Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

Improvements in energy efficiency in Industry 4.0 is the main imperative in manufacturing. An important challenge in many fields of complex industrial processes is energy-efficient optimization. The basic idea of the energy efficiency optimization algorithm is to find the optimal assigned value of process parameters to achieve the lowest energy cost and the best working conditions. Applying the multi-objective approach for solving real industry optimization problems is a challenging task. Therefore, this chapter provides an overview of the most significant issues in multi-objective optimization problem. Improving energy efficiency with the multi-objective optimization has opened new opportunities for technological progress in Industry 4.0.

: Autonomous cooperative driving systems require the integration of research activities in the field of embedded systems, robotics, communication, control and artificial intelligence in order to create a secure and intelligent autonomous drivers behaviour patterns in the traffic. Beside autonomous vehicle management, an important research focus is on the cooperation behaviour management. In this paper, we propose hybrid automaton modelling to emulate flexible vehicle Platoon and vehicles cooperation interactions. We introduce novel coding function for Platoon cooperation behaviour profile generation in time, which depends of vehicles number in Platoon and behaviour types. As the behaviour prediction of transportation systems, one of the primarily used methods of artificial intelligence in Intelligent Transport Systems, we propose an approach towards NARX neural network prediction of Platoon cooperation behaviour profile. With incorporation of Platoon manoeuvres dynamic prediction, which is capable of analysing traffic behaviour, this approach would be useful for secure implementation of real autonomous vehicles cooperation.

ABSTRACT The thermal power plant systems are one of the most complex dynamical systems which must function properly all the time with least amount of costs. More sophisticated monitoring systems with early detection of failures and abnormal behaviour of the power plants are required. The detection of anomalies in historical data using machine learning techniques can lead to system health monitoring. The goal of the research is to build a neural network-based data-driven model that will be used for anomaly detection in selected sections of thermal power plant. Selected sections are Steam Superheaters and Steam Drum. Inputs for neural networks are some of the most important process variables of these sections. All of the inputs are observable from installed monitoring system of thermal power plant, and their anomaly/normal behaviour is recognized by operator's experiences. The results of applying three different types of neural networks (MLP, recurrent and probabilistic) to solve the problem of anomaly detection confirm that neural network-based data-driven modelling has potential to be integrated in real-time health monitoring system of thermal power plant.

— Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. It is very important to timely detect parameter anomalies in real-world running thermal power plant system, which is one of the most complex dynamical systems. Artificial neural networks are one of anomaly detection techniques. This paper presents the Elman recurrent neural network as method to solve the problem of parameter anomaly detection in selected sections of thermal power plant (steam superheaters and steam drum). Inputs for neural networks are some of the most important process variables of these sections. In addition to the implementation of this network for anomaly detection, the effect of key parameter change on anomaly detection results is also shown. Results confirm that recurrent neural network is good approach for anomaly detection problem, especially in real-time industrial applications.

Anomalies are integral part of every system's behavior and sometimes cannot be avoided. Therefore it is very important to timely detect such anomalies in real-world running power plant system. Artificial neural networks are one of anomaly detection techniques. This paper gives a type of neural network (probabilistic) to solve the problem of anomaly detection in selected sections of thermal power plant. Selected sections are steam superheaters and steam drum. Inputs for neural networks are some of the most important process variables of these sections. It is noteworthy that all of the inputs are observable in the real system installed in thermal power plant, some of which represent normal behavior and some anomalies. In addition to the implementation of this network for anomaly detection, the effect of key parameter change on anomaly detection results is also shown. Results confirm that probabilistic neural network is excellent solution for anomaly detection problem, especially in real-time industrial applications.

Abstract Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and distributed information acquisition and dissemination infrastructure. With the accelerated development of wireless Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) communications, the integrated acquiring and processing of information is becoming feasible at an increasingly large scale. Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems. It is a challenging problem due to the dynamic changes of the traffic states caused by many uncertain factors along a travelling route. In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength values of communications between pairs of vehicles. The V2VSPP was trained and evaluated by using traffic data provided by the Australian Centre for Field Robotics. It contains vehicle state information, vehicle-to-vehicle communications and road maps with high temporal resolution for large numbers of interacting vehicles over a long time period. The experimental results show that the proposed approach (V2VSPP) has the capability of providing accurate predictions of speed profiles in multi-vehicle trajectories setup.

This paper investigates the genetic based re-planning search strategy, using neural learned vibration behavior for achieving tolerance compensation of uncertainties in robotic assembly. The vibration behavior was created from complex robot assembly of cogged tube over multistage planetary speed. Complex extensive experimental investigations were conducted for the purpose of finding the optimum vibration solution for each planetary stage reducer in order to complete the assembly process in defined real-time. However, tuning those parameters through experimental discovering for improved performance is a time consuming process. Neural network based learning was used to generate wider scope of parameters in order to improve the robot behavior during each state of the assembly process. As a novel modelling formalism of reactive hybrid automata, we propose the Wormhole Model with both learning and re-planning capacities (WOMOLERE). For our application, the states of hybrid automaton include amplitudes and frequencies of robot vibration module. The transition action is a function of minimal distance and uncertainty effects due to jamming during the assembly process. The results suggest that the methodology is adequate and could be recognized as an idea for designing of robot surgery assistance methods, especially in soft-robotics.

This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

As an inspiration for robot behavior, it is possible to make analogies between behavior of biological organisms and robot models. Inspired by the behavior of the beetles, we designed reactive, sensor-based behavior for mobile robot. The focus is on intelligent control algorithm, which approximates the behavior of the dor beetle with a Finite State Machine-based Design method. This finite state machine–based approach could be usefull as methodologie for the improved planning of the real-time complex tasks in robot-based manufactoring systems, using information from factory sensor networks, and taking into account the constraints from factory environments. In this paper, we will present the motor control results obtained through experiments, which confirm the effectiveness of the control based on behavior algorithm of living organisms. INTRODUCTION A biomimetic system is basically a system that mimics/copiess biology, in a way that it adopts mechanisms found in natural organisms and applies them in engineering [Na et al., 2009]. In recent years, the field of biomimetics made a great progress in related fields, including mechanical intelligence, materials science, biological science, understanding the wonders of nature and biology. These new findings have enabled scientists and engineers to apply the reverse engineering on the many functions of the animal world and to implement these technological capabilities [Argyros et. al., 2004]. Currently in robotics, a lot of research has been done in biomimetics. Biomimetic robots do not copy the complete behavior of the animals. Only the most useful abilities are extracted and modified so they can be used with the body structure of the robot and in this way the robot reacts more practically. The key features of biomimetic robots are their capacity to adapt to the environment and the ability to learn and react faster. A biomimetic robot also involves mechanisms and manipulator structures capable of meeting the requirements of enhanced performance [Vepa, 2009]. The biomimetic robots are designed to be substantially more compliant and stable than conventionally controlled robots and they use the advantage of new developments in materials, microsystems technology and also the developments that have led to a deeper understanding of biological behavior. For small, insect-like robots, applications include autonomous or semi-autonomous tasks such as reconnaissance and de-mining, and for human interaction tasks at a larger scale.

This paper presents implementation of optimal search strategy (OSS) in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration) and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.

This paper presents a visual/motor behavior learning approach, based on neural networks. The behavior description is introduced in order to create behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for off-line learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to gripe a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest the methodology is adequate and could be recognized as an idea for designing mobile robot assistance for blind people guiding or for performing manipulation tasks, as a helping hand to disabled people.