• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.307-313
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• 2024

This study explores the integration of Explainable Artificial Intelligence (XAI) and network science in healthcare, focusing on enhancing healthcare data interpretation and improving diagnostic and treatment methods. Key methodologies like Graph Neural Networks, Community Detection, Overlapping Network Models, and Time-Series Network Analysis are examined in depth for their potential in patient health management. The research highlights the transformative role of XAI in making complex AI models transparent and interpretable, essential for accurate, data-driven decision-making in healthcare. Case studies demonstrate the practical application of these methodologies in predicting diseases, understanding drug interactions, and tracking patient health over time. The study concludes with the immense promise of these advancements in healthcare, despite existing challenges, and underscores the need for ongoing research to fully realize the potential of AI in this field.

• Korean System Dynamics Review
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• v.13 no.1
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• pp.113-131
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• 2012

Applying social network analysis techniques, this study examines complex system characteristics of industry-university joint research R&D networks. In specific, it focuses on whether these R&D networks comply with the power law, whose system typically presents the-rich-get-richer and the-poor-get-poor patterns. The basic data come from 7,751 industry-university joint research projects, all of which were carried out by Daejeon, Chungbuk, and Chungnam-based universities from January 2005 to October 2008. The empirical results reveal that the R&D networks abide by the power law. That is, a handful of business units and universities command an overwhelming majority in the joint links, indicating positive feedback dominance within the system.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.73-82
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• 2002

It is difficult to find the shortest paths using existing algorithms (Dijkstra, Floyd-Warshall algorithm, and etc) in high complex and large size real traffic networks The objective of this paper is to develop an evolution program to find the multiple shortest paths within reasonable time in these networks including turn-restrictions, U-turns, and etc.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.94-100
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• 2010

Nowadays, several nontraditional machining(NTM) processes are widely used to machine a complex and accurate shape part of hard materials, such as titanium, ceramics, high strength temperature resistant and refractory materials which are difficult to machine and having high strength, hardness, toughness. Machining of these complex shapes in such materials by traditional machining processes are very difficult. The NTM processes is important in the areas of micro- and nano scale machining, where high accuracy and superior surface characteristics are required, which can only be achieved using these NTM processes. So, for effective selection of different NTM processes, careful decision making for a given NTM application is often necessary. An appropriate NTM process for a given material and shape condition is very difficult for the novice engineers. In this paper, an expert system based on an analytic network process(ANP) is suggested for a best selection of NTM process in a NTM application considering an prior interdependency effect among various factors.

• Development and Reproduction
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• v.16 no.4
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• pp.265-270
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• 2012

Ethanol actions in the amygdala formation may underlie in part the reinforcing effects of ethanol consumption. Previously a physiological phenomenon in the basolateral amygdala (BLA) that is dependent on neuronal network activity, compound postsynaptic potentials (cPSPs) were characterized. Effects of acute ethanol application on the frequency of cPSPs were subsequently investigated. Whole cell patch clamp recordings were performed from identified projection neurons in a rat brain slice preparation containing the amygdala formation. Acute ethanol exposure had complex effects on cPSP frequency, with both increases and decreases dependent on concentration, duration of exposure and age of the animal. Ethanol produces complex biphasic effects on synaptically-driven network activity in the BLA. These findings may relate to subjective effects of ethanol on arousal and anxiolysis in humans.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

• IE interfaces
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• v.3 no.2
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• pp.39-51
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• 1990

One of the most important and powerful tools available for design and/or study of the operation of complex systems and processes is simulation. Since automated material handling systems like AS/RS are often quite complex, a network-based simulation model is developed to analyze an automobile part supplier's automated storage and retrieval system(AS/RS). The network simulation model is implemented in the SLAM Ⅱ on a VAX 8800 computer. Performance of the AS/RS was tested for 3 dispatching rules, 3 work load levels, 2 storage policies, 3 levels of stacker crane break-down, and 2 conveyor system layouts. Results indicate that the AS/RS performance is primarily affected by the dispatching rule and work load level.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.543-552
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• 1994

This paper presents a decentralized quadratic regulation architecture with feedforward neural networks for the control problem of complex systems. In this method, the decentralized technique was used to treat several simple subsystems instead of a full complex system in order to reduce training time of neural networks, and the neural networks' nonlinear mapping ability is exploited to handle the nonlinear interaction variables between subsystems. The decentralized regulating architecture is composed of local neuro-controllers, local neuro-identifiers and an overall interaction neuro-identifier. With the interaction neuro-identifier that catches interaction characteristics, a local neuro-identifier is trained to simulate a subsystem dynamics. A local neuro-controller is trained to learn how to control the subsystem by using generalized Backprogation Through Time(BTT) algorithm. The proposed neural network based decentralized regulating scheme is applied in the power System Stabilization(PSS) control problem for an imterconnected power system, and compared with that by a conventional centralized LQ regulator for the power system.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.7-11
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• 2007

The aim of this paper is to design and implement a new ECG signal monitoring and analysis method for the home care of elderly persons or patients, using wireless sensor network (WSN) technology. The wireless technology for home-care purpose gives new possibilities for monitoring of vital parameter with wearable biomedical sensors and will give the patient freedom to be mobile and still be under continuously monitoring. Developed platform for portable real-time analysis of ECG signals can be used as an advanced diagnosis and alarming system. The ECG features are used to detect life-threatening arrhythmias, with an emphasis on the software for analyzing the P-wave, QRS complex, and T-wave in ECG signals at server after receiving data from base station. Based on abnormal ECG activity, the server transfer diagnostic results and alarm conditions to a doctor's PDA. Doctor can diagnose the patients who have survived from arrhythmia diseases.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.487-494
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• 2000

본 논문에서는 배경잡음이 섞여 있는 QRS 파의 증대를 위해 신경망에 근거한 적응라인증대기(ALE) 적용을 다루고 있다. Elman과 Jordan RNN 구조의 합성형태를 갖는 수정된 완전연결 리커런트 신경망이 ALE의 비션형 적응필터로 사용되고 있다. 신경망 노드사이의 연결계수와 이득, 기울기, 지연과 같은 노드 활성함수의 변수들이 기울기 강하 알고리즘을 사용하여 학습이 반복될 때마다 갱신된다. 수정된 신경망은 먼저 미지의 선형과 비선형 시스템 identification을 수행함으로써 평가하였다. 그리고 미약한 QRS를 증대시키기 위해서 적당한 크기의 잡음과 매우 심한 잡음이 포함된 실제의 ECG 신호를 비선형 신경망 적응필처를 사용하는 ALE에 입력하였다. 수정된 신경망은 시스템 identification에 사용하기가 적합함을 확인하였으며, 시뮬레이션 결과에 의하면 신경망 ALE는 잡음 ECG 신호로부터 QRS 파를 증대를 잘 수행하였다.