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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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SmartMote: Energy and VoI aware solar-powered sensor network design for environment monitoring

SmartMote: Energy and VoI aware solar-powered sensor network...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Shao-Jie Tang Cheng Bo Xiang-Yang Li Yunhao Li Xiaohua Xu Jing Yuan Department of Computer Science Illinois Institute of Technology USA The Key Laboratory of Embedded System and Service Computing Tongji University China Tsinghua University Beijing Beijing CN Department of Computer Science University of Illinois at Chicago USA

Due to advances in low power micro-sensor technology, energy harvesting techniques, we can now build large scale solar-powered sensor networks to support long-running operations. Solar powered sensors often harvest variable amounts of energy in different weather conditions. Then a primary requirement for an efficient and a long-running solar-powered sensor system is to adapt to changing environment conditions and resources, and to gather as much valuable data as possible. Sensing and collecting data at a constant rate, without taking into account energy availability or data deliverability, will either drain the battery or waste resources. In this work, we design and test a highly efficient and robust solar-powered system SmartMote; and we further present an energy and value of information (VoI) aware routing strategy, that balances the rates of sensing with packet delivery for SmartMote. SmartMote achieves fairness and near maximum utility across the network. We deploy SmartMote in a forest with 100 sensors in order to monitor the humidity, temperature and luminance intensity. Our experimental results corroborate our design.

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Object rigidity and reflectivity identification based on motion analysis

Object rigidity and reflectivity identification based on mot...

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IEEE International Conference on Image Processing

作者： Di Zang Paul R. Schrater Katja Doerschner Department of Computer Science and Engineering University of Minnesota USA Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji China National Research Center of Magnetic Resonance and Department of Psychology Bilkent University Turkey

Rigidity and reflectivity are important properties of objects, identifying these properties is a fundamental problem for many computer vision applications like motion and tracking. In this paper, we extend our previous work to propose a motion analysis based approach for detecting the object's rigidity and reflectivity. This approach consists of two steps. The first step aims to identify object rigidity based on motion estimation and optic flow matching. The second step is to classify specular rigid and diffuse rigid objects using structure from motion and Procrustes analysis. We show how rigid bodies can be detected without knowing any prior motion information by using a mutual information based matching method. In addition, we use a statistic way to set thresholds for rigidity classification. Presented results demonstrate that our approach can efficiently classify the rigidity and reflectivity of an object.

关键词： Optical imaging Adaptive optics Mutual information Shape Computer vision Integrated optics Object recognition

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Structure-Aware Generative Adversarial Network for Text-to-Image Generation

Structure-Aware Generative Adversarial Network for Text-to-I...

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IEEE International Conference on Image Processing

作者： Wenjie Chen Zhangkai Ni Hanli Wang Department of Computer Science and Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai P. R. China

Text-to-image generation aims at synthesizing photo-realistic images from textual descriptions. Existing methods typically align images with the corresponding texts in a joint semantic space. However, the presence of the modality gap in the joint semantic space leads to misalignment. Meanwhile, the limited receptive field of the convolutional neural network leads to structural distortions of generated images. In this work, a structure-aware generative adversarial network (SaGAN) is proposed for (1) semantically aligning multimodal features in the joint semantic space in a learnable manner; and (2) improving the structure and contour of generated images by the designed content-invariant negative samples. Experimental results show that SaGAN achieves over 30.1% and 8.2% improvements in terms of FID on the datasets of CUB and COCO when compared with the state-of-the-art approaches.

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Information Security Evaluation by Information Flow Analysis Based on Stochastic Petri Nets

Information Security Evaluation by Information Flow Analysis...

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IEEE International Conference on systems, Man and Cybernetics

作者： Hanqian Tu Dongming Xiang Wang Lin Guanjun Liu Department of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China Department of Computer Science Key laboratory of Embedded System and Service Computing Ministry of Education Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quantitative evaluation on information leakage. In this paper, we propose a novel method for quantitative evaluation of information security based on stochastic labeled Petri nets (SLPNs) and information flow analysis. Specifically, we introduce four different levels of security metrics, and provide a methodology for evaluating the information security. Furthermore, a case study is presented to show the feasibility of our method.

关键词： Measurement Petri nets Semantics Stochastic processes Information leakage Explosions Complexity theory Cybernetics Business

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Petri net modeling and one-wafer scheduling of single-arm multi-cluster tools

Petri net modeling and one-wafer scheduling of single-arm mu...

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2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Zhu, Qinghua Wu, Naiqi Qiao, Yan Zhou, Mengchu School of Computer Guangdong University of Technology Guangzhou 510006 China Macau University of Science and Technology China Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou 510006 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102-1982 United States

ISBN: (纸本)9781479915156

It is very challenging to schedule a multi-cluster tool for it needs to coordinate the activities of multiple robots. This paper studies one-wafer cyclic scheduling for multi-cluster tools whose bottleneck cluster tool is process-bound. A Petri net model is developed to describe the system. Based on the model, it is shown that, for any multi-cluster tool whose bottleneck cluster tool is process-bound, there is always a one-wafer cyclic schedule. Then, a method is presented to find the minimal cycle time and the optimal one-wafer cyclic schedule. The method requires only simple calculation. An example illustrates the application and effectiveness of the proposed method. © 2013 IEEE.

关键词： Scheduling

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Knowledge Granulation in Interval-Valued Information systems

Knowledge Granulation in Interval-Valued Information Systems

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International Workshop on Intelligent systems and Applications, ISA

作者： Nan Zhang Duoqian Miao Can Gao Department of Computer Science and Technology University of Tongji Shanghai China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Key Laboratory of Embedded System and Service Computing of Ministry of Education University of Tongji Shanghai China

Rough set theory, proposed by Pawlak, has been proved to be a mathematical tool to deal with vagueness and uncertainty in intelligent information processing. In this paper, we propose the concept of knowledge granulation in interval-valued information systems, and discuss some important properties. From these properties, it can be shown that the proposed knowledge granulation provides important approaches to measuring the discernibility of different knowledge. It may be helpful for rule evaluation and knowledge discovery in interval-valued information systems.

关键词： Information systems Approximation methods Uncertainty Rough sets Computers Entropy

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VCFGAN: A Two-stage Collaborative Filtering Framework based on Variational Autoencoders and Generative Adversarial Networks

VCFGAN: A Two-stage Collaborative Filtering Framework based ...

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Information systems and computing Technology (ISCTech), International Conference on

作者： Zhiqiang Huang Jinzhong Li Xueyu Huang Junqi Liu School of Software Engineering Jiangxi University of Science and Technology Nanchang China College of Electronics and Information Engineering Jinggangshan University Ji’an China Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Deep generative models, such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), are widely used in collaborative filtering. They usually learn users’ preferences for items directly from a highly sparse user rating matrix (URM), and then recommend the top-N items to users. Due to the high sparsity of URM, GAN has limited ability to handle sparse data, while VAE’s encoder can extract features. Therefore, we propose a two-stage collaborative filtering framework based on variational autoencoders and generative adversarial networks, named VCFGAN. It first uses two VAEs to extract features from URM and side information (SI), and then uses the extracted latent vectors to train the GAN network. To evaluate the performance of our proposed VCFGAN model, some experiments are conducted on two real datasets, and the experimental results show that our model outperforms other representative models.

关键词： Training Collaborative filtering Autoencoders Generative adversarial networks Feature extraction Vectors Data mining Sparse matrices Recommender systems Information systems

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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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On Theoretical Optimization of the Sensing Matrix for Sparse-Dictionary Signal Recovery

On Theoretical Optimization of the Sensing Matrix for Sparse...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Jianchen Zhu Shengjie Zhao Xu Ma Gonzalo R. Arce Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai CHN School of Optics and Photonics Beijing Institute of Technology Beijing CHN Department of Electrical and Computer Engineering University of Delaware Newark Delaware USA

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. Here, we first propose the Signal space Subspace Pursuit (SSSP) algorithm, and then we derive a low bound on the number of measurements required. The algorithm has low computational complexity and provides high recovery accuracy.

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