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A Parallel Implementation of Q-learning

A Parallel Implementation of Q-learning

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Vivek Kuchibhotla Shashank Kuchibhotla Bangalore Institute of Technology Bangalore India Andhra University Vishakhapatnam India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Q-learning has been one of the most used algorithms in reinforcement learning. There have been a lot of improvements in the speed of convergence of algorithm and many other improvements for various types of environments. Parallelizing the algorithm is one of the very useful improvements for Q-learning. Parallelizing the algorithm can mean the algorithm can run at multiple times the speed on capable systems. In this paper a technique to parallelize the algorithm is proposed. Experimental results are presented with the improvements achieved and a theoretical reasoning of the algorithm's merit in comparison with other works done in the field are also written.

关键词： Reinforcement learning machine learning algorithms Convergence Message passing conferences Prediction algorithms Parallel processing

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Effect of EEG Dual-Channel Acquisition and Gender Specification Subjects on the Classification of Sleep Stages Using machine learning Techniques 3rd

Effect of EEG Dual-Channel Acquisition and Gender Specificat...

引用

3rd international conference on Emerging Technology Trends in Electronics, Communication and Networking, ET2ECN 2020

作者： Satapathy, Santosh Kumar Loganathan, D. Mitra, Anirban Pondicherry Engineering College Puducherry India Amity University Kolkata India

ISBN: (纸本)9789811572180

Nowadays sleep staging is considered one of the major issues in all age groups. Proper sleep scoring of sleep stages can give clinical information for selecting a suitable diagnosing of sleep disorder. Since the manual visual scoring of sleep stage classification is highly time-consuming and it also depends on expert experiences. To overcome this problem automatic sleep stage classification obtained to diagnosis sleep disorder. The main objective of this study is to automatic sleep stage classification on dual channels of EEG signals in gender-specific subjects. This study round up a wide range of research findings concerning sleep stage classification. Here we have combined the NREM and REM stage into one stage, called the sleep stage. Basically here we have proposed two-state classification and practically implemented on public available sleep dataset. In this study, we have considered two channels of EEG signals with different sex subjects. Feature selection and classifiers are assessed the accuracy level of which channel recordings and which classification algorithm is more suitable to discriminating the different sleep stages in subject-gender specific accurately. According to our achieved results from both the gender-specific subjects from dual channels of EEG signal, subject male category with F3-A2 channel and Ensemble classifier are chosen as the best channel and classifier. The mentioned channel and classifier have reached 90.7% accuracy, in the same manner for the female subject category with the C3-A2 channel and LSVM classifier are chosen as the best channel and classifier and have reached 91.5% accuracy levels in discriminating the different stages of sleep. This study shows that gender-specific and channel-specific recordings can be classifying sleep stages with a level accuracy that makes it more suitable for scientific and clinical sleep disorder assessment. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Gear Fault Diagnosis and Classification Using machine learning Classifier 2019

Gear Fault Diagnosis and Classification Using Machine Learni...

引用

3rd international conference on Intelligent Systems, Metaheuristics and Swarm Intelligence (ISMSI)

作者： Sahoo, Sudarsan Laskar, R. A. Das, J. K. Laskar, S. H. NIT Silchar Deptt E&I Engn Silchar India KIIT Univ Sch Elect Bhubaneswar India

ISBN: (纸本)9781450372114

In industry the condition monitoring of rotating machinery gear is very important. The defect in gear mesh may cause the failure in machinery and that causes a severe loss in industry. The failure in gear mesh reduces the efficiency and hence decreases the productivity in industrial operation. Therefore the health monitoring of gear mesh is very important. Proper health monitoring of gears can avoid the failure in machinery and can save money in industrial applications. The acoustic emission and vibration are the two widely used measuring parameters which is used for the condition monitoring of gear mesh. In this work the gear fault detection by using the acoustic emission monitoring technique is used. This experimentation is done by using an efficient instrumentation system. The experimental set-up is designed which consists of a gear mesh driving system and a hand-held sound analyzer. To carry out the experiment the measuring signals from the defective and healthy gears are captured and compared. In this work the measuring signal is the acoustic emission from the tested gears. Then for the fault detection, two signal processing techniques are followed. These are statistical analysis and adaptive wavelet transform (AWT) analysis. The comparison in statistical as well as in AWT analysis used to detect the fault present in gears. In AWT analysis the adaptive noise cancellation is used to enhance the signal to noise ratio (SNR). Finally faults in gears are classified using the machine learning classifier. The statistical parameter data are used as the input data for the classifiers to train the system to classify the fault.

关键词： Gear fault statistical analysis Active noise cancellation (ANC) Adaptive wavelet transform (AWT) fault classification Random forest classifier

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Pre-processing Task for Classifying Satire in Indonesian News Headline

Pre-processing Task for Classifying Satire in Indonesian New...

引用

international conference on Information and Communications Technology (ICOIACT)

作者： Mas Siti Imrona Widyawan Lukito Edi Nugroho Departement of Electrical Engineering and Information Technology Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728173566

ISBN: (纸本)9781728173573

One of challenges in the topic of natural language processing is detecting satire sentences. In the news headlines, satire commonly used to criticize the goverment. This study aims to know the impact of preprocessing in the classification of satire sentences in Indonesian news headlines. Besides, feature extraction was conducted by using Term Frequency Inverse Document frequency (TF-IDF) employing machine learning: Naive Bayes to classify the satire in Indonesian news headline. In this study, there were six preprocessing combinations used which were; breaking down the word into tokens (tokenizing), changing word into base form (stemming), removing stopword, and removing punctuation. The result showed that preprocessing affected the accuracy of sarcastic sentence classification which are not on the dataset of Indonesian news headlines. The highest accuracy of 90.38% obtained by combination of standardized preprocessing which were tokenizing and lowercasing.

关键词： machine learning Feature extraction Natural language processing Information and communication technology Task analysis

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SVM classification based fuzzy understanding of eye electroencephalography movements for robotics applications

SVM classification based fuzzy understanding of eye electroe...

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3rd Smart Cities Symposium (SCS 2020)

作者： E. Mattar I. Rashid M. Rahat College of Engineering Robotics and Cybernetics Laboratory P.O. Box 32038 Sakheer Kingdom of Bahrain

This research is related to Electroencephalography (EEG) analysis for eye movement, events feature detection, classification, hence building a fuzzy decision system for arobotic learning applications. The adopted BMI, EEG-technique, uses non-invasive technique in reading brain signals by sensors (electrodes) located at top of a skull. Works were towards EEG analysis for EYE Movements, Event Features Detection, the Recognition eye EEG brainwaves, while developing a SVM for classification of multi-related for individuals’ actions. The adopted procedures are: First the signal analysis was done, where we filtered the EEG signal and segment significant segments of the analysis and get rid of noise. Secondly, the feature extraction, where we used algorithm such as mean, average, std. deviation and more to get a specific feature which will be used in other parts of the research. The final stage is related to the classification the resulted EEG signals of the sample. In this respect, Support Vector machine (SVM) classifier was used to identify and classifier the EEG signal into three main eye events, the ( T 0 , T 1 , T 2 ). The purpose of this experiment is to use this technology to solve real life problems and robotics application like moving a wheelchair for disabled people, or more interactive EEG related systems.

关键词： electroencephalography handicapped aids medical signal processing support vector machines feature extraction signal classification filtering theory eye

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Remote Sensor Networks for Chilli Crop Disease Prediction Using Thermal Image processing Techniques

Remote Sensor Networks for Chilli Crop Disease Prediction Us...

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international conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA)

作者： Shafaque Fatma Syed Ditty Varghese Amiya Kumar Tripathy Don Bosco Institute of Technology Mumbai India

ISBN: (数字)9781728123394

ISBN: (纸本)9781728123400

Chilli is one of the most important crops to the Indian economy as India is the largest producer and exporter of chilli, so it is essential that the crop is grown in a healthy and suitable environment. As chilli crops are highly susceptible to infection by pests, fungi or bacteria and any changes in the environmental conditions have an adverse effect on the crop, a methodology needs to be devised so as to monitor the growth of the plant and ensure proper growth conditions. This paper discusses a methodology that focuses on monitoring the stress level of the plant as a primary way of identifying if the plant is at a risk of a disease or not. Using image processing and a wireless sensor network, the crop is monitored and sensor values of various climatic conditions are generated. A machine learning algorithm takes these sensor values as input and predicts if the crop is healthy or not, which will help the farmer make effective decisions so as to increase the yield of the crop.

关键词： machine learning Stress Analysis Thermal Imaging chilli crop disease prediction crops Stress analysis Avian Crop Thermography Artificial Intelligence machine learning THERMAL IMAGES plants Sensor networks Stress intensity stress level Wireless sensor networks

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Training Encrypted Models with Privacy-preserved Data on Blockchain 2019

Training Encrypted Models with Privacy-preserved Data on Blo...

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Proceedings of the 3rd international conference on Vision, Image and signal processing

作者： Lifeng Liu Yifan Hu Jiawei Yu Fengda Zhang Gang Huang Jun Xiao Chao Wu Zhejiang University China Cybervein Foundation China Zhejiang Lab China Zhejiang University Data Science Institute Imperial

ISBN: (纸本)9781450376259

Currently, training neural networks often requires a large corpus of data from multiple parties. However, data owners are reluctant to share their sensitive data to third parties for modelling in many cases. Therefore, Federated learning (FL) has arisen as an alternative to enable collaborative training of models without sharing raw data, by distributing modelling tasks to multiple data owners. Based on FL, we premodel sent a novel and decentralized approach to training encrypted models with privacy-preserved data on Blockchain. In our approach, Blockchain is adopted as the machine learning environment where different actors (i.e., the model provider, the data provider) collaborate on the training task. During the training process, an encryption algorithm is used to protect the privacy of data and the trained model. Our experiments demonstrate that our approach is practical in real-world applications.

关键词： learning Privacy machine Blockchain learningDistributed

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Defect Straw Inspection Method Based on machine Vision

Defect Straw Inspection Method Based on Machine Vision

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Ying Zhu Hui Zhang Zhisheng Zhang Zhijie Xia School of Mechanical Engineering Southeast University Nanjing China

ISBN: (数字)9781728188232

ISBN: (纸本)9781728188249

The visual inspection of defect straw can overcome the shortcomings of manual inspection, such as low accuracy, low efficiency, and poor real-time performance. It plays an important role in improving the production capacity and automation level of the enterprise. This paper takes telescopic straws as the research object, divides the defect types into global defects and local defects by analyzing the characteristics of each defect, and elaborate on the detection process and detection algorithm involved for each defect. A detection method from global to local is proposed by using image processing technology. In addition, this paper also proposes a new corner detection method, which has strong robustness to target corner detection in noisy images after experimental comparison. Finally, after experimental verification, the defect detection rate of the detection method proposed in this paper reached 98.8%.

关键词： Electron tubes Image segmentation machine vision Image edge detection Corner detection Surface treatment Fitting

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Research and Application of Object Recognition Method of Visual Grasping Robot Based on Deep learning

Research and Application of Object Recognition Method of Vis...

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Electron Device and Mechanical Engineering (ICEDME), international conference on

作者： Minggang Li Yanping Du Zhenqing Gao Yuan Zhang Zhimin Qin School of mechanical and electrical engineering Beijing Institute of Graphic Comminication Beijing China

ISBN: (数字)9781728181455

ISBN: (纸本)9781728181462

In order to improve the flexibility and intelligence of the grasping robots in the field of logistics, a research on visual robot objects recognition method based on deep learning was carried out. Aiming at this problem, a real-time object recognition and grasp detection scheme is proposed, the rectangle of the items to be operated is defined, the convolutional neural network structure model is established, and the loss function is defined. Finally, experimental verification was performed, including data processing and network model training, and the results analysis showed the effectiveness of this research method.

关键词： Deep learning Grasping Robot Object Detetion Pose Predition object recognition Grasp Robot machine learning LOSS FUNCTION Intelligence Visualization Flexibility

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A Comparative Study of Meningioma Tumors Segmentation Methods from MR Images

A Comparative Study of Meningioma Tumors Segmentation Method...

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international conference on signal processing and Information Security (ICSPIS)

作者： Mohanad Alkhodari Omnia Hassanin Salam Dhou Biomed. Eng. Graduate Program American University of Sharjah Sharjah United Arab Emirates Sharjah United Arab Emirates

ISBN: (数字)9781728189987

ISBN: (纸本)9781728189994

Brain tumor segmentation from magnetic resonance (MR) images can have a great impact on improving diagnostics, growth rate prediction, and treatment planning. In this paper, we provide a comparative study of four well-known segmentation algorithms, namely k-means clustering, histogram thresholding (Otsu), fuzzy c-means thresholding, and region growing. For the region growing algorithm, the seed selection process is automated and enhanced by preprocessing the images and approximating the tumor regions using initial clustering and/or thresholding approaches. The evaluation and comparison of the algorithms is conducted using a data-set of T1-Weighted Contrast-Enhanced magnetic resonance imaging (MRI) brain images. Ground truth tumor images were provided by three experienced radiologists and are used in the evaluation process. Results showed that the enhanced region growing method had the highest mean dice similarity coefficient with a score of 0.87, and the lowest under-segmentation rate (17.46%). The fuzzy c-means thresholding method had the lowest over-segmentation rate (0.03%). This study serves as a baseline for other advanced tumor segmentation studies such as the ones using the emergent machine learning approaches.

关键词： Image segmentation Thresholding (Imaging) Clustering algorithms signal processing algorithms Approximation algorithms Prediction algorithms Tumors

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