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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MSE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MSE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

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Anomaly Detection in Pediatric and Adults Brain MRI with Generative Model 46

Anomaly Detection in Pediatric and Adults Brain MRI with Gen...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chen, Chaojun Namdar, Khashayar Wagner, Matthias W. Ertl-Wagner, Birgit B. Khalvati, Farzad Department of Mechanical and Industrial Engineering University of Toronto Canada Institute of Medical Science University of Toronto Canada Neurosciences & Mental Health Research Program SickKids Research Institute TorontoON Canada Department of Diagnostic & Interventional Radiology The Hospital for Sick Children TorontoON Canada Department of Diagnostic and Interventional Neuroradiology University Hospital Augsburg Germany Department of Medical Imaging University of Toronto Canada Department of Computer Science University of Toronto Canada Vector Institute TorontoON Canada

ISBN: (纸本)9798350371499

Magnetic Resonance Imaging (MRI) serves as a valuable tool for detecting abnormalities in brain structures. However, a notable 5-10% of pathologies remain unnoticed in MRI scans. To address this challenge and reduce the burden on radiologists, machine learning methods have been used to automate anomaly detection. In this work, we propose to use Denoising Diffusion Probabilistic Model (DDPM) for anomaly detection in MRI. DDPM learns to reconstruct healthy brain MRI and generates anomaly maps to facilitate the classification of the input MRI as healthy or unhealthy. The proposed model correctly differentiated all healthy or unhealthy test patients. Additionally, localized regions of interest in the generated anomaly maps, and the diffusion model achieved a Dice coefficient of 0.376, outperforming other generative models such as generative adversarial network (GAN). © 2024 IEEE.

关键词： Generative adversarial networks

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Shao-Yu Chien Ching-Chang Wong Doctoral Program in Robotics College of Engineering Tamkang University New Taipei City 25137 Taiwan Electrical and Computer Engineering Department Tamkang University New Taipei City 25137 Taiwan

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method.

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LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models

arXiv

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arXiv 2025年

作者： Peng, Zengqi Wang, Yubin Han, Xu Zheng, Lei Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China

Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To tackle these issues, we introduce LearningFlow, an innovative automated policy learning workflow tailored to urban driving. This framework leverages the collaboration of multiple large language model (LLM) agents throughout the RL training process. LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions. Particularly, each process is supported by an analysis agent that evaluates training progress and provides critical insights to the generation agent. Through the collaborative efforts of these LLM agents, LearningFlow automates policy learning across a series of complex driving tasks, and it significantly reduces the reliance on manual reward function design while enhancing sample efficiency. Comprehensive experiments are conducted in the high-fidelity CARLA simulator, along with comparisons with other existing methods, to demonstrate the efficacy of our proposed approach. The results demonstrate that LearningFlow excels in generating rewards and curricula. It also achieves superior performance and robust generalization across various driving tasks, as well as commendable adaptation to different RL algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design of Volumetric Magnetic Induction Tomography (VMIT) System Using COMSOL Simulation

Design of Volumetric Magnetic Induction Tomography (VMIT) Sy...

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International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

作者： Muhammad Nurul Puji Prawito Prajitno Warsito Purwo Taruno Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Electronic and Instrumentation Physics of The Department of Physics Universitas Indonesia Depok Indonesia C-TECH LABS Edwar Technology Tangerang Indonesia

ISBN: (数字)9798350389920

ISBN: (纸本)9798350389937

Magnetic Induction Tomography (MIT) is a low-resolution imaging technique used to reconstruct the magnetic permeability distribution of an object. COMSOL Simulation of VMIT (Volumetric Magnetic Induction Tomography) system has been developed by using 8 coils as transmitter and 8 coils as receiver. The existence of sine excitation signal in the transmitter coil cause magnetic field changing, it causing emf at the receiver coil. The magnitude of the magnetic field induction is also influenced by the permeability of the medium between the transmitter and receiver coils. COMSOL and MATLAB simulation of VMIT 8 coil systems indicate that, by using ILBP algorithms, ball-shaped object with ferromagnetic materials (iron) can be reconstructed to produce a similar image. The sensitivity matrix significantly influences image reconstruction in MIT, leading to distorted elliptical images of objects, particularly when positioned at the top center, as the matrix shows homogeneity mainly in the middle area.

关键词： Coils Microwave antennas Transmitters Magnetic resonance imaging Receivers Tomography Radar antennas Permeability Magnetic fields Image reconstruction

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Modeling the Self-Capacitance of Individual Plates in a Multi-Conductor System using PEEC 5

Modeling the Self-Capacitance of Individual Plates in a Mult...

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Wolf, Henry Striker, Ryan Cleveland, Jerika Gieser, Anna Braaten, Benjamin D. Enochson, Avery Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica Vitola, Sabina North Dakota State University Department of Electrical and Computer Engineering Fargo United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Eglin Air Force Base Eglin Afb Air Force Research Laboratory Munitions Directorate FL32542 United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

In many areas of electronics design, it is necessary to understand the different aspects of capacitance associated with various conducting surfaces in a particular layout. This is because as operating frequencies increase and dimensions decrease, capacitive coupling can become the dominant means in which noise is induced in a design. Therefore, having the ability to extract an equivalent circuit for modeling capacitive coupling and understanding the coupling can be very important, and challenging. This paper presents insight on capacitive coupling by evaluating the capacitance between two parallel plates, with particular attention paid to the self-capacitance (with a reference at infinity) of each of the individual plates. More specifically, the Partial Element Equivalent Circuit (PEEC) method is used to compute the self-capacitance of each individual plate in the parallel plate capacitor problem, and results are verified by comparison to values from James C. Maxwell's original works and the electrostatic solver ANSYS Maxwell 3D. Overall, it is shown how the self-capacitance of each individual plate changes as a function of distance between the parallel plates. © 2022 IEEE.

关键词： Capacitance

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Diabetes Classification Using Support Vector Machine: Binary Classification Model 4

Diabetes Classification Using Support Vector Machine: Binary...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. This paper presents experimental results related to the exploration of the use of a Super Vector Machine (SVM) for data classification of Diabetes. The model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. The Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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Neuronal Modeling Tool Using DynaSim 5

Neuronal Modeling Tool Using DynaSim

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Gieser, Anna Vitola, Sabina Enochson, Avery Wolf, Henry Striker, Ryan Cleveland, Jerika Braaten, Benjamin D. Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica North Dakota State University Department of Electrical and Computer Engineering Fargo United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Munitions Directorate Air Force Research Laboratory Eglin Air Force Base FL32542 United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

Human cognitive processes remain an area of strong interest and ongoing research. One tool to gain greater insight into this process is neuronal modeling. The following features are desirable in a neuronal modeling tool: a library of known parameters for different neurons and species, the ability to select the neuron and species of interest, the ability to quickly simulate neuronal behavior, and the ability to calculate metabolic requirements and efficiencies for various neuronal activity. Many of these can be found in software today, but not all elements can be found in one entity. The goal of this work is to develop a neuronal modeling tool that incorporates these features. © 2022 IEEE.

关键词： Neurons

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Unifying Complementarity Constraints and Control Barrier Functions for Safe Whole-Body Robot Control

arXiv

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arXiv 2025年

作者： Muchacho, Rafael I Cabral Laha, Riddhiman Pokorny, Florian T. Figueredo, Luis F.C. Chakraborty, Nilanjan Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Munich Institute of Robotics and Machine Intelligence TUM Germany School of Computer Science University of Nottingham United Kingdom Department of Mechanical Engineering Stony Brook University NY United States

Safety-critical whole-body robot control demands reactive methods that ensure collision avoidance in real-time. Complementarity constraints and control barrier functions (CBF) have emerged as core tools for ensuring such safety constraints, and each represents a well-developed field. Despite addressing similar problems, their connection remains largely unexplored. This paper bridges this gap by formally proving the equivalence between these two methodologies for sampled-data, first-order systems, considering both single and multiple constraint scenarios. By demonstrating this equivalence, we provide a unified perspective on these techniques. This unification has theoretical and practical implications, facilitating the cross-application of robustness guarantees and algorithmic improvements between complementarity and CBF frameworks. We discuss these synergistic benefits and motivate future work in the comparison of the methods in more general cases. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimal control systems

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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