• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.94-94
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• 2011

배관 구조물에서는 내부 미세 균열에서부터 국부 좌굴, 볼트 풀림, 피로 균열 등과 같이 다양한 형태의 손상이 복합적으로 발생 가능하다. 이러한 복합 손상은 배관 구조물의 누수, 누유 등의 사고를 야기할 수 있다. 하지만 기존의 단일 스케일 계측 시스템으로부터 복합 손상에 의한 실시간 누수를 진단하기는 매우 어렵다. 본 연구 단계에서는 누수를 야기하는 복합 손상을 효율적으로 진단하기 위하여 선행 연구에서 제안된 압전센서를 이용한 자가 계측 회로 기반의 다중 스케일 계측 시스템을 구조물의 복합 손상 진단에 적용하였다. 자가 계측 회로 기반 다중 스케일 계측 시스템은 크게 두 가지 형태의 신호를 계측한다. 첫 번째 스케일은 임피던스 계측으로부터 특정 주파수 대역폭에 대한 구조 응답을 계측하며, 두 번째 스케일은 유도 초음파 계측으로부터 단일 중심 주파수에 해당하는 구조물의 응답을 계측한다. 복합 손상을 손상 유형별로 분류하기 위하여 E/M 임피던스(Electro-mechanical impedance)및 유도 초음파(Guided wave) 계측으로부터 추출한 특성을 이용하여 2차원 손상지수를 계산하고 이를 지도학습 기반 패턴인식 기법(Supervised learning based pattern recognition) 중 확률론적 신경망 기법(Probabilistic Neural Network, PNN)에 적용한다. 제안된 기법의 적용성 검토를 위하여 파이프 구조물에 인위적으로 다중 손상을 생성시켜 시험을 수행하였다. 본 연구에서 제안된 기법이 실제 배관 구조물에 성공적으로 적용된다면 손상 부재의 거동 및 구조물 성능의 손상에 대한 영향을 효율적으로 진단하고 평가함으로써 배관 구조물의 효과적인 유지관리가 가능할 것으로 예상된다.

• The Journal of Korean Association of Computer Education
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• v.5 no.2
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• pp.39-48
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• 2002

It is general that the ICAI(Intelligent Computer Assisted Instruction) consists of 4 modules. Export module, Teacher module, Student module and Interface module. In each module construction, there should be some rules to control strategies efficiently and systematically that are related to the texts and assessment instruments, assessment results and evaluation, feedback, etc. It is necessary to use a method to classify the curriculum into sections with units and to represent the identified relationships between them. These relationships are available to all the process of learning, assessment, evaluation and feedback. In this paper, we propose the method to represent these units and relationships as a graph. In addition, we implement an internet-based courseware authoring tool to support the environment in which several expert can construct concurrently the courseware with cooperation between them.

• Journal of The Korean Association of Information Education
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• v.6 no.3
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• pp.298-307
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• 2002

Recent advances in the Web technology have been changing our life in various aspects. These advances have brought us new paradigms of education. The Web provides teachers with many opportunities to implement wide ranges of new teaching and learning practices, which supplement the traditional classroom teaching-learning. Especially, the Web enables so-called WBI (Web-based instruction) system as a teaching aid. Now the WBI system can incorporate multimedia information with various communication and collaborative tools. In order for the WBI system to be successful, various supports are necessary. One of such supports comes from assessment. In this work, an item pool system for leveled assessment is designed and implemented. The proposed system has the following characteristics. First, the item pool is classified into three categories subject, semester, and chapter. This categorization makes lookup easier and faster. Second, any teacher can use the item pool system and enter their questions into the item pool. Third, the proposed system reflects various levels of students for each course. Thus, students can select their exams based on their progress and background. Finally, it can make difficulty of each item to be objective by repeated tests and refinements.

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.107-118
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• 2004

This study aims to design and implement individual complete learning system based on IPI(Individually Prescribed Instruction) model. Most of current web based learning systems do not consider individual students' ability and just follow the sequence of instructing contents $\rightarrow$ providing problems $\rightarrow$ presenting the result of evaluating. However, this system focuses on individual ability prior to studying subjects. In individual complete learning system, it is acknowledged that a period and a pace to complete each task will differ from students to students, therefore until they complete the whole unit, they are not allowed to move onto the next unit. After completing each unit, there will be a process of evaluating students' performance. It is necessary to show the correct completion of 80% of the evaluation to move onto next step; for those who are evaluated as inadequate to move on, an individual supplementary instruction will be provided. Therefore, this study intends to supplement the deficit of prior learning and provide feedback dependent on individual's learning ability so that the goal of Individual Whole Complete Learning could be accomplished.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.801-807
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• 2024

Linear motion robots are devices that perform functions such as transferring parts or positioning devices, and require high precision. In companies that develop linear robot application systems, human workers are in charge of quality control and fault diagnosis of linear robots, and the result and accuracy of a fault diagnosis varies depending on the skill level of the person in charge. Recently, there have been many attempts to utilize artificial intelligence to diagnose faults in industrial devices. In this paper, we present a system that automatically diagnoses linear rail and ball screw misalignment of a linear robot using transfer learning. In industrial systems, it is difficult to obtain a lot of learning data, and this causes a data imbalance problem. In this case, a transfer learning model configured by retraining an established model is widely used. The information obtained by using an acceleration sensor and torque sensor was used, and its usefulness was evaluated for each case. After converting the signal obtained from the sensor into a spectrogram image, the type of abnormality was diagnosed using an image recognition artificial intelligence classifier. It is expected that the proposed method can be used not only for linear robots but also for diagnosing other industrial robots.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.153-161
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• 2013

Recently, there has been a tremendous increase in the availability of educational materials for foreign language learning. As part of this trend, there has been an increase in the amount of electronically mediated materials available. However, conventional educational contents developed using computer technology has provided typically one-way information, which is not the most helpful thing for users. Providing the user's convenience requires additional off-line analysis for diagnosing an individual user's learning. To improve the user's comprehension of texts written in a foreign language, we propose an intelligent learning tool based on the analysis of the user's eyeball movements, which is able to diagnose and improve foreign language reading ability by providing necessary supplementary aid just when it is needed. To determine the user's learning state, we correlate their eye movements with findings from research in cognitive psychology and neurophysiology. Based on this, the learning tool can distinguish whether users know or do not know words when they are reading foreign language sentences. If the learning tool judges a word to be unknown, it immediately provides the student with the meaning of the word by extracting it from an on-line dictionary. The proposed model provides a tool which empowers independent learning and makes access to the meanings of unknown words automatic. In this way, it can enhance a user's reading achievement as well as satisfaction with text comprehension in a foreign language.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.70-77
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• 2019

Breast ultrasound readings are very important to diagnose early breast cancer. In Ultrasonic inspection, it shows a significant difference in image quality depending on the ultrasonic equipment, and there is a large difference in diagnosis depending on the experience and skill of the inspector. Therefore, objective criteria are needed for accurate diagnosis and treatment. In this study, we analyzed texture characteristics by applying GLCM (Gray Level Co-occurrence Matrix) algorithm and extracted characteristic parameters and diagnosed breast cancer using neural network classifier. Breast ultrasound images were classified into normal, benign and malignant tumors and six texture parameters were extracted. Fourteen cases of normal, malignant and benign tumor diagnosed by mammography were studied by using the extracted six parameters and learning by multi - layer perceptron neural network back propagation learning method. As a result of classification using 51 normal images, 62 benign tumor images, and 74 malignant tumor images of the learned model, the classification rate was 95.2%.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• Journal of Gifted/Talented Education
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• v.21 no.4
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• pp.861-884
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• 2011

In this study, we developed and applied 15 evaluation questions about light to 30 middle school students in K University Science Education Institute for the Gifted. Then we used the theory of knowledge space and analyzed the middle school Science Gifted Students' knowledge state about light. By schematizing the hierarchy from it, we intended to not only measure students' knowledge state about light, but use it as the basic materials to improve teaching methods. To achieve the purpose of this study, we analyzed the evaluation results and individual knowledge state and hierarchy of questions. As a result, there were different knowledge structures in the individual, and we found that we should diagnose them differently. In addition, we have had implications that it has the connection with each questions and the individual knowledge state.