• Journal of Korean Society for Quality Management
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• v.52 no.1
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• pp.95-113
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• 2024

Purpose: This study focused on the societal changes associated with the entry into an ultra-aged society and the increase in single-person households. The core objective of this research is to investigate how social intelligent robots can bring about positive changes in the lives of individuals in single-person households and how such changes influence user satisfaction and the intention to use these robots. Methods: The study employed a cross-sectional analysis using a structural equation model. A survey designed to assess the impact of social intelligent robots' characteristics, such as perceived encouragement, empathy, presence, appearance, and attachment, on user satisfaction and usage intentions was conducted. Data were collected from a total of 335 users and analyzed using the structural equation model. Results: In the characteristics of social intelligent robots for single-person households, it was found that empathy, presence, and attachment significantly influenced satisfaction, while perceived encouragement, empathy, and attachment significantly influenced usage intentions. The research results indicate differences between enhancing user satisfaction and increasing the intention to use social intelligent robots. The findings suggest the essential need for a user-centric approach in the design and development of social intelligent robots. Additionally, it was observed that emotional support plays a crucial role in users' experiences with social intelligent robots. Conclusion: This study verified the impact of social intelligent robots on satisfaction and usage intentions based on users' experiences. It examined the influence of linguistic, visual, and personal characteristics of robots on user experiences, providing insights into how technological and human aspects of social intelligent robots interact to shape user satisfaction and usage intentions. Consequently, the study confirmed that social intelligent robots can bring positive changes to human life, emphasizing the necessity for the advancement of robot technology in a human-centric direction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.1-12
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• 2016

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.262-267
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• 2008

The focus of this paper is on devising an efficient clustering task for arbitrary color data. In order to tackle this problem, the inherent uncertainty and vagueness of color are represented by a fuzzy color model. By taking a fuzzy approach to color representation, the proposed model makes a soft decision for the vague regions between neighboring colors. A definition on a three-dimensional fuzzy color ball is introduced, and the degree of membership of color is computed by employing a distance measure between a fuzzy color and color data. With the fuzzy color model, a novel fuzzy clustering algorithm for efficient partition of color data is developed.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.397-402
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• 2007

In this paper, we present a method for clustering incomplete Microarray data using alternating optimization in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster Information such as cluster centroids and all available non-missing values in each iteration. The clustering results of the proposed method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.136-146
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• 2013

Truck demand analysis based on TCS data has limitation in that TCS data can not provide truck O/D data for each type of truck vehicle. This study conducted video survey for classifying truck vehicle types. By using TCS data and vehicle ratio by region/cities type, truck O/D data on expressway were estimated. It was found that average travel distances of small truck, medium truck and large truck were 52km/veh, 56km/veh and 97km/veh, respectively by analysing truck O/D data estimated in this study. The reliability analysis showed that check points where error rate is lower than 30% comprise of 87.3%. It is considered that estimated O/D data by truck vehicle types would be useful for the analysis of truck demand of expressway.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.445-451
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• 2013

Cyber terrorism has lately aimed at major domestic financial institutions and broadcasters. A large number of PCs have been infected, so normal service is difficult. As a result, the monetary damage was reported to be very high. It is important to recognize the importance of big data. But security and privacy efforts for big data is at a relatively low level, therefore the marketing offort is very active. This study concerns the analysis of Big Data industry and Big data security threats that are intelligent and the changes in defense technology. Big data, security countermeasures for the future are also presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.876-880
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• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.32-45
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• 2013

Globally, intelligent vehicles and telematics research and development through the integration of IT technology in the vehicle are significant increasing. Real-time data communication for intelligent transportation system (ITS) is very important. It collects real-time data from the vehicle and provides the information collected from ITS center. We propose an effective and secure communication scheme for these communication procedures. In particular, our proposed SIP-based MVPN reduces signaling cost and has many advantages in security aspects. In addition, our proposed scheme performs the mobility management applying NEMO (Network Mobility) for the communication between the vehicles. In other words, we propose an ITS communication mechanism of SIP-based mobile VPN and V2V NEMO. Finally, our performance analysis show that the ITS of SIP-based MVPN is significantly reducing the handoff signaling cost.