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Experiments and modeling on thermal performance evaluation of standalone and M-cycle based desiccant air-conditioning systems

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Energy Reports 2024年 11卷 1445-1454页

作者： Bhat, Showkat Ahmad Sajjad, Uzair Hussain, Imtiyaz Yan, Wei-Mon Raza, Hafiz M.U. Ali, Hafiz Muhammad Sultan, Muhammad Omar, Hani Azam, Muhammad Waheed Bozzoli, Fabio Huang, Nen-Fu Center of Innovative Incubator National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology Taipei10608 Taiwan Research Center of Energy Conservation for New Generation of Residential Commercial and Industrial Sectors National Taipei University of Technology Taipei10608 Taiwan Department of Agricultural Engineering Bahauddin Zakariya University Bosan Road Multan60800 Pakistan Dhahran31261 Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan Department of Architecture and Engineering University of Parma Parco Area delle Science 181/A Parma43124 Italy IRC-Renewable Energy and Power Systems King Fahd University of Petroleum and Minerals Dammam Dhahran Saudi Arabia

This study aims to evaluate the performance of standalone and Maisotsenko-cycle based desiccant air-conditioning systems with silica gel through experimental and explainable artificial intelligence methods. In order to examine the desiccant systems' performance, deep neural networks have been constructed based on the optimal hyper-parameters provided by the Bayesian surrogacy. The influential factors, such as regeneration air temperature and relative humidity, primary air temperature and relative humidity, among other system features like regeneration temperature and humidity, are chosen as the inputs using feature engineering and correlation heat maps. The results indicated that with the right framework and training data, the proposed model can forecast the performance of the standalone and M-cycle-based desiccant air conditioning systems with a great accuracy (i.e. R2 = 0.99). In addition, regeneration temperature and humidity, and inlet regeneration air temperature are considered to be the primary parameters that affect the thermal performance of Maisotsenko-cycle and stand-alone desiccant systems. The regeneration conditions are shown to have a positive correlation with the primary and regeneration outlet temperatures while having a negative correlation with the primary and regeneration outlet relative humidity. The primary inlet relative humidity has the greatest impact on the primary outlet relative humidity while the regeneration inlet temperature has the highest impact on the regeneration outlet relative humidity of the system. In addition, the primary inlet relative humidity is found to have positive correlations with the outlet regeneration and primary relative humidity and outlet regeneration temperature. Also, the primary air inlet temperature is found to be directly associated with the outlet primary relative humidity and temperature for the considered systems. © 2024 The Authors

关键词： Air conditioning

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Enhancing Sentiment Analysis on SEED-IV Dataset with Vision Transformers: A Comparative Study 23

Enhancing Sentiment Analysis on SEED-IV Dataset with Vision ...

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Proceedings of the 2023 11th International Conference on Information technology: IoT and Smart City

作者： Imad Eddine Tibermacine Ahmed Tibermacine Walid Guettala Christian NAPOLI Samuele Russo Department of Computer Automation and Management Engineering Sapienza Unviersity of Rome Italy Department of Computer Science University of Biskra Algeria Department of Computer Automation and Management Engineering Sapienza Unviersity of Rome Italy and Institute for Systems Analysis and computer Science Italian National Research Council Italy and Department of Computational Intelligence Czestochowa University of Technology Poland Department of Psychology Sapienza Unviersity of Rome Italy

ISBN: (纸本)9798400709043

This paper introduces a new approach to emotion classification utilising deep learning models, specifically the Vision Transformer (ViT) model, in the analysis of electroencephalogram (EEG) signals. A dual-feature extraction approach was implemented in our study, utilising Power Spectral Density and Differential Entropy, to analyse the SEED IV dataset. This methodology resulted in the detailed classification of four distinct emotional states. The ViT model, which was originally designed for image processing, has been successfully applied to EEG signal analysis. It demonstrated remarkable performance by attaining a test accuracy of 99.02% with little variance. Notably, it outperformed conventional models like GRUs, LSTMs, and CNNs in this context. The findings of our study indicate that the ViT model has a high level of effectiveness in accurately identifying complex patterns present in EEG data. Specifically, the precision and recall rates achieved by the model surpass 98%, while the F1 score is estimated to be about 98.9%. The results of this study not only demonstrate the efficacy of transformer-based models in analysing cognitive states, but also indicate their considerable potential in improving systems for sympathetic human-computer interaction.

关键词： Classification Deep Learning EEG Signal Analysis Vision Transformer

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A Clustering-BIST Design for Inter-Layer Vias in 3D ICs Based on Walking Pattern Approach

TechRxiv

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TechRxiv 2023年

作者： Menbari, Ahmad Rahimi, Hemin Jahanirad, Hadi Ziener, Daniel The Department of Electrical Engineering University of Kurdistan Sanandaj Iran The Department of Computer Architecture and Embedded Systems Ilmenau University of Technology Ilmenau Germany

In the realm of 3D monolithic integrated circuits, inter-layer vias are prone to defects during fabrication, assembly, and operation that necessitates robust Built-In Self-Test solutions. This paper introduces an innovative BIST design aimed at the detection and localization of different types of faults in irregularly placed ILVs using a walking pattern methodology. In the proposed BIST methodology, the ILVs are clustered based on the fault occurrence probability distribution. Then, an effective detection approach is developed for all stuck-at faults, bridging faults, and almost all multiple faults in a single cluster. This approach allows designers to adjust different parameters such as fault coverage percentage, fault localization, and test time in alignment with specific requisites. The proposed BIST method reduces test configurations to two and test time to just 20 cycles by dividing ILVs into more clusters, overcoming a key limitation of recent approach. This methodology also demonstrates improved efficiency in terms of power consumption, area, and hardware utilization, particularly for large-scale benchmarks. For instance, when considering LU32PEENG and the scenario where ILVs are divided into 64 clusters, the power, area, and hardware overhead are 0.82% and 1.03%, and 1.14%, respectively. © 2023, CC BY.

关键词： Built-in self test

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Generative AI for Secure Physical Layer Communications: A Survey

arXiv

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arXiv 2024年

作者： Zhao, Changyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Letaief, Khaled B. The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of security from physical layer perspectives, traditional AI approaches frequently struggle, primarily due to their limited capacity to dynamically adjust to the evolving physical attributes of transmission channels and the complexity of contemporary cyber threats. This adaptability and analytical depth are precisely where GAI excels. Therefore, in this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We first emphasize the importance of advanced GAI models in this area, including Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs). We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity. Furthermore, we also present future research directions focusing model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication, highlighting the multifaceted potential of GAI to address emerging challenges in secure physical layer communications and sensing. © 2024, CC BY.

关键词： Wireless sensor networks

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Reinforcement Learning with Large Language Models (LLMs) Interaction for Network Services

Reinforcement Learning with Large Language Models (LLMs) Int...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Hongyang Du Ruichen Zhang Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Artificial Intelligence-Generated Content (AIGC)-related network services, especially image generation-based services, have garnered notable attention due to their ability to cater to diverse user preferences, which significantly impacts the subjective Quality of Experience (QoE). Specifically, different users can perceive the same semantically informed image quite differently, leading to varying levels of satisfaction. To address this challenge and maximize network users' subjective QoE, we introduce a novel interactive artificial intelligence (IAI) approach using Reinforcement Learning With Large Language Models Interaction (RLLI). RLLI leverages Large Language Model (LLM)-empowered generative agents to simulate user interactions, thereby providing real-time feedback on QoE that encapsulates a range of user personalities. This feedback is instrumental in facilitating the selection of the most suitable AIGC network service provider for each user, ensuring an optimized, personalized experience.

关键词： Image synthesis Computational modeling Instruments Focusing Reinforcement learning Real-time systems Quality of experience

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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IEEE International Conference on Industrial technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz School of Automation China University of Geosciences Wuhan China School of Automation the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises 42

Design and Application of a One-Stop Mobile Application Shar...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Xinzhe Chen, Luefeng Hu, Jie Yang, Qingfeng Wu, Min Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada

ISBN: (纸本)9789887581543

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Informatization

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The ForTune Toolbox: Building Solutions for Condition-Based and Predictive Maintenance Focusing on Retrofitting

The ForTune Toolbox: Building Solutions for Condition-Based ...

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MikroSystemTechnik Kongress 2023: Mikroelektronik, Mikrosystemtechnik und ihre Anwendungen - Nachhaltigkeit und TechnologiesouveranitatMicrosystems technology Congress 2023: Microelectronics, Microsystems technology and their Applications - Sustainability and technology Sovereignty

作者： Assafo, Maryam Lautsch, Martin Suawa, Priscile Jongmanns, Marcel Huebner, Michael Reichenbach, Marc Brockmann, Carsten Reinhardt, Denis Langendoerfer, Peter Department of Wireless Systems BTU Cottbus-Senftenberg Cottbus Germany Department of Automation Technology BTU Cottbus-Senftenberg Cottbus Germany Department of Computer Engineering BTU Cottbus-Senftenberg Cottbus Germany Fraunhofer IPMS Dresden Germany IZM Berlin Germany IHP-Leibniz-Institut für Innovative Mikroelektronik Frankfurt/Oder Germany

ISBN: (纸本)9783800762040

Condition-based and predictive maintenance (PdM) will play a pivotal role in improving sustainability. This holds true not only for production systems but also for other machinery, e.g., trains, construction machines, etc. A common feature of these systems is that they are in use for long terms, often several decades. Thus, in order to use an innovative approach such as an artificial intelligence-based maintenance optimization, appropriate means to select, deploy, and evaluate the data for these systems are essentially needed. Empowering retrofitting is what the ForTune Toolbox aims for by providing the hardware and software needed to enable the deployment of PdM solutions. In this paper, we present the concept of the toolbox. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Retrofitting

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HIL Simulation and Testing of the Distribution Network's Automatic Reconfiguration

HIL Simulation and Testing of the Distribution Network's Aut...

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IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Sikho Ntshiba Senthil Krishnamurthy Sinawo Nomandela Department of Electrical Electronic and Computer Engineering Center for Substation Automation and Energy Management Systems (CSAEMS) Cape Peninsula University of Technology (CPUT) Cape Town South Africa

ISBN: (数字)9798350389388

ISBN: (纸本)9798350389395

Distribution system network reconfiguration involves the optimization of the configuration of electrical distribution networks to enhance their performance, reliability, and efficiency. In this research work, distribution networks can be designed to automatically reconfigure themselves in response to faults, load changes, or other disturbances. This automatic reconfiguration can help minimize outage durations and improve system reliability. The hardware-in-the-loop (HIL) simulation results can significantly improve distribution networks' flexibility, reliability, and efficiency.

关键词： Hardware-in-the-loop simulation Distribution networks Reliability engineering Circuit faults Logic Protection Optimization Testing Load modeling Load flow

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