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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Multilevel Particle Filters for Partially Observed McKean-Vlasov Stochastic Differential Equations

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

In this paper we consider the filtering problem associated to partially observed McKean-Vlasov stochastic differential equations (SDEs). The model consists of data that are observed at regular and discrete times and the objective is to compute the conditional expectation of (functionals) of the solutions of the SDE at the current time. This problem, even the ordinary SDE case is challenging and requires numerical approximations. Based upon the ideas in [3, 12] we develop a new particle filter (PF) and multilevel particle filter (MLPF) to approximate the afore-mentioned expectations. We prove under assumptions that, for ϵ > 0, to obtain a mean square error of O(ϵ2) the PF has a cost per-observation time of O(ϵ−5) and the MLPF costs O(ϵ−4) (best case) or O(ϵ−4 log(ϵ)2) (worst case). Our theoretical results are supported by numerical experiments. © 2024, CC BY.

关键词： Mean square error

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Global-Local Active Contour for Segmenting Images with Simultaneous Inhomogeneity and Texture

SSRN

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SSRN 2024年

作者： Shao, Pei-Chiang Department of Applied Mathematics Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan Undergraduate Program in College of Electrical Engineering and Computer Science Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan

Severe intensity inhomogeneity (InH) and complex real-world textures cause great difficulties and become two important issues in image segmentation and object extraction applications. Plenty of methods are proposed to tackle these issues, however, they usually perform poorly on images possess both InH and texture. In this paper, we propose a region-based active contour model for segmenting images with simultaneous inhomogeneity and texture. A new bias field possesses both global and local smoothness properties is proposed to handle the InH issue, and a pixel inhomogeneity factor (PIF) is employed to jointly deal with InH and textured images. We also employ the total variation (TV) regularization to further smooth and denoise the transformed PIF image, making the segmentation process to have the nice properties of global convergence and independence of contour initialization. A large number of numerical experiments and comparisons are conducted to demonstrate the effective performance of the newly proposed method. © 2024, The Authors. All rights reserved.

关键词： Textures

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Deep Reinforcement Learning for Mapless Robot Navigation Systems

Deep Reinforcement Learning for Mapless Robot Navigation Sys...

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IEEE Latin American Robotics Symposium, LARS

作者： Iure Rosa L. Oliveira Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil

DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorithm, state representation, and training procedure. In this paper we explore various cutting-edge DRL algorithms, such as policy-, value-, and actor-critic-based approaches. Our results demonstrate the effectiveness of the ranging sensor approach, which achieves robust navigation policies capable of generalizing to unseen virtual environments with a high success rate. We combine Behavior Cloning with Imitation Learning to expedite the training process, leveraging expert demonstrations and reinforcement learning. Our methodology enables faster training while enhancing the learning efficiency and performance of the robot, obtaining better results in terms of crash and success rate, and being able to reach a cumulative reward of approximately 12000.

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN computer Science 2021年第1期2卷 48页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Q-Learning based Local Path Planning for UAVs with Different Priorities

Q-Learning based Local Path Planning for UAVs with Different...

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IEEE Latin American Robotics Symposium, LARS

作者： Kevin B. de Carvalho O. B. Hiago Batista Leonardo A. Fagundes-Júnior Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil

Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context of unmanned autonomous vehicles. In many applications, the agent may have to react to environment shifting. Algorithms such as geometric and dynamic programming as well as techniques such as artificial potential fields have been employed to tackle this local planning problem. In recent years, machine learning has gained more evidence in many research fields due to its flexibility and generalization capabilities. In this study, we propose a Q-learning-based approach to local planning, which weighs three crucial factors- path length, safety, and energy consumption- that can be freely adjusted by the user to suit its application’s needs. The performance of the proposed method was tested in simulated static and dynamic scenarios as well as benchmarked with a baseline approach. The results show that it can perform well in both kinds of environments without struggling with the commom pitfalls of other local planning algorithms.

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Wearable Active Vibration Sensing for Mid-Activity Knee Health Assessment

Wearable Active Vibration Sensing for Mid-Activity Knee Heal...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Goktug C. Ozmen Christopher J. Nichols Lan Lan Emily Moise Christopher Sugino Alper Erturk Omer T. Inan School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA BioEngineering Graduate Program Georgia Institute of Technology Atlanta GA USA Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA Coulter Department of Biomedical Engineering School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Non-invasive vibration measurements from the knee offer a convenient and affordable alternative to benchtop or biomechanics lab joint health monitoring systems. Recently, joint acoustic emissions (JAEs) measured from the knee were shown to be an indicator of knee health. However, the origin of JAEs is still not fully understood, which limits its acceptance and use by clinical experts. In this proof-of-concept study, rather than relying on the movements of the knee and corresponding frictional rubbing of internal surfaces to produce vibrations, we propose using an active vibration sensing approach with a known vibration source interrogating the knee. We aim to elucidate the linkage between knee vibration characteristics and structural changes in the joint following injuries. We measured tibial vibration responses of two participants using a laser vibrometer system to quantify the frequency band where the most repeatable tibial vibration measurement can be taken. Subsequently, a custom-designed wearable system measured mid-activity tibial vibration characteristics from four participants (five healthy knees and three knees with prior acute injury) during unloaded knee flexion-extensions. An active sensing knee health score was defined as the ratio of the changes in low- to high-frequency response during flexion-extension. Since changes in the boundary of tibia would alter low-frequency response more than high frequency response, we found that increased knee laxity with acute injuries resulted in an increased active sensing knee health score. Our findings demonstrate the potential of active vibration sensing as an interpretable, computationally inexpensive alternative to JAEs for wearable knee health assessment.

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Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

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Processes 2023年第8期11卷

作者： Sajid, Injila Gautam, Ayushi Sarwar, Adil Tariq, Mohd Liu, Hwa-Dong Ahmad, Shafiq Lin, Chang-Hua Sayed, Abdelaty Edrees Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh202002 India Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions. © 2023 by the authors.

关键词： Efficiency

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Characteristics Fluctuation of Sub-3-nm Bulk FinFET Devices Induced by Random Interface Traps

Characteristics Fluctuation of Sub-3-nm Bulk FinFET Devices ...

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IEEE Conference on Nanotechnology

作者： Sekhar Reddy Kola Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering Institute of Communications Engineering Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan

Three-dimensional (3D) bulk fin-typed field effect transistors (FinFETs) have emerged as key devices that can scale down the technology node beyond 22-nm. However, the scaled devices have created new sources of fluctuation inherent in 3D geometry. The interface trap is one such fluctuation that is caused by the trapping and de-trapping of charge carriers and has an adverse effect on device characteristics and variability. In this work, we study impacts of random interface traps (RITs) on electrical characteristic of bulk FinFETs by using a 3D quantum-mechanically corrected device simulation. RIT position effects on short channel effects (SCEs) are examined with physical governed influence to show the major fluctuations. More than 50% reductions of the RITs-induced characteristic fluctuation of the germanium (Ge) devices are observed, compared with Si devices. The Ge ones can reduce SCE variations and exhibit high immunity to RITs.

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Enhanced Prognostics and Health Management in Automated Driving Systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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