scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image. (2) region adjacency graph conversion. (3) graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements such as walls, doors, and symbols as well as spatial elements such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

scholarly journals Research on the Method of Urban Jobs-Housing Space Recognition Combining Trajectory and POI Data

2021 ◽  
Vol 10 (2) ◽  
pp. 71
Author(s):  
Ya Zhang ◽  
Jiping Liu ◽  
Yong Wang ◽  
Yungang Cao ◽  
Youda Bai
Keyword(s):  
Flow Model ◽  
Trajectory Data ◽  
Dynamic Flow ◽  
Public Facilities ◽  
Point Of Interest ◽  
Recognition Result ◽  
Different Types ◽  
Core Issue ◽  
Space Recognition ◽  
New Framework

With the gradual emergence of the separation and dislocation of urban jobs-housing space, rational planning of urban jobs-housing space has become the core issue of national land-spatial planning. To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective. This method provides a new framework for the identification of urban jobs-housing space by integrating point-of-interest (POI) and trajectory data. The method involves three steps: Firstly, based on the trajectory data, we analyze the characteristics of the dynamic flow of passengers in the grid and construct the living factors and working factors to identify the distribution of jobs-housing space. Secondly, we reclassify the POIs to calculate the category ratios of different types of POIs in the grid to identify the jobs-housing space. Finally, an OR operation is performed on the results obtained by the two methods to obtain the final recognition result. We selected Haikou City as the experimental area to verify the method proposed in this paper. The experimental results show that the recognition accuracy of the travel flow model is 72.43%, the POI quantitative recognition method’s accuracy is 74.94%, and the accuracy of the method proposed in this paper is 85.90%, which is significantly higher than the accuracy of the previous two methods. Therefore, the method proposed here can serve as a reference for subsequent research on urban jobs-housing space.

scholarly journals Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

2021 ◽  
Vol 11 (11) ◽  
pp. 5235
Author(s):  
Nikita Andriyanov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Inference ◽  
Recognition Accuracy ◽  
Network Architectures ◽  
Reduced Dimensions ◽  
Different Types ◽  
Rectangular Area ◽  
Noise Impulse ◽  
Selection Of

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

scholarly journals An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Process ◽  
Photovoltaic Arrays ◽  
Mode Of Operation ◽  
Different Types ◽  
The Neural Networks ◽  
Operational Modes ◽  
Types Of Faults ◽  
Electrical Data

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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