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2nd Conference on Biomedical Photonics and Cross-Fusion, BPC 2023

作者： Haonan, Xing Xiaoqi, Wei Lin, Gui Mingyu, Cao Xuhuix, Chen Cunliang, Cheng School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China

ISBN: (数字)9781510667556

ISBN: (纸本)9781510667549

As milk consumption increases, rapid detection of milk concentration is becoming more and more crucial. In this research, we propose an LED-based milk concentration detection system, which is a new detection method that detects milk concentration by loading the LED with RF signals. The system includes an arbitrary waveform generator (AWG), LED, lampshade, silicon photo dioxide (PD), oscilloscope, and other components. After passing through different concentrations of milk solution, the light signal carrying a specific frequency is received by the PD, and after photoelectric conversion, the signal carrying a specific frequency in the light signal is converted out, and the concentration of the milk solution to be measured is obtained by reading the peak value measured by the oscilloscope. The experiments validated different brands of milk solutions with varying concentrations, demonstrating the feasibility of LED-based milk concentration detection, and the test analysis was performed for the same brand of milk solutions with varying concentrations, yielding a quadratic fit R2=0.9169, confirming that the system has good sensing performance for milk concentration and validating the method's feasibility. © 2023 SPIE.

关键词： Light emitting diodes

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Viewpoint Optimization using Genetic Algorithm for Flying Robot Inspection of Electricity Transmission Tower Equipment

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Chinese Journal of Electronics 2025年第2期23卷 426-431页

作者： Guotian Yang Ruifang Dong Hua Wu Chang'an Liu School of Control and Computer Engineering North China Electric Power University Beijing China

To fully exploit the limited flight-time of the flying robot, and ensure the successful visibility of target, viewpoint optimization is proposed in this paper for the inspection of electricity transmission tower equipment with an optimization function to determine the best viewpoints in a local viewpoint region. The local viewpoint regions are generated from the local objective regions which are determined by the geometrical structure of a priori 3D model for the electricity transmission tower equipment. The optimization function is structured based on three factors including visibility, viewing quality and observation distance. In addition, the fitness function of genetic algorithm is used to find the optimal viewpoint. The experimental results demonstrate the effectiveness and efficiency of the proposed viewpoint selection algorithm.

关键词： Solid modeling Three-dimensional displays Electricity Poles and towers Inspection Optimization Robots Drones Genetic algorithms

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Cloud-Edge Intelligent Collaborative Computing Model Based on Transfer Learning in IoT 9th

Cloud-Edge Intelligent Collaborative Computing Model Based o...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Long, Yang Li, Zhixin School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China

ISBN: (纸本)9789819959679

The deep neural network is a reliable technical support for cloud computing and edge computing. It has excellent nonlinear approximation and generalization capabilities, making it suitable for classifying and predicting Internet of Things data in cloud computing and edge computing fields. However, the increasing size of neural networks poses a challenge for their deployment on devices with limited computing and storage resources. Traditional cloud computing services also suffer from high latency, which hinders real-time tasks. To address these challenges, this paper proposes a cloud-side cooperation model for deep learning based on migration learning technology. This model used migration learning technology to reduce the size of deep neural networks. Specifically, it deployed the deep neural network model (CDLM) in the cloud and the shallow neural network model (EDLM) at the edge. CDLM is used to help train EDLM and improve its performance, enabling it to run independently on edge devices with high accuracy and respond to real-time tasks. This approach reduced the amount of user data transmitted to the cloud, alleviated bandwidth pressure, and protected user privacy. Experimental results show that the proposed model improved the accuracy of EDLM by 19.58% compared with traditional neural network models. These findings provide a theoretical and experimental foundation for the study of cloud-edge collaborative models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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Simultaneous Placement of Tie-lines and Distributed Generations to Optimize Distribution System Post-outage Operations and Minimize Energy Losses

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 318-328页

作者： Farzaneh Shojaei Mohammad Rastegar Morteza Dabbaghjamanesh Department of Power and Control Engineering School of Electrical and Computer EngineeringShiraz UniversityShirazIran Electrical and Computer Engineering Department The University of Texas at DallasUSA.

This study proposes a new method which aims to optimally install tie-lines and distributed generations *** is done to optimize the post-outage reconfiguration and minimize energy losses and energy not supplied of distribution *** number and location of tie-lines,as well as the number,size,and location of DGs,are pinpointed through teaching the learning-based optimization(TLBO)*** objective function in the current research is to minimize the costs pertaining to the investment,operation,energy losses,and energies not *** addition to the normal operational condition,fault operational condition is also ***,the optimal post-fault reconfigurations for fault occurrences in all lines are ***,the operational constraints such as the voltage and line current limits are taken into account in both normal and post-fault operational ***,the modified IEEE 33-bus and 69-bus distribution test systems are selected and tested to demonstrate the effectiveness of the simultaneous placement of DGs and tie-line technique proposed in this paper.

关键词： Distribution system reliability distributed generation energy loss post-outage reconfiguration tie-lines

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A Trust Prediction Method Based on Heterogeneous Information Networks 8

A Trust Prediction Method Based on Heterogeneous Information...

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8th International Conference on Systems and Informatics, ICSAI 2022

作者： Xiao, Ruili Tong, Xiangrong Li, Yinggang Yantai University School of Computer and Control Engineering Yantai264005 China

ISBN: (纸本)9781665474481

It is essential to predict the level of trust among users before they interact to reduce the risk of interaction. Due to the sparsity of trust relationships, it is inefficient to simply use explicit trust relationships to predict the trust among users, and even the trust path may be lost. On the other hand, there are implicit trust relationships among users such as the joint items that several users all rated. Once the trust relationship is extracted, it will greatly expand the number of trusted users. To this end, a trust prediction method incorporating rating information is proposed to address this problem. It first constructs a heterogeneous information network consisting of social and rating information. Secondly, in the trust prediction period, if the user has no trusted users to choose from, the joint item is used as a bridge to find implicit trusted users from users who have jointly rated the item. Finally, the Dueling DQN algorithm is used to calculate the strength of the trust path, and the predicted trust value is derived by aggregating multiple trust paths based on an aggregation function. The experimental results on two datasets indicate the presented approach outperforms most existing trust prediction methods. © 2022 IEEE.

关键词： Forecasting

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Social Recommendation System Based on Multi-agent Deep Reinforcement Learning 8

Social Recommendation System Based on Multi-agent Deep Reinf...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Yinggang, Li Xiangrong, Tong Yantai University School of Computer and Control Engineering Yantai Shandong China

ISBN: (纸本)9781665477352

Currently, deep reinforcement learning is widely used to transform the recommendation process into a sequential task. Despite this, the recommendation system continues to be plagued by data sparsity and cold start issues as a result of high online costs and a sparse user-item rating matrix. To address the aforementioned issues, this article proposes a social multi-agent reinforcement learning framework (MATR). Two agents collaborate within the framework to enhance the performance of conventional solutions. Specifically, one agent is responsible for capturing the dynamic preferences of users based on their interaction with the system. The other agent leverages a ubiquitous social network to minimise cold starts and data sparsity for some users. In addition, we designed a module for learning state representations from social networks and user rating matrices. To optimise the use of social networks in the state representation module, in addition to first-order social neighbour features, we used trust inference to discover high-level social neighbour features, and we integrated Feature aggregation modelling state representation with graph convolutional neural networks and attention mechanisms. Our method has been shown to be effective and advantageous using actual data sets. © 2022 IEEE.

关键词： Reinforcement learning

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Optimal decarbonisation pathway for mining truck fleets

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Journal of Automation and Intelligence 2024年第3期3卷 129-143页

作者： Gang Yu Xianming Ye Yuxiang Ye Hongxu Huang Xiaohua Xia Department of Electrical Electronic and Computer EngineeringUniversity of PretoriaPretoria 0002South Africa School of Economics and Finance University of the WitwatersrandJohannesburg 2000South Africa School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

The fossil fuel powered mining truck fleets can contribute up to 80%of total emissions in open pit *** study investigates the optimal decarbonisation pathway for mining truck ***,our proposed pathway incorporates power generation,negative carbon technologies,and carbon ***,financial,and environmental models of decarbonisation technologies are established,capturing regional variations and time dynamic characteristics such as cost trends and carbon capture *** dynamic natures of characteristics pose challenges for using the cost-effective analyses approach to find the optimal decarbonisation *** address this,we introduce a mixed-integer programming optimisation framework to find the decarbonisation pathway with minimum life cycle costs during the planning period.A case study for the optimal decarbonisation pathway of truck fleets in a South African coal mine is conducted to illustrate the applicability of the proposed *** indicate that the optimal decarbonisation pathway is significantly influenced by factors such as land cost,annual budget,and carbon trading *** proposed method provides invaluable guidance for transitioning towards a cleaner and more sustainable mining industry.

关键词： Coal mine Truck fleet Carbon emission Optimal decarbonisation pathway

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Dynamic event-triggered formation control of second-order nonholonomic systems

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Journal of Systems engineering and Electronics 2023年第2期34卷 501-514页

作者： WANG Xiaoyu SUN Sijia XIAO Feng YU Mei School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

In this paper,the formation control problem of secondorder nonholonomic mobile robot systems is investigated in a dynamic event-triggered ***-triggered control protocols combined with persistent excitation(PE)conditions are *** event-detecting processes,an inactive time is introduced after each sampling instant,which can ensure a positive minimum sampling *** increase the flexibility of the event-triggered scheme,internal dynamic variables are included in event-triggering ***,the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive *** addition,event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation(low-pass)*** asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.

关键词： nonholonomic system dynamic event-triggered control consensus-based formation

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Experimental Study on Motion controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel Robot 11

Experimental Study on Motion Controller based on NN-ARX and ...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Jalali, Hasan Damavandi, Hossein Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran University of Tehran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers. © 2023 IEEE.

关键词： controllers

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A Location Selection Method for Infected Areas Material Distribution Based on SIR Model and GA-K - means Algorithm

A Location Selection Method for Infected Areas Material Dist...

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2023 China Automation Congress, CAC 2023

作者： You, Jiangwei Jia, Jianfang Zhang, Wenwen Zhang, Jianguo Ren, Xiaoqing Dong, Yuanchang School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China

ISBN: (纸本)9798350303759

The sudden outbreak of the COVID-19 pandemic has had a serious impact on the health and daily life of people. The problem of determining where to put household goods during an epidemic is particularly important. This paper proposes a siting method for the distribution of supplies in disaster area relief missions. Firstly, a qualitative analysis of the SIR prediction model for infectious diseases is established, and it is learned that how subsistence supplies are distributed has a direct impact on the direction of the number of infected people in the disaster area. Secondly, the entropy weight is applied to comprehensively evaluate the reasonableness of the number of material drop-off points. The optimal number, location, and size of drop-off points are determined by the GA-K-means clustering algorithm. This algorithm resulted in a 22.88% reduction in the overall number of task drop-off points. The average contour coefficient is then used to find the number and location of large-scale material sorting sites. The siting method for drop-off points provides practical implications for the solution of material or personnel deployment problems during various natural disasters. © 2023 IEEE.

关键词： Drops

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