The book "The Fundamentals and Empirical Design of a Smart Fire Detection System"

The book entitled "The Fundamentals and Empirical Design of a Smart Fire Detection System" was published by Cambridge Scholars Publishing in November 2020. This book introduces a smart fire detection system designed using a wireless sensor network and fuzzy methods. This system predicts, controls, and provides alerts to various events based on intelligent techniques. Routing protocols are performed based on intelligent procedures in which they are classified into two main groups: static and dynamic. Static protocols are used to transmit data packets between stationary nodes, while dynamic protocols are applied to transmit messages between rescue teams and fire departments. The active and passive states are specified for sensor nodes to balance the remaining energy of the nodes and prolong the network lifetime. The probability of explosion, fire, burn, and suffocation is determined based on fuzzy procedures. People affected can be guided to the exit at event places based on an intelligent method. In addition, members and dispatch routes of rescue and support teams are selected using intelligent methods to reduce financial losses and human casualties. The book will be useful for professors, researchers, and engineers in computer and electrical engineering.

You can obtain more information about this book via the link.

The paper "Implementation of an Autonomous Intelligent Mobile Robot for Climate Purposes"

The paper entitled "Implementation of an Autonomous Intelligent Mobile Robot for Climate Purposes" was published by International Journal of Ad Hoc and Ubiquitous Computing on June 30, 2019. This paper proposes an autonomous intelligent mobile robot for climate purposes, called ClimateRobo, to notify the weather condition based on environmental data. An ATmega32 microcontroller is used to measure temperature, gas, light intensity, and distance to obstacles using the LM35DZ, MQ-2, photocell, and infrared (IR) sensors. A utility function is proposed to calculate the weather condition according to the temperature and gas data. Afterwards, the weather condition will be monitored on a liquid crystal display (LCD), an appropriate light-emitting diode (LED) will be illuminated, and an audio alarm would be enabled when weather condition is emergency as well as ambient brightness is high. The ambient brightness is calculated by a proposed supervised machine learning using sensed data of the photocell sensor. A fuzzy decision system is proposed to adjust the speed of DC motors based on weather condition and light intensity. The robot can detect and pass stationary obstacles with the six reflective sensors installed in the left, front, and right sides under six detection scenarios. Simulation results show performance of the proposed supervised machine learning, fuzzy decision system, and obstacle detection mechanism under various simulation parameters. The robot, initially, is simulated in the Proteus simulator and, then, is implemented by electronic circuits and mechanical devices.

You can find more information about this paper via the link.

The paper "Behavior-Based Decision Making: A Tutorial"

The paper entitled "Behavior-Based Decision Making: A Tutorial" was published by International Journal of Dynamics and Control in December 2018. This paper proposes a novel, knowledge and learning based method called behavior-based decision making, BBDM, in control and system engineering. It is an expert decision support system containing the learning ability to work based on humanistic behavioral reasoning. The knowledge base is built by the system based on various behavioral styles (e.g., safe) associated to other systems and humans. BBDM uses the knowledge-based information to make appropriate decisions when any desired behavioral style is requested from the system. It specifies a success rate for any desired style based on the obtained knowledge base with the aid of a behavioral inference system. This procedure can be used to select a proper system or human to accomplish a requested job. All operations of the BBDM method are performed by a proposed behavioral decision system, called BDS, which consists of three main units: decomposition, behavioral inference, and composition. The decomposition unit splits any behavioral style into several optional features (e.g., safety). The behavioral aggregation sub-unit aggregates all behavioral styles obtained by the system to define the total behavior. The behavioral inference unit produces a success set for any desired behavioral style. Finally, the composition unit converts success set to success rate to specify the success probability of the desired style.

You can find more information about this paper via the link.