• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.3
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• pp.157-168
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• 2014

From a complex systems perspective, ecohydrological systems in forests may be characterized with (1) large networks of components which give rise to complex collective behaviors, (2) sophisticated information processing, and (3) adaptation through self-organization and learning processes. In order to demonstrate such characteristics, we applied the recently proposed 'process networks' approach to a temperate deciduous forest in Gwangneung National Arboretum in Korea. The process network analysis clearly delineated the forest ecohydrological systems as the hierarchical networks of information flows and feedback loops with various time scales among different variables. Several subsystems were identified such as synoptic subsystem (SS), atmospheric boundary layer subsystem (ABLS), biophysical subsystem (BPS), and biophysicochemical subsystem (BPCS). These subsystems were assembled/disassembled through the couplings/decouplings of feedback loops to form/deform newly aggregated subsystems (e.g., regional subsystem) - an evidence for self-organizing processes of a complex system. Our results imply that, despite natural and human disturbances, ecosystems grow and develop through self-organization while maintaining dynamic equilibrium, thereby continuously adapting to environmental changes. Ecosystem integrity is preserved when the system's self-organizing processes are preserved, something that happens naturally if we maintain the context for self-organization. From this perspective, the process networks approach makes sense.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.438-449
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• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.41-55
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• 1995

In this paper, an optimal idenfication method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together wlth optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzzy-neural networks(FNNs) are tuned automatically using improved modified complex method and modified learning algorithm. For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activateti sluge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.363-372
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• 2012

In this study, a new information-based hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all features of system response are amenable to mathematical modeling, hence considering informational alternatives. This may be because (i) the underlying theory is not available or not sufficiently developed, or (ii) the existing theory is too complex and therefore not suitable for modeling within building frame analysis. The role of informational methods is to model aspects that the mathematical model leaves out. Autoprogressive algorithm and self-learning simulation extract the missing aspects from a system response. In a hybrid framework, experimental data is an integral part of modeling, rather than being used strictly for validation processes. The potential of the hybrid methodology is illustrated through modeling complex hysteretic behavior of beam-to-column connections.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.9-11
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• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".

• Journal of Information Science Theory and Practice
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• v.11 no.4
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• pp.14-39
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• 2023

Cybercrime is a significant threat to Internet users, involving crimes committed using computers or computer networks. The landscape of cyberspace presents a complex terrain, making the task of tracing the origins of sensitive data a formidable and often elusive endeavor. However, tracing the source of sensitive data in online cyberspace is critically challenging, and detecting cyber-criminals on the other hand remains a time-consuming process, especially in social networks. Cyber-criminals target individuals for financial gain or to cause harm to their assets, resulting in the loss or theft of millions of user data over the past few decades. Forensic professionals play a vital role in conducting successful investigations and acquiring legally acceptable evidence admissible in court proceedings using modern techniques. This study aims to provide an overview of forensic investigation methods for extracting digital evidence from computer systems and mobile devices to combat persistent cybercrime. It also discusses current cybercrime issues and mitigation procedures.

• Steel and Composite Structures
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• v.50 no.4
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• pp.375-382
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• 2024

The network theory studies interconnection between discrete objects to find about the behavior of a collection of objects. Also, nanomaterials are a collection of discrete atoms interconnected together to perform a specific task of mechanical or/and electrical type. Therefore, it is reasonable to use the network theory in the study of behavior of super-molecule in nano-scale. In the current study, we aim to examine vibrational behavior of spherical nanostructured composite with different geometrical and materials properties. In this regard, a specific shear deformation displacement theory, classical elasticity theory and analytical solution to find the natural frequency of the spherical nano-composite structure. The analytical results are validated by comparison to finite element (FE). Further, a detail comprehensive results of frequency variations are presented in terms of different parameters. It is revealed that the current methodology provides accurate results in comparison to FE results. On the other hand, different geometrical and weight fraction have influential role in determining frequency of the structure.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.204-211
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• 2020

The era of the 4th industrial revolution is a complex environment in which the cyber world and the physical world are integrated and interacted. In order to successfully implement and be sustainable the 4th industrial revolution of hyper-connectivity, hyper-convergence, and hyper-intelligence, not only the technological aspects that implemented digitalization but also the social aspects must be recognized and dealt with as important. There are socio-technical systems and socio-technical systems theory as concepts that describe systems involving complex interactions between the environmental aspects of human, mechanical and tissue systems. This study confirmed how the Socio-technical System was applied in the research literature for the last 10 years through machine learning-based analysis. Eight clusters were derived by performing co-occurrence keywords network analysis, and 13 research topics were derived and analyzed by performing a structural topic model. This study provides consensus and insight on the social and technological perspectives necessary for the sustainability of the 4th industrial revolution.

• Journal of the Korean Ceramic Society
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• v.53 no.2
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• pp.150-161
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• 2016

The conventional brick-layer model is not satisfactory either in theory or in practice for the description of dispersive responses of polycrystalline solid electrolytes with current-constriction effects at the grain boundaries. Parallel networks of complex dielectric functions have been shown to successfully describe the AC responses of polycrystalline sodium conductors over a wide temperature and frequency range using only around ten model parameters of well-defined physical significance. The approach can be generally applied to many solid electrolyte systems. The present work illustrates the approach by simulation. Problems of bricklayer model analysis are demonstrated by fitting analysis of the simulated data under experimental conditions.

• Advances in Computational Design
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• v.7 no.1
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• pp.1-17
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• 2022

In this study, a new strategy is presented to transmit the fundamental elastic beam problem into the modern optimization platform and solve it by using artificial intelligence (AI) tools. As a practical example, deflection of Euler-Bernoulli beam is mathematically formulated by 2nd-order ordinary differential equations (ODEs) in accordance to the classical beam theory. This fundamental engineer problem is then transmitted from classic formulation to its artificial-intelligence presentation where the behavior of the beam is simulated by using neural networks (NNs). The supervised training strategy is employed in the developed NNs implemented in the heuristic optimization algorithms as the fitness function. Different evolutionary optimization tools such as genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve this non-linear optimization problem. The step-by-step procedure of the proposed method is presented in the form of a practical flowchart. The results indicate that the proposed method of using AI toolsin solving beam ODEs can efficiently lead to accurate solutions with low computational costs, and should prove useful to solve more complex practical applications.