• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.351-354
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• 2008

유비쿼터스 네트워크의 실현을 위하여 다양한 연구가 진행되면, CR (Cognitive Radio) 구성의 연구는 주파수 자원의 효율적인 활용에 관한 연구를 부각시키고 있다. 또한, CR 적용형 소출력 무선기기의 활용에 따른 공유주파수대역의 소요대역폭에 대한 논의와 공통으로 사용하는 주파수 대역 내에서 혼용되는 통신기기들의 user 수를 만족하는 총 소요대역폭 산출은 더불어 중요한 작업이 되었다. 공통으로 사용하는 주파수 대역의 총 소요대역폭 산출은 소출력 무선기기의 간섭 회피 기술로 사용되는 Frequency Hopping (FH) 및 Listen Before Talk (LBT) 방식이 고려되어야 한다. 본 논문에서는 Cognitive Radio (CR) 기반의 LBT 방식을 사용하는 ZigBee와 FH 방식을 사용하는 DCP, RFID, Bluetooth 등의 소출력 무선기기가 공통으로 사용하는 주파수 대역에 공존할 경우를 가정하고, 대기행렬이론 (Queuing Theory)을 적용하여 소요대역폭 산출을 수행했다. 채널수 별 user의 통신시도에 따른 throughput을 분석한 결과, throughput 84% 이상의 조건에서 FH 및 LBT 방식을 사용하는 250mW 소출력 무선기기들이 공존하는 공유주파수대역의 적정 채널수는 35개를 가지며, 총 소요대역폭은 채널수와 채널대역폭의 곱으로 산출이 가능함을 보였다. 이러한 접근방법은 다른 통신시스템의 소요대역폭 산출에도 유용할 것이다.

• Computational Structural Engineering
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• v.7 no.3
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• pp.4-15
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• 1994

현재 구조최적화는 아직도 실무설계에서 제 위상을 찾지 못하고 있다. 그 원인은 주로 지금까지의 대부분의 연구가 알고리즘 위주로, 교과서적 예제위주로 치달았기 때문이며 따라서 오늘날과 같은 고도의 전산화시대에도 실무설계자들에게 외면당하고 있는 실정이다. 앞으로 구조최적화 분야의 전문가들이 실무설계문제 응용위주의 연구개발에 주력함으로써 이러한 문제는 쉽게 극복될 것이며, 실무설계자들도 최적설계가 무엇인지 제대로 알지도 못하면서 외면만 하고 매도만 할 것이 아니라, 오늘날 멀티미디어 초고성능 PC시대에 막대한 정보 및 자료의 처리능력을 갖춘 CD롬과 고성능 통신기능, 고도의 음성, 문자, 영상인식 Input Media, 그리고 윈도우, 펜티엄 같은 현재의 OS와 OS/2, 시카고 같은 차세대 OS체계 하에서 고도의 CAD/CAD Expert 시스템이 실용화 되려면 최적설계는 재래적인 설게방법을 대치하는 시스템 내의 핵심설계코드가 되지 않을 수 없다는 점을 인식해야 할 것이다. 어차피 가까운 장래에 현재의 이론과 응용사이의 lAG와 실무설계자들의 오해가 해소되는 날이 오면 최적설계는 지금의 MPC시대는 물론 인공지능형, 사고형 차세대 컴퓨터 시대에 적합한 현대적인 구조설계법이 될 것임을 확신하는 바이다.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.283-289
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• 2021

Many people with disabilities have shown interest in artificial intelligence speakers that serves as the main hub of the smart home. Therefore, the purpose of this study was to identify the intention of people with disabilities to use such speakers. The focus is on those with physical disabilities, a segment that accounts for the largest number of disability types. Based on the theoretical model of technology acceptance, the effect of perceived ease of use and perceived usefulness of artificial intelligence speakers by people with disabilities was analyzed using Structural Equation Modeling (SEM). Research has confirmed that the technology acceptance model is suitable for identifying the intention to use artificial intelligence speakers by people with disabilities, and specifically that the perceived ease of use has a significant impact on usefulness. Furthermore, the perceived ease of use for people with disabilities did not have a statistically significant effect on their intent to use whereas the perceived usefulness was shown to have a significant effect on the same. This study is meaningful as a foundation for developing customized artificial intelligence speaker services and improving the use of artificial intelligence speakers by people with disabilities.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.140-145
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• 2010

In this work, to theoretically analyze the nonlinear charging characteristic, a Radial Basis Function Neural Network (RBFNN) is adopted. Based on the RBFNN, an charging characteristic tendency of a Linear Type Magnetic Flux Pump (LTMFP) is analyzed. In the paper, we developed the LTMFP that generates stable and controllable charging current and also experimentally investigated its charging characteristic in the cryogenic system. From these experimental results, the charging current of the LTMFP was also found to be frequency dependent with nonlinear quality due to the nonlinear magnetic behaviour of superconducting Nb foil. On the whole, in the case of essentially cryogenic experiment, since cooling costs loomed large in the cryogenic environment, it is difficult to carry out various experiments. Consequentially, in this paper, we estimated the nonlinear characteristic of charging current as well as realized the intelligent model via the design of RBFNN based on the experimental data. In this paper, we view RBF neural networks as predominantly data driven constructs whose processing is based upon an effective usage of experimental data through a prudent process of Fuzzy C-Means clustering method. Also, the receptive fields of the proposed RBF neural network are formed by the FCM clustering.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.101-110
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• 2020

Devices that add emotion-specific services or various functions are appearing in AI speakers and related devices. To this end, this study performed topic modeling analysis on the topics of post-purchase texts written by AI speaker users, and compared them with the data collected via survey questionnaires. Furthermore, data on the emotional intelligence of AI speakers and relationship quality were collected from 600 users and analyzed using structural equation modeling. The findings of the study are as follows: First, the analysis results of topic modeling showed that most of the articles mainly mention the functional aspects of AI speakers. Second, emotional intelligence of AI speaker perceived by consumer affected relationship quality, and relationship quality had a positive effect on customer satisfaction. Therefore, this study expands the area of AI research by integrating the concept of emotional intelligence and relationship quality to provide new theoretical and practical implications.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.674-682
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• 2003

Ship autonomous navigation is designated as what computerizes mental faculties possessed of navigation experts, which are building navigation plans, grasping the situation, forecasting the fluctuation, and coping with the situation. An autonomous navigation system, which consists of several subsystems such as navigation system, a collision avoidance system, several data fusion systems, and a motion control system, is based on an intelligent control architecture for the sake of integrating the systems. The motion control system, which is one of the most essential system in autonomous navigation system, controls its propulsion and steering gears to move the ship satisfying its hydrodynamic characteristics. This paper is the study on the ship movement control system and its implementation which are totally developed and run on virtual-world system. Receiving the high-level control values such as a waypoint presented from the collision avoidance system, the motion control system generates them to low-level control values for propulsion and steering devices. In the paper, we develop a ship motion controller using Oldenburger's theory based on mathematical fundamentals, and simulate it with various scenarios in order to verify its performance.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.11
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• pp.610-620
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• 2003

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.