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3rd International Symposium on computer Technology and Information Science, ISCTIS 2023

作者： Lan, Qiuyan Du, Yao Gao, Caixia Zheng, Haotian School of Computer Science and Engineering Southwest Minzu University Chengdu China Southwest Minzu University Key Laboratory of Computer System State Ethnic Affairs Commission Chengdu China

ISBN: (纸本)9798350325386

In recent years, computer vision technology has achieved rapid development and is widely used in the field of information security. Researchers have carried out a lot of research on the malicious code analysis technology based on image analysis, and many excellent image analysis models have successfully migrated to the malware detection area. This paper systematically collates the malware detection technology based on image analysis technology from different angles. Firstly, the development of computer vision technology in the field of malware, the main representation methods of feature images, malicious code detection models, and other issues are sorted out. After that, the relevant representative literature is collected and sorted out. Finally, the potential research direction of computer vision in the realm of malware detection is deliberated. © 2023 IEEE.

关键词： Image analysis

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Enhancing Ocean Scene Video Captioning with Multimodal Pre-Training and Video-Swin-Transformer 49

Enhancing Ocean Scene Video Captioning with Multimodal Pre-T...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Xinyu Zhao, Meng Shi, Fan Zhang, Meng'en He, Yu Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Laboratory of Computer Vision and System of Ministry of Education Tianjin300384 China Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization Beijing100190 China

ISBN: (纸本)9798350331820

With the success of multimodal pre-training models in the video-language field and various downstream tasks, previous multimodal models used 3DCNN networks as video feature extractors, which have limitations in interacting and fusing with text features. This paper proposes a multimodal pre-training model that utilizes a Video-Swin-Transformer-based network to encode both video and text data, to achieve better performance in video understanding. The model consists of four modules: video encoder, text encoder, interact encoder, and caption decoder to accomplish the task of ocean scene video captioning. A dataset of ocean scene videos, including various content types such as sea surfaces and shores, is also constructed. The training process is divided into two stages: pre-training and fine-tuning. Pre-training is performed on the Howto100m dataset to allow the model to learn video captions in natural scenes and complete video-language matching tasks. The fine-tuning stage is then performed on the ocean1000 dataset to better understand the events and content in ocean scene videos and generate captions that conform to ocean scene video descriptions. The model achieves satisfying results on both the public dataset YouCook2 and the proprietary dataset Ocean1000, demonstrating its ability in video-text information fusion and interaction. © 2023 IEEE.

关键词： Signal encoding

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs 8

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Du, Weiming Lin, Junmou Du, Binqing Borhan, Uddin Md. Li, Jianqiang Chen, Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate. © 2023 IEEE.

关键词： Landing

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Representative Period Selection for Robust Capacity Expansion Planning in Low-carbon Grids

Representative Period Selection for Robust Capacity Expansio...

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2024 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2024

作者： Anderson, Osten Yu, Nanpeng Oikonomou, Konstantinos Maloney, Patrick Wu, Di University of California Riverside Department of Electrical and Computer Engineering RiversideCA United States Pacific Northwest National Laboratory System Evaluation Group RichlandWA United States

ISBN: (纸本)9798350316377

Amid the urgency to decarbonize power systems, while mitigating extreme weather events, capacity expansion models can play a vital role in reliably planning the expansion of power systems and facilitating the integration of renewable energy (RE) sources. Optimizing capacity expansion generally involves selecting surrogate representative days from forecasts of load and the generation profiles of variable RE resources. To properly select those representative days, we propose a novel input-based clustering approach that utilizes three unique operational characteristics: load shedding, renewable curtailment, and transmission congestion. The proposed method allows for more robust and cost-effective capacity planning. The method is validated using a capacity expansion model and a production cost model aligned with California Independent system Operator (CAISO)'s decarbonization goals, and results in significant cost reduction and substantial decreases in load shedding. © 2024 IEEE.

关键词： Renewable energy

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A Composite Decomposition Method for Large-Scale Global Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6304-6319页

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education Shanghai200237 China Westlake University School of Engineering Hangzhou310030 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74048 United States

Cooperative coevolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of nonadditively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups nonseparable variables by recursively considering the interactions between each nonseparable variable and the formed nonseparable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a nonseparable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group nonseparable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG-series designs. © 2024 IEEE.

关键词： Computational efficiency

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

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A Study of Data Augmentation for Imbalance Problem in Network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

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3rd International Conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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Economic Operation Strategy for Fast Charging Station Assisted by HESS Consisting of Retired Batteries Considering Concurrent Decommissioning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7897-7909页

作者： Zhang, Ronghui Zhang, Kai Manandhar, Ujjal Wang, Benfei Qu, Xiaobo Chen, Hong Sun Yat-sen University Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Guangdong 510275 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Tsinghua University School of Vehicle and Mobility Beijing100084 China Tongji University College of Electronics and Information Engineering Shanghai200092 China

The wide adoption of electric vehicles (EVs) leads to the need for fast charging stations (FCSs) with battery energy storage systems (BESSs) due to charging anxieties and various charging behaviors, and results in numerous retired batteries (RBs) necessitating the recycling solutions. To address these issues simultaneously, this study proposes an economic operation strategy for FCSs assisted by the RB-based hybrid energy storage system (HESS). In the proposed strategy, the FCS economic operational model is established, where a rolling mixed-integer linear programming (RMILP) mechanism is adopted considering uncertain charging behaviors, and the fuzzy C-means (FCM) algorithm is adopted to cluster RBs to form the RB-based HESS. Moreover, a concurrent decommissioning method is introduced for the RB-based HESS lifespan extension and cost reduction, with the assistance of a state-of-health-based droop control for power allocation optimization among RBs. Finally, various simulation and experimental studies have been conducted to verify the effectiveness of the proposed economic operation strategy. The results demonstrate that FCSs with the RB-based HESSs achieve 67.32% annual net revenue (ANR) superiority over FCSs without BESSs, and 12.69% ANR increment over those with BESSs. Moreover, the lifespan of the RB-based HESS can be extended by 11.23% with the concurrent decommissioning method. © 2015 IEEE.

关键词： Charging stations

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Additively Manufactured Electric Machine Conductors with Integrated End Turn Heat Exchangers

Additively Manufactured Electric Machine Conductors with Int...

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2022 International Conference on Electrical Machines, ICEM 2022

作者： Pecotich, James Klink, David Heins, Greg Bahrani, Behrooz Monash University Power Engineering Advanced Research Laboratory Department of Electrical and Computer System Engineering Melbourne Australia

ISBN: (数字)9781665414326

ISBN: (纸本)9781665414326

Conductors within a loaded electric machine commonly induce the highest loss. Numerous cooling solutions exist for high-power density electric machine conductors with external water jackets being the most common. The inherent disadvantage of water jacket cooling is the thermal resistance between the conductor and the cooling medium, which reduces the maximum cooling capacity, regardless of the jacket design. This restriction commonly leads designers to implement solutions such as end-turn cooling. The advances in additive manufacturing have unlocked new potential for conductor end-turn cooling. This paper investigates an additively-manufactured conductor with an integrated end-turn heat exchanger. The conductor is based around a Formula Student in-hub motor. This concept utilizes air-cooling and demonstrates a steady-state current density of 11 A/mm2 for a AlSi10Mg end-turn heat-exchanger conductor, without the added mass and complexity of liquid cooling solutions. If copper is used instead of AlSi10Mg, this value increases to approximately 22 A/mm2. © 2022 IEEE.

关键词： Electric losses

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