• Annals of Clinical Neurophysiology
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• v.22 no.2
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• pp.82-91
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• 2020

Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer's disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.139-148
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• 2024

In this paper, we propose a two-degree-of-freedom snake robot head system and an I-PID (Intelligent Proportional-Integral-Derivative)-based controller utilizing RBF (Radial Basis Function) neural network and adaptive robust terms as a control strategy to reduce rotation occurring in the snake robot head. This study proposes a two-degree-of-freedom snake robot head system to avoid complex snake robot dynamics. This system has a control system independent of the snake robot. Subsequently, it utilizes an I-PID controller to implement a control system that can effectively manage rotation at the snake robot head, the robot's nonlinearity, and disturbances. To compensate for the time delay estimation errors occurring in the I-PID control system, an RBF neural network is integrated. Additionally, an adaptive robust term is designed and integrated into the control system to enhance robustness and generate control inputs responsive to signal changes. The proposed controller satisfies stability according to Lyapunov's theory. The proposed control strategy was tested using a 9-degreeof-freedom snake robot. It demonstrates the capability to reduce rotation in Lateral undulation, Rectilinear, and Sidewinding locomotion.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.109-127
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• 2014

This study is devised out of the recognition that the existing standardization-related research has not sufficiently examined the overall social environment where a standard is actually made and diffused and the roles of the actors and the changes in them in the complex social system where multiple stakeholders exist. Against this backdrop, this study purports to reconstruct the dynamic process of developing and standardizing an innovative technology through a socio-technical approach involved by multiple stakeholders with different interests in the context of a socio-technical institutional environment. The specific goals to achieve the purpose include first, inspecting the characteristics of the interactions between the human actors and between the human and non-human actors in the socio-technical network surrounding a standardization process. Second, the study aimed to observe the activities of the focal actor who led the standardization process and its changing role. To that end, it analyzed the dynamic features of the process of standardizing a HD 3DTV broadcasting technology that took place in South Korea based on the actor network theory. As for the analysis method, the researchers personally took part in the actor network involving the new technology to analyze the dynamic characteristics of the network, applying the qualitative research method of survey and in-depth interviews and exploring the overall dynamics of environment, behavior and technology observed over the course of the entire standardization process.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1443-1450
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• 2017

The purpose of this study is to develop an intelligent parking dispatching system based on LoRa network technology. During the local festival, many tourists come into the festival site simultaneously after sunset. To handle the traffic jam and parking dispatching, many traffic management staffs are engaged in the main road to guide the cars to available parking lots. Nevertheless, the traffic problems are more serious at the peak time of festival. Such parking dispatching problems are complex and real-time traffic information dependent. We used Queuing theory to predict inbound traffics and to measure parking service performance. Q-learning algorithm is used to find fastest routes and dispatch the vehicles efficiently to the available parking lots.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.427-439
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• 2013

With the fast development of complex science, lots of social networks are studied. We know that the social network is widely applied in analyzing issues in human culture, economics and web sciences. Recently we witness that some researchers began to compare the social network constructed from fiction literatures(literature social network) and the real social network obtained from practice. But we point that previous approaches for literature social network have some drawbacks since they completely depend on the biographical dictionary constructed for a designated literature. So since the previous approach focus on the few important characters and peoples around them, we can not understand the global structure of all characters appeared in the literature at least once. We propose one method to extract all characters appeared in the literature and how to make the social network from that information. Also we newly propose K-critical network by applying frequency of the named characters and the strength of relationship among all textual characters. Our experiment shows that the K-critical measure could be one crucial quantitative measure to compute the relationship strength among characters appeared in the object literature.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.321-329
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• 2000

Welding is essential for the manufacture of a range of engineering components which may vary from very large structures such as ships and bridges to very complex structures such as aircraft engines, or miniature components for microelectronic applications. Especially, a domestic situation of the welding automation is still depend on the arc sensing system in comparison to the vision sensing system. Specially, the gap-detecting of workpiece using conventional arc sensor is proposed in this study. As a same principle, a welding current varies with the size of a welding gap. This study introduce to the fuzzy membership filter to cancel a high frequency noise of welding current, and ART2 which has the competitive learning network classifies the signal patterns the filtered welding signal. A welding current possesses a specific pattern according to the existence or the size of a welding gap. These specific patterns result in different classification in comparison with an occasion for no welding gap. The patterns in each case of 1mm, 2mm, 3mm and no welding gap are identified by the artificial neural network.

• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1021-1025
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• 2009

A novel supramolecular network containing binuclear copper unit $[Cu(phen)_{2}({\mu}-ID\;A)Cu(phen){\cdot}(NO_{3})](NO_{3}){\cdot}4(H_{2}O)$ (1) was synthesized through the self-assembly of iminodiacetic acid ($H_2IDA$) and 1,10-phenanthroline (phen) in the condition of pH = 6. It has been characterized by the infrared (IR) spectroscopy, elemental analysis, single crystal X-ray diffraction, and thermogravimetric analysis (TGA). 1 shows a 2-D supramolecular structure assembled through strong and unique $\pi-\pi$ packing interactions. Density functional theory (DFT) calculations show that theoretical optimized structures can well reproduce the experimental structure. The TGA and powder X-ray diffraction (PXRD) curves indicate that the complex 1 can maintain the structural integrity even at the loss of free water molecules. The magnetic property is also reported in this paper.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.26-51
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• 2003

Large scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in thePDB. PSIMAP incorporates both functional and evolutionary information into a single network. It makes it possible to age protein domains in terms of taxonomic diversity, interaction and function. One consequence of it is to predict the most important protein domain structure in evolution. We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: ${\bullet}$ Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. ${\bullet}$ Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. ${\bullet}$ Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. This led to the prediction of the oldest and most important protein domain in evolution of lift. ${\bullet}$ Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.100-107
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• 2024

Currently, in Korea, due to the rapid aging and deterioration of facilities, the minimum Maintenance Level and Performance Level' of facilities are required by the 'Facility Safety Act' or 'Infrastructure Management Act'. Since infrastructure assets have a long lifespan and the pattern of deterioration over time is complex, it is very difficult to maintain infrastructure as 'minimum maintenance state' or 'minimum performance state' by the current way of management. 'Asset Management' shall be performed not only by a technical perspective, but also by an accounting perspective such as cost and asset value. However, due to lack of awareness of 'asset management' among stakeholder, only technical perspective management is being carried out in practice. In order to effectively manage infrastructure assets, complex consideration of various asset value factors such as budget and service as well as safety and durability are required. In this paper, we presented a theory to evaluate and quantify the road network value for efficient asset management of the road network. We also presented a method of simulation to apply the theory presented in this paper. Through simulation and the results derived from this study, it is possible to specify the budget for the future national asset management, and to optimize the strategy for the management of old road facilities.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.12
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• pp.885-893
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• 2006

The Bayesian network(BN) has emerged in recent years as a powerful technique for handling uncertainty iii complex domains. Parameter learning of BN to find the most proper network from given data set has been investigated to decrease the time and effort for designing BN. Off-line learning needs much time and effort to gather the enough data and since there are uncertainties in real world, it is hard to get the complete data. In this paper, we propose an online learning method of Bayesian network parameters from incomplete data. It provides higher flexibility through learning from incomplete data and higher adaptability on environments through online learning. The results of comparison with Voting EM algorithm proposed by Cohen at el. confirm that the proposed method has the same performance in complete data set and higher performance in incomplete data set, comparing with Voting EM algorithm.