Search

Proportional–Integral–Derivative controller technique continues to provide the easiest and effective solutions to most of the industrial applications in recent years. However PID controller is poorly tuned in practice compared to most other tuning methods and is complicated with poor performance. This research presents a multi objective optimization approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization and Bacterial foraging optimization. The proposed multi objective optimization algorithm is used to tune the PID controller parameters and their performances have been compared with the conventional methodologies like Ziegler Nichols method. The results proved that the use of multi objective optimization approach based controller tuning improves the performance of process in terms of time domain specifications and performance index, set point tracking and regulatory changes and also provides stability. This paper describes the various multi objective optimization algorithms and its implementation to tune the PID Controller used in paper machine DCS as real time processing of a Pulp and paper industry processes.

Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on-demand edge cloud services in three-dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low-orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space-borne MEC, enabling intelligent, personalized, and distributed on-demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence-based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes. European Union in the Horizon 2020 EU-Korea project 5G-ALLSTAR, GA no. 815323, by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT No. 2018-0-00175) and by Grant No. P102-18-27023S, funded by the Czech Science Foundation.

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

The paper describes the stages of paper production, considers the structure of a paper-making machine. Questions related to the proof and use of the Pontryagin’s maximum principle in the theory of optimal control are considered. Optimal paper web weight control system based on the Pontryagin’s maximum principle is presented. Adaptive learning methods for modeling nonlinear systems represent some of the latest advances in adaptive algorithms and machine learning techniques designed to model and identify nonlinear systems. Real-world problems always involve a certain degree of non-linearity, which makes linear models a suboptimal choice. This article may be of interest to research engineers and practitioners in the study and application of control systems using adaptive regulators. This book serves as an essential resource for researchers, graduate students and doctoral students working in the field of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, and computational intelligence. This book may also be of interest to the industry market and practitioners working with a wide range of nonlinear systems.

The Web of Data (WoD) is growing at an amazing rate and it will no longer be feasible to deal with it in a global way, by centralising the data or reasoning processes making use of that data. We believe that Computational Intelligence tech- niques provides the adaptiveness, robustness and scalability that will be required to exploit the full value of ever growing amounts of dynamic SemanticWeb data.

In the new era of computational intelligence, the ever-expanding abundance of information storage and processing power enables researchers and users to tackle applications on various scientific domains of Decision Support Systems DSS. The general focus of this special issue is to provide insights on how computational intelligence methodologies and algorithms can be employed in real world applications, so as to produce information systems capable of solving important and hard decision problems. This special issue on "Innovative Decision Systems" contains extended versions of seven 7 papers selected from the 12th EANN/7th AIAI 2011 Joint Conference. The manuscripts were accepted for publication, after passing through a peer review process by at least two independent academic referees. AIAI and EANN are two well-established annual events, technically sponsored by the Technical Committee 12, Working Group 12.5 TC12-WG12.5 of the International Federation for Information Processing IFIP and the International Neural Network Society INNS respectively. The 2011 event was held on September 15--18 2011 in Corfu, Greece.

The 2012 International Conference on Computational Intelligence and Security (CIS) is the ninth one focusing on all areas of two crucial fields in information processing: computational intelligence (CI) and information security (IS). In particular, the CIS Conference provides a platform to explore the potential applications of CI models, algorithms, and technologies to IS.

Real-world applications include the complex behavior of natural phenomena and the human mind-issue not easily dealt with using conventional engineering. This calls for the use of softcomputing and computational intelligence. The International Symposium on Computational Intelligence and Industrial Applications (ISCIIA), targets the development of computational intelligence technology and increasing activity in applications to real-world systems in industry. The first ISCIIA conference, held in December 2004 at Hainan University, China, covered image processing, networks, signal processing, optimization, data mining, rough sets, and e-Learning. The second ISCIIA conference, held in Guangzhou, China, in November 2006 centered on 55 papers published in the ISCIIA 2006 proceedings. This special JACIII issue features seven papers from these proceedings dealing with intelligent image processing, brain science, human-computer interaction, data mining, and industrial applications. All are interesting, and the paper by Jing et al. was chosen as the best paper of the conference. This conference was chaired by Professor Lefu Wang, president of Guangdong Polytechnic Normal University, and Professor Shibin Zhao. The local organizing committee consisted of Professor Fengmei Zhang, Professor Shibin Zhao, Professor Jin Zhang, and Professor Jianxiong Zhang, all of whom worked to make this conference a success. Founding chairs, Professor Kaoru Hirota, Professor Shibin Zhao, and Dr. Raymond Tay also contributed much to the conference's interesting discussions and presentations, for which we are most grateful. The conference was sponsored by the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT), Guangdong Polytechnic Normal University (GPNU), the Singapore Industrial Automation Association (SIAA), and the Center of Excellence 21-Agent Based Social Science Systems (COE21-ABSSS). The third ISCIIA will be held in 2008 in Dali, China, and we look forward to the advances in computational intelligence technology and its novel applications that this event will present.

The Eighth International Conference on Knowledge Based Intelligent Information and Engineering Systems was held at the Intercontinental Hotel, Wellington, hosted by Wellington Institute of Technology, New Zealand in September 2004. KES2004 aimed to provide a high-tech forum for the presentation of recent research into the theory and applications of intelligent systems and techniques. However, it also focused on some significant emerging intelligent technologies including evolvable hardware, evolutionary computation in computational intelligence, DNA computing, artificial immune systems, bioinformatics using intelligent and machine learning techniques, and intelligent web mining. The conference attracted about 500 delegates from 55 countries and the proceedings contained approximately 500 papers. This Special Issue contains extended versions of nine papers presented at KES2004, selected for qualities of innovation, application of leading-edge intelligent techniques, or overall excellent quality research. The first paper,by A. Kusiak, A. Burns and F. Milster, describes a data-mining approach applied to the analysis of parameters relating to a circulating fluidisedbed boiler. The outcome of the research has interesting implications on the direction of research into the optimisation of energy production. The second paper, by B. Kostek and J. Wojcik, describes innovative work in which techniques often used in data-mining were applied to improve the effectiveness of the retrieval of stored musical rhythms. The next paper, by J.A. Rose, describes work relating to recent developments in DNA computing. Theory and results are presented. The fourth paper, by M.F. Ursu, B. Virginas, G. Owusu and C. Voudouris, describes an approach to workforce allocation, modelled as a distributed system. The work combines an agent-based model combined with rulebased expressions in an original combination. Good global solutions are obtained from the distributed algorithm. The fifth paper, by V.K. Murthy, describes research in which contextual knowledge management in peer to peer computing is applied to mobile-multiplayer games and robotics. Paper number six, by M. Ong, X. Ren, G. Allan, V. Kadirkamanathan, H.A. Thompson and P.J. Fleming, presents a practical framework under which to build a decision support system using a Grid computing paradigm. The system is applied to aeroengine monitoring. The next paper, authored by R. Ranawa, V. Palade and G.E.M.D.C. Bandara, describes an approach to the automatic generation of a fuzzy rule base for on-line hand-written alphanumeric character recognition. The method was found by the authors to be reliable and simple. The penultimate paper of the selection, written by D. Kim, N.-H. Kim, S.-J. Seo and G.-T. Park, describes simulation-based work that uses a fuzzy system to effectively model a practical walking bipedal robot. The final paper of the special issue, authored by V. Gorodetsky, O. Karsaev and V. Samoilov, describes an intelligent systems based generic approach to the on-line updating of situation assessment. We would like to thank the authors for informing us of the results of their work through their papers. We would also like to thank the reviewers for their comments, which resulted in improvements in the papers. We hope that readers appreciate from the papers some of the challenges of modern intelligent systems research, and some of the approaches that are being adopted to overcome them.

In this paper, we propose optimization models to address flexible staff scheduling problems and some main issues arising from efficient workforce management during the Covid-19 pandemic. The adoption of precautionary measures to prevent the pandemic from spreading has raised the need to rethink quickly and effectively the way in which the workforce is scheduled, to ensure that all the activities are conducted in a safe and responsible manner. The emphasis is on novel optimization models that take into account demand requirements, employees’ personal and family responsibilities, and anti-Covid-19 measures at the same time. It is precisely considering the anti-Covid-19 measures that the models allow to define the working mode to be assigned to the employees: working remotely or on-site. The last optimization model, which can be viewed as the most general and the most flexible formulation, has been developed to capture the specificity of a real case study of an Italian University. In order to improve employees’ satisfaction and ensure the best work/life balance possible, an alternative partition of a workday into shifts to the usual two shifts, morning and afternoon, is proposed. The model has been tested on real data provided by the Department of Mechanical, Energy and Management Engineering, University of Calabria, Italy. The computational experiments show good performance and underline the potentiality of the model to handle worker safety requirements and practicalities and to ensure work activities continuity. In addition, the non-cyclic workforce policy, based on the proposed workday organization, is preferred by employees, since it allows them to better meet their needs.

Picture fuzzy set is an efficient tool for dealing with uncertainty and vagueness, particularly in situations that require assimilation of more dimensions of linguistic assessment such as human voting, feature selection, etc. The correlation coefficient of picture fuzzy sets is a tool to determine the association of two picture fuzzy sets. It has several applications in various disciplines like science, engineering, and management. The prominent applications include decision-making, pattern recognition, clustering analysis, medical diagnosis, etc. In this paper, we introduce a new correlation coefficient for picture fuzzy sets with the justification of its advantages. This correlation coefficient is better than the existing correlation coefficients and other such measures in the picture fuzzy theory because it considers the picture fuzzy set as a whole and also expresses the nature (positive or negative) as well as the extent of association between two PFSs. By performing some comparative analysis based on the computation of correlation degree and linguistic hedges, we establish the effectiveness of the suggested correlation measure over some available correlation measures in a picture fuzzy environment. Further, in the context of pattern recognition, we examine the performance of the proposed correlation measure over some existing picture fuzzy correlation measures. Finally, we apply the suggested picture fuzzy correlation coefficient to a decision-making problem involving the selection of an appropriate COVID-19 mask.

Fermatean fuzzy set theory is emerging as a novel mathematical tool to handle uncertainties in different domains of real world. Fermatean fuzzy sets were presented in order that uncertain information from quite general real-world decision-making situations could be mathematically tractable. To that purpose, these sets are more flexible and reliable than intuitionistic and Pythagorean fuzzy sets. This paper presents a novel hybrid model, namely, the Fermatean fuzzy bipolar soft set (FFBSS, in short) model as a general extension of two powerful existing models, that is, fuzzy bipolar soft set and Pythagorean fuzzy bipolar soft set models. Some fundamental properties of the proposed FFBSS model, namely, subset-hood, equal FFBSSs, relative null and relative absolute FFBSSs, restricted intersection and union, extended intersection and union, AND operation and OR operation are investigated along with numerical examples. In addition, certain basic operations, including Fermatean fuzzy weighted average and score function of FFBSSs are proposed. Furthermore, two applications of FFBSS are explored to deal with different multiattribute decision-making situations, that is, selection of best surgeon robot and analysis of most affected country due to COVID-19 (‘CO’ stands for corona, ‘VI’ for virus, ‘D’ for disease, and ‘19’ stands for its year of emergence, that is, 2019). The proposed methodology is supported by an algorithm. At the end, a comparison analysis of the proposed hybrid model with some existing models, including Pythagorean fuzzy bipolar soft sets is provided.

In the era of autonomous robots, multi-targets search methods inspired researchers to develop adapted algorithms to robot constraints, and with the rising of Swarm Intelligence (SI) approaches, Swarm Robotics (SRs) became a very popular topic. In this paper, the problem of searching for an exponentially increasing number of targets in a complex and unknown environment is addressed. Our main objective is to propose a Robotic target search strategy based on the EHO (Elephants Herding Optimization) algorithm, namely Robotic-EHO (REHO). The main additions were the collision-free path planning strategy, the velocity limitation, and the extension to the multi-target version in discrete environments. The proposed method has been the subject of many experiments, emulating the search of infected individuals by COVID-19 in a context of containment within complex and unknown random environments, as well as in the real case study of USA. The particularity of these environments is their increasing targets' number and the dynamic Containment Rate (CR) that we propose. The experimental results show that REHO reacts much better in high Containment Rate, early start search mission, and where the robots' speed is higher than the virus spread speed.

Picture Fuzzy Sets (PFSs) originated by Cuong and Kreinovich are more capable to capture uncertain, inconsistent and vague information in multi-criteria decision making. In this paper, we propose a new picture fuzzy divergence measure based on Jensen-Tsallis function between PFSs. Further, the concept has been extended from fuzzy sets to novel picture fuzzy divergence measure. Besides the validation of the proposed measure, some of its key properties with specific cases are additionally talked about. The performance of the proposed measure is compared with other existing measures in the literature. Some illustrative examples are provided in the context of novel rapacious COVID-19 and pattern recognition which demonstrate the adequacy and practicality of the proposed approach in solving real-life problems.

The present biometric market segment has been captured by compact, lightweight sensors which are capable of reading the biometric fluctuations of a user in real-time. This biometric market segment has further facilitated rise of a new ecosystem of wearable devices helpful in tracking the real-time physiological data for Healthcare-related analysis. However, the devices in the smart-wearable ecosystem are limited to capturing and displaying the biometrics without any prescriptive analytics. This paper addresses this gap to analyse the human emotion space based on an individual’s state of mind over the past 60 min and employs Deep Learning and Bayesian prediction techniques to predict the possibility of an impulsive outburst within upcoming few minutes. A lightweight smart processing device mounted with sensors captures the biometrics of the user and calibrate the same to the mental state of the user on a scale of zero to hundred. The results reveal that the deep learning algorithm along with the Bayesian probability module can predict the future mood fluctuations of the user with lower error than the other contemporary models. The predicted mood fluctuations has matched with the actual mood changes of the experimental subject within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 10 $$\end{document}±10 min of the predicted time index in 93% of the cases and within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 5 $$\end{document}±5 min in 82% of the cases.

Peer-to-peer (P2P) networks support a wide variety of network services including elastic services such as file-sharing and downloading and inelastic services such as real-time multiparty conferencing. Each peer who acquires a service will receive a certain level of satisfaction if the service is provided with a certain amount of resource. The utility function is used to describe the satisfaction of a peer when acquiring a service. In this paper we consider optimal resource allocation for elastic and inelastic services and formulate a utility maximization model which is an intractable and difficult non-convex optimization problem. In order to resolve it, we apply the successive approximation method and approximate the non-convex problem to a serial of equivalent convex optimization problems. Then we develop a gradient-based resource allocation scheme to achieve the optimal solutions of the approximations. After a serial of approximations, the proposed scheme can finally converge to an optimal solution of the primal utility maximization model for resource allocation which satisfies the Karush–Kuhn–Tucker conditions.