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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Zhang, Zhengxu Yang, Jie Ma, Kai Guo, Shiliang Li, Hui School of Electrical Engineering Yanshan University Qinhuangdao China Yanshan University Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province Qinhuangdao China Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education Qinhuangdao China

ISBN: (纸本)9798331504755

With the development of the maritime industry, the number and capacity of existing port cold-ironing (CI) equipment can no longer meet the power demand of ships during berthing, resulting in significant carbon emissions from ships within the port area. Therefore, a ship power sharing strategy based on Shapley value method and Cartel mechanism is proposed. Firstly, the rolling optimization model of ship power scheduling is established to adjust the ship power scheduling strategy according to the real-time information. Secondly, the cooperative game model of ship power sharing is established, and the cost sharing among cooperative ships is realized by Shapley value method. Finally, in order to reduce the situation that ships increase their own profits by deviating from cooperation and affect the overall profits, this paper introduces Cartel mechanism to punish the behavior of deviating from cooperation. The simulation results show that the power sharing strategy proposed in this paper can reduce the total ship cost and carbon emission by 5.28% and 3.78%, respectively. The introduction of Cartel mechanism can increase the cost of non-cooperative behavior, thus ensuring that all ships take cooperative behavior. The power sharing strategy based on Shapley method and Cartel mechanism proposed in this paper is helpful to the construction of green ports. © 2024 IEEE.

关键词： Profitability

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Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism 41

Enhance Smart Grid Security With A Coordinated Cyber-Physica...

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第41届中国控制会议

作者： Zhenyong Zhang Xin Che Xuguo Jiao Jingpei Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou University College of Control Science and Engineering Zhejiang University School of Information and Control Engineering Qingdao University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Smart grid,as one of the largest evolutionary critical infrastructures,witnesses the deep integration of cyber technologies and physical ***,recent events show that the vulnerabilities exposed in the cyber side and the physical side can be exploited to cause coupled and severe ***,the adversaries are able to mask the pernicious physical attack using the cyber attack by compromising the digitized data transmitted to the control *** cooperative cyber-physical attack(CCPA) is stealthy to the current defense *** this article,we propose a cyber-physical defensive mechanism(CPDM) to thwart the *** changing the physical value of the line admittance,the model parameters known by the adversary are not useful anymore,which is the basic idea to design the *** implementation of CPDM depends on the deployment of the distributed flexible AC transmission system(D-FACTS).To reduce the infrastructure cost for the CPDM,we investigate the minimum number of required D-FACTS devices to thwart the *** consider both weak constraints on the CCPA and CPDM and practical limitations for the CCPA and *** each case,we propose a method to compute the optimal deployment of D-FACTS devices,that is,where the DFACTS device should be deployed.

关键词： CPA Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism Security

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Workflow as a Service Broker in Cloud Environment: A Systematic Literature Review

arXiv

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arXiv 2025年

作者： Abrishami, Saeid Zandi, Faridreza Momtaz Nourbakhsh, Alireza Department of Computer Engineering Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran Departement of Control and Computer Engineering Politecnico di Torino Torino Italy

Cloud computing has emerged as a promising platform for running scientific workflows across various domains. Scientists can take advantage of different cloud service models, such as serverful or serverless, to execute workflows based on their specific requirements, along with diverse pricing models like on-demand, reserved, or spot instances to reduce execution costs. However, the challenge of selecting appropriate resources and pricing models, coupled with the orchestration and scheduling of workflow tasks, creates significant complexity for users. To mitigate this burden, Workflow as a Service (WaaS) brokers have been introduced to facilitate workflow execution. In recent years, numerous studies have been published, either directly or indirectly related to this research area, highlighting the need for a comprehensive and systematic review of WaaS brokers to identify key trends and challenges in this field. In this paper, we conduct a Systematic Literature Review (SLR) on WaaS brokers within cloud environments. The SLR employs a thorough 3-tier strategy (database search, backward snowballing, and forward snowballing) to answer five research questions. A total of 74 high-quality articles, published in 43 prestigious venues, are analyzed to derive a taxonomy based on the architecture of WaaS brokers. The articles are classified and surveyed according to this taxonomy, and future research directions for the design and implementation of WaaS brokers are explored. This study provides valuable insights for researchers and developers, helping them identify major trends and issues in the field of WaaS brokers. © 2025, CC BY.

关键词： Cloud platforms

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Emotion detection on phone calls during emergency using ensemble model with hyper parameter tuning

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International Journal of Information Technology (Singapore) 2023年第2期15卷 745-757页

作者： Selvan, A. Kalai Nimmi, K. Janet, B. Sivakumaran, N. C-DAC Thiruvananthapuram India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India

The paper’s main focus is on speech-based emotion detection in Malayalam phone call records, including emergency calls [(emergency response support system (ERSS)] and elicited *** emotions taken into consideration are fear, anger, sad, disgust, panic, anxious are among the emotions considered, while all other emotions are labeled as others. When reporting an emergency, callers commonly exhibit emotional stress, which results in speech output discrepancies. In such uncontrolled situations, determining caller qualities is exceedingly challenging due to poor speech quality and background noise. Fear, anger, and sadness are the three basic emotions expressed during an emergency. In order to classify the emotions, this paper used a bagging and boosting ensemble model. Using the Malayalam speech emotion dataset (MSED), the proposed support vector machine ensembles with Bagging with radial basis function kernel function were found to have an accuracy of 93.01%. Elicited and emergency speech from emergency calls are used to train support vector machine ensembles. For comparison of the performance of the model, we have used RAVDESS and Emo-DB. The accuracy obtained were 81.40% while using the RAVDESS dataset and 93.01% while using Emo-DB dataset which is high when compared to existing machine learning and ensemble model. The performance of boosting models was comparatively lesser than that of the Bagging model. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Emergency response support system (ERSS) Ensemble model Mel frequency cepstral co-efficient (MFCC) Speech emotion detection (SED) Support vector machine (SVM) ZeroCrossing rate (ZCR)

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Large Language Models for topic analysis: insights from robotic accident narratives

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Procedia computer Science 2025年 253卷 1462-1472页

作者： Francesco Costantino Nicolò Sabetta Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 00185 Rome Italy

The integration of robotics in manufacturing has significantly enhanced productivity and safety by performing hazardous tasks. However, it also introduces new risks and accident profiles that require thorough analysis. This study explores the application of Large Language Models (LLMs) for topic modeling on accident narratives from the Occupational Safety and Health Administration (OSHA) database, focusing on incidents involving robots. Advanced Natural Language Processing (NLP) techniques, specifically LLMs, were employed to uncover prevalent themes, common risk factors, and patterns in these narratives. The methodology includes data collection, text processing, statistical analysis, and various topic modeling techniques. The analysis identifies six topics, and key themes including maintenance-related accidents, types of machinery involved, and specific body parts frequently affected. The findings highlight critical areas for improving safety measures, such as rigorous lockout/tagout procedures and enhanced training for maintenance personnel. This study provides a novel approach to analyze accident narratives.

关键词： Robot collaborative robot health safety NLP LLM topic modelling

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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An Ensemble of UNet Frameworks for Lung Nodule Segmentation

An Ensemble of UNet Frameworks for Lung Nodule Segmentatio...

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International Conference on Actual Problems of Applied Mathematics and computer Systems, APAMCS 2022

作者： Gautam, Nandita Basu, Abhishek Kaplun, Dmitry Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031341267

One of the most common types of cancer in the world is lung cancer, which is a cause of increasing mortality. It is most often discovered in the middle and later stages as it does not have obvious symptoms due to which its treatment is often missed. Studies show that most lung cancers are in the form of lung nodules, which can be categorized as benign or malignant. Thus, accurate early identification of malignant lung nodules that might later become cancerous is essential for the prevention of lung cancer. Computed Tomography (CT) images can be useful for identifying and segmenting these nodules. In this paper, we propose an ensemble model, called BUS-UNet++, to segment lung nodules using CT images. We use a combination of the ConvLSTM up-sampling architecture from BUS-UNet and skip connections in aggregation blocks from the UNet++ model to build the BUS-UNet++ ensemble. The dataset for this study is collected from the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), where we have selected the modality of these images as CT images. These are further preprocessed to obtain nodule masks and nodule images, which constitute the Region of Interest (RoI). The accuracy achieved with the Adam Optimizer is 0.9731, the Intersection over Union (IoU) of about 0.8439, and the dice score coefficient (DSC) of about 0.958 are obtained by the proposed system. This ensemble model outperforms several state-of-the-art models used for the same purpose. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image segmentation

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A Mean Pyramid Strategy for Binary Pattern Family 11

A Mean Pyramid Strategy for Binary Pattern Family

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Xu, Xiaochun Shen, Yifan Lei, Jiaxing Wang, Xin Guo, Ente Minjiang University College of Computer and Control Engineering Fuzhou350108 China Minjiang University International Digital Economy College Fuzhou350108 China

ISBN: (纸本)9798350326277

This paper proposes a novel mean pyramid strategy for binary pattern family. The mean pyramid strategy can help the binary pattern family to capture robust multilayer local texture structure instead of the traditional single pixel value. To validate the effectiveness of the proposed strategy, we apply the mean pyramid strategy to the classical completed local binary pattern and present the completed mean pyramid local binary pattern for texture classification tasks. The experimental results on Outex database show that the proposed mean pyramid strategy can significantly enhances the classification performance of binary pattern family. © 2023 IEEE.

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MulDoor: A Multi-target Backdoor Attack Against Federated Learning System

MulDoor: A Multi-target Backdoor Attack Against Federated Le...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuan Wu, Longfei Guan, Zhitao Du, Xiaojiang Aitsaadi, Nadjib Guizani, Mohsen North China Electric Power University School of Control and Computer Engineering China Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Université Paris-Saclay Uvsq David Versailles France Machine Learning Department United Arab Emirates

ISBN: (纸本)9798350351255

In recent years, with the development of wireless communication networks, federated learning (FL) has been widely deployed in distributed scenarios as a privacy-preserving machine learning paradigm. Due to its inherent features, FL shows vulnerability to backdoor attacks. In a backdoor attack, an adversary manipulates the global model's output by compromising the model of one or multiple participants. Existing backdoor attacks are constrained to outputting a single specified target label during the inference phase, limiting the adversary's flexibility to alter the model's output when different target labels are required. In this paper, we study the multi-target attack scenario within the federated learning context, where the adversary aims to manipulate the global model to output various specified labels by inserting different types of triggers. To effectively insert multiple backdoors simultaneously without reducing the attack's effectiveness, we propose MulDoor, a novel multi-target backdoor attack scheme. MulDoor incorporates the concept of supervised contrastive learning to learn the discrepancies among different types of triggers and mitigate interference between them. The experimental results demonstrate that MulDoor achieves better attack effectiveness compared to existing backdoor attacks in a multi-target backdoor attack setting. © 2024 IEEE.

关键词： Contrastive Learning

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An adaptive enhanced windowed correlation filter for visual tracking 11

An adaptive enhanced windowed correlation filter for visual ...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Xu, Xiaochun Feng, Huibin Wu, Qihang Guo, Ente Wang, Xinxin Minjiang University College of Computer and Control Engineering Fuzhou350108 China Minjiang University International Digital Economy College Fuzhou350108 China

ISBN: (纸本)9798350326277

Boundary effect, as an inherent drawback of discriminative correlation filter (DCF) trackers, cannot be handled well in most existing studies. This paper proposes an adaptive enhanced windowed correlation filter tracker (AEWCF), which can alleviate boundary effect adaptively and capture more reliable target information effectively. Firstly, a target enhanced likelihood map is introduced to extract reliable target information from searching window. Then, this paper further proposes an adaptive parted searching window including two sub-windows: the object region window depended on object likelihood maximization strategy and the background window generated by background suppression scheme. The extensive evaluation on OTB2015 benchmarks demonstrate that the AWCF tracker performs favorably against some popular trackers and keeps real-time speed. © 2023 IEEE.

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