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Rice Variety Classification Based on Optimized Near-Infrared Spectral Classification Model

Rice science 2024年第1期31卷 6-9,I0009-I0012页

作者： YANG Sen WANG Zhenmin ZHANG Houqing SONG Wenlong College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040China The 49th Research Institute of China Electronics Technology Group Corporation Harbin 150000China

Accurate classification of rice variety is essential to ensure the brand value of high-quality rice *** the impact of sample state on modeling optimization algorithms,rice samples after grinding and sealing were *** enhance the accuracy of rice variety classification,we introduced a spectral characteristic wavelength selection method based on adaptive sliding window permutation entropy(ASW-PE).

关键词： sealing Infrared grinding

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Recent advances in heterogeneous catalysis of solar-driven carbon dioxide conversion

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Journal of Environmental Sciences 2024年第6期140卷 165-182页

作者： Jun Xu Farzaneh Arabpour Roghabadi Ying Luo Vahid Ahmadi Qian Wang Zheng Wang Hong He State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China College of Chemistry and Chemical Engineering Henan Polytechnic UniversityJiaozuoHenan 454000China Department of Process Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityTehranIran Optoelectronics and Nanophotonics Research Group Faculty of Electrical and Computer EngineeringTarbiat Modares UniversityTehranIran Graduate School of Engineering Nagoya UniversityFuro-choChikusa-kuNagoya 464-8603Japan

Solar-driven carbon dioxide(CO_(2))conversion including photocatalytic(PC),photoelectrochemical(PEC),photovoltaic plus electrochemical(PV/EC)systems,offers a renewable and scalable way to produce fuels and high-value chemicals for environment and energy *** review summarizes the basic fundament and the recent advances in the field of solar-driven CO_(2)*** the visible-light absorption is an important strategy to improve solar energy conversion *** separation and migration of photogenerated charges carriers to surface sites and the surface catalytic processes also determine the photocatalytic *** engineering including co-catalyst loading,defect engineering,morphology control,surface modification,surface phase junction,and Z-scheme photocatalytic system construction,have become fundamental strategies to obtain high-efficiency *** to photocatalysis,these strategies have been applied to improve the conversion efficiency and Faradaic efficiency of typical PEC *** PV/EC systems,the electrode surface structure and morphology,electrolyte effects,and mass transport conditions affect the activity and selectivity of electrochemical CO_(2)***,the challenges and prospects are addressed for the development of solar-driven CO_(2)conversion system with high energy conversion efficiency,high product selectivity and stability.

关键词： Solar-driven CO_(2)reduction Photocatalytic system Photoelectrochemical system Photovoltaic plus electrochemical system Surface/interface engineering

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Modeling a Single Camera System based on Predicted Bounding Box and Pan-Tilt Angles 13

Modeling a Single Camera System based on Predicted Bounding ...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Putri, Adi Novitarini Maria, Fabiola Hidayat, Egi Mahayana, Dimitri Machbub, Carmadi Institut Teknologi Bandung Control and Computer Research Group School of Electrical Engineering and Informatics Bandung Indonesia Institut Teknologi Sains Bandung Bekasi Indonesia

ISBN: (纸本)9798350340891

The control schemes that are related to visual servoing systems are often done through trial and error. Based on the classical control concept, it is necessary to have a mathematical model of the plant before designing the controller. Therefore, this research focuses on producing a mathematical model of the pan-tilt system. We propose the combination of the computer vision method with the classic system identification method, namely N4SID to obtain the dynamics model of a pan-tilt system. In this research, the system identification procedure was successful in producing a state-space model of a visual servoing system using a pan-tilt system and a single camera. This study merged traditional system identification techniques with computer vision to create a discrete-time linear state-space model. When the system order value equals three, the approximation with the minimum MSE value is obtained. © 2023 IEEE.

关键词： Visual servoing

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Energy Harvesting Integrated Sensor Node Architecture for Sustainable IoT Networks 5th

Energy Harvesting Integrated Sensor Node Architecture for S...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Gopikrishnan, S. Kokila, M. Rivera, Marco Wheeler, Patrick School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Power Electronics Machines and Control PEMC Research Group University of Nottingham Nottingham United Kingdom Universidad de Talca Curicó Chile

ISBN: (纸本)9789819735228

Internet of Things (IoT) is a life changing technology which is build on various devices which are connected to the Internet. Wireless sensor nodes (WSN) is one of such devices which plays a major role in deploying IoT in large scale of real-time even monitoring applications. But the energy limitations of those sensor nodes limits the features of IoT networks. Hence improving the energy limitations in WSN plays a vital role in last decade. This chapter presents the possibilities of enhance the energy limitations of sensor nodes with renewable energy resources. The proposed integrated architecture of this proposed model discusses the implementation of sensor node with flexible solar energy harvester module, multi mode communication capability and multi sensor data module. The special contribution in this chapter includes the hardware specification with budget analysis, sleep-awake strategy to effective use of energy and maximum possible data collection from the environment. The novelty and improvement of the proposed model has been proved with mathematical model as well as the real-time measurements from the prototype. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar energy

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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A UAV-based compressive algorithm for minimizing the energy consumption of large-scale WSNs 2

A UAV-based compressive algorithm for minimizing the energy ...

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2023 2nd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence, SIUSAI 2023

作者： Lv, Cuicui Yang, Linchuang Zhang, Xinxin Wang, Peijin Du, Zhenbin Li, Xiangming Wang, Peng School of Computer and Control Engineering YanTai University YanTai264005 China Central Research Institute Yantai Jereh Oilfield Services Group Co. Ltd. YanTai264003 China School of Environment and Materials Engineering YanTai University YanTai264005 China

In large-scale environmental monitoring, massive sensor nodes regularly collect data and transmit them over long distances, which burdens the network and leads to the increased energy consumption. To deal with this problem, we develop an Unmanned Aerial Vehicle (UAV)-based compressive algorithm. In this algorithm, the network clustering technologies and Compressive Sensing are exploited to decrease the amount of data throughout the network. Monte Carlo method is used to group sensor nodes into clusters. The UAV is dispatched as a mobile agent to collect data from the cluster heads. Compared with the benchmark algorithms, the simulations demonstrate that the algorithm proposed in this paper can decrease the network's energy consumption and obtain better environmental monitoring results. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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Pallet Detection and Distance Estimation with YOLO and Fiducial Marker Algorithm in Industrial Forklift Robot

Pallet Detection and Distance Estimation with YOLO and Fiduc...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Eric Sean Kesuma Pranoto Hidaya Rusmin Devira Anggi Maharani Control and Computer System Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Utilising technology such as artificial intelligence and robotics potentially improves E-Commerce in efficiency. In this trends, the usage of autonomous forklifts in the warehouse to lift and arrange things should be implemented. The picking system in the warehouse needs pallet detection and tracking to carry out the things. This research will find the best performance of the YOLOv5 model and correct the distance estimation model to the fiducial marker. In this paper, we used the ArUco fiducial marker to mark the pallet target and estimate the pose and distance in real time. The insertion points of the pallet were also detected using the YOLOv5 algorithm to validate the pallet and get the coordinate variables of the holes. The YOLOv5n gives the best performance at 24 fps in real-time detection. Distance measurement from the marker detection had an average error of 2.28 cm with linear regression.

关键词： Robot kinematics Linear regression Transportation Cameras Fiducial markers Real-time systems Distance measurement

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An Integrated Environment for the Reliability Assessment of CNNs Accelerators Implemented in FPGAs

An Integrated Environment for the Reliability Assessment of ...

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Latin American Test Workshop, LATW

作者： Luis Ariel Mesa Juan-David Guerrero-Balaguera Erika D. Castañeda Ernesto Sanchez Wilson-Javier Pérez-Holguín GIRA Research Group Universidad Pedagogica y Tecnologica de Colombia (UPTC) Department of Control and Computer Engineering (DAUIN) Politecnico di Torino

ISBN: (数字)9798350365559

ISBN: (纸本)9798350365566

This work presents the development of an integrated framework that facilitates the resilience evaluation of CNNs w.r.t. hardware faults by resorting to fault emulation strategies. The proposed framework leverages the flexibility of Field-Programmable Gate-Arrays (FPGAs) to implement and evaluate any DL accelerator architecture. In addition, we describe the detailed procedure to emulate faults inside a DL architecture. We report the cost, simulation time, and hardware overhead required by the proposed technique when using a stream-processing DL accelerator to deploy some of the most relevant layers of LeNet5. The experimental results were gathered in four different FPGA-based platforms, demonstrating the flexibility of the proposed approach.

关键词： Costs Emulation Accelerator architectures Life estimation Logic gates Hardware Reliability

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NARX Neural Network Modeling of Batch Distillation Process

NARX Neural Network Modeling of Batch Distillation Process

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Putri, Adi Novitarini MacHbub, Carmadi Hidayat, Egi Muhammad Idris School of Electrical Engineering and Informatics Institut Teknologi Control Computer System Research Group Bandung Indonesia

ISBN: (纸本)9781665437660

The batch distillation model is highly nonlinear due to the influence of mass and composition of the initial material to be separated. It is also caused by the thermodynamics of the system which is not ideal. Therefore, analytical batch distillation modeling does not fulfill superposition theory, or is nonlinear. Thus, any well-established linear control schemes can not be applied here. Meanwhile, modeling a system using the Nonlinear Autoregressive with eXogenous inputs (NARX) is quite widely used today because of its ability to represent non-linear system dynamics. This paper propose a system identification using NARX Neural Network (NARX-NN) to modeling batch distillation process. In order to prove the superiority of NARX-NN's accuracy in the system identification process, a comparison with linear ARMA models is done. In this study, ARMA approximation was carried out in two ways. The first way is to use a neural network, while the second method is through approximation to the discrete transfer function. The validation results show that the NARX-NN achieves significantly better fit compared to the linear models. NARX-NN and ARMA-NN were compared and their MSE ratio for delay input and output 1 and 3, respectively have the smallest value, i.e 1.71e-04 © 2021 IEEE.

关键词： Thermodynamics Analytical models System dynamics Conferences Neural networks Transfer functions Systems engineering and theory

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