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A Safe and Reliable Anti-lock Wheel Control with Enhanced Forgotten Factor for Brake Operation of Heavy Train 15

A Safe and Reliable Anti-lock Wheel Control with Enhanced Fo...

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15th IEEE international Symposium on parallel and distributed Processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Zhang, Shuai Huang, Zhiwu Yang, Yingze Gao, Kai Zhu, Cong Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China Changsha Univ Sci & Technol Coll Automot & Mech Engn Changsha Hunan Peoples R China Hunan Engn Lab Rail Vehicles Braking Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781538637906

With the development of science and technologies, the locomotive is getting faster and faster and the load becomes heavier. the inertia and vertical force generated by the heavy train are increasing, and there are higher requirements of the braking and anti-slip control of the heavy train. In order to address the issues of safe and reliable anti-lock wheel control for heavy train operation, a sliding mode control based on recursive least square algorithm with enhanced forgotten factor is proposed. In this algorithm, the recursive least square method is exerted to seek optimal slip ratio. the wheel pair model is introduced to construct PI closed-loop observer to estimate unmeasured adhesive torque. On the one hand, the value of adhesion force torque can be used to calculate the actual adhesion coefficient which is an input of the algorithm for calculating the optimal slip rate;on the other hand, it can be also used to calculate the reference speed which is a necessary parameter of controller. the simulation result demonstrates the effect of real-time adjustment for braking torque, which guarantees the braking performance.

关键词： safety reliablity optimal slip ratio forgotten factor recursive least square closed loop observer

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A Novel Approach of Constructing Chinese Handwriting Mobile System and Data Service for Elementary Education Domain 15

A Novel Approach of Constructing Chinese Handwriting Mobile ...

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15th IEEE international Symposium on parallel and distributed Processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Lu, Ye Li, Qingcheng Li, Tao Liu, Jiaxin Nankai Univ Coll Comp & Control Engn Tianjin Peoples R China

ISBN: (纸本)9781538637906

Handwriting is the most basic and natural behaviors in the teaching activities or homework. At present, various mobile or media system and devices are applied in the elementary education stage,but the input mode based on traditional keyboard has many obstacles which is not suitable for primary and middle school's students and teachers in or after class. Although there are lots of handwriting recognition software or solutions provided written services, they are always depending on recognition technologies or methods. these approaches were not the direct and natural way because of the complexity of Chinese characters and manual assistance, for example, selecting a item from numbers of alternatives in the input method. In this paper, a novel approach of constructing Chinese handwriting mobile system and data service oriented elementary education based on Android platform is proposed. A series of non-recognition document format processing and human-machine interaction methods supporting Chinese handwriting are implemented by means of the view layers structure, gesture classification and recognition, buffer mechanism for screen loading handwriting characters, log-structured storage and data synchronization. Students and Teachers can use the system in any education scenarios or activities by writing, editing, storing and sharing handwriting characters or other multimedia as writing as the real paper even simulating Chinese writing brush. Results show that system can be applied to teaching activities to archive handwriting easily and conveniently.

关键词： Chinese handwriting non-recognition document format hierarchical display log-structured storage data synchronization

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A case of electrical circuit switched interconnection network for parallel computers 18

A case of electrical circuit switched interconnection networ...

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18th international conference on parallel and distributed computing, applications and technologies, PDCAT 2017

作者： Hu, Yao Kudoh, Tomohiro Koibuchi, Michihiro National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo101-8430 Japan University of Tokyo 2-11-16 Yayoi Bunkyo-ku Tokyo113-8658 Japan

ISBN: (纸本)9781538631515

Circuit switching is a way to minimize network latency and maximize network bandwidth when a limited number of source-and-destination pairs exchange messages which are predictable. Although there are a large number of studies of optical circuit switching (OCS) on HPC systems and datacenters, it is still not mature. In this context, we explore the use of electrical circuit switching (ECS) for the low-latency purpose on HPC systems and datacenters. ECS has the same link bandwidth as existing electrical packet switched networks, and inherits quick update of input-and-output connections from electrical switches. We develop a network topology generator for ECS to minimize the number of time slots optimized to target applications whose traffic patterns are predictable. By performing a quantitative discrete-event simulation, we present that an ideal ECS network outperforms counterpart EPS networks. Evaluation results show that the minimum necessary number of slots (MNNS) can be reduced to a small number in a generated topology while keeping resource amount less than that in a standard mesh network. © 2017 IEEE.

关键词： Timing circuits

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Delay Compensated Asynchronous Adam Algorithm for Deep Neural Networks 15

Delay Compensated Asynchronous Adam Algorithm for Deep Neura...

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15th IEEE international Symposium on parallel and distributed Processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Shan, Lei Guan, Naiyang Yang, Canqun Xu, Weixia Zhang, Minxuan Natl Univ Def Technol Coll Comp Inst Software Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Inst Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538637906

Deep neural network (DNN) learns hierarchical representations from big data in a multilayer network structure and has achieved great successes in many fields such as computer vision and speech analysis. Since DNN usually contains several billions of parameters, the asynchronous stochastic gradient descent (ASGD) algorithm is often used to train an effective DNN model on a computer cluster. However, as the increase of computing nodes and data size, ASGD suffers from serious slow convergence deficiency because the parameters might be wrongly updated by long-term delayed gradients. In this paper, we propose a delay compensated asynchronous Adam (DC-Adam) algorithm to train DNN. In particular, DC-Adam updates the parameters with the moment increment which is the division of the first and the second moments to take the advantage of the original Adam algorithm, and compensates the gradient with the first-order component in its Taylor expansion. Since the delay compensation technique reduces the error of delayed gradients, and the moment increment further counteracts the influence of approximated compensation, DC-Adam converges much more rapidly than ASGD on a computer cluster with moderate computing nodes. We theoretically analyze the Ergodic convergence rate of DC-Adam and compare with DC-ASGD. We implement our DC-Adam algorithm on 61 computing nodes in a computer cluster, and conduct image classification by using LeNet and ResNet, respectively, on the MNIST and CIFAR-10 datasets. the experimental results demonstrate that DC-Adam greatly accelerates the training progress and achieves almost linear speedup rate as increasing the computing nodes.

关键词： Asynchronous algorithm stochastic gradient descent deep neural networks Adam delay compensation

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Benefits of new CoolSiCTM MOSFET in HybridPACKTM Drive package for electrical drive train applications.

Benefits of new CoolSiCTM MOSFET in HybridPACKTM Drive packa...

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CIPS 2018;10th international conference on Integrated Power Electronics Systems

作者： Waldemar Jakobi Andre Uhlemann Markus thoben Christian Schweikert Christian Strenger Ajay Poonjal Pai Laurent Beaurenaut Mark Muenzer

For hybrid and electric vehicles it is necessary to increase the power density in order to keep the system compact and to expand the power range of the car. there are different options to increase the power density like better chips with wide band gap materials, an improved cooling system or an innovative interconnection- and joining technology for elevated operation temperatures. In this paper Infineon's new 1200V CoolSiC(Exp TM) MOSFET is presented, integrated and tested in the upcoming HybridPACK (Exp TM) Drive power module. the power module is designed for high power main inverter applications up to 300kW and for switching currents up to 500A RMS. For this application several SiC-MOSETS devices must be arranged and controlled in parallel connection. For this reason a new low inductive DCB/AMB layout concept is developed. Different investigations (experimental and simulative ones) were performed to reduce current fail distribution to a minimum. these considerations are also interesting from a reliability point of view, because current fail distribution could limit the life time expectation. Furthermore the switching behavior of SiC MOSFET is discussed especially with focus on overvoltage and oscillations effects which can be critical for inverter operations. Influence of e.g. RC snubber solutions and operation modes will be also discussed in this paper. In addition to these aspects, the switching losses will be compared with state of the art chip technologies to figure out the increase of efficiency in dependence on the considered working points.

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A Prototype Simulator for the Simulation of Complicated Hadoop Framework Behaviors 13

A Prototype Simulator for the Simulation of Complicated Hado...

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13th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Yang Jing, Weizhe Sichuan Univ Sch Elect Engn & Informat Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9781538621653

distributed computing and parallel computing have become the most effective tools for solving complex problems in amount of academia and industrial fields. Among a number of distributed and parallel computing technologies, MapReduce has been proved as one of the most efficient and reliable computing model to deal with distributed tasks. Especially, Hadoop framework, a Java based open source implementation of MapReduce has been widely used and studied. Hadoop supplies tremendously abilities to process large-scale tasks with large volume of data, which significantly facilitates the big data applications. However, Hadoop is lack of a function that to predict the job processing time before actually running a job. And also. Hadoop cannot tell that how the system performs under the condition that the system parameters have been tuned. therefore, this paper presents a prototype Hadoop simulator which aims at simulating the parameters and interactions of the Hadoop framework using agent based simulation. the experimental results show that the simulator offers potentials to be an instrument to study complicated behaviors of the Hadoop framework.

关键词： MapReduce Hadoop simulator parameters agent simulation

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DSP Based Acceleration for Long Short-Term Memory Model Based Word Prediction Application 10

DSP Based Acceleration for Long Short-Term Memory Model Base...

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10th international conference on Intelligent Computation Technology and Automation (ICICTA)

作者： Zhu, Keqian Jiang, Jingfei Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410000 Hunan Peoples R China

ISBN: (纸本)9781538612309

Neural network based deep learning algorithm is a study hotspot in artificial intelligence. Moreover, embedded artificial intelligence and mobile computing are becoming more and more important in industry. For these applications, not only high performance computing is required, but also low power consumption restriction cannot be ignored. DSP has special hardware architecture with characteristics of high performance and low power consumption, which is an ideal computing platform for embedded artificial intelligence. this paper concerns the energy efficiency of DSP under deep learning applications. However, many relative researches are insufficient in application scale and optimization techniques. this research extends application scale and puts forward some optimization methods in detail. Specifically, for the Long Short-Term Memory (LSTM) model based word prediction application, we use TI's high-performance multi-core DSP to accelerate its inference process. We apply a variety of optimization techniques to our initial DSP program. Relative experimental results show that these techniques bring notable performance improvement. Furthermore, we regard the MATLAB program which runs on general CPU and C program which runs on ARM as contrast. In terms of performance and power ratio, DSP is 7.79 times over general CPU and 2.28 times over ARM, which indicates that DSP is a suitable platform for embedded artificial intelligence.

关键词： Deep Learning Embedded Artificial Intelligence Multi-core DSP Long Short-Term Memory Model Acceleration

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Emerge: Self-emerging Data Release using Cloud Data Storage 10

Emerge: Self-emerging Data Release using Cloud Data Storage

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10th IEEE international conference on Cloud computing (CLOUD)

作者： Li, Chao Palanisamy, Balaji Univ Pittsburgh Sch Informat Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781538619933

In the age of Big Data, advances in distributed technologies and cloud storage services provide highly efficient and cost-effective solutions to large scale data storage and management. Supporting self-emerging data using clouds is a challenging problem. While straight-forward centralized approaches provide a basic solution to the problem, unfortunately they are limited to a single point of trust. Supporting attack-resilient timed release of encrypted data stored in clouds requires new mechanisms for self emergence of data encryption keys that enables encrypted data to become accessible at a future point in time. Prior to the release time, the encryption key remains undiscovered and unavailable in a secure distributed system, making the private data unavailable. In this paper, we propose Emerge, a self-emerging timed data release protocol for securely hiding data encryption keys of private encrypted data in a large-scale distributed Hash Table (DHT) network that makes the data available and accessible only at the defined release time. We develop a suite of erasure-coding-based routing path construction schemes for securely storing and routing encryption keys in DHT networks that protect an adversary from inferring the encryption key prior to the release time (release-ahead attack) or from destroying the key altogether (drop attack). through extensive experimental evaluation, we demonstrate that the proposed schemes are resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks.

关键词： Encryption Receivers Protocols Cloud computing Routing distributed databases

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Insecurity of Cheng et al.'s efficient revocation in ciphertext-policy attribute-based encryption based cryptographic cloud storage 15

Insecurity of Cheng et al.'s efficient revocation in ciphert...

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15th IEEE international Symposium on parallel and distributed Processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Wang, Changji Wu, Jiayuan Yuan, Yuan Liu, Jing Guangdong Univ Foreign Studies Collaborat Innovat Ctr 21st Century Maritime Silk Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Sch Data Sci & Comp Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Foreign Studies Sch Finance Guangzhou 510006 Guangdong Peoples R China Yunnan Univ Natl Pilot Sch Software Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9781538637906

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising solution to the problem of fine-grained access control over encrypted data in the cloud. Several CP-ABE based cryptographic cloud storage systems have been proposed in recent years. However, access policy revocation is expensive in these systems, because data owner has to retrieve, re-encrypt and re-upload the data when access policy updates. To optimize the access policy revocation procedure, Cheng et al. proposed a revocation scheme for CP-ABE based cryptographic cloud storage. In their scheme, the original data is first divided into a number of slices, and then uploaded to the cloud storage. When a revocation occurs, the data owner needs only to retrieve, re-encrypt and re-upload one slice instead of the entire data. they claimed that their scheme is efficient and computationally secure. In this paper, we first point out Cheng et al.'s scheme only preserves all-or-nothing property for one time, then we show that it is susceptible to a fatal attack from the malicious revoked data user who stores the symmetric key or generates extra valid slices.

关键词： cloud storage ciphertext-policy attribute-based encryption access policy revocation information dispersal algorithm all-or-nothing transform

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A DAG Model of Synchronous Stochastic Gradient Descent in distributed Deep Learning

A DAG Model of Synchronous Stochastic Gradient Descent in Di...

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international conference on parallel and distributed Systems (ICPADS)

作者： Shaohuai Shi Qiang Wang Xiaowen Chu Bo Li Hong Kong Baptist University Hong Kong Baptist University Kowloon HK HK The Hong Kong University of Science and Technology

With huge amounts of training data, deep learning has made great breakthroughs in many artificial intelligence (AI) applications. However, such large-scale data sets present computational challenges, requiring training to be distributed on a cluster equipped with accelerators like GPUs. With the fast increase of G PU computing power, the data communications among GPUs have become a potential bottleneck on the overall training performance. In this paper, we first propose a general directed acyclic graph (DAG) model to describe the distributed synchronous stochastic gradient descent (S-SG D) algorithm, which has been widely used in distributed deep learning frameworks. To understand the practical impact of data communications on training performance, we conduct extensive empirical studies on four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet and TensorFlow) over multi-GPU and multi-node environments with different data communication techniques, including PCIe, NVLink, 10GbE, and InfiniBand. through both analytical and experimental studies, we identify the potential bottlenecks and overheads that could be further optimized. At last, we make the data set of our experimental traces publicly available, which could be used to support simulation-based studies.

关键词： Task analysis Computational modeling Training Graphics processing units Data models Deep learning Data communication

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