2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)最新文献_第8页

An Improved Association Rule Mining Algorithm Based on the Prior Information 一种改进的基于先验信息的关联规则挖掘算法

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967736

X. Cai, Shengbing Xu, Lei Chen, Jinzhang Li, Xin Qiu, Boqi Zheng

引用次数: 0

Semi-Supervised Prototype Network with CBAM and Data Selector for Few-Shot Bearing Fault Diagnosis 基于CBAM和数据选择器的半监督原型网络少弹轴承故障诊断

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967716

Wenkang Zhou, Ning Li

引用次数: 0

Prescribed-Time Practical Tracking Control of Output-Constrained Time-Delay Nonlinear Systems 输出约束时滞非线性系统的规定时间实际跟踪控制

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967780

Kaidi Xu

引用次数: 0

Reinforcement Learning for Robust Neuro-Control of Constrained Nonlinear Systems 约束非线性系统鲁棒神经控制的强化学习

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967737

Mengmeng Xu, Xiong Yang

引用次数: 0

Clustering-based Autoencoding for Dynamic Multiobjective Evolutionary Optimization 基于聚类的动态多目标进化优化自编码

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967742

Yulong Ye, Qingling Zhu, Lingjie Li, Jianyong Chen

{"title":"Clustering-based Autoencoding for Dynamic Multiobjective Evolutionary Optimization","authors":"Yulong Ye, Qingling Zhu, Lingjie Li, Jianyong Chen","doi":"10.1109/DOCS55193.2022.9967742","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967742","url":null,"abstract":"Dynamic multiobjective optimization problems (DMOPs) usually contain multiple conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, transfer learning (TL)-based methods have been considered promising in solving DMOPs. However, most existing TL-based methods are computationally extensive and therefore time-consuming. In this paper, a clustering based autoencoding for DMOEA, called CAE-DMOEA, is proposed, which aims to generate a high-quality initial population to accelerate the evolutionary process and improve the optimization performance. In particular, by learning the mapping relationship between the regional centroids of the approximate Pareto-optimal solutions (POS) from the previous two environments, the CAE-DMOEA can effectively predict the regional centroids of the POS in the new environment, which helps to tracking the moving POS and enhance the optimization efficiency. To study the performance of the proposed method, extensive experiments have been carried out by comparing three state-of-the-art DMOEAs. The experimental results show that the overall performance of the CAE-DMOEA is superior to that of the compared algorithms.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124737076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unbalanced data processing for software defect prediction 软件缺陷预测的不平衡数据处理

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967755

Yang Qu, Zhenming Li, Jiaoru Zhao, Hui Li

{"title":"Unbalanced data processing for software defect prediction","authors":"Yang Qu, Zhenming Li, Jiaoru Zhao, Hui Li","doi":"10.1109/DOCS55193.2022.9967755","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967755","url":null,"abstract":"How to solve the imbalance of defect classification in software defect prediction and improve the accuracy of prediction is an important problem in software testing. Thus many machining learning based model, such as self-adaptive Robust Synthetic Minority Over-Sampling Technique (RSMOTE), ware presented for software defect prediction. However, the imbalanced data distribution limited the prediction performance. Addressing to this issue, a RSMOTE-based Data Imbalance Processing (RDIP) model is presented in this paper. Specifically, the normalized outlier data is removed according to the European distance between points in data denoising, and then the fuzzy membership and fuzzy labels of each point are calculated using the Computational Class Fuzzy Algorithm (FCMD), which removes the hazard points and noise points according to the selection boundary point algorithm (BRS). Experimental results the date sets of NASA, Promise show that the average F1-measure of software defect prediction method for data imbalance is 6.98% higher than other comparison algorithms, which can effectively solve the problem of defect classification imbalance in software defect prediction.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128150889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Off-COMA Algorithm for Multi-UCAV Intelligent Combat Decision-Making 多无人机智能作战决策的非昏迷算法

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967776

Zhengkang Shi, Jingcheng Wang, Hongyuan Wang

{"title":"An Off-COMA Algorithm for Multi-UCAV Intelligent Combat Decision-Making","authors":"Zhengkang Shi, Jingcheng Wang, Hongyuan Wang","doi":"10.1109/DOCS55193.2022.9967776","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967776","url":null,"abstract":"Unmanned Combat Aerial Vehicle (UCAV) has played an important role in modern military warfare, whose level of intelligent decision-making needs to be improved urgently. In this paper, a simplified multi-UCAV combat environment is established, which is modeled as a multi-agent Markov games. There are many difficulties in multi-UCAV combat problem, including strong randomness and complexity, sparse rewards, and no strong opponents for training. In order to solve the above problems, an algorithm called Off Conterfactual Multi-Agent (Off-COMA) is proposed. This algorithm extends the COMA algorithm to the off-policy version, and can reuse historical data for training, which improves data utilization. In addition, the proposed Off-COMA algorithm exploits an improved prioritized experience replay method to deal with the sparse reward. This paper presents an asymmetric policy replay self-play method, which provides a guarantee for the algorithm to generate a powerful policy. Finally, compared with several classical multi-agent reinforcement learning algorithms, the superiority of Off-COMA algorithm in solving the multi-UCAV combat problem is verified.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapless Path Planning of Multi-robot Systems in Complex Environments via Deep Reinforcement Learning 基于深度强化学习的复杂环境下多机器人系统无映射路径规划

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967756

Wanbin Han, Chongrong Fang, Jianping He

引用次数: 0

Fully Distributed Containment Control of Nonlinear Multi-agent Systems 非线性多智能体系统的全分布遏制控制

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967718

Tianqi Liu, Yuezu Lv, G. Wen

引用次数: 0

Causal Discovery of Medical Test Parameters Based on Improved PC Algorithm 基于改进PC算法的医学检验参数因果发现

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967738

Xueyao Qiu, Fangqing Gu, Yiqun Zhang

{"title":"Causal Discovery of Medical Test Parameters Based on Improved PC Algorithm","authors":"Xueyao Qiu, Fangqing Gu, Yiqun Zhang","doi":"10.1109/DOCS55193.2022.9967738","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967738","url":null,"abstract":"Causal discovery from observational data is extremely challenging, especially in obtaining precise causal relationships in observational data. Existing methods for such issue can be roughly categorized into Constrained-based and Score-based causal discovery methods. A common independence test in PC algorithms is Fisher’s exact test, which can only cope with the complete data set. However, missing data is common in many application domains including the healthcare data analysis. When processing data set with missing values, the independence of observed data may differ from that of the corresponding full data generated by the underlying causal processes, and thus unsatisfactory results may occur if we simply applied the Fisher’s exact test-based PC causal discovery method to observational data. Medical test parameters are often used to reflect the patient’s physical condition, and mastering the causal relationship between medical test parameters can manage patients more efficiently. However, in most cases, medical test parameters have missing values. This paper, consequently, proposes an algorithm to first perform a testwise-deletion Fisher-z independence test to data sets with missing values, fill in missing data by generating virtual data to perform the CI relations test, and then use the rule of resolving conflicts between unshielded colliders confirmed as orient bi-directed. Finally, the K2 and Bayesian-Dirichlet equivalent uniform (BDeu) scoring functions were used to score the causal structure discovered by the PC algorithm and the causal structure found by the PC algorithm based on the Missing-value Fisher-z test with orient bi-directed, respectively. Experimental results demonstrate that the causal structure discovered by the proposed algorithm yields a more precise casual analysis.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127392336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0