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Analysis and Evaluation of PHEV Development and Technical Routes of Major Manufacturers in China

Analysis and Evaluation of PHEV Development and Technical Ro...

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Power and Energy Engineering (ICPEE), international conference on

作者： Baodi Zhang Fuyu Yang Hewu Wang Hongren Zhao Xin Zhang School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China National International Science and Technology Cooperation Base Beijing Jiaotong University Beijing China State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (纸本)9781665420471

the evolution of PHEV architecture represents the development of the PHEV technical route. In order to explore the evolution process of typical PHEV technical routes in the market, the proportion of Chinese PHEV and the composition of the market segments is analyzed. the attitude of each manufacture towards the development of PHEV is clarified. the detailed PHEV architectures are classified based on different motor positions. the technical route trend of Chinese PHEV manufacturers is analyzed with the unified technical route classification standard. the technical route of each manufacturer is quantitatively evaluated. the fuel economy of each technical route is evaluated from the whole situation. the analysis results show that the number of technical iterations and application models of each manufacture are increasing. It is not obvious which technical route has the best fuel economy. On the whole, the serial-parallel architecture has better fuel economy.

关键词： Analytical models Biological system modeling Companies Fuel economy Market research Hybrid electric vehicles Vehicle dynamics

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the Challenge of Onboard SAR processing: A GPU Opportunity 20th

The Challenge of Onboard SAR Processing: A GPU Opportunity

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20th Annual international conference on Computational Science (ICCS)

作者： Romano, Diego Mele, Valeria Lapegna, Marco CNR Inst High Performance Comp & Networking ICAR Naples Italy Univ Naples Federico II Naples Italy

ISBN: (纸本)9783030504205;9783030504199

Data acquired by a Synthetic Aperture Radar (SAR), onboard a satellite or an airborne platform, must be processed to produce a visible image. For this reason, data must be transferred to the ground station and processed through a time/computing-consuming focusing algorithm. thanks to the advances in avionic technology, now GPUs are available for onboard processing, and an opportunity for SAR focusing opened. Due to the unavailability of avionic platforms for this research, we developed a GPU-parallel algorithm on commercial off-the-shelf graphics cards, and with the help of a proper scaling factor, we projected execution times for the case of an avionic GPU. We evaluated performance using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 on both NVIDIA Kepler and Turing architectures.

关键词： Onboard SAR focusing GPU-parallel Range-Doppler algorithm

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HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics 8

HeAT - a Distributed and GPU-accelerated Tensor Framework fo...

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8th IEEE international conference on Big Data (Big Data)

作者： Goetz, Markus Debus, Charlotte Coquelin, Daniel Krajsek, Kai Comito, Claudia Knechtges, Philipp Hagemeier, Bjorn Tarnawa, Michael Hanselmann, Simon Siggel, Martin Basermann, Achim Streit, Achim German Aerosp Ctr Inst Software Technol SC Cologne Germany Forschungszentrum Julich Inst Rio & Geosci Agrosphere IBG 3 Julich Germany Forschungszentrum Julich FZJ Julich Supercomp Ctr JSC Julich Germany Karlsruhe Inst Technol KIT Steinbuch Ctr Comp SCC Karlsruhe Germany

ISBN: (纸本)9781728162515

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce IleAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly I owering the bartier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.

关键词： IleAT Tensor Framework High-performance Computing PyTorch NumPy Message Passing Interface CPU Rig Data Analytics Machine Learning Dask Model parallelism parallel Application Frameworks

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Pandemic rumor identification on social networking sites: A case study of COVID-19 21

Pandemic rumor identification on social networking sites: A ...

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Proceedings of the 2021 5th international conference on Natural Language processing and Information Retrieval

作者： Mohsan Ali Iqbal Murtza Ayesha Ejaz National Center for Cybersecurity Air University Islamabad Pakistan Department of Creative Technologies Air University Islamabad Pakistan Department of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9781450387354

Digital Rumors, because of the ease and innovations in social networking technologies, has become an important issue. these rumors become a critical issue in a disaster, epidemic, or pandemic. Considering classification power of conventional and deep learning techniques, we propose a hybrid learning technique that identifies rumors effectively. For this, TF-IDF description has been used to build a stack of multiple conventional learning techniques; logistic regression, Naïve Bayes, and random forest. Whereas, word-embedding features have been used for purpose of deep learning; LSTM and LSTM-RNN. the combination of LSTM and RNN makes this study unique in the field of rumor detection. With LSTM and RNN gated architectures, huge series rumor tweets may be efficiently managed. To aggregate the decisions, the labels of deep learning and the stack of conventional learning have been combined using majority voting based ensemble classification. To evaluate the performance of the proposed technique, we used publically available standard COVID-19 RUMOR dataset. the proposed technique obtains 99.02% accuracy, which shows its effectiveness. the dataset utilized and the ensemble model created for rumor identification distinguish our work from existing methods.

关键词： Covid-19 Deep Learning Ensemble Learning Long-short term memory Machine learning Natural language processing

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A GPU-based jde algorithm applied to continuous unconstrained optimization 18th

A GPU-based jde algorithm applied to continuous unconstraine...

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Joint conferences on 18th international conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018

作者： Boiani, Mateus Dominico, Gabriel Stubs Parpinelli, Rafael Santa Catarina State University JoinvilleSC Brazil

ISBN: (纸本)9783030166564

Population-based search algorithms, such as the Differential Evolution approach, evolve a pool of candidate solutions during the optimization process and are suitable for massively parallel architectures promoted by the use of GPUs. Hence, this paper proposes a GPU-based self-adaptive Differential Evolution employing the jDE mechanism to control its parameters, named cujDE. Two CUDA structures are employed to model the kernel functions in cujDE. the proposed algorithm is compared with another GPU-based self-adaptive DE (cuSaDE) in four continuous unconstrained benchmark functions. Also, the speedup between the CPU-based and the GPU-based jDE is measured. Results obtained suggest that the proposed approach is well suited to and competitive for continuous optimization. © Springer Nature Switzerland AG 2020.

关键词： Graphics processing unit

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Enabling Blind Area Coverage for the Smart Grids: Integrating Energy-efficient LoRa Technologies in the 5G

Enabling Blind Area Coverage for the Smart Grids: Integratin...

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6th international conference on Intelligent Computing and Signal processing (ICSP)

作者： Jianliang Zhang Yang Li Junwei Ma Honglin Xue Jian Wu Min Zhao Chao Han Xiaoyan Dang Sheng Bi Information Communication Branch Company Shanxi Electric Power Company Taiyuan China

ISBN: (纸本)9781665447300

the convergence of the Internet of things (IoT) and 5G will open a range of innovations for the deployment of enhanced sensing and novel applications, controlling and interactive systems. the current smart grid (SG) will be more reliable, secure, flexible and durable by implementing 5GIoT monitoring. However, 5GIoT, is inefficiently and costly to travel over large areas and penetrate physical structures. As a complement, the proposed 5G1oRa converged network can offer a low-cost and large scale of coverage without blind areas. there are notable concerns regarding certain energy conservation issues to be overcome in order to achieve a successful integration of multi-hop LoRa systems within 5G architectures. Based on the Charnes-Cooper transform and Lagrange Multiplier iteration algorithm, a non-convex relaxation optimization is proposed to allocate transmit power for multi-hop LoRa to further maximize the system energy efficiency. the solution has been deployed, implemented and validated in a real and integrated 5GLoRa testbed, showing its feasibility to meet the requirements of SG data collection, transmission, cloud storage, and calculation in a wide area. Simulation results also validate the efficiency of our proposed model, which significantly outperforms other benchmark algorithms in terms of energy efficiency and QoS requirements.

关键词： 5G mobile communication Simulation Signal processing algorithms Transforms Energy efficiency Smart grids Resource management

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parallel Solution to LIS Using Divide-and-Conquer Approach 5th

Parallel Solution to LIS Using Divide-and-Conquer Approach

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5th international conference on Recent Developments in Science, Engineering and Technology, REDSET 2019

作者： Rani, Seema Rajpoot, Dharamveer Singh Jaypee Institute of Information Technology Noida India Dronacharya Government College Gurugram India Computer Science and Engineering Department Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9789811558269

this paper presents parallel solution to the LIS problem. the presented approach works by dividing the problem into smaller sub problems and then combining their solutions. the individual sub problems are handled in parallel by multiple processors. the solution produces optimal result in O(n log n) time using n/2 processors. the time taken by a single processor to implement this approach is O(n/2 (log2 n)2). © Springer Nature Singapore Pte Ltd. 2020.

关键词： parallel processing systems

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Power Quality Enhancement in AC Microgrids Using Integrated Genetic Algorithm in ANN

Power Quality Enhancement in AC Microgrids Using Integrated ...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M. Vijay Karthik Priyanka Chaudhary Noida International University Greater Noida India

ISBN: (纸本)9781665435253

this article presents the detailed analysis of power quality improvement in microgrids by using the multiple schemes. the microgrid contains the various renewable energy sources, and they are interconnected with the power electronic converters. these converters are faced the many issues with respect to the reactive inrush currents and harmonic voltages. these currents also resulted in voltage drop across the line impedances. the voltage harmonics and fluctuations can generate the malfunctioning, overheating and equipment tripping issues. the microgrid stability mainly depends upon the optimization method and compensation of various micro grid units. this article mainly focused on development of smart grid system by combining the two individual microgrids using electronically coupled distributed energy resources (EC-DER) inverter and smart impedance (SI) converter. thus, the optimal power flow was achieved based on the each individual microgrid available power. the two “parallel optimization” methods are used to optimize various power quality challenges such as operation mode transfer transients, harmonic load sharing and parallel control of current and voltage quality. thus, artificial neural network (ANN) approach is used for improving EC-DER performance and genetic algorithm (GA) is used for improving SI performance. the stability of the system is achieved by operating the ANN-GA in parallel processing manner. thus, two microgrids develops the individual powers and optimal power flow is generated in smart grid. the simulation results shows that the proposed ANN-GA controller results the reduced total harmonic distortion (thD) and fault setting time in microgrids as compared to the conventional model predictive control (MPC) approach.

关键词： Power quality Microgrids Artificial neural networks Power system stability parallel processing Harmonic analysis Stability analysis

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A Dynamic Acceleration Method for Remote Sensing Image processing Based on CUDA 12th

A Dynamic Acceleration Method for Remote Sensing Image Proce...

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12th EAI international conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Zuo, Xianyu Zhang, Zhe Qiao, Baojun Tian, Junfeng Zhou, Liming Zhang, Yunzhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Laboratory of Spatial Information Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China National Cultural Heritage Administration Beijing100010 China

ISBN: (纸本)9783030727918

the incredible increase in the volume of remote sensing data has made the concept of Remote Sensing as Big Data reality with recent technological developments. Remote sensing image processing is characterized with features of massive data processing and intensive computation, which makes the processes difficult. To optimize the remote sensing image processing for GPU, compute unified device architecture (CUDA) is widely used to implement remote sensing algorithms. However, the usage of GPU in remote sensing image processing has been constrained by the complexity of its implementation and configuration. therefore, how to take fully advantage of the parallel organization of GPU architecture is awfully challenging. In this paper, a dynamic adaptive acceleration (DAA) method is proposed to determine calculation parameters of GPU adaptively and preprocess the input remote sensing images on host dynamically. By this method, we determine calculation parameters according to the hardware parameters of GPU firstly. And then, the input remote sensing images are reconstructed based on the calculation parameters. Finally, the preprocessed image blocks are arranged to stream tasks and executed on GPU respectively. Effectiveness of the proposed DAA method in accelerate remote sensing algorithm with point operations were verified by experiments in this paper, and the experimental results indicated that the DAA method can obtain better performance than traditional methods. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Graphics processing unit

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Highly parallel SPARQL Engine for RDF 6th

Highly Parallel SPARQL Engine for RDF

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6th international conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Feng, Fan Zhou, Weikang Zhang, Ding Pang, Jinhui Tianjin University Tianjin China Beijing Institute of Technology Beijing China

ISBN: (纸本)9789811579806

In this paper, a highly parallel batch processing engine is designed for SPARQL queries. Machine learning algorithms were applied to make time predictions of queries and reasonably group them, and further make reasonable estimates of the memory footprint of the queries to arrange the order of each group of queries. Finally, the query is processed in parallel by introducing pthreads. Based on the above three points, a spall time prediction algorithm was proposed, including data processing, to better deal with batch SPARQL queries, and the introduction of pthread can make our query processing faster. Since data processing was added to query time prediction, the method can be implemented in any set of data-queries. Experiments show that the engine can optimize time and maximize the use of memory when processing batch SPARQL queries. © 2020, the Author(s).

关键词： Forecasting

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