• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.19-32
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• 1999

In this study, the trip characteristics of workers in the city are systematically analyzed. The trip behaviors and socioeconomic characteristics of workers are analyzed using Person Trip Survey Data of 1988 and 1992 in Taegu Metropolitan area. With the results of behavioral analyses, the daily travel pattern of workers is shown as one tour contained two trips and it is relatively simple and stable. Also the rate using the same mode in a day is Presented as high ratio. So, it can be explained that the choice of worker\`s first trip is fixed his/her travel mode for his/her daily travel mode. Based on these analyses, the mode choice model for workers is developed by applying the Multi-nominal Logit Model with the choice set of bus, taxi, and car. The explanatory variables of this model include sex, age, auto, travel time, and cost. Empirical tests of the model show encouraging results. After that, the temporal transferability of the model is examined by the Pairwise t-test and five indexes far the model of 1988 and 1992. The results of examination are satisfied with each significance level of the explanatory variables and five indexes. Therefore. it can be concluded that the temporal transferability of this model developed in this study is resonable.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.125-138
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• 2006

The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in data acquisition system andinformation processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, damage detection of bridge structures using neural networks technique based on the modal properties is presented, which can effectively consider the modeling uncertainty in the analysis model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness and applicability of the proposed method.

• Journal of the Korean Society of Costume
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• v.53 no.4
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• pp.1-16
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• 2003

The purpose of this study is to investigate the sensibility images of Korean Traditional Chumoni. The detailed methodology of this study is as follows. Selections of stimuli to analyse the sensibility images of Korean Traditional Chumoni were made up of 15 stimuli. The survey has been done for the 15 slide stimuli with semantic differential hi-polar scales which are consist of 23 couples of sensibility words. The subjects were 150 female students majoring in clothing and textile. 150 male students majoring in other department and 150 female students majoring in other department in the twenties between 2001. 3. 30 and 2001. 4. 4. The obtained data were analyzed by factor analysis, cluster analysis. ANOVA. The major finds were as follows. 1. To explain the hierarchy of the sensibility of Korean Traditional Chumoni, two image groups were classified, one is noble and characteristic image the other is splendid and intensive image. Finally it represented noble and splendid image. 2. As result of the factor analysis. 3 factors which are Attraction, Decorativeness, Gravity were found to be constructing factors for the sensibility images of Korean Traditional Chumoni. 3. By cluster analysis, 4 clusters were determined according to Korean Traditional Chumoni. Cluster 1 is splendid. multi-colored and realistic in patteren. Cluster 2 is consist of 'true chumonis' and one-colored. Cluster 3 is modal in pattern. Cluster 4 is simple without any decorations. As to the difference of image of Korean Traditional Chumoni, there were significant differences amang 3 factors by cluster Cluster 1 was found most attractive and grave. Cluster 2 was found most decorative. 4. As to the difference of image of Korean Traditional Chumoni, there were significant differences amang 3 factors by decoration. Gold foil was found most attractive and grave. Embroidery was found most decorative. 5. As to the difference of image of Korean traditional chumoni, there were differences in Decorativeness and Gravity by sex and there were differences in Attraction by major.

• Cartoon and Animation Studies
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• s.41
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• pp.307-336
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• 2015

Virtual reality technology is one of the important technologies that will affect our life in many ways. This novel technology will draw new paradigm into the medical field due to its advantages in physical safety and environment that are fully controlled. Phobia treatment using VR technology has been implemented and its feasibility has been proved through a number of researches in many institutions. This study has observed the current progress of its technology environment and the trend of research. Also, This study has analyzed the results from domestic and international data. Analysis shows that other countries are ahead of korea in all aspects of the phobia treatment using virtual reality method. Although the authors limited the kinds of journals, the amount of quantity in international researches are two times more than domestics. Also, The researchers in other countries concentrate on the multi-modal studies. To use virtual reality in the phobia treatment, we need to understand the needs of the society members and the government has responsibility to support what the researchers need.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.203-214
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• 2023

The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.141-147
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• 2024

Recently, there has been growing interest in personalized services tailored to an individual's preferences. This has led to ongoing research aimed at recognizing and leveraging an individual's personality traits. Among various methods for personality assessment, the OCEAN model stands out as a prominent approach. In utilizing OCEAN for personality recognition, a multi modal artificial intelligence model that incorporates linguistic, paralinguistic, and non-linguistic information is often employed. This paper examines the impact of the margin value set for extracting facial areas from video data on the accuracy of a personality recognition model that uses facial expressions to determine OCEAN traits. The study employed personality recognition models based on 2D Patch Partition, R2plus1D, 3D Patch Partition, and Video Swin Transformer technologies. It was observed that setting the facial area extraction margin to 60 resulted in the highest 1-MAE performance, scoring at 0.9118. These findings indicate the importance of selecting an optimal margin value to maximize the efficiency of personality recognition models.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.619-624
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• 2016

Social-aware video displays not only the relationships between characters but also diverse information on topics such as economics, politics and culture as a story unfolds. Particularly, the speaking habits and behavioral patterns of people in different situations are very important for the analysis of social relationships. However, when dealing with this dynamic multi-modal data, it is difficult for a computer to analyze the drama data effectively. To solve this problem, previous studies employed the deep concept hierarchy (DCH) model to automatically construct and analyze social networks in a TV drama. Nevertheless, since location knowledge was not included, they can only analyze the social network as a whole in stories. In this research, we include location knowledge and analyze the social relations in different locations. We adopt data from approximately 4400 minutes of a TV drama Friends as our dataset. We process face recognition on the characters by using a convolutional- recursive neural networks model and utilize a bag of features model to classify scenes. Then, in different scenes, we establish the social network between the characters by using a deep concept hierarchy model and analyze the change in the social network while the stories unfold.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.1
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• pp.331-351
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• 2016

Digitalized data and literature in scholarly community has developed the concept of digital humanities and cyberscholarship which indicate the characteristics of a new aspect and approach in scholarly activities with digitalized resources or new media. This study was performed in order to identify the changes in national research activities of art and humanities by using a multi-modal approach. The combined methodology of in-depth interview and content analysis on publishing and citing behaviors in literature was executed. The steps of research process is identified as a non-linear combination of 3 parts: developing research idea, developing the research idea to write, and submitting manuscript to publish. Prominent implementations of cyberscholarship were found in the 2nd step for accessing and using research data and literatures. Understanding the characteristics of scholar communication using cyberscholarhip factors in humanities for interdisciplinarity, sophisticating the environment of cyberscholarhip for data sharing, investing and developing archivist and archives, and providing a various platform for accelerating scholarly communication were derived by the panel discussion for developing interdisciplinary research for humanities.