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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

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Traffic Crash Severity Prediction with Convolutional Neural Network and Long Short-Term Memory: An Automatic Feature Selection Approach

SSRN

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SSRN 2022年

作者： Mansoor, Umer Ayub, Mohammad Ratrout, Nedal Alam, Gulzar Assi, Khaled Civil & Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Information and Computer Science Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Interdeciplinary Research for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia School of Computing Ulster University Northern Ireland United Kingdom

Road traffic crashes lead to substantial economic and life losses. An accurate crash prediction model is essential to instantly deploy proper emergency units, provide appropriate medical treatment and manage rescue facilities at the crash location. Few studies have explored the efficiency of deep learning models for predicting traffic crash severity. Still, no study investigated the suitability of various standalone and combined (hybrid) models of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and the effect of automatic crash feature selection techniques taking into consideration training time. As such, keeping Deep Neural Network (DNN) as a baseline model, we investigated three standalone models of CNN and three combined models of CNN and LSTM, with rigorous experiments using two publicly available datasets. Our findings reveal that CNN standalone is more efficient than other tested models with acceptable higher overall performance than the baseline model in terms of commonly used and established evaluation metrics. Moreover, the findings recommend subsets of optimal features for both datasets from which most important features contributing to the severity of the crash are shown based on feature importance techniques. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Traffic Crash Severity Prediction with Convolutional Neural Network and Long Short-Term Memory: An Automatic Feature Selection Approach

SSRN

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SSRN 2022年

关键词： Forecasting

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Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Network and Systems Medicine

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Network and Systems Medicine 2020年第1期3卷 36-56页

作者： Stalidzans, Egils Zanin, Massimiliano Tieri, Paolo Castiglione, Filippo Polster, Annikka Scheiner, Stefan Pahle, Jürgen Stres, Blaž List, Markus Baumbach, Jan Lautizi, Manuela Van Steen, Kristel Schmidt, Harald H.H.W. Computational Systems Biology Group University of Latvia Riga Latvia Latvian Biomedical Reasearch and Study Centre Riga Latvia Centro de Tecnología Biomédica Universidad Politécnica de Madrid Pozuelo de Alarcón Spain CNR National Research Council IAC Institute for Applied Computing Rome Italy Oslo Norway Institute for Mechanics of Materials and Structures Vienna University of Technology Vienna Austria BioQuant Heidelberg University Heidelberg Germany Department of Animal Science University of Ljubljana Ljubljana Slovenia Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Big Data in BioMedicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Computational Systems Medicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany BIO-Systems Genetics GIGA-R University of Liège Liège Belgium BIO3 - Systems Medicine Department of Human Genetics KU Leuven Leuven Belgium Department of Pharmacology and Personalised Medicine Faculty of Health Medicine and Life Science Maastricht University Maastricht Netherlands

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body. © Egils Stalidzans et al. 2020 Published b

关键词： Iterative methods

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Machine learning prediction of higher heating value of biomass

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Biomass Conversion and Biorefinery 2021年第5期13卷 3659-3667页

作者： Dai, Zuocai Chen, Zhengxian Selmi, Abdellatif Jermsittiparsert, Kittisak Denić, Nebojša M. Nеšić, Zoran College of Mechanical and Electrical Engineering Hunan City University Yiyang China Key Laboratory Energy Monitoring and Edge Computing for Smart City of Hunan Province Yiyang China Department of Mechanical Engineering Columbia University New York USA Department of Civil Engineering College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Ecole Nationale d’Ingénieurs deTunis (ENIT) Civil Engineering Laboratory Tunis Tunisia Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Humanities and Social Sciences Duy Tan University Da Nang Vietnam MBA School Henan University of Economics and Law Zhengzhou China Faculty of Sciences and Mathematics University of Priština Kosovska Mitrovica Serbia Faculty of Technical Sciences Čačak University in Kragujevac Čačak Serbia

Recently, biomass sources are important for energy applications. There is need for analyzing of the biomass model based on different components such as carbon, ash, and moisture content since the biomass sources are important for energy applications. In this paper, an extreme learning machine (ELM) is used to estimate efficiency. ELM was implemented for single-layer feed-forward neural network (SLFN) architectures. Because biomass modeling could be a very challenging task for conventional mathematical, it is suitable to apply machine learning models which could overcome nonlinearities of the process. The main attempt in this study was to develop a machine learning model for prediction of the higher heating values of biomass based on proximate analysis. According the prediction accuracy (coefficient of determination and root mean square error) of the higher heating value of the biomass, the inputs’ influence was determined on the higher heating value. According to the obtained results, fixed carbon has less moderate coefficient, ash has less correlation coefficient, and volatile matter has the most correlation coefficient. Therefore, the volatile matter percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the ash has the smallest relevance on the higher heating value of the biomass based on machine learning approach.

关键词：

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Simulation of Passenger Evacuation of High-speed Train Cars

Simulation of Passenger Evacuation of High-speed Train Cars

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Fire Science and Fire Protection engineering (ICFSFPE), Conference on

作者： Shenshi Huang Ruichao Wei Kai Shi Jinxing Hu Shouxiang Lu Liping Lian Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China Institute of Advanced Materials Science and Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China The Smart City Research Institute of China Electronics Technology Group Corporation Shenzhen China State Key Laboratory of Fire Science University of Science and Technology of China Hefei China School of Urban Planning and Design Peking University Shenzhen China

ISBN: (数字)9781728153223

ISBN: (纸本)9781728153230

This study is to evaluate the passenger evacuation of the high-speed train. Geometry was built up based on current Chinese train type CRH380B, and scenarios were designed according to the current emergency response plans. Simulation runs were performed by Pathfinder and uncertainty of evacuation time was obtained. It is observed that the passenger flow rates in train coaches are generally stable. Passenger transfer time and evacuation time are approximately proportional to passenger attendance rate. However, in some complicated scenarios, it is found that the process of passenger transfer affects the evacuation results a lot. Reasonable passenger transfer scale can maximize the effectiveness of emergency plans. This study provides an evaluation method on the topic of train car evacuation, and future work can be carried out to optimize the design of emergency response.

关键词： passenger evacuation high-speed train monteCarlo simulation high-speed train Evacuation passengers Evacuation Time evaluation method Educational attendance simulating Cars

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A more compact object detector head network with feature enhancement and relational reasoning

arXiv

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arXiv 2021年

作者： Zhang, Wenchao Fu, Chong Chang, Xiangshi Zhao, Tengfei Li, Xiang Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Baidu Smart Life Group NO. 10 Xibeiwang East Street Ke Ji Yuan Haidian District Beijing100193 China School of Computer Science University of Auckland New Zealand

Modeling implicit feature interaction patterns is of significant importance to object detection tasks. However, in the two-stage detectors, due to the excessive use of hand-crafted components, it is very difficult to reason about the implicit relationship of the instance features. To tackle this problem, we analyze three different levels of feature interaction relationships, namely, the dependency relationship between the cropped local features and global features, the feature autocorrelation within the instance, and the cross-correlation relationship between the instances. To this end, we propose a more compact object detector head network (CODH), which can not only preserve global context information and condense the information density, but also allows instance-wise feature enhancement and relational reasoning in a larger matrix space. Without bells and whistles, our method can effectively improve the detection performance while significantly reducing the parameters of the model, e.g., with our method, the parameters of the head network is 0.6× smaller than the state-of-the-art Cascade R-CNN, yet the performance boost is 1.3% on COCO test-dev. Without losing generality, we can also build a more lighter head network for other multi-stage detectors by assembling our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

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Frontiers in high entropy alloys and high entropy functional materials

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Rare Metals 2024年第10期43卷 4639-4776页

作者： Wen-Tao Zhang Xue-Qian Wang Feng-Qi Zhang Xiao-Ya Cui Bing-Bing Fan Jia-Ming Guo Zhi-Min Guo Rui Huang Wen Huang Xu-Bo Li Meng-Ru Li Yan Ma Zhi-Hua Shen Yong-Gang Sun De-Zhuang Wang Fei-Yang Wang Li-Qiang Wang Nan Wang Tian-Li Wang Wei Wang Xiao-Yang Wang Yi-Han Wang Fu-Jie Yu Yu-Zhen Yin Ling-Kun Zhang Yi Zhang Jian-Yang Zhang Qi Zhao Yu-Ping Zhao Xin-Dong Zhu Yasir Sohail Ya-Nan Chen Tao Feng Qi-Long Gao Hai-Yan He Yong-Jiang Huang Zeng-Bao Jiao Hua Ji Yao Jiang Qiang Li Xiao-Ming Li Wei-Bing Liao Huai-Jun Lin Hui Liu Qi Liu Qing-Feng Liu Wei-Di Liu Xiong-Jun Liu Yang Lu Yi-Ping Lu Wen Ma Xue-Fei Miao Jie Pan Qing Wang Hong-Hui Wu Yuan Wu Tao Yang Wei-Ming Yang Qian Yu Jin-Yu Zhang Zhi-Gang Chen Liang Mao Yang Ren Bao-Long Shen Xun-Li Wang Zhe Jia He Zhu Zhen-Duo Wu Si Lan School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Jiangsu Key Laboratory for Advanced Metallic Materials School of Materials Science and EngineeringSoutheast UniversityNanjing 211189China Department of Physics City University of Hong KongHongKong 999077China School of Materials Science and Engineering Tianjin UniversityTianjin 300072China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China Department of Mechanical Engineering Research Institute for Advanced ManufacturingThe Hong Kong Polytechnic UniversityHong Kong 100872China Center of Electron Microscopy and State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China Institute of Advanced Wear&Corrosion Resistance and Functional Materials Jinan UniversityGuangzhou 510632China School of Mechanics and Civil Engineering China University of Mining and TechnologyXuzhou 221116China School of Physical Sciences Great Bay UniversityDongguan 523000China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical EngineeringNanjing Tech UniversityNanjing 211816China Beijing Advanced Innovation Center for Materials Genome Engineering Research Institute for Carbon NeutralityUniversity of Science and Technology BeijingBeijing 100083China Department of Mechanical Engineering The University of Hong KongHong Kong 999077China School of Computing Macquarie UniversitySydney 2113Australia Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laborato

Owing to their exceptional properties,high-entropy alloys(HEAs)and high-entropy materials have emerged as promising research areas and shown diverse ***,the recent advances in the field are comprehensively reviewed,organized into five *** first section introduces the background of HEAs,covering their definition,significance,application prospects,basic properties,design principles,and *** subsequent section focuses on cutting-edge high-entropy structural materials,highlighting developments such as nanostructured alloys,grain boundary engineering,eutectic systems,cryogenic alloys,thin films,micro-nano-lattice structures,additive manufacturing,high entropy metallic glasses,nano-precipitate strengthened alloys,composition modulation,alloy fibers,and refractory *** the following section,the emphasis shifts to functional materials,exploring HEAs as catalysts,magneto-caloric materials,corrosion-resistant alloys,radiation-resistant alloys,hydrogen storage systems,and materials for ***,the review encompasses functional high-entropy materials outside the realm of alloys,including thermoelectric,quantum dots,nanooxide catalysts,energy storage materials,negative thermal expansion ceramics,and high-entropy wave absorption *** paper concludes with an outlook,discussing future directions and potential growth areas in the *** this comprehensive review,researchers,engineers,and scientists may gain valuable insights into the recent progress and opportunities for further exploration in the exciting domains of high-entropy alloys and functional materials.

关键词： High entropy alloys Cutting-edge structural materials Cutting-edgefunctional materials

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Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware

arXiv

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arXiv 2022年

作者： Sudharsan, Bharath Sundaram, Dineshkumar Patel, Pankesh Breslin, John G. Ali, Muhammad Intizar Dustdar, Schahram Zomaya, Albert Ranjan, Rajiv Confirm SFI Research Centre for Smart Manufacturing Data Science Institute NUI Galway Ireland AVM Solutions United Kingdom Artificial Intelligence Institute University of South Carolina Columbia United States School of Electronic Engineering Dublin City University Ireland Distributed Systems Group TU Wien Austria Albert Zomaya The University of Sydney Sydney Australia School of Computing Newcastle University Newcastle upon Tyne United Kingdom

The majority of IoT devices like smartwatches, smart plugs, HVAC controllers, etc., are powered by hardware with a constrained specification (low memory, clock speed and processor) which is insufficient to accommodate and execute large, high-quality models. On such resource-constrained devices, manufacturers still manage to provide attractive functionalities (to boost sales) by following the traditional approach of programming IoT devices/products to collect and transmit data (image, audio, sensor readings, etc.) to their cloud-based ML analytics platforms. For decades, this online approach has been facing issues such as compromised data streams, non-real-time analytics due to latency, bandwidth constraints, costly subscriptions, recent privacy issues raised by users and the GDPR guidelines, etc. In this paper, to enable ultra-fast and accurate AI-based offline analytics on resource-constrained IoT devices, we present an end-to-end multi-component model optimization sequence and open-source its implementation. researchers and developers can use our optimization sequence to optimize high memory, computation demanding models in multiple aspects in order to produce small size, low latency, low-power consuming models that can comfortably fit and execute on resource-constrained hardware. The experimental results show that our optimization components can produce models that are;(i) 12.06 x times compressed;(ii) 0.13% to 0.27% more accurate;(iii) Orders of magnitude faster unit inference at 0.06 ms. Our optimization sequence is generic and can be applied to any state-of-the-art models trained for anomaly detection, predictive maintenance, robotics, voice recognition, and machine vision. © 2022, CC BY.

关键词： Internet of things

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Towards Distributed Multiscale Simulation of Biological Processes

Towards Distributed Multiscale Simulation of Biological Proc...

引用

IEEE International Conference on E-Science Workshops

作者： Jorg Bernsdorf Guntram Berti Bastien Chopard Jan Hegewald Manfred Krafczyk Dinan Wang Eric Lorenz Alfons Hoekstra German Research School for Simulation Sciences RWTH Aachen University Aachen Germany Consulting mathematical methods Berti CMM Bonn Germany Scientific and Parallel Computing Group University of Geneva Geneva Switzerland Institute for Computational Modeling in Civil Engineering Technische Universität Braunschweig Brunswick Germany Meteorological Institute University of Bonn Bonn Germany Section Computational Science University of Amsterdam Amsterdam Netherlands

The understanding of biological processes, e.g. related to cardio-vascular disease and treatment, can significantly be improved by numerical simulation. In this paper, we present an approach for a multiscale simulation environment, applied for the prediction of in-stent re-stenos is. Our focus is on the coupling of distributed, heterogeneous hardware to take into account the different requirements of the coupled sub-systems concerning computing power. For such a concept, which is an extension of the standard multiscale computing approach, we want to apply the term Distributed Multiscale computing.

关键词： Computational modeling Couplings Biological system modeling Numerical models Muscles Automata Drugs

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