• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.182-196
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• 2024

This study introduces driving pattern analysis and change detection methods using smartphone sensors, based on contrastive learning. These methods characterize driving patterns without labeled data, allowing accurate classification with minimal labeling. In addition, they are robust to domain changes, such as different vehicle types. The study also examined the applicability of these methods to smartphones by comparing them with six lightweight deep-learning models. This comparison supported the development of smartphone-based driving pattern analysis and assistance systems, utilizing smartphone sensors and contrastive learning to enhance driving safety and efficiency while reducing the need for extensive labeled data. This research offers a promising avenue for addressing contemporary transportation challenges and advancing intelligent transportation systems.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.1-18
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• 2008

By using previous similar case plans, the case-based planning (CBP) systems can generate efficiently plans for new problems. However, most existing CBP systems show limited functionalities for case retrieval and case generalization. Moreover, they do not allow their users to participate in the process of plan generation. To support efficient memory use and case retrieval, the proposed case-based planning system, JCBP, groups the set of cases sharing the same goal in each domain into individual case bases and maintains indexes to these individual case bases. The system applies the heuristic knowledge automatically extracted from the problem model to the case adaptation phase. It provides a sort of case generalization through goal regression. Also JCBP can operate in an interactive mode to support a mixed-initiative planning. Since it considers and utilizes user's preference and knowledge for solving the given planning problems, it can generate solution plans satisfying more user's needs and reduce the complexity of plan generation.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.321-330
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• 2023

In this paper, we propose a method of automatically creating an ontology by extracting various relationships between terms necessary for constructing an ontology of a specific domain. The extracted relationship is constructed as an ontology by encoding it into an axiomatic set in the structure of the ontology. To solve efficiently, we represent the search space of the set as an integer programming problem, and we reduce the matrix by using a simple reduction that eliminates rules that are not very helpful for optimization. In conclusion, this paper proposes a way to generalize patterns using given data, reduce search space while maintaining useful patterns, and automatically generate efficient ontology using extracted relationships by applying algorithms composed of structured ontology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.117-123
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• 2018

In this study, we suggest a fast image reconstruction scheme using Poisson equation from image gradient domain. In this approach, using the Poisson equation, a guided vector field is created by employing source and target images within a selected region at the first step. Next, the guided vector is used in generating the result image. We analyze the problem of reconstructing a two-dimensional function that approximates a set of desired gradients and a data term. The joined data and gradients are able to work like modifying the image gradients while staying close to the original image. Starting with this formulation, we have a screened Poisson equation known in physics. This equation leads to an efficient solution to the problem in FFT domain. It represents the spatial filters that solve the two-dimensional screened Poisson model and shows gradient scaling to be a well-defined sharpen filter that generalizes Laplace sharpening. We demonstrate the results using a discrete cosine transformation based this Poisson model.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.139-148
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• 2006

This study is intended to propose quality criteria and Web metrics for usability quality evaluation of multimedia Web sites. Recently, applications of the Web sites are not limited to the area of industry and operating as it integrates the new technology. Also as information super highway becomes common with development or network technology, multimedia Web site is on the rise due to various use of multimedia attributes. Therefore this study expands to apply HTML based Web site quality evaluation studied in Software engineering, HCI, Hypermedia to multimedia Web site, and suggests new metrics by integrating flash usability which is expanding its portion in multimedia Web site lately. The Web metrics proposed in this study are verified by heuristic evaluation from a group of expert. It analyses the results of quantitative and qualitative qualify evaluation on Web Award Korea by comparing the award-winning, high-quality Web site with non-winning Web sites. This study can be used to establish guideline for high-quality multimedia Web site development in the future.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.9-15
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• 2007

As the information of the internet increased, importance of sematic web for collecting and integration of these informations to support decision making of some group or ordinary people are growing as well. Basis structure that composes semantic web is ontology and languages like XML, RDF/RDF schema and OWL are basis means that compose ontology schema. When composes and manages Ontology schema, one of the important consideration point is that schema is changed as times go by. Therefore, change of domain of schema, change of data concept or change of relation between resource etc. are reflected in the ontology system. In this study, we suggest semantic web schema management skill in terms of version management. We categorized version change forms and created version graph for checking of version transition. With created version graph, we define transitivity rule and propose schema tag for detail application which enables extending of applicable version schema.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.667-680
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• 2007

The bio-data processing is used for a suitable purpose with bio-signals, which are obtained from human individuals. Recently, there is increasing demand that the bio-data has been widely applied to various applications. However, it is often that the number of data within each class is limited and the number of classes is large due to the property of problem domain. Therefore, the conventional pattern recognition systems and classification methods are suffering form low generalization performance because the system using the lack of data is influenced by noises of that. To solve this problem, we propose a modified additive factor model for bio-data generation, with two factors; the class factor which affects properties of each individuals and the environment factor such as noises which affects all classes. We then develop a classification system through defining a new similarity function using the proposed model. The proposed method maximizes to use an information of the class classification. So, we can expect to obtain good generalization performances with robust noises from small number of datas for bio-data. Experimental results show that proposed method outperforms significantly conventional method with real bio-data.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.302-313
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• 2021

As the domestic consulting industry matured, consultants were required to have insight into customer's business and consulting business. Gaining these insights requires deep understanding of the business domains and high degree of competencies. This study empirically analyzed the data collected through the survey in order to apply the ICMCI competence model to domestic consultants. As a result of the study, it was found that business competency and technical competency had a positive effect on project performance, but values & behavior competency were not statistically significant. On the other hand, it was found that only technical competency, values & behavior competency had a positive effect on social-relational competency. Through this study, it was confirmed that a deep understanding and perception of the consulting business is necessary to grow into a professional consultant, but there is a limit to generalizing the research results because the characteristics of the population cannot be sufficiently reflected with a small sample.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.466-476
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• 2006

The objective of this study was to compare and analyze the acceptability and consumption attitude for soy foods between Korean and Canadian university students as young consumers. This survey was carried out by questionnaire and the subjects were n=516 in Korea and n=502 in Canada. Opinions for soy foods in terms of general knowledge were that soy foods are healthy (86.5% in Korean and 53.4% in Canadian) or neutral (11.6% in Korean and 42.8% in Canadian), dairy foods can be substituted by soy foods (51.9% in Korean and 41.8% in Canadian), and soy foods are not only for vegetarians and milk allergy Patients but also for ordinary People (94.2% in Korean and 87.6% in Canadian). In main sources of information about soy foods, the rate by commercials on TV, radio or magazine was the highest (58.0%) for Korean students and the rate by family or friend was the highest(35.7%) for Canadian students. In consumption attitude, all of Korean students have purchased soy foods but only 55.4% of Canadian students have purchased soy foods, and soymilk was remarkably recognized and consumed then soy beverage and margarine in order. 76.4% of Korean students and 65.1% of Canadian students think soy foods are general and popular and can purchase easily, otherwise, in terms of price, soy foods were expensively recognized as 'more expensive than dairy foods' was 59.1% (Korean) and 54.7% (Canadian), and 'similar to dairy foods' was 36.8% (Korean) and 39.9% (Canadian). Major reasons for the rare consumption were 'I am not interested in soy foods' in Korean students (27.3%) and 'I prefer dairy foods to soy foods' in Canadian students (51.7%). However, consumption of soy foods in both countries are very positive and it will be increased.