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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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Robust image modeling technique with a bioluminesence image segmentation application

Robust image modeling technique with a bioluminesence image ...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Zhong, Jianghong Wang, Ruiping Tian, Jie Department of Biomedical Engineering School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Medical Image Processing Group Key Laboratory of Complex Systems and IntelligenceScience Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780819475138

A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixture of a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved. Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What's more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the ***, it will be ***,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved. © 2009 SPIE.

关键词： Bioluminescence

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Video-based Person Re-identification Based on Distributed Cloud Computing

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 110-120页

作者： Chengyan Zhong Xiaoyu Jiang Guanqiu Qi College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Intelligence Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Computer Information Systems Department State University of New York at Buffalo StateBuffaloNY 14222USA

Person re-identification has been a hot research issues in the field of computer *** recent years,with the maturity of the theory,a large number of excellent methods have been ***,large-scale data sets and huge networks make training a time-consuming *** the same time,the parameters and their values generated during the training process also take up a lot of computer ***,we apply distributed cloud computing method to perform person re-identification *** distributed data storage method,pedestrian data sets and parameters are stored in cloud *** speed up operational efficiency and increase fault tolerance,we add data redundancy mechanism to copy and store data blocks to different nodes,and we propose a hash loop optimization algorithm to optimize the data distribution ***,we assign different layers of the re-identification network to different nodes to complete the training in the way of model *** comparing and analyzing the accuracy and operation speed of the distributed model on the video-based dataset MARS,the results show that our distributed model has a faster training speed.

关键词： person re-identification distributed cloud computing data redundancy mechanism

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Cloud-Fog automation: The New Paradigm towards Autonomous Industrial Cyber-Physical systems

arXiv

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arXiv 2025年

作者： Jin, Jiong Pang, Zhibo Kua, Jonathan Zhu, Quanyan Johansson, Karl H. Marchenko, Nikolaj Cavalcanti, Dave School of Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Intelligent Systems KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Technology ABB Corporate Research Sweden Västerås72226 Sweden School of Information Technology Deakin University GeelongVIC3220 Australia Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm10044 Sweden Corporate Research Robert Bosch GmbH Renningen71272 Germany Intel Corporation Santa ClaraCA95054 United States

Autonomous Industrial Cyber-Physical systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security. © 2025, CC BY-SA.

关键词： computer control

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CFUI: Collaborative filtering with unlabeled items

CFUI: Collaborative filtering with unlabeled items

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20th Annual Workshop on information Technologies and systems, WITS 2010

作者： Peng, Jing Zeng, Daniel Liu, Bing Zhao, Huimin Institute of Automation Chinese Academy of Sciences China Department of Management Information Systems University of Arizona United States Department of Computer Science University of Illinois Chicago United States Sheldon B. Lubar School of Business University of Wisconsin-Milwaukee United States

As opposed to Web search, social tagging can be considered an alternative technique tapping into the wisdom of the crowd for organizing and discovering information on the Web. Effective tag-based recommendation of information items is critical to the success of this social information discovery mechanism. Over the past few years, there have been a growing number of studies aiming at improving the item recommendation quality of collaborative filtering (CF) methods by leveraging tagging information. However, a critical problem that often severely undermines the performance of tag-based CF methods, i.e., sparsity of user-item and user-tag interactions, is still yet to be adequately addressed. In this paper, we propose a novel learning framework, which deals with this data sparsity problem by making effective use of unlabeled items and propagating users' preference information between the item space and the tag space. Empirical evaluation using real-world tagging data demonstrates the utility of the proposed framework.

关键词： Collaborative filtering

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Associative learning mechanism for drug‐target interaction prediction

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1558-1577页

作者： Zhiqin Zhu Zheng Yao Guanqiu Qi Neal Mazur Pan Yang Baisen Cong College of Automation Chongqing University of Posts and TelecommunicationsChongqingChina Computer Information Systems Department State University of New York at Buffalo StateBuffaloNew YorkUSA Department of Cardiovascular Surgery Chongqing General HospitalUniversity of Chinese Academy of SciencesChongqingChina Emergency Department The Second Affiliated Hospital of Chongqing Medical UniversityChongqingChina Data Scientist Diagnostics Digital DH(Shanghai)Diagnostics Co.Ltd.Danaher CompanyShanghaiChina

As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and *** drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug ***,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of *** paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder *** proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural *** not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular *** this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular *** performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting ***,attention transformer models with different configurations can improve the feature expression of drug/protein *** model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art *** addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new *** DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of ***,this paper theoretically proves that the proposed method maximises evidence lower bound

关键词： deep learning medical applications

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Energy efficient indoor localisation for narrowband internet of things

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1150-1163页

作者： Ismail Keshta Mukesh Soni Mohammed Wasim Bhatt Azeem Irshad Ali Rizwan Shakir Khan Renato RMaaliw III Arsalan Muhammad Soomar Mohammad Shabaz Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia Department of CSE University Centre for Research and Development Chandigarh UniversityMohaliPunjabIndia Department of Computer Science and Engineering National Institute of TechnologySrinagarIndia Asghar Mall College Rawalpindi Higher Education Department(HED)Govt.of the PunjabRawalpindiPakistan Department of Industrial Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Automation Electronics and Electrical EngineeringGdansk University of TechnologyGdańskPoland Arba Minch University Arba MinchEthiopia

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops *** high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these *** maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a *** on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot *** a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is *** investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT *** current work primarily considers the NOMA Narrow Band IoT network under a single EH ***,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance *** simulation,the proposed approach is successfully *** improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.

关键词： artificial inteligence detection of moving objects internet of things

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation 63

Model predictive control for tracking using artificial refer...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Krupa, P. Kohler, J. Ferramosca, A. Alvarado, I. Zeilinger, M.N. Alamo, T. Limon, D. Computer Science Group L'Aquila Italy Institute for Dynamic Systems and Control Eth Zürich ZürichCH-8092 Switzerland University of Bergamo Information and Production Engineering Department of Management Via Marconi 5 BG Dalmine24044 Italy Universidad de Sevilla Department of Systems Engineering and Automation Seville Spain

ISBN: (纸本)9798350316339

This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation. © 2024 IEEE.

关键词： Predictive control systems

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Developing an optimized application hosting framework in Clouds

Developing an optimized application hosting framework in Clo...

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作者： Shi, Xuanhua Jiang, Hongbo He, Ligang Jin, Hai Wang, Chonggang Yu, Bo Chen, Xueguang Service Computing Technology and Systems Lab School of Computer Huazhong University of Science and Technology China Department of Electronics and Information Engineering Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom InterDigital Communications Corp. United States Department of Automation Control and Engineering Huazhong University of Science and Technology China

A Cloud system, which is also often called a data center, may host a large number of application servers (termed as applications for short in this paper) that consume various types of resource, such as CPU, memory, and I/O. In such a scenario, the application placement strategy, which specifies how to host applications across the physical servers in the Cloud, is critical to achieve good system- and/or client-oriented performance. The existing application placement algorithms in literature assume that the consumption of system resources is proportional to the level of workloads submitted to the system. In this paper, we reveal that it may not be the case in some circumstances. Based on this observation, we design and implement an application placement framework, called EAPAC, for Clouds. The developed framework is able to achieve the optimized performance by judiciously allocating to the applications a proper mixture of different types of application request as well as an appropriate number of requests in each type. Further, we investigate the issue of resource conflicts among different applications when there exist concurrent requests in the system. We have conducted extensive experiments to evaluate the performance of the developed framework. The experiment results show that compared with the existing method in literature, EAPAC can improve the performance by 35% in terms of the reply rate. © 2013 Elsevier Inc.

关键词： Application placement Cloud Resource conflicts

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Signal Strength Based Ranging for Livestock Localization for Smart Farms 11

Signal Strength Based Ranging for Livestock Localization for...

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2023 IEEE 11th International Conference on information, Communication and Networks, ICICN 2023

作者： Tu, Chunling Du, Shengzhi Tong, Jigang Wang, Lushuo Plessis, Deon Du Tshwane University of Technology Department of Computer Systems Engineering Pretoria South Africa Tshwane University of Technology Department of Electrical Engineering Pretoria South Africa Tianjin University of Technology Electrical Engineering and Automation Tianjin China Tshwane University of Technology Department of Information Technology Pretoria South Africa

ISBN: (纸本)9798350314014

An intelligent livestock monitoring system has become increasingly popular for monitoring and managing animals in smart farms. Various technologies have been developed to localize the animals with wearable sensors. This paper addresses ranging from Received Signal Strength Indicator (RSSI). ESP32C3-Mini chips are used to construct the localization system, where both ear tags and base stations are built on these chips. Based on the distances captured by multiple base stations, triangulation is employed to estimate the position of the concerned tags. To improve the positioning accuracy, a weighted triangulation method is used. The advantage and distances of the proposed RSSI-based ranging method are discussed using real data. © 2023 IEEE.

关键词： Triangulation

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