• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1372-1380
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• 1999

본 논문에서는 자연언어 문장의 객관적 정량적인 질 측정 방법의 구축에 대해 설명하고, 이를 문장 퇴고 시스템의 사례에 적용해 본다. 문장의 질을 평가한다는 것은 본질적으로 주관적이고 정량화가 어려운 작업이기 때문에, 이 과정에서 질의 객관적 계량화가 가능한지 여부가 가장 중요한 문제가 된다. 이 논문에서는 이러한 문제를 해결하기 위해 유전자 알고리즘을 이용한 진화적 접근 방법을 통해 객관적이고 정량적인 질의 측정 공식을 유도하는 방법론을 제시하였다. 이 논문에서 제시한 방법론의 핵심은 간단히 말해서 사람이 행하는 정성적인 판단을, 이에 가장 근접하는 정량적 측정 체계로 전환시키는 것이라고 보면 된다. 이것을 위해 정량화 문제를 문장의 단순 언어 특징들의 변화값을 이용한 최적화 문제로 환원시키고, 다시 이 최적화 문제를 유전자 알고리즘을 이용해 해결함으로써 문제를 효과적으로 해결할 수 있었다. 실험 결과를 보면, 본 논문에서 제시한 최적화 방법은 주어진 훈련용 예제와 검증용 예제 중 각각 99.84%, 99.88%를 만족시키는 해를 찾아내었으므로 정량적 질 평가 공식의 유도에 매우 효과적임을 알 수 있었다. 또한 도출된 측정 공식을 이용해서 실제 퇴고 시스템 평가에 적용한 결과 문장 질의 측정에 매우 유용하게 이용될 수 있음을 알 수 있었다. 이와 같이 질의 정량적 평가가 가능하다는 사실이 갖는 또 한가지 중요한 의미는 최종 사용자의 구매 의사나 개발자의 공학적 의사 결정을 위한 객관적 성능 평가 자료의 제공에 이 방법이 유용하게 사용될 수 있다는 점이다.Abstract This paper describes a method of building a quantitative measure of the quality of natural language sentences, particularly produced by document revision systems. Evaluating the quality of natural language sentences is intrinsically subjective, so what is most important as to the evaluation is whether the quality can be measured objectively. To solve such problem of objective measurability, genetic algorithm, an evolutionary learning method, is employed in this paper. The underlying standpoint of this approach is that building the quality measures is a task of constructing a formulae that produces as close results as can to the qualitative decisions made by humans. For doing this, the problem of measurability has been simply reduced to an optimization problem using the change of the values of simple linguistic parameters found in sentences, and the reduced problem has been solved effectively by the genetic algorithm. Experimental result shows that the optimization task satisfied 99.84% and 99.88% of the given objectives for training and validation samples, respectively, which means the method is quite effective in constructing the quantitative measure of the quality of natural language sentences. The actual evaluation result of a revision system shows that the measure is useful to quantize the quality of sentences. Another important contribution of this measure would be to provide an objective performance evaluation data of natural language systems on a basis of which end-users and developers can make their decision to fit their own needs.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.469-480
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• 2019

One of the goals of the new 2015 revised curriculum is to cultivate the creativity of students who will live in the era of the Fourth Industrial Revolution to create new things through diverse ideas and challenges based on basic learning skills. Accordingly, in order to solve the given problems rationally, the convergence problem solving ability that can process and utilize various areas of knowledge and information is becoming important. Therefore, in this study, we designed the integrative education using a 3D printer based on Tinkercad modeling and applied it to the class to investigate the effect on the improvement of computing thinking ability of elementary school students. To verify the contents of the study, two classes of 25 sixth-grade elementary school students were divided into an experimental group and a controlled group. For the experimental group, 12 classes of convergence education programs using a 3D printer were applied for about three months, and the same amount of general curriculum was conducted for the control group. After that, the t-tests were carried out using the pre-post test to measure the effectiveness of the computational thinking ability. After the application of the program, the experimental group showed statistically significant improvement in computational thinking ability, but the controlled group showed no statistically significant difference. The results show that convergence education using the Tinkercad modeling-based 3D printer has a positive effect on the improvement of computing thinking ability of elementary school students.

• Journal of Industrial Convergence
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• v.17 no.3
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• pp.9-16
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• 2019

In this study, we wanted to analyze the causes of food poisoning and its major occurrence in September 2018 and analyze the relevance of the HACCP certification system to report the correlation. Based on three-year food poisoning cases and causative substances data, and big data on HACCP certification companies and food poisoning frequency, Hygiene 1: 'Salmonella would have spread through school food processing medium.' Hypothesis 2: The difference in the number of food poisoning cases in the last three years as the number of HACCP certifier increases, the number of food poisoning cases will be verified and the cause of food poisoning in September 2018. Studies show that the food poisoning in September 2018 was caused by salmonella bacteria and that outsourced food provided through school meals was the cause. It was also shown that the expansion of HACCP certification did not significantly contribute to the reduction of food poisoning. Therefore, the management operation measures were proposed as a solution to prevent salmonella and to become HACCP certification that could reduce food poisoning.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.7-7
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• 2017

범람파동 개념에 따르면 하도와 홍수터의 횡적 연결성은 하천 생태계의 생물다양성과 생산성 증대에 중요한 역할을 한다. 제방에 의하여 제내지 홍수터가 하도와 차단된 우리나라 하천에서 생태적 서비스를 증대하기 위해서 횡적 연결성을 복원하는 기술 개발이 필요하다. 횡적 연결성의 복원 기술을 개발하기 위해서는 우선 하도와 홍수터 사이에 생태적 연결성의 현황을 파악하고 연결성을 저해하는 요인을 진단하는 평가 기술 개발이 시급히 요청되고 있다. 따라서 본 연구에서는 제방에 의하여 차단된 제내지 하천환경에서 수리적, 생태적 횡적 연결성을 평가하고 진단하는 기술을 개발하고 연결성 회복 방안을 제안하고자 한다. 차단된 제내지 하천환경 평가는 1) 지리정보시스템을 이용하여 차단된 하천공간을 탐색하고, 2) 탐색된 전체 재내지에서 원격평가에 의하여 간편하게 횡적 연결성 평가를 실시하고, 3) 선정된 특정 제내지 대상지에서 현장평가에 의하여 상세하게 연결성을 평가하는 순서로 수행된다. 차단된 하천공간의 획정은 홍수가 범람할 수 있는 제내지 공간을 잠재적 하천공간으로 정의하고 수치표고모델 (DEM)과 하천기본계획의 30년 빈도 홍수위 자료를 이용하여 제내지 홍수터를 탐색하였다. 제내지 홍수터의 원격 연결성평가는 지리정보시스템에서 수치지도와 토지피복도 등 공간자료를 이용하여 수리 및 서식처 환경성, 제방 차단성과 하도 및 육상 연결성을 평가하고 원격평가 결과를 토대로 현장평가 대상지를 선정하였다. 횡적 연결성의 현장평가를 위하여 크게 하도-홍수터 연결성과 제내지 서식처 보존성으로 평가 항목을 선정하였다. 또한 연결성 평가는 수리연결성과 생물연결성으로, 서식처 보존성 평가는 습지유지율, 습지보존성, 육역지보존성을 세부항목으로 구성하였다. 평가 항목별로 5 등급의 평가 기준에 따라서 평가 점수를 부여하고 평가 총점을 산출하여 최종 연결성 평가 등급을 5 단계로 구분하였다. 현장평가를 위한 MS Access 기반 소프트웨어를 개발하여, 데이터 입력과 관리 및 평가 결과 산출과 비교를 편리하게 하였다. 개발된 제내지 하천환경 평가법을 청미천과 만경강에 적용하여 검증하였다. 개발된 평가법을 바탕으로 차단된 제내지 하천환경에서 연결성 회복에 따른 어류와 식생의 분포를 예측하는 수리생태 결합모델을 개발하였다. 먼저 차단된 제내지에서 연결 수로를 복원하여 유속, 수심 분포를 준이차 수리수문 모델로 예측하였다. 예측된 수리 환경에 따라서 지표어종의 서식처 적합도 지수 (HSI)를 이용하여 서식 분포 확률을 모의하였다. 또한 일반화가법모델 (GAM)을 이용하여 환경구배에 의한 우점식생의 분포를 예측하였다. 차단된 제내지 하천환경의 생태적 연계성 평가 기술을 기반으로 제방제거, 제방후퇴, 제방고 하강, 수문 및 연결수로 개선, 생물이동 저해 장벽 제거 등의 다양한 복원기술이 개발되어야 할 것으로 생각된다.

• Journal of the Korean Magnetics Society
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• v.24 no.1
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• pp.22-27
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• 2014

North indicating azimuth angle sensors have been used in airplanes, ships traditionally and nowadays employed in smart phones. For the azimuth and roll angle measurement of the sensor, 3-axis acceleration sensor was added to the 3-axis magnetic field sensor. In this work, we have constructed a measuring system for the measurement of the magnetic field and the angle uncertainty of the magnetic field sensors. Measuring system could be useful not only in non-magnetic laboratory but also in normal laboratory, we constructed small size of 3-axis Helmholtz coils for the compensation environment magnetic field (Earth magnetic field and magnetic field from building) and the generation of magnetic field for the test of magnetic field sensor. The constructed measuring system could compensate environment magnetic field below 10 nT level and generate 3-dimensional magnetic field with magnitude uncertainty of 0.2 % and angle error of $0.2^{\circ}$ within the volume of ${\pm}30mm$ diameter at center of Helmholtz coils. For the conformation of developed measuring system, We tested commercially available 3-axis magnetometer and heading sensor.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.433-440
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• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.426-432
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• 2021

Continuous wave (CW) Doppler radar has the advantage of being able to solve the privacy problem unlike camera and obtains signals in a non-contact manner. Therefore, this paper proposes a human activity recognition (HAR) system using CW Doppler radar, and presents the hardware design and implementation results for acceleration. CW Doppler radar measures signals for continuous operation of human. In order to obtain a single motion spectrogram from continuous signals, an algorithm for counting the number of movements is proposed. In addition, in order to minimize the computational complexity and memory usage, binarized neural network (BNN) was used to classify human motions, and the accuracy of 94% was shown. To accelerate the complex operations of BNN, the FPGA-based BNN accelerator was designed and implemented. The proposed HAR system was implemented using 7,673 logics, 12,105 registers, 10,211 combinational ALUTs, and 18.7 Kb of block memory. As a result of performance evaluation, the operation speed was improved by 99.97% compared to the software implementation.