• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.523-535
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• 2017

Local intelligent services aim at controlling local services such as cooling or lightening services in a certain local area, using Internet-of-Things (IoT) sensor data in the area. As the IoT paradigm has evolved, local intelligent services have gained increasing attention. However, most of the local intelligent service mechanism proposed so far do not directly take the users' presence and service preference information into account for controlling local services. This study proposes an individual presence-and-preference-based local service system (IPP-LISS). We present a intelligent service control algorithm and implement a prototype system of IPP-LISS. Typically, the intelligence part of IPP-LISS including the prediction models, is generated on remote server in the cloud because of their compute-intense aspect. However, this can cause huge data traffic between IoT devices and servers in the cloud. The emerging mobile edge computing technology will be a promising solution of this challenge of IPP-LISS. In this paper, we implement IPP-LISS in the cloud, and then, based on the implementation result, we discuss applying the mobile edge computing technology to the IPP-LISS application.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.242-264
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• 2019

This study focuses on considering the factors affecting the user intention to adopt an intelligent service - A.I. speaker services. Currently there can be a considerable difference between the expectation and the realized diffusion of IT-based intelligent services. This study aims to find out this gap based on the idea of diver previous researches including TAM and UTAUT studies and to identify the direct and indirect effects of diverse factors such as security issues, perceived time pressure, service innovativeness, and the experience of these IT-based intelligent services. And this study considers the expected impact of perceived time pressure factor on the user acceptance of A.I. speaker services. In analysis results, not only the traditional factors such as the perceived usefulness and the hedonic/utilitarian motives but also the perceived time pressure, the perceived security issues, and the experience of the services should be considered as meaningful factors to affect the users adopting A.I. speaker services.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.379-389
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• 2020

This is a qualitative study about Google 'Nest Hub Max' that displays proactive intelligent personal assistant. Following the step of grounded theory, an in-depth interview for 6 users who had used this device for a month was taken. 186 concepts were discovered, categorized as 11 top-categories and 24 sub-categories. Paradigm diagram, considering axis-coding, was made and it have been narrowed down to 'Usage patterns' of proactive IPA, considering selective coding aspects. 'Usage patterns' were divided to passive and active user. Thus, neither passive user nor active user was satisfied about device and proactive IPA. This study is meaningful that it constructed basic data about the user experience of proactive IPA on this device. It will support the device or service that consists proactive IPA in the future.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.301-313
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• 2015

Robot technology has become a crucial part of today's business operation. In fact, more manufacturing firms have been utilizing robot technology in order to increase operational efficiency and productivity. Thus, this study develops the research model investigating firms; behavior for process innovation with intelligent robot. Three categories - Technical, Entrepreneur, and Environmental characteristic - are proposed in the research model as determinants of process innovation. These three characteristics include six variables(Perceived Direct Usefulness, Perceived Indirect Usefulness, Innovation, Risk Senstivity, Perceived Industry Pressure, and Perceived Government Pressure) as influencing factors on process innovation. The data from 77 employee at manufacturing firms were analyzed to test proposed hypotheses. The results reveal that all variables with exception of Perceived Government Pressure have a significant influence on process innovation. Based on the study results, theoretical and practical implications for process innovation with intelligent robot technology are discussed.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.223-233
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• 2013

At local emerging stock markets such as Korea, Hong Kong, Singapore and Taiwan, foreign investors (FI) are recognized as important investment community due to the globalization and deregulation of financial markets. Therefore, it is required to monitor the behavior of FI against a sudden enormous selling stocks for the concerned local governments or private and institutional investors. The main aim of this study is to propose an early warning system (EWS) which purposes issuing a warning signal against the possible massive selling stocks of FI at the market. For this, we suggest machine learning algorithm which predicts the behavior of FI by forecasting future conditions. This study is empirically done for the Korean stock market.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.379-384
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• 2002

개발적인 측면에서 온라인 게임 환경에서의 NPC(Non Playable Character)들은 환경인식능력, 이동능력, 특수 능력 및 아이템의 소유 배분 등을 원활히 하기 위한 능력들을 소유해야 하며, 게임 환경을 인식, 저장하기 위한 데이터구조와 자신만의 독특한 임무(mission)를 달성하기 위한 계획을 갖고 행위를 해야 한다. 이런 의미에서 NPC는 자신만의 고유한 규칙과 행동 패턴, 그리고 목표(Goal)와 이를 실행하기 위한 계획(plan)을 소유하는 에이전트로 인식되어야 할 것이다. 그러나, 기존 게임의 NPC 제어 구조나 구현 방법은 이러한 요구조건에 부합되지 못한 부분이 많았다. C/C++ 같은 컴퓨터 언어들을 이용한 구현은 NPC의 유연성이나, 행위에 많은 문제점이 있었다. 이들 언어의 switch 문법은 NPC의 몇몇 특정 상태를 묘사하고, 그에 대한 행위를 지정하는 방법으로 사용되었으나, 게임 환경이 복잡해지면서, 더욱더 방대한 코드를 만들어야 했고, 해석하는데 많은 어려움을 주었으며, 동일한 NPC에 다른 행동패턴을 적용시키기도 어려웠다. 또한, 대부분의 제어권을 게임 서버 폭에서 도맡아 함으로써, 서버측에 많은 과부하 요인이 되기도 하였다. 이러한 어려움을 제거하기 위해서 게임 스크립트를 사용하기도 하였지만, 그 또한 단순 반복적인 패턴에 사용되거나, 캐릭터의 속성적인 측면만을 기술 할 수 있을 뿐이었다 이러한 어려움을 해소하기 위해서는 NPC들의 작업에 필요한 지식의 계층적 분화를 해야 하고, 현재 상황과 목표 변화에 적합한 반응을 표현할 수 있는 스크립트의 개발이 필수 적이라 할 수 있다 또한 스크립트의 실행도 게임 서버 측이 아닌 클라이언트 측에서 수행됨으로써, 서버에 걸리는 많은 부하를 줄일 수 있어야 할 것이다. 본 논문에서는, 대표적인 반응형 에이전트 시스템인 UMPRS/JAM을 이용하여, 에이전트 기반의 게임 캐릭터 구현 방법론에 대해 알아본다.퓨터 부품조립을 사용해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.211-227
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• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.153-161
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• 2002

다사용자계층 통행배정(Multiple User Class Assignment) 문제란 교통망을 이용하는 통행자들이 이질적인 통행계층으로 구성된 경우, 이들 각 계층의 통행수요를 교통망에 배정하는 문제를 의미한다. 이는 기존 통행 배정모형들이 모든 통행자의 통행특성이 동질적이라고 가정함으로서 발생하는 불합리한 통행배정 결과를 완화시키기 위한 방법이다. 또한, 최근 지능형교통체계(Intelligent Transportation Systems, ITS)사업에서 교통정보제공시스템이 구현될 예정임에 따라, 교통정보를 제공받는 계층과 그렇지 못한 계층간의 영향을 분석하거나 혼잡통행료부과 등과 같은 교통관리전략을 정확히 평가하기 위해서 다사용자계층 통행배정모형에 대한 관심이 증가하고 있다. 그러나, 다사용자계층 통행배정모형의 경우, 사용자간의 상호영향으로 통행비용함수의 1차 편미분행렬(Jacobian matrix)이 비대칭(Asymmetric)이 되어 동등 수리최소화문제(Equivalency mathematical Minimization program)로 구성할 수 없고 또한 수치적으로 풀기가 어렵다는 문제가 있다. 본 연구는 이런 문제점을 극복할 수 있는 모형식과 알고리듬을 제시코자 한다. 본 연구에서 제시된 모형은 2가지 특징이 있다. 먼저, 각 사용자 계층간의 상호영향을 모형내에 반영하며, 기종점쌍간의 통행시간변화에 따른 수요변화를 고려한다는 점이다. 이를 위하여 변동부등모형(Variational Inequality Model. VI)으로 문제를 구성하며, 이에 대한 해석 알고리듬도 제시한다. 또한, 변동부등모형으로 구축된 다사용자계층 모형이 다사용자계층 균형조건과 동일함을 보여주는 동등성조건(Equivalency condition)도 제시한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.229-233
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• 2005

모집단의 최적군집 수를 자동으로 결정하고 군집내의 분산은 최소로 하고 군집 간의 분산은 최대로 하는 최적 군집화에 대한 연구는 대부분의 지능형 시스템에서 필요로 하는 모형전략이다. 하지만 아직도 대부분의 군집화 과정에서 분석가의 주관적인 경험에 의존하여 군집수가 결정되어 군집화가 이루어지고 있다. 예를 들어 K-평균 군집화 알고리즘에서도 초기에 K 값을 결정해 주어야 한다. 모집단을 제대로 대표하지 못한 K 값에 의한 군집화 결과는 심각한 오류를 범하게 된다. 본 논문에서는 통계적 학습이론을 이용하여 이러한 문제점을 해결하려고 하였다. VC-차원에 의한 Support Vector를 이용하여 최적의 군집화 기법을 제안하였다. 제안 방법의 성능 평가를 위하여 UCI 기계학습 데이터를 이용하여 객관적인 실험을 수행하였다.