Моделювальні дослідження випадкового керування доступом в безпровідній сенсорній мережі

In this paper we present the research results of WSN network work with random access control, using the PASTA system (Poisson Arrivals See Time Averages). We present the results of simulation tests of the efficiency of the network operation, for the two cases: 1) when all network nodes transmit protocols with the same average time between transmissions, 2) when the network nodes were divided into groups that have different average time between transmissions. This approach has many practical advantages which we present.В статье представлено результаты исследования работы сети БСС со случайным управлением доступом, используя систему PASTA (среднее значение за время наблюдения поступления пуассоновского потока). Приведено результаты моделирующих тестирований эффективности функционирования сети для двух случаев: 1) если все узлы сети передают протоколы с тем же средним временем между передачами, 2) если узлы сети разделены на группы, которые имеют разные средние времена между передачами. Этот подход имеет много практических преимуществ, которые отражено в статье.У статті представлено результати дослідження роботи мережі БСМ з випадковим керуванням доступом, використовуючи систему PASTA (середнє значення за час спостереження надходження пуассонівського потоку). Наведено результати моделювальних тестувань ефективності функціонування мережі для двох випадків: 1) коли всі вузли мережі передають протоколи з тим самим середнім часом між передачами, 2) якщо вузли мережі поділено на групи, які мають різні середні часи між передачами. Цей підхід має багато практичних переваг, які висвітлено у статті

Motivated learning as an extension of reinforcement learning

National Research Foundation (NRF) Singapore under IDM Progra

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty

Service oriented architecture based web application model for collaborative biomedical signal analysis

The rapid growth in availability of new biomedical systems and devices capable of acquiring biosignals for disease diagnosis and health monitoring require rigorous processing. Biomedical research by nature depends on integrated problem solving software environment and often involves people located at different geographical positions. The reusability of different personalized tools are limited due to the complex architectural constrains and restricted interoperability among different devices mostly requiring individual tools. Thus, new computational environments are required to provide robust, user friendly, and scalable systems capable of interoperate seamlessly. This work proposes a service oriented architecture (SOA) based web application model for collaborative biosignal analysis and research to facilitate the seamless integration of various existing tools and different Health Information Systems