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Conceptual model of real-time IoT systems

Frontiers of Information Technology & Electronic Engineering 2019年第11期20卷 1457-1464页

作者： Bo YUAN De-ji CHEN Dong-mei XU Ming CHEN Department of Computer Science and Technology Tongji UniversityShanghai 201804China School of Electronics and Information Engineering Jinggangshan UniversityJi'an 343009China MOE Key Laboratory of Embedded System and Service Computing Tongji UniversityShanghai 201804China China Electronics Standardization Institute Beijing 100007China Chinesisch-Deutsche Hochschule for Angewandte Wissenschaften Tongji UniversityShanghai 201804China

We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RT-IoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronization, deadline, and so on. The timing constraints turn to be more stringent as we get closer to the physical things. Based on the reference architecture of IoT (ISO/IEC 30141), the RT-IoT conceptual model is established. The idea of edge subsystem is introduced. The sensing & con-trolling domain is the basis of the edge subsystem, and the edge subsystem usually must meet the hard real-time constraints. The model includes four perspectives, the time view, computation view, communication view, and control view. Each view looks, from a different angle, at how the time parameters impact an RT-IoT system.

关键词： Internet of Things(IoT) Real-time system Conceptual model View Hard/Soft real-time

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Feature selection for semi-supervised multi-label learning with application to gene function analysis 10

Feature selection for semi-supervised multi-label learning w...

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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

作者： Li, Guo-Zheng You, Mingyu Ge, Lei Yang, Jack Y. Yang, Mary Qu MOE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Department of Control Science and Engineering Tongji University Shanghai 201804 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China Purdue Univ. ECE West Lafayette IN 47907 United States Indiana Univ. Bioinformatics Center Indianapolis IN 46202 United States

ISBN: (纸本)9781450304382

This paper investigates gene function annotation of Yeast by using semi-supervised multi-label learning. Multi-label learning has been a hot topic in the bioinformatics field, but there are many samples unlabeled. Semi-supervised learning may be employed to utilize the unlabeled data. This paper proposes a novel semi-supervised multi-label learning algorithm COMN by combining Co-Training with ML-kNN to utilize the unlabeled yeast gene data to improve modeling accuracy of function annotation. Furthermore, an embedded feature selection algorithm PRECOMN is proposed to perform feature selection for COMN to remove the irrelevant and redundant features. Experimental results on one benchmark data set of Yeast show COMN and PRECOMN perform better than the original multi-label learning algorithm ML-kNN. Furthermore PRECOMN improves generalization performance of COMN. Copyright © 2010 ACM.

关键词： Feature Selection

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Optimal scheduling of single-arm multi-cluster tools with two-space buffering modules

Optimal scheduling of single-arm multi-cluster tools with tw...

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2014 IEEE International Conference on Automation Science and Engineering, CASE 2014

作者： Yang, Fajun Wu, Naiqi Qiao, Yan Zhou, Mengchu Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou510006 China Institute of Systems Engineering Macau University of Science and Technology China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102-1982 United States

A multi-cluster tool is composed of a number of single-cluster tools linked by buffering modules. The capacity of a buffering module can be one or two. Aiming at finding an optimal one-wafer cyclic schedule, this work explores the effect of two-space buffering modules on the performance of a multi-cluster tool. The tool is modeled by a kind of Petri nets. The dynamic behavior of robot waiting and tasks, process modules, and buffers is well captured by the net model. With the model, this work shows that there is always a one-wafer cyclic schedule that reaches the lower bound of the cycle time of a process-dominant tool. Furthermore, a constant-time algorithm is revealed to find such a schedule for the first time for such multi-cluster tools. An illustrative example is given to show the application and power of this new method. © 2014 IEEE.

关键词： Petri nets

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Mobile message-aware enhancement using fuzzy lattice reasoning

Mobile message-aware enhancement using fuzzy lattice reasoni...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Ghaidaa Al-Sultany Maozhen Li Mahesh Ponraj Hamid Al-Raweshidy School of Engineering and Design Brunel University Uxbridge UK The Key Laboratory of Embedded Systems and Service Computing Ministry of Education University of Tongji China

Developing service discovery, information retrieval and context-aware to adapt to user everywhere/anytime and attain the ambient intelligence arise the issue of handling misclassification, imperfect reasoning and estimation with respect to the current context. To deal with this issue, we propose the use of Fuzzy set theory with the purpose of inferring and reasoning that adopt fuzzy lattice classifier for decision making. It is applied on top of SOIM framework to increase the accuracy of the classification process with clearer decisions.

关键词： Lattices Cognition Context Ontologies Semantics Decision making Mobile communication

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Mining Hierarchical Decision Rules from Hybrid Data with Categorical and Continuous Valued Attributes

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浙江海洋学院学报（自然科学版） 2010年第5期29卷 420-427页

作者： MIAO Duo-qian QIAN Jin LI Wen ZHANG Ze-hua Department of Computer＇Science and Technology Tongji University Shanghai 201804 China College of Computer Engineering Jiangsu Teachers University of Technology Changzhou 213015 China Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai 201804 China

Decision rules mining is an important issue in machine learning and data ***,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for ***,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid *** approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.

关键词： Similarity relation Attribute reduction Hierarchical decision rules Hybrid data

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A Recommender system for Ordering Platform Based on an Improved Collaborative Filtering Algorithm

A Recommender System for Ordering Platform Based on an Impro...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Chengchao Yu Qingshi Tang Zheng Liu Bin Dong Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Shanghai Zhuoji Information Technology ltd. Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the development of ordering platform, an increasing number of people are paying their attention to design a suitable recommender system. Most of the traditional recommender systems are based on the abundant rating information of users. However, Only historical order data can be provided to the recommender system in ordering platform as training data. This paper proposes an improved Collaborative Filtering algorithm based on historical order data of restaurants. The recommender system includes two parts: 1) rule generation module, we define a new method for measuring the similarity between dishes. Furthermore, we incorporate an incremental learning method in this module. 2) recommendation module, we design user interest vector and propose a noise filtering method. Experimental results demonstrate that the proposed algorithm can effectively improve the performance of recommendation in terms of the accuracy and coverage ratio. Moreover, our recommender system has been successfully put into service.

关键词： Recommender systems Collaboration Training Measurement Filtering algorithms Training data Learning systems

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An Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN

An Energy-efficient Traffic Scheduling Method based on Slime...

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International Conference on Information systems and Computer Networks (ISCON)

作者： Zheyuan Wang Junli Wang Chungang Yan Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Software defined network (SDN) enables efficient and green traffic management by separating the control and data planes. However, the existing itemized scheduling approach is prone to waste of network resources and energy consumption due to the different sensitivity of traffic to time delay. To address these problems, we design a Slime Mould Algorithm based Energy-efficient Traffic Scheduling Method (SMA-ETSM) for SDN. First, we model the traffic scheduling problem with delay requirements to evaluate the decision. Second, in order to minimize the generation of invalid solutions in traffic scheduling, the encoding of the slime mould algorithm is improved, and a slime mould adaptation and update mechanism suitable for energy-efficient traffic scheduling is designed. The experiments show that SMA-ETSM can effectively reduce the energy consumption and improve the overall bandwidth utilization of the network compared with ECMP and ACO algorithm, and it also has some improvement in the operation efficiency compared with the original slime mould algorithm.

关键词： Energy consumption Sensitivity Processor scheduling Scheduling Energy efficiency Real-time systems Time factors

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Reader Activation Scheduling in Multi-reader RFID systems: A Study of General Case

Reader Activation Scheduling in Multi-reader RFID Systems: A...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Shaojie Tang Cheng Wang Xiang-Yang Li Changjun Jiang Department of Computer Science Illinois Institute of Technology Chicago IL USA Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China

Radio frequency identification (RFID) is a technology where a reader device can "sense'' the presence of a close by object by reading a tag device attached to the object. To guarantee the coverage quality, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of activation schedule for readers in a multi-reader environment. In particular, we try to design a schedule for readers to maximize the number of served tags per time-slot while avoiding various interferences. We first develop a centralized algorithm under the assumption that different readers may have different interference and interrogation radius. Next, we propose a novel algorithm which does not need any location information of the readers. Finally, we extend the previous algorithm in distributed manner in order to suit the case where no central entity exists. We conduct extensive simulations to study the performances of our proposed algorithm. And our evaluation results corroborate our theoretical analysis.

关键词： Interference Radiofrequency identification Schedules Scheduling Processor scheduling Geometry Algorithm design and analysis

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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International Conference on Computer Supported Cooperative Work in Design

作者： Chengwu Tu Shengjie Zhao Hao Deng School of Software Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings.

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Detecting targets of variable acceleration with High Frequency Surface Wave Radar

Detecting targets of variable acceleration with High Frequen...

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International Radar Symposium (IRS)

作者： Gui renzhou Robert Weigel Key Laboratory of Embedded System and Service Computing Ministry of Education College of Electronics & Information Engineering University of Tongji Shanghai China Institute of Electronic Engineering Friedrich-Alexander-University of Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9788372077578

According to the conventional hypothesis, the velocity of target in the coherent integration time (CIT) is invariable in the application of utilizing high frequency surface wave radar to detect target. First, this paper proposes a more proper hypothesis in which the velocity and acceleration of target are treated as time-varying. However the acceleration of target can be regarded as invariable within the small fraction of coherent integration time. Then the model of echo signal in the hypothesis is analyzed. Considering the disadvangtages of some conventional methods of time-frequency analysis such as short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD), the paper proposes the method of time-frequency analysis based on chirplets signal decomposition which can improve the time and frequency resolution simultaneously and solve the cross-term problem in WVD method. Firstly, we process the small fraction of coherent integration time with the chirplets signal decomposition. Then according to the differences between target signal and ocean clutter echo, the BP neural network classifier can be exploited to suppress ocean clutter. The different fractions of coherent integration time are composed together with the method of the nearest correlation. Lastly, the feasibility of the method for detecting targets of variable acceleration is proved by its processing the actual data.

关键词： Time frequency analysis Acceleration Chirp Frequency modulation Oceans Radar Distance measurement

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