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Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models

Journal of Bionic engineering 2024年第2期21卷 1022-1054页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Xin Zhang College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Biomedical Engineering National Engineering Research Center of Ophthalmology and OptometryEye HospitalWenzhou Medical UniversityWenzhou325027China Key Lab of Biohealth Materials and Chemistry of Wenzhou Wenzhou UniversityWenzhouChina Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhouChina

Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV *** accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,*** experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and ***,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for *** addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results *** evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other *** addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and *** conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV *** that the source code of INMVO is available at https://***/woniuzuioupao/INMVO.

关键词： Photovoltaic models Multi-verse optimizer Nelder-Mead simplex Iterative chaos map

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Using Goal-Conditioned Reinforcement Learning with Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task

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IEEE Transactions on Automation Science and engineering 2024年第4期21卷 6217-6228页

作者： Li, Jingchen Shi, Haobin Hwang, Kao-Shing Northwestern Polytechnical University School of Computer Science Xi'an710072 China National Sun Yat-sen University Department of Electrical Engineering Kaohsiung81164 Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung80708 Taiwan

Leveraging reinforcement learning on high-precision decision-making in Robot Arm assembly scenes is a desired goal in the industrial community. However, tasks like Flexible Flat Cable (FFC) assembly, which require highly trained workers, pose significant challenges due to sparse rewards and limited learning conditions. In this work, we propose a goal-conditioned self-imitation reinforcement learning method for FFC assembly without relying on a specific end-effector, where both perception and behavior plannings are learned through reinforcement learning. We analyze the challenges faced by Robot Arm in high-precision assembly scenarios and balance the breadth and depth of exploration during training. Our end-to-end model consists of hindsight and self-imitation modules, allowing the Robot Arm to leverage futile exploration and optimize successful trajectories. Our method does not require rule-based or manual rewards, and it enables the Robot Arm to quickly find feasible solutions through experience relabeling, while unnecessary explorations are avoided. We train the FFC assembly policy in a simulation environment and transfer it to the real scenario by using domain adaptation. We explore various combinations of hindsight and self-imitation learning, and discuss the results comprehensively. Experimental findings demonstrate that our model achieves fast and advanced flexible flat cable assembly, surpassing other reinforcement learning-based methods. Note to Practitioners - The motivation of this article stems from the need to develop an efficient and accurate FFC assembly policy for 3C (computer, Communication, and Consumer Electronic) industry, promoting the development of intelligent manufacturing. Traditional control methods are incompetent to complete such a high-precision task with Robot Arm due to the difficult-to-model connectors, and existing reinforcement learning methods cannot converge with restricted epochs because of the difficult goals or trajectories. To

关键词： Manipulators

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A LoRa-Based Emotion Estimation Scheme for Smart Home Automated Actions Using ELMs 8

A LoRa-Based Emotion Estimation Scheme for Smart Home Automa...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Karras, Christos Karras, Aristeidis Drakopoulos, Georgios Tsolis, Dimitrios Mylonas, Phivos Sioutas, Spyros Decentralized Systems Computing Group University of Patras Computer Engineering and Informatics Department Greece Ionian University Humanistic and Social Informatics Laboratory Department of Informatics Greece

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Emotion detection is crucial in many IoT deployments from an operational perspective with examples ranging from digital health to smart cities. This is particularly true in smart homes where the interaction between the local IoT ecosystem and the inhabitants are continuous, pervasive, and nuanced. More specifically, emotion estimation from human speech attributes is an integral architectural component of such ecosystems. In this work, we survey the emerging LPWAN technologies and after selecting the most optimal for our use-case, we propose an emotion estimation scheme based on LoRa wireless technology for automated actions in smart home environments. In particular, a voice recognition module coupled with a transmitter are installed in a car. Then, depending on the estimation outcome, the smart home may undertake one or more preemptive actions according to its configuration as the car passenger approaches. The prototype LoRa system has been tested with the widely-used TESS dataset with encouraging results as expressed in the emotion confusion matrix obtained from a extreme learning machine with various kernels. The preceding paves the way for adaptive smart homes tailored to the needs of their inhabitants. © 2022 IEEE.

关键词： Automation

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Analysis of selected dielectric parameters of insulating liquids 23

Analysis of selected dielectric parameters of insulating liq...

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23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Pluzek, Aleksandra Nagi, Lukasz Koziol, Michal Zygarlicka, Malgorzata Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Electrical Power and Renewable Energy Opole Poland Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Computer Science Opole Poland

ISBN: (纸本)9798350335934

During the last few years, there has been a growing interest in the topic of using natural or synthetic esters as an alternative to mineral oils in oil transformers due to the easier way to obtain them and their ability to exhibit better electrical properties. In addition, more and more researchers are describing in their work that doping esters with nanoparticles is capable of further improving these properties. Therefore, in the current work, it was decided to see what effect the addition of a hydrophobic carbon shell iron nanoparticle to ORLEN mineral oil and Midel 7131 synthetic ester would have on selected electrical parameters of the insulating fluid, i.e. breakdown voltage and electrical strength. © 2023 IEEE.

关键词： Nanoparticles

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Relevance Vector Machine for Code Smell Detection 22

Relevance Vector Machine for Code Smell Detection

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Putro, Hanson Prihantoro Yuhana, Umi Laili Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798331527471

Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection, especially Magic Value smells, which often affect source code readability and maintainability in a software development industry. This approach utilizes source code analysis using two source code representations (raw source code and token stream) and feature extraction using two text mining techniques (Bag of Words/BoW and Term Frequency-Inverse Document Frequency/TF-IDF). The experiment involves analyzing 983 records of labeled source code datasets. The results show that RVM can detect code smells with a very high level of accuracy, reaching 99.9% on some kernel configurations, also nearly perfect precision, and F1 scores. This paper contributes to introducing the use of RVM for detecting code smells in source code, explores source code representation and feature extraction techniques, and shows that RVM can achieve high detection accuracy. © 2024 IEEE.

关键词： Software design

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A framework for object tracking in videos with complex backgrounds and obstructions

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Neural Computing and Applications 2025年 1-20页

作者： Chang, Tsui-Ping Chen, Tzer-Long Hsiao, Tsung-Chih Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Gaoxiong807 Taiwan Department of Radio Television and Film Shih Hsin University Taipei11604 Taiwan

The research of object tracking in videos utilizes computer vision and machine learning techniques to identify and track objects in the consecutive image frames of videos. The popular algorithms used in the research are YOLO, DeepSORT, StrongSORT, and ByteTrack. YOLO has gained recognition for its hardware efficiency and impressive performance in object detection tasks. It efficiently detects objects in the consecutive image frames of videos. On the other hand, DeepSORT, StrongSORT, and ByteTrack leverage the prediction capabilities to estimate the positions of objects in the next image frames. These algorithms play a significant role to enhance the capabilities of object tracking systems. Recently, many researchers have proposed their methods to enhance the accuracy in object tracking tasks. However, the division of videos into the consecutive image frames for tracking objects can pose challenges, especially when the videos have complex backgrounds and many obstructions in the image frames. In fact, the complex backgrounds with many obstructions in image frames decrease the accuracy of tracking objects in applications. In this study, we present TraObs, a framework for object Tracking in complex backgrounds with Obstructions in the image frames of videos. In TraObs, two novel mechanisms CFM and M-controller are proposed for detectors and trackers to enhance the accuracy of object tracking performance. CFM addresses the tracked objects which are missing in some image frames of videos, while M-controller makes sure the tracked objects with the correct object ids in the system. Some experiments with four testing datasets and benchmark datasets are designed and used respectively to analyze the performance of TraObs. Furthermore, the HOTA and MOTA metrics are used to evaluate our proposed methods. The experimental results also prove the effectiveness of CFM and M-controller in our framework TraObs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd.,

关键词： Image enhancement

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A Process Oriented Integration Model for Smart Health Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1369-1386页

作者： Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal Department of Computer Science Superior UniversityLahore54600Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Faculty of Computer Science&IT Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efﬁcient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atﬁrst,the service participants,processes and tasks of smart services are identiﬁed and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

关键词： Process modeling business process business process integration business process modeling and notation

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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Evaluation and Integration of Advanced AI Chatbots for Biological Pathway Curation

Evaluation and Integration of Advanced AI Chatbots for Biolo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Azam, Muhammad He, Fei Santosh, Krpns Xu, Dong Popescu, Mihail University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Department of Health Management and Informatics ColumbiaMO United States

ISBN: (纸本)9798350386226

The rapid expansion of biological literature presents significant challenges in manually curating pathway knowledge from images for biological and medical research. Recent advancements in AI, particularly multimodal AI chatbots like GPT-4V, Claude, and Gemini, offer promising capabilities for image understanding and extraction of biological interactions. This study evaluates the effectiveness of these AI chatbots in curating gene interactions from 50 curated tumor signaling pathway images. Our results highlight the superior performance of GPT-4V, achieving a precision of 0.778, a recall of 0.664, an F1-score of 0.716 in non-directional analysis, and a precision of 0.566, a recall of 0.471, an F1-score of 0.512 in directional analysis. Claude followed with a precision of 0.735, recall of 0.594, and F1-score of 0.6571 in non-directional analysis, and a precision of 0.500, recall of 0.400, and F1-score of 0.445 in directional analysis. Gemini recorded the lowest performance metrics across both analyses. We further developed a model fusion integrating Claude-3 chatbots and our pathway curation pipeline, achieving notable improvements. © 2024 IEEE.

关键词： AI Chatbots Bio-Curation Gene Interactions Precision Oncology Signaling Pathways

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Neural Collaborative Filtering for Improved Tourism Destination Recommendation 8

Neural Collaborative Filtering for Improved Tourism Destinat...

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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Marzuki, Imam Hariadi, Mochamad Rachmadi, Reza Fuad Arif, Yunifa Miftahul Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Indonesia Universitas Islam Negeri Maulana Malik Ibrahim Department of Informatics Engineering Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350368970

This study evaluates the effectiveness of Neural Collaborative Filtering (NCF) in enhancing tourism destination recommendation. The researchers employed a methodology encompassing a comprehensive literature review, integration of user preference dataset, and the application of the NCF algorithm. The evaluation utilized MAE and RMSE metrics, demonstrating significant improvement compared to the Neural Network method. The results highlight the superior accuracy and reliability of the NCF-based recommendation system in providing tourism destination recommendation. This study emphasizes the practical significance of implementing NCF, offering a more sophisticated and efficient approach to enhancing user experience. The implication of this research contribute to academic needs and are expected to provide practical benefits for recommendation system in the tourism sector. The researchers elucidate critical differences in the NCF algorithm compared to benchmarks, and the introduction explores gaps in NCF applications for tourism, reinforcing the importance of this study. Experimental results show that the NCF model achieved an MAE of 0.306 and an RMSE of 0.371, outperforming the Neural Network's MAE of 0.794 and RMSE of 0.960, confirming superior predictive accuracy. © 2024 IEEE.

关键词： Collaborative filtering

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