Hasil untuk "Cybernetics"

Shirantha Welikala, Hai Lin, Panos J. Antsaklis

Recent global and local phenomena have exposed vulnerabilities in critical supply chain networks (SCNs), drawing significant attention from researchers across various fields. Typically, SCNs are viewed as static entities regularly optimized to maintain their optimal operation. However, the dynamic nature of SCNs and their associated uncertainties have motivated researchers to treat SCNs as dynamic networked systems requiring robust control techniques. In this paper, we address the SCN inventory consensus problem, which aims to synchronize multiple parallel supply chains, enhancing coordination and robustness of the overall SCN. To achieve this, we take a novel approach exploiting dissipativity theory. In particular, we propose a dissipativity-based co-design strategy for distributed consensus controllers and communication topology in SCNs. It requires only the dissipativity information of the individual supply chains and involves solving a set of convex optimization problems, thus contributing to scalability, compositionality, and computational efficiency. Moreover, it optimizes the robustness of the SCN to various associated uncertainties, mitigating both bullwhip and ripple effects. We demonstrate our contributions using numerical examples, mainly by comparing the consensus performance with respect to standard steady-state control, feedback control, and consensus control strategies.

Parth G. Dangi, Yogesh Kumar Meena

Brain-machine interfaces (BMIs) have significantly advanced neuro-rehabilitation by enhancing motor control. However, accurately decoding continuous grasp force remains a challenge, limiting the effectiveness of BMI applications for fine motor tasks. Current models tend to prioritise algorithmic complexity rather than incorporating neurophysiological insights into force control, which is essential for developing effective neural engineering solutions. To address this, we propose EEGForceMap, an EEG-based methodology that isolates signals from the premotor-parietal region and extracts task-specific components. We construct three distinct time-frequency feature sets, which are validated by comparing them with prior studies, and use them for force prediction with linear, non-linear, and deep learning-based regressors. The performance of these regressors was evaluated on the WAY-EEG-GAL dataset that includes 12 subjects. Our results show that integrating EEGForceMap approach with regressor models yields a 61.7% improvement in subject-specific conditions (R-squared = 0.815) and a 55.7% improvement in subject-independent conditions (R-squared = 0.785) over the state-of-the-art kinematic decoder models. Furthermore, an ablation study confirms that each preprocessing step significantly enhances decoding accuracy. This work contributes to the advancement of responsive BMIs for stroke rehabilitation and assistive robotics by improving EEG-based decoding of dynamic grasp force.

Simona-Vasilica Oprea, Adela Bara

This paper explores the evolving landscape of smart grids and super smart grids (SSG) through a review of recent academic publications (2019-2024). An SSG expands the traditional smart grid concept by interconnecting power grids across vast regions, facilitating the efficient transfer of renewable energy sources (RES) across borders. While the potential of SSG lies in enhancing global energy security and reducing carbon emissions, significant challenges remain, including high infrastructure costs, geopolitical risks and cybersecurity concerns. Additionally, controversies surrounding energy control and the environmental impact of large-scale infrastructure development add complexity to the SSG debate. Our paper focuses on emerging research trends and key topics related to SSG, aiming to address several concerns: 1) SSG viability given the current global energy landscape and conflicts; 2) the prevailing academic sentiment towards SSG in recent publications; 3) the most prominent research topics and emergent themes in the field. By analyzing recent publications from the past five years, we aim to shed light on the future of SSG and their interactions with local energy systems such as microgrids. The first topic revolves around data-driven approaches within network contexts, highlighting load management, distribution systems and optimization techniques. The second topic explores performance analysis and comparative studies. The third topic concentrates on control methods in distributed networks. The fourth topic centers on voltage, current and control methods in direct current (DC) systems. Our research identifies seven new research directions for SSG development.

E.A. Kuvshinov, R.R. Trofimov, N.N. Konobeeva

The relevance of modeling the interaction of electromagnetic waves with various materials exhibiting nonlinear properties is increasing every year. In this work, we studied the dynamics of laser beams propagating in a medium of single-walled carbon nanotubes with impurities, placed in a dielectric. By multilevel impurity, we mean an impurity whose energy levels are separated from the conduction band and valence band in carbon nanotubes and lie inside the band gap of the dielectric medium. The novelty of this work lies in the development of a model for the evolution of electromagnetic radiation in the infrared range is constructed using the Madelung transform for the nonlinear Schrödinger equation, the numerical implementation of which is carried out using the smoothed-particle hydrodynamics. The influence of impurity parameters on the laser beam propagation in a given medium, namely, the energy of electron transitions from impurity levels to the first and second sublattices of nanotubes, is analyzed.

Roman Yu. Dobretsov, Dmitrii S. Popov, Yaroslav N. Smirnov et al.

The paper considers the principles of constructing an energy balance equation for a mobile robotic articulated wheeled chassis with an electromechanical drive of the transport system. The chassis is designed to accommodate in-pipe diagnostics equipment or other process equipment. The approach is based on the analysis of the machine operating conditions and the chassis design features. The article proposes calculation dependencies for the operational forecasting of energy costs for chassis movement along a pipeline with specified characteristics. The dependencies are obtained based on the methods and approaches of the theory of wheeled and tracked vehicles motion, taking into account the design features of the robot transport system, wheel loading scheme, and track characteristics. An example of calculation for a pipeline of a given configuration is given. Organizational and technical solutions are proposed aimed at improving the safety of operation of the mobile chassis in question as part of a robotic using the principles of duplication and redundancy of systems responsible for movement, and the introduction of a mobile reconnaissance module into the complex for the operational construction of a profile of the pipeline under study in the absence of reliable information about its configuration and actual parameters. Bibliographic references to sources are provided that make it possible to obtain a detailed understanding of the current state of the issue of technical support for in-pipe diagnostics.

Simona-Vasilica Oprea, Adela Bâra

In this paper, we propose a methodology designed to deliver actionable insights that help businesses retain customers. While Machine Learning (ML) techniques predict whether a customer is likely to churn, this alone is not enough. Explainable Artificial Intelligence (XAI) methods, such as SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), highlight the features influencing the prediction, but businesses need strategies to prevent churn. Counterfactual (CF) explanations bridge this gap by identifying the minimal changes in the business–customer relationship that could shift an outcome from churn to retention, offering steps to enhance customer loyalty and reduce losses to competitors. These explanations might not fully align with business constraints; however, alternative scenarios can be developed to achieve the same objective. Among the six classifiers used to detect churn cases, the Balanced Random Forest classifier was selected for its superior performance, achieving the highest recall score of 0.72. After classification, Diverse Counterfactual Explanations with ML (DiCEML) through Mixed-Integer Linear Programming (MILP) is applied to obtain the required changes in the features, as well as in the range permitted by the business itself. We further apply DiCEML to uncover potential biases within the model, calculating the disparate impact of some features.

Ayush Mohanty, Jason Dekarske, Stephen K. Robinson et al.

Robotic manipulators are critical in many applications but are known to degrade over time. This degradation is influenced by the nature of the tasks performed by the robot. Tasks with higher severity, such as handling heavy payloads, can accelerate the degradation process. One way this degradation is reflected is in the position accuracy of the robot's end-effector. In this paper, we present a prognostic modeling framework that predicts a robotic manipulator's Remaining Useful Life (RUL) while accounting for the effects of task severity. Our framework represents the robot's position accuracy as a Brownian motion process with a random drift parameter that is influenced by task severity. The dynamic nature of task severity is modeled using a continuous-time Markov chain (CTMC). To evaluate RUL, we discuss two approaches -- (1) a novel closed-form expression for Remaining Lifetime Distribution (RLD), and (2) Monte Carlo simulations, commonly used in prognostics literature. Theoretical results establish the equivalence between these RUL computation approaches. We validate our framework through experiments using two distinct physics-based simulators for planar and spatial robot fleets. Our findings show that robots in both fleets experience shorter RUL when handling a higher proportion of high-severity tasks.

Xin Jin, Zhu Cao, Yang Tang et al.

In this paper, we consider the partial quantum consensus problem of a qubit network in a distributed view. The local quantum operation is designed based on the Hamiltonian by using the local information of each quantum system in a network of qubits. We construct the unitary transformation for each quantum system to achieve the partial quantum consensus, i.e., the directions of the quantum states in the Bloch ball will reach an agreement. A simple case of two-qubit quantum systems is considered first, and a minimum completing time of reaching partial consensus is obtained based on the geometric configuration of each qubit. Furthermore, we extend the approaches to deal with the more general N-qubit networks. Two partial quantum consensus protocols, based on the Lyapunov method for chain graphs and the geometry method for connected graphs, are proposed. The geometry method can be utilized to deal with more general connected graphs, while for the Lyapunov method, the global consensus can be obtained. The numerical simulation over a qubit network is demonstrated to verify the validity and the effectiveness of the theoretical results.

S.V. Chuprov, A.V. Babkin

Background. With the increasing digitalization of the Russian economy, understanding the dominant trends in the distribution and protection of information flows in a non-stationary space is gaining theoretical and practical significance. An evolving industrial system immersed in such environments is characterized by highly disturbed processes and metamorphoses displayed by nonlinear phenomena, measures of chaos and order of its behavior. On this basis, taking into account the discussed phenomena, the article is aimed at analyzing and ensuring the stability of the industrial system in the face of the impact of both flows of technological and product innovations of the digitalized economy and geopolitical and economic threats that aggravate the behavior of the system. Materials and methods. The theoretical and methodological basis of the study was formed by the doctrines of thermodynamics, statistical physics, nonlinear dynamics, concepts and methods of theories catastrophe, communication and information, cybernetics and synergetics, evolutionary and innovative economics and production management. Results. Using their ideas and analytics, the concepts of entropy, chaos and the effect of the functioning of the system supported by the control information coming into it are revealed. Within the framework of information theory, the influence of signal and noise parameters on information processes in the economy, the features of their transmission and distortion are characterized. In the context of the statistical dependence of the effect of the economic system's activity on the control information accumulated in it, the transformation and interpretation of this exponential dependence with measures of the order of behavior of the industrial system are performed. Nonlinear phenomena of this dependence and the evolution of the industrial system are interpreted in order to achieve its stable effect. Conclusions. The study complements theoretical ideas about the factors ensuring the stability of the functioning of modernized industrial systems and argues for the need for a symbiosis of natural and economic sciences to deepen the analysis and interpretation of the phenomena of the evolution of chaotic industrial systems in a digitalized economy.

Alesia Rudnik

Protest mobilisation and coordination require competences that extend beyond political leadership and communication. Technology, which has become a daily part of humanity, pushes protest leaders to obtain skills in navigating social media to achieve effective communication and leadership. Labour practices behind protest mobilisation are gradually complexifying and require a broadening of our understanding of human actions behind the implementation of technological solutions in the context of political protests. Focusing on the example of the Belarusian protests of 2020, this article examines the human and non-human labour behind the production of protest mobilisation content, protest coordination, and protest reporting. Based on semi-structured interviews with 18 respondents, the paper is the first to examine the practices and routines of Telegram channel editors and moderators, activists, politicians, and marketing specialists. The analysis contributes to our understanding of protest-related labour, which is often unseen and divided between humans and technology, and its consequences for the protest movement.

P. Das, S. Meher, Rutuparna Panda et al.

The automatic segmentation of blood cells for detecting hematological disorders is a crucial job. It has a vital role in diagnosis, treatment planning, and output evaluation. The existing methods suffer from the issues like noise, improper seed-point detection, and oversegmentation problems, which are solved here using a Laplacian-of-Gaussian (LoG)-based modified highboosting operation, bounded opening followed by fast radial symmetry (BOFRS)-based seed-point detection, and hybrid ellipse fitting (EF), respectively. This article proposes a novel hybrid EF-based blood-cell segmentation approach, which may be used for detecting various hematological disorders. Our prime contributions are: 1) more accurate seed-point detection based on BO-FRS; 2) a novel least-squares (LS)-based geometric EF approach; and 3) an improved segmentation performance by employing a hybridized version of geometric and algebraic EF techniques retaining the benefits of both approaches. It is a computationally efficient approach since it hybridizes noniterative-geometric and algebraic methods. Moreover, we propose to estimate the minor and major axes based on the residue and residue offset factors. The residue offset parameter, proposed here, yields more accurate segmentation with proper EF. Our method is compared with the state-of-the-art methods. It outperforms the existing EF techniques in terms of dice similarity, Jaccard score, precision, and F1 score. It may be useful for other medical and cybernetics applications.

K. G, A. J, S. B et al.

We, the Editors and Publisher of the journal Cybernetics and Systems, have retracted the following article: G, Kalaiarasi, Ashok J, Saritha B, and Manoj Prabu M (2023). A Deep Learning Approach to Detecting Objects in Underwater Images. Cybernetics and Systems, 1–16. https://doi.org/10.1080/01969722.2023.2166246 Following publication, the Publisher identified concerns regarding the peer review process for this article. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the article was not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract the article. The Editor-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

Cheng Feng, Kedi Zheng, Yi Wang et al.

The proliferation of novel industrial applications at the wireless edge, such as smart grids and vehicle networks, demands the advancement of cyber-physical systems. The performance of CPSs is closely linked to the last-mile wireless communication networks, which often become bottlenecks due to their inherent limited resources. Current CPS operations often treat wireless communication networks as unpredictable and uncontrollable variables, ignoring the potential adaptability of wireless networks, which results in inefficient and overly conservative CPS operations. Meanwhile, current wireless communications often focus more on throughput and other transmission-related metrics instead of CPS goals. In this study, we introduce the framework of goal-oriented wireless communication resource allocations, accounting for the semantics and significance of data for CPS operation goals. This guarantees optimal CPS performance from a cybernetic standpoint. We formulate a bandwidth allocation problem aimed at maximizing the information utility gain of transmitted data brought to CPS operation goals. Since the goal-oriented bandwidth allocation problem is a large-scale combinational problem, we propose a divide-and-conquer and greedy solution algorithm. The information utility gain is first approximately decomposed into marginal utility information gains and computed in a parallel manner. Subsequently, the bandwidth allocation problem is reformulated as a knapsack problem, which can be further solved greedily with a guaranteed sub-optimality gap. We further demonstrate how our proposed goal-oriented bandwidth allocation algorithm can be applied in four potential CPS applications, including data-driven decision-making, edge learning, federated learning, and distributed optimization.

Yunkang Cao, Xiaohao Xu, Chen Sun et al.

We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models. Existing anomaly segmentation models typically rely on domain-specific fine-tuning, limiting their generalization across countless anomaly patterns. In this work, inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly to leverage diverse multi-modal prior knowledge for anomaly localization. For non-parameter foundation model adaptation to anomaly segmentation, we further introduce hybrid prompts derived from domain expert knowledge and target image context as regularization. Our proposed SAA+ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA, MVTec-AD, MTD, and KSDD2, in the zero-shot setting. We will release the code at \href{https://github.com/caoyunkang/Segment-Any-Anomaly}{https://github.com/caoyunkang/Segment-Any-Anomaly}.

P. Mishro, Sanjay Agrawal, Rutuparna Panda et al.

The fuzzy $C$ -means (FCM) clustering procedure is an unsupervised form of grouping the homogenous pixels of an image in the feature space into clusters. A brain magnetic resonance (MR) image is affected by noise and intensity inhomogeneity (IIH) during the acquisition process. FCM has been used in MR brain tissue segmentation. However, it does not consider the neighboring pixels for computing the membership values, thereby misclassifying the noisy pixels. The inaccurate cluster centers obtained in FCM do not address the problem of IIH. A fixed value of the fuzzifier ( ${m}$ ) used in FCM brings uncertainty in controlling the fuzziness of the extracted clusters. To resolve these issues, we suggest a novel type-2 adaptive weighted spatial FCM (AWSFCM) clustering algorithm for MR brain tissue segmentation. The idea of type-2 FCM applied to the problem on hand is new and is reported in this article. The application of the proposed technique to the problem of MR brain tissue segmentation replaces the fixed fuzzifier value with a fuzzy linguistic fuzzifier value ( ${M}$ ). The introduction of the spatial information in the membership function reduces the misclassification of noisy pixels. Furthermore, the incorporation of adaptive weights into the cluster center update function improves the accuracy of the final cluster centers, thereby reducing the effect of IIH. The suggested algorithm is evaluated using T1-w, T2-w, and proton density (PD) brain MR image slices. The performance is justified in terms of qualitative and quantitative measures followed by statistical analysis. The outcomes demonstrate the superiority and robustness of the algorithm in comparison to the state-of-the-art methods. This article is useful for the cybernetics application.

H. Caswell