• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.640-645
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• 2011

The educational exchange through e-learning is working very well in such case as develop e-learning, development of various learning tools, cooperative practical use of e-learning contents, etc. However because there were no considerations of LMS(Learning Management System) interconnection when each systems were developed, the exchange through e-learning is starting to raise a problem. Hence in this thesis, this paper presents designed model about efficient LMS interconnection through analysis case of exchange through e-learning and deduce problem. In the first place essential part for is defied study such as lecture establishment data, lecture data, user data, class data, student learning tracking to interconnection data, then constituted data interconnection table used view by data interconnection process. By experiment result, the accessibility between students and professors was more convenience, and decreased work process by less data exchange. Henceforth there are researches in development of various essential parts for study, considered security of LMS interconnection.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.45-50
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• 2009

The educational exchange through e-learning is working very well in such case as develop e-learning, development of various learning tools, cooperative practical use of e-learning contents, etc. However because there were no considerations of LMS(Learning Management System) interconnection when each systems were developed, the exchange through e-learning is starting to raise a problem. Especially the exchange through e-learning between university produced problem for a variety of reasons by absence of direct exchange in every case such as communication of students information, communication of lecture information, etc. Hence in this thesis, I will present designed model about efficient LMS interconnection through analysis case of exchange through e-learning and deduce problem. In the first place I define essential part for study such as lecture establishment data, lecture data, user data, class data, student learning tracking to interconnection data, then constituted data interconnection table used view by data interconnection prcess. By experiment result, the accessibility between students and professors was more convenience, and decreased work process by less data exchange. Henceforth there are researches in development of various essential parts for study, considered security of LMS interconnection.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1083-1085
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• 2017

본 논문에서는 우리 주변의 생활 공간을 지능화하고 편리하게 만들기 위한 연구 방향으로서 공간 정보를 토대로 한 스마트 공간 기술을 제안한다. 이를 위해 3D 공간 모델을 구성하고 공간 내의 객체를 인식 및 식별하고 추적함으로써 변화하는 공간정보를 수집하고, 이를 복합 공간 상황 모델로 구성 및 관리하는 방법을 제시한다. 또, 수집된 정보를 토대로 각 사용자의 작업 이력을 학습하여 적절한 서비스를 능동적으로 제안하기 위한 학습 기술 및 응용 서비스를 구현하고, 간단한 실험을 통해 제안 기술의 가능성을 검증한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.982-983
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• 2020

추천 시스템은 사용자들의 과거 구매 이력 등을 학습해서 사용자들이 미래에 구매할 것 같은 상품을 추천한다. 대부분의 추천 시스템 관련 연구들은 사용자들과의 상호작용을 고려하지 않은 채 한 번의 모델 학습과 한 번의 추천만 수행하며, 사용자로부터 추천 결과에 대한 피드백을 받아서 더 나은 추천을 수행하려는 시도는 거의 이루어지지 않았다. 본 논문에서는 기존의 추천 모델들이 사용자와의 상호작용을 추가적으로 고려했을 때 어느 정도의 정확도 향상을 이룰 수 있는지에 대해서 분석한다. 특히 사용자와의 상호작용을 통해 사용자 취향의 다양성을 파악하고 이를 반영하여 더 나은 추천을 제공하는 방법에 대해서 논의한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

• Journal of Engineering Education Research
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• v.13 no.1
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• pp.78-86
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• 2010

Training Curriculum Integrated Information Network is allowed to searching the all curriculums and courses related with training but also is a one stop handling integrated learning system to cover a course registration, learning and analysis of learning performance. Through developing and managing the Training Curriculum Integrated Information Network, it is available to get the various curriculum thus it is for trainers able to enforce the self oriented course choice and then high quality of training could be proposed by the diverse training curriculums and competitions. To manage Training Curriculum Integrated Information Network more effectively, active public relations marketing activities, high reliable correct information service and rich contents are required. It is essential to manage the learner and learning contents supplier, stable financial resources, personal security issue and protecting a copyright of training curriculum to be a successful network system.

• Journal of Engineering Education Research
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• v.8 no.4
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• pp.20-30
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• 2005

The purpose of this study is the development of SPMP(Student Portfolio Management Program) to aid performance of student program outcomes related to program educational objectives. SPMP is developed to manage accreditation conditions, student materials, academic results through system database and to provide synthetic data for engineering education through statistical process. There are three functions to construct SPMP. First, the function of student information management is organized on various input data related to academic program, resume, etc. Second, the function of accreditation condition management is identified by the evaluation of program outcomes, subject grades, etc. Third, the function of course work data management is made by the operation of the academic reports and productions. The result of this study is expected to effectively provide constructive directions for continuous management in accreditation of engineering education and to support student portfolio for submission in required application.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.