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IEEE International Conference on Big Data

作者： Ali Aghazadeh Ardebili Andreas Livera George E. Georghiou Antonella Longo Antonio Ficarella Dept. of Research and Development HSPI Rome Dept. of Engineering for Innovation CRISR University of Salento Lecce Italy PV Technology Laboratory FOSS Research Centre for Sustainable Energy Dept. of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Gaps in current numerical models highlight challenges in accurately simulating photovoltaic (PV) systems, particularly their sensitivity to weather forecast outputs and the impact of environmental factors such as wind speed and humidity. These factors introduce discrepancies between simulated and real-life PV system performance. This study investigates the potential of open-source PV physical models, leveraging pvlib-python, to address these challenges. The research focuses on enhancing the accuracy of these models to generate synthetic data that closely replicate real-world conditions. Using the single diode equation implemented in pvlib-python, DC power production data from a real-life PV module is simulated, with initial discrepancies identified and mitigated through the integration of Huld model. This integration aims to improve the accuracy of synthetic data, thereby bridging the gap between simulated and actual performance metrics. The efficacy of the enhanced predictive models is evaluated using historical field measurements from a PV system testbed, demonstrating advancements in simulating PV system behaviors with enhanced accuracy and reliability. The results unveil a significant enhancement in synthetic data generation using PVLib and Huld model.

关键词： Photovoltaic systems Accuracy Sensitivity Wind speed System performance Data models Mathematical models Numerical models Wind forecasting Synthetic data

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Providing a labeled statements dataset to enhance the trans-compilation-based tools 4

Providing a labeled statements dataset to enhance the trans-...

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4th Novel Intelligent and Leading Emerging Sciences Conference, NILES 2022

作者： Soliman, Abdelrahman M. Radwan, Moataz-Bellah A. Eida, Hussain E. Mahmoud, Amira T. Mohammed, Youmna E. Asal, Walaa M. Yousef, Ahmed H. School of Information Technology and Computer Science Nile University Giza Egypt Center for Informatics Science Nile University Giza Egypt Ain Shams University Faculty of Engineering Cairo Egypt Banha University Faculty of Computer and AI Banha Egypt

ISBN: (数字)9781665452410

ISBN: (纸本)9781665452410

Nowadays Mobile Applications are a necessity as everyone is depending on them in their everyday tasks. We use them for communication, entertainment, and utilities. Every day new devices are introduced to the market. The diversity in these devices resulted in many platforms like Android and iOS. These different mobile platforms used by different companies and manufacturers made it challenging for mobile developersto build an application or a system that can be deployed on different platforms. Developers will be required to go through multiple development cycles to develop the application for each of the targeted platforms. To solve these issues, cross-platform frameworks emerged as a solution that would help the developers to write one codebase and deploy it on multiple platforms at the same time. Even though cross-platform frameworks reduced the time and cost needed to develop an application, but they did not provide the native feel and performance of the native applications. It was also challenging for developers to integrate cross-platform applications with a pre-existing system and to maintain the system. Some approaches were introduced to solve these issues by allowing the developer to write one codebase and generate the native code for each platform. One of the recently presented approaches is the trans-compilation approach that depends on the parser code to recognize the source code and generate the corresponding code for it in the target language. Even though the trans-compiler-based approaches were effective in providing an application that combines the advantages of the cross-platform and native development, it faced some problems in dealing with statements that is not expressed in the language's grammar rules, eternal APIs, and APIs that require code definition, like Firebase. In this paper, our aim is to provide a solution that can be integrated with the trans-compilation approach to solve the previously mentioned issues to enhance the conversion success

关键词： Data mining

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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Combining Drone-based Monitoring and Machine Learning for Online Reliability Evaluation of Wind Turbines 5

Combining Drone-based Monitoring and Machine Learning for On...

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5th International Conference on Computing, Electronics and Communications engineering. iCCECE 2022

作者： Kabir, Sohag Aslansefat, Koorosh Gope, Prosanta Campean, Felician Papadopoulos, Yiannis University of Bradford Faculty of Engineering and Informatics Bradford United Kingdom University of Hull Dept. Computer Science and Technology Kingston Upon Hull United Kingdom University of Sheffield Dept. Computer Science Sheffield United Kingdom

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-Assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach. © 2022 IEEE.

关键词： Offshore wind farms

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On Board-Level Failure Localization in Optical Transport Networks Using Graph Neural Network

On Board-Level Failure Localization in Optical Transport Net...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Yan Jiao Pin-Han Ho Xiangzhu Lu János Tapolcai Limei Peng Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Hungary School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea

ISBN: (数字)9798350348972

ISBN: (纸本)9798350348989

This paper investigates a novel framework for board-level failure localization in the Optical Transport Networks (OTN), dubbed Board-Alarm Propagation Tree based Failure Localization (BAPT-FL). Foremost, a collection of functional graphs (FGs) is garnered by iteratively tagging each board in the network topology, serving as the ground of the proposed framework. Concretely, BAPT-FL is designed to build a range of BAPTs by correlating the tagged boards and alarms involved in the FGs, where each BAPT deems a failed board and its corre-lated alarms as the root and leaves, respectively. To evaluate the edge weights of potential BAPTs induced by FGs, a graph neural network (GNN) with the graph transformer operator is employed as an edge classifier, which characterizes each vertex/edge from diverse dimensions including time, traffic distribution, network topology, and board/alarm attributes. Subsequently, we frame an integer linear programming (ILP) problem to construct the best possible BAPT(s). Extensive case studies are conducted to showcase BAPT-FL's advantage over its counterparts in terms of the metrics assessing the identified failed boards/root alarms. We also delve into its performance in volatile environmental variations such as diverse failure scenarios, network topologies, traffic distributions, and noise alarms.

关键词： Location awareness Correlation Network topology Optical propagation Optical fiber networks Tagging Transformers

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Partition-Based Cache Replacement to Manage Shared L2 Caches

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Chinese Journal of Electronics 2025年第3期23卷 464-467页

作者： Juan Fang Jing Wang Chengyan Li Zhicheng Yao Weimao Ke College of Computer Science Beijing University of Technology Beijing China College of Information Engineering Capital Normal University Beijing China College of Computing and Informatics Drexel University Chapel Hill NC USA

The Least recently used (LRU) replacement has been widely employed by on-chip shared caches. With the increase of cache associativity, the performance gap between LRU and theoretical optimal re-placement algorithm is significant, motivating us to design alternative replacement algorithms to improve cache performance. We propose a Partition-based cache replacement (PCR) algorithm to manage multi-core shared L2 caches. PCR takes into consideration recency and frequency information of cache access, and cache resource interference among multiple competing applications. PCR is effective to filter out the less frequently reused blocks through a dynamic insertion/promotion policy based on the target partitions, and ensures that some fraction of the working set is retained in the cache to contribute to more hits. Our evaluation shows that PCR improves performance of an 8-core system by an average of 11.3% over LRU-based cache.

关键词： Power demand Multicore processing Heuristic algorithms Education Interference Filtering algorithms Information filters Partitioning algorithms System-on-chip

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AOCN: Appendix Object Correction Network Utilizing Relationships Across CT Slices

AOCN: Appendix Object Correction Network Utilizing Relations...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ng, Wing W. Y. Xu, Jing Liang, Yinhao Wang, Ting Zhang, Jianjun Zhou, Hui Liang, Dan Li, Guangming Wei, Xinhua School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Medicine South China University of Technology Guangzhou First People's Hospital Department of Radiology Guangzhou510006 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Guangzhou Medical University The Sixth Affiliated Hospital Department of Radiology Qingyuan511500 China

ISBN: (纸本)9781665410205

When analyzing CT images of patients with suspected appendicitis, radiologists need to observe and examine consecutive 2D CT slices. computer-assisted detection of the appendix in 2D CT slices significantly improve the diagnostic efficiency of radiologists. However, existing 2D medical image object detection methods primarily focus on spatial features within a single CT slice, which overlook spatial relationships between consecutive slices. We propose an Appendix Object Correction Network (AOCN) to refine predictions of universal object detectors. Although AOCN is a 2D network, it effectively leverages spatial relationships across consecutive CT slices. AOCN requires only a few training epochs to improve the accuracy of bounding boxes significantly, which offers advantages such as high scalability, low cost, and reduced training time. It consists of a global case feature learning module for extracting global feature map from the CT case and an object feature relation module for modeling the relationships between objects across slices. Experimental results demonstrate the effectiveness and efficiency of AOCN in correcting the output bounding boxes of several mainstream object detection networks, with a 6% to 14% improvement in Recall while requiring only a few training epochs. © 2024 IEEE.

关键词： computerized tomography

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping

arXiv

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arXiv 2025年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Digital Futures United Kingdom

In modern large-scale systems with sensor networks and IoT devices it is essential to collaboratively solve complex problems while utilizing network resources efficiently. In our paper we present three distributed optimization algorithms that exhibit efficient communication among nodes. Our first algorithm presents a simple quantized averaged gradient procedure for distributed optimization, which is shown to converge to a neighborhood of the optimal solution. Our second algorithm incorporates a novel event-triggered refinement mechanism, which refines the utilized quantization level to enhance the precision of the estimated optimal solution. It enables nodes to terminate their operation according to predefined performance guarantees. Our third algorithm is tailored to operate in environments where each message consists of only a few bits. It incorporates a novel event-triggered mechanism for adjusting the quantizer basis and quantization level, allowing nodes to collaboratively decide operation termination based on predefined performance criteria. We analyze the three algorithms and establish their linear convergence. Finally, an application on distributed sensor fusion for target localization is used to demonstrate their favorable performance compared to existing algorithms in the literature. © 2025, CC BY.

关键词： Dynamic programming

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Machine Learning Model for Predicting Community Integrity Towards Solid Waste Management and Environmental Care 5

Machine Learning Model for Predicting Community Integrity To...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Zulkipli, Faridah Jamian, Nurul Husna Masrom, Suraya Khalid, Khairul Irwan Nopiah, Zulkifli Mohd Kerdprasop, Nittaya College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah CampusTapah Road Malaysia Block J MKN Embassy Techzone Jalan Teknokrat 2 Cyberjaya Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Selangor Malaysia School of Computer Engineering Suranaree University of Technology Thailand

ISBN: (纸本)9798331528553

Solid waste management and environmental conservation are global concerns that play a crucial role in both developed and developing countries. This worldwide issue makes a substantial contribution to the achievement of sustainable development goals and, as a result, helps to create a brighter future for a sustainable environment. The primary objective of this project is to create a machine learning model that can accurately assess and monitor the level of community integrity in relation to the management of solid waste and environmental conservation. Community integrity was used as a dependent variable (predictor variable). The independent factors considered in this study include gender, cumulative grade point average (CGPA), residence status, awareness, practice, attitude, perception, and knowledge. Five machine learning algorithms were utilized to determine the optimal prediction model for assessing community integrity in solid waste management and environmental care. The models used were Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Model. The Generalized Linear Model machine learning algorithm was identified as the most appropriate model, based on its ability to predict community integrity in solid waste management and environmental care, as indicated by its lowest Root Mean Square Error (RMSE). © 2024 IEEE.

关键词： Adversarial machine learning

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