In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not *** pro...
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Within the paradigm of Evolving and Adaptive Intelligent systems, the Electrical Power System (EPS) represents a Critical Infrastructure demanding robust reliability metrics. This study pioneers the application of Var...
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ISBN:
(数字)9798350366235
ISBN:
(纸本)9798350366242
Within the paradigm of Evolving and Adaptive Intelligent systems, the Electrical Power System (EPS) represents a Critical Infrastructure demanding robust reliability metrics. This study pioneers the application of Variable Neighborhood Search (VNS) heuristic to the Optimal Switch Allocation (OSA) problem in the EPS. Adapting neighborhood structures and local search methods, we consider the joint allocation of Manual Switches and Remote-Controlled switches, addressing interdependencies. Results highlight VNS efficacy in navigating OSA challenges, showcasing its adaptability in an evolving system. Comparative analyses endorse VNS as a valuable tool for addressing EPS reliability within the dynamic landscape of evolving and adaptive intelligent systems.
The key goals in learning Bayesian networks (BNs) from data are to identify significant statistical relationships between variables and to build a Directed Acyclic Graph (DAG) that represents these relationships throu...
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ISBN:
(数字)9798350366235
ISBN:
(纸本)9798350366242
The key goals in learning Bayesian networks (BNs) from data are to identify significant statistical relationships between variables and to build a Directed Acyclic Graph (DAG) that represents these relationships through Joint Probability Distributions. Most research relies on score-based or conditional test methods for model selection. However, when using real-world data, it can be challenging to identify whether the learned DAG represents the underlying relations inherent in the limited datasets, particularly when evaluating data obtained from multiple independent sources. This study presents a methodology to assess the credible interval for both the existence and direction of each edge within Bayesian networks derived from data. Furthermore, it explores the fusion of models acquired from distinct and independent datasets. This approach enables the Bayesian learning of Bayesian Networks (BNs) from data by treating the uncertainty associated with the existence and orientation of each edge as a random variable. By evaluating the probability of the orientation of each edge, it is possible to suggest the existence of a potential latent variable within the dataset. If an edge exhibits equiprobable directions and is verified to exist, it becomes a plausible hypothesis for a latent variable. The Fast Causal Algorithm, originally introduced by [1], is the foundation of this approach. Finally, by employing a maximum a posteriori estimation, the most prominent edges and their respective orientations are identified and employed to create the leading DAG. We present our findings in simulated datasets with different length sizes. By comparing the structure of the learned DAGs with existing structures and evaluating the inference capabilities of the final BN, we establish that our approach achieves results comparable to the most recent studies in the field, while offering insights into the model’s reliability and improving the use of the data.
The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition ...
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The article presents the development of an ontology for the Taxonomic List of Soils in Bulgaria (according to the FAO World System). In addition to soil types, the ontology includes knowledge about the different agro-...
The article presents the development of an ontology for the Taxonomic List of Soils in Bulgaria (according to the FAO World System). In addition to soil types, the ontology includes knowledge about the different agro-climatic regions in Bulgaria that are suitable for cultivating various agricultural crops. The ontology is part of the knowledge base of the ZEMELA platform for smart agriculture.
Effective communication is crucial when working on complex or wicked problems in interdisciplinary, international teams. Developing a shared understanding of critical concepts and aligning knowledge, focus, and resour...
Effective communication is crucial when working on complex or wicked problems in interdisciplinary, international teams. Developing a shared understanding of critical concepts and aligning knowledge, focus, and resources can be problematic when stakeholders speak different languages, span several disciplines and work within different national or industry-based norms. Using visual tools to represent critical concepts and their relations, and map out our strategic goals has greatly helped combat such issues. This paper recounts the use and impact of adopting visual tools within the transnational and interdisciplinary collaborative environment in the BC4ECO project's international consortium. The Miro Board and the canvas approach greatly helped represent, visualise, and thus concretise the structure and contents of our collaboration, which takes place within a 3 years ERASMUS+ framework that builds and delivers Summer School, MOOCS as well as teacher and multiplier events about harnessing the potential of Distributed Ledger Technologies (DLTs) for environmental sustainability. As the materials and MOOCS are expected to become Open Education Resources and have a wide impact, it is particularly crucial to get the contents as well as the pedagogy right, in spite of the team's differences in almost any dimension of background and culture.
The increasing reliance on intelligent transportation systems (ITS) for traffic management has simultaneously heightened the potential for cybersecurity threats. Malicious cyber attacks on such systems can lead to ope...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
The increasing reliance on intelligent transportation systems (ITS) for traffic management has simultaneously heightened the potential for cybersecurity threats. Malicious cyber attacks on such systems can lead to operational inefficiencies, heightened congestion, and compromised safety. This work introduces a stealthy integrity attack tailored for ramp metered freeway systems. The attack model is conceptualized as a closed-loop dynamical system, formulated as an optimization problem based on the Cell Transmission Model. This work further proposes a distributed detection mechanism designed to identify attack residuals, which, by incorporating an adaptive threshold scheme, can detect the presence of an attack. To demonstrate the efficacy of our detection scheme, we present a scenario illustrating its application in freeway road networks.
Rapid urbanization has expanded urban agglomerations, escalating carbon emissions from increased transportation needs. These agglomerations have distinct multi-modal transportation systems. Analyzing their carbon foot...
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Considering potential threats to cyber-physical systems such as component faults and stealthy cyber-attacks, an adaptive observer-based threat discrimination method is proposed for identifying the occurring threat typ...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
Considering potential threats to cyber-physical systems such as component faults and stealthy cyber-attacks, an adaptive observer-based threat discrimination method is proposed for identifying the occurring threat type. Typically, stealthy attacks have only weak effects easily obscured by disturbances on the system outputs. To solve this problem, a parameter adaptation algorithm based on a newly designed dynamic adaptive gain generator is proposed, aiming at improving the sensitivity of the adaptive threat discrimination scheme to potential threats. Only the strictly positive real condition of the proposed gain generator sufficiently ensures the stability of the adaptive observer error system. A moment-matching method is then developed to determine the proper parameters of the gain generator, allowing for the improvement of the sensitivity of the threat discriminators. A numerical example to demonstrate the effectiveness of the proposed methodology is presented.
We use fixed relays deployed by network operators to reduce re-transmission (thereby reducing network power requirements) in addition to providing excellent end-to-end error performance in a revisit to automatic repea...
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