School of Advanced Technology

ADDRESS
School of Advanced Technology
Xi'an Jiaotong-Liverpool University
111 Ren'ai Road Suzhou Dushu Lake Science and Education Innovation District , Suzhou Industrial Park
Suzhou,Jiangsu Province,P. R. China,215123
1. Vehicle Logo Recognition and Attributes Prediction by Multi-task Learning with CNN

Author:Xia, YZ;Feng, J;Zhang, BL

Source:2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD),2016,Vol.

Abstract:Vehicle Logo Recognition(VLR) has been an important study field in intelligent Transportation system (ITS). This paper proposes to recognize vehicle logo and predict logo attributes by combining Convolutional Neural Network (CNN) with Multi-Task Learning(MTL). In order to accelerate convergence of multi-task model, an adaptive weight training strategy is employed. To verify the algorithm, the Xiamen University Vehicle logo recognition dataset is extended into a larger vehicle logo dataset including 15 brands, 6 visual attributes and 3 no-visual attributes. The experiment results indicate that the proposed multi-task CNN model perform well for both of logo classification and attribution prediction with overall accuracy 98.14 %%.
2. E-marketing services and e-marketing performance: the roles of innovation, knowledge complexity and environmental turbulence in influencing the relationship

Author:Chong, WK;Bian, D;Zhang, N

Source:JOURNAL OF MARKETING MANAGEMENT,2016,Vol.32

Abstract:The business-to-business electronic marketplace (e-marketplace) is becoming critical for small- and medium-sized enterprises (SMEs). However, which e-marketing services determine a firm's e-marketing performance and how innovation, knowledge complexity and environmental turbulence influence the relationship between e-marketing services and e-marketing performance are under-researched topics in the field. We first empirically tested 176 SMEs from China to evaluate which e-marketing services are significantly related to e-marketing performance and how these services collectively influence the performance. Then, we used an NK model to examine how innovation, knowledge complexity and environmental turbulence mediate/moderate the relationship. The results show that five e-marketing services (e-CRM, e-SCM, e-competitiveness, IS/IT integration and information transparency) can greatly influence e-marketing performance; innovation positively mediates the relationship between e-marketing services and performance; and knowledge complexity and environmental turbulence positively moderate the relationship.
3. Usable Authentication Mechanisms for Mobile Devices: An Exploration of 3D Graphical Passwords

Author:Yu, Z;Olade, I;Liang, HN;Fleming, C

Source:2016 INTERNATIONAL CONFERENCE ON PLATFORM TECHNOLOGY AND SERVICE (PLATCON),2016,Vol.

Abstract:Current authentication systems in mobile devices such as smart phones have many shortcomings. Users tend to use simple textual passwords such as PINs, which are easily cracked by intruders. Meanwhile, graphical passwords suffer from shoulder surfing attack In this paper, a new authentication system using 3D graphical passwords, will be proposed and tested to offer more security for mobile devices. This authentication system allows users to interact with the 3D objects in a 3D virtual environment and these actions are tracked in the virtual environment and used to create unique passwords. Based on the previous studies of the 3D password scheme, this paper developed a simple testing program that enables users to create their own 3D password easily. At the end of the paper, some improvements of the program and this authentication system are discussed.
4. Computation of macro-fiber composite integrated thin-walled smart structures

Author:Zhang, SQ;Zhang, SY;Chen, M;Bai, J;Li, J

Source:2016 GLOBAL CONFERENCE ON POLYMER AND COMPOSITE MATERIALS (PCM 2016),2016,Vol.137

Abstract:Due to high flexibility, reliability, and strong actuation forces, piezo fiber based composite smart material, macro-fiber composite (MFC), is increasingly applied in various fields for vibration suppression, shape control, and health monitoring. The complexity arrangement of MFC materials makes them difficult in numerical simulations. This paper develops a linear electro-mechanically coupled finite element (FE) model for composite laminated thin-walled smart structures bonded with MFC patches considering arbitrary piezo fiber orientation. Two types of MFCs are considered, namely, MFC-d31 in which the d(31) effect dominates the actuation forces, and MFC-d33 which mainly uses the d(33) effect. The proposed FE model is validated by static analysis of an MFC bonded smart plate.
5. Modeling and Simulation of Energy Control Strategies in AC Microgrid

Author:Wen, HQ;Yang, RZ

Source:2016 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC),2016,Vol.2016-December

Abstract:Microgrid is an intelligent power system wh ich contains distributed generations, energy storages, local loads, monitoring and protecting units to realize autonomous control, protect and manage functions. It's regarded as the trend for the future power system since it satisfies the rapidly-developing electrical power demand and reduces environmental concerns. Considering different operation modes of Microgrid such as grid-connected and stand-alone, the control strategies need be automatically adjusted with support from Grid, battery or Fuel Cells. In this paper, a Microgrid is buHt and simulated in PSCAD, including PV, wind power, Fuel Cell, lead acid battery, and microturbine. Both component and system level models are verified tor different scenarios with appropriate control strategies. Main results are illustrated in order to show the efTectiveness of the control strategy tor different modes.
6. SecIoT: a security framework for the Internet of Things

Author:Huang, X;Craig, P;Lin, HY;Yan, Z

Source:SECURITY AND COMMUNICATION NETWORKS,2016,Vol.9

Abstract:The 5th generation wireless system (5G) will support Internet of Things (IoT) by increasing the interconnectivity of electronic devices to support a variety of new and promising networked applications such as the home of the future, environmental monitoring networks, and infrastructure management systems. The potential benefits of the IoT are as profound as they are diverse. However, the benefits of the IoT come with some significant challenges. Not the least of these is that the increased interconnectivity integral to an IoT network increases its vulnerability to malevolent attacks. There is still no proven methodology for the design of security frameworks with device authentication and access control. This paper attempts to address this problem through the development of a prototype security framework with robust and transparent security protection. This includes an investigation into the security requirements of three different characteristic IoT scenarios (concretely, body IoT, home IoT, and hotel IoT), a design of new authentication mechanisms, and an access control subsystem with fine-grained roles and risk indicators. Our prototype security framework gives us an insight into some of the major difficulties of IoT security as well as providing some feasible solutions. Copyright (C) 2015 John Wiley & Sons, Ltd.
7. Anomalous Capacitance-Voltage Hysteresis in MOS Devices with ZrO2 and HfO2 Dielectrics

Author:Lu, QF;Qi, YF;Zhao, CZ;Zhao, C;Taylor, S;Chalker, PR

Source:2016 5TH INTERNATIONAL SYMPOSIUM ON NEXT-GENERATION ELECTRONICS (ISNE),2016,Vol.

Abstract:Anomalous behaviors in capacitance-voltage (CV) characteristics are observed on MOS devices with ZrO2 and HfO2 oxides. The relative positions of forward and reverse CV traces measured by pulse technique are opposite to those obtained by LCR meter. This unusual phenomenon cannot be consistently explained by trapping/de-trapping of charges. A hypothesis related to interface dipoles is proposed to provide a possible explanation.
8. An Improved Beta Method With Autoscaling Factor for Photovoltaic System

Author:Li, XS;Wen, HQ;Jiang, L;Hu, YH;Zhao, CH

Source:IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,2016,Vol.52

Abstract:Maximum power point tracking (MPPT) is essential to improve the energy yield of solar energy systems. However, conventional MPPT algorithms show obvious problems such as the conflict of the steady-state oscillations and dynamic speed, and the clash of high computational burden and accuracy. Originated from the beta method, which shows the advantages of fast tracking speed in the transient stage, small oscillations in the steady-state, and medium complexity of implementation, this paper proposed an improved beta method to further improve the overall performance, especially for practical applications. Instead of manually tuning key parameters such as the range of beta parameter and scaling factor N for different operating conditions, an autoscaling factor is used, which make the method easier in practical implementation and suitable for wider conditions. The meteorological data of two distinct locations are used to verify that the beta parameters derived from photovoltaic (PV) modules are valid for one whole year under different environmental conditions. A PV system with the proposed MPPT method was built in MATLAB/Simulink, and different indices such as the rise time, the setting time, and the tracking energy loss are used to evaluate the performance of various MPPT algorithms. Finally, two experimental tests were carried out, including the indoor test with solar array emulator and the outdoor test with an actual PV module, respectively, to show the effectiveness of the proposed MPPT algorithm.
9. Understanding Quantum Protocols Using Information Visualization

Author:Huang, X;Wang, QL

Source:2015 7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY IN MEDICINE AND EDUCATION (ITME),2015,Vol.

Abstract:There are many cases where people need to be aware of the security status of their network in order to be able to respond effectively to security risks. Most security mechanisms are however too complex and most users find it difficult to understand the system and respond effectively. There is therefore a need to design understandable security mechanisms. In this paper, we focus on the learnability of complex security protocols which is improved using symmetrical information visualization. From preliminary results, we can see that visualization and symmetry are useful tools for usable security. They are aesthetically pleasing for the user and can improve the understanding of protocols that would otherwise be difficult to comprehend for normal users.
10. 3D VIDEO SUPER-RESOLUTION USING FULLY CONVOLUTIONAL NEURAL NETWORKS

Author:Xie, YC;Xiao, JM;Tillo, T;Wei, YC;Zhao, Y

Source:2016 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO (ICME),2016,Vol.2016-

Abstract:Large amount of redundant information and huge data size have been a serious problem for multiview video systems. To address this problem, one popular solution is mixedresolution, where only few viewpoints are kept with full resolution and other views are kept with lower resolution. In this paper, we propose a super-resolution (SR) method, where the low-resolution viewpoints in the 3D video are up-sampled using a fully convolutional neural network. By simply projecting the neighboring high resolution image to the position of the low resolution image, we learn the relationship of high and low resolution patches, and reconstruct the low resolution images into high resolution ones using the projected image information. We propose to use a fully convolutional neural network to establish a mapping between those images. The network is barely trained on 17 pairs of multiview images, and tested on other multiview images and video sequences. It is observed that our proposed method outperforms existing methods objectively and subjectively, with more than 1 dB average gain achieved. Meanwhile, our network training procedure is efficient, with less than 3 hours using one Titan X GPU.
11. Particle Swarm Optimization With Interswarm Interactive Learning Strategy

Author:Qin, QD;Cheng, S;Zhang, QY;Li, L;Shi, YH

Source:IEEE TRANSACTIONS ON CYBERNETICS,2016,Vol.46

Abstract:The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.
12. S(2)Net: A Security Framework for Software Defined Intelligent Building Networks

Author:Xue, N;Huang, X;Zhang, J

Source:2016 IEEE TRUSTCOM/BIGDATASE/ISPA,2016,Vol.

Abstract:During the last ten years, the progress of the Internet of Things (IoT) has continued unabated and gradually expanded into almost every corner of our daily lives. One very typical use case of IoT is the intelligent building (IB). Along with the remarkable rise of IoT, an increasing number of smart devices will be connected to IB networks, which brings many challenges to current IB network architectures. SDN (Software Defined Networking) is a potential solution to future IB networks; however, there are still few studies on SDN-based IB networks. In this paper, we have proposed a secure SDN framework named as S(2)Net, offering authentication and key agreement between the central controller and smart devices. In addition, we have designed a secure OpenFlow message issuing protocol for IB networks; it is used to securely exchange messages between the controller and smart devices. Also, these security protocols are evaluated regarding their security and performance. In doing so, we take an important step towards integrating SDN with extant IB networks.
13. Comparative performance on photovoltaic model parameter identification via bio-inspired algorithms

Author:Ma, JM;Bi, ZQ;Ting, TO;Hao, SY;Hao, WJ

Source:SOLAR ENERGY,2016,Vol.132

Abstract:Photovoltaic (PV) models are usually composed by nonlinear exponential functions, where several unknown parameters must be identified from a set of experimental measurements. Owing to the ability to handle nonlinear functions regardless of the derivatives information, bio-inspired algorithms for parameter identification have gained much attention. In this work, six bio-inspired optimization algorithms, i.e. genetic algorithm, differential evolution, particle swarm optimization, bacteria foraging algorithm, artificial bee colony, and cuckoo search are compared statistically by testing over single-diode models to evaluate their performance in terms of accuracy and stability under uniform solar irradiance and various environmental conditions. Various parameter settings of these algorithms are used in the study. Results indicate that cuckoo search algorithm is more robust and precise among these bio-inspired optimization algorithms. In addition, this paper shows that bio-inspected algorithms are capable of improving the existing PV models by using optimized parameters. (C) 2016 Elsevier Ltd. All rights reserved.
14. Preface

Author:Tan, Ying ; Shi, Yuhui ; Li, Li

Source:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2016,Vol.9713 LNCS

Abstract:[No abstract available]
15. Low-Complexity Noncoherent Signal Detection for Nanoscale Molecular Communications

Author:Li, B;Sun, MW;Wang, SY;Guo, WS;Zhao, CL

Source:IEEE TRANSACTIONS ON NANOBIOSCIENCE,2016,Vol.15

Abstract:Nanoscale molecular communication is a viable way of exchanging information between nanomachines. In this investigation, a low-complexity and noncoherent signal detection technique is proposed to mitigate the inter-symbol-interference (ISI) and additive noise. In contrast to existing coherent detection methods of high complexity, the proposed noncoherent signal detector is more practical when the channel conditions are hard to acquire accurately or hidden from the receiver. The proposed scheme employs the molecular concentration difference to detect the ISI corrupted signals and we demonstrate that it can suppress the ISI effectively. The difference in molecular concentration is a stable characteristic, irrespective of the diffusion channel conditions. In terms of complexity, by excluding matrix operations or likelihood calculations, the new detection scheme is particularly suitable for nanoscale molecular communication systems with a small energy budget or limited computation resource.
16. Brain Storm Optimization Algorithms for Optimal Coverage of Wireless Sensor Networks

Author:Wei, M;Shi, YH

Source:2015 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI),2015,Vol.

Abstract:Optimal coverage plays an essential role in the service quality of wireless sensor networks because the monitoring can be accurate and meaningful if and only if information from all interesting areas is collected. To ensure high coverage and reduce cost in coverage problems, efficient deployments of sensor nodes in wireless sensor networks become the target of coverage optimization. This paper focuses on using the fewest number of sensor nodes to cover more areas in regular or irregular interesting areas with irregular obstacles inside. To optimize the deployment of sensor nodes, in this paper, a brain storm optimization algorithm is utilized and simulation results show that the algorithm performs well on optimizing the coverage percentage and minimizing the needed sensor nodes under complex environments. In addition, the balance of coverage percentage and needed sensor nodes' number can be adjusted according to specific requirements of different networks. For better optimization results in coverage problems, the step size in the generation process of the brain storm optimization algorithm has also been modified to reach higher coverage using fewer sensor nodes under the same environment.
17. Convergence Analysis of Brain Storm Optimization Algorithm

Author:Zhou, ZW;Duan, HB;Shi, YH

Source:2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC),2016,Vol.

Abstract:Brain storm optimization (BSO) algorithm is a new kind of swarm intelligence algorithm, which is inspired by collective behavior of human beings. In this paper, a Markov model for brain storm optimization algorithm is derived. The model gives the theoretical probability of the occurrence of each possible population as the number of generation count goes to infinity. Using the Markov model, the convergence of the brain storm optimization is analyzed.
18. Driver Behavior Recognition Based on Deep Convolutional Neural Networks

Author:Yan, SY;Teng, YX;Smith, JS;Zhang, BL

Source:2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD),2016,Vol.

Abstract:Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R*CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R*CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76%% on the dataset which prove the proposed method is effective in drivers's action recognition.
19. Neural Incremental Attribute Learning Based on Principal Component Analysis

Author:Wang, T;Zhou, W;Zhu, XY;Liu, FZ;Guan, SU

Source:PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA),2016,Vol.

Abstract:Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features and improve classification results. This study proposed a preprocessing for neural IAL algorithm based on feature extraction with PCA. Experimental results show that this approach is not only very efficient, but also applicable for pattern classification performance improvement.
20. Device-to-Device Communications in LTE-Unlicensed Heterogeneous Network

Author:Yuan, H;Guo, WS;Wang, SY

Source:2016 IEEE 17TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC),2016,Vol.2016-August

Abstract:In this paper, the authors examine how the envisaged Device-to-Device (D2D) networks can efficiently scale its capacity by utilizing the unlicensed spectrum with appropriately designed LTE-Unlicensed (LTE-U) protocols. The LTE-U Listen Before Talk (LBT) algorithm is adapted for collision avoidance between traditional unlicensed user equipment (UEs), e.g. Wi-Fi UEs, and the LTE-U enabled D2D UEs. By considering different traffic loads, the analysis found that whilst the D2D UEs reduce the unlicensed network capacity, it increases the combined licensed and unlicensed network capacity by 63%%.
Total 173 results found
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