An FPGA Implementation of Finite Physical Quantity Neural Network

2003 ◽  
Vol 15 (2) ◽  
pp. 136-142
Author(s):  
Takashi Sotohebo ◽  
◽  
Minoru Watanabe ◽  
Funtinori Kobayashi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Image Recognition ◽  
Physical Quantity ◽  
Neural Network Model ◽  
Field Programmable Gate Array ◽  
High Speed ◽  
Density Field ◽  
Experimental Results ◽  
Field Programmable

We propose installing a finite physical quantity neural network model on a high-density field programmable gate array (FPGA) at high speed by reducing multipliers. We could thereby downsize circuits without loss of precision. We evaluated its installation and experimental results for image recognition.

scholarly journals Computer Assisted Chord Detection Using Deep Learning and YOLOV4 Neural Network Model

2021 ◽  
Vol 2083 (4) ◽  
pp. 042017
Author(s):  
Yingdong Ru
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Training Model ◽  
Experimental Results ◽  
Fine Tuning ◽  
Computer Assisted ◽  
Single Voice ◽  
Music Information ◽  
Chord Recognition

Abstract Music symbol recognition is an important part of Optical Music Recognition (OMR), Chord recognition is one of the most important research contents in the field of music information retrieval. It plays an important role in information processing, music structure analysis, and recommendation systems. Aiming at the problem of low chord recognition accuracy in the OMR recognition model, the article proposes a chord recognition method based on the YOLOV4 neural network model. First, the YOLOV4 network model is used to train single-voice scores to obtain the best training model. Then, the scores containing chords are trained through neural network fine-tuning technology. The experimental results show that the method recognizes the chords with great results, the model was tested on the test set generated by MuseScore. The experimental results show that the accuracy of note recognition is high, which can reach the accuracy of duration value of 0.96 which is higher than the accuracy of note recognition of other score recognition models.

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.

A quantum Hopfield neural network model and image recognition

2020 ◽  
Vol 17 (4) ◽  
pp. 045201 ◽  
Author(s):  
Ge Liu ◽  
Wen-Ping Ma ◽  
Hao Cao ◽  
Liang-Dong Lyu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Image Recognition ◽  
Neural Network Model ◽  
Hopfield Neural Network

A field-programmable gate array system for sonar image recognition based on convolutional neural network

2020 ◽  
pp. 095965182093934
Author(s):  
Chong Wang ◽  
Yu Jiang ◽  
Kai Wang ◽  
Fenglin Wei
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Field Programmable Gate Array ◽  
Oil And Gas ◽  
Sonar Image ◽  
Field Programmable ◽  
Sonar Images ◽  
Gate Array

Subsea pipeline is the safest, most reliable, and most economical way to transport oil and gas from an offshore platform to an onshore terminal. However, the pipelines may rupture under the harsh working environment, causing oil and gas leakage. This calls for a proper device and method to detect the state of subsea pipelines in a timely and precise manner. The autonomous underwater vehicle carrying side-scan sonar offers a desirable way for target detection in the complex environment under the sea. As a result, this article combines the field-programmable gate array, featuring high throughput, low energy consumption and a high degree of parallelism, and the convolutional neural network into a sonar image recognition system. First, a training set was constructed by screening and splitting the sonar images collected by sensors, and labeled one by one. Next, the convolutional neural network model was trained by the set on the workstation platform. The trained model was integrated into the field-programmable gate array system and applied to recognize actual datasets. The recognition results were compared with those of the workstation platform. The comparison shows that the computational precision of the designed field-programmable gate array system based on convolutional neural network is equivalent to that of the workstation platform; however, the recognition time of the designed system can be saved by more than 77%, and its energy consumption can also be saved by more than 96.67%. Therefore, our system basically satisfies our demand for energy-efficient, real-time, and accurate recognition of sonar images.

scholarly journals Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1662
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
High Speed ◽  
Prediction Method ◽  
High Speed Train ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

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%. 

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