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Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling. [ABSTRACT FROM AUTHOR]

This paper presents a new method for condition monitoring, especially for monitoring progressive faults such as wear and fatigue. Based on the literature survey, the existing condition monitoring methods are based on either probability or fuzzy logic. The new method, called the fuzzy transition probability (FTP), combines the transition probability (Markov process) as well as the fuzzy set. From a theoretical point of view, the new method uses the available information from the training samples to the maximum extent (finding both the transition probability and the fuzzy membership) and hence, is more effective than the existing methods. This paper is the first part of a two-part paper. It presents the basic theory and shows how to compute the transition probability from the available training samples step by step. A simple demonstration example is also included. The second part of the paper presents two practical applications: one is material tensile strength testing and the other is tool condition monitoring in boring. Based on the testing results, the new method outperforms the popular artificial neural networks. Future research and applications are also discussed. [Copyright &y& Elsevier]

People have always tried to understand natural phenomena. In computer science natural phenomena are mostly used as a source of inspiration for solving various problems in distributed systems such as optimization, clustering, and data processing. In this paper we will give an overview of research in field of computer science where fireflies in nature are used as role models for time synchronization. We will compare two models of oscillators that explain firefly synchronization along with other phenomena of synchrony in nature (e.g., synchronization of pacemaker cells of the heart and synchronization of neuron networks of the circadian pacemaker). Afterwards, we will present Mirollo and Strogatz׳s pulse coupled oscillator model together with its limitations. As discussed by the authors of the model, this model lacks of explanation what happens when oscillators are nonidentical. It also does not support mobile and faulty oscillators. Finally, it does not take into consideration that in communication among oscillators there are communication delays. Since these limitations prevent Mirollo and Strogatz׳s model to be used in real-world environments (such as Machine-to-Machine systems), we will sum up related work in which scholars investigated how to modify the model in order for it to be applicable in distributed systems. However, one has to bear in mind that there are usually large differences between mathematical models in theory and their implementation in practice. Therefore, we give an overview of both mathematical models and mechanisms in distributed systems that were designed after them. [ABSTRACT FROM AUTHOR]

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms. • Provide an up-to-date comprehensive review of the different clustering techniques. • Highlight novel and most recent practical applications areas of clustering. • Provide a convenient research path for new researchers. • Help experts develop new algorithms for emerging challenges in the research area. [ABSTRACT FROM AUTHOR]

Abstract: To some extent, explanations in computer science are answers to questions. Often an explanatory dialogue is necessary to satisfy needs of software users. In this paper, we introduce the concept of intuitive explanation representing the first explanations in an explanatory dialogue. This kind of explanation does not require a situational context to be established or that there is a user model. Depending on an abstract model of explanation generation we present the generic explanation component Kalliope applying Semantic Technologies to construct intuitive explanations. We illustrate our generation approach by means of the semantic search engine KOIOS++ enabling keyword-based search on medical articles. Since semantic search results are often hard to understand Kalliope was integrated into KOIOS++ in order to justify search results. In this work we describe in detail the construction of intuitive explanations for inexperienced users in the medical domain building on the concepts of Semantic Frequency Classes and Semantic Cooccurrence Classes. Various user experiments illustrate that these concepts enable the explanation component to rate the understandability of labels and of label connections. We show how Kalliope exploits these valuations to construct and select understandable explanations. [Copyright &y& Elsevier]

Abstract: Computer science in general, and artificial intelligence and multiagent systems in particular, are part of an effort to build intelligent transportation systems. An efficient use of the existing infrastructure relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. In particular, traffic signal controllers located at intersections can be seen as autonomous agents. However, challenging issues are involved in this kind of modeling: the number of agents is high; in general agents must be highly adaptive; they must react to changes in the environment at individual level while also causing an unpredictable collective pattern, as they act in a highly coupled environment. Therefore, traffic signal control poses many challenges for standard techniques from multiagent systems such as learning. Despite the progress in multiagent reinforcement learning via formalisms based on stochastic games, these cannot cope with a high number of agents due to the combinatorial explosion in the number of joint actions. One possible way to reduce the complexity of the problem is to have agents organized in groups of limited size so that the number of joint actions is reduced. These groups are then coordinated by another agent, a tutor or supervisor. Thus, this paper investigates the task of multiagent reinforcement learning for control of traffic signals in two situations: agents act individually (individual learners) and agents can be “tutored”, meaning that another agent with a broader sight will recommend a joint action. [Copyright &y& Elsevier]

This paper presents three integral methods of a fuzzy knowledge-based control system, to diminish the steady-state output errors. The three controllers, viz the three-input fuzzy controller, fuzzy plus integral controller and are fuzzy-PI controller, have been described and are applied to the two systems. The first is a zero-type-number first-order system whose input is step, and the other is a linearized one-type-number fifth-order electrohydraulic servo system whose input is ramp. Responses of both systems have steady-state errors without integral control. Simulation results have shown that all these three integral methods can effectively eliminate steady-state errors. The three-input fuzzy controller that produces the least overshoot and shortest rise time is the first choice for producing rapid dynamic response and minimum output errors. Fuzzy-PI controller is the second best for producing small output errors. [Copyright &y& Elsevier]

Jet engines are nonlinear dynamical systems for which an exact mathematical model cannot be used for estimator design, because it is either not available or so complex that it does not fit the necessary assumptions. Thus, classical analytical tools for studying standard system properties like observability, which is very important in estimator design, cannot be directly applied. Generally, for practical jet engine applications, the designer faces two closely related problems: first, given a non-measurable parameter, find the minimal set of estimator inputs that facilitates achieving a satisfactory estimation performance (input selection); second, given a predetermined set of inputs, derive an “observability” measure that characterizes the estimation feasibility of a specific non-measurable parameter. In this paper, techniques for solving these two problems are developed and applied to estimator design for jet engine thrust, stall margins, and an unmeasurable state. [Copyright &y& Elsevier]

The application of pattern recognition techniques, expert systems, artificial neural networks, fuzzy systems and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The paper outlines the most important steps of this process and introduces some new results with special emphasis on hybrid AI and multistrategy machine learning approaches. Agent-based (holonic) systems are highlighted as promising tools for managing complexity, changes and disturbances in production systems. Further integration of approaches is predicted. [Copyright &y& Elsevier]

A standard iterative thinning algorithm which has been widely used to extract features for character recognition may destroy information due to several defects such as spurious loops in the skeleton and deformation in touched strokes. This is because most thinning algorithms rely on the steady erosion of character boundaries while maintaining the connectivity of the shape. To solve this problem, this paper proposes a knowledge-based thinning method which removes the spurious loops by a preprocessing stage and makes use of average stroke widths and domain knowledge on Hangul (Korean script) to extract intuitive strokes. The experimental results on 2000 handwritten Hangul characters in PE92 benchmark database indicate that the proposed method has reduced the number of defects and led to more intuitive strokes. [Copyright &y& Elsevier]