To address safety concerns in industrial systems, we propose a framework for forecasting CPU load with respect to a predetermined threshold, allowing customers to add tasks from a predefined library. Existing tools, akin to Windows Task Manager, provide limited insights due to their aggregate nature and high computational overhead. Our approach uses conformal prediction for rapid uncertainty-aware forecasts and Shapley value analysis to quantify individual task contributions to the CPU load. This proof-of-concept framework improves system safety assessment by addressing key research questions in load prediction and validation, paving the way for refined measurement methodologies in industrial applications.

Abstract Integrating machine learning into computer architecture simulation offers a new approach to performance analysis, moving away from traditional algorithmic methods. While existing simulators accurately replicate hardware, they often suffer from slow execution, complex documentation, and require deep CPU knowledge, limiting their usability for quick insights. This paper presents a deep learning-based approach for simulating a key CPU component, cache memory. Our model “learns” cache characteristics by observing cache miss distributions, without needing detailed manual modeling. This method accelerates simulations and adapts to different program needs, demonstrating accuracy comparable to traditional simulators. Tested on Sysbench and image processing algorithms, it shows promise for faster, scalable, and hardware-independent simulations.

Digital Twins (DTs) are virtual representations of physical products in many dimensions, such as geometry and behaviour. As a backbone of Industry 4.0, DTs help interpret and even predict the behaviour of physical processes, provide a virtual testbed for maintenance and upgrade, and enable automatic decision-making supported by artificial intelligence. Despite the promising future, challenges exist, such as the absence of a framework that facilitates the development and application of DTs in industrial contexts. We propose a service-oriented architecture (SOA) DT framework for dynamic and robust distributed systems. The framework contains two types of services. One includes the services provided to the users and is supported by an orchestration mechanism to ensure a quality of service (QoS). The other one refers to the common functions of all DTs. Further, we describe the DT-based decision-making enabled by our QoS-oriented learning of the framework and a Hoare-logic-based verification of QoS.

Digital Twins (DTs) serve as the backbone of Industry 4.0, offering virtual representations of actual systems, enabling accurate simulations, analysis, and control. These representations help predict system behaviour, facilitate multiple real-time tests, and reduce risks and costs while identifying optimization areas. DTs meld cyber and physical realms, accelerating the design and modelling of sustainable innovations. Despite their potential, the complexity of DTs presents challenges in their industrial application. We continue here the development of our approach to build an adaptable and trustable framework for building and operating DT systems - A Digital Twin Framework for Dynamic and Robust Distributed Systems (D-RODS). D-RODS aims to address the challenges above, aiming to advance industrial digitalization and targeting areas like system efficiency, incorporating AI and verification techniques with formal support. We employ existing large-usage tools to illustrate the approach in development based on a synthetic adaptable use case.

The paper introduces elements of a service based perspective of a scalable and dynamic automation system architecture. The approach is based on potentially multi-role devices (implementing node management, processing and networking functionalities) hosting a set of services requested by input nodes. In addition, artificial intelligence support is described to provide means of reaching deployment optimality and reliability. Formal approaches are deemed necessary for both verification of the artificial intelligence approach and of the resulting solutions. A model-based design path is complementary considered in order to lead to an increased efficiency in resource utilization, to lowering design efforts, and ensure a formally correct allocation of services, according to system requirements and constraints.

Federated learning (FL) is an emerging and privacy-preserving machine learning technique that is shown to be increasingly important in the digital age. The two challenging issues for FL are: (1) communication overhead between clients and the server, and (2) volatile distribution of training data such as class imbalance. The paper aims to tackle these two challenges with the proposal of a federated fuzzy learning algorithm (FFLA) that can be used for data-based construction of fuzzy classification models in a distributed setting. The proposed learning algorithm is fast and highly cheap in communication by requiring only two rounds of interplay between the server and clients. Moreover, FFLA is empowered with an an imbalance adaptation mechanism so that it remains robust against heterogeneous distributions of data and class imbalance. The efficacy of the proposed learning method has been verified by the simulation tests made on a set of balanced and imbalanced benchmark data sets.

The selection of ElectroEncephaloGram (EEG) features with functional relevance to Motor Imagery (MI) is a crucial task for successful outcome in Brain-Computer Interface (BCI)-based motor rehabilitation. Individual EEG patterns during MI requires subject-dependent feature selection, which is an arduous task due to the complexity and large number of features. One solution is to use metaheuristics, e.g. Genetic Algorithm (GA), to avoid an exhaustive search which is impractical. In this work, one of the most widely used GA, NSGA-II, is used with an hierarchical individual representation to facilitate the exclusion of EEG channels irrelevant for MI. In essence, the performance of different objectives in NSGA-II was evaluated on a previously recorded MI EEG data set. Empirical results show that k-Nearest Neighbors (k-NN) combined with Pearson's Correlation (PCFS) as objective functions yielded higher classification accuracy as compared to the other objective-combinations (73% vs. 69%). Linear Discriminant Analysis (LDA) combined with Feature Reduction (FR) as objective functions maximized the reduction of features (99.6%) but reduced classification performance (65.6%). All classifier objectives combined with PCFS selected similar features in accordance with expected activity patterns during MI. In conclusion, PCFS and a classifier as objective functions constitutes a good trade-off solution for MI data.

Abstract Differential evolution (DE) is a population-based metaheuristic algorithm that has been proved powerful in solving a wide range of real-parameter optimization tasks. However, the selection of the mutation strategy and control parameters in DE is problem dependent, and inappropriate specification of them will lead to poor performance of the algorithm such as slow convergence and early stagnation in a local optimum. This paper proposes a new method termed as Joint Adaptation of Parameters in DE (JAPDE). The key idea lies in dynamically updating the selection probabilities for a complete set of pairs of parameter generating functions based on feedback information acquired during the search by DE. Further, for mutation strategy adaptation, the Rank-Based Adaptation (RAM) method is utilized to facilitate the learning of multiple probability distributions, each of which corresponds to an interval of fitness ranks of individuals in the population. The coupling of RAM with JAPDE results in the new RAM-JAPDE algorithm that enables simultaneous adaptation of the selection probabilities for pairs of control parameters and mutation strategies in DE. The merit of RAM-JAPDE has been evaluated on the benchmark test suit proposed in CEC2014 in comparison to many well-known DE algorithms. The results of experiments demonstrate that the proposed RAM-JAPDE algorithm outperforms or is competitive to the other related DE variants that perform mutation strategy and control parameter adaptation, respectively.

Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.

Differential evolution has many mutation strategies which are problem dependent. Some Adaptive Differential Evolution techniques have been proposed tackling this problem. But therein all individuals are treated equally without taking into account how good these solutions are. In this paper, a new method called Ranked-based Mutation Adaptation (RAM) is proposed, which takes into consideration the ranking of an individual in the whole population. This method will assign different probabilities of choosing different mutation strategies to different groups in which the population is divided. RAM has been integrated into several well-known adaptive differential evolution algorithms and its performance has been tested on the benchmark suit proposed in CEC2014. The experimental results shows the use of RAM can produce generally better quality solutions than the original adaptive algorithms.

JADE is an state-of-the-art adaptive differential evolution algorithm which implements “DE/current-to-pbest” as its mutation strategy, adapts its mutation factor and crossover rate, and uses an optional external archive to keep track of potential removed individuals in previous generations. This paper proposes MPADE, which extends JADE by using a multi-populated approach to solve high dimensional real-parameter optimization problems. This mechanism helps preventing the two well-known problems affecting the differential evolution algorithm performance, which are premature convergence and stagnation. The algorithm was tested using the benchmark functions in IEEE Congress on Evolutionary Computation 2014 test suite. MPADE was compared using Wilcoxon test to JADE algorithm and with other state-of-the-art algorithms that either use a multi-population approach or adapt their parameters. The experimental results show that the proposed new algorithm improves significantly its precursor and it is also suggested that other state-of-the-art algorithms could benefit from the multi-populated based approach.

Differential Evolution represents a class of evolutionary algorithms that are highly competitive for solving numerical optimization problems. In a Differential Evolution algorithm, there are a few alternative mutation strategies, which may lead to good or a bad performance depending on the property of the problem. A new mutation strategy, called DE/Alopex/1, is proposed in this paper. This mutation strategy distinguishes itself from other mutation strategies in that it uses the fitness values of the individuals in the population in order to calculate the probabilities of move directions. The performance of DE/Alopex/1 has been evaluated on the benchmark suite from CEC2013. The results of the experiments show that DE/Alopex/1 outperforms some state-of-the-art mutation strategies.

Harmonic filtering has been widely applied to reduce harmonic distortion in power distribution systems. This paper investigates a new method of exploiting Differential Evolution (DE) to support the optimal design of harmonic filters. DE is a class of stochastic and population-based optimization algorithms that are expected to have stronger global ability than trajectory-based optimization techniques in locating the best component sizes for filters. However, the performance of DE is largely affected by its two control parameters: scaling factor and crossover rate, which are problem dependent. How to decide appropriate setting for these two parameters presents a practical difficulty in real applications. Greedy Adaptive Differential Evolution (GADE) algorithm is suggested in the paper as a more convenient and effective means to automatically optimize filter designs. GADE is attractive in that it does not require proper setting of the scaling factor and crossover rate prior to the running of the program. Instead it enables dynamic adjustment of the DE parameters during the course of search for performance improvement. The results of tests on several problem examples have demonstrated that the use of GADE leads to the discovery of better filter circuits facilitating less harmonic distortion than the basic DE method.

Thermal process control plays a crucial role in energy management in large data centers. This paper proposes a conceptual view of the framework for integrated modelling and optimization for controlling airflow and temperature of server rooms. The main spirit is to continuously evolve the model to adapt to varied situations so as to support optimal decisions of control actions in a dynamic environment inside data centers. Various online learning and optimization methods are discussed as interesting techniques and tools that are recommended to be utilized in the framework.

Differential evolution (DE) is a very attractive evolutionary and meta-heuristic technique to solve many optimization problems in various real-world scenarios. However, the proper setting of control parameters of DE is highly dependent on the problem to solve as well as on the different stages of the search process. This paper proposes a new greedy adaptation method for dynamic adjustment of mutation factor and crossover rate in DE. The proposed method is based on the idea of greedy search to find better parameter assignment in the neighborhood of a current candidate. Our work emphasizes reliable evaluation of candidates via applying a candidate with a number of times in the search process. As our purpose is not merely to increase the success rate (the survival of more trial solutions) but also to accelerate the speed of fitness improvement, we suggest a new metric termed as progress rate to access the quality of candidates in support of the greedy search. This greedy parameter adaptation method has been incorporated into basic DE, leading to a new DE algorithm called Greedy Adaptive Differential Evolution (GADE). GADE was tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtains the best ranking among the counterparts in terms of the summation of relative errors across the benchmark functions.

The health sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available in the market as commercial products. However, many of the projects/systems are intended to be commercialized.

This paper proposes a new approach to discover knowledge about key features together with their degrees of importance in the context of case-based reasoning. A hierarchical memetic algorithm is designed for this purpose to search for the best feature subsets and similarity models at the same time. The objective of the memetic search is to optimize the possibility distributions derived for individual cases in the case library under a leave-one-out procedure. The information about the importance of selected features is revealed from the magnitudes of parameters of the learned similarity model. The effectiveness of the proposed approach has been shown by evaluation results on the benchmark data sets from the UCI repository and in comparisons with other machine learning techniques.

One significant challenge in building fuzzy systems for complex problems is the "curse of dimensionality". For the sake of a reduced size of the knowledge base, some rules with incomplete premise structures covering larger areas of the input domain are often desirable. This paper presents a genetic algorithm based approach to searching for suitable antecedents of rules under which specific fuzzy consequences can be recommended. The rule premises are coded in a flexible way allowing for presence as well as absence of an input variable in them, in combination with a certain class of input and output fuzzy sets. On the other hand, a consistency index is introduced to give a numerical evaluation of the coherence among individual rules. This index is incorporated into the fitness function of the genetic algorithm to search for a set of optimal rule premises yielding not only good problem solving performances but also little conflict in the rule base. The effectiveness of our work is demonstrated through experiment results on controlling an inverted pendulum.

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of ldquosimilarityrdquo can vary in different situations and remains an issue. This paper proposes a novel similarity model consisting of fuzzy rules to represent the semantics and evaluation criteria for similarity. We believe that fuzzy if-then rules present a more powerful and flexible means to capture domain knowledge for utility oriented similarity modeling than traditional similarity measures based on feature weighting. Fuzzy rule-based reasoning is utilized as a case matching mechanism to determine whether and to which extent a known case in the case library is similar to a given problem in query. Further, we explain that such fuzzy rules for similarity assessment can be learned from the case library. The key to achieving this is pair-wise comparisons of cases with known solutions in the case library such that sufficient training samples can be derived for fuzzy rule learning. The evaluations conducted have shown that the proposed method yields more precise similarity values to approximate case utility than conventional ways of similarity modeling and that fuzzy similarity rules can be learned from a rather small case base without the risk of over-fitting.

Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho‐physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case‐based decision support system to assist clinicians in performing such tasks. Case‐based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty‐nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness‐of‐fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho‐physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.

This paper proposes a new fuzzy case-based reasoning system in which fuzzy rule-based reasoning is utilized as a mechanism for matching between cases. The motivation is that fuzzy if-then rules present a more powerful and flexible means to represent the knowledge about case relevance than traditional distance based similarity measurements. With such fuzzy rules available, every case in the case base can be examined via fuzzy reasoning to predict whether it is relevant to a target problem in query. Those cases that are predicted as relevant are then retrieved and delivered to the next stage of decision fusion. Further, we claim that the set of fuzzy rules for case relevance prediction can be learned from the case base. The key to this is doing pair-wise comparisons of cases with known solutions in the case base such that sufficient samples of case relevance can be derived for fuzzy rule learning. The evaluations conducted on a benchmark data set have shown that the fuzzy rules in demand can be learned from a rather small case base without the risk of over-fitting and that the proposed system yields high information recall rate by capturing more cases that are relevant while not undermining the precision for the set of retrieved cases.

Dealing with conflicting and target-specific requirements is an important issue in multisensor and multitarget tracking. This paper aims to allocate sensing resources among various targets in reaction to individual information requests. The proposed approach is to introduce agents for every relevant target responsible for its tracking. Such agents are expected to bargain with each other for a division of resources. A bilateral negotiation model is established for resource allocation in two-target tracking. The applications of agent negotiation to target covariance tuning are illustrated together with simulation results presented. Moreover, we suggest a way of organizing simultaneous one-to-one negotiations, making our negotiation model still applicable in scenarios of tracking more than two targets.

This article proposes an agent negotiation model for target distribution across a set of geographically dispersed sensors. The key idea is to consider sensors as autonomous agents that negotiate over the division of tasks among them for obtaining better payoffs. The negotiation strategies for agents are established based upon the concept of subgame perfect equilibrium from game theory. Using such negotiation leads to not only superior measuring performance from a global perspective but also possibly balanced allocations of tasks to sensors, benefiting system robustness and survivability. A simulation test and results are given to demonstrate the ability of our approach in improving system security while keeping overall measuring performance near optimal. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1251–1269, 2007.

This paper discusses the role and integration of knowledge discovery (KD) in case‐based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case‐specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co‐occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica‐ tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co‐occurrence between certain sequences and classes.

Input selection is a crucial stage for empirical modeling of complex processes with numerous features. This correspondence proposes a new hybrid method of case-based reasoning and genetic algorithm (GA) to identify significant inputs from a set of features. Case-based reasoning is performed repeatedly on a "leave-one-out" procedure to yield an unbiased error estimate for a hypothesis. This error estimate is then combined with the number of selected attributes to provide an evaluation function for the GA, which serves as a search engine to find the optimal hypothesis for the input selection problem. Simulation examples and their results are presented to demonstrate the effectiveness of the proposed approach.

The task of fuzzy modeling involves specification of rule antecedents and determination of their consequent counterparts. Rule premises appear a critical issue because they correspond to the structure of a fuzzy model. The paper proposes an approach to extracting fuzzy rules from training examples by means of premise optimization. In order to construct a 'parsimonious' fuzzy model with high generalization ability, general premise structure allowing incomplete compositions of input variables as well as OR-connections of linguistic terms is considered. A genetic algorithm is utilized to optimize both premise structure of rules and fuzzy set membership functions at the same time. Determination of rule conclusions is nested in the premise learning, where consequences of individual rules are determined under fixed preconditions. The proposed method was applied to the well-known gas furnace data of Box and Jenkins to show its validity and to compare its performance with that of other works.

The mapping of environment using sensory information has generated a great deal of interest. This paper presents a technique for learning, which allows the mobile robot to acquire the environment map automatically. The technique is based on the principle of explicitly representing and reasoning about the uncertainty in robot's position and orientation, as well as uncertainty in sensing process. The learning procedure is composed of matching and merging. Matching is to compare the sensed-map with the known-map of the robot to find a set of correspondences; merging is to incorporate new or more accurate information in sensed-map into known-map using the correspondences for guidance. By performing matcher and merger constantly, the robot will ultimately acquire the complete map of its environment.>