• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.76.5-76
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• 2001

This paper considers the parameter estimation for the state-space model based time-delay systems in the case that the Lyapunov stability of the system is guaranteed. In order to estimate the parameters, two estimation methods can be proposed which are known as the parallel model and the series parallel model. It is shown that the parameters can be estimated using each method, and also certied that the results are correct by simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• 한국지능시스템학회논문지
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• 제7권5호
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• pp.21-27
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• 1997

Neuro-fuzzy network that combined advantages of the neural network in learning and fuzzy system in inferencing can be used to establish a system model in the design of a controller. In this paper, we presented the neuro-fuzzy system that can be able to generated a linguistic fuzzy model which results in a similar input/output response to the original system. The network was used to model a system. We tested the performance ot the neuro-fuzzy network through computer simulations.

• 한국통신학회논문지
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• 제35권11B호
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• pp.1715-1724
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• 2010

본 논문은 미군의 국방정보체계에 대한 상호운용성 평가모델로 개발된 LISI(Level of Information System Interoperability) 모델을 기반으로 국방과학연구소에서 한국군 환경에 적합하도록 개선시킨 한국군의 LISI 모델의 제한사항을 분석하고 대안을 제시한다. 한국군의 LISI 모델은 국방 상호운용성의 유일한 평가기관인 국군지휘통신사령부(이하 "국통사") 예하의 "합동 상호운용성 기술센터"에서 획득규정에서 정의한 모든 체계(정보체계, 무기체계, 비무기체계)의 상호운용성 평가시 적용하고 있다. 그러나, LISI 모델은 기본적으로 정보체계간 상호운용성을 평가하는 모델로 개발되었으므로 NCW 환경에서 요구되는 정보체계와 무기 및 비무기체계를 포함하는 복합체계 상호운용성 평가에 미흡하기 때문에 최근 미군은 SOSI(System of Systems Interoperability) 모델을 연구개발하고 있다. 따라서 미군의 LISI 모델을 벤치마킹하여 개선시킨 한국군의 LISI 모델은 국방정보체계는 물론 우기 및 비무기체계의 상호운용성을 평가하는 도구로 사용하기에는 근본적인 제한사항을 가질 수밖에 없으며, 이러한 제한사항을 보완하기 위해 국방부와 관련기관에서 연구를 통하여 개선대책을 제안하고 있다. 본 논문은 LISI 모델의 응용(P), 기반(I), 데이터(D) 분야 평가기준 및 평가가 완료된 단위체제간의 비교평가 절차에 대한 제한사항을 분석하고, 이를 극복하기 위한 정책적 대안을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

• 대한전기학회논문지:전력기술부문A
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• 제53권5호
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• pp.266-274
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• 2004

This paper presents a scheme for the interconnection of dispersed generator systems(DGs) based on load .unbalance and load model in composite distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The unbalance is involved with many single-phase line segment. . Voltage profile improvement and system loss minimization by installation of DGs depend greatly on how they are placed and operated in the distribution systems. So, DGs can reduce distribution real power losses and replace large-scale generators if they are placed appropriately in the distribution systems. The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm. The method proposed is applied to IEEE 13 bus and 34 bus test systems to demonstrate its effectiveness.

• Journal of Multimedia Information System
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• 제8권4호
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• pp.259-266
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• 2021

Security vulnerabilities have been reported in major design software systems such as Adobe Photoshop and Illustrator, which are recognized as de facto standard design tools in most of the design industries. Companies need to evaluate and manage their risk levels posed by those vulnerabilities, so that they could mitigate the potential security bridges in advance. In general, security vulnerabilities are discovered throughout their life cycles repeatedly if software systems are continually used. Hence, in this study, we empirically analyze risk levels for the three major graphical design software systems, namely Photoshop, Illustrator and GIMP with respect to a software vulnerability discovery model. The analysis reveals that the Alhazmi-Malaiya Logistic model tends to describe the vulnerability discovery patterns significantly. This indicates that the vulnerability discovery model makes it possible to predict vulnerability discovery in advance for the software systems. Also, we found that none of the examined vulnerabilities requires even a single authentication step for successful attacks, which suggests that adding an authentication process in software systems dramatically reduce the probability of exploitations. The analysis also discloses that, for all the three software systems, the predictions with evenly distributed and daily based datasets perform better than the estimations with the datasets of vulnerability reporting dates only. The observed outcome from the analysis allows software development managers to prepare proactively for a hostile environment by deploying necessary resources before the expected time of vulnerability discovery. In addition, it can periodically remind designers who use the software systems to be aware of security risk, related to their digital work environments.

• 지능정보연구
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• 제5권1호
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• pp.13-34
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• 1999

Building a knowledge base effectively has been an important research area in the expert systems field. A variety of approaches have been studied including rules, semantic networks, and frames to represent the knowledge base for expert systems. As the size and complexity of the knowledge base get larger and more complicated, the integration of knowledge based with database technology cecomes more important to process the large amount of data. However, relational database management systems show many limitations in handing the complicated human knowledge due to its simple two dimensional table structure. In this paper, we propose Object-Oriented Knowledge Store (OOKS), a knowledge base model on the basis of a frame sturcture using an object-oriented database. In the proposed model, managing rules for inferencing and facts about objects in one uniform structure, knowledge and data can be tightly coupled and the performance of reasoning can be improved. For building a knowledge base, a knowledge script file representing rules and facts is used and the script file is transferred into a frame structure in database systems. Specifically, designing a frame structure in the database model as it is, it can facilitate management and utilization of knowledge in expert systems. To test the appropriateness of the proposed knowledge base model, a prototype system has been developed using a commercial ODBMS called ObjectStore and C++ programming language.

• 대한산업공학회지
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• 제41권2호
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• pp.173-184
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• 2015

The application of radio frequency identification (RFID) sensor-tags in cold chain systems has recently received a great deal of attention. To design cold chain systems with RFID sensor-tags that minimize the initial investment and operational cost while fulfilling the functional and operational requirements, simulation study is one of the preferable and effective approaches. To simulate the possible design configurations, the individual components in a cold chain system can be extracted and implemented as a DEVS (Discrete Event System Specification) model. Based on the proposed DEVS model, a new cold chain simulation model can be efficiently created by simply connecting each DEVS model around the RFID sensor-tag of interest in sequence according to the structure of the cold chain system, and then executed (or simulated) on Java programming environments by the DEVSJAVA simulator. As a result of simulation, some key performance indexes such as reliability, accuracy or timeliness can be calculated and used to choose better components or to compare different system configurations of cold chain systems.

• 제어로봇시스템학회논문지
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• 제8권5호
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• pp.385-392
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• 2002

This paper introduces a new approach to analysis of error convergence for a class of iterative teaming control systems. Firstly, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established if the form of T-S fuzzy model. We analyze the error convergence in the sense of induced L$_2$-norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative teaming controller design problem to guarantee the error convergence can be reduced to the linear matrix inequality problem. This method provides a systematic design procedure for iterative teaming controller. A simulation example is given to illustrate the validity of the proposed method.