• Anxiety and mood
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• v.9 no.1
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• pp.45-53
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• 2013

Objectives : The purpose of this study was to develop the Korean Strength Scale and to examine its validity and reliability. Methods : The Korean Strength Scale is a self-report questionnaire that measures 25 valued strengths and is comprised of 124 items ; each item had a 0-5 rating on a 6-point scale. In order to test validity and reliability, data were collected from 355 adults. The measures included the Korean Strength Scale, HEXACO Personality Inventory (HEXACO-PI), Satisfaction with life scale (SWLS), Positive Affect and Negative Affect Schedule (PANAS), and Orientations to Happiness Questionnaire (OHQ). Results : The resulting exploratory factor analysis of the Korean Strength Scale suggested 4 factor structures. The Korean Strength Scale was shown to have acceptable psychometric properties, including acceptable internal-consistency reliabilities, factorial validity, and high convergent correlations. Conclusion : Although there is room on improvement for some facet scales, the Korean Strength Scale appears to be a useful tool for assessing an individual's signature strengths.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.132-139
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• 2002

본 실험은 청각 사상관련전위(ERP)를 이용하여 다양한 성격 특질 모형들의 생물학적 근거와, 구조의 공통점과 차이점을 명확히 하기 위해서 설계되었다. 48명의 대학생 피험자들에게 4가지 성격 측정 검사(NEO-PI-R, EPQ-R, BIS/BAS 척도, IVE)를 시행하고, 이어 두 종류의 청각 자극 세트(50ms와 300ms)를 이용한 오드볼 과제를 통해 ERP를 측정하였다. 설문지 간의 상관과, 설문지와 ERP 내외생적 구성 요소 간의 상관, 그리고 그 상관의 두피 분포 양상을 통합적으로 고려하여 해석하였다. 각 성격 검사의 외향성 척도에 해당한다 할 수 있는 E, E, BAS-FUN은 양성 모두에서 높은 수준의 설문지 척도 간 정적 상관을 보였으나, 50ms 자극으로 유발된 P3 진폭은 E와 BAS-FUN에 대해서만 유의하게 높은 상관을 나타내었다. 이 결과는 Eysenck의 모델과 일견 일치하는 결과이지만, 그가 개발한 EPQ-R의 외향성 척도(E) 보다는 오히려 다른 척도들이 뇌파에 반영된 성격의 생물학적 기제를 측정하기 위한 적절한 도구일 수도 있음을 시사한다. 신경증적 경향성을 반영하는 N과 N 척도는 50ms 자극에 대한 여성 피험자의 N1, P2 진폭과 유의한 상관을 보였다. 그러나 이론적으로 두 척도에 등가적인 것으로 예상되었던 BIS 척도는 설문지 간 상관이나 ERP 요소와의 상관에서 모두 유의한 결과를 보이지 않았다. Eysenck가 성격의 세 번째 축(차원)으로 제시했던 P(정신병적 경향성) 역시 여성 피험자들에게서 두드러지는 설문지 간 상관을 보였으나, 300ms 자극에 대한 P2 진폭과의 정적인 상관은 양성을 합한 자료에서만 발견되었다. 여성의 상관 분포는 비록 통계적으로 유의한 수준에는 이르지 못하였으나, 양성 자료의 분포와 유사함이 확인되었다. 이 실험의 결과는 P3 진폭과 외향성 간의 부적인 상관 관계를 보고하였던 기존 연구들을 지지한다. 또한 본 실험에서 확인된 Eysenck 모델의 신경증적 경향성이나 정신병적 경향성이 ERP의 비교적 초기에 나타나는 외생적 구성 요소들(N1, P2)과의 상관 양상은, P3 요소에 집중하였던 기존 연구들이 외향성 이외의 차원으로 일관된 결과를 얻어내지 못했던 이유를 설명해줄 수 있을 것이다. 또한 성격 차원과 그것을 지표 하는 ERP 구성 요소 간의 관계를 해석할 때에 성별이나 유발 자극의 속성을 고려해야 함을 보여준다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.507-520
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• 2017

The set-based similar sequence matching method measures similarity not for an individual data item but for a set grouping multiple data items. In the method, the similarity of two sets is represented as the size of intersection between them. However, there is a critical performances issue for the method in twofold: 1) calculating intersection size is a time consuming process, and 2) the number of set pairs that should be calculated the intersection size is quite large. In this paper, we propose an index-based search method for improving performance of set-based similar sequence matching in order to solve these performance issues. Our method consists of two parts. In the first part, we convert the set similarity problem into the intersection size comparison problem, and then, provide an index structure that accelerates the intersection size calculation. Second, we propose an efficient set-based similar sequence matching method which exploits the proposed index structure. Through experiments, we show that the proposed method reduces the execution time by 30 to 50 times then the existing methods. We also show that the proposed method has scalability since the performance gap becomes larger as the number of data sequences increases.

• Journal of Advanced Navigation Technology
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• v.16 no.3
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• pp.455-462
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• 2012

The loss due to caching replacement in the wireless mobile proxy caching structure has a significant effect on streaming QoS. This paper proposes a similarity-based caching loss minimization (SCLM) for minimizing the loss caused by the caching replacement. The proposed scheme divides object segments, and then it performs the similarity relation about them. Segments that perform the similarity relation generates similarity relation tree (SRT). The similarity is an important metric for deciding a relevance feedback, and segments that satisfy these requirements in the cache block for caching replacement. Simulation results show that the proposed scheme has better performance than the existing prefix caching scheme, segment-based caching scheme, and bi-directional proxy scheme in terms of QoS, average delayed startup ratio, cache throughput, and cache response ratio.

• Korean Journal of Culture and Social Issue
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• v.24 no.3
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• pp.385-410
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• 2018

Materialistic values can be a important variable to understand Koreans' psychological well-being and mental health. This study aimed to validate the Korean version of the Material Values Scale (K-MVS)(Richins & Dawson, 1992). In study 1, we performed confirmatory factor analysis(CFA) to ascertain the three factor model of the original MVS using 417 Korean undergraduate student data(sample 1). The CFA confirmed the three-factor model of the MVS. Yet, three items that yielded low factor loadings in this study as well as in other MVS validation studies were excluded from the final model. In study 2, content, construct, and concurrent validity of the K-MVS were examined with 650 undergraduate student data(Sample 2). We also tested measurement invariance across two groups(i.e., college student group of Sample 2 and employee group of Sample 3). The result revealed that the three-factor model of the K-MVS hold true across the two groups. Lastly test-retest reliability was calculated with 408 female college student data(Sample 4) that filled out K-MVS twice within 6 months. These findings suggest that the K-MVS is a reliable and valid measure for assessing materialistic values in Korea.

• Korean Journal of Culture and Social Issue
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• v.25 no.4
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• pp.353-375
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• 2019

The purpose of this study was to examine the validity and reliability of the Playfulness Scale for Adults. The Korean version of the Playfulness Scale for adults was developed by Proyer, R.T (2017) to measure the playfulness of adults. To validate the OLIW in Korean, item translation, back-translation, item analysis, and exploratory factor analysis (EFA) were conducted with 406 adults in study 1. Of the results obtained from study1, three items and one factor (7 items) were discarded because they turned out to be improper. In addition, 4 factors that were not the same as the original scale were extracted. This was checked by conducting confirmatory factor analysis (CFA) with 336 adults in study 2. CFA supported the 4 factors structure and all 4 factors showed adequate internal consistency. To check the concurrent validity of the Korean adults playfulness scale, correlation analysis with the APS, SMAP, PSYA, and NEO Personality Assessment was conducted. It showed significant positive correlation to APS, SMAP, PSYA, and showed the similar patterns of correlation with the sub factors of NEO Personality Assessment. Adult playfulness is related to the participation frequency of leisure. In conclusion, the Korean version of the playfulness scale for adults is a valid measure of playfulness for adults in Korea. The implications, practical use and suggestions for future study were discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.354-358
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• 2001

This paper presents a fuzzy neural network model which solves the underutilization problem. This fuzzy neural network has both stability and flexibility because it uses the control structure similar to AHT(Adaptive Resonance Theory)-l neural network. And this fuzzy nenral network does not need to initialize weights and is less sensitive to noise than ART-l neural network is. The learning rule of this fuzzy neural network is the modified and fuzzified version of Kohonen learning rule and is based on the fuzzification of leaky competitive leaming and the fuzzification of conditional probability. The similarity measure of vigilance test, which is performed after selecting a winner among output neurons, is the relative distance. This relative distance considers Euclidean distance and the relative location between a datum and the prototypes of clusters. To compare the performance of the proposed fuzzy neural network with that of Kohonen Self-Organizing Feature Map the IRIS data and Gaussian-distributed data are used.

• IE interfaces
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• v.8 no.3
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• pp.269-278
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• 1995

본 논문은 분산환경하에서 생산정보, 데이터 분석의 일 방안으로서 데이터의 관리 주체와 데이터들간의 유사성에 기초한 분산 기준을 제시한다. 제안된 분산기준은 데이터베이스 설계의 초기과정에서 네트워크 환경의 각 노드에서의 트랜젝션의 발생 수와 데이터 이용량의 예측이 용이하지 않은 경우에도 적용 가능하다는 점에서 가치를 지닌다. 제시된 설계 기준을 이용하여 생산정보 데이터베이스의 분산 모형을 설계, 제시한다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.59-64
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• 1998

정상 성인의 경우, 감성 개념의 내적 차원 구조는 쾌/불쾌 차원과 각성 차원이라는 2차원 구조에 원형의 체계적인 분포를 보이는 것으로 알려져 왔다. 본 연구에서는 이와 같은 2차원 구조가 얼마나 보편적이고 일관된 양상으로 나타나고 있는지를 살펴보고자 하였다 이를 위해 연구1에서는 아동 및 청소년에게 15개의 정서 관련 어휘로 이루어진 10i개의 단어 쌍에 대한 7점 척도의 유사성 평정을 시행하였으며, 연구 2에서는 정신분열증 환자에게 통일한 절차를 시행하였다 다차원 분석 결과, 1차원(초등5년:74%, 중등2 년:72%, 정신분열증 환자: 60%)과 2차원(초등5년: 18%, 중등2년16%, 정신분열증 환자: 11%)이 도출되었다 정상 성인의 경우와 마찬가지로 1차원은 쾌/불쾌 차원, 2차원은 각성 차원으로 해석될 수 있었다. 따라서, 감성 개념의 구조에 있어서 쾌/불쾌 및 각성은 인지적 성숙의 단계나 인지, 정서적 손상에 관계 없이 매우 일관되게 나타나는 비교적 안정적인 차원 구조인 것으로 생각된다. 다만, 발달 단계나 병리적 속성에 따라 각 차원의 비중치는 다소 차이를 보이는데, 아동 및 청소년의 경우, 주로 쾌/불쾌 차원을 통해 감성을 개념화 하는 특징을 보이며, 정신분열증 환자 집단의 경우, 1,2차원 모두 상대적으로 설명량이 낮은 것으로 나타나는데, 의 경우, 비중치가 더욱 낮은 것으로 나타났다. 이러한 결과를 통해 내적 차원 구조의 타당성 및 제한점에 관해 논의하였다.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.