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IEEE International WIE Conference on electrical and computer engineering (WIECON-ECE)

作者： P. Venkatesan C. Gopinath P. Prabhavathy Department of Electrical and Electronics Engineering Center for Integrated Smart Waste Management System Department of Electrical and Electronics Engineering Sri Venkateswara College of Engineering (Autonomous) Sriperumbudur India Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Increasing energy demands and the need for efficient management necessitate innovative solutions. This study introduces an integrated Demand Side Management (DSM) framework tailored for residential buildings within smart grid systems. The proposed methodology employs Earth Worm Optimization Algorithm (EWOA) and Virulence Optimization Algorithm (VOA) to optimize scheduling for household appliance using integration of Renewable Energy Storage (RES) and Renewable Energy Resources (RERs). Real-Time Electricity Scheduling (RTES) is implemented using IoT technologies to enable adaptive energy management. This system demonstrates significant performance improvements, achieving a 76.19% reduction in the Peak-to-Average Ratio (PAR) using VOA and a 73.8% reduction with EWOA when RES is included. Comparative analyses highlight substantial cost reductions, enhanced grid stability, and improved energy efficiency. This work not only showcases the novelty of integrating advanced optimization algorithms with IoT but also provides a robust approach to achieve sustainable and cost-effective energy management.

关键词： Renewable energy sources Demand side management Virulence Peak to average power ratio Energy efficiency Scheduling Real-time systems Smart grids Internet of Things Optimization

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GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation 28th

GRU-Net: Gaussian Attention Aided Dense Skip Connection Base...

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28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Roy, Ayush Pramanik, Payel Ghosal, Sohom Valenkova, Daria Kaplun, Dmitrii Sarkar, Ram Department of Electrical Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Electronics and Communication Engineering International Institute of Information Technology Chhattisgarh India Department of Automation and Control Processes Saint Petersburg Electrotechnical University LETI Saint Petersburg Russia Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China

ISBN: (纸本)9783031669545

Breast cancer is a major global health concern. Pathologists face challenges in analyzing complex features from pathological images, which is a time-consuming and labor-intensive task. Therefore, efficient computer-based diagnostic tools are needed for early detection and treatment planning. This paper presents a modified version of MultiResU-Net for histopathology image segmentation, which is selected as the backbone for its ability to analyze and segment complex features at multiple scales and ensure effective feature flow via skip connections. The modified version also utilizes the Gaussian distribution-based Attention Module (GdAM) to incorporate histopathology-relevant text information in a Gaussian distribution. The sampled features from the Gaussian text feature-guided distribution highlight specific spatial regions based on prior knowledge. Finally, using the controlled Dense Residual Block (CDRB) on skip connections of MultiResU-Net, the information is transferred from the encoder layers to the decoder layers in a controlled manner using a scaling parameter derived from the extracted spatial features. We validate our approach on two diverse breast cancer histopathology image datasets: TNBC and MonuSeg, demonstrating superior segmentation performance compared to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Gaussian distribution

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Structural design and simulation analysis of tomato soft picking manipulator 5

Structural design and simulation analysis of tomato soft pic...

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5th International Conference on Mechanical engineering, Intelligent Manufacturing, and Mechatronics, MEIMM 2024

作者： Liu, Deqing Xiong, Zhonggang Liu, Zhong Wu, Dandan Cai, Jinyun Wang, Shuguang School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace Technology Guilin541004 China Key Laboratory of Special Engineering Equipment Design and Intelligent Driving Technology Guilin University OfAerospace Technology Education Department of Guangxi Zhuang Autonomous Region Guilin541004 China

ISBN: (数字)9781510686786

ISBN: (纸本)9781510686779

Aiming at the existing picking manipulator's fingers are primarily integrated with rigid materials, which have the problem of poor flexibility and self-adaptability, this study designs a multi-chamber soft picking manipulator for tomato picking. The structural model of the soft body picking manipulator is established, and the mathematical model of the single chamber is constructed using the strain energy density function, the principle of virtual work and the volume solution method, otherwise, it is concluded that the pressure in the finger chamber is positively correlated with the bending angle in the material's limit range . Yeoh's third order and control variable method are used in workbench finite element simulation to simulate and analyze the finger's structure model .The simulation results demonstrate the ideal combinations of parameters for the finger: 6 chambers, the chamber spacing is 6mm and the chamber wall thickness is 3mm;The fingers intracavity pressure can pick tomatoes when the minimum pressure is 0.03MPa, and it can pick fruits similar to the shape of the tomato. Additionally the soft picking robot model has some degree of universality, providing new models and parameters for fruit and vegetable picking robots. © 2024 SPIE.

关键词： Photons

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Trade-off Between Execution Time and Memory Consumption in Fuzzy Average-Utility Mining 12

Trade-off Between Execution Time and Memory Consumption in F...

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Hong, Tzung-Pei Ku, Meng-Ping Tsai, Yu-Chuan Huang, Wei-Ming Li, Shu-Min Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. Kaohsiung Taiwan

ISBN: (纸本)9798350324693

Fuzzy utility mining considers high-utility fuzzy itemsets as valuable knowledge by integrating quantities of items, their profits, and meaningful fuzzy terms derived by quantities according to membership functions. In fuzzy utility mining, the utility value of a fuzzy itemset in a transaction will always be greater than or equal to those of its subsets, so the measurement of fuzzy utility is an unfair evaluation method. Therefore, the fuzzy average-utility mining problem was issued in 2020, and three solutions were proposed to solve fuzzy average-utility itemsets as two-phase fuzzy average-utility algorithm (TPFAU), two-phase method with tree-based structure (HFAUIM) and one-phase approach with tree-based structure (FHFAUIM), respectively. The second and third methods decrease the candidates generated compared to the first. However, the sorting strategy for mining steps for the last two approaches is based on the frequencies of items in a database and then inserting items of a transaction into a tree in descending order of their frequencies, thus spending more computing time on deriving the actual fuzzy utility value of itemsets. To overcome the above-mentioned problem, this paper adopts a different sorting strategy with a tree-based structure and then by using it to design an algorithm named IFHFAUIM to mine high fuzzy average-utility itemsets. It reduces the storage of required fuzzy utility values in tree nodes and recovers them through tree traversal. Computational experiments show that the proposed method could make a good trade-off between execution time and memory usage. © 2023 IEEE.

关键词： Membership functions

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Erasable-Itemset Mining for Sequential Product Databases 22nd

Erasable-Itemset Mining for Sequential Product Databases

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Hong, Tzung-Pei Chen, Yi-Li Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9783031274084

Erasable-itemset mining has become a popular research topic and is usually used for product production planning in the industry. If some products in a factory may be removed without critically affecting production profits, the set composed of them is called an erasable itemset. Erasable-itemset mining is to find all the removable material sets for saving funds. This paper extends the concept of erasable itemsets to consider customer behavior with a sequence of orders. We consider the scenario that when an item (material) is not purchased, a product using that material cannot be manufactured, and clients will cancel all their orders if at least one such order exists. We propose a modified erasable-itemset mining algorithm for solving the above problem. Finally, experiments with varying thresholds are conducted to evaluate the execution time and mining results of the proposed algorithm considering customer behavior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education 7

The Emergence of Artificial Intelligence in Education and it...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes. © 2024 IEEE.

关键词： Students

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The Impact of the Digital Transformation of Education on Literacy 7

The Impact of the Digital Transformation of Education on Lit...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills. © 2024 IEEE.

关键词： digital education digital literacy digital-informatics literacy

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CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots

arXiv

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arXiv 2025年

作者： Sejersen, Jonas le Fevre Kayacan, Erdal Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMETA framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ETA prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMETA framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMETA improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ETA prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method (A∗) which does not consider conflicts. The performance of the CAMETA framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners. © 2025, CC BY.

关键词： Mobile robots

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Convergent Wheeled Robot Navigation Based on an Interpolated Potential Function and Gradient

SSRN

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SSRN 2023年

作者： Seder, Marija Klančar, Gregor Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of System Control and Cybernetics Faculty of Electrical Engineering University of Ljubljana Slovenia

The article presents a novel idea to construct a smooth navigation function based on the grid-based search that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predicted control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. Monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTM) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from MODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTM-RNN model achieves high predictive accuracy, with root mean square error (RMSE) values as low as 0.08 m for ice thickness and a mean absolute error (MAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTM (RMSE 0.12 m, MAE 0.09 m) and CNN-LSTM hybrid (RMSE 0.10 m, MAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTM-RNN Machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

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