• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.155-160
• /
• 2023

In principle, data from all databases and systems managed by the Ministry of Defense or public institutions must be guaranteed to have a certain level of quality or higher, but since most information systems are built and operated, data quality management for all systems is realistically limited. Most defense data is not disclosed due to the nature of the work, and many systems are strategically developed or integrated and managed by the military depending on the need and importance of the work. In addition, many types of data that require data quality management are being accumulated and generated, such as sensor data generated from weapon systems, unstructured data, and artificial intelligence learning data. However, there is no data quality management guide for defense data and a guide for selecting quality control targets, and the selection criteria are ambiguous to select databases and systems for quality control of defense data according to the standards of the public data quality management manual. Depends on the person in charge. Therefore, this paper proposes criteria for selecting a target system for quality control of defense data, and describes the relationship between the proposed selection criteria and the selection criteria in the existing manual.

• 한국지구과학회:학술대회논문집
• /
• 2005.02a
• /
• pp.239-248
• /
• 2005

이 논문은 과학영재교육원의 학생 선발과정을 중심으로 과학영재아 판별과정을 분석해보고, 과학영재교육원의 학생 선발과정에서 발생할 수 있는 오류를 점검하여, 이를 보완할 수 있는 개선안을 마련하고자 하는데 목적을 두고 있다. 분석결과 과학, 수학과 관련된 창의적 문제의 지필평가 성적이 선발의 가장 중요한 기준이었다. 지필평가 단계에서 지망분야에 관계없이 과학, 수학 및 창의력검사를 모두 치러야 하는 교육원에 초점을 맞추어 그 점수 활용에 있어서 발생할 수 있는 오류를 점검하였다. A 교육원의 경우 학생 선발은 다단계 평가를 원칙으로 하고 있었으며, 1단계 지필평가에서 과학, 수학, 창의력검사라는 세 과목 시험의 합산점수로서 선발하고 있었다. 이 교육원의 ○○년도 중등과정 응시자 276명을 대상으로 합격자와 불합격자의 지필평가 점수를 분석하여 과학, 수학, 창의력검사의 시행에 오류가 없었는지 점검하였다. 또 이들의 합산에 의한 선발이 의미 있는 결과를 보이는지 분석하였다. 그 결과 과학, 수학, 창의력검사의 상관도분석에서 과학은 수학 및 창의력검사와 유의미한 상관이 있었으나, 수학과 창의력검사는 독립적으로 분석되었다. 또 이들의 합산에 지원분야별 배점으로 계산한 선발은 본래의 취지, 즉 과학, 수학, 창의력에서 모두 우수한 학생을 선발하고자 하는 의도대로 진행되었으나, 판별분석 결과 합격과 불합격자 판별에서 88.1%의 정확도를 보여 다소 오류가 있었음을 발견하였다. 이는 해당년도에 출제된 문제의 난이도 및 시험 과목별 평균점수 차를 고려하지 않아 발생하는 문제로 파악되어져 원점수 대신 표준점수로 변환하여 오류를 보완할 것을 제안한다. 자체와 직접 관련되는 영역으로는 좌반구의 측두엽과 전두엽 부분이 관찰되었다. 특히 한국어 어말어미 산출시 나타나는 형태점화 양상과 관련된 대뇌영역으로 발견된 브로카 영역에서의 활성화는 어미 변환과 관련된 영역이라기보다는 산출시 관련되는 articulation, motor coordinate관련 영역으로 추정되고, 측두엽의 활성화는 형태소, 의미 관련 지식의 data base로 추정된다. 또한 우반구 전두엽 부분에서 관찰된 활성화는 억제관련 영역으로 짐작된다.러한 동물실험이 그 기초를 제공해 줄 수 있을 것이다. 또한 행동성향 및 기억의 종류에 따른 약물효과의 차이는 기억과 관련된 질병인 알츠하이머 환자에 있어 개개인에게 맞는 적절한 특징적인 치료약물이 존재할 것이라는 가능성을 제공해줄 뿐만 아니라 학습과 기억력 증진 효과를 기대해 볼 수 있을 것이라고 생각된다. 및 지역산업발전의 기획${\sim}$조정기구로서, 선진국의 지역발전기구(Regional Development Agency : RDA)인 지역전략산업기획단이 2002년도부터 산업자원부와 9개 시도에 의해 설립되어 지역네트워크의 활성화와 클러스터의 형성 촉진을 하게 되었고 2004년도에는 13개시도로 확대${\sim}$운영되고 있고, 지역특화사업(H/W)과 지역산업기술개발과제(S/W)와 함께 패케지 형태로 지원되며, 주요역할은 크게 지역산업의 정책기획 분야와 평가관리, 지역혁신역량 조사 및 DB구축 등으로 구분된다. 그중에서도 권역별, 지역별, 지역산업진흥사업 육성과 중장기 산업발전계획을 수립하기 위하여 지역혁신역량을 바탕으로 한 지역 Technol

• Journal of Korea Water Resources Association
• /
• v.56 no.12
• /
• pp.939-953
• /
• 2023

Shallow water equations (SWE) serve as fundamental equations governing the movement of the water. Traditional numerical approaches for solving these equations generally face various challenges, such as sensitivity to mesh generation, and numerical oscillation, or become more computationally unstable around shock and discontinuities regions. In this study, we present a novel approach that leverages the power of physics-informed neural networks (PINNs) to approximate the solution of the SWE. PINNs integrate physical law directly into the neural network architecture, enabling the accurate approximation of solutions to the SWE. We provide a comprehensive methodology for formulating the SWE within the PINNs framework, encompassing network architecture, training strategy, and data generation techniques. Through the results obtained from experiments, we found that PINNs could be an accurate output solution of SWE when its results were compared with the analytical method. In addition, PINNs also present better performance over the Artificial Neural Network. This study highlights the transformative potential of PINNs in revolutionizing water resources research, offering a new paradigm for accurate and efficient solutions to the SVE.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.6
• /
• pp.260-268
• /
• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Science of Emotion and Sensibility
• /
• v.14 no.3
• /
• pp.395-402
• /
• 2011

As the consumer market for odor products grows, companies producing healthcare products are beginning to pay more attention to the emotional aspect of an odor product in order to differentiate their products from competitors. In the following research, the affective effect of odor product was investigated while focusing on relaxation and working contexts using orange and pine scents, since these are typical odors in current domestic market. Two empirical studies were carried out. First, in experiment I, 18 subjects, all of whom were university students, spent 20 minutes sitting comfortably on a sofa while electrocardiogram assessments were made. After a five-minute break, in experiment II, the same subjects were provided with both arithmetic and geometric questions and their electroencephalogram readings was recorded from eight channels. All subjects participated in three sessions - no odor, an orange scent, and then a pine scent - with a minimum time interval of 24 hours. The results show that in the context of a pine scent, both the activation ratio of subjects' parasympathetic system and those of the Sensory Motor Rhythm waves and Mid Beta waves were at the highest peak. Therefore, the pine scent helped the subjects to feel more comfortable and more focused at the same time. In other words, it gave them a state of meditated attention. In addition, it was found that the right brain was activated twice the intensity when the subjects worked through the geometric questions, whereas both sides of the brain were activated in equal magnitude during the process of arithmetic tasks. This replicates previous studies of the functional aspect of the right brain - being responsible for spatial and creative thinking.

• Journal of the Society of Disaster Information
• /
• v.17 no.2
• /
• pp.245-253
• /
• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• Journal of The Korean Association For Science Education
• /
• v.29 no.2
• /
• pp.137-155
• /
• 2009

The purpose of the present study was to analyse the effectiveness of a gifted education program. To analyse the effectiveness of an education program for the gifted affiliated with a university, the study carried out a quasi-experimental design to compare the 153 gifted students who enrolled in an education center for the gifted and the 131 potentially gifted students who were nominated by teachers for their high achievements and interests in science but without any education services for the gifted. These two groups of students were compared in the aspects of problem finding ability in science, motivation, self regulation, science-related attitudes, and science anxiety through the pre- and post-treatment settings. The results indicated that the gifted group showed a significant improvement in originality and elaboration of problem-finding ability, but the potentially gifted group showed significant decrease in most variables of problem finding. Related to motivation and self-regulated learning, gifted students showed an increase in cognitive strategy use and decrease in intrinsic value, but the potentially gifted students showed significant decreases in most variables related to motivation and self-regulation, except intrinsic value. Related to the scientific attitudes and science anxiety, there were no significant changes between pre- and post-tests in the gifted group, but significant decreases in most variables were found in the potentially gifted group. The results of paired t-test and Ancova indicate that significant differences between the gifted and the potentially gifted groups are mainly due to the significantly lowered performance in post tests in the potentially gifted group, rather than a significant increase in gifted group.

• Journal of Korean Society of Forest Science
• /
• v.111 no.4
• /
• pp.461-472
• /
• 2022

Natural and anthropogenic factors cause forest types to continuously change. Since the ratio of forest area by forest type is important information for identifying the characteristics of national forest resources, an accurate understanding of the prospect of forest type change is required. The study aim was to use National Forest Inventory (NFI) time-series data to understand the characteristics of forest type change and to estimate future prospects of nationwide forest type change. We used forest type change information from the fifth and seventh NFI datasets, climate, topography, forest stand, and disturbance variables related to forest type change to analyze trends and characteristics of forest type change. The results showed that the forests in Korea are changing in the direction of decreasing coniferous forests and increasing mixed and broadleaf forests. The forest sites that were changing from coniferous to mixed forests or from mixed to broadleaf forests were mainly located in wet topographic environments and climatic conditions. The forest type changes occurred more frequently in sites with high disturbance potential (high temperature, young or sparse forest stands, and non-forest areas). We used a climate change scenario (RCP 8.5) to establish a forest type change model (SVM) to predict future changes. During the 40-year period from 2015 to 2055, the SVM predicted that coniferous forests will decrease from 38.1% to 28.5%, broadleaf forests will increase from 34.2% to 38.8%, and mixed forests will increase from 27.7% to 32.7%. These results can be used as basic data for establishing future forest management strategies.

• Journal of Internet Computing and Services
• /
• v.19 no.5
• /
• pp.21-31
• /
• 2018

Cyber penetration attacks can not only damage cyber space but can attack entire infrastructure such as electricity, gas, water, and nuclear power, which can cause enormous damage to the lives of the people. Also, cyber space has already been defined as the fifth battlefield, and strategic responses are very important. Most of recent cyber attacks are caused by malicious code, and since the number is more than 1.6 million per day, automated analysis technology to cope with a large amount of malicious code is very important. However, it is difficult to deal with malicious code encryption, obfuscation and packing, and the dynamic analysis technique is not limited to the performance requirements of dynamic analysis but also to the virtual There is a limit in coping with environment avoiding technology. In this paper, we propose a machine learning based malicious code analysis technique which improve the weakness of the detection performance of existing analysis technology while maintaining the light and high-speed analysis performance applicable to commercial endpoints. The results of this study show that 99.13% accuracy, 99.26% precision and 99.09% recall analysis performance of 71,000 normal file and malicious code in commercial environment and analysis time in PC environment can be analyzed more than 5 per second, and it can be operated independently in the endpoint environment and it is considered that it works in complementary form in operation in conjunction with existing antivirus technology and static and dynamic analysis technology. It is also expected to be used as a core element of EDR technology and malware variant analysis.

• Journal of The Korean Association For Science Education
• /
• v.38 no.5
• /
• pp.667-680
• /
• 2018

The purpose of this study is to investigate 1) the types of knowledge students ask for in their reading comprehension of science textbooks using an online annotation system, 2) the accuracy of the knowledge provided by the students to their peers, 3) the frequency of knowledge sharing behaviors, 4) the evaluation of the effect of collaborative reading, and 5) the trust among peers as knowledge sharers. Questions made by 241 students in the second grade of middle school using an online annotation system in two chapters of the science textbook were analyzed using Bloom's revised taxonomy and their answers were grouped according to five accuracy categories. Also, questionnaires for the evaluation of the effectiveness of collaborative reading comprehension and of trust among the students were used. The students asked their peers 'understanding questions' which comprised almost 80% of the total questions they made and were similar with individual metacognitive strategies for reading comprehension. Of the total threads, 71% has scientifically correct threads shared by the students. The frequency of the knowledge sharing behaviors was high but this was affected by the rewards (point system). Students evaluated that collaborative reading comprehension conducted through an online annotation system were helpful in their learning. In addition, the ratio of students trusting their peers who did the knowledge sharing is over 80%. This study shows that when students use an online annotation system, they can fill one another's cognitive gaps in the reading process by sharing knowledge. Also, collaborative reading using an online annotation system has proved that cognitive individualization is possible through sharing knowledge interactively and dynamically, unlike reading hard copies of textbooks which are a one way information transfer.