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SSRN 2023年

作者： Zhang, Zhi Ding, Xiang He, Xinyou Gong, Yang Xiao, Biaobiao Shen, Jixue Ou, Xing Hunan Provincial Key Laboratory of Water Treatment Functional Materials Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraf College of Chemistry and Materials Engineering Hunan University of Arts and Science Changde415000 China Engineering Research Center of the Ministry of Education for Advanced Battery Materials School of Metallurgy and Environment Central South University Changsha410083 China Institute of Advanced Battery Materials and Devices Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China Tianmu Lake Institute of Advanced Energy Storage Technologies Co. Ltd Liyang213300 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China

Developing high-voltage (≥4.3 V vs Li/Li+) single-crystal Ni-rich LiNixCoyMn1-x-yO2 offers an enticing strategy to achieve high energy density for lithium-ion batteries. However, at high-voltage operation, the cathode will be vulnerable to induce the intrinsic Oα- (α 0.6Co0.1Mn0.3O2 (SC-NCM) cathode materials by introducing the surface Se element, exhibiting an "anti-aging" effect of inhibiting the escape of oxygen on SC-NCM particles during the ultra-high voltage (4.7 V) cycling. Both theoretical calculation and experimental results confirm that the outside-in nanostructure would substitute some mobile Oα- at the surface of highly charged SC-NCM particle, stabilizing the oxygen lattice and suppressing O2 release during long-term cycling. Meanwhile, the surface modification of thin Se layer will alleviate the parasitic reactions and improve the electronic conductivity. Under the synergistic strategy of surface modification and interface doping, the obtained SC-NCM exhibits boosted cyclic stability in coin half-cell and pouch full-cell simultaneously. Therefore, the reversible capacity of LiNi0.6Co0.1Mn0.3O2 at high voltage is competitive with comercial LiNixCoyMn1-x-yO2 (x ≥ 0.8), demonstrating more superior safety ability and cyclic property. It provides an effective approach for improving the long-term performance of Ni-rich cathode materials for practical application under ultra-high working voltage. © 2023, The Authors. All rights reserved.

关键词： Single crystals

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffi...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xin Wang Shuhui Chen Jinshu Su College of Computer Science and Technology National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

关键词： Cryptography Feature extraction Payloads Recurrent neural networks Machine learning Convolutional neural networks

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Research on Fine-Grained Sentiment Classification 8th

Research on Fine-Grained Sentiment Classification

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8th CCF International Conference on Natural Language processing and Chinese Computing, NLPCC 2019

作者： Wang, Zhihui Wang, Xiaodong Chang, Tao Lv, Shaohe Guo, Xiaoting National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

ISBN: (纸本)9783030322359

Aiming at the fine-grained sentiment classification that distinguishes the emotional intensity, the commonly used dataset SST-1 is analyzed in depth. Through the analysis, it is found that the dataset has serious problems such as data imbalance and small overall scale, which seriously restricts the classification effect. In order to solve the related problems, data augmentation method is adopted to realize the optimization of the dataset. The IMDB and other data which are relatively homologous to the original dataset are annotated, and the focus is to expand the categories with fewer numbers. By this way, the problem of data imbalance is effectively alleviated and the original data scale is expanded. Then, based on the Bidirectional Encoder Representations from Transformers (BERT) model, which has good overall performance on natural language processing, the benchmark classification model is built. Through multiple comparison experiments on the original dataset and the enhanced data, the influence of the deficiency of the original dataset on the classification effect is verified. And, it is fully demonstrated that the enhanced data can effectively improve the test results and solve the problem of large differences in performance between different categories well. © 2019, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Optimizing One by One Direct Convolution on ARMv8 Multi-core CPUs

Optimizing One by One Direct Convolution on ARMv8 Multi-core...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Qinglin Wang Dongsheng Li Songzhu Mei Siqi Shen Xiandong Huang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

Convolutional layers are ubiquitous in a variety of deep neural networks. Due to the lower computation complexity and the smaller number of parameters, convolutions with small filter sizes are often used, such as one by one convolution. Nevertheless, these small convolution operations are still time-consuming. A common approach to implementing convolutions is to transform them into matrix multiplications, known as GEMM-based convolutions. The approach maybe incurs additional memory overhead and calls matrix multiplication routines, which are not optimized for matrices generated by convolutions. In this paper, we present a new parallel one by one direct convolution implementation on ARMv8 multi-core CPUs, which doesn't incur any additional memory space requirement. Our implementation is verified on two ARMv8 CPUs, Phytium FT-1500A and FT-2000plus. In terms of performance and scalability, our implementation is better than GEMM-based implementations in all the tests on Phytium FT-1500A. On Phytium FT-2000plus, our approach gives much better performance and scalability than GEMM-based approaches in most cases.

关键词： Convolution Registers Layout Neural networks Parallel processing Computer architecture Tensile stress

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features

Research on Integrated Detection of SQL Injection Behavior B...

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作者： Ming Li Bo Liu Guangsheng Xing Xiaodong Wang Zhihui Wang College of Intelligence Science and Technology National University of Defence Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users' data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile,the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved.

关键词： SQL injection Integrated method SQL-LSTM Traffic features

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ME-TCAM: Memory-Efficient Ternary Content Addressable Memory Based on Multipumping-Enabled LUTRAM on FPGA

ME-TCAM: Memory-Efficient Ternary Content Addressable Memory...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Zilin Shi Hui Yang Rulin Liu Jinli Yan Peng Qiao Baosheng Wang College of Computer Science and Technology National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China

Static random access memory (SRAM) on field programmable gate arrays (FPGAs) can be emulated to offer ternary content addressable memory (TCAM) functionality. However, SRAM-based TCAM wastes storage resources. This is due to the limited capacity of the physical addresses in the SRAM unit. This work proposes a LUTRAM-based TACM scheme on the FPGA called Memory-Efficient TCAM (ME-TCAM). METCAM divides SRAM unit into multiple virtual blocks mapping to a portion of the TCAM table to store the more address information of the TCAM table. Operation on SRAM block means that increasing the overall emulated TCAM bits/SRAM. Moreover, ME-TCAM exploits Xilinx primitives to conFigure lookup tables (LUTs) as 32 × 2 lookup table RAMs (LUTRAMs). We implement ME-TCAM using LUTRAM with a size of 512 × 48 and 1024 × 144 on a Virtex-7 FPGA device. Compared with the state-of-the-art research DUR, ME-TCAM achieves at least 2.6 times more memory efficiency.

关键词： Human computer interaction Associative memory Memory management Random access memory Logic gates Table lookup Parallel architectures

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Deep Learning for Social Network Information Cascade Analysis: a survey

Deep Learning for Social Network Information Cascade Analysi...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Liqun Gao Bin Zhou Yan Jia Hongkui Tu Ye Wang Chenguang Chen Haiyang Wang Hongwu Zhuang College of Computer National University of Defense Technology ChangSha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology ChangSha China

The following topics are dealt with: learning (artificial intelligence); text analysis; natural language processing; pattern classification; graph theory; data mining; security of data; social networking (online); fea...

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Zhenpeng Xu Xinhai Chen Lihua Chi Jie Liu Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.

关键词： Convolution Computational fluid dynamics Convolutional neural networks Jacobian matrices Artificial intelligence distributed processing Software engineering

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International Conference on Computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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