IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第10页

MoAR-CNN: Multi-Objective Adversarially Robust Convolutional Neural Network for SAR Image Classification

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-02 DOI: 10.1109/TETCI.2024.3449908

Hai-Nan Wei;Guo-Qiang Zeng;Kang-Di Lu;Guang-Gang Geng;Jian Weng

{"title":"MoAR-CNN: Multi-Objective Adversarially Robust Convolutional Neural Network for SAR Image Classification","authors":"Hai-Nan Wei;Guo-Qiang Zeng;Kang-Di Lu;Guang-Gang Geng;Jian Weng","doi":"10.1109/TETCI.2024.3449908","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449908","url":null,"abstract":"Deep neural networks (DNNs) have been widely applied to the synthetic aperture radar (SAR) images detection and classification recently while different kinds of adversarial attacks from malicious adversary and the hidden vulnerability of DNNs may lead to serious security threats. The state-of-the-art DNNs-based SAR image detection models are designed manually by only considering the test accuracy performance on clean datasets but neglecting the models' adversarial robustness under various types of adversarial attacks. In order to obtain the best trade-off between the clean accuracy and adversarial robustness in robust convolutional neural networks (CNNs)-based SAR image classification models, this work makes the first attempt to develop a multi-objective adversarially robust CNN, called MoAR-CNN. In the MoAR-CNN, we propose a multi-objective automatic design method of the cells-based neural architectures and some critical hyperparameters such as the optimizer type and learning rate of CNNs. A Squeeze-and-Excitation (SE) layer is introduced after each cell to improve the computational efficiency and robustness. The experiments on FUSAR-Ship and OpenSARShip datasets against seven types of adversarial attacks have been implemented to demonstrate the superiority of the proposed MoAR-CNN to six classical manually designed CNNs and four robust neural architectures search methods in terms of clean accuracy, adversarial accuracy, and model size. Furthermore, we also demonstrate the advantages of using SE layer in MoAR-CNN, the transferability of MoAR-CNN, search costs, adversarial training, and the developed NSGA-II in MoAR-CNN through experiments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"57-74"},"PeriodicalIF":5.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feature Autonomous Screening and Sequence Integration Network for Medical Image Classification

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-02 DOI: 10.1109/TETCI.2024.3448490

Hongfeng You;Xiaobing Chen;Kun Yu;Guangbo Fu;Fei Mao;Xin Ning;Xiao Bai;Weiwei Cai

{"title":"Feature Autonomous Screening and Sequence Integration Network for Medical Image Classification","authors":"Hongfeng You;Xiaobing Chen;Kun Yu;Guangbo Fu;Fei Mao;Xin Ning;Xiao Bai;Weiwei Cai","doi":"10.1109/TETCI.2024.3448490","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3448490","url":null,"abstract":"This article proposes a feature self-selection and sequence integration network, namely FASSI-Net, for medical image classification, which can extract representative deep features and contextual semantic information. In this network, FASSI-Net uses a new feature selection and integration module (FSIM) to compress the depth features, which uses a sequence model to replace the Flatten layer. This strategy introduces two sets of multi-scale convolutions, where a cross-attention mechanism assigns two sets of weights (i.e., vertical and horizontal weights) to each convolution. We then calculate the Euclidean distance between different scale feature points to measure the correlation between them. Specifically, the feature points are divided into useful features and redundant features. In addition, a feature dimension compression (CRI) module is constructed to reconstruct the redundant feature structure, and the residual structure is used to extract the representative features from the redundant features. Meantime, a sequence model is introduced to compress the deep features and obtain the context relationship between feature points. Experimental results on three datasets show that the proposed method significantly outperforms previous methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"1034-1048"},"PeriodicalIF":5.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for Capacitated Electric Vehicle Routing Problems

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-30 DOI: 10.1109/TETCI.2024.3444698

Chao Wang;Mengmeng Cao;Hao Jiang;Xiaoshu Xiang;Xingyi Zhang

{"title":"A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for Capacitated Electric Vehicle Routing Problems","authors":"Chao Wang;Mengmeng Cao;Hao Jiang;Xiaoshu Xiang;Xingyi Zhang","doi":"10.1109/TETCI.2024.3444698","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3444698","url":null,"abstract":"The Capacitated Electric Vehicle Routing Problem (CEVRP) poses a novel challenge within the field of vehicle routing optimization, as it requires consideration of both customer service requirements and electric vehicle recharging schedules. In addressing the CEVRP, Adaptive Large Neighborhood Search (ALNS) has garnered widespread acclaim due to its remarkable adaptability and versatility. However, the original ALNS, using a weight-based scoring method, relies solely on the past performances of operators to determine their weights, thereby failing to capture crucial information about the ongoing search process. Moreover, it often employs a fixed single charging strategy for the CEVRP, neglecting the potential impact of alternative charging strategies on solution improvement. Therefore, this study treats the selection of operators as a Markov Decision Process and introduces a novel approach based on Deep Reinforcement Learning (DRL) for operator selection. This approach enables adaptive selection of both destroy and repair operators, alongside charging strategies, based on the current state of the search process. More specifically, a state extraction method is devised to extract features not only from the problem itself but also from the solutions generated during the iterative process. Additionally, a novel reward function is designed to guide the DRL network in selecting an appropriate operator portfolio for the CEVRP. Experimental results demonstrate that the proposed algorithm excels in instances with fewer than 100 customers, achieving the best values in 7 out of 8 test instances. It also maintains competitive performance in instances with over 100 customers and requires less time compared to population-based methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"131-144"},"PeriodicalIF":5.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Deep Learning Framework for Automatic Detection of Brain Tumours Using Different Modalities 使用不同模式自动检测脑肿瘤的混合深度学习框架

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-29 DOI: 10.1109/TETCI.2024.3442889

Adyasha Sahu;Pradeep Kumar Das;Indraneel Paul;Sukadev Meher

{"title":"A Hybrid Deep Learning Framework for Automatic Detection of Brain Tumours Using Different Modalities","authors":"Adyasha Sahu;Pradeep Kumar Das;Indraneel Paul;Sukadev Meher","doi":"10.1109/TETCI.2024.3442889","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3442889","url":null,"abstract":"Nowadays, deep convolutional neural networks (DCNNs) are the focus of substantial research for classification and detection applications in medical image processing. However, the limited availability and unequal data distribution of publicly available datasets impede the broad use of DCNNs for medical image processing. This work proposes a novel deep learning-based framework for efficient detection of brain tumors across different openly accessible datasets of different sizes and modalities of images. The introduction of a novel end-to-end Cumulative Learning Strategy (CLS) and Multi-Weighted New Loss (MWNL) function reduces the impact of unevenly distributed datasets. In the suggested framework, the DCNN model is incorporated with regularization, such as DropOut and DropBlock, to mitigate the problem of over-fitting. Furthermore, the suggested augmentation approach, Modified RandAugment, successfully deals with the issue of limited availability of data. Finally, the employment of K-nearest neighbor (KNN) improves the classification performance since it retains the benefits of both deep learning and machine learning. Moreover, the effectiveness of the proposed framework is also validated over small and imbalanced datasets. The proposed framework outperforms others with an accuracy of up to <inline-formula><tex-math>$ 99.70%$</tex-math></inline-formula>.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1216-1225"},"PeriodicalIF":5.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing Security and Correctness in Code Generation: An Empirical Study on Commercial Large Language Models

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-29 DOI: 10.1109/TETCI.2024.3446695

Gavin S. Black;Bhaskar P. Rimal;Varghese Mathew Vaidyan

{"title":"Balancing Security and Correctness in Code Generation: An Empirical Study on Commercial Large Language Models","authors":"Gavin S. Black;Bhaskar P. Rimal;Varghese Mathew Vaidyan","doi":"10.1109/TETCI.2024.3446695","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3446695","url":null,"abstract":"Large language models (LLMs) continue to be adopted for a multitude of previously manual tasks, with code generation as a prominent use. Multiple commercial models have seen wide adoption due to the accessible nature of the interface. Simple prompts can lead to working solutions that save developers time. However, the generated code has a significant challenge with maintaining security. There are no guarantees on code safety, and LLM responses can readily include known weaknesses. To address this concern, our research examines different prompt types for shaping responses from code generation tasks to produce safer outputs. The top set of common weaknesses is generated through unconditioned prompts to create vulnerable code across multiple commercial LLMs. These inputs are then paired with different contexts, roles, and identification prompts intended to improve security. Our findings show that the inclusion of appropriate guidance reduces vulnerabilities in generated code, with the choice of model having the most significant effect. Additionally, timings are presented to demonstrate the efficiency of singular requests that limit the number of model interactions.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"419-430"},"PeriodicalIF":5.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SR-ABR: Super Resolution Integrated ABR Algorithm for Cloud-Based Video Streaming

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-28 DOI: 10.1109/TETCI.2024.3446449

Haiqiao Wu;Dapeng Oliver Wu;Peng Gong

{"title":"SR-ABR: Super Resolution Integrated ABR Algorithm for Cloud-Based Video Streaming","authors":"Haiqiao Wu;Dapeng Oliver Wu;Peng Gong","doi":"10.1109/TETCI.2024.3446449","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3446449","url":null,"abstract":"Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the received video can be enhanced with a trained super-resolution model running on the client-side. However, all the existing solutions ignore the content-induced performance variability of Super-Resolution Deep Neural Network (SR-DNN) models, which means the same super-resolution models have different enhancement effects on the different parts of videos because of video content variation. That leads to unreasonable bitrate selection, resulting in low video QoE, e.g., low bitrate, rebuffering, or video quality jitters. Thus, in this paper, we propose SR-ABR, a super-resolution integrated adaptive bitrate (ABR) algorithm, which considers the content-induced performance variability of SR-DNNs into the bitrate decision process. Due to complex network conditions and video content, SR-ABR adopts deep reinforcement learning (DRL) to select future bitrate for adapting to a wide range of environments. Moreover, to utilize the content-induced performance variability of SR-DNNs efficiently, we first define the performance variability of SR-DNNs over different video content, and then use a 2D convolution kernel to distill the features of the performance variability of the SR-DNNs to a short future video segment (several chunks) as part of the inputs. We compare SR-ABR with the related state-of-the-art works using trace-driven simulation under various real-world traces. The experiments show that SR-ABR outperforms the best state-of-the-art work NAS with the gain in average QoE of 4.3%–46.2% and 18.9%–42.1% under FCC and 3G/HSDPA network traces, respectively.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"87-98"},"PeriodicalIF":5.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lightweight Self Attention Based Multi-Task Deep Learning Model for Industrial Solar Panel and Environmental Monitoring 用于工业太阳能电池板和环境监测的轻量级自关注多任务深度学习模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-26 DOI: 10.1109/TETCI.2024.3444590

Tuhinangshu Gangopadhyay;Tanushree Meena;Debojyoti Pal;Sudipta Roy

{"title":"A Lightweight Self Attention Based Multi-Task Deep Learning Model for Industrial Solar Panel and Environmental Monitoring","authors":"Tuhinangshu Gangopadhyay;Tanushree Meena;Debojyoti Pal;Sudipta Roy","doi":"10.1109/TETCI.2024.3444590","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3444590","url":null,"abstract":"Environmental monitoring has become a serious topic of discussion and is gaining mass attention. The reason is the severe consequences of environmental depletion, which has led to circumstances like climate change, rise in floods and droughts, changed rainfall patterns, etc. So, various measures are being taken to protect the environment, like shifting to renewable and pollution-free energy alternatives, like solar energy, and handling the after-effects of disasters, like flood management and oil spill accident management. However, their identification still remains a huge challenge, which is laborious and extensive. Thus, this work proposed a lightweight and efficient segmentation model, SA U-Net++, for the automatic identification of solar panels and their associated defects, flood affected-areas and oil spill accident regions. The model's novel blend of level-wise self-attention modules is embedded with the revised bridge connections and the dropouts. It has helped in better efficient global context understanding and feature extraction from the inputs, besides maintaining the integrity of the training process and avoiding some major learning and run-time issues, like overfitting and memory exhaustion. Our detailed experiments demonstrate that the proposed model outperforms state-of-the-art models. The results confirm its high generalizability, cost-effectiveness, and robustness.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1502-1513"},"PeriodicalIF":5.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-26 DOI: 10.1109/TETCI.2024.3445450

Sibo Cheng;Hector Chassagnon;Matthew Kasoar;Yike Guo;Rossella Arcucci

{"title":"Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale","authors":"Sibo Cheng;Hector Chassagnon;Matthew Kasoar;Yike Guo;Rossella Arcucci","doi":"10.1109/TETCI.2024.3445450","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3445450","url":null,"abstract":"Global wildfire models play a crucial role in anticipating and responding to changing wildfire regimes. JULES-INFERNO is a global vegetation and fire model simulating wildfire emissions and area burnt on a global scale. However, because of the high data dimensionality and system complexity, JULES-INFERNO's computational costs make it challenging to apply to fire risk forecasting with unseen initial conditions. Typically, running JULES-INFERNO for 30 years of prediction will take several hours on High Performance Computing (HPC) clusters. To tackle this bottleneck, two data-driven models are built in this work based on Deep Learning techniques to surrogate the JULES-INFERNO model and speed up global wildfire forecasting. More precisely, these machine learning models take global temperature, vegetation density, soil moisture and previous forecasts as inputs to predict the subsequent global area burnt on an iterative basis. Average Error per Pixel (AEP) and Structural Similarity Index Measure (SSIM) are used as metrics to evaluate the performance of the proposed surrogate models. A fine tuning strategy is also proposed in this work to improve the algorithm performance for unseen scenarios. Numerical results show a strong performance of the proposed models, in terms of both computational efficiency (less than 20 seconds for 30 years of prediction on a laptop CPU) and prediction accuracy (with AEP under 0.3% and SSIM over 98% compared to the outputs of JULES-INFERNO).","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"444-454"},"PeriodicalIF":5.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-20 DOI: 10.1109/TETCI.2024.3406691

Linhao Zhang;Li Jin;Guangluan Xu;Xiaoyu Li;Xian Sun

{"title":"COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport","authors":"Linhao Zhang;Li Jin;Guangluan Xu;Xiaoyu Li;Xian Sun","doi":"10.1109/TETCI.2024.3406691","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3406691","url":null,"abstract":"Counter-narratives, which are direct responses consisting of non-aggressive fact-based arguments, have emerged as a highly effective approach to combat the proliferation of hate speech. Previous methodologies have primarily focused on fine-tuning and post-editing techniques to ensure the fluency of generated contents, while overlooking the critical aspects of individualization and relevance concerning the specific hatred targets, such as LGBT groups, immigrants, etc. This research paper introduces a novel framework based on contrastive optimal transport, which effectively addresses the challenges of maintaining target interaction and promoting diversification in generating counter-narratives. Firstly, an Optimal Transport Kernel (OTK) module is leveraged to incorporate hatred target information in the token representations, in which the comparison pairs are extracted between original and transported features. Secondly, a self-contrastive learning module is employed to address the issue of model degeneration. This module achieves this by generating an anisotropic distribution of token representations. Finally, a target-oriented search method is integrated as an improved decoding strategy to explicitly promote domain relevance and diversification in the inference process. This strategy modifies the model's confidence score by considering both token similarity and target relevance. Quantitative and qualitative experiments have been evaluated on two benchmark datasets, which demonstrate that our proposed model significantly outperforms current methods evaluated by metrics from multiple aspects.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"740-756"},"PeriodicalIF":5.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Re-Stabilizing Large-Scale Network Systems Using High-Dimension Low-Sample-Size Data Analysis 利用高维低样本数据分析的再稳定大型网络系统

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-08-20 DOI: 10.1109/TETCI.2024.3442824

Xun Shen;Hampei Sasahara;Jun-ichi Imura;Makito Oku;Kazuyuki Aihara

{"title":"Re-Stabilizing Large-Scale Network Systems Using High-Dimension Low-Sample-Size Data Analysis","authors":"Xun Shen;Hampei Sasahara;Jun-ichi Imura;Makito Oku;Kazuyuki Aihara","doi":"10.1109/TETCI.2024.3442824","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3442824","url":null,"abstract":"Dynamical Network Marker (DNM) theory offers an efficient approach to identify warning signals at an early stage for impending critical transitions leading to system deterioration in extensive network systems, utilizing High-Dimension Low-Sample-Size (HDLSS) data. It is crucial to explore strategies for enhancing system stability and preventing critical transitions, a process known as re-stabilization. This paper aims to provide a theoretical basis for re-stabilization using HDLSS data by proposing a computational method to approximate pole placement for re-stabilizing large-scale networks. The proposed method analyzes HDLSS data to extract pertinent information about the network system, which is then used to design feedback gain and input placement for approximate pole placement. The novelty of this method lies in adjusting only the diagonal elements of the system matrix, thus simplifying the re-stabilization process and enhancing its practicality. The method is applicable to systems experiencing either saddle-node bifurcation or Hopf bifurcation. A theoretical analysis was performed to examine the perturbation of the maximum eigenvalues of the system matrix using the proposed approximate pole placement method. We validated the proposed method via simulations based on the Holme-Kim model.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1638-1649"},"PeriodicalIF":5.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0