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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Qiang Li Haowen Gai Kaijie Hu Northeastern University Shenyang China Tianjin College University of Science and Technology Beijing Tianjin China Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

To address the problem of low accuracy and poor stability of bearing diagnostic models under strong background noise, a bearing fault image recognition method is proposed that reduces the randomness of the model by avoiding the need for artificial parameterization, which can introduce random factors. This method is based on hyperparameter optimization of the GoogLeNet convolutional neural network model and decision fusion. First of all, two-dimensional wavelet time-frequency variation of the original signal of the bearing vibration to construct an image dataset, one-dimensional classification is into a two-dimensional image problem; Secondly, three lightweight convolutional neural network architectures are selected for the noise immunity test, to get the best noise-resistant network architecture GoogLeNet; Finally, hyper-parameter optimization is performed for the network, comparing the mesh method, the progressive mesh method, and the group optimization algorithm, respectively, and getting The optimal parameters are then analyzed for decision fusion and visualization of the network. To verify the method proposed in this paper, we used the open Case Western Reserve University bearing dataset. The experimental verification demonstrates that the proposed method achieves a correctness rate of 94.33% in decision fusion under noise, with good accuracy and stability.

关键词： Accuracy image recognition Computer architecture Power system stability White noise Stability analysis Robustness Classification algorithms Convolutional neural networks Optimization

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Improvement of Edge-Tracking Methods using Genetic Algorithm and neural Network

Improvement of Edge-Tracking Methods using Genetic Algorithm...

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International conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Sajjad Ghazanfari Shabankareh Saeid Ghazanfari Shabankareh Faculty of Electrical Engineering Islamic Azad University Shiraz Iran Faculty of Science and Technology Aix-Marseille University Marseille France

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

One of the most basic and important operations in the field of image processing is image extraction and detection. Edge recognition is very important for image clarity and image segmentation. The importance of edge detection is that the human eye can recognizes the existence of different objects by observing the edges. So it makes sense to do edge detection before interpreting images in automated systems. Edges are points in the image where the two pixels are two different values, or two values very large in numerical value. It can be said that one of the goals of edge reduction in data size in images is to preserve the original structure and shape of the images. Editing has various applications, including object recognition, segmentation and image coding, in a variety of medical images. One of the problems we encounter when editing images has noises. In this paper, a combination of several standard edge- matching algorithms, neural network and multi-objective genetic algorithm NonDominated Sorting Genetic Algorithm (NSGA-ii) is used to edge detection. In the proposed method, we give the standard multiedge finder to a forward propagating (FP) neural network as input. A multi-objective genetic algorithm with Non- Dominated Sorting Genetic Algorithm (NSGA-ii was used to select the number of edge detectors to access the neural network and to select the most accurate ones. The genetic algorithm selects the least accurate number of edge detectors to enter the neural network. To evaluate the proposed method, TP, TN, FP, FN criteria were used to compare with other methods. Finally, by comparing the results obtained on different images using existing methods and the proposed method it is observed that the proposed method has better accuracy in detecting the edge of noise images.

关键词： image edge detection Detectors Signal processing algorithms Genetic algorithms Biological neural networks Sociology

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ANN-based Control Method Implemented in a Voltage Source Converter for Industrial Micro-grid

ANN-based Control Method Implemented in a Voltage Source Con...

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International conference on Bio-Inspired Computing: Theories and applications, BICTA

作者： Jinbang Xu Zhizhuo Wu Xuan Yang Jie Ye Anwen Shen Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science & Technology Huazhong University of Science & Technology Wuhan China

With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWM-VSC) have been investigated. Conventional PI controller has shown limitations such as sensitivity to load and system parameter variation. Even the stability of the system can be threatened under a large and sudden load change. In this paper, the practical situation of a VSC for industrial Micro Grid (MG) is considered and an artificial neural network (ANN) based control method is employed to solve the problem. Meanwhile, an on-line parameter tuning algorithm is introduced for its advantage of self-tuning and system character identification. The proposed control scheme is verified through simulation based on SABER software. The simulation results have shown the advantage of the proposed method and the performance of the parameter tuning session.

关键词： Voltage control artificial neural networks Power conversion Regulators Voltage measurement Frequency control

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RRAM based Learning acceleration

RRAM based Learning acceleration

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Proceedings of the International conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)

作者： Yu Wang Lixue Xia Ming Cheng Tianqi Tang Boxun Li Huazhong Yang Dept. of E.E. Tsinghua University Beijing China Tsinghua University Beijing Beijing CN

ISBN: (纸本)9781509035892

Deep Learning (DL) is becoming popular in a wide range of domains. Many emerging applications, ranging from image and speech recognition to natural language processing and information retrieval, rely heavily on deep learning techniques, especially the neural networks (NNs). NNs have led to great advances in recognition accuracy compared with other traditional methods in recent years. NN-based methods demand much more computation and memory resource, and therefore a number of NN accelerators have been proposed on CMOS-based platforms, such as FPGA and GPU [1]. However, it becomes more and more difficult to obtain substantial power efficiency and gains directly through the scaling down of traditional CMOS technique. Meanwhile, the large data amount in DL applications also meets an ever-increasing “memory wall” challenge because of the efficiency of von Neumann architecture. Consequently, there is a growing research interest of exploring emerging nano-devices and new computing architectures to further improve power efficiency [2].

关键词： artificial neural networks Learning (artificial intelligence) Computer architecture Design automation Computational modeling Acceleration

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Thermodynamics proof of sensory learning and implication of mammal homeostasis

Thermodynamics proof of sensory learning and implication of ...

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International Joint conference on neural networks (IJCNN)

作者： H. Szu Dahlgren Division B Department Naval Surface Warfare Center Dahlgren VA USA

While Oja et al. have derived a Hebbian learning neural net algorithm of PCA reproducing Petland's eigenfaces for examples, Bell-Sejnowski and Amari have improved an unsupervised maximum output entropy learning algorithm for ICA sensory pre-processing reproducing the Hubel-Wiesel edge maps, among others. This paper unifies proofs of the convergence of both supervised and unsupervised learning artificial neural networks in the Lyapunov sense. It implies the effortless division of labor such as the cocktail party effect of supervised word spotting under the unsupervised rejection of severe clutter noise. The proof is based on the minimization of Helmholtz thermodynamic free energy: A=U -TS; Hopfield dynamics: du/sub i//dt=-/spl part/U(v/sub i/)//spl part/v/sub i/; v/sub i/=/spl sigma/o(u/sub i/); and Bell-Sejnowski dynamics: du/sub i//dt=+/spl part/S(w/sub ij/)//spl part/w/sub ij/ for arbitrary brain-like open systems which suggests homeostasis constant body temperature T reservoir. This is characteristic of all mammals, as opposed to cold-blooded reptiles. Other than the known cocktail party effect, applications are given to unsupervised image de-noise generalization of PCA eigenfaces and the supervised recognition of ICA faces.

关键词： Thermodynamics artificial neural networks Principal component analysis Independent component analysis Hebbian theory Biological neural networks Entropy Convergence Unsupervised learning Open systems

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3D Objects Recognition Using artificial neural networks

3D Objects Recognition Using Artificial Neural Networks

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Diogo Santos Ortiz Correa Fernando Santos Osório Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR

ISBN: (纸本)9781728104386

The recent advances in computational processing and the low prices of sensors able to capture three-dimensional information have contributed for the progress of computer vision researches involving 3D data and 3D images. Object recognition allows us to develop complex applications for intelligent mobile robotics, augmented reality, systems for the visually impaired, among other applications. In this context, this paper presents a method for recognizing and classifying objects which are represented in three dimensions through depth maps. The data used in this study comes from the "UW RGB-D Object Dataset" from University of Washington, which is available online and is largely used to evaluate 3D object classifiers. This object database is composed of depth maps captured by the Microsoft's Kinect sensor. The obtained results are promising and contribute positively to the computer vision area.

关键词： Three-dimensional displays image segmentation Sensors Two dimensional displays neural networks image sensors Object recognition

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Quantitative Analysis of Different Land-use and Land-cover Classifiers using Hyperspectral Dataset

Quantitative Analysis of Different Land-use and Land-cover C...

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International conference on image Information processing

作者： Neelam Dahiya Sheifali Gupta Sartajvir Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665433624

Remote sensing plays a vital role in analyzing land-use and land-cover (LULC) areas using hyperspectral imagery which was not feasible with multispectral imagery. The analysis of LULC is an important factor for environmental application. Classification is one of the methods used for the categorization of different types of class cateogries. The performance evaluation of various classifiers using hyperspectral dataset is essential to be performed for the categorization of various classes. Therefore, the main aim of this paper is to analyze and implement the various supervised classifiers such as random forest (RF), neural network (NN), and minimum distance classifier (MDC). The hyperspectral dataset has been used for these classifiers over a part of Haryana and Uttar Pradesh states, India. The results have shown that NN (88%) algorithm has achieved higher accuracy than other algorithms. These experimental outcomes highlights the potential of NN in handling complex problems and useful in the mapping of changes over LULC using hyperspectral imagery. This study is beneficial in applications such as crop monitoring, mapping seasonal variations, and snow detection.

关键词： Performance evaluation Statistical analysis Snow Crops artificial neural networks Information processing Classification algorithms

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Ensemble learning-based approach for crack detection using CNN

Ensemble learning-based approach for crack detection using C...

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International conference on Trends in Electronics and Informatics (ICEI)

作者： Vibhu Kailkhura S. Aravindh Shashank Shekhar Jha N. Jayanthi Delhi Technological University

ISBN: (数字)9781728155180

ISBN: (纸本)9781728155197

Crack detection is of pivotal importance in civil engineering and other related applications. Traditional methods of human inspection are tedious and severely limited. Automated crack detection by conventional image processing techniques is challenging due to their inability to discriminate crack features from background noise. Inhomogeneous lighting, shadows, and surface finish hinder the performance of digital image processing methods. The use of convolutional neural networks has helped achieve remarkably better results in the field of computer vision. Ensemble learning is an approach to aggregate the results of a number of individual models for classification or regression. Ensemble learning for crack detection has been implemented using deep convolutional neural networks (DCNN) in this paper. The models are evaluated on a number of performance metrics, namely-(i) accuracy, (ii) precision, (iii) recall (iv) Matthews correlation coefficient (MCC), (v) AUROC, and (vi) F1 score. Experimental results show the robustness of the ensembling method and offer promising scope in crack detection. They outperform the current best performance on open source concrete crack dataset. The ensemble models achieved much better performance than their individual counterparts with the best ensemble achieving a validation accuracy of 99.67%.

关键词： Feature extraction Convolutional neural networks Predictive models Analytical models Surface cracks image segmentation

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Estimated of coordinates of user's looked point on laptop screen by ANN

Estimated of coordinates of user's looked point on laptop sc...

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IEEE Signal processing and Communications applications (SIU)

作者： Bülent Turan Halil İbrahim Eskikurt Mehmet Serhat Can Zile Meslek Yüksekokulu Mekatronnik Programı Gaziosmanpaşa Üniversitesi Tokat Türkiye Elektrik-Elektronik Mühendisliği Bölümü Sakarya Üniversitesi Teknoloji Fakültesi Tokat Türkiye

In this study, is aimed to estimated of the region which observed on the screen by the artificial neural network (ANN) trained with using images of laptop user. Study were formed of two phases. In the first stage, an ANN has been developed for determining eye region in the captured image with webcam. In the second stage, the data obtained from the eye region are used for training of another ANN which will be estimated to region of coordinate. For the creation of the training set, the laptop screen is divided to 57×32 pieces region of coordinate (24×24 pixels in size). 100 pieces of these regions identified as random. And user when looking at each of this regions were taken 20 different images via internal webcam. For the creation of test set randomly selected 20 regions. And each of this regions were taken 20 different images. In the training set was used 2000 pieces image. Similarly, test set was created using 400 images. The result of the test set processing with ANN, the region of coordinates in close proximity to the actual region of coordinates were obtained. Of the regions estimated via the ANN, was seen to cluster around of actual region and does not interfere the values obtained for the different zones.

关键词： Webcams artificial neural networks conferences Feature extraction Portable computers Training

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artificial intelligence-powered precision: Unveiling the landscape of liver disease diagnosis-A comprehensive review

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ENGINEERING applications OF artificial INTELLIGENCE 2024年 138卷

作者： Vadlamudi, Sireesha Kumar, Vimal Ghosh, Debjani Abraham, Ajith Bennett Univ Sch Comp Sci Engn & Technol TechZone II Greater Noida Uttar Pradesh India Bennett Univ Sch Artificial Intelligence TechZone II Greater Noida Uttar Pradesh India

The global significance of diagnosing liver diseases is heightened, particularly in under-resourced regions with limited healthcare facilities. Traditional diagnostic methods, characterized by time and labor-intensive processes, have led to a growing demand for telemedicine-based solutions. The incorporation of artificial Intelligence is deemed essential to enhance the efficiency and accuracy of diagnostic models. This review explores the seamless integration of diverse data modalities, including clinical records, demographics, laboratory values, biopsy specimens, and imaging data, emphasizing the importance of combining both types for comprehensive liver disease diagnosis. The study rigorously examines various approaches, focusing on pre-processing and feature engineering in non-image data diagnostic model development. Additionally, it analyzes studies employing Convolutional neural networks for cutting-edge solutions in image data interpretation. The paper provides insights into existing liver disease datasets, encompassing both image and non-image data, offering a comprehensive understanding of crucial research data sources. Emphasis is placed on performance evaluation metrics and their correlation in assessing diagnostic model efficiency. The review also explores open-source software tools dedicated to computer-aided liver analysis, enhancing exploration in liver disease diagnostics. Serving as a concise handbook, it caters to novice and experienced researchers alike, offering essential insights, summarizing the latest research, and providing a glimpse into emerging trends, challenges, and future trajectories in liver disease diagnosis.

关键词： Liver diagnosis Telemedicine image & non-image data Pathology Deep learning artificial intelligence

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