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IEEE International Conference on Systems, Man and Cybernetics

作者： Lingjuan Lyu Yee Wei Law Kee Siong Ng Shibei Xue Jun Zhao Mengmeng Yang Lei Liu National University of Singapore School of Engineering University of South Australia Software Innovation Institute Australian National University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University School of Computer Science and Engineering Nanyang Technological University Singapore Unicloud Engine Technology Co. Ltd China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models' predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.

关键词： Privacy Training data Machine learning Predictive models Data models Encryption Noise measurement

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Data-based Formation control for Underactuated Quadrotor Team via Reinforcement Learning

Data-based Formation Control for Underactuated Quadrotor Tea...

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第三十九届中国控制会议

作者： Hao Liu Wanbing Zhao Frank L.Lewis Zhong-Ping Jiang Hamidreza Modares School of Astronautics Beihang University Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies of Ministry of Education Beihang University the University of Texas at Arlington Research Institute University of Texas at Arlington the Control and Networks Lab Department of Electrical and Computer EngineeringTandon School of EngineeringNew York University the University of Texas at Arlington Research Institute

In this paper, the formation problem of unknown multi-quadrotor systems with underactuation and nonlinearities is addressed. A formation controller including a position controller and an attitude controller is designed. The designed formation controller is based on hierarchical scheme and reinforcement learning method is used to learn the control weights of the formation controller. A simulation of formation of multiple quadrotor systems shows the effectiveness of the proposed controller.

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Lightweight Distributed Attribute Based Keyword Search System for Internet of Things 1

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12th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2019

作者： Long, Jiahuan Zhang, Ke Wang, Xiaofen Dai, Hong-Ning School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Science and Technology on Electronic Information Control Laboratory Chengdu610000 China Department of Information Technology Macau University of Science and Technology Macau China

ISBN: (数字)9783030249007

ISBN: (纸本)9783030248994

Internet of Things (IoT) is a promising networking paradigm that connects various kinds of sensors and exchanges data from smart devices. Since IoT always related to user’s daily life, the problems of security and privacy are of great importance. Presently, the attribute based encryption (ABE) is a popular solution to guarantee the fine-grained sharing of encrypted data in IoT. In this paper, an attribute based keyword search with lightweight decryption in multi-authority (ABKS-LD-MA) is proposed. Our system supports multi-keyword search in cloud by using searchable attribute based encryption. We also integrate the lightweight decryption to searchable ABE scheme that largely reduces the computing overhead for users. Furthermore, our ABKS-LD-MA scheme supports multi-authority scenario, which is more adaptive to the real IoT environment. The experiment analysis shows that our scheme has relatively lowered the communication cost on IoT devices. © 2019, Springer Nature Switzerland AG.

关键词： Internet of things

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Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network

Automatic Fault Diagnosis of Smart Water Meter Based on BP N...

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2020国际计算机前沿大会

作者： Jing Lin Chunqiao Mi School of Computer Science and Engineering Huaihua University Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province

The smart water meter in water supply network can directly affect water production and usage when faults occur. The traditional method of fault detection is inefficient with time lagging, which is not helpful for modernization of water supply system. The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply. In this paper, an automatic fault diagnosis method for the smart device is proposed based on BP neural network. And it was applied on Google Tensorflow platform. Fault symptom vectors were constructed using water meter status data and were used to train the neural network model. In order to improve the learning convergence speed and fault classification effect of the network, a method of weighted symptom was also employed. Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%.

关键词： Automaticfaultdiagnosis Smartwatermeter BPneuralnetwork Tensorflow

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A Real-Time Mp3 Copyright Protection System for Mobile Terminals:Practicalissues

A Real-Time Mp3 Copyright Protection System for Mobile Termi...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Zhangqian Wu Zhaopin Su Qinfang Wu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology) Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

With the development of the Internet and mobile terminals, more and more pirate digital music resources are easily disseminated online. To prevent the illegal copies propagating among mobile devices and protect the right of content providers. In this paper, we designed a practical real-time mp3 Copyright Protection System(MCPS) for mobile terminals which is based on Client/Server model and contains three parts: security channel, server and client. In particular, we established a secure channel based on SSL(Secure Sockets Layer) protocol, defined an identity authentication procedure between the server and the client. In the server, we designed RC4 based encryption algorithm for frame header, and chaotic based watermarking algorithms for frame body, respectively. In the client, we designed a novel mp3 streamd ecoder, where, for each frame, frame header is decrypted, and the embedded information is extracted and compared with its IMEI to decide whether to decode or not. The practical experiments show that the system can effectively protect copyright for digital_music and is of great.

关键词： Watermarking Servers Protocols Copyright protection Authentication Digital audio players Encryption

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Multi-loss-aware channel pruning of deep networks

arXiv

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arXiv 2019年

作者： Hu, Yiming Sun, Siyang Li, Jianquan Zhu, Jiagang Wang, Xingang Gu, Qingyi Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

Channel pruning, which seeks to reduce the model size by removing redundant channels, is a popular solution for deep networks compression. Existing channel pruning methods usually conduct layer-wise channel selection by directly minimizing the reconstruction error of feature maps between the baseline model and the pruned one. However, they ignore the feature and semantic distributions within feature maps and real contribution of channels to the overall performance. In this paper, we propose a new channel pruning method by explicitly using both intermediate outputs of the baseline model and the classification loss of the pruned model to supervise layer-wise channel selection. Particularly, we introduce an additional loss to encode the differences in the feature and semantic distributions within feature maps between the baseline model and the pruned one. By considering the reconstruction error, the additional loss and the classification loss at the same time, our approach can significantly improve the performance of the pruned model. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Cluster regularized quantization for deep networks compression

arXiv

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arXiv 2019年

作者： Hu, Yiming Li, Jianquan Long, Xianlei Hu, Shenhua Zhu, Jiagang Wang, Xingang Gu, Qingyi Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

Deep neural networks (DNNs) have achieved great success in a wide range of computer vision areas, but the applications to mobile devices is limited due to their high storage and computational cost. Much efforts have been devoted to compress DNNs. In this paper, we propose a simple yet effective method for deep networks compression, named Cluster Regularized Quantization (CRQ), which can reduce the presentation precision of a full-precision model to ternary values without significant accuracy drop. In particular, the proposed method aims at reducing the quantization error by introducing a cluster regularization term, which is imposed on the full-precision weights to enable them naturally concentrate around the target values. Through explicitly regularizing the weights during the re-training stage, the full-precision model can achieve the smooth transition to the low-bit one. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Dynamic analysis of high-dimensional fractional-order gene regulation networks with time delay

Dynamic analysis of high-dimensional fractional-order gene r...

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Chinese control Conference (CCC)

作者： Zhe Zhang Can Ding Jing Zhang Feng Liu Yingjie Zhang Fanyong Cheng College of Electrical and Information Engineering Hunan University Changsha China School of Automation China University of Geosciences Wuhan China College of Computer Science and Electronic Engineering Hunan University Changsha China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fujian China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This paper mainly analyzes the stability of high-dimensional fractional-order gene regulatory network systems with time delay. Based on the judgment of eigenvalues, this paper proposes sufficient information about the stability of high-dimensional fractional-order gene regulatory network systems with time delay. Conditions, and by analyzing the characteristic equations, the bifurcation conditions of the high-dimensional fractional-order gene regulatory network system with time delay are derived. Finally, the theoretical part of this paper is verified by numerical simulation.

关键词： Bifurcation Timing Stability analysis Mathematical model Delay effects Numerical stability Electronic mail

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Coordinating EV Charging and Dynamic CVR in a LV Network: A UK Case Study

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IFAC-PapersOnLine 2018年第10期51卷 193-198页

作者： Gharavi, Hani McLorn, Gareth Liu, Xueqin McLoone, Seán Energy Power and Intelligent Control Research Cluster School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

The high cost associated with installing new infrastructure to meet the increasing demand in the electricity network, particularly in the context of supporting the increasing deployment of low carbon technologies, such as electric vehicles and rooftop solar PV systems, presents distribution network operators with many challenges. This paper explores the potential for coordinating Conservation Voltage Reduction (CVR) and Electric Vehicle Demand control (EVDC) on distribution networks to reduce overall energy consumption. The study is conducted using a detailed OpenDSS simulation of a low voltage network in the greater Manchester area in the UK under time-varying load conditions representative of a winter weekday. Matlab is employed as a wrapper program to run the optimization, which is performed using uses Particle Swarm Optimisation, while OpenDSS is used for power flow simulation. The results show that energy savings of 4.2% are possible for the scenario considered. © 2016

关键词： Charging (batteries) Electric automobiles Electric load flow Electric power distribution Energy conservation Energy utilization MATLAB Particle swarm optimization (PSO) Voltage control Conservation voltage reductions CVR implementation Distribution network operators Electricity networks Low carbon technologies Optimisations Particle swarm optimisation Power flow simulation

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Reliability Analysis of a Multirotor Flight Module of a High-altitude Telecommunications Platform Operating in a Random Environment

Reliability Analysis of a Multirotor Flight Module of a High...

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engineering and Telecommunication (En&T), International Conference

作者： Duy Phuong Nguyen Dmitry Vladimirovich Kozyrev School of Radio Engineering and Computer Technology Moscow Institute of Physics and Technology (National Research University) Dolgoprudny Moscow Region Russian Federation V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russian Federation

ISBN: (纸本)9781728188300

One of the promising directions in the framework of the concept of creating the next-generation 5G/IMT-2020 networks is the development of broadband wireless networks based on autonomous unmanned aerial vehicles (UAVs). In addition to the interest in autonomous UAV-based high-altitude platforms (HAPs), leading researchers are currently engaged in the design and implementation of tethered unmanned HAPs, which are intended to be long-term operating and are widely used in civilian and military areas. Their possibility of long-term operation, which is one of the main advantages over autonomous UAVs, puts forward new reliability requirements. In this paper we study an analytical reliability model of the multi rotor flight module of the tethered HAP as a homogeneous hot-standby system consisting of n elements operating in a random environment. A general Markov reliability model of the considered system operating in a random environment is proposed, which accounts for the increase in the functional load and the location of the failed components.

关键词： Analytical models Wireless networks Rotors Markov processes Unmanned aerial vehicles Numerical models Telecommunications

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