipment surface defect simulation data synthesis method based on real texture is also proposed. The experimental results on the simulation dataset show that the network has high detection accuracy for surface defects across devices, while having advantages such as small size and fast operation speed.
Semantic Segmentation Model Based On Adaptive Fusion And Attention Refinement, Yun Wei, Qi Luo, Yingzhi Zhao
Semantic Segmentation Model Based On Adaptive Fusion And Attention Refinement, Yun Wei, Qi Luo, Yingzhi Zhao
Journal of System Simulation
Aiming at the insufficient use of context information and loss of detail information of the existing semantic segmentation, a model based on adaptive fusion and attention refinement is proposed.The model introduces an adaptive fusion module in the process of coding, and solves the insufficient use of context information by fusing each feature map according to the corresponding weight. An attention thinning module is designed in the process of decoding, so that the low-order features and high-order features can guide and optimize each other to solve the loss of detail information.The experimental results show that the average intersection union …
Multi-Agent Cooperative Combat Simulation In Naval Battlefield With Reinforcement Learning, Ding Shi, Xuefeng Yan, Lina Gong, Jingxuan Zhang, Donghai Guan, Mingqiang Wei
Multi-Agent Cooperative Combat Simulation In Naval Battlefield With Reinforcement Learning, Ding Shi, Xuefeng Yan, Lina Gong, Jingxuan Zhang, Donghai Guan, Mingqiang Wei
Journal of System Simulation
Abstract: Due to the rapidly-changed situations of future naval battlefields, it is urgent to realize the high-quality combat simulation in naval battlefields based on artificial intelligence to comprehensively optimize and improve the combat effectiveness of our army and defeat the enemy. The collaboration of combat units is the key point and how to realize the balanced decision-making among multiple agents is the first task. Based on decoupling priority experience replay mechanism and attention mechanism, a multi-agent reinforcement learning-based cooperative combat simulation (MARL-CCSA) network is proposed. Based on the expert experience, a multi-scale reward function is designed, on which a naval …
Research On Image Super-Resolution Reconstruction Based On Loss Extraction Feedback Attention Network, Hong Sun, Yuxiang Zhang, Yuelan Ling
Research On Image Super-Resolution Reconstruction Based On Loss Extraction Feedback Attention Network, Hong Sun, Yuxiang Zhang, Yuelan Ling
Journal of System Simulation
Abstract: Since the first application of convolutional neural network to the field of super-resolution image reconstruction (super-resolution convolutional neural network, SRCNN), a large number of studies have proved that deep learning can improve the effect of image reconstruction. Aiming at the too many parameters in the image super-resolution network and the insufficient utilization of image features resulting in less available high-frequency information, a loss extraction feedback attention network (LEFAN) is proposed to reuse parameters in a circular way and increase the reuse of low-resolution image features to capture more high-frequency information. The loss caused in the reconstruction process is extracted …
Image Dehazing Network Based On Densely Connected Residual Block And Channel Pixel Attention, Weidong Jin, Shuli Zhang, Peng Tang, Man Zhang
Image Dehazing Network Based On Densely Connected Residual Block And Channel Pixel Attention, Weidong Jin, Shuli Zhang, Peng Tang, Man Zhang
Journal of System Simulation
Abstract: Abstruct: A lot of research achievements have been made in image dehazing based on neural network,but there aiming at the fog residue, even the color distortion and texture loss, in complex outdoor image dehazing, an image dehazing network based on densely connected residual block and channel pixel attention is proposed. Densely connected residual blocks are used to extract and fuse the features of foggy images,and the repair module with channel pixel attention mechanism is used to repair the color and texture of the feature maps. The experimental results show that, compared with the existing methods, the proposed method and …
Research On Prediction Of Model Based On Multi-Scale Lstm, Junjie Qiu, Hong Zheng, Yunhui Cheng
Research On Prediction Of Model Based On Multi-Scale Lstm, Junjie Qiu, Hong Zheng, Yunhui Cheng
Journal of System Simulation
Abstract: Aircraft engine remaining useful life (RUL) prediction is the core issue in equipmentfailure prognostics and health management (PHM). Aiming at the characteristics of high dimensionality, high lag and complexity of engine data, a multi-scale attention-based bidirectional long short-term memory neural network model based on self-training weights is proposed. Multi-scale features are extracted through bidirectional long short-term memory neural network (BiLSTM) of different scales. A fusion algorithm based on self-training weights is proposed, and an attention mechanism is introduced to screen features at different scales to improve prediction accuracy. Various models are compared on the NASA's C-MAPSS data set. The …
Vehicle Detection Method Based On Multi Scale Feature Fusion, Yin Wang, Feixiang Wang, Qianlai Sun
Vehicle Detection Method Based On Multi Scale Feature Fusion, Yin Wang, Feixiang Wang, Qianlai Sun
Journal of System Simulation
Abstract: Vehicle detection is the important research content and hotspot in the intelligent transportation. Aiming at the low detection accuracy and poor small-scale recognition effect of the traditional vehicle detection algorithm, an improved detection method based on YOLOv4(you only look once v4) is proposed to improve the detection performance of small target vehicles in traffic scenes. By redesigning the YOLOv4 network, the MobileNetv2 deep separable convolution module is used to replace the traditional convolution, and the convolutional block attention module (CBAM) attention module is integrated into the feature extraction network to ensure the detection accuracy of the model and reduce …
Prediction Method For Health Degree Of Front Bearing Of Wind Turbine Generator And Implementation, Yin Shi, Guolian Hou, Chi Yan, Linjuan Gong, Xiaodong Hu
Prediction Method For Health Degree Of Front Bearing Of Wind Turbine Generator And Implementation, Yin Shi, Guolian Hou, Chi Yan, Linjuan Gong, Xiaodong Hu
Journal of System Simulation
Abstract: Aiming at the deterioration trend of front bearing of doubly-fed wind turbine generator, a new combined modeling method is proposed to predict health degree of front bearing of generator. The GMM is used to identify operating conditions of wind turbines. The temperature model of front bearing based on ELM is established respectively in each sub-condition. Combining with temperature residual characteristics and time-frequency characteristics of vibration signal, the health degree of front bearing is calculated. Based on attention mechanism, the Bi-LSTM neural network is proposed to model and predict health degree of front bearing. The result shows that the …
Research On Cgf-Oriented Virtual Human Perceptual Attention Model, Weilong Yang, Xu Kai, Xie Xu, Sun Lin
Research On Cgf-Oriented Virtual Human Perceptual Attention Model, Weilong Yang, Xu Kai, Xie Xu, Sun Lin
Journal of System Simulation
Abstract: Perception model is an important research field of CGF behavior modeling. As the input of the reasoning module, the information obtained from the perception model has an important influence on the fidelity of the simulation. The CGF perception model is researched, and the attention mechanism, effective time of attention model is analyzed, compared with the traditional model of perception. By modeling the knowledge base and information feedback, a virtual human perception framework based on attention mechanism is proposed, and the experiment is carried out in the simulation environment.
Attention Mechanism In Battlefield Situation Awareness, Yisi Kong, Xiaofeng Hu, Zhu Feng, Jiuyang Tao
Attention Mechanism In Battlefield Situation Awareness, Yisi Kong, Xiaofeng Hu, Zhu Feng, Jiuyang Tao
Journal of System Simulation
Abstract: Battlefield situation awareness (BSA) is the basis of C2 and decision making. Research on BSA can clarify the mechanism and lay the foundation for designing intelligent model and algorithm to assist commanders with situation awareness. Taking attention mechanism as the point, the relevant discussion in psychology was summarized, a framework of the attention mechanism in BSA was put forth, and its application in operational target value assessment was explored.