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14th International Conference on Cooperative Design, Visualization, and Engineering, CDVE 2017

作者： Xu, Xiujuan Liu, Yu Ma, Ruixin Sheng, Quan Z. School of Software Dalian University of Technology Dalian116620 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Department of Computing Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783319668048

Scheduling analysis, which focuses on the evaluation, testing and verification of the scheduling systems and the algorithms used in real-time operations, is critical to a number of research areas such as databases and transaction management. In order to better understand the field of scheduling, we select and investigate 9,611 papers about scheduling from five SCI journals which have published many excellent papers about scheduling. This paper presents a collaboration analysis of the scheduling field. In addition, we generate three networks to analyze collaboration patterns between scientists, including co-authorship network, keyword co-occurrence network and author co-keyword network to show the collaboration relationship between authors in the field of scheduling. The research findings from our work can help researchers understand the research status of scheduling and gain valuable insights on future technical trends in the scheduling field. © 2017, Springer International Publishing AG.

关键词： Scheduling

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Research on Hepatitis Auxiliary Diagnosis Model Based on Fuzzy Integral and GA-BP Neural Network

Research on Hepatitis Auxiliary Diagnosis Model Based on Fuz...

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2017 IEEE 8th International Conference on Software Engineering and service Science

作者： Chunyan Yang JinJie Song Tianjin Key Laboratory of Intelligent Computing & Novel Software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology

In this pa per, in order to impro ve the accuracy of hepatitis diagnosis in computer-aided diagnosis system, the medical data from the network public database were optimized by GA-BP neural network algorithm, and then we obtained the important index of judging the hepatitis and the weight of the index through thi s algorithm, we regarded the weight as the single point measure of non-additive, and then using the fuzzy integral calculated the proba bility of a person being diagnosed with hepatitis. From the results of calculation, using fuzzy integral can greatly improve the accuracy of hepatitis auxiliary diagnosis.

关键词： hepatitis auxiliary diagnosis GA-BP neural network fuzzy integral

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Leveraging geometric correlation for input-adaptive facial landmark regression

Leveraging geometric correlation for input-adaptive facial l...

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2017 IEEE International Conference on Multimedia and Expo, ICME 2017

作者： Feng, Yuyao Liu, Risheng Fan, Xin Huyan, Kang Luo, Zhongxuan DUT-RU International School of Information and Software Engineering Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781509060672

Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions on controlled facial images can successfully capture the appearance variations, the performance of these appearance-based models are tightly related to the quantity and quality of the training data. In this paper, we develop a novel framework, named geometric correlated landmark regression (GCLR), to inherit the advantages but overcome limitations of these two categories of methods. Specifically, we first establish a landmark-to-landmark regression to estimate the geometry of facial images. By further incorporating a sparse coding term into the regression framework, we can successfully leverage the geometric correlations between the test image and the shape dictionary, thus significantly enhance the geometry regression performance. Experimental results on various challenging facial data sets verify the effectiveness and efficiency of GCLR. © 2017 IEEE.

关键词： Geometry

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Learning converged propagations with deep prior ensemble for image enhancement

arXiv

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arXiv 2018年

作者： Liu, Risheng Ma, Long Wang, Yiyang Zhang, Lei DUT-RU International School of Information Science & Engineering Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Dalian China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Institute of Atmospheric Sciences Fudan University Shanghai Institute of Meteorological Science Shanghai China Department of Computing Hong Kong Polytechnic University Hong Kong

Enhancing visual qualities of images plays very important roles in various vision and learning applications. In the past few years, both knowledge-driven maximum a posterior (MAP) with prior modelings and fully data-dependent convolutional neural network (CNN) techniques have been investigated to address specific enhancement tasks. In this paper, by exploiting the advantages of these two types of mechanisms within a complementary propagation perspective, we propose a unified framework, named deep prior ensemble (DPE), for solving various image enhancement tasks. Specifically, we first establish the basic propagation scheme based on the fundamental image modeling cues and then introduce residual CNNs to help predicting the propagation direction at each stage. By designing prior projections to perform feedback control, we theoretically prove that even with experience-inspired CNNs, DPE is definitely converged and the output will always satisfy our fundamental task constraints. The main advantage against conventional optimization-based MAP approaches is that our descent directions are learned from collected training data, thus are much more robust to unwanted local minimums. While, compared with existing CNN type networks, which are often designed in heuristic manners without theoretical guarantees, DPE is able to gain advantages from rich task cues investigated on the bases of domain knowledges. Therefore, DPE actually provides a generic ensemble methodology to integrate both knowledge and data-based cues for different image enhancement tasks. More importantly, our theoretical investigations verify that the feed-forward propagations of DPE are properly controlled toward our desired solution. Experimental results demonstrate that the proposed DPE outperforms state-of-the-arts on a variety of image enhancement tasks in terms of both quantitative measure and visual perception quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

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A QUANTUM MULTI-AGENT BASED NEURAL NETWORK MODEL FOR FAILURE PREDICTION

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Journal of systems Science and systems Engineering 2016年第2期25卷 210-228页

作者： Wei Wu Min Liu Qing Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai 200092 China

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.

关键词： Failure prediction complex equipment quantum-inspired multi-agent algorithm back propagation neural network

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Detecting Phishing Websites via Aggregation Analysis of Page Layouts

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Procedia Computer Science 2018年 129卷 224-230页

作者： Jian Mao Jingdong Bian Wenqian Tian Shishi Zhu Tao Wei Aili Li Zhenkai Liang School of Electronic and Information Engineering Beihang University Beijing 100183 China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai 200240 China Baidu USA LLC. Sunnyvale CA 94089 USA Information Technology Service Center China National Petroleum Corporation Beijing 100007 China School of Computing National University of Singapore Singapore 117417 Singapore

Phishing websites are typical starting points of online social engineering attacks, including many recent online scams. The attackers develop web pages mimicking legitimate websites, and send the malicious URLs to victims to lure them to input their sensitive information. Existing phishing defense mechanisms are not sufficient to detect with new phishing attacks. In this paper, we aim to improve phishing detection techniques using machine learning techniques. In particular, we propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. Our experiment results shows that our approach is accurate and effective in detecting phishing pages.

关键词： Anti-phishing Web security Machine learning Aggregation analysis

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Design and Experimental Study of Tunable Fiber Ring Laser of C+L Band

Design and Experimental Study of Tunable Fiber Ring Laser of...

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2017 2nd International Conference on Electrical and Electronics: Techniques and Applications（EETA2017）

作者： Dan WANG Yun-wei JIA Yan-bing XUE Chun-lei ZHAO Key Laboratory of Computer Vision and System Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology School of Mechanical Engineering Tianjin University of Technology

A tunable fiber ring laser based on L band EDFA(erbium-doped fiber amplifier) was proposed. It has high signal-to-noise ratio and wide tuning range. The influence factor of the tuning range and the time delay of relaxation oscillation were studied. And the FFP-TF(Fabry-Perot tunable filter) was used as a wavelength selector to produce tunable laser. Experiments show that by using appropriate time-delay and auto-adjusting the intra-cavity loss, the signal-to-noise ratio can be increased and the tunable range can be broadened to C+L band.

关键词： Fiber laser Tunable filter Erbium-doped fiber Intra-cavity loss Laser sensing

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A data acquisition system of CNC based on Zigbee and GPRS technology

A data acquisition system of CNC based on Zigbee and GPRS te...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Xiao Bin CAO Xiang Li ZHANG Hong Mei ZHANG Key Laboratory of Cloud Computing & Complex System Guilin University of Electronic Technology

ISBN: (纸本)9781509001668

A data acquisition and monitoring system of machine tools is designed,which is based on Zigbee and GPRS wireless communication *** monitoring and alerting about the safety status of the machine tools operates via the acquisition of such physical parameters as temperature and *** hardware such as the CC2530 RF circuit and power module is *** on the CC2530 module and SIM900 module,the wireless transmission and SMS alarm can be *** detailed design process of the software is also given in this paper,besides,the data monitoring is carried out on the PC software *** turns out to be a success of this security monitoring system which integrates the data acquisition and transmission of machine tool and alarm system under the industrial *** software platform overcomes the shortcomings such as the poor realtime performance of traditional CNC machine tools monitoring system and the difficulty of data transmission,and shows an extensive range of applicability.

关键词： CNC monitoring realtime performance Zigbee GPRS

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Image captioning with deep LSTM based on sequential residual

Image captioning with deep LSTM based on sequential residual

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Kaisheng Xu Hanli Wang Pengjie Tang Tongji University Shanghai Shanghai CN Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai CN Key Laboratory of Embedded System and Service Computing Tongji University Shanghai P. R. China Tongji University Shanghai P. R. China

ISBN: (纸本)9781509060689

Image captioning is a fundamental task which requires semantic understanding of images and the ability of generating description sentences with proper and correct structure. In consideration of the problem that language models are always shallow in modern image caption frameworks, a deep residual recurrent neural network is proposed in this work with the following two contributions. First, an easy-to-train deep stacked Long Short Term Memory (LSTM) language model is designed to learn the residual function of output distributions by adding identity mappings to multi-layer LSTMs. Second, in order to overcome the over-fitting problem caused by larger-scale parameters in deeper LSTM networks, a novel temporal Dropout method is proposed into LSTM. The experimental results on the benchmark MSCOCO and Flickr30K datasets demonstrate that the proposed model achieves the state-of-the-art performances with 101.1 in CIDEr on MSCOCO and 22.9 in B-4 on Flickr30K, respectively.

关键词： Computational modeling Feature extraction Semantics Training Logic gates Recurrent neural networks

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Linked Data Crowdsourcing Quality Assessment based on Domain Professionalism

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Lu Yang Li Huang Zhenzhen Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the rapid development of Internet technology, crowdsourcing, as a flexible, effective and low-cost problem-solving method, has begun to receive more and more attention. The use of crowdsourcing to evaluate the quality of linked data has also become a research hotspot. This paper proposes the concept of Domain Specialization Test (DST), which uses domain professional testing tasks DSTs to evaluate the professionalism of workers, and combines the idea of Mini-batch Gradient Descent (MBGD) to improve the EM algorithm, and the MBEM algorithm is proposed to achieve efficient and accurate evaluation of task results. The experimental results show that the proposed method can screen out the appropriate workers for the linked data crowdsourcing task and improve the accuracy and iteration efficiency of the results.

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