• Journal of KIISE:Software and Applications
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• v.34 no.4
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• pp.377-388
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• 2007

In this paper, we propose a multi-modal biometric system based on face, iris and fingerprint recognition system. To effectively aggregate two systems, we use statistical distribution models based on matching values for genuine and impostor, respectively. And then, We performed reveal fusion algorithms including weighted summation, Support Vector Machine(SVM), Fisher discriminant analysis, Bayesian classifier. From the various experiments, we found that the performance of multi-modal biometric system was influenced with the normalization methods and classifiers.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.242-246
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• 2010

In this paper, we propose a quality assessment method of biometrics for estimating an authentication result in an user authentication system. The proposed quality assessment method is designed to compute a quality score called CIMR (Confidence Interval Matching Ratio) as a result by small-sample analysis like T-test. We use the C/MR-based quality assessment method for testing how to well draw a distinction between various biometrics in a multimodal biometric system. We also test a predictability for authentication results of obtained biometrics using the mean $\bar{X}$ and the variance $s^2$ in T-test-based CIMR. As a result, we achieved the maximum 88% accuracy for estimation of user authentication results.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.10a
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• pp.240-257
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• 2006

In an effort to gain competitiveness, recently many companies are trying to outsource their logistics activities to the logistics specialists, while concentrating on their core and strategic business area. Because of this trend, the third party logistics comes to the fore, catching people's attention, and expanding its market rapidly. Under these circumstances, the third party logistics companies are making every effort to improve their logistics services and to develop an information system in order to enhance their competitiveness. In particular, among these efforts one of the critical parts is the decision support system for effective transportation network planning. To this end, this study has developed an efficient decision support system for an optimal transportation network planning by comprehensively considering the transportation mode, routing, assignment, and schedule. As a result of this study, the new system enables the expansion of the third party logistics companies' services including the multimodal transportation, not to mention one mode of transportation, and also gets them ready to plan an international transportation network.

• Journal of KIISE:Software and Applications
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• v.27 no.11
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• pp.1133-1140
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• 2000

다중모드 대화에서 나타나는 대용어는 언어만을 사용하는 대화에서 나타나는 것과 비교하여 매우 다른 형태와 특징을 가진다. 그것은 행위나 시각이 대용 행위로 사용될 수 있기 때문이다. 본 논문에서는 터치스크린 인터페이스를 이용한 홈쇼핑 가구점 영역의 다중모드 대화 시스템에서 나타나는 다양한 대용어의 처리 방법을 알아본다. 먼저, 화면 대용어와 참조 대용어를 정의하여 다양한 형태의 대용어를 분류한다. 그리고 각 대용어를 처리할 수 있는 두 가지의 일반적인 방법을 제안한다. 하나는 지시 행위를 수반하거나 생략한 채 발화되어 현재 화면에 나타나 있는 아이템을 참조하는 대용어를 처리하는 단순한 매핑 알고리즘이다. 다른 하나는 다중 모드 대화 시스템을 위해 워커(Walker)의 센터링 알고리즘을 확장한 이중 캐시 구조의 센터링 알고리즘이다. 확장된 센터링 알고리즘은 발화와시각 정보 그리고 화면 전환 시간을 유지할 수 있기 때문에 다중모드 대화에서 발생하는 다양한 대용어를 처리하기에 적합하다. 실험에서 제안된 시스템은 40개의 대화에서 나타난 402개의 대용어(발화당 0.54)중에서 387개를 처리하여 96.3%의 정확도를 보였다.

• Korea Trade Review
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• v.44 no.6
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• pp.303-325
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• 2019

The purpose of this study is to determine optimal transportation routes through the comparison of Korean - European transportation routes of automotive parts and to suggest information that can be utilized in Korea - Europe trade activities or trade route selection by establishing multipath. This study analyzed the direct transportation cost, inventory cost, and warehouse inventory cost of the sea and TSR / TCR railroad transport based on characteristics of automotive parts logistics and multimodal transportation. Also, this study identifies the most effective transportation route from the viewpoint of total logistics cost. In addition to the economic factors, we conducted an in-depth analysis through interviews with corporate executives to identify the importance of the factors with the behavioral factors, and the reliability was further secured through interviews. Through this study, it is possible to understand various aspects of international logistics by analyzing the factors of transportation choice in terms of economic and behavioral perspectives concurrently by differentiating from existing research.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• Journal of The Korean Association of Information Education
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• v.16 no.4
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• pp.419-427
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• 2012

This paper presents the effect of a interactive learning experience and student's response to technological components We develop the interactive learning environment and learning model in lessons relying on educational robots and augmented reality in the school classroom. The developed learning model is based on the problem-based learning model. The experiments of the study conduct with 18 students, the $5^{th}$ and $6^{th}$ graders of an elementary school for 8 weeks using developed system. We find out the interactive learning experiences have an influence on the creative ability of children. We know that students who scored lower on the school exam scored higher on the score of creativity compared to top students through educational robots and augmented reality.

• Journal of Korean Society of Transportation
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• v.35 no.5
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• pp.409-422
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• 2017

TOD (Transit-Oriented Development) is one of the urban structure concentrated on the multifunctional space/district with public transportation system, which is introduced for maintaining sustainable future cities. With such trends, the project of building complex transferring centers located at a urban railway station has widely been spreaded and a comprehensive and systematic analytical framework is required to clarify and readily understand the complicated procedure of estimation with the large scale of the project. By doing so, this study is to develop a comprehensive analytical framework for estimating a pedestrian OD matrix using a spatial information and an integrated smart card data, which is so called a data depository and it has been applied to the Samseong station for the model validation. The proposed analytical framework contributes on providing a chance to possibly extend with digitalized and automated data collection technologies and a BigData mining methods.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.