Gear fault detection is one of the underlying research areas in the field of condition monitoring of rotating machines. Many methods have been proposed as an approach. One of the major tasks to obtain the best fault detection is to examine what type of feature(s) should be taken out to clarify/improve the situation. In this paper, a new method is used to extract features from the vibration signal, called 1D local binary pattern (1D LBP). Vibration signals of a rotating machine with normal, break, and crack gears are processed for feature extraction. The extracted features from the original signals are utilized as inputs to a classifier based onk-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) for three classes (normal, break, or crack). The effectiveness of the proposed approach is evaluated for gear fault detection, on the vibration data obtained from the Prognostic Health Monitoring (PHM’09) Data Challenge. The experiment results show that the 1D LBP method can extract the effective and relevant features for detecting fault in the gear. Moreover, we have adopted the LOSO and LOLO cross-validation approaches to investigate the effects of speed and load in fault detection.
In the higher education sector, web based facilities perform a vital aspect to offer success of an academic institution, due to the users depend on the universities websites to achieve different academic instructions. Simultaneously, users may face many usability difficulties while having access to the websites. For that reason, this research investigates user based testing and questionnaires methods from user perspective to evaluate three of lowermost university websites in KRG/Iraq according to Ranking Web of Universities (webometrics); university of Raparin, university of Garmian, and university of Halabja. Thirty participants contribute to implementing six tasks of user-based methods and ten questions of questionnaire approach. Based on the analysing process, the accuracy of universities websites are; 86.7%, 79.5%, and 61.1% for each University of Raparin, University of Halabja, and University of Garmian respectively. Moreover, user satisfaction for the University of Raparin is 3.59, while 3.24 and 3.01 are the rates of satisfaction for University of Halabja and University of Garmian.
Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three different 1D Convolutional Neural Network (CNN) structures is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami. The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects.
A smart clock is any digital clock that has at least one intelligent feature. Moreover, it provides time with synchronizing automatically base on the standard measurement, which is determined during the implementation software on the hardware architecture design. This study presents an efficient cost-effective smartwatch for disable people based on the Atmega328p microcontroller (Arduino Uno) that is programmed in “Arduino” (C based) programming language. Moreover, the system uses DS1302 real time clock, SD card memory, push button, voice recognition module, liquid crystal display (LCD), and sound speaker. The clock sounds time in Kurdish language when a related switch is pressed or asked via microphone, it also shows the time on LCD. Finally, the system is applied successfully with a satisfactory cost and performance for the proposed application according to the achieved test results.
Sign Language Recognition (SLR) has an important role among the deaf-dump community since it is used as a medium of instruction to execute daily activities such as communication, teaching, learning, and social interactions. In this paper, a real-time model has been implemented for Kurdish sign recognition using Convolutional Neural Network (CNN) algorithm. The main objective of this study is to recognize the Kurdish alphabetic. The model has been trained and predicted on the KuSL2022 dataset using different activation functions for a number of epochs. The dataset consists of 71,400 images for the 34 Kurdish sign languages and alphabets collected from two different datasets. The accuracy of the proposed method is evaluated on a dataset of real images collected from many users. The obtained results show that the proposed system's performance increased for both classification and prediction models, with an average train accuracy of 99.91 %. These results outperform previous studies on Kurdish sign language in term of accuracy detection and recognition.
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