• 정보교육학회논문지
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• 제6권3호
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• pp.288-297
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• 2002

정보 통신 기술이 빠른 속도로 발전함에 따라 교육의 페러다임이 변하고 있다. 학교현장에서는 교수-학습을 위하여 웹활용에 대한 많은 자료와 방법들이 개발되고 있고, 활발한 논의가 되고 있지만 우리 아이들의 정의적 측면을 신장시키는데 웹활용 교육은 상대적으로 소홀한 측면이 있었다. 자아 개념 증진을 위한 선행연구들의 공통점은 off-line에서 실시되기 때문에 정보의 공유가 어렵고, 향상된 형태의 프로그램으로 발전하는데 제약이 있다. 본 연구에서는 선행연구들의 단점을 보완한 W-SET(웹기반 자기 표현 훈련)시스템을 설계 구현하였다. 이 시스템을 충북 청주시내 초등학교 학생들에게 실험 적용한 결과, 자아개념 형성에 긍정적인 효과를 가져왔다.

• ETRI Journal
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• 제38권5호
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• EDISON SW 활용 경진대회 논문집
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• 제5회(2016년)
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• pp.455-462
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• 2016

Recently, followed by rapid growth of robotics field, multi-linkage mechanism which can even pass by rough road is getting lots of attention. In this paper, I focused on Jansen mechanism. It's a kinematics object which is named after Dutch artist Theo jansen. Jansen mechanism embraces structure and mechanism which creates locomotion with the combination of the power and simple structure. Theo jansen suggests a 'Holy number'. It's an ideal ratio of leg components length. However, if there's desired gait locomotion, you have to adjust the ratio and the length. But even slight change of the length could cause a big change at the end-point. To solve this problem, I suggest a reverse engineering method to get a ratio of each links by nonlinear optimization with pre-set desired trajectory. First, we converted a movement of the joint of Jansen mechanism to vectors by kinematics analysis of multi-linkage structure. And we showed the trajectory at the end-point. After that, we set desired trajectory which we found most ideal. Then we got the length of the leg components which draws a trajectory as same as trajectory we set, using Multi-objective genetic algorithm toolbox in MATLAB. Result is verified by Edison designer and mSketch. And we analyzed if it could pass through the obstruction which is set dynamically.

• International Journal of Arrhythmia
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• 제18권2호
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• pp.66-76
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• 2017

Background and Objectives: Although an anterior linear ablation is an effective lesion set in radiofrequency catheter ablation (RFCA) for longstanding persistent atrial fibrillation (L-PeAF), its durability for bidirectional block (BDB) is only about 60% at repeat procedure. We hypothesized that changes in electrocardiogram (ECG) may predict an anterior line block state and the clinical outcome of L-PeAF ablation. Subjects and Methods: We studied 304 L-PeAF patients (77% male, $60{\pm}10yrs$), who consistently underwent RFCA Dallas lesion set (circumferential pulmonary vein isolation, posterior box lesion, and anterior line) protocol with subsequent comparison of pre-procedural and post-procedural P wave axes, and one year follow-up (n=205) sinus rhythm (SR) ECGs. Results: 1. P wave axis shifted inferiorly at immediate post-procedure (p<0.001), and was independently correlated with BDB of anterior line (${\ss}=10.4$, 95% confidence interval [CI] 2.79-17.94, p=0.008). 2. The degree of post-procedural inferior shift of P wave axis did not reflect clinical recurrence within one-year (n=205, p=0.923), potentially due to conduction recovery of an anterior line. However, among 160 patients without clinical recurrence within one-year, P wave axis at one-year ECG was independently associated with very late recurrence of AF after one-year (n=160, hazard ratio [HR] 0.98; 95% CI 0.97-0.99, p=0.001), during $45.6{\pm}16.7$ months of follow-up. 3. Among 22 patients who underwent repeat procedures, P wave axis shift was more significant in patients with maintained BDB of an anterior line than in those without (p=0.015). Conclusion: An inferior shift of P wave axis reflects the achievement and the maintenance of an anterior line BDB, and is associated with better long-term clinical outcome after catheter ablation for L-PeAF based on Dallas lesion set.

• Smart Structures and Systems
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• 제29권1호
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• pp.29-39
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• 2022

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted high-resolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The Recall reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The IoU of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The IoU of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the IoU by 2.9%. In general, our method is of great significance for crack detection.

• 한국통신학회논문지
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• 제33권8C호
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• pp.633-641
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• 2008

무선 센서 네트워크에서는 물리적인 접근과는 무관하게 jamming이나 eavesdropping과 같은 공격이 쉽게 발생하기 때문에 무선 센서 네트워크에서의 보안은 중요한 요구 사항 중에 하나이다. 무선 센서 네트워크에서 보안을 향상시키기 위해 최근 키 관련 기법들이 활발히 연구되고 있지만 현재까지 연구된 기법들은 노드가 공유하고 있는 공유키의 발견을 위하여 시간과 에너지가 많이 소요되므로 무선 네트워크 환경에 적합하지 않다. 특히, 무선센서 네트워크를 구성하고 있는 구성 요소 중 게이트웨이 역할을 담당하는 노드의 안정성은 여러 보안 공격에 취약하다. 따라서, 이 논문에서는 확률적 키에 의존하지 않으면서 게이트웨이 역할을 담당하는 노드의 안전성을 향상시키기 위해 랜덤 키 사전 분배 기술과 이중 해쉬 체인을 조합한 키 사전 분배 기법을 제안한다. 제안 기법은 기존 기법보다 적은 저장 공간과 강한 보안 강도를 유지할 수 있기 때문에 동일 보안 강도를 가지고 있는 기존 기법들보다 효율성이 좋고, 작은 크기의 키 생성 키 셋을 사용하기 때문에 네트워크 확장성에 효율적이며 센서노드의 저장 오버헤드를 크게 줄일 수 있다.

• 대한지구과학교육학회지
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• 제12권2호
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• pp.141-150
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• 2019

이 연구의 목적은 초등예비교사에게 메이커 교육을 도입한 프로젝트 학습으로 자기주도적 학습능력 및 창의적 인성에 미치는 효과를 알아보는 것이다. 본 연구는 2019년 3월부터 4월까지 8주간의 실험처치 기간을 설정하였으며, 연구에 참여한 학생들은 B 교육대학교 2학년 1학기에 재학 중이고 '과학과 교재연구 1' 강좌를 수강하는 심화과정 3개 반 75명의 초등예비교사를 대상으로 연구집단을 구성하였다. 연구집단의 실험처치는 메이커 교육으로 창의적인 산출물을 만들어 내는 과정의 수업이 이루어졌다. 모둠별로 창의적인 산출물을 만들기 위해 주제를 설정하였으며, 주 도구인 3D펜으로 작업을 하였고 리사이클 재료를 활용하였다. 창의적인 산출물이 완성되면 모둠별로 실물을 제시하면서 발표를 하였다. 연구의 결과는 다음과 같다. 첫째, 초등예비교사의 프로젝트 학습은 자기주도적 학습능력에 미치는 효과가 있었다. 둘째, 초등예비교사의 프로젝트학습은 창의적 인성에 미치는 효과가 있었다. 셋째, 초등예비교사는 프로젝트 학습에 대해 흥미를 느끼며 인식에 긍정적인 반응을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.757-770
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• 2020

We propose a hand gesture recognition method that is compatible with a head-up display (HUD) including small processing resource. For fast link adaptation with HUD, it is necessary to rapidly process gesture recognition and send the minimum amount of driver hand gesture data from the wearable device. Therefore, we use a method that recognizes each hand gesture with an inertial measurement unit (IMU) sensor based on revised correlation matching. The method of gesture recognition is executed by calculating the correlation between every axis of the acquired data set. By classifying pre-defined gesture values and actions, the proposed method enables rapid recognition. Furthermore, we evaluate the performance of the algorithm, which can be implanted within wearable bands, requiring a minimal process load. The experimental results evaluated the feasibility and effectiveness of our decomposed correlation matching method. Furthermore, we tested the proposed algorithm to confirm the effectiveness of the system using pre-defined gestures of specific motions with a wearable platform device. The experimental results validated the feasibility and effectiveness of the proposed hand gesture recognition system. Despite being based on a very simple concept, the proposed algorithm showed good performance in recognition accuracy.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• 한국운동역학회지
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• 제25권2호
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• pp.167-174
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• 2015

Purpose : The purpose of this study was to investigate the effects of loaded vertical jumps on the following vertical jumps and to find how long the transient effect of warm-ups would continue. Methods : Twelve healthy college male students, majoring in physical education, participated in this study voluntarily. They performed three sets of unloaded jumps (pre-jump, 5% post jump, and 10% post jump) and two sets of loaded jumps (5% and 10% loaded jumps) according to the counter-balanced order. At each set, three trials of maximal vertical jumps were performed by a 30 second interval between trials and a 3 minute break after warm-up jumps. Force platform and motion capturing system were used to record motions and ground reaction force. Results : Only 5% post-warm-up jumps ($48.29{\pm}2.06cm$) showed significant increase in the jump height compared with pre-warm-up jumps ($47.35{\pm}2.21cm$). The transient effects of loaded warm-ups disappeared 4 minutes after loaded jumps. Conclusion : Conclusively, a decent amount of loading (around 5% extra of body weight) during sport specific warm-ups would give a positive, transient effect on the performance of the vertical jump.