To solve the problem of dynamic and sequential air combat intention recognition in complex battlefield environment, a target tactical intention recognition method based on the extended multi-entities Bayesian network ...
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The integrated calibration method of micro-nano satellite attitude benchmark is studied. Considering the magnetic effects and residual magnetism caused by the complex magnetic environment of the satellite, an accurate...
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Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, a...
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In recent years, the number of software vulnerabilities has gradually increased, posing a threat to the security of software, and identifying whether there are vulnerabilities in software is crucial for assuring its q...
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Scheduling algorithms in distributed heterogeneous systems have been the subject of extensive research. However, with the advancement of technology, addressing real-time, reliability, and energy consumption issues in ...
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To solve the problem of dynamic and sequential air combat intention recognition in complex battlefield environment, a target tactical intention recognition method based on the extended multi-entities Bayesian network ...
To solve the problem of dynamic and sequential air combat intention recognition in complex battlefield environment, a target tactical intention recognition method based on the extended multi-entities Bayesian network (EMEBN) is proposed in this paper. First the dynamic series Bayesian network (DSBN) model is established to describe the process of intention representation and reasoning, the deficiency of the multi-entities Bayesian network (MEBN) in expressing the probability inference knowledge of the planning process is analyzed. Then the probability transfer MEBN fragments (PT-MFrags) and the series relation MEBN fragments (SR-MFrags) are introduced to depict the probability transfer and series relationship of rule knowledge. Finally, the feasibility and effectiveness of this method is verified by an experimental example.
The integrated calibration method of micro-nano satellite attitude benchmark is studied. Considering the magnetic effects and residual magnetism caused by the complex magnetic environment of the satellite, an accurate...
The integrated calibration method of micro-nano satellite attitude benchmark is studied. Considering the magnetic effects and residual magnetism caused by the complex magnetic environment of the satellite, an accurate measurement model for magnetometers was adopted. By using the relationship between the measured and theoretical values of the geomagnetic field, the residual magnetism and magnetic sensitivity coefficients in the magnetometer measurement model were calibrated, through the least square method of uncertain parameters of the calibration. After modeling the installation error of the solar sensor, the calibration of the solar sensor is completed, so as to realize the integrated calibration of the attitude benchmark of the micro-nano satellite. Finally, this article conducted ground experiments using telemetry data from an in orbit satellite. The experimental results showed that the proposed method reduced sensor measurement errors, improved sensor benchmark consistency, and improved attitude determination accuracy from around 10 degrees to better than 0.2 degrees, verifying the effectiveness of the proposed method.
This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassemb...
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This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.
Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, a...
Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy.
In recent years, the number of software vulnerabilities has gradually increased, posing a threat to the security of software, and identifying whether there are vulnerabilities in software is crucial for assuring its q...
ISBN:
(数字)9798350367041
ISBN:
(纸本)9798350367058
In recent years, the number of software vulnerabilities has gradually increased, posing a threat to the security of software, and identifying whether there are vulnerabilities in software is crucial for assuring its quality. With the development of deep learning, vulnerability detection based on deep learning has achieved good results. However, real-world software vulnerability datasets often suffer from class imbalance and a lack of labeled data, limiting the performance of deep learning models. Therefore, this paper proposes a data augmentation method to alleviate these two problems. Firstly, we treats samples with vulnerabilities in the dataset as code, and generates more samples with vulnerabilities through code refactoring to alleviate class imbalance issues. Secondly, we use the mixup method to perform linear interpolation on the embedding vectors which represent codes, and add the interpolated vectors into the training set, thereby increasing the amount of labeled training data. To evaluate our method, we conducted experiments on three projects of the Promise dataset. The experimental results show that our data augmentation method can effectively improve the effect of vulnerability detection.
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