scholarly journals Exploiting the Relationship between Pruning Ratio and Compression Effect for Neural Network Model Based on TensorFlow

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Bo Liu ◽  
Qilin Wu ◽  
Yiwen Zhang ◽  
Qian Cao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Computing Time ◽  
Experimental Results ◽  
Data Set ◽  
Compression Effect ◽  
Model Based ◽  
The Relationship

Pruning is a method of compressing the size of a neural network model, which affects the accuracy and computing time when the model makes a prediction. In this paper, the hypothesis that the pruning proportion is positively correlated with the compression scale of the model but not with the prediction accuracy and calculation time is put forward. For testing the hypothesis, a group of experiments are designed, and MNIST is used as the data set to train a neural network model based on TensorFlow. Based on this model, pruning experiments are carried out to investigate the relationship between pruning proportion and compression effect. For comparison, six different pruning proportions are set, and the experimental results confirm the above hypothesis.

Phát hiện mã độc IoT botnet dựa trên đồ thị PSI với mô hình Skip-gram

2020 ◽  
Vol 7 (1) ◽  
pp. 29-36
Author(s):  
Ngô Quốc Dũng ◽  
Lê Văn Hoàng ◽  
Nguyễn Huy Trung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Data Set ◽  
Iot Devices

 Tóm tắt— Trong bài báo này, nhóm tác giả đề xuất một phương pháp phát hiện mã độc IoT botnet dựa trên đồ thị PSI (Printable String Information)  sử dụng mạng nơ-ron tích chập (Convolutional Neural Network - CNN). Thông qua việc phân tích đặc tính của Botnet trên các thiết bị IoT, phương pháp đề xuất xây dựng đồ thị để thể hiện các mối liên kết giữa các PSI, làm đầu vào cho mô hình mạng nơ-ron CNN phân lớp. Kết quả thực nghiệm trên bộ dữ liệu 10033 tập tin ELF gồm 4002 mẫu mã độc IoT botnet và 6031 tập tin lành tính cho thấy phương pháp đề xuất đạt độ chính xác (accuracy) và độ đo F1 lên tới 98,1%. Abstract— In this paper, the authors propose a method for detecting IoT botnet malware based on PSI graphs using Convolutional Neural Network (CNN). Through analyzing the characteristics of Botnet on IoT devices, the proposed method construct the graph to show the relations between PSIs, as input for the CNN neural network model. Experimental results on the 10033 data set of ELF files including 4002 IoT botnet malware samples and 6031 benign files show Accuracy and F1-score up to 98.1%. 

A Neural Network Model for Simulating Cloth Texture Deformation

2012 ◽  
Vol 157-158 ◽  
pp. 1608-1613
Author(s):  
Zhi Hong Zhao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Experimental Results ◽  
Curved Surface ◽  
Topological Structures ◽  
The Relationship ◽  
Smooth Texture

This study describes a method to simulate cloth texture deformation using a neural network model. The cloth texture may be represented by its texture colors, positions and its topological structures. In addition, the relationship between the texture colors can be deduced based on the smooth texture and the two and three dimensional texture deformation are correspondingly concerned. A multilayered single direction neural network model is adopted to numerically represent the cloth texture for the purpose of speeding up the simulation. The color values of the points on the cloth deformed curved surface can be calculated with such neural network model. The experimental results show that such method is efficient and executable for the regularized texture deformation.

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

Digital twin of the chip formation process

2020 ◽  
pp. 81-86
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
A.M. Kuz'mishina
Keyword(s):  
Neural Network ◽  
Control System ◽  
Crystal Lattice ◽  
Network Model ◽  
Intelligent Control ◽  
Neural Network Model ◽  
Formation Process ◽  
Chip Formation ◽  
Intelligent Control System ◽  
The Relationship

The formation of chips during the processing of various materials was studied. The relationship between the type of chips, the type of crystal lattice of the material and the number of sliding systems is shown. A neural network model of chip formation is developed, which allows predicting the type of chips. An intelligent control system for the process of chip formation during cutting is proposed. Keywords: chip formation, crystal lattice, neural network model, type of chips. [email protected]

Sign in / Sign up

Export Citation Format

Share Document