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IEEE International Conference on Networking, Sensing and Control

作者： GuanJun Liu ChangJun Jiang MengChu Zhou Key Laboratory of the MoE for Embedded System and Service Computing University of Tongji Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Li and Zhou propose an important concept for Petri nets: elementary siphons. They partition siphons into elementary and dependent ones. The controllability of the latter can be ensured by the former's proper control. They give a sufficient condition to decide whether a dependent siphon is controlled by its elementary ones in S 3 PR. However, this condition is so loose that in many cases the controllability of a dependent SMS cannot be determined although it is actually controlled. In this paper, we propose an improved condition to decide the controllability of strongly dependent SMS.

关键词： Controllability system recovery Nickel Flexible manufacturing systems Petri nets Sufficient conditions Control theory

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Collaborative Tensor Factorization and its Application in POI Recommendation

Collaborative Tensor Factorization and its Application in PO...

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IEEE International Conference on Networking, Sensing and Control

作者： Wenjing Luan Guanjun Liu Changjun Jiang Department of Computer Science & Technology Tongji University Shanghai P. R. China MoE Key Lab of Embedded System & Service Computing Tongji University Shanghai P. R. China

ISBN: (纸本)9781467399760

The rapid prevalence of location-based services provides an opportunity to understand people's mobility behavior, helps people easily decide where to go at the next moment, and helps salesmen select the most suitable commercial sites to delivery advertisements. In this paper, we propose a POI recommendation method based on collaborative tensor factorization. Firstly, we present a generalized objective function for collaboratively factorizing an n-mode tensor with m feature matrices. Secondly, all users' check-in behaviors are modeled as a 3-mode tensor and three feature matrices are extracted to characterize the time distribution, category distribution and POI correlation. Thirdly, we fill the missing entries by using our collaborative tensor factorization approach such that a user's rating to a POI at a specific time is recovered. Our experiment on a real check-in database shows that our method can provide more accurate recommendation.

关键词： Tensor factorization context feature extraction POI recommendation

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Improving Utilization and Parallelism of Hadoop Cluster by Elastic Containers

Improving Utilization and Parallelism of Hadoop Cluster by E...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yinggen Xu Wei Chen Shaoqi Wang Xiaobo Zhou Changjun Jiang The Key Lab of Embedded System and Service Computing Tongji University Shanghai China Department of Computer Science University of Colorado Colorado Springs Colorado USA

ISBN: (纸本)9781538641293

Modern datacenter schedulers apply a static policy to partition resources among different tasks. The amount of allocated resource won't get changed during a task's lifetime. However, we found that resource usage during a task's runtime demonstrates high dynamics and it only reaches full usage at few moments. Therefore, the static allocation policy doesn't exploit the dynamic nature of resource usage, leading to low system resource utilization. To address this hard problem, a recently proposed task-consolidation approach packs as many tasks as possible on the same node based on real-time resource demands. However, this approach may cause resource over-allocation and harm application performance. In this paper, we propose and develop ECS, an elastic container based scheduler that leverages resource usage variation within the task lifetime to exploit the potential utilization and parallelism. The key idea is to proactively select and shift tasks backward so that the inherent paralleled tasks can be identified without over-allocation. We formulate the scheduling scheme as an online optimization problem and solves it using a resource leveling algorithm. We have implemented ECS in Apache Yarn and performed evaluations with various MapReduce benchmarks in a cluster. Experimental results show that ECS can efficiently utilize resource and achieves up to 29% reduction on average job completion time while increasing CPU utilization by 25%, compared to stock Yarn.

关键词： Task analysis Containers Dynamic scheduling Resource management Benchmark testing Parallel processing Runtime

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Rotational invariant LBP-SURF for fast and robust image matching

Rotational invariant LBP-SURF for fast and robust image matc...

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International Conference on Signal Processing and Communication systems, ICSPCS

作者： Penghui Jiang Shengjie Zhao Samuel Cheng School of Electronic and Information Engineering Tongji University Shanghai China Key Lab of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Speeded Up Robust Features (SURF) is one of the most robust and widely used image matching algorithms based on local features. However, the performance for rotation image is poor when one image is a rotated version of the other. To improve the matching accuracy of rotation image, we present an modified image matching algorithm combining Haar wavelet and the rotation invariant Local Binary Patterns (LBP). Firstly, keypoints are extracted from the images for matching by applying the Hessian matrix and integral images. Secondly, each keypoint is described by the Rotation Invariant LBP patterns and Haar wavelet, which are computed from the image patch centered at the keypoint. Finally, the matching pairs between the two sets of keypoints are determined by using the nearest neighbor distance based on matching strategy. The experimented results show that in comparison with prior works the proposed algorithm is efficient when tested on the images of scaling, rotation, blurring and brightening.

关键词： Robustness Feature extraction Image matching Brightness Lighting Euclidean distance embedded systems

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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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Conceptual model of real-time IoT systems

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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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A three-stage method to find refining schedules of crude oil operations in refinery

A three-stage method to find refining schedules of crude oil...

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IEEE International Conference on service Operations and Logistics, and Informatics, SOLI

作者： Naiqi Wu LiPing Bai MengChu Zhou School of Mechatronics Engineering Guangdong University of Technology Guangzhou China MoE Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China ECE New Jersey Institute of Technology Newark NJ USA

A short-term scheduling problem for crude oil operations is highly challenging. There lack efficient techniques and software tools for its solution. Our prior research shows that it may be solved in a hierarchical way. At the upper level, we find a realizable refining schedule to optimize concerned objectives. At the lower level, a detailed schedule is obtained to realize it. This work attempts to present a three-stage method to solve the the upper level problem from a control perspective. The first stage solves a linear programming problem to determine the maximal production rate. The second one solves a transportation problem to optimally assign crude oil types and volume to the distillers. The last one adjusts and sequences oil parcels. Consequently, the proposed method is computationally efficient. An industrial case study is presented to show the result.

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Trust reasoning and optimization based on associate petri net

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ICIC Express Letters 2011年第3期5卷 791-796页

作者： Fang, Xianwen Yan, Chungang Fan, Xiaoqin Yin, Zhixiang Department of Information and Computing Science Anhui University of Science and Technology No. 168 Shungengzhong Road Huainan 232001 China Key Lab of Embedded System and Service Computing Ministry of Education Tongji University No. 1239 Siping Road Shanghai 201804 China

Trust issue is an important problem both in social field and information field. At present, there are few methods considering the interaction behavior relativity between models which can judge the interaction influence, so the obtained trust value is imprecise and unreliable. In the paper, behavior-aware trust computation and reasoning methods based on Associate Petri net (APN) are presented, which use the APN reasoning and take the interaction relativity into account. Using Genetic Algorithm (GA) combined with the properties of APN, a trust optimization method is obtained. Theoretical analysis and experimental results indicate that the method owns both lower computation cost and lower error rate. © 2011 ISSN 1881-803X.

关键词： Genetic algorithms

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Closed-form solution for cycle time of revisiting processes in single-arm cluster tool scheduling with atomic layer deposition

Closed-form solution for cycle time of revisiting processes ...

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IEEE International Conference on Networking, Sensing and Control

作者： YuXi Sun NaiQi Wu MengChu Zhou School of Mechatronics Engineering Guangdong University of Technology Guangzhou China Key Laboratory of MoE for Embedded System and Service Computing University of Tongji Shanghai China ECE Department New Jersey Institute of Technology Newark NJ USA

Atomic layer deposition (ALD) is a typical process with wafer revisiting that should be performed by cluster tools. This paper discusses the scheduling problem of single-arm cluster tools for the ALD process. In scheduling such system, the most difficult part is to schedule the revisiting process and obtain its optimal cycle time. This paper studies the revisiting process of ALD with revisiting times k = 3, 4, and 5. Analytical expressions are obtained to calculate the cycle time for the k possible schedules with k = 3, 4, and 5, respectively, so as to obtain the optimal one. In this way, the scheduling problem of such a revisiting process becomes simple and this is a significant improvement in scheduling cluster tools with wafer revisiting.

关键词： Schedules Semiconductor device modeling Robots Load modeling system recovery Color Integrated circuit modeling

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MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition

arXiv

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arXiv 2024年

作者： Gu, Qiwen Wang, Xufei Zhang, Fenglin Zhao, Junqiao Tao, Siyue Ye, Chen Feng, Tiantian Jiang, Changjun Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous System Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance. To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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