2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)最新文献_第7页

A Reference Point and Multi-direction Search Based Evolution Algorithm for Large-scale Multi-objective Optimization 基于参考点和多方向搜索的大规模多目标优化进化算法

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967781

Shuai Tian, Ziqing Wang, Xiangjuan Wu, Yuping Wang

{"title":"A Reference Point and Multi-direction Search Based Evolution Algorithm for Large-scale Multi-objective Optimization","authors":"Shuai Tian, Ziqing Wang, Xiangjuan Wu, Yuping Wang","doi":"10.1109/DOCS55193.2022.9967781","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967781","url":null,"abstract":"This paper proposes a new algorithm based on a reference point selection mechanism and a multi-direction search strategy for large-scale multi-objective optimization problems. Firstly, a center point symmetry strategy is designed to select uniformly distributed reference points and transform the original problem into several low-dimensional single-objective optimization problems. Based on the reference points, a multi-directional weight variable association strategy is proposed to add search directions for the original problem and to improve the search ability of the algorithm. Then, to solve the transformed single-objective problem effectively, an improved differential evolution algorithm based on center mutation is presented. Finally, the numerical experiments are conducted on the large-scale optimization problem benchmarks LSMOP with 200, 500, and 1000 decision variables and the comparison of the proposed algorithm with four state-of-the-art algorithms is made. The results show that the proposed algorithm significantly outperforms the compared algorithms.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"1 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":"131303648","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

Multi-scale Enhanced Fine-grained Feature-based Person Re-identification Algorithm 基于多尺度增强细粒度特征的人物再识别算法

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967712

Zhen Ding, Kangning Yin, Tingting Huang, Lin Xiao, Zhi-hua Dong, Guangqiang Yin

{"title":"Multi-scale Enhanced Fine-grained Feature-based Person Re-identification Algorithm","authors":"Zhen Ding, Kangning Yin, Tingting Huang, Lin Xiao, Zhi-hua Dong, Guangqiang Yin","doi":"10.1109/DOCS55193.2022.9967712","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967712","url":null,"abstract":"The key to solve the problem of Person Re-identification is to improve the extraction and application of Person effective features. Convolutional neural networks have powerful capabilities in this regard. This paper proposes a Person re-recognition algorithm based on multi-scale enhanced fine-grained features. Resnet50 is used as the backbone network to extract Person features at different scales, and the EFOM module is proposed to enable the extraction of fine-grained features by adding relevant global features while compensating for the shortcomings of its own attention mechanism to obtain enhancement and refinement. Finally, the MFFP module is used to obtain the fused features at different scales and then stitched into the BNNeck module. The fused feature vectors are supervised and trained using a variant triplet loss function with less overhead and a more flexible central loss function. Experimental results of the method on the DukeMTMC-ReID and Market-1501 datasets show that it achieves 86.7%% and 92.0% on the mAP evaluation metric; 91.1% and 94.8% on the Rank-1 evaluation metric. The experimental results show that the method makes full use of different scale feature information and key fine-grained features. It enhances the recognition degree of person features and improves the efficiency of person Re-ID.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"6 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":"129101228","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

ADMM-based Distributed Electric Vehicle Charging Optimization Algorithm 基于admm的分布式电动车充电优化算法

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967749

Qiutong Ji, Yuezu Lv, Zhongyuan Zhao

引用次数: 0

Autistic Motor Skill Analysis via ICF-based Protocols 基于icf协议的自闭症运动技能分析

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967751

Carrie M. Toptan, Dinghuang Zhang, Gongyue Zhang, Honghai Liu

引用次数: 1

Research on calibration method of extrinsic parameters of lidar and camera carried by UAV 无人机机载激光雷达与相机外部参数标定方法研究

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967772

Z. Wang, Han Shen, Haibo Du, Jun Zhou, Xiaozheng Jin

引用次数: 1

Fault Diagnosis of Indicator Diagram of Pumping Well Based on Stochastic Configuration Network 基于随机组态网络的抽油井指示图故障诊断

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967768

Baojun Zhao, C. Zang, Na Li, Peng Zeng

{"title":"Fault Diagnosis of Indicator Diagram of Pumping Well Based on Stochastic Configuration Network","authors":"Baojun Zhao, C. Zang, Na Li, Peng Zeng","doi":"10.1109/DOCS55193.2022.9967768","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967768","url":null,"abstract":"In China’s oil exploitation, rod pumping wells occupy an important position. Once the pumping well breaks down, the oil production work will not be carried out in an orderly manner, which will affect the progress target and cause certain safety accidents in serious cases. Therefore, accurate fault diagnosis of pumping wells is a very necessary work. According to the coordinate points of oil well data acquisition, this paper carries out normalization processing, uses wavelet transform and singular value decomposition (SVD) to reduce noise, then draws the image, extracts the gray level co-occurrence matrix (GLCM)and contour features, and uses stochastic configuration network (SCN) to model the typical fault diagnosis of rod pumping wells. Finally, an example is used to verify the correctness of this method. Experiments show that the system has a high fault recognition rate, which verifies the efficiency of SCN classification. It can identify faults faster and more accurately in actual oilfield projects, and is of great significance to improve oil well production.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"1 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":"125862471","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

Decentralized Robust Control of Nonlinear Coupled Tanks Systems 非线性耦合储罐系统的分散鲁棒控制

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967777

Junguo Song

引用次数: 0

Stacking Based LightGBM-CatBoost-RandomForest Algorithm and Its Application in Big Data Modeling 基于堆叠的LightGBM-CatBoost-RandomForest算法及其在大数据建模中的应用

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967714

Zhihong Wang, Hongru Ren, Renquan Lu, Lirong Huang

引用次数: 0

Continuous-time Value-Iteration-Based Learning for Constrained-Input Nonlinear Nonzero-Sum Game 基于连续时间值迭代的约束输入非线性非零和博弈学习

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967754

Geyang Xiao, Yuan Liang, Linlin Yan, Xiaoyu Yi, Congqi Shen, Huifeng Zhang

引用次数: 0

Evolutionary Neural Architecture Search Based on Variational Inference Bayesian Convolutional Neural Network 基于变分推理贝叶斯卷积神经网络的进化神经结构搜索

2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) Pub Date : 2022-10-28 DOI: 10.1109/DOCS55193.2022.9967744

Jialiang Yu, Song Gao, Jie Tian, H. Bian, Hui Liu, Junqing Li

{"title":"Evolutionary Neural Architecture Search Based on Variational Inference Bayesian Convolutional Neural Network","authors":"Jialiang Yu, Song Gao, Jie Tian, H. Bian, Hui Liu, Junqing Li","doi":"10.1109/DOCS55193.2022.9967744","DOIUrl":"https://doi.org/10.1109/DOCS55193.2022.9967744","url":null,"abstract":"In past decades, Bayesian neural networks have attracted much attention due to their advantages of being less prone to over-fitting and being able to generate uncertain measurements with discriminant results. However, Compared with traditional neural networks, Bayesian neural network has too many hyper-parameters to be optimized, so that its performance in classification or regression problems on large-scale datasets is not much superior to ordinary neural networks. Therefore, in order to design a Bayesian network with superior performance, we propose VIBCNN-EvoNAS, a Bayesian convolutional neural network architecture search framework based on variational inference, which constructs a search space through a fixed length integer encoding scheme, and uses evolutionary algorithm as a search strategy to deeply explore the influence of convolution kernel size and other related parameters on the network architecture. In addition, in order to reduce the time loss caused by individual evaluation, we adopt the early stop mechanism in the performance evaluation stage. The proposed method is evaluated on CIFAR10 and CIFAR100 datasets, and the experimental results show the effectiveness of the proposed method.","PeriodicalId":348545,"journal":{"name":"2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS)","volume":"103 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":"134190909","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