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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Sparse State Space Model for System Identification and Fault Detection of Hydrogen Production Plants

SSRN

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SSRN 2024年

作者： Zhang, Qi Xie, Lei Liu, Xueqin Su, Hongye College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT9 5AH United Kingdom

Alkaline water electrolysis (AWE) stands out as a well-established and widely adopted technology for green hydrogen production. With the rapid development and large-scale application of industrial hydrogen plants, achieving efficient energy conversion becomes a critical factor for their economic viability. System faults can disrupt their stability and reliability, leading to decreased energy conversion efficiency and posing significant safety *** detection and diagnosis (FDD) techniques play a crucial role in safeguarding AWE plants by promptly identifying potential safety hazards and ensuring the stability of plant. However, the inherent complexity of AWE plants, coupled with the presence of significant noise disturbances, poses a significant challenge to the effectiveness of conventional FDD methods. This study proposes a novel approach for system identification and FDD using a lasso sparse state space model (Sparse SSM). This model leverages the strengths of both sparse regularization and the Kalman filter. The lasso component promotes sparsity, leading to a more concise representation of the system dynamics. By incorporating a Laplace prior, the Sparse SSM facilitates filter and smoother inference during the estimation process. This allows for more robust extraction of temporal structure information from the data, ultimately enhancing the reliability of system identification and FDD. Test results indicate critical faults in the hydrogen production process can be detected, which in turn helps to improve the safety of energy conversion efficiency in hydrogen production plants. © 2024, The Authors. All rights reserved.

关键词： Kalman filters

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Infrared and Visible Image Registration Based on Compound 2S Network

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Science Technology and engineering 2024年第16期24卷 6783-6791页

作者： Zheng, Bo-Wen Wang, Zhuo Cao, Xin-Yu College of Computer and Control Engineering Northeast Forestry University Harbin150040 China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin150040 China

Aiming at the problem that traditional image registration methods have poor effect in infrared and visible image registration tasks. A feature matching method based on Superpoint + Superglue (2S) composite network was proposed for infrared and visible image registration. The method first used Superpoint's unique feature extraction method to fully extract common features between infrared and visible light images. Secondly, the idea of adding matching constraints and using attention mechanism in Superlube feature matching method was used to give full play to the advantages of neural network and improve the matching efficiency. In the training phase, the method of using self built datasets was used to improve the generalization and accuracy of the neural network. The results show that the repeatability and accuracy scores of traditional registration methods for feature point extraction on three sets of experimental images are (0. 006 7, 0. 006 1), (0. 001 0, 0. 000 8), and (0, 0), respectively. The correct matching logarithms of feature points are 7 pairs, 1 pair, and 0 pairs, with an average number lower than the minimum four matching point pairs required to estimate the transformation matrix. The scores of infrared and visible image registration methods based on Superpoint + Superglue are (0. 240 2, 0. 262 5), (0. 193 9, 0. 172 2), (0. 263 0, 0. 264 4), and the correct matching logarithms of feature points are 252, 165, and 252 pairs. The evaluation index of feature point extraction and the number of correct matching of feature point pairs are significantly increased compared with traditional methods, which can better complete the registration task. © 2024 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Feature Selection

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controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification

arXiv

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arXiv 2023年

作者： Xing, Dong Gu, Pengjie Zheng, Qian Wang, Xinrun Liu, Shanqi Zheng, Longtao An, Bo Pan, Gang The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore College of Control Science and Engineering Zhejiang University Hangzhou China

Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation of types and then pre-train the best response for each type. However, most of them do not consider the distribution of teammate instances within a type. This could expose the agent to the hidden risk of type confounding. In the worst case, the best response for an abstract teammate type could be the worst response for all specific instances of that type. This work addresses the issue from the lens of causal inference. We first theoretically demonstrate that this phenomenon is due to the spurious correlation brought by uncontrolled teammate distribution. Then, we propose our solution, CTCAT, which disentangles such correlation through an instance-wise teammate feedback rectification. This operation reweights the interaction of teammate instances within a shared type to reduce the influence of type confounding. The effect of CTCAT is evaluated in multiple domains, including classic ad hoc teamwork tasks and real-world scenarios. Results show that CTCAT is robust to the influence of type confounding, a practical issue that directly hazards the robustness of our trained agents but was unnoticed in previous works. Copyright © 2023, The Authors. All rights reserved.

关键词： Feedback

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Technology Development of Automated Contactless Hand Sanitizer Dispenser System with IR Sensor and Based on A PIC Microcontroller 22

Technology Development of Automated Contactless Hand Sanitiz...

引用

23rd International Conference on computer Systems and Technologies, CompSysTech 2022

作者： Nokerov, Suleyman Ishangulyyev, Rovshen Hojagulyyev, Perman Venkataraman, Aravind Pitchai Khan, Khalid Ali Department of Physics of High Technologies Oguz Han Engineering and Technology University of Turkmenistan Turkmenistan Department of Economics of Innovations Oguz Han Engineering and Technology University of Turkmenistan Turkmenistan Department of Cyberphysics Systems Oguz Han Engineering and Technology University of Turkmenistan Turkmenistan Department of Instrumentation and Control Engineering Saranathan College of Engineering India Department of Electrical and Computer Engineering Mettu University Ethiopia

ISBN: (纸本)9781450396448

Hand hygiene (HH) is one of the most important activities of protection against pandemic disease in the current difficult situation around the world. To achieve this, World Health Organization (WHO) recommends frequent hand washing with soap and hand sanitization using hand sanitizers. Hand sanitizers are generally applied by squirting the sanitizer liquid by hand and it may cause to spread of viral carriers to many peoples. To reduce the spread of pandemic disease and to reduce contact with potential carriers of viruses, bacteria, fungi and parasites in public places, in this paper, the authors have designed an automated contactless hand sanitizer dispenser system (ACHSDS) with an infrared (IR) sensor and based on a PIC18F4550 microcontroller for use in public places, workplaces, kindergartens, schools, universities and etc. The ACHSDS can be used without touching or holding, and may decrease the risk of contamination. © 2022 ACM.

关键词： Microcontrollers

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Development of a 5G-Enabled and AI-Powered Agricultural Picking Robot control Module

Development of a 5G-Enabled and AI-Powered Agricultural Pick...

引用

Applied Physics and Computing (ICAPC), International Conference on

作者： Jianxiang Yu Maher Maalla Allam Maalla School of Engineering Guangzhou College of Technology and Business Guangzhou Guangdong China School of Computer Technologies and Control ITMO University Saint Petersburg Russian Federation

The escalating challenges faced by the agricultural industry, characterized by labor shortages, and rising costs, underscore the imperative for innovative solutions. This study explores the transformative potential of Intelligent Harvesting Systems in revolutionizing fruit harvesting, with a focus on assessing feasibility and understanding implications within the fruit industry. The integration of cutting-edge technologies, specifically 5G communication and artificial intelligence (AI), plays a pivotal role in the design and evaluation of a control module for an agricultural picking robot. The feasibility assessment encompasses an in-depth exploration of the technological, economic, operational, and environmental. Experimental evaluations conducted in controlled environments and real-world settings showcase the system's high picking speed, accuracy, and adaptability to diverse produce sizes. The outcomes of this research not only align with the initial objectives of enhancing harvesting efficiency and reducing labor costs but also hold promise for a sustainable and resource-efficient agricultural future. The positive synergy between 5G communication and AI technology emerges as a driving force behind the system's exceptional performance. This study not only contributes to the immediate advancements in intelligent harvesting but also lays a robust foundation for continued innovation, setting the stage for further exploration and refinement in this dynamic field.

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An Integrated Online-Offline Hybrid Particle Swarm Optimization Framework for Medium Scale Expensive Problems*

An Integrated Online-Offline Hybrid Particle Swarm Optimizat...

引用

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Hanyu Hu Jian Wang Xiaodong Huang Sergey V. Ablameyko College of Computer Science and Technology China University of Petroleum (East China) Qingdao China College of science) China University of Petroleum (East China) Qingdao China College of Control Science and Engineering China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Real-world optimization problems often involve computationally expensive evaluations, making efficient optimization crucial. To address medium-scale expensive optimization problems, this paper proposes and investigates an Integrated Online-Offline Hybrid Particle Swarm Optimization Framework (IOOPSOF). The framework integrates concepts from active learning and ensemble learning, iteratively querying the most uncertain and highest-precision solutions to achieve efficient utilization of evaluation resources. IOOPSOF employs two distinct sample selection criteria, with offline optimization for exploration and online optimization for exploitation. The effectiveness of the proposed method is validated through experiments on a series of low to medium-dimensional benchmark functions and the operational parameter optimization of Enhanced Geothermal Systems (EGS). Results demonstrate that IOOPSOF significantly outperforms existing methods, achieving global optimum (0) on most benchmark functions and showing superior performance over single surrogate-assisted evolutionary algorithms (SAEAs) on real-world problems, thereby showcasing its exceptional potential for solving expensive optimization problems.

关键词： Decision making Benchmark testing Computational efficiency Ensemble learning Particle swarm optimization Complex systems Optimization Standards

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Automatic Identification for Projector Brand and Model Number 1

引用

6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Zuoyong Wang, Weice Zhang, Fuquan Fan, Haoyi Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

The projector production process needs to pack manufactured projectors. The key step of projector packing is to check whether the brand and model number of the projector is correct or not, for avoiding the projector’s packing error. To achieve automation of identifying projector brand and model number for improving the efficiency of the projector packing, we proposed an automatic identification method by using image processing, character recognition, and string matching. Specifically, image grayscale processing, image gradient calculation, morphological operations, and image thresholding are sequentially performed to determine candidate target regions located by a projector's brand and model number. Then, two shape prior-based image features are designed to perform target region screening, i.e., excluding fake target regions as far as possible. Next, the character recognition technology is used to extract the texts in the target regions. Finally, string matching is performed to obtain identification results of the projector's brand and model number. Experimental results on several projectors demonstrate the effectiveness of the proposed method on automatic identification of projector brand and model number. The proposed method provides a general framework for identifying the brand and model number of other similar electronic products. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Bone Marrow Cell Segmentation Based on Improved U-Net 1

引用

6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Jin, Lingmin Yu, Zhaochai Fan, Haoyi Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

Automatic segmentation of bone marrow cells plays an important role in the diagnosis of many blood diseases such as anemia and leukemia. Due to the complex morphology and wide variety of bone marrow cells, their segmentation is still a challenging task. To improve the accuracy of bone marrow cell segmentation, we propose an end-to-end U-shaped network based on the pyramid residual convolution and the attention mechanism. Specifically, the standard convolution and dilated convolution are combined as its feature encoder, which designs a pyramid residual convolution block to extract multi-scale features. Then, the attention gating mechanism is introduced into each skip connection module for fusing the shallow and deep information. Finally, the proposed method combines convolution and deconvolution for feature decoding to achieve the final segmentation of bone marrow cells. Experiments with quantitative and qualitative comparisons are carried out on a self-built bone marrow smear dataset. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

引用

Data Driven control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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