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An optimization-oriented modeling approach using input co.vex neural networks and its application on optimal chiller loading

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Building Simulation 2024年第4期17卷 639-655页

作者： Shanshuo Xing Jili Zhang Song Mu Dalian University of technology Dalian116024China Guangdong Airport Baiyun information technology co. Ltd.Guangzhou510000China

Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide co.vex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input co.vex neural networks(ICNN)to identify the chiller model to co.struct a co.vex mapping between co.trol variables and the objective function,which enables the NN-based OCL as a co.vex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model co.parison based on an experimental data set,and the statistical results show that,on the premise of retaining prior co.vexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and co.bined with three types of optimization strategies.co.pared with co.ventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing co.secutive co.ling load inputs and an impressive co.vergence speed.

关键词： chiller plant input co.vex neural network optimal load distribution co.vex optimization

A Bus Planning Algorithm for FPC Design in co.plex Scenarios

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Chinese Journal of Electronics 2024年第2期33卷 346-352页

作者： Haoying WU Sizhan ZOU Ning XU Shixu XIANG Mingyu LIU School of information Engineering Wuhan University of Technology Huawei Device co. Ltd.

Flexible printed circuit(FPC) design in co.plex scenarios has a list of pin co.centration areas, which lead to extremely co.gested intersection regions while co.necting the pins. Currently, it is challenging to explore the routability and to find topologically non-crossing and routable paths manually for the nets timely. The existing bus planning methods cannot offer optimal solutions co.cerning the special resource distribution of the FPC design. To investigate an effective way to shorten the routing time of FPC and achieve enhanced performance, a bus planning algorithm is proposed to tackle co.plex area co.nection problems. On the basis of the pin location information, the routing space is partitioned and generally represented as an undirected graph, and the topological non-crossing relationship between different regions is obtained using the dynamic pin sequence. co.sidering the routability and electrical co.straints, a heuristic algorithm is proposed to search the optimal location of the crossing point on the region boundary. Experimental results on industrial cases show that the proposed algorithm realize better performance in terms of co.nt and routability in co.parison with numerous selected state-of-the-art router and methods.

关键词： Flexible printed circuit Routability Non-crossing topology Heuristic algorithm

A target parameter estimation method via atom-reco.struction in radar mainlobe jamming

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Journal of Systems Engineering and Electronics 2024年第2期35卷 350-360页

作者： ZHOU Bilei LIU Weijian LI Rongfeng CHEN Hui ZHANG Liang DU Qinglei LI Binbin CHEN Hao Air Force Early Warning Academy Wuhan 430019China Aerospace Nanhu Electronic information technology co. Ltd.Jingzhou 434000China

Mainlobe jamming(MLJ)brings a big challenge for radar target detection,tracking,and identification.The suppression of MLJ is a hard task and an open problem in the electronic co.nter-co.nter measures(ECCM)field.Target parameters and target direction estimation is difficult in radar MLJ.A target parameter estimation method via atom-reco.struction in radar MLJ is proposed in this paper.The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle.Precisely,the eigen-projection matrix processing(EMP)algorithm is adopted to suppress the MLJ,and the target range is estimated effectively through the beamforming and pulse co.pression.Then the target angle can be effectively estimated by the atom-reco.struction method.Without any prior knowledge,the MLJ can be canceled,and the angle estimation accuracy is well preserved.Furthermore,the proposed method does not have strict requirement for radar array co.struction,and it can be applied for linear array and planar array.Moreover,the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth(or elevation)equals to the jamming azimuth(or elevation),because the MLJ is suppressed in spatial plane dimension.

关键词： mainlobe jamming anti-jamming atom-reco.struction radar

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Mechanism of action, benefits, and research gap in fermented soybean meal utilization as a high-quality protein source for livestock and poultry

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Animal Nutrition 2024年第1期16卷 130-146页

作者： Modinat T.Lambo Haokai Ma Haosheng Zhang Peng Song Hongxiang Mao Guowen Cui Baisheng Dai Yang Li Yonggen Zhang co.lege of Animal Science and technology Northeast Agricultural University Wilmar (Shanghai) Biotechnology Research and Development Center co. Ltd co.lege of Electrical Engineering and information Northeast Agricultural University

Animal nutritionists have incessantly worked towards providing livestock with high-quality plant protein feed resources.Soybean meal (SBM) has been an essential and predominantly adopted vegetable protein source in livestock feeding for a long time;however,several SBM antinutrients co.ld potentially impair the animal's performance and growth,limiting its use.Several processing methods have been employed to remove SBM antinutrients,including fermentation with fungal or bacterial microorganisms.Acco.ding to the literature,fermentation,a traditional food processing method,co.ld improve SBM's nutritional and functional properties,making it more suitable and beneficial to livestock.The current interest in health-promoting functional feed,which can enhance the growth of animals,improve their immune system,and promote physiological benefits more than co.ventional feed,co.pled with the ban on the use of antimicrobial growth promoters,has caused a renewed interest in the use of fermented SBM (FSBM) in livestock diets.This review details the mechanism of SBM fermentation and its impacts on animal health and discusses the recent trend in the application and emerging advantages to livestock while shedding light on the research gap that needs to be critically addressed in future studies.FSBM appears to be a multifunctional high-quality plant protein source for animals.Besides removing soybean antinutrients,beneficial bioactive peptides and digestive enzymes are produced during fermentation,providing probiotics,antioxidants,and immunomodulatory effects.Critical aspects regarding FSBM feeding to animals remain uncharted,such as the duration of fermentation,the influence of feeding on digestive tissue development,choice of microbial strain,and possible environmental impact.

关键词： Soybean meal Protein feed Fermentation Microorganism Livestock

Text Difficulty Study:Do Machines Behave the Same as Humans Regarding Text Difficulty?

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Machine Intelligence Research 2024年第2期21卷 283-293页

作者： Bowen Chen Xiao Ding Yi Zhao Bo Fu Tingmao Lin Bing Qin Ting Liu Research Center for Social co.puting and information Retrieval Harbin Institute of TechnologyHarbin 150001China Fundamental technology Center China Construction Bank Financial Technology Co.Ltd.Beijing 100033China

With the emergence of pre-trained models,current neural networks are able to give task performance that is co.parable to humans.However,we know little about the fundamental working mechanism of pre-trained models in which we do not know how they approach such performance and how the task is solved by the model.For example,given a task,human learns from easy to hard,whereas the model learns randomly.Undeniably,difficulty-insensitive learning leads to great success in natural language processing(NLP),but little attention has been paid to the effect of text difficulty in NLP.We propose a human learning matching index(HLM Index)to investigate the effect of text difficulty.Experiment results show:1)LSTM gives more human-like learning behavior than BERT.Additionally,UID-SuperLinear gives the best evaluation of text difficulty among four text difficulty criteria.Among nine tasks,some tasks’performance is related to text difficulty,whereas others are not.2)Model trained on easy data performs best in both easy and medium test data,whereas trained on hard data only performs well on hard test data.3)Train the model from easy to hard,leading to quicker co.vergence.

关键词： co.nition inspired natural language processing psycholinguistics explainability text difficulty curriculum learning

Online diagnosis platform for tomato seedling diseases in greenhouse production

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International Journal of Agricultural and Biological Engineering 2024年第1期17卷 80-89页

作者： Xin Jin Xiaowu Zhu Jiangtao Ji Mingyong Li Xiaolin Xie Bo Zhao co.lege of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471003HenanChina Science&technology Innovation Center for co.pleted Set Equipment Longmen LaboratoryLuoyang 471023HenanChina Institute of Mechatronics technology and Application China Mechanized Agriculture Science Research Institute Group Co.LtdBeijing 100020China

The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by co.lecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform co.sists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall co.struction was designed to be co.pressed and lightweight;Seco.dly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.co.pared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest co.trol research and the development of smart agriculture.

关键词： pest and disease detection YOLO diagnosis platform k-means clustering facility production base

A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1720-1722页

作者： Yishuai Lin Gang Hu Liang Wang Qingshan Li Jiawei Zhu the School of co.puter Science and technology Xidian UniversityXi’an 710000China Suzhou Mingyi Intelligence Warehousing information technology co. Ltd.Kunshan 215300China the School of information Engineering Chang’an UniversityXi’an 710000China

Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimi... 详细信息

关键词： network AGV Deep

Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

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China co.munications 2024年第1期21卷 88-101页

作者： Shanzhi Chen Xinxin He Rui Zhao Jinling Hu Xin Zhang China information and co.munication technology Group co. Ltd.(CICT)the State Key Laboratory of Wireless Mobile CommunicationsBeijing 100083China Beijing Laboratory of Advanced information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing 100876China CICT co.nected and Intelligent Technologies co. LtdChina Academy of Telecommunication TechnologyBeijing 100191China CICT Mobile co.munications technology co.Ltd.

Since the evolving standardization of cellular V2X(C-V2X)technologies is co.tinuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and industries.Though millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary stage.In this article,a co.prehensive discussion of mm Wave NR-V2X is given co.ering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage management.The vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic co.trol display and improved in-car infotainment for the future.

关键词： C-V2X enabling technologies mmWave NR-V2X

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Group-wise co.salient object detection via multi-view self-labeling novel class disco.ery

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Frontiers of co.puter Science 2024年第2期18卷 259-261页

作者： Yang WU Gang DONG Lingyan LIANG Yaqian ZHAO Kaihua ZHANG Inspur Electronic information Industry co. Ltd.Beijing 100080China Jiangsu Key Laboratory of Big Data Analysis technology and Jiangsu co.laborative Innovation Center of Atmospheric Environment and Equipment technology Nanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction co.salient object detection(co.OD)aims to extract the salient object(s)that are co.mon across a group of relevant images[1].Group-wise clue plays a crucial role in accurately predicting the co.salient regions.Therefore,numerous groupwise deep models have been proposed by exploring co.sistency across images in unsupervised clustering manners[2-4]or the semantic co.nections guidance information[5].

关键词： co.sistency Group semantic