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SSRN 2024年

作者： Lei, Mingyue Wang, Haoran Li, Duo Li, Zhenning Dhamaniya, Ashish Hu, Jia Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Environmental Engineering University of California Los AngelesCA90024 United States State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Transportation Engineering & Planning Section Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Key Laboratory of Road and Traffic Engineering of the Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China

Ecological Cooperative and Adaptive Cruise Control (Eco-CACC) is widely focused to enhance sustainability of CACC. However, state-of-the-art Eco-CACC studies are still facing challenges in adopting on rolling terrain. Furthermore, they cannot ensure both ecology optimality and computational efficiency. Hence, this paper proposes a nonlinear optimal control based Eco-CACC controller. It has the following features: i) enhancing performance across rolling terrains by modeling in space domain;ii) enhancing fuel efficiency via globally optimizing all vehicle's fuel consumptions;iii) ensuring computational efficiency by developing a differential dynamic programming-based solving method for the non-linear optimal control problem;iv) ensuring string stability through theoretically proving and experimentally validating. The performance of the proposed Eco-CACC controller was evaluated. Results showed that the proposed Eco-CACC controller can improve average fuel saving by 37.67% at collector road and about 17.30% at major arterial. © 2024, The Authors. All rights reserved.

关键词： Dynamic programming

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An Intelligent Design for Ni-Based Superalloy Based on Machine Learning and Multi-Objective Optimization

SSRN

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SSRN 2022年

作者： Deng, Yuedan Zhang, Yu Gong, Xiufang Hu, Wang Wang, Yucheng Liu, Ying Lian, Lixian College of Materials Science and Engineering Sichuan University No.24 South Section 1 Yihuan Road Chengdu610065 China School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Avenue Chengdu611731 China State Key Laboratory for Long-Life High Temperature Materials 666 Jinshajing West Road Deyang618000 China Dongfang Electric Corporation Dongfang Turbine Co. Ltd. 666 Jinshajing West Road Deyang618000 China

The simultaneous consideration of multiple conflicting properties in alloy optimization design is necessary yet remains challenging. A comprehensive strategy of machine learning and multi-objective optimization based on thermodynamic simulation data was proposed to accelerate the composition design of Ni-base superalloys. The microscopic parameters were determined by Pearson correlation analysis and domain knowledge as the key affect factors of tensile strength and elongation. The Multi-objectives Evolutionary Algorithm (MOEA) was adopted to search the well-built surrogate by ML meta-heuristically for the Pareto front of three objectives and its responding Pareto optimal solution set of composition. Furthermore, nine high-performance superalloy samples selected from the obtained Pareto front were well verified by fabricating and testing in the laboratory. Specially, a new composition among the nine as-fabricated samples was the best one according to the pre-defined design preferences with the γ′ solvus temperature, γ′ volume fraction, and TCP phases content approximating to 1210 ℃, 65%, and 0.01%, respectively. This intelligent cooperation strategy based on ML and MOEA extends the methodology for multi-composition and multi-property design materials, which can optimize multiple conflicting objectives simultaneously rather than do one by one. © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Low-Profile 3D Printed Transmit-array for Wide-Angle Beam Scanning at Ka-Band

Low-Profile 3D Printed Transmit-array for Wide-Angle Beam Sc...

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International Conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Álvaro. F. Vaquero Sérgio Matos Manuel Arrebola Jorge R. Costa João Felício Carlos Fernandes Nelson J. G. Fonseca Dept. Electrical Engineering Universidad de Oviedo Gijón Spain Institute de Telecomunicações (IT) Lisbon Portugal Dept. Information Science and Technology University Institute of Lisbon (ISCTE-IUL) Lisbon Portugal Centro de Investigação Naval (CINAV) Escola Naval Almada Portugal Dept. Electrical and Computer Engineering Instituto Superior Tecnico (IST) Univ. of Lisbon Portugal European Space Agency Antenna and Sub-Millimeter Waves Section Noordwijk The Netherlands

Low-cost and compact antennas for wide-angle beam scanning in the mm-Wave band play a key role in the development of the next generation of terrestrial and satellite communication systems. Phased array antennas are currently getting a lot of attention for their excellent scanning with a low profile and mature manufacturing process [1]. However, the power consumption and losses due to their complex feeding network and large count of active components are still their main drawbacks. Both issues are overcome using spatially fed antennas (SFA) such as reconfigurable reflectarray (RA) or transmitarray (TA) antennas. These solutions avoid the feeding network, and the beam scanning is obtained using power-efficient controlling elements (PIN, MEMs, Liquid Crystal, etc.) [2]. However, they are still more costly and complex than solutions based on passive periodic structures combined with mechanical scanning (mainly reflector-based antennas, RA, or TA).

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Survey on limitations, applications and challenges for machine learning aided hybrid FSO/RF systems under fog and smog influence

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Optica Acta: International Journal of Optics 2024年第4-6期71卷

作者： Hira Khalid Sheikh Muhammad Sajid Hector E. Nistazakis Muhammad Ijaz a Department of Electrical Engineering National University of Computer and Emerging Sciences Lahore Pakistan b Section of Electronic Physics and Systems Department of Physics National and Kapodistrian University of Athens Zografou Greece c Department of Engineering Faculty of Science and Engineering Manchester Metropolitan University UK

This paper provides a concise overview of hybrid Free Space Optical (FSO)/Radio Frequency (RF) systems operating under fog and smog channels. With the rapid growth of wireless communication and the emergence of the Internet of Things (IoT) have brought about unprecedented connectivity and transformed various aspects of our daily lives. However, certain environmental conditions pose significant challenges to wireless communication systems like FSO and RF technologies. Fog and smog with its dense moisture-laden atmosphere and presence of particulate matter cause absorption and scattering of light, leading to reduced link quality and limited range for FSO communication system. Whereas, RF systems are although less affected by fog/smog as compared to rain but still have inherent limitations in terms of bandwidth and capacity. To overcome these challenges and ensure reliable communication, researchers have turned their attention to hybrid FSO/RF systems which can combine the strengths of FSO and RF technologies and mitigate the limitations of each technology. This work summarizes and explores the hybrid system architecture and machine learning techniques used to enhance the performance and adaptability of wireless communication systems. Additionally, we present the existing experimental research on hybrid FSO/RF systems, performance evaluation metrics, and future research directions to pave the way for further ***: Free Space Optical; RF: Radio Frequency; IoT: Internet-of-Things; OS: Operating Systems; OWC: Optical Wireless Communication; LOS: Line-of-Sight; LPF: Low Pass Filter; AM: Amplitude Modulation; FM: Frequency Modulation; PSK:Phase Shift key; QAM: Quadrature Amplitude Modulation; OFDM: OrthogonalFrequency Division Multiplexing; LF: Low frequency; M-L: Medium frequency; UHF:Ultra High Frequency; SHF: Super High Frequency; EHF: Extremely High frequency; PM: Phase Modulation; QPSK: Quadrature Phase Shift Key; DSSS: Direct SequenceSpread Spec

关键词： Free space optical communication fog and smog attenuation hybrid FSO/RF machine learning algorithms

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A Shape-Aware Total Body Photography System for In-focus Surface Coverage Optimization

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Huang, Wei-Lun Liu, Joshua Tashayyod, Davood Kang, Jun Gandjbakhche, Amir Kazhdan, Misha Armand, Mehran Johns Hopkins University Department of Computer Science BaltimoreMD United States Eunice Kennedy Shriver National Institute of Child Health and Human Development BethesdaMD United States United States Lumo Imaging United States Johns Hopkins School of Medicine Faculty of the Department of Dermatology BaltimoreMD United States Eunice Kennedy Shriver National Institute of Child Health and Human Development Principal Investigator in Section on Translational Biophotonics BethesdaMD United States Johns Hopkins University Faculty of the Department of Computer Science BaltimoreMD United States University of Arkansas Faculty of Institute for Integrative and Innovative Research Department of Mechanical Engineering FayettevilleAR United States

Total Body Photography (TBP) is becoming a useful screening tool for patients at high risk for skin cancer. While much progress has been made, existing TBP systems can be further improved for automatic detection and analysis of suspicious skin lesions, which is in part related to the resolution and sharpness of acquired images. This paper proposes a novel shape-aware TBP system automatically capturing full-body images while optimizing image quality in terms of resolution and sharpness over the body surface. The system uses depth and RGB cameras mounted on a 360-degree rotary beam, along with 3D body shape estimation and an in-focus surface optimization method to select the optimal focus distance for each camera pose. This allows for optimizing the focused coverage over the complex 3D geometry of the human body given the calibrated camera poses. We evaluate the effectiveness of the system in capturing high-fidelity body images. The proposed system achieves an average resolution of 0.068 mm/pixel and 0.0566 mm/pixel with approximately 85% and 95% of surface area in-focus, evaluated on simulation data of diverse body shapes and poses as well as a real scan of a mannequin respectively. Furthermore, the proposed shape-aware focus method outperforms existing focus protocols (e.g. auto-focus). We believe the high-fidelity imaging enabled by the proposed system will improve automated skin lesion analysis for skin cancer screening. © 2013 IEEE.

关键词： Digital cameras

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Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data

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Expert Systems with Applications 2023年 213卷

作者： Nguyen, Thi Phuong Quyen Phuc, Phan Nguyen Ky Yang, Chao-Lung Sutrisno, Hendri Luong, Bao-Han Le, Thi Huynh Anh Nguyen, Thanh Tung Faculty of Project Management The University of Danang- University of Science and Technology 54 Nguyen Luong Bang Danang Viet Nam Department of Industrial System Engineering International University- Ho Chi Minh City VNU Quarter 6 Linh Trung Ward Thu Duc District HCMC Viet Nam Department of Industrial Management National Taiwan University of Science and Technology No. 43 Sec. 4 Keelung Rd. Daan Dist. Taipei Taiwan Institute of Statistical Science Academia Sinica No. 128 Academia Road Section 2 Nankang Taipei11529 Taiwan Faculty of Computer Science and Engineering University of Technology- Ho Chi Minh City VNU 268 Ly Thuong Kiet Street District 10 HCMC Viet Nam

This study proposes a novel approach to time-series anomaly detection by solving the longest common subsequence (LCS) problem of two time-series data. The conventional LCS problem is used to measure the similarity of two input strings. However, it cannot be applied to real-time data collected from sensors in smart manufacturing, which are mostly real numbers instead of strings. Thus, two algorithms—an extension of fixed gap longest common subsequence (extent FGLCS) and the modified dynamic programming-based LCS (MDP-LCS) algorithm—are proposed to deal with real-time series data. Further, a gap search constraint is investigated to limit the search range in finding the LCS. This study also provides a threshold to define the matching of a pair that contains real numbers rather than a string in the conventional LCS or FGLCS problems. In addition, the proposed methods employ the gap search constraint and the threshold to reduce computational time. Furthermore, the proposed algorithms can be implemented on both univariate and multivariate time-series data. The LCS length, known as the similarity of the two time-series data, is used to detect the anomalies based on the user's expectation. The accuracy and computational time of the proposed algorithm show a significant positive result compared with the exact solution provided by the dynamic programming-based LCS algorithm. Moreover, this study also uses the proposed methods for anomaly detection in a case study of big multisensory data in smart manufacturing. The result shows that the MDP-LCS can detect approximately 98% of normal data and more than 85% of anomalies with a shorter computational time. © 2022 Elsevier Ltd

关键词： Time series

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Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization

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engineering Applications of Artificial Intelligence 2023年 124卷

作者： Zaidan, A.A. Alnoor, Alhamzah Albahri, O.S. Mohammed, R.T. Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Garfan, Salem Hameed, Hamsa Al-Samarraay, Mohammed S. Jasim, Ali Najm Malik, R.Q. Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia SP Jain School of Global Management Sydney Australia Management Technical College Southern Technical University Basrah Iraq Department of Computer Science and Information Technology La Trobe University MelbourneVIC Australia Computer Techniques Engineering Department Mazaya University College Nassiriya Thi-Qar Iraq Sulaymaniyah Iraq Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Tanjung Malim Malaysia Foundation of Alshuhda Baghdad Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq MEU Research Unit Middle East University Amman Jordan

This study presents a review of literature on the usage of artificial neural networks (ANNs) architecture contribution method and structural equation modeling (SEM), and proposes a new selection process in the context of algorithm -based SEM-ANNs schemes. This study enriches academic literature by providing a review of all the main aspects of customization in ANNs and contribution methods in combination with SEM. Academic databases are examined for exhibition findings, yielding 253 papers published between 2016 and 2022. The retrieved papers are categorized according to inclusion criteria, and the final set of 73 articles are discussed based on two directions, namely, ‘Sector-based’ and ‘Algorithm-based’ as a new representation of taxonomy research. A state-of-the-art bibliographic analysis is presented. This review also identifies modern challenges and open issues in terms of multiple evaluation criteria, importance criteria, and data variations related to the selection of customizations in ANNs and contribution methods combined with SEM in different industrial cases. Several issues fall under multicriteria decision making for handling complexity problems in different ANNs and contribution methods. Thus, this study also presents a research proposal and recommends a solution based on a three-phase methodology for handling the selection and overcoming the identified issues, subsequently completing a strategic guideline solution. © 2023 Elsevier Ltd

关键词： Neural networks

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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Numerical treatment of fractional order Buruli ulcer and cholera model by using neural network approach

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Knowledge-Based Systems 2025年 320卷

作者： Sabir, Zulqurnain Abdelkawy, M.A. Raja, Muhammad Asif Zahoor Ali, M․ R. Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Riyadh11989 Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha National University Egypt

Purpose: The purpose of these investigations is to design a neuro computing solver based on the Levenberg-Marquardt backpropagation neural network for the fractional order confection Buruli ulcer and cholera model, which is divided into ten different categories. Method: The numerical solutions of the Buruli ulcer and cholera model are obtained through the reliable stochastic approach. A dataset based Adam scheme is designed that implemented to decrease the mean square error by splitting the data 12%, 12% for both endorsement and testing, while 76% is applied for training. Three cases based on the fractional order values 0.5, 0.7 and 0.9 are used to present the numerical performances of the model. The structure of neural network contains twelve neurons, single layer, and log-sigmoid transfer function for the Buruli ulcer and cholera model. Results: The precision of the proposed scheme is checked using the comparison of the outputs, best validation performances around 10–06 to 10–07, and small absolute error as 10–04 to 10–06. Moreover, the some test performances based on taking different proportional indices are programmatic to validate the dependability of the solver. Novelty: The proposed Levenberg-Marquardt backpropagation neural network approach together with twelve neurons, single layer, and log-sigmoid transfer function is applied first time for the fractional order Buruli ulcer and cholera model. © 2025 Elsevier B.V.

关键词： Mean square error

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Deep learning for drug repurposing: methods, databases, and applications

arXiv

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arXiv 2022年

作者： Pan, Xiaoqin Lin, Xuan Cao, Dongsheng Zeng, Xiangxiang Yu, Philip S. He, Lifang Nussinov, Ruth Cheng, Feixiong School of Computer Science and Engineering Hunan University China School of Computer Science Xiangtan University China Xiangya School of Pharmaceutical Sciences Central South University China Department of Computer Science University of Illinois at Chicago United States Department of Computer Science and Engineering Lehigh University United States Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick FrederickMD21702 United States Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv69978 Israel Genomic Medicine Institute Lerner Research Institute Cleveland Clinic ClevelandOH44195 United States Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University ClevelandOH44195 United States Case Comprehensive Cancer Center Case Western Reserve University School of Medicine ClevelandOH44106 United States

Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic, and outline its future challenges. © 2022, CC BY-NC-SA.

关键词： Coronavirus

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