Patent Landscape Reports (PLR) have long been used in business, science and RD among countries, it is USA and China. However, patents of high technological relevance and/or commercial value may appear in small tech companies, or as a “by-product” of other corporate activities. The research results are of interest to researchers and practitioners involved in the development and implementation of 5G technologies.
The control of room temperature and humidity is important for ensuring of the necessary indoor human comfort for optimal work capacity and effective rest. The plant nonlinearity and the variables coupling require intelligent control techniques in order to satisfy the high performance demands. The present paper suggests a procedure for the design of a simple for industrial implementation fuzzy logic controller on the principle of parallel distributed compensation (PDC) that consists of linear local decoupling two-variable controllers. It is based on a Takagi-Sugeno-Kang (TSK) plant model, derived from experimentally obtained plant step responses using expert knowledge and parameter optimisation via genetic algorithms. The design is applied for the control of the temperature and the relative humidity of a laboratory air-conditioning system. The PDC system outperforms an existing Mamdani two-variable control system with adaptive properties in shorter settling time, higher robustness and reduced overshoot, estimated from simulations.
The COVID-19 pandemic situation keeps on ruining and affecting the wellbeing and prosperity of the worldwide population and due to this situation, the doctors around the world are working restlessly, as the coronavirus is increasing exponentially and the situation for testing has become quite a problematic and with restricted testing units, it’s impossible for every patient to be tested with available facilities. Effective screening of infected patients through chest X-ray images is a critical step in combating COVID-19. With the help of deep learning techniques, it is possible to train various radiology images and detect COVID-19. The dataset used in our research work is gathered from different sources and a specific new dataset is generated. The proposed methodology implemented is beneficial to the medical practitioner for the diagnosis of coronavirus infected patients where predictions can be done automated using deep learning. The deep learning algorithms that are used to predict the COVID with the help of chest X-ray images are evaluated for their prediction based on performance metrics such as accuracy, precision, Recall, and F1-score. In this work, the proposed model has used deep learning techniques for COVID-19 prediction and the results have shown superior performance in prediction of COVID-19. © 2021, International Institute for General Systems Studies. All rights reserved.
This paper describes models, methods and software tool for testability analysis of aviation systems. It comprises analytical and programing aspects of calculations of main testability, reliability and availability indices. It presents general description of the software, XML schema of input data and technique of their mapping to the database structure. The procedure for generating the initial data for testability analysis based on the line replaceable units failure modes report is described. Fault tree model for analysis of the built-in test conformity is suggested. Markov models have been created for analyzing reliability and availability, taking into account the features of the built-in test and the specifics of the aircraft operation. An approach to the construction of trends in the operative availability of aviation systems in the inter-maintenance interval is proposed.
EMG data processing and muscle activity recognition has become the most popular method for upper limb prosthetics. The high sensitivity of EMG sensors with respect to external disturbances and other factors prevent from accurate muscle activity recognition. The aim of the paper is to investigate robustness of window recognition method with respect to muscle fatigue and perspiration of the forearm skin. The current experiment was carried out using Arduino nano microcontroller connected to EMG sensors. The subject under study is a healthy man of 26 years old with an average build. The subject was asked to do physical exercises, thereby loading the muscles of the fingers of the hand to achieve partial or complete fatigue and perspiration. During the whole process, EMG sensors have installed on the subject and transmitted the signal to the computer using Arduino. All signal processing is done directly on the computer with a pre-recorded signal. Experimental results have been shown that with the appearance of external factors during prosthesis operation recognition accuracy may degrade to unsatisfactory. False positives occur with perspiration of skin surface and complete muscle fatigue. An algorithm for automatic self-correction of the boundaries of motion detection zones has been introduced. Instead of identification of causes that leads to performance degradation, we use correction scheduling started by timer. Experimental results have shown that proposed automatic adaptive correction is effective. Despite higher recognition delay, proposed auto-tuning method provides satisfactory muscle activity identification and feature extraction in real-time.
