Purpose – The purpose of this paper to classify a set of Turkish sign language (TSL) gestures by posture labeling based finite-state automata (FSA) that utilize depth values in location-based features. Gesture classification/recognition is crucial not only in communicating visually impaired people but also for educational purposes. The paper also demonstrates the practical use of the techniques for TSL. Design/methodology/approach – Gesture classification is based on the sequence of posture labels that are assigned by location-based features, which are invariant under rotation and scale. Grid-based signing space clustering scheme is proposed to guide the feature extraction step. Gestures are then recognized by FSA that process temporally ordered posture labels. Findings – Gesture classification accuracies and posture labeling performance are compared to k-nearest neighbor to show that the technique provides a reasonable framework for recognition of TSL gestures. A challenging set of gestures is tested, however the technique is extendible, and extending the training set will increase the performance. Practical implications – The outcomes can be utilized as a system for educational purposes especially for visually impaired children. Besides, a communication system would be designed based on this framework. Originality/value – The posture labeling scheme, which is inspired from keyframe labeling concept of video processing, is the original part of the proposed gesture classification framework. The search space is reduced to single dimension instead of 3D signing space, which also facilitates design of recognition schemes. Grid-based clustering scheme and location-based features are also new and depth values are received from Kinect. The paper is of interest for researchers in pattern recognition and computer vision.
It is necessary for the companies to use a structured method to manage a powerful reliability, and to made risk analysis is essential. Risk analysis refers to the studies required to determine the existing or potential hazards for companies, to analyze and rate the risks arising from these hazards and to prevent them from occurring. There are several methods of risk analysis. Failure Mode and Effect Analysis (FMEA) is a method that is to ascertain the failures and dangers in the system without causing any accidents and to make them better by starting from the top priority of them. This method is used to define how the system can be developed to increase reliability and make free from failures. In this study, it is aimed to determine the mistakes that may occur in the processes of a company that produces transformers and to take precautions and increase the reliability of the company. The power transformer product group of the company has been focused on, and fuzzy logic based failure mode and effect analysis (Fuzzy FMEA) has been used to prioritize risks. High risk priority values and failures that need to be improved have been identified by design and process FMEA studies in the classical FMEA. Considering the deficiencies such as use of linguistic expressions in the calculation of risk priority value and the depens on the person of the evaluation, it was concluded that using Fuzzy Logic based FMEA method would be beneficial. When the risk priorities of the failures obtained as a result of the Fuzzy FMEA studies are evaluated, it is seen that the method used is closer to the truth, and a FMEA monitoring system has been developed to ensure the effectiveness and continuity of the work carried out in the company.
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