scholarly journals Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hong-Chan Chang ◽  
Shang-Chih Lin ◽  
Cheng-Chien Kuo ◽  
Hao-Ping Yu
Keyword(s):  
Support Vector Machine ◽  
Information System ◽  
Monitoring System ◽  
Solar Power ◽  
Video Data ◽  
Failure Diagnosis ◽  
Support Vector ◽  
Management Platform ◽  
System A ◽  
Cloud Monitoring

This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P) streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM) theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

Research on Video Encryption and Privacy Protection in Cloud Monitoring System Based on H.264

2019 ◽  
Vol 12 ◽  
Author(s):  
Qingtao Wu ◽  
Zaihui Cao
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Video Data ◽  
Encryption Algorithm ◽  
Management Tool ◽  
Video Encryption ◽  
Wireless Multimedia ◽  
Monitoring Technology ◽  
Security Issues ◽  
Cloud Monitoring

: Cloud monitoring technology is an important maintenance and management tool for cloud platforms.Cloud monitoring system is a kind of network monitoring service, monitoring technology and monitoring platform based on Internet. At present, the monitoring system is changed from the local monitoring to cloud monitoring, with the flexibility and convenience improved, but also exposed more security issues. Cloud video may be intercepted or changed in the transmission process. Most of the existing encryption algorithms have defects in real-time and security. Aiming at the current security problems of cloud video surveillance, this paper proposes a new video encryption algorithm based on H.264 standard. By using the advanced FMO mechanism, the related macro blocks can be driven into different Slice. The encryption algorithm proposed in this paper can encrypt the whole video content by encrypting the FMO sub images. The method has high real-time performance, and the encryption process can be executed in parallel with the coding process. The algorithm can also be combined with traditional scrambling algorithm, further improve the video encryption effect. The algorithm selects the encrypted part of the video data, which reducing the amount of data to be encrypted. Thus reducing the computational complexity of the encryption system, with faster encryption speed, improve real-time and security, suitable for transfer through mobile multimedia and wireless multimedia network.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

Action Recognition with Spatiotemporal Analysis and Support Vector Machine for Elderly Monitoring System

2021 ◽  
Author(s):  
Mahaputra Ilham Awal ◽  
Luqmanul Hakim Iksan ◽  
Rizky Zull Fhamy ◽  
Dwi Kurnia Basuki ◽  
Sritrusta Sukaridhoto ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Monitoring System ◽  
Action Recognition ◽  
Spatiotemporal Analysis ◽  
Support Vector

scholarly journals Implementation of Integrated Bayes Formula and Support Vector Machine for Analysing Airline’s Passengers Review

2020 ◽  
Vol 202 ◽  
pp. 15004
Author(s):  
Aditya Tegar Satria ◽  
Mustafid ◽  
Dinar Mutiara Kusumo Nugraheni
Keyword(s):  
Support Vector Machine ◽  
Information System ◽  
Text Mining ◽  
Input Data ◽  
Tourism Industry ◽  
Support Vector ◽  
Accuracy Score ◽  
Bayes Formula ◽  
Performance Results ◽  
Mining Algorithms

Nowadays, the utilization of Internet of Things (IoT) is commonly used in the tourism industry, including aviation, where passengers of flight services can rate their satisfaction levels towards the product and service they use by writing their reviews in the form of text-based data on many popular websites. These passenger reviews are collections of potential big data and can be analyzed in order to extract meaningful informations. Some text mining algorithms are already in common use, including the Bayes formula and Support Vector Machine methods. This research proposes an implementation of the Bayes and SVM methods where these algorithms will operate independently yet integrated with other modules such as input data, text pre-processing and shows output result concisely in one single information system. The proposed system was successfully delivered 1000 documents of passenger reviews as input data, then after implemented the pre-processing method, the Bayes formula was used to classify the document reviews into 5 categories, including plane condition, flight comfort, staff service, food and entertainment, and price. While simultanously, the positive and negative sentiment contained in the review document was analyzed with SVM method and shows the accuracy score of 83.6% for a training to testing set ratio of 50:50, while 82.75% accuracy for the 60:40 ratio, and 83.3% accuracy for the 70:30 ratio. This research shows that two different text mining algorithms can be implemented simultaneously in a effective and efficient way, while still providing an accurate and satisfying performance results in one integrated information system.

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