Theoretical and Experimental Results for Planning with Learned Binarized Neural Network Transition Models

Sorption thermal heat storage is a promising solution to improve the development of renewable energies and to promote a rational use of energy both for industry and households. These systems store thermal energy through physico-chemical sorption/desorption reactions that are also termed hydration/dehydration. Their introduction to the market requires to assess their energy performances, usually analysed by numerical simulation of the overall system. To address this, physical models are commonly developed and used. However, simulation based on such models are time-consuming which does not allow their use for yearly simulations. Artificial neural network (ANN)-based models, which are known for their computational efficiency, may overcome this issue. Therefore, the main objective of this study is to investigate the use of an ANN model to simulate a sorption heat storage system, instead of using a physical model. The neural network is trained using experimental results in order to evaluate this approach on actual systems. By using a recurrent neural network (RNN) and the Deep Learning Toolbox in MATLAB, a good accuracy is reached, and the predicted results are close to the experimental results. The root mean squared error for the prediction of the temperature difference during the thermal energy storage process is less than 3K for both hydration and dehydration, the maximal temperature difference being, respectively, about 90K and 40K.

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Research of Personalized Recommendation Technology Based on Knowledge Graphs

Applied Sciences ◽

10.3390/app11157104 ◽

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.

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Simulation of ATPG neural network and its experimental results

Journal of Computer Science and Technology ◽

10.1007/bf02943500 ◽

1995 ◽

Vol 10 (4) ◽

pp. 310-324

Author(s):

Zhong Zhang

Keyword(s):

Neural Network ◽

Experimental Results

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Recognition of 3D Objects from 2D Views Features

Journal of Electronic Commerce in Organizations ◽

10.4018/jeco.2015040105 ◽

2015 ◽

Vol 13 (2) ◽

pp. 50-58

Author(s):

R. Khadim ◽

R. El Ayachi ◽

Mohamed Fakir

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Color Image ◽

Recognition Rate ◽

Experimental Results ◽

Support Vector ◽

K Nearest Neighbor ◽

3D Objects ◽

Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

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Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i4.pp328-341 ◽

2019 ◽

Vol 8 (4) ◽

pp. 328

Author(s):

Sachin B. Jadhav

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Plant Diseases ◽

Experimental Results ◽

Disease Classification ◽

Soybean Leaves ◽

Soybean Diseases ◽

Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

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Nucleosome Positioning with Set of Key Positions and Nucleosome Affinity

The Open Biomedical Engineering Journal ◽

10.2174/1874120701408010166 ◽

2014 ◽

Vol 8 (1) ◽

pp. 166-170 ◽

Cited By ~ 1

Author(s):

Jia Wang ◽

Shuai Liu ◽

Weina Fu

Keyword(s):

Neural Network ◽

Experimental Data ◽

Dna Sequence ◽

Dna Sequences ◽

Nucleosome Positioning ◽

Experimental Results ◽

Negative Effects ◽

Sequence Structure ◽

Precise Positioning ◽

Histone Octamer

The formation and precise positioning of nucleosome in chromatin occupies a very important role in studying life process. Today, there are many researchers who discovered that the positioning where the location of a DNA sequence fragment wraps around a histone octamer in genome is not random but regular. However, the positioning is closely relevant to the concrete sequence of core DNA. So in this paper, we analyzed the relation between the affinity and sequence structure of core DNA, and extracted the set of key positions. In these positions, the nucleotide sequences probably occupy mainly action in the binding. First, we simplified and formatted the experimental data with the affinity. Then, to find the key positions in the wrapping, we used neural network to analyze the positive and negative effects of nucleosome generation for each position in core DNA sequences. However, we reached a class of weights with every position to describe this effect. Finally, based on the positions with high weights, we analyzed the reason why the chosen positions are key positions, and used these positions to construct a model for nucleosome positioning prediction. Experimental results show the effectiveness of our method.

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/4/042017 ◽

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.

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A Single Target Grasp Detection Network Based on Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/5512728 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Longzhi Zhang ◽

Dongmei Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

High Accuracy ◽

Experimental Results ◽

Detection Accuracy ◽

Single Object ◽

Single Target ◽

High Detection ◽

Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

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