• 대한임베디드공학회논문지
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• 제19권1호
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• 한국식생활문화학회지
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• 제23권5호
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• pp.605-615
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• 2008

The principal objective of this study was to categorize service attributes on the basis of the asymmetric and non-linear relationship existing between service attributes and customer satisfaction. Researchers generally assume that service attribute performances and customer satisfaction are both symmetrical and linear. That is to say, improvements in attribute performance will inevitably result in increased customer satisfaction. However, this is not always the case. Certain attributes have been shown not to create satisfaction even when improved, and others do not create dissatisfaction even when their performance ratings become negative. Understanding this relationship is crucial not only to researchers, but also to service managers. Service managers can arrange their priorities with regard to which attributes must be improved or promoted first, in an environment of limited technical, financial, and human resources. Many studies into this asymmetric and non-linear relationship have recently been conducted, beginning with Herzberg's motivation-hygiene theory (1976) and the disconfirmation theory, which was eventually developed into Kano's model (1984). This study attempted to determine the impact level of service attributes on incidents of satisfaction or dissatisfaction. It used 30 service attributes generated by Park (2008) in the CIT research into family restaurants. The data were collected from 600 participants, 300 incidences of satisfaction and 300 incidents of dissatisfaction, via an online survey. The t-test was used to confirm the difference between the satisfaction group's and dissatisfaction group's attributes. 11 attributes were found to be significant at a level of p>0.05. This indicates that the 11 attributes exerted different impacts on satisfaction and dissatisfaction, which confirmed the asymmetric and non-linear relationship. 14 attributes were categorized into the core service, 1 attribute into the quality service, 7 attributes into the basic service, and 8 attributes into the neutral service. Strategic customer service management was recommended for the 'A' family restaurant as an example, on the basis of the asymmetric and non-linear relationship and the characteristics of the four service factors.

• 한국지능시스템학회논문지
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• 제24권2호
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• pp.166-172
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• 2014

이미지를 각각의 카테고리로 분류하는 일은 컴퓨터 비전 분야의 중요한 문제 중 하나이다. 그러나 이미지에 존재하는 가변성, 모호성, 스케일 문제 등으로 인해 매우 도전적인 문제라고 할 수 있다. 본 논문에서는 장면 이미지를 구성하는 시멘틱 속성들의 고차원의 상호작용 관계를 고려 가능한 하이퍼그래프 기반의 모델링 기법을 제시하고 이를 장면 이미지 분류에 적용한다. 각 장면 카테고리에 준최적화된 하이퍼그래프를 생성하기 위해 확률 부분공간 기법에 기반을 둔 탐색기법을 제안하고, 이들 부분 공간 내에 속한 시멘틱 속성들의 발현량을 축약하기 위한 우도비 기반의 선형 변환 기법을 제안한다. 제안한 기법의 우수성을 검증하기 위한 실험을 통하여 제시한 기법을 통해 생성된 특징 벡터의 분별력이 기존의 기법들에서 사용된 특징 벡터들의 분별력보다 우수함을 보인다. 또한 제안한 기법을 장면 분류 데이터에 적용한 결과 기존의 기법들과 비교하여 경쟁력 있는 분류 성능을 보인다. 제안 한 기법은 이미지 분류에서 일반적으로 사용 되는 기법인 BoW+SPM 모델과 비교하여 3~4%이상의 성능 향상을 보였다.

• 대한화학회지
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• 제60권6호
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• pp.415-427
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• 2016

This study analyzed the attributes of the conceptions of $3^{rd}$ to $6^{th}$ grade elementary school students on three states of matter and investigated the characteristics of the classified results of various examples of matter by grades. Through discussion activities, we confirmed the stabilization of conception attributions. For this study, 113 participants from two $3^{rd}$ to $6^{th}$ grade elementary school classes were selected. The concentration analysis (C-factor) and normalized gain (G-factor) of the conceptions for the quantitative analysis of the conception changes were used. The elementary school students retained different percentages of the attributes for states of matter. The characteristic of the grades were different between the 3rd grade and other grades. Based on these results, we pointed out the problems with the present teaching methods in science textbooks and stated the advantages of the effects of the representation of mixtures.

• IEIE Transactions on Smart Processing and Computing
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• 제5권5호
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• pp.358-365
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• 2016

We propose a novel application of random forest, a machine learning-based general classification algorithm, to analyze the influence of design attributes on the silicon-to-SPICE (S2S) gap. To improve modeling accuracy, we introduce magnification of learning data as well as randomization for the counting of design attributes to be used for each tree in the forest. From the automatically generated decision trees, we can extract the so-called importance and impact indices, which identify the most significant design attributes determining the S2S gap. We apply the proposed method to actual silicon data, and observe that the identified design attributes show a clear trend in the S2S gap. We finally unveil 10nm key fin-shaped field effect transistor (FinFET) structures that result in a large S2S gap using the measurement data from 10nm test vehicles specialized for model-hardware correlation.

• Journal of Information Technology Applications and Management
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• 제10권4호
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• pp.29-50
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• 2003

This research provide the basic information to enhance the user-orientedness of usability design guidelines for software products and an effective empirical guidance to classify design attributes of internet shopping mall sites. The results of analysis show that design attributes can be classified into the procedural attribute group, the shopping tool attribute group, the visual attribute group, linguistic attribute group, and others. The results show that shopping tool attribute group can be divided further into the search tool attribute group and purchase tool attribute group and that the visual attribute group can be divided further into the screen condition attribute group and the character legibility attribute group. The research reveals that when designers design software interfaces and features they should take the compound effect of a group of design attributes into consideration to enhance the usability of the system.

• 한국산업융합학회 논문집
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• 제25권1호
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• pp.37-45
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• 2022

It is well known that the Kano model measures customer satisfaction and classifies quality attributes into must-be, attractive as well as one-dimensional. The main purpose of this study is to investigate the dynamics of e-learning quality attributes by applying the proposed method using Kano's satisfaction index in the rapidly changing online learning environment. For this, the current study examined 27 e-learning quality attributes and conducted a comparative study using Kano's results obtained in 2013 and 2020. The result shows that the dynamics of quality attributes suggested by Kano(2001) is confirmed in the case of e-learning. The proposed approach shows better results in terms of Kano's direct classification method, and has potential application areas such as IPA(Importance-Performance Analysis) in the area of risk assemement. Some suggestions for better understanding of the proposed SI-DI diagram are also included in this study.

• 한국멀티미디어학회논문지
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• 제20권4호
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• pp.704-712
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• 2017

Various researches on content sensibility with the development of digital contents are under way. Emotional research on fonts is also underway in various fields. There is a requirement to use the content expressions in the same way as the content, and to use the font emotion and the textual sensibility of the text in harmony. But it is impossible to select a proper font emotion in Korea because each of more than 6,000 fonts has a certain emotion. In this paper, we analysed emotional classification attributes and constructed the Hangul font recommendation system. Also we verified the credibility and validity of the attributes themselves in order to apply to Korea Hangul fonts. After then, we tested whether general users can find a proper font in a commercial font set through this emotional recommendation system. As a result, when users want to express their emotions in sentences more visually, they can get a recommendation of a Hangul font having a desired emotion by utilizing font-based emotion attribute values collected through the crowdsourced method.

• 전자공학회논문지CI
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• 제48권5호
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• pp.83-87
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• 2011

나이브 베이시안은 기계학습에서 많이 사용되고 상대적으로 좋은 성능을 보인다. 하지만 전통적인 나이브 베이시안 학습의 환경은 두 가지의 가정을 기반으로 학습을 수행한다: (1) 각 속성들의 값은 서로 독립적이다. (2) 각 속성들의 중요도는 동일하다. 본 연구에서는 각 속성의 중요도가 동일하다는 가정에 대하여 새로운 방법을 제시한다. 즉 각 속성은 현실적으로 다른 중요도를 가지며 본 논문은 나이브 베이시안에서 각 속성의 중요도를 계산하는 새로운 방식을 제안한다. 제안된 알고리즘은 다수의 데이터를 이용하여 기존의 나이브 베이시안과 SBC 등의 다른 확장된 나이브 베이시안 방법들과 비교하였고 대부분의 경우에 더 좋은 성능을 보임을 알 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1031-1033
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• 2005

This paper presents a system of handwritten numerals recognition, which is based on Ant-miner algorithm (data mining based on Ant colony optimization). At the beginning, three distinct fractures (also called attributes) of each numeral are extracted. The attributes are Loop zones, End points, and Feature codes. After these data are extracted, the attributes are in the form of attribute = value (eg. End point10 = true). The extraction is started by dividing the numeral into 12 zones. The numbers 1-12 are referenced for each zone. The possible values of Loop zone attribute in each zone are "true" and "false". The meaning of "true" is that the zone contains the loop of the numeral. The Endpoint attribute being "true" means that this zone contains the end point of the numeral. There are 24 attributes now. The Feature code attribute tells us how many lines of a numeral are passed by the referenced line. There are 7 referenced lines used in this experiment. The total attributes are 31. All attributes are used for construction of the classification rules by the Ant-miner algorithm in order to classify 10 numerals. The Ant-miner algorithm is adapted with a little change in this experiment for a better recognition rate. The results showed the system can recognize all of the training set (a thousand items of data from 50 people). When the unseen data is tested from 10 people, the recognition rate is 98 %.