• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.139-143
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• 2004

최근 고 해상도 위성영상정보의 민간 활용에 대한 수요가 증가하면서 기존의 공간 정보를 다루는 여러 응용분야에서 이에 관련된 많은 연구를 하고 있다. 도시교통 환경 분석을 위하여 위성영상정보를 처리하는 과정에 있어서 도로, 건물, 기타 선 구조와 같은 지형지물을 분석하는 과정은 사용자에 따라 주관적일 수 있다. 이러한 배경에서 수학적 그레이 레벨 모폴로지는 하나의 효과적인 접근으로 간주된다. 본 연구에서 지형지물 추출을 위해 윈도우 운영체제에서 실행되는 실질적인 응용 프로그램을 구현하였다. 이 프로그램에서 주요한 지형지물은 그레이 레벨 영상을 이용하여 개방(opening), 폐쇄(closing), 침식(erosion), 팽창(dilation)의 순차적 처리를 통하여 자동적으로 추출된다. 결과적으로, GDPA, 허프 변환 또는 다른 알고리듬들과 비교시 하나의 이점이 된다. 모폴로지 처리와 같이 본 프로그램은 그레이 레벨 값의 범위에 기반하여 지형지물을 추출을 위한 density slicing 기능 또는 주어진 경계치 보다 작은 화소 군집을 제거하는 처리인 'sieve filtering'을 제공한다. 이러한 기능들은 형태학적으로 처리된 결과를 증대하고 지형지물 종류들을 분리하는데 유용하다. 또한 배경의 제거, 잡음 탐지, 도시 환경 원격 탐사에서의 지형지물 특성화에 기여한다. 본 프로그램을 이용하는데 있어서 IKONOS 위성영상을 이용하여 시험 구현하였다. 결과, 다중 경계치 또는 steve filtering을 이용한 그레이 레벨 모폴로지 처리는 복잡한 지형지물과 많은 데이터로 구성된 고해상도 영상 내의 주어진 대상에서 자동적인 처리와 사용자 정의 sieve filtering으로 인한 효과적인 지형지물 추출 방법으로 간주 된다. 시안을 작성 표준화를 위한 첫 단계 시도를 소개하였다.분석 결과는 문장, 그림 및 도표, 장 끝의 질문, 학생의 학습 활동 수 등이 $0.4{\sim}1.5$ 사이의 값으로 학생 참여를 적절히 유도하는 발견 지향적 인 것으로 조사되었다. 그러나 장의 요약은 본문 내용을 반복하는 내용으로 구성되었다. 이와 같이 공통과학 과목은 새로운 현대 사회에 부응하는 교과 목표와 체계를 지향하고 있지만 아직도 통합과학으로서의 내용과 체계를 완전히 갖추고 있지 못할 뿐만 아니라 현재 사용되고 있는 7종의 교과서가 교육 목표를 충분히 반영하지 못하고 있다. 따라서 교사의 역할이 더욱더 중요하게 되었다.괴리가 작아진다. 이 결과에 따르면 위탁증거금의 징수는 그 제도의 취지에 부합되고 있다. 다만 제도운용상의 이유이거나 혹은 우리나라 주식시장의 투자자들이 비합리적인 투자형태를 보임에 따라 그 정책적 효과는 때로 역기능적인 결과로 초래하였다. 그럼에도 불구하고 이 연구결과를 통하여 최소한 주식시장(株式市場)에서 위탁증거금제도는 그 제도적 의의가 여전히 있다는 사실이 확인되었다. 또한 우리나라 주식시장에서 통상 과열투기 행위가 빈번히 일어나 주식시장을 교란시킴으로써 건전한 투자풍토조성에 저해된다는 저간의 우려가 매우 커왔으나 표본 기간동안에 대하여 실증분석을 한 결과 주식시장 전체적으로 볼 때 주가변동율(株價變動率), 특히 초과주가변동율(超過株價變動率)에 미치는 영향이 그다지 심각한 정도는 아니었으며 오히려 우리나라의 주식시장은 미국시장에 비해 주가가 비교적 안정적인 수준을 유지해 왔다고 볼 수 있다.36.4%)와 외식을 선호(29.1%)${\lrcorner}$ 하기 때문에 패스트푸드를 이용하게 된 것으로 응답 하였으며,

• Information Systems Review
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• v.13 no.2
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• pp.1-16
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• 2011

Recently, many companies have started to manage and support CoPs formally at the organizational level because of strategic usability of CoP. These companies are also seeking ways to motivate CoP members to actively participate in their groups. Accordingly, this paper proposes one way of increasing CoP activities by rearranging CoP members. In practice, active CoP members often lead their groups. Therefore, rearranging members can, eventually, be one method to motivate more individuals to participate in CoP activities. This paper first suggests a new approach in order to improve knowledge sharing activities at the organizational level based on rearranging members of current CoPs. Second, a mathematical model is presented which maximizes total BLS (Balanced Level Score) of company A with several constraints. Then a real world problem is changed to a popular problem, VRP to solve this problem. Third, the solution program was developed to find a meaningful solution.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.94-101
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• 2012

This paper proposes an improved image steganalysis scheme to raise the detection rate of stego images out of cover images. To improve the detection rate of stego image in the steganalysis, tiny variation caused by data hiding should be amplified. For this, we extract feature vectors of cover image and stego image by two steps. First, we separate image into upper 4 bit subimage and lower 4 bit subimage. As a result, stego noise is expanded more than two times. We decompose separated subimages into twelve subbands by applying 3-level Haar wavelet transform and calculate co-occurrence probabilities of two different subbands in the same scale. Since co-occurrence probability of the two wavelet subbands is affected by data hiding, it can be used as a feature to differentiate cover images and stego images. The extracted feature vectors are used as the input to the multilayer perceptron(MLP) classifier to distinguish between cover and stego images. We test the performance of the proposed scheme over various embedding rates by the LSB, S-tool, COX's SS, and F5 embedding method. The proposed scheme outperforms the previous schemes in detection rate to existence of hidden message as well as exactness of discrimination.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1233-1242
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• 2021

In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1627-1634
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• 2021

This paper introduces a low-power compact ADC circuit for analog Convolutional filter for low-power neural network accelerator SOC. While convolutional neural network accelerators can speed up the learning and inference process, they have drawback of consuming excessive power and occupying large chip area due to large number of multiply-and-accumulate operators when implemented in complex digital circuits. To overcome these drawbacks, we implemented an analog convolutional filter that consists of an analog multiply-and-accumulate arithmetic circuit along with an ADC. This paper is focused on the design optimization of a low-power 8bit SAR ADC for the analog convolutional filter accelerator We demonstrate how to minimize the capacitor-array DAC, an important component of SAR ADC, which is three times smaller than the conventional circuit. The proposed ADC has been fabricated in CMOS 65nm process. It achieves an overall size of 1355.7㎛², power consumption of 2.6㎼ at a frequency of 100MHz, SNDR of 44.19 dB, and ENOB of 7.04bit.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the Korean Chemical Society
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• v.62 no.4
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• pp.299-313
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• 2018

Inquiry-centered education is important in science education, but in the actual education field, scientific research is being done in a uniform manner due to realistic difficulties. In this study, we use RS (Reverse Science) as a secondary chemistry class to provide opportunities for students to engage in inquiry learning and scientific thinking through process-oriented activities. In this study, we developed and applied it to explore the effects on the scientific inquiry abilities of middle school students and checked the students' perception of it. For the application of the program, 128 students were selected from 6 classes of the 2nd grade in D district middle school, 64 from the experimental group and 64 from the comparative group. The experimental group taught RSP-based the chemistry inquiry programs and the comparative group taught instructor-led classes and verification experiments on the same topic over the seventh hour with three themes. In addition, we analyzed the results of the pre- and post-test by using the science inquiry ability test, and discussed the effects of the program based on the students' perceptions through class observation, student activity area, questionnaire and interview. As a result, the class using the program showed statistically significant changes in the science inquiry ability of secondary school students. Specifically, the experimental group was found to be significant in its prediction among the subcomponents of basic exploration ability compared to the comparative group. The differences have also been shown to be significant in terms of data translation, hypothesis setup and variable control, which are subcomponents of integrated exploration capabilities (p <. 05). In addition, students became interested in the process of creating the theory of science, and were highly interested in collaborating with their friends. It also provided students with opportunities to experience scientific thinking through process-oriented inquiry. Finally, based on the positive impact of the RSP-based chemistry inquiry program on students, we were able to identify the potential use of the program.