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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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Component Fault Diagnosability of Hierarchical Cubic Networks

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ACM Transactions on Design Automation of Electronic Systems 2021年第3期28卷 1-19页

作者： Huang, Yanze Wen, Kui Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology No. 3 Xueyuan Road Fujian Fuzhou China College of Computer and Cyber Security Fujian Normal University No. 8 Xuefu South Road Fujian Fuzhou350117 China Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan70101 Taiwan

The fault diagnosability of a network indicates the self-diagnosis ability of the network, thus it is an important measure of robustness of the network. As a neoteric feature for measuring fault diagnosability, the r-component diagnosability ctr(G) of a network G imposes the restriction that the number of components is at least r in the remaining network of G by deleting faulty set X, which enhances the diagnosability of G. In this article, we establish the r-component diagnosability for n-dimensional hierarchical cubic network HCNn, and we show that, under both PMC model and MM∗model, the r-component diagnosability of HCNn is rn-½(r-1)r+1 for n≥ 2 and 1≤ r≤ n-1. Moreover, we introduce the concepts of 0-PMC subgraph and 0-MM∗subgraph of HCNn. Then, we make use of 0-PMC subgraph and 0-MM∗subgraph of HCNn to design two algorithms under PMC model and MM∗model, respectively, which are practical and efficient for component fault diagnosis of HCNn. Besides, we compare the r-component diagnosability of HCNn with the extra conditional diagnosability, diagnosability, good-neighbor diagnosability, pessimistic diagnosability, and conditional diagnosability, and we verify that the r-component diagnosability of HCNn is higher than the other types of diagnosability. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Failure analysis

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DeceFL:a principled fully decentralized federated learning framework

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National science Open 2023年第1期2卷 35-51页

作者： Ye Yuan Jun Liu Dou Jin Zuogong Yue Tao Yang Ruijuan Chen Maolin Wang Chuan Sun Lei Xu Feng Hua Yuqi Guo Xiuchuan Tang Xin He Xinlei Yi Dong Li Guanghui Wen Wenwu Yu Hai-Tao Zhang Tianyou Chai Shaochun Sui Han Ding School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Department of Applied Mathematics University of WaterlooWaterloo N2L 3G1Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China School of Mathematical and Physical Sciences Wuhan Textile UniversityWuhan 430200China School of Electrical Engineering and Computer Science and Digital FuturesKTH Royal Institute of TechnologyStockholm 10044Sweden Shenyang Institute of Automation(SIA) Chinese Academy of SciencesShenyang 110169China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu 610091China

Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently *** a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy *** there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central *** we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning *** has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly ***,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.

关键词： decentralized federated learning smart manufacturing control systems privacy

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Quadratically Constrained Myopic Adversarial Channels

Quadratically Constrained Myopic Adversarial Channels

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作者： Zhang, Yihan Vatedka, Shashank Jaggi, Sidharth Sarwate, Anand D. Department of Information Engineering Chinese University of Hong Kong Hong Kong Technion-Israel Institute of Technology Henry and Marilyn Taub Faculty of Computer Science Haifa3200003 Israel Institute of Science and Technology Austria Klosterneuburg3400 Austria Department of Communications and Electronics Palaiseau91120 France Institute of Network Coding Chinese University of Hong Kong Hong Kong Indian Institute of Technology Hyderabad Department of Electrical Engineering Hyderabad502285 India Department of Electrical Engineering Indian Institute of Technology Hyderabad Hyderabad502285 India University of Bristol School of Mathematics BristolBS8 1TH United Kingdom State University of New Jersey Rutgers Department of Electrical and Computer Engineering New BrunswickNJ08854 United States

We study communication in the presence of a jamming adversary where quadratic power constraints are imposed on the transmitter and the jammer. The jamming signal is allowed to be a function of the codebook, and a noncausal but noisy observation of the transmitted codeword. For a certain range of the noise-to-signal ratios (NSRs) of the transmitter and the jammer, we are able to characterize the capacity of this channel under deterministic encoding or stochastic encoding, i.e., with no common randomness between the encoder/decoder pair. For the remaining NSR regimes, we determine the capacity under the assumption of a small amount of common randomness (at most 2\log (n) bits in one sub-regime, and at most \Omega ({n}) bits in the other sub-regime) available to the encoder-decoder pair. Our proof techniques involve a novel myopic list-decoding result for achievability, and a Plotkin-type push attack for the converse in a subregion of the NSRs, both of which may be of independent interest. We also give bounds on the strong secrecy capacity of this channel assuming that the jammer is simultaneously eavesdropping. © 1963-2012 IEEE.

关键词： Transmitters

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Harmonic-Free AC/DC/AC Structure with Innovative Modulation Technique for Asymmetrical Cascade H-Bridge Multi-Level Inverter

Harmonic-Free AC/DC/AC Structure with Innovative Modulation ...

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Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Nabil Karania Mohamad Alaaeddin Alali Stefano Di Gennaro Jean–Pierre Barbot Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy QUARTZ Laboratory EA7393 ENSEA 6 Avenue du Ponceau Cergy-Pontoise France LS2N-UMR 6004-CNRS Centrale Nantes1 Rue de la Noë Nantes France

This paper presents an advanced solution for AC/DC/AC converter structure using a novel modulation approach for Asymmetrical Cascade H-Bridge Multi-Level Inverter (ACHB-MLI). This solution improves the structure performance and power quality of the output stepping voltage to fulfill the smart industrial grid requirements. Besides, a harmonic-free structure is employed to ensure integration with the grid, using conventional rectifiers combined with a shunt active *** novel approach presents a combination of two modulation techniques: Staircase Modulation Technique (SMT) and Selective Harmonics Elimination method (SHE). This combined approach (SMT-SHE) achieves an output stepping voltage closer to the sinusoidal waveform and free of predominant harmonics by determining the appropriate SHE switching ***-SHE approach will minimize the THD ratio, eliminate specific harmonic ranks, and allow an output voltage amplitude variation according to load specifications. Moreover, switching angles’ limitations are proposed to prevent the appearance of undesired harmonics (zeros, even), while minimum number of H bridge modules with a lower rating of the capacitors is ***, the proposed AC/DC/AC converter is validated on an industrial network for supplying a specific load and tested within a MATLAB environment.

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Approximating Tensor Norms via Sphere Covering: Bridging the Gap Between Primal and Dual

arXiv

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arXiv 2023年

作者： He, Simai Hu, Haodong Jiang, Bo Li, Zhening Research Institute for Interdisciplinary Sciences School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Department of Computer Science and Technology School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China School of Mathematics and Physics University of Portsmouth PortsmouthPO1 3HF United Kingdom

The matrix spectral and nuclear norms appear in enormous applications. The generalizations of these norms to higher-order tensors is becoming increasingly important but unfortunately they are NP-hard to compute or even approximate. Although the two norms are dual to each other, the best known approximation bound achieved by polynomial-time algorithms for the tensor nuclear norm is worse than that for the tensor spectral norm. In this paper, we bridge this gap by proposing deterministic algorithms with the best bound for both tensor norms. Our methods not only improve the approximation bound for the nuclear norm, but are also data independent and easily implementable comparing to existing approximation methods for the tensor spectral norm. The main idea is to construct a selection of unit vectors that can approximately represent the unit sphere, in other words, a collection of spherical caps to cover the sphere. For this purpose, we explicitly construct several collections of spherical caps for sphere covering with adjustable parameters for different levels of approximations and cardinalities. These readily available constructions are of independent interest as they provide a powerful tool for various decision making problems on spheres and related problems. We believe the ideas of constructions and the applications to approximate tensor norms can be useful to tackle optimization problems over other sets such as the binary hypercube. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning

arXiv

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arXiv 2023年

作者： Masuyama, Naoki Takebayashi, Takanori Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao Wermter, Stefan Graduate School of Informatics Department of Core Informatics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Informatics Faculty of Mathematics Computer Science and Natural Sciences University of Hamburg Vogt-Koelln-Str. 30 Hamburg22527 Germany

In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in a topological clustering algorithm plays an important role in adaptively generating well-separated clusters during a self-organizing process. In this paper, we propose a new parameter-free ART-based topological clustering algorithm capable of continual learning by introducing parameter estimation methods. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to the state-of-the-art clustering algorithms without any parameter pre-specifications. © 2023, CC BY-NC-ND.

关键词： Clustering algorithms

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Trajectory Planning of Multiple Dronecells in Vehicular Networks: A Reinforcement Learning Approach

IEEE Networking Letters

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IEEE Networking Letters 2020年第1期2卷 14-18页

作者： Samir, Moataz Ebrahimi, Dariush Assi, Chadi Sharafeddine, Sanaa Ghrayeb, Ali Concordia Institute for Information Systems Engineering Concordia University MontrealQCH4V1S1 Canada Department of Computer Science Lakehead University Thunder BayONP7B 5E1 Canada Department of Computer Science and Mathematics Lebanese American University Beirut1102 2801 Lebanon Electrical and Computer Engineering Department Texas AandM University at Qatar Doha Qatar

The agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this letter, multiple UAVs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This letter minimizes the number of deployed UAVs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAVs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAVs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAVs adapt their velocities in order to cover the vehicles. © 2019 IEEE.

关键词： Trajectories

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A NOVEL SYMMETRY IN NANOCARBONS: PRE-CONSTANT DISCRETE PRINCIPAL CURVATURE STRUCTURE

arXiv

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arXiv 2023年

作者： Kabata, Yutaro Matsutani, Shigeki Noda, Yusuke Ogata, Yuta Onoe, Jun School of Information and Data Sciences Nagasaki University 1-14 Bunkyo-machi Nagasaki City852-8131 Japan Electrical Engineering and Computer Science Graduate School of Natural Science & Technology Kanazawa University Kakuma Kanazawa920-1192 Japan Department of Information and Communication Engineering Okayama Prefectural University 111 Kuboki Okayama Soja719-1197 Japan Department of Mathematics Faculty of Science Kyoto Sangyo University Motoyama Kamigamo Kita-ku Kyoto603-8555 Japan Department of Energy Science and Engineering Nagoya University Furo-cho Chikusa-ku Aichi Nagoya464-8603 Japan

Since the first-principles calculations in quantum chemistry precisely provide possible configurations of carbon atoms in nanocarbons, we have analyzed the geometrical structure of the possible carbon configurations and found that there exists a novel symmetry in the nanocarbons, i.e., the pre-constant discrete principal curvature (pCDPC) structure. In terms of the discrete principal curvature based on the discrete geometry for trivalent oriented graphs developed by Kotani, Naito, and Omori [Comput. Aided Geom. Design, 58, (2017), 24-54], we numerically investigated discrete principal curvature distribution of the nanocarbons, C60, carbon nanotubes, C120 (C60 dimer), and C60-polymers (peanut-shaped fullerene polymers). While the C60 and nanotubes have the constant discrete principal curvature (CDPC) as we expected, it is interesting to note that the C60-polymers and C60 dimer also have the almost constant discrete principal curvature, i.e., pCDPC, which is surprising. A nontrivial pCDPC structure with revolutionary symmetry is available due to discreteness, though it has been overlooked in geometry. In discrete geometry, there appears a center axisoid which is the discrete analogue of the center axis in the continuum differential geometry but has three-dimensional structure rather than a one-dimensional curve due to its discrete nature. We demonstrated that such pCDPC structure exists in nature, namely in the C60-polymers. Furthermore, since we found that there is a positive correlation between the degree of the CDPC structure and stability of the configurations for certain class of the C60-polymers, we also revealed the origin of the pCDPC structure from an aspect of materials science. Copyright © 2023, The Authors. All rights reserved.

关键词： Dimers

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Enhancement of Computational Efficiency for Deadlock Recovery of Flexible Manufacturing Systems Using Improved Generating and Comparing Aiding Matrix Algorithms

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Processes 2023年第10期11卷 3026页

作者： Pan, Yen-Liang Tseng, Ching-Yun Chen, Ju-Chin Mathematics and Physics Division General Education Center R.O.C. Air Force Academy No. Sisou 1 Jieshou W. Rd. Gangshan Dist Kaohsiung City820009 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung413310 Taiwan Department of Electronic Engineering National Kaohsiung University of Sciences and Technology Kaohsiung City807618 Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Sciences and Technology Kaohsiung City807618 Taiwan

After the fourth industrial evolution, precision and automatic manufacturing have become increasingly widely accepted in production. With highly variable productivity and flexibility, flexible manufacturing systems (FMS) lower production costs and increase efficiency. Due to its resource shareability, unexpected system deadlock may occur in some specific situations. Many existing works use deadlock prevention as the primary control methodology in research on system deadlock control, while this type of control policy would constrain the transportation resources and reduce the system’s liveness. This paper adopts a new transition-based deadlock recovery policy as the direct control strategy, which uses generating and comparing aiding matrix (GCAM) to determine the optimal control transition. We also improve the existing GCAM-based method by reducing the computational redundancy. This kind of control strategy and its benefit could be demonstrated through two typical systems of simple sequential processes with resource (S3PR) nets and their Petri nets model. © 2023 by the authors.

关键词： Petri nets

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