• The Korean Journal of Coaching Psychology
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• v.7 no.3
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• pp.153-196
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• 2023

The purpose of this study is to development and validate kkondae tendency scale. Kkondae tendencies are defined as "a response pattern to others in a way that values authority in social relationships, is self-centered, and does not accept other people's opinions," and the subjects of the study are workers aged 19 or older who act as seniors, seniors, and bosses in the workplace. In Study 1, 65 preliminary questions were produced with 7 factors for the compositional concept of kkondae tendency through literature review, expert interviews, and open questionnaire survey. In Study 2, a preliminary survey was conducted with 65 questions derived from Study 1. Exploratory factor analysis was conducted based on the responses of a total of 395 people, and 22 items for 4 factors were derived. In Study 3, this survey was conducted with 22 questions derived from Study 2. A total of 880 responses were analyzed, and cross-validation verification was conducted by dividing the data into two groups (Group 1 and Group 2). Exploratory factor analysis was conducted on Group 1 (N=429) to derive 19 items with 4 factors. The four factors are authoritarianism(3 items), egocentrism (5 items), inertial thinking (5 itemss), and one-sided communication (6 items). A confirmatory factor analysis was conducted on 19 questions obtained from Group 1 for Group 2 (N = 451), and 19 questions of four factors were accepted due to the good fit of the model. To verify the convergent validity of the Kkondae tendency scale, the correlation with the Kkondae scale was examined, and to verify the criterion-related validity, the relationship between self-reflection, relationship conflict, social connectedness was examined. All were statistically significant, and convergence validity and criterion-related validity were verified. Finally, discussions on the process and results of this study, differences from related measures, academic significance, practical implications, limitations of the study, and future research directions were presented.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.63-76
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• 2024

Multi-family housing plays a crucial role as a living and experiencing space, and its environment has a direct impact on the well-being and stability of its residents. Therefore, Crime Prevention Design (CPTED) for multi-family housing is of utmost importance. However, crime-related data in China is not disclosed to the public because of its specificity, making it difficult for researchers to conduct further in-depth studies based on accurate crime data. As a result, the establishment and application of CPTED theory in terms of crime prevention is limited and delayed. This study aims to explore three aspects of CPTED in multi-family housing as perceived by home-buying consumers. It investigated consumer perception of the CPTED, the importance of each element and ways to increase awareness of CPTED in multifamily housing in order to effectively improve multifamily crime prevention design principles and further enhance public safety. This study examined the current state and future trends of CPTED in China by analyzing relevant research reports and literature, aiming to gain insights into the crime prevention awareness of Chinese homeowners. In addition, a survey was conducted on Chinese consumers to unravel the importance of CPTED and increase awareness of its various elements in multifamily-family. This study used a Likert scale and SPSS reliability analysis to determine the cognitive status of multi-family CPTED, the importance of each element, and proposed an improvement plan based on the analysis results. As this study was limited by the difficulty of implementation and the lack of validation of its practical effectiveness, it is recommended that future research needs to validate the effectiveness of crime prevention designs and produce more practical results. Furthermore, it is crucial to utilize this study to inform the implementation of security solutions that are tailored to the unique characteristics of each district. Additionally, it is important to offer guidance on how to enhance community safety by increasing residents' awareness of security through education and information dissemination. The author hopes that the representative multi-family CPTED awareness, the importance of each element, and plans for improvement shall be summarized from this study, and provide foundational data for the future development of CPTED based on the Chinese region.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.67-96
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• 2023

The purpose of this study was to develop and validate a scale that can grasp the reality of the three systems of action for middle and high school students in home economics. For this purpose, a total of 105 questions, 35 questions for each systems of action, were developed as a 5-point Likert scale in order to measure technical action, communicative action, and emancipative action as preliminary questions by reviewing domestic and international literature related to the three systems of action. The procedure for revising and supplementing the developed preliminary questions by reviewing the content validity of the home economics education expert was executed twice. A preliminary survey was conducted on middle and high school students with 70 developed preliminary questions, and 166 copies were collected. As a result of exploratory factor analysis of the collected questionnaires to test the validity of the scale, it was found that 38 questions 7 factors were appropriate. After constructing this survey based on the results of exploratory factor analysis, this survey was conducted on middle and high school students, and 548 copies were collected and a confirmatory factor analysis was performed. A total of 38 questions were finally selected through confirmatory factor analysis, including basic living ability 5 questions, self-management ability 4 questions, information processing ability 4 questions, communication/interpersonal ability 12 questions, critical thinking ability 3 questions, decision-making ability 7 questions, empowerment 3 questions. The Model Fit was χ²=1846.741(p<.001), CFI=0.865, TLI=0.853, RMSEA=0.058, and the Standardized Regression Weights for each question was more than 0.5, so it can be seen as a suitable measurement instrument for measuring the status of the three systems of action of middle and high school students in home economics. The three systems of action scales were found to have significant correlations with self-acceptance, future planning, intimacy, uniqueness, which are sub-factors of the self-identity scale, and social participation scales therefore confirmed that they have recognized concurrent validity.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.366-376
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• 2023

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the 'Cheongmyeong Gaual' variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37' N 128°32' E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.35-48
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• 2024

This study analyzed the value empathy of environmentally sustainable fashion products, encompassing environmental, economic, and social values, drawing from existing literature. We sought to verify the relationship between empathic value and the likability and purchase intention towards these products. To validate these relationships, we formulated research hypotheses and conducted an online survey targeting female college students residing in Guangzhou, Guangdong Province, China, who have experience purchasing environmentally sustainable fashion products. The survey was conducted from August 10th to August 20th, 2023, with a total distribution of 352 questionnaires. Among the collected responses, 313 valid responses were utilized for data analysis. The collected survey data underwent frequency analysis, exploratory factor analysis, reliability and validity analysis, correlation analysis, and multiple regression analysis using SPSS 26.0 software. The analysis yielded the following results. First, the empathy value of environmentally sustainable fashion products was classified into environmental protection values, economic values, and social values. Second, the economic and social values of environmentally sustainable fashion products were found to have a positive effect on favorability. Third, it was found that the environmental protection value and social value of environmentally sustainable fashion products had a positive effect on purchase intention. Fourth, it was found that Chinese female college students' favorability toward environmentally sustainable fashion products had a positive effect on their purchase intention. Based on these results, it is judged that companies need to emphasize the characteristics of products such as environmental protective value, economic value, and social value in order to promote consumers' purchase of environmentally sustainable fashion products. The purpose of this study is to help develop marketing strategies for environmentally sustainable fashion products by providing basic data, development ideas, and methods useful for environmentally sustainable fashion-related industries and companies by analyzing the relationship between empathy value, favorability, and purchase intention.

• Journal of Naturopathy
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• v.12 no.2
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• pp.56-66
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• 2023

Background: Self-gravity acupressure (SGA), which complements the limitations of conventional manual therapies, is expected to have a positive effect on mood, closely related to reduction in stress. Purpose: This study aims to evaluate changes in mood states by SGA and to discuss its effects on stress relief and immunity. Methods: For 118 subjects (21 males and 97 females) who experienced the SGA program for 75 minutes, their mood states were assessed before and after the SGA session on 5 scales (0-4 points) using K-POMS consisting a total of 65 items grouped in six factors. For calculating the total mood disturbance score (iTMDs), the scores of the items in the only positive mood factor 'vigor-comfort' were reversed to have iTMDS increase the degree of the positive correlation with negative mood states. Results: The iTMDS decreased by 11.50% from 1.09±0.54 before SGA to 0.63±0.40 after SGA (p<0.001). The average score of the only positive factor 'vigor-comfort' increased by 10.78%, from 1.93±1.17 before SGA to 2.38±1.31 after SGA (p<0.001). On the other hand, the factor 'fatigue-inertia' of the 5 negative factors decreased most significantly in its average score by 16.73%, from 1.19±1.24 before SGA to 0.40±0.58 after SGA (p<0.001). The remaining 4 negative factors (depressed state, anxiety-fear, anger-hostility, and uncertainty-helplessness) decreased by within the range of 7.75% to 11.33% (p<0.001). Conclusions: Changes in K-POMS scores observed in this study indicate that the SGA program improves significantly mood. Since a mood state is closely related to stress and immunity, SGA is expected to have effects on stress relief and immunity enhancement (p<0.001). Continued studies are suggested to further validate the present results and to enhance the clinical utility, which include physiological signal measurements and clinical pathological examinations to test the effetcs of SGA on stress management and immunity enhancement.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.