• Clinical and Experimental Reproductive Medicine
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• v.24 no.3
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• pp.335-340
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• 1997

Fluorescence in situ hybridization (FISH) techniques allow the enumeration of chromosome abnormalities and from a great potential for many clinical applications. In order to produce quantitative and reproducible results, expensive tools such as a cooled CCD camera and a computer software are required. We have developed a Chromosome Image Processing System (Chips) using FISH that allows the detection and mapping of the genetic aberrations. The aim of our study, therefore, is to evaluate the capabilities of our original system using a black-and-white video camera. As a model system, three repetitive DNA probes (D18Z1, DXZ1, and DYZ3) were hybridized to variety different clinical samples such as human metaphase spreads and interphase nuclei obtained from uncultured peripheral blood lymphocytes, uncultured amniocytes, and germ cells. The visualization of the FISH signals was performed using our system for image acquisition and pseudocoloring. FISH images were obtained by combining images from each of probes and DAPI counterstain captured separately. Using our original system, the aberrations of single or multiple chromosomes in a single hybridization experiment using chromosomes and interphase nuclei from a variety of cell types, including lymphocytes, amniocytes, sperm, and biopsied blastomeres, were enabled to evaluate. There were no differences in the image quality in accordance with FISH method, fluorochrome types, or different clinical samples. Always bright signals were detected using our system. Our system also yielded constant results. Our Chips would permit a level of performance of FISH analysis on metaphase chromosomes and interphase nuclei with unparalleled capabilities. Thus, it would be useful for clinical purposes.

• Journal of the Korean GEO-environmental Society
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• v.11 no.12
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• pp.47-56
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• 2010

The construction of new roads and the consistent extension of already-existing roads or the line-shape revision of those roads are increased with the governmental investment to SOC facilities currently. Accordingly, the road cut slopes are in the trend of rapidly increasing. As the road slope has increased, a lot of human and property damages has entailed consequently and in the local case, numerous studies have carried out aiming at minimizing this damages caused by the rockfall and landslide. In general, standard falling rock prevention facility has employed for most of the local road slope based on "Guide for Installation and Management of Road Safety Facilities" published by MLTM(the Ministry of Land, Transport, and Maritime Affairs) but profound doubt has raised as to whether this rockfall prevention facility would function properly enough to prevent rockfall efficiently without any damages in case of actual occurrence of rockfall. In addition, it is a reality that in most cases, such work is relied on overseas technology as a whole as the local technical level is low and in case of rockfall prevention net, it is judged that a study on rockfall prevention net that is able to endure more powerful rockfall energy is required as the problem including net bursting is taken place as a result of enough bearing force being failed to be demonstrated due to its partial weak point(not uniformly made). Under this background, in this study, three kinds of model depending on mesh size of expanded metal that is considered to have an adoptability as rockfall prevention net, as target are selected and characteristics depending on mesh size of expanded metal is intended to be researched through a pull-out test performance by using pull-out test equipment rockfall prevention net.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.771-782
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• 2017

This paper describes the development of a wearable robot to assist ankle power for the elderly. Previously developed wearable robots have generally used motors and gears to assist muscle power during walking. However, the combination of motor and reduction gear is heavy and has limitations on the simultaneous control of stiffness and torque due to the friction of the gear reducer unlike human muscles. Therefore, in this study, Mckibben pneumatic muscle, which is lighter, safer, and more powerful than an electric motor with gear, was used to assist ankle joint. Antagonistic actuation using a pair of pneumatic muscles assisted the power of the soleus muscles and tibialis anterior muscles used for the pitching motion of the ankle joint, and the model parameters of the antagonistic actuator were experimentally derived using a muscle test platform. To recognize the wearer's walking intention, foot load and ankle torque were calculated by measuring the pressure and the center of pressure of the foot using force and linear displacement sensors, and the stiffness and the torque of the pneumatic muscle joint were then controlled by the calculated ankle torque and foot load. Finally, the performance of the developed ankle power assistive robot was experimentally verified by measuring EMG signals during walking experiments on a treadmill.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.855-865
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• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• Journal of Music and Human Behavior
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• v.18 no.1
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• pp.79-102
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• 2021

The purpose of this study was to identify the teaching methods used in inclusive music classes at elementary schools by of music in elementary school inclusive classes through the application of understanding by design and differentiated instruction, and to explore the feasibility of inclusive education. To this end, based on the 2.0 version of the backward design template, a unit for music lessons for 3rd and 4th grade inclusive classes was developed. The unit presented elements of differentiated instruction that considered students with intellectual disabilities at each stage. In the first stage, goals and essential questions were presented by analyzing the curriculum's achievement standards. In the second stage, a performance task was developed using the GRASPS technique, guidelines and examples were presented. Various evaluation methods based on students' readiness, interest, and learning type were suggested. In the third stage, the unit's seven lessons were planned using the WHERETO model. Examples of differentiated instruction for students with intellectual disabilities were presented by flexibly using classroom elements. This study indicated that understanding by design and differentiated instruction can be applied to inclusive education. Future studies on more diversified educational design and strategies are needed for promoting inclusive education.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.