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Measurement: Energy 2025年 7卷

作者： Sametah Macine Ngong Ftatsi Mbetmi Guy-de-patience Mohaman Gonza Ndjiya Ngasop Laboratory of Energy Signal Images and Automatic (LESIA) National School of Agro-Industrial Sciences (ENSAI) University of Ngaoundere Cameroon Department of Electrical Engineering Energy and Automation National School of Agro-Industrial Sciences University of Ngaoundere Ngaoundere Cameroon Laboratory of Analyses Simulations and Testing (LASE) University Institute of Technology University of Ngaoundere Cameroon

Power transformers are important components of electrical systems. Their failure or malfunction can have serious consequences, affecting the overall functionality or safety of the electrical system. Rapid and accurate diagnosis of transformer internal faults are key factors of efficient and safe operation. in the literature, several techniques of power transformers windings diagnosis exist. The voltage current diagram is a promising and powerful technique acknowledged to be efficient and quick in winding faults diagnosis. However, two major limitations of this technique reported in the literature are: its inability to detect incipient faults and the method of faults classification which is manual. In this study, two machine learning algorithms (SVM and linear regression) are combined with voltage current diagram to diagnose internal incipient faults by analysing data collected during fault simulations on a layer type power transformer. Three main faults responsible of power transformer failure are considerate: turn to turn short-circuit, buckling stress and axial displacement. For each type of fault, a dataset is generated and the model is trained. The SVM algorithm is used to identify the type of fault (classification), and the linear regression algorithm is used to determine its degree of severity. The highest performance of classification was obtained using the RBF kernel (82 %) and the determination of the degree of severity using R-squared gave a score of 99,9 %.

关键词： Power transformer winding machine learning algorithm Diagnostic Classification Degree

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A design and development of an intelligent jammer and jamming detection methodologies using machine learning approach

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1期22卷 93-101页

作者： Sivaprakash, S. Venkatesan, M. KSR Coll Engn Dept Comp Sci & Engn Namakkal 637215 Tamil Nadu India KSR Inst Engn & Technol Dept Comp Sci & Engn Namakkal 637215 Tamil Nadu India

Nowadays, the utilization of mobile phones has increased rapidly. Due to this evolution schools, mosques, prisons, etc., require silence and security. This is achieved by using the mobile phone jammers. An intelligent mobile jammer is designed for allowing only the emergency calls by utilizing the microcontroller. Here, the jammer utilizes the successive approximation for reducing the transmission power. However, it requires few modifications. Therefore in this paper, an improved successive approximation based on divide and conquer algorithm is proposed in order to design the improved intelligent mobile jammer and reduce transmission power. Subsequently, the proposed jammer is analysed based on the different scenarios and frequency bands to illustrate their performance effectiveness. Furthermore, the normal activities are distinguished from jamming by using machine learning-based detection system according to the various parameters. Finally, the proposed algorithm is compared with conventional algorithms to demonstrate its performance efficiency in terms of detection accuracy.

关键词： Binary search algorithm Divide and conquer algorithm machine learning algorithm Mobile phone jammer Successive approximation

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An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems

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SENSORS 2021年第2期21卷 457-457页

作者： Kim, Tae-Wook Oh, Jaewon Min, Cheonhong Hwang, Se-Yun Kim, Min-Seok Lee, Jang-Hyun Korea Res Inst Ships & Ocean Engn KRISO Offshore Ind R&BD Ctr 1350 Geojebuk Ro Gyeongsangnam Do 53201 Geoje Si South Korea Inha Univ Res Inst Ind Technol 100 Inha Ro Incheon 22212 South Korea Inha Univ Dept Naval Architecture & Ocean Engn 100 Inha Ro Incheon 22212 South Korea

In order to utilize wave energy, various wave power systems are being actively researched and developed and interest in them is increasing. To maximize the operational efficiency, it is very important to monitor and maintain the fault of components of the system. In recent years, interest in the management cost, high reliability and facility utilization of such systems has increased. In this regard, fault diagnosis technology including fault factor analysis and fault reproduction is drawing attention as an important main technology. Therefore, in this study, to reproduce and monitor the faults of a wave power system, firstly, the failure mode of the system was analyzed using FMEA analysis. Secondly, according to the derived failure mode and effect, the thrust bearing was selected as a target for fault reproduction and a test equipment bench was constructed. Finally, with the vibration data obtained by conducting the tests, the vibration spectrum was analyzed to extract the features of the data for each operating status;the data was classified by applying the three machine learning algorithms: naive Bayes (NB), k-nearest neighbor (k-NN), and multi-layer perceptron (MLP). The criteria for determining the fault were derived. It is estimated that a more efficient fault diagnosis is possible by using the standard and fault monitoring method of this study.

关键词： wave power system oscillating water column type wave power system thrust bearing fault reproduction fault diagnosis FMEA vibration spectrum machine learning algorithm

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Comparing methods for measuring dialect similarity in Norwegian 12

Comparing methods for measuring dialect similarity in Norweg...

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12th International Conference on Language Resources and Evaluation (LREC)

作者： Kasen, Andre Johannessen, Janne Bondi Hagen, Kristin Noklestad, Anders Priestley, Joel Natl Lib Norway Pb 2674 N-0203 Oslo Norway Univ Oslo Dept Linguist & Scandinavian Studies Pb 1102 N-0317 Oslo Norway

ISBN: (纸本)9791095546344

The present article presents four experiments with two different methods for measuring dialect similarity in Norwegian: the Levenshtein method and the neural long short term memory (LSTM) autoencoder network, a machine learning algorithm. The visual output in the form of dialect maps is then compared with canonical maps found in the dialect literature. All of this enables us to say that one does not need fine-grained transcriptions of speech to replicate classical classification patterns.

关键词： dialectometry Norwegian transcription geolinguistics maps the Levenshtein method neural networks long short term memory (LSTM) autoencoder network machine learning algorithm

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Mathematical Analysis on Information-Theoretic Metric learning With Application to Supervised learning

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IEEE ACCESS 2019年 7卷 121998-122005页

作者： Choi, Jooyeon Min, Chohong Lee, Byungjoon Ewha Womans Univ Dept Math Seoul 03760 South Korea Catholic Univ Korea Dept Math Bucheon 14662 South Korea

This article presents a concrete mathematical analysis on Information-Theoretic Metric learning (ITML). The analysis provides a theoretical foundation for ITML, by supplying well-posedness, strong duality, and convergence. Our analysis suggests the correction of a typo in the original ITML article that may lead to the loss of accuracy in the metric learning. The necessity of this correction is confirmed by several numerical experiments on supervised learning.

关键词： Bregman iteration machine learning algorithm mathematical analysis metric learning convex optimization

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Review of Deep learning algorithms and Architectures

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IEEE ACCESS 2019年 7卷 53040-53065页

作者： Shrestha, Ajay Mahmood, Ausif Univ Bridgeport Dept Comp Sci & Engn Bridgeport CT 06604 USA

Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The painstakingly handcrafted feature extractors used in traditional learning, classification, and pattern recognition systems are not scalable for large-sized data sets. In many cases, depending on the problem complexity, DL can also overcome the limitations of earlier shallow networks that prevented efficient training and abstractions of hierarchical representations of multi-dimensional training data. Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures. This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time. We delve into the math behind training algorithms used in recent deep networks. We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and others.

关键词： machine learning algorithm optimization artificial intelligence deep neural network architectures convolution neural network backpropagation supervised and unsupervised learning

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Modeling and optimizing in vitro seed germination of industrial hemp (Cannabis sativa L.)

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INDUSTRIAL CROPS AND PRODUCTS 2021年 170卷

作者： Hesami, Mohsen Pepe, Marco Monthony, Adrian Scott Baiton, Austin Jones, Andrew Maxwell Phineas Univ Guelph Dept Plant Agr Guelph ON N1G 2W1 Canada

In vitro seed germination of cannabis as the first physiological stage in the plant life cycle is not only important for studying factors affecting cultivation conditions but also crucial for obtaining juvenile tissue as a potential explant for different in vitro procedures. On the other hand, in vitro seed germination is a multi-variable biological process that can be influenced by genetic (genotype) and physical factors (medium composition and environ-mental conditions). Therefore, a powerful mathematical methodology such as artificial neural networks (ANNs) is well suited to analyze the data and optimize the conditions this complex system. The current study was aimed to evaluate the effect of different types and concentrations of carbohydrate sources (sucrose and glucose) as well as different strengths of DKW (Driver and Kuniyaki Walnut) and mMS (Murashige and Skoog Medium, Van der Salm modification) media on seed germination indices as well as morphological features of in vitro-grown cannabis seedlings by using three ANNs including multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN). The GRNN model displayed higher predictive accuracy (r(2)>0.70) in both training and testing sets for all germination indices and morphological traits in comparison to RBF or MLP. Moreover, non-dominated sorting genetic algorithm-II (NSGA-II) was subjected to the GRNN to find the optimal type and level of media and carbohydrate source for obtaining the best seed germination indices (germination rate and mean germination time). According to the optimization process, 0.43 strength mMS medium supplemented with 2.3 % sucrose would result in the best outcomes. This result showed that a moderate level of salts existing in culture media (0.43 strength of mMS medium) supplemented with a moderate level of sucrose (2.3 %) can improve in vitro seed germination of hemp. The results of a validation experiment revealed that there was a neglig

关键词： Artificial intelligence Carbohydrate sources Culture medium machine learning algorithm Optimization algorithm Plant tissue culture

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machine learning based adaptive congestion window adjustment for Congestion Aware Routing in Cross Layer Approach Handling of Wireless Mesh Network

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 S9929-S9939页

作者： Yuvaraj, N. Thangaraj, P. Aishwarya Coll Engn & Technol Dept Comp Sci & Engn Erode 638312 India Bannari Amman Inst Technol Dept Comp Sci & Engn Erode 638401 India

Among different researches on Wireless Mesh Networks (WMN), the Cross-layer Handling Link Asymmetry (CHLA) scheme has been enhanced with QoS-based Congestion Avoidance using Congestion Aware Routing (QSCACAR) technique. In this approach, QoS requirements were achieved by estimation of signal strength, network capacity, MAC scheduling, link scheduling and slot assignment. In addition, congestion was controlled based on the bandwidth management mechanism which is according to the congestion window on the dynamic traffic conditions. Based on these approaches, congestion over the network was controlled. However, the congestion window size was not precisely adjusted and selected by using these approaches. Therefore in this article, CHLA-QSCACAR is enhanced by including adaptive congestion window size based on the machine learning algorithm (CHLA-MQSCACAR). In this approach, congestion window size is predicted for adjusting the congestion in the next transmission. The Support Vector machine (SVM) algorithm utilizes the congestion balance status as label and congestion control parameters as input attributes. Initially, congestion balance status of different transmissions and their corresponding input congestion control parameters are gathered. The collected database is trained with aid of SVM classifier. Then by using the trained SVM model, the congestion status of current transmission with congestion window size is predicted. The predicted congestion window is utilized for achieving congestion balance for the consecutive transmissions. Finally, the experimental results show that the performance effectiveness of the proposed CHLA-MQSCACAR compared to the existing CHLA-QSCACAR method in terms of different metrics such as routing overhead, bit error rate, end-to-end delay, throughput and latency.

关键词： Wireless mesh network Quality-of-service CHLA-QSCACAR Congestion control machine learning algorithm Support vector machine

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A new hybrid approach for intrusion detection using machine learning methods

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APPLIED INTELLIGENCE 2019年第7期49卷 2735-2761页

作者： Cavusoglu, Unal Sakarya Univ Dept Comp Engn TR-54187 Serdivan Sakarya Turkey

In this study, a hybrid and layered Intrusion Detection System (IDS) is proposed that uses a combination of different machine learning and feature selection techniques to provide high performance intrusion detection in different attack types. In the developed system, firstly data preprocessing is performed on the NSL-KDD dataset, then by using different feature selection algorithms, the size of the dataset is reduced. Two new approaches have been proposed for feature selection operation. The layered architecture is created by determining appropriate machine learning algorithms according to attack type. Performance tests such as accuracy, DR, TP Rate, FP Rate, F-Measure, MCC and time of the proposed system are performed on the NSL-KDD dataset. In order to demonstrate the performance of the proposed system, it is compared with the studies in the literature and performance evaluation is done. It has been shown that the proposed system has high accuracy and a low false positive rates in all attack types.

关键词： Intrusion detection system machine learning algorithm Hybrid system Feature selection NSL-KDD

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Computational model for identifying stereotyped behaviors and determining the activation level of pseudo-autistic

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APPLIED SOFT COMPUTING 2021年 99卷

作者： Camada, Marcos Y. O. Cerqueira, Jes J. F. Lima, Antonio M. N. Inst Fed Baiano Rua Barao Camacari 118 BR-48110000 Catu BA Brazil Univ Fed Bahia Dept Elect Engn & Comp Rua Prof Aristides Novis02 BR-40210630 Salvador BA Brazil Univ Fed Campina Grande Dept Elect Engn Rua Aprigio Veloso882 BR-58429900 Campina Grande PB Brazil

Affective state recognition of an individual is based on the emotional cues, such as the activation level. Body expression is a modal able to convey emotions and can be used for autism diagnosis through the presence of stereotyped behaviors (SBs). These behaviors are atypical and repetitive movements of the body, which can be related to a low mental health condition. The development of systems able to both recognize SBs and inferring activation level can automatically aid some therapeutic approaches. In this paper, a computational model of low intrusiveness is proposed to infer activation levels from recognized SBs, machine learning algorithms (MLAs) are for identifying the SBs and for determining the related activation levels. A metric performance is also proposed to evaluate the performance of MLAs considering the time for classification of the SBs, accuracy, and precision. For classifying the SBs, the Hidden Markov Models and Multilayer Perceptron presented the best performance than Support Vector machine and Convolutional Neural Network. The Adaptive Neuro-Fuzzy technique based on the Fuzzy C-Means algorithm allowed one to determine and differentiate the activation levels of the stereotyped behaviors considered in the present study. The experiments were performed with non-autistic participants, here referred to as pseudo-autistic. (C) 2020 Elsevier B.V. All rights reserved.

关键词： machine learning algorithm Performance analysis Affective state Stereotyped behavior Autism

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