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Application of Multimodal Information Fusion Technology and System Construction for Vocational Education

Application of Multimodal Information Fusion Technology and ...

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Information Science and Education (ICISE-IE), international conference on

作者： Shuping Yuan Dongying Vocational Institute Dongying

ISBN: (数字)9798331506766

ISBN: (纸本)9798331506773

Modern vocational education poses new requirements for teaching models and technical support. this study addresses the challenges of processing and integrating multimodal data in vocational education by designing and implementing a multimodal information fusion teaching system. the system uses an improved SIFT algorithm and MFCC feature extraction methods for data processing, integrates features and recognition based on the Transformer architecture, and constructs a teaching platform through a microservice architecture. Experiments show that the system excels in skill action recognition, real-time interaction performance, and system stability, providing technical support for the advancement of vocational education informatization.

关键词： Deep learning Education Microservice architectures Feature extraction Transformers Data processing Stability analysis Real-time systems Time factors Mel frequency cepstral coefficient

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DCNN-Based Vegetable Image Classification Using Transfer Learning: A Comparative Study 5

DCNN-Based Vegetable Image Classification Using Transfer Lea...

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5th international conference on Computer, Communication, and Signal processing, ICCCSP 2021

作者： Ahmed, M. Israk Mahmud Mamun, Shahriyar Zaman Asif, Asif Uz Daffodil International University Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9781665432771

In this paper, an attempt is addressed towards accurate vegetable image classification. A dataset consisting of 21,000 images of 15 classes is used for this classification. Convolutional neural network, a deep learning algorithm is the most efficient tool in the machine learning field for classification problems. But CNN requires large datasets so that it performs well in natural image classification problems. Here, we conduct an experiment on the performance of CNN for vegetable image classification by developing a CNN model from the ground. Additionally, several pre-trained CNN architectures using transfer learning are employed to compare the accuracy with the typical CNN. this work proposes the study between such typical CNN and its architectures(VGG16, MobileNet, InceptionV3, ResNet etc.) to build up which technique would work best regarding accuracy and effectiveness with new image datasets. Experimental results are presented for all the proposed architectures of CNN. Besides, a comparative study is done between developed CNN models and pre-trained CNN architectures. And the study shows that by utilizing previous information gained from related large-scale work, the transfer learning technique can achieve better classification results over traditional CNN with a small dataset. And one more enrichment in this paper is that we build up a vegetable images dataset of 15 categories consisting of a total of 21,000 images. © 2021 IEEE.

关键词： Image classification

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Performance Comparison of Various YOLO architectures on Object Detection of UAV Images 8

Performance Comparison of Various YOLO Architectures on Obje...

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8th IEEE international conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2022

作者： Gunawan, Teddy Surya Ismail, Islam Mohamed Mahmoud Kartiwi, Mira Ismail, Nanang International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40614 Indonesia

ISBN: (数字)9781665488006

ISBN: (纸本)9781665488006

Today, the rapid development of deep learning offers an extraordinary opportunity to enhance the performance and efficiency of various industries, including business, the military, medicine, and transportation. Using deep learning algorithms in the transportation industry, for instance, makes UAVs vital and efficient in this industry. Current Unmanned Aerial Vehicles (UAVs) applications in transportation systems encourage the development of object detection methods to collect real-time traffic data using UAVs. Due to the versatility and portability of UAVs, particularly drones, individuals require systems that operate with UAVs to identify objects in real-time for military, safety observation, and protection. the culmination of the evolution of computer vision technology is the development of sophisticated algorithms centered on extensive training and testing datasets. this research aims to compare the performance of object detection of UAV images using various YOLO architectures. Tiny YOLOv3 and YOLOv5s models were implemented to extract the object's features and classify them into the dataset's multiple classes. this paper selected the VisDrone2019 dataset for its various object classes: pedestrian, person, bicycle, car, van, truck, tricycle, awning-tricycle, bus, and motor. Results demonstrated that YOLOv5s have acceptable precision and processing speed. © 2022 IEEE.

关键词： Drones

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Research on Computer Neural Network processing Chip in Data Heterogeneous processing System

Research on Computer Neural Network Processing Chip in Data ...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Xie Binming Zheng Lei NARI Group Corporation Information System Integration Company Nanjing China

Nonlinear frequency modulation scaling algorithm can effectively solve the problem of strongly coupled echo, but sequential NFM signal scaling method is difficult to meet the real-time imaging requirements. In this project, the nonlinear frequency modulation scaling method based on subaperture is used to improve the computational efficiency of the algorithm. the real-time nonlinear frequency modulation scale-up imaging algorithm is implemented in parallel on a heterogeneous multi-core prototype chip. Compared with the traditional serial method, this method has higher parallel speed and operation speed. Compared with the traditional single subaperture method, the real-time efficiency of this method is as high as 90.06%.

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Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs 30

Precision Batching: Bitserial Decomposition for Efficient Ne...

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30th international conference on parallel architectures and Compilation Techniques (PACT)

作者： Lam, Maximilian Yedidia, Zachary Banbury, Colby R. Reddi, Vijay Janapa Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781665442787

We present PrecisionBatching, a quantized inference algorithm for speeding up neural network inference on traditional hardware platforms at low bitwidths. PrecisionBatching is based on the following insights: 1) neural network inference with low batch sizes on traditional hardware architectures (e.g: GPUs) is memory bound, 2) activation precision is critical to improving quantized model quality and 3) matrix-vector multiplication can be decomposed into binary matrix-matrix multiplications, enabling quantized inference with higher precision activations at the cost of more arithmetic operations. Combining these three insights, PrecisionBatching enables inference at extreme quantization levels (< 8 bits) by shifting a memory bound problem to a compute bound problem and achieves higher compute efficiency and runtime speedup at fixed accuracy thresholds against standard quantized inference methods. Across a variety of applications (MNIST, language modeling, natural language inference, reinforcement learning) and neural network architectures (fully connected, RNN, LSTM), PrecisionBatching yields end-to-end speedups of over 8x on a GPU within a < 1 - 5% error margin of the full precision baseline, outperforming traditional 8-bit quantized inference by over 1.5 x -2x at the same error tolerance.

关键词： Recurrent neural networks Runtime Computational modeling Natural languages Graphics processing units Reinforcement learning Hardware

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Distributed Exact Structural Clustering on Large Graph

Distributed Exact Structural Clustering on Large Graph

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international conference on parallel and Distributed Systems (ICPADS)

作者： Jinyu Zhou Chuitian Rong Ding Liu Zhengyi Chai Tiangong University Tianjin China

Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. the existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. the graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. the distributed processing algorithm is called for processing large graphs. this work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.

关键词： Distributed processing Costs Social networking (online) Scalability Clustering algorithms Focusing Cyberspace

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architectures for processing Large Data Arrays in Distributed Systems Based on Open Source Frameworks

Architectures for Processing Large Data Arrays in Distribute...

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international conference on Problems of Cybernetics and Informatics (PCI)

作者： Olga Blinova Oleg Kuprikov Daniil Komanich Ekaterina Gorina Laboratory No. 17 Automated Queuing Systems and Signal Processing Institute of Control Sciences V.A. Trapeznikov Academy of Sciences Moscow Russia

Currently, relational and object-relational databases have become the de facto standard for organizing data storage in most information systems. But such structures are not suitable for volumes of information that cannot be processed by the capacity of a single server. To process such big data, databases with a key-value structure are used. the article discusses the main problems that arise when creating distributed big data repositories and modern technologies that allow these problems to be leveled. there is no single universal solution related to the storage and processing of big data. When choosing technologies and implementing storage, it is important to consider together the tasks that require solutions, technical capabilities, types and regularity of necessary treatments, data features, for example, the frequency of their updates.

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Design and Simulation of a Photovoltaic Inverter parallel Microgrid

Design and Simulation of a Photovoltaic Inverter Parallel Mi...

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Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Shan Gu Hanye Shi Ke Chen Fengqin Jiang Zhejiang University of Water Resources and Electric Power Hangzhou China

Microgrid technology based on photovoltaic distributed power generation is becoming more and more mature. With the rapid development of clean energy in China, its application will be more extensive. the control of parallel operation of inverters is very important to the stable operation of microgrid and the circulation control is the key to ensure the reliable operation of parallel operation of inverters. the working principle of three-phase photovoltaic inverter was analyzed in this paper. A master-slave control mode was proposed to control circulation of the parallel inverter system. the voltage and current double closed-loop control parameters was analyzed. the microgrid system was simulated by PSIM software. the simulation results show that the system circulation decreases from the maximum value of 0.16A to 1.4×10 -8 A after the control strategy is added. the voltage and current of three-phase output basically reach the requirement of balance. the control effect of the system circulation is obvious, and the simulation results meet the design requirements.

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Scalable parallel Static Learning 5

Scalable Parallel Static Learning

引用

5th IEEE international Test conference in Asia (ITC-Asia)

作者： Lin, Xiaoze Lai, Liyang Li, Huawei Shantou Univ Guangdong Prov Key Lab Digital Signal & Image Pro Elect Engn Shantou Guangdong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9781665413343

Static learning is a learning algorithm for finding additional implicit implications between gates in a netlist. In automatic test pattern generation (ATPG) the learned implications help recognize conflicts and redundancies early, and thus greatly improve the performance of ATPG. though ATPG can further benefit from multiple runs of incremental or dynamic learning, it is only feasible when the learning process is fast enough. In the paper, we study speeding up static learning through parallelization on heterogeneous computing platform, which includes multi-core microprocessors (CPUs), and graphics processing units (GPUs). We discuss the advantages and limitations in each of these architectures. With their specific features in mind, we propose two different parallelization strategies that are tailored to multi-core CPUs and GPUs. Speedup and performance scalability of the two proposed parallel algorithms are analyzed. As far as we know, this is the first time that parallel static learning is studied in the literature.

关键词： static learning parallel acceleration GPU multicore CPU

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LSTM-Based Encoder–Decoder Attention Model for Text Translation and Simplification on the Constitution of India 5th

LSTM-Based Encoder–Decoder Attention Model for Text Transla...

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5th international conference on Recent Innovations in Computing , ICRIC 2022

作者： Navlakha, Meith Lobo, Russel Bhargava, Rishabh Karani, Ruhina B. Department of Computer Engineering Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9789819906000

Natural language processing techniques can be used on judicial and legislative documents like the Constitution for making it more accessible to the general audience. Various approaches such as Natural Machine Translation (NMT), text simplification of complex sentences can be performed on the Constitution. the model proposed in this paper can be used for the purpose of translation and simplification of the Constitution of India. the model is a LSTM variant forming an encoder–decoder network integrated with an attention layer and is trained using teacher forcing method. the data set used for the task of translation consists of parallel Corpus from IIT Bombay English–Hindi concatenated with our own curated data set of 300 English articles of the Constitution of India translated to Hindi, whereas for simplification task it consists of our own curated data set of complex 300 English articles of the Constitution of India translated to simplified sentence. the proposed model for translation has a BLEU score of 15.34, and the research has elaborated on the performance analysis of the generated outputs and the BLEU score. For simplification, the results show some inconsistencies which can be improved by increasing the data set for simplification task. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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