Computational intelligence for industrial and environmental applications

Abstract

Computational Intelligence (CI) techniques are being increasingly used for automatic monitoring and control systems, especially regarding industrial and environmental applications, due to their performance, their capabilities in fusing noisy or incomplete data obtained from heterogeneous sensors, and the ability in adapting to variations in the operational conditions. Moreover, the increase in the computational power and the decrease of the size of the computing architectures allowed a more pervasive use of CI techniques in a great variety of situations. In this paper, we propose a brief review of the most important CI techniques applied in each step of the design of a monitoring and control system for industrial and environmental applications, and describe how these techniques are integrated in the development of efficient industrial and environmental applications.

A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

“…In the majority of the cases, a feature extraction step computes the one-dimensional input signals [7] from data with higher dimensionality (e.g., an image). The feature extraction step requires a priori knowledge of the problem to efficiently reduce the dimensionality of the input data, while maintaining the most significant information [25].…”

Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

“…Applications of agent-based systems (ABSs) include the following: diverse problems solving in Industry 4.0 [5]; adaptive clustering [6]; modeling strategic interactions in diverse democratic systems [7]; investigation on supply chain of product recycling [8]; detecting the proportion of traders in the stock market [9]; control design in the presence of actuator saturation [10]; agent-based simulator for environmental land change [11]; distributed intrusion detection [12]; investigations of complex information systems [13]; discovering Semantic Web services through process similarity matching [14]; power system control and protection [15]; studies on task type and critic information in credit assignments [16]; planning with joint actions [17]; patient scheduling [18]; study compliance with safety regulations [19]; multi-objective optimization [20]. Studies present applications of computational intelligence in domains including the [21] industry and environment. Problem-solving based on computational intelligence techniques include adaptive reflection detection and location in iris biometric images [22], arithmetic codes for concurrent error detection in artificial neural networks [23], and support in medical decision-making [24,25].…”