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An adaptive multitasking optimization algorithm based on population distribution

Mathematical Biosciences and engineering 2024年第2期21卷 2432-2457页

作者： Li, Xiaoyu Wang, Lei Jiang, Qiaoyong Xu, Qingzheng School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China School of Electronic and Information Engineering Ankang University Ankang725000 China The Key Laboratory of Industrial Automation of Shaanxi Province Shaanxi University of Technology Hanzhong723001 China College of Information and Communication National University of Defense Technology Wuhan430019 China

Evolutionary multitasking optimization (EMTO) handles multiple tasks simultaneously by transferring and sharing valuable knowledge from other relevant tasks. How to effectively identify transferred knowledge and reduce negative knowledge transfer are two key issues in EMTO. Many existing EMTO algorithms treat the elite solutions in tasks as transferred knowledge between tasks. However, these algorithms may not be effective enough when the global optimums of the tasks are far apart. In this paper, we study an adaptive evolutionary multitasking optimization algorithm based on population distribution information to find valuable transferred knowledge and weaken the negative transfer between tasks. In this paper, we first divide each task population into K sub-populations based on the fitness values of the individuals, and then the maximum mean discrepancy (MMD) is utilized to calculate the distribution difference between each sub-population in the source task and the subpopulation where the best solution of the target task is located. Among the sub-populations of the source task, the sub-population with the smallest MMD value is selected, and the individuals in it are used as transferred individuals. In this way, the solution chosen for the transfer may be an elite solution or some other solution. In addition, an improved randomized interaction probability is also included in the proposed algorithm to adjust the intensity of inter-task interactions. The experimental results on two multitasking test suites demonstrate that the proposed algorithm achieves high solution accuracy and fast convergence for most problems, especially for problems with low relevance. © 2024 the Author(s).

关键词： Optimization

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ECG Classification Based on Multimodal Feature Extraction and Deep Learning Model 21

ECG Classification Based on Multimodal Feature Extraction an...

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21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Sekar, Rajeev Kumar, Ranjeet School of Computer Science and Engineering Vellore Institite of Technology Chennai India School of Electronics Engineering Vellore Institite of Technology Chennai India

ISBN: (纸本)9798331522193

Heart health diagnosis plays a vital role in preventing and managing cardiovascular diseases, which remain a leading cause of death globally. Early and precise detection of heart related problems is vital for effective treatment and enhancing patient outcomes. Traditional methods often rely on single-modal data, which can limit diagnostic accuracy. In this study, we present a comprehensive approach to heart health analysis by extracting multimodal features derived from Electrocardiogram (ECG) signals and training a Deep Learning model. The methodology involves extraction of both time-domain features using Continuous Wavelet Transform: Morlet transform (CWT) and frequency-domain features using Fast Fourier Transform (FFT), enabling a thorough examination of the ECG data. These features were then input into a deep neural network model. The model achieved an impressive accuracy of 99.1% in classifying various heart conditions, highlighting its robustness and effectiveness. Notably, the neural network architecture employed is relatively simple, making it feasible to deploy on mobile or edge devices. This characteristic ensures that the heart health analysis can be performed in real-time without relying on extensive computational resources, thereby making it practical for widespread, on-device use. This approach not only enhances the accessibility of heart health monitoring but also promotes timely and accurate diagnosis in everyday settings. © 2025 IEEE.

关键词： Electrocardiography

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Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding

Multilingual Synopses of Movie Narratives: A Dataset for Vis...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Sun, Yidan Yu, Jianfei Li, Boyang College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798891761681

Story video-text alignment, a core task in computational story understanding, aims to align video clips with corresponding sentences in their descriptions. However, progress on the task has been held back by the scarcity of manually annotated video-text correspondence and the heavy concentration on English narrations of Hollywood movies. To address these issues, in this paper, we construct a large-scale multilingual video story dataset named Multilingual Synopses of Movie Narratives (M-SYMON), containing 13,166 movie summary videos from 7 languages, as well as manual annotation of fine-grained video-text correspondences for 101.5 hours of video. Training on the human annotated data from M-SYMON outperforms the SOTA methods by 15.7 and 16.2 percentage points on Clip Accuracy and Sentence IoU scores, respectively, demonstrating the effectiveness of the annotations. As benchmarks for future research, we create 6 baseline approaches with different multilingual training strategies, compare their performance in both intra-lingual and cross-lingual setups, exemplifying the challenges of multilingual video-text alignment. The dataset is released at: https://***/insundaycathy/M-SyMoN. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Research on Network Security Game Based on System Dynamics 24

Research on Network Security Game Based on System Dynamics

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2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Zeng, Qiufen Pei, Tingrui School of Mechanical Engineering and Mechanics Xiangtan University Hunan Xiangtan China School of Information Science and Engineering Hunan Institute of Engineering Hunan Xiangtan China College of Information Science and Technology Jinan University Guangdong Zhuhai China School of Computer Science School of Cyberspace Science Xiangtan University Hunan Xiangtan China

ISBN: (纸本)9798400711237

Network security game was usually considered as attack-defense confrontation;however, we need to think out of the box because any network entity may get a chance to launch an attack. The problem of network security which was described as the game between regulatory authority and network entity needs to be analyzed from the perspective of macro management. The model of system dynamic which was combined with traditional game theory was established. Several strategies which were most commonly used were verified and evaluated through simulation model. In order to weaken the instability of network security game, selective punishment strategy was proposed and the simulation result showed that not only in astringency but also in network security maintenance the proposed strategy can achieve good results. © 2024 Copyright held by the owner/author(s).

关键词： System theory

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Comparative Analysis of News Articles Summarization using LLMs

Comparative Analysis of News Articles Summarization using LL...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Archanaa, N. Shivanesh, B. Suwin Kumar, J.D.T. Bharathi Mohan, G. Doss, Srinath Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350361537

In the rapidly evolving domain of Natural Language Processing (NLP), the efficiency of Large Language Models (LLMs) in generating abstractive text summaries plays a pivotal role in information synthesis. This study advances the understanding of LLM performance by conducting a comprehensive evaluation of seven cutting-edge models on four distinct datasets. The models selected for comparison include Distilbart-cnn-12-6, Led-base-16384, Google's Bigbird, Microsoft's ProphetNet, Facebook's BART, T5 fine-tuned, and Google's PEGASUS. Each model's summarization prowess is rigorously assessed using a battery of metrics: ROUGE, METEOR, BERTScore, Cosine Similarity, and BLEU. The goal is to discern the intricate relationship between dataset characteristics and model efficacy, delivering insights into the inherent advantages and limitations of each model in handling specific data contexts. The results contribute to a refined understanding of LLM applicability, offering empirical evidence to aid in the selection of the most suitable model for varied summarization needs. © 2024 IEEE.

关键词： Natural language processing systems

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Integration Framework Solution for Healthcare Monitoring

Integration Framework Solution for Healthcare Monitoring

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2022 International Conference on Recent Advances in Wireless Communications and Emerging Technologies, RAWCET 2022

作者： Usharani, Shola Rajakumaran, Gayathri Nandam, Anjana Devi Ibrahim, Mohamed School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

Huge volumes of data have flowed in recent years of healthcare research to follow a few factors of a person and inform the guardian in the event of a patient's emergency. This necessitates the creation of a single platform where consumers may monitor data in real time. This study discusses an interoperable health monitoring system. The module provides the essential chance for patients to obtain all-day service, which may be documented by the doctor and can be notified in the event of an emergency. When a patient requires regular check-ups or long-term home care, this platform comes in handy. It may be accomplished utilizing market sensors and includes monitoring devices that allow patients to be monitored without having to visit a doctor. Because it eliminates the need for data transcription and copying by humans. Because it reduces the need for human data transcription and copying, interoperability helps make patient data more secure. When computer systems are set up for optimum interoperability, with databases and other applications communicating and exchanging information, employees will be more productive. XMPP is an integrating platform for IoT available in open source for uploading instant messaging using generic XML data processing. The XMPP protocols are free and there are several implementations in the form of clients, servers, server components, and code libraries. The work is demonstrated using the XMPP interoperable IoT communication protocol for the health monitoring system.. © Published under licence by IOP Publishing Ltd.

关键词： Interoperability

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A Practical Predefined-Time Stability Criterion and Its Application to Uncertain Nonlinear Systems

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Journal of Systems science & Complexity 2024年第6期37卷 2556-2578页

作者： XU Hao YU Dengxiu SUI Shuai XU Bowen CHEN C.L.Philip Unmanned System Research Institute Northwestern Polytechnical UniversityXi'an 710072China College of Science Liaoning University of TechnologyJinzhou 121001China School of Artificial Intelligence Optics and ElectronicsNorthwestern Polytechnical UniversityXi'an 710072China School of Computer Science and Engineering South China University of Technology and Pazhou LabGuangzhou 510641China

In this paper,the singularity-free predefined-time fuzzy adaptive tracking control problem is studied for non-strict feedback(NSF)nonlinear systems considering mismatched external *** innovative practical predefined-time stability(PPTS)criterion is proposed to provide the theoretical basis for subsequent control *** to the existing predefined-time stability(PTS)criterion,this criterion has a broader application range and can solve the control design issues of nonlinear systems with system *** logic systems(FLSs)are employed to identify unknown nonlinear *** on the backstepping control technology,a singularity-free predefined-time control(PTC)design method is proposed,in which the hyperbolic tangent function is utilized to avoid the singular problem,and the nature of fuzzy basis function is adopted to resolve the algebraic loop *** PPTS of the closed-loop NSF nonlinear system is proven with the Lyapunov theory and the proposed PPTS ***,the efficiency of the presented PTC design method is verified by several sets of simulations on a single link manipulator system.

关键词： Fuzzy adaptive control nonlinear systems practical predefined-time stability predefinedtime control singularity-free control

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Adaptive Control Strategy for Primary Frequency Regulation of BESS in Wind Farm 9

Adaptive Control Strategy for Primary Frequency Regulation o...

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Gu, Yichao Zhuang, Kai Zhang, Jimiao Xie, Jianfeng Chongqing University of Science and Technology School of Electrical Engineering Chongqing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350309638

This paper proposes a novel adaptive synthetic inertia (SI) control scheme for the battery energy storage system (BESS) in the wind farm. The proposed approach dynamically adjusts the amplitude of the direct current (DC) of the voltage source converter (VSC) in response to grid frequency disturbances. The unique feature of the proposed control is its ability to emulate droop and inertia in real time by adjusting parameter values according to the rate of change of frequency (RoCoF) and frequency deviation. Within the SI control, a first-order low-pass element is incorporated into the virtual inertia control to augment the system inertia. Its advantages and effectiveness are rigorously demonstrated on a modified IEEE benchmark system under load changes and wind speed fluctuations. © 2024 IEEE.

关键词： Battery storage

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Developing Smart Skin: A Force Sensor-Based Touch and Pressure Detector 12

Developing Smart Skin: A Force Sensor-Based Touch and Pressu...

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12th International Conference on Information and Communication technology, ICoICT 2024

作者： Zain, Muhammad Aulia Rifqi Mutiara, Giva Andriana Periyadi Alfarisi, Muhammad Rizqy Sitorus, Angelica Adhira, Raka Duta Applied Science School Computer Technology Telkom University Bandung Indonesia Applied Science School Multimedia Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350363432

This research discusses the advancements in developing smart skin by employing Force Sensitive Resistor (FSR). The objective of this research is to advance research on the fundamental concept of artificial skin that relies on the human sense of touch sensors especially in pressure to imitate the sensory capabilities of human skin, since humans are incapable of accurately quantifying the pain induced by external pressure. Therefore, by employing FSR sensors, the acquired data will be stabilized, thereby serving as a dependable reference. A prototyping method is implemented in this research to construct and observe the smart skin design. This research is utilized RP-S40-ST and FS7548 sensors for gathering data pressure and touch. The smart skin is coated with an elastomeric substance that closely resembles human skin in terms of its flexibility, softness, and mechanical properties. The FSR sensor is linked to a microcontroller board called TTGO LoRa OLED, which enables the integration of data transfer for further development through the Long Range (LoRa) protocol. The AMF-500 force gauge functions as a standard for converting data obtained from analog sensors into precise force (N) and pressure measurements (kPa). This research represents a significant step towards developing smart skin using FSR sensors that aim to mimic human touch by detecting external pressure up to a maximum of under 60 Newtons and a maximum bend of under 90 degrees. © 2024 IEEE.

关键词： Contact sensors

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A fast anomaly network traffic detection method based on the constrained k-nearest neighbor 13

A fast anomaly network traffic detection method based on the...

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Zhu, Zhiguang Xie, Ying Yang, Xu Hu, Wei University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Southwest Minzu University School of Computer Science and Engineering Chengdu China National Engineering Research Center of Classified Protection Safeguard Technology for Cybersecurity Beijing China

ISBN: (纸本)9781665462631

The k-nearest neighbor algorithm has been widely used in network anomaly detection works, but its query efficiency decreases significantly when the number of samples and feature dimensions increase. To meet the demand for real-time detection, an accurate and timely anomaly detection solution is particularly important. This paper proposes a fast anomaly traffic detection method based on the constrained k-nearest neighbor (CKNN) algorithm. The method uses equilibrium modified k-means and randomized incremental method to optimize the ball tree construction scheme. Specifically, randomized incremental method is used to solve the minimum coverage ball, which optimizes the selection process of the centeroid and radius while reducing the depth of the ball tree. And the equilibrium modified k-means method replaces the original k-center principle used in the division of subtrees, which solves the problem of unbalanced search binary tree division. Meanwhile, by reducing the required number of backtracking to search the K nearest neighbors, which reduces the classification time overhead of the algorithm. We validate the effectiveness of the method on several benchmark datasets. The experimental results show that the CKNN maintains a higher query rate without loss of detection accuracy when dealing with high-dimensional, massive sample data compared with the traditional KNN algorithm. And with the growth ratio of up to 99.68% for some samples, our method also exhibits higher detection accuracy and less time consumption compared with other machine learning algorithms. © 2023 IEEE.

关键词： Anomaly detection

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