• Journal of Environmental Science International
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• v.22 no.11
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• pp.1443-1449
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• 2013

This was an experimental study to evaluate temperature reduction and evapotranspiration of extensive green roof. Three test cells with a dimension of $1.2(W){\times}1.2(D){\times}1.0(H)$ meters were built using 4-inch concrete blocks. Ten-centimeter concrete slab was installed on top of each cell. The first cell was control cell with no green roof installed. The second and third cells were covered with medium-leaf type Zoysiagrass (Zoysia japonica) above a layer of soil. Soil thickness on the second cell was 10cm and that on the third cell was 20cm. Air temperature, relative humidity and solar irradiance were measured using AWS (automatic weather system). Temperature on top surface and ceiling of the control cell and temperature on top surface, below soil and ceiling of green roof cells was measured. Evapotranspiration of the green roof cells were measured using weight changes. Compared with temperature difference on the control cell, temperature difference was greater on green roof cells. Between two green roof cells, the temperature difference was greater on the third cell with a thicker soil layer. Temperature differences below soil and on ceilings of green roof cells were found greater than those of the control cell. Between the green roof cells, there was no difference in the temperature reduction effects below soil and on ceilings based on substrate depth. In summary, green roof was found effective in temperature reduction due to evapotranspiration and shading effect.

• Imaging Science in Dentistry
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• v.50 no.2
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• pp.169-174
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• 2020

Purpose: Periodontal disease causes tooth loss and is associated with cardiovascular diseases, diabetes, and rheumatoid arthritis. The present study proposes using a deep learning-based object detection method to identify periodontally compromised teeth on digital panoramic radiographs. A faster regional convolutional neural network (faster R-CNN) which is a state-of-the-art deep detection network, was adapted from the natural image domain using a small annotated clinical data- set. Materials and Methods: In total, 100 digital panoramic radiographs of periodontally compromised patients were retrospectively collected from our hospital's information system and augmented. The periodontally compromised teeth found in each image were annotated by experts in periodontology to obtain the ground truth. The Keras library, which is written in Python, was used to train and test the model on a single NVidia 1080Ti GPU. The faster R-CNN model used a pretrained ResNet architecture. Results: The average precision rate of 0.81 demonstrated that there was a significant region of overlap between the predicted regions and the ground truth. The average recall rate of 0.80 showed that the periodontally compromised teeth regions generated by the detection method excluded healthiest teeth areas. In addition, the model achieved a sensitivity of 0.84, a specificity of 0.88 and an F-measure of 0.81. Conclusion: The faster R-CNN trained on a limited amount of labeled imaging data performed satisfactorily in detecting periodontally compromised teeth. The application of a faster R-CNN to assist in the detection of periodontally compromised teeth may reduce diagnostic effort by saving assessment time and allowing automated screening documentation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.23-34
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• 2020

In this paper, we proposes a method to automatically construct SHACL schemas for RDF knowledge graphs(KGs) generated by Direct Mapping(DM). DM and SHACL are all W3C recommendations. DM consists of rules to transform the data in an RDB into an RDF graph. SHACL is a language to describe and validate the structure of RDF graphs. The proposed method automatically translates the integrity constraints as well as the structure information in an RDB schema into SHACL. Thus, our SHACL schemas are able to check integrity instead of RDBMSs. This is a consideration to assure database consistency even when RDBs are served as virtual RDF KGs. We tested our results on 24 DM test cases, published by W3C. It was shown that they are effective in describing and validating RDF KGs.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1258-1270
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• 2017

'Semantic gap' has been a year-old problem in automatic video summarization, which refers to the gap between semantics implied in video summarization algorithms and what people actually infer from watching videos. Using the external EEG bio-feedback obtained from video watchers as a solution of this semantic gap problem has several another issues: First, how to define and measure noises against ERP waveforms as signals. Second, whether individual differences among subjects in terms of noise and SNR for conventional ERP studies using still images captured from videos are the same with those differently conceptualized and measured from videos. Third, whether individual differences of subjects by noise and SNR levels help to detect topic-irrelevant shots as signals which are not matched with subject's own semantic topical expectations (mis-match negativity at around 400m after stimulus on-sets). The result of repeated measures ANOVA test clearly shows a 2-way interaction effect between topic-relevance and noise level, implying that subjects of low noise level for video watching session are sensitive to topic-irrelevant visual shots, while showing another 3-way interaction among topic-relevance, noise and SNR levels, implying that subjects of high noise level are sensitive to topic-irrelevant visual shots only if they are of low SNR level.

• Journal of Oral Medicine and Pain
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• v.41 no.3
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• pp.85-90
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• 2016

Purpose: The aim of this study was to develop an automated software to extract tooth and pulpal area from sectional cone-beam computed tomography (CBCT) images, which can guarantee more reproducible, objective and time-saving way to measure pulp/tooth volume ratio. Methods: The software program was developed using MATLAB (MathWorks). To determine the optimal threshold for the region of interest (ROI) extraction, user interface to adjust the threshold for extraction algorithm was added. Default threshold was determined after several trials to make the outline of extracted ROI fitting to the tooth and pulpal outlines. To test the effect of starting point location selected initially in the pulpal area on the final result, pulp/tooth volume ratio was calculated 5 times with different 5 starting points. Results: Navigation interface is composed of image loading, zoom-in, zoom-out, and move tool. ROI extraction process can be shown by check in the option box. Default threshold is adjusted for the extracted tooth area to cover whole tooth including dentin, cementum, and enamel. Of course, the result can be corrected, if necessary, by the examiner as well as by changing the threshold of density of hard tissue. Extracted tooth and pulp area are reconstructed three-dimensional (3D) and pulp/tooth volume ratio is calculated by voxel counting on reconstructed model. The difference between the pulp/tooth volume ratio results from the 5 different extraction starting points was not significant. Conclusions: In further studies based on a large-scale sample, the most proper threshold to present the most significant relationship between age and pulp/tooth volume ratio and the tooth correlated with age the most will be explored. If the software can be improved to use whole CBCT data set rather than just sectional images and to detect pulp canal in the original 3D images generated by CBCT software itself, it will be more promising in practical uses.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.122-122
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• 2017

Recently, environmental problems have become an area of growing interests. In-situ monitoring of water quality is fundamental to most environmental applications. The accurate measurement of nitrate concentrations is fundamental to understanding biogeochemistry in aquatic ecosystems. Several studies have reported that one of the most feasible methods to measure nitrate concentration is the use of Ion Selective-electrodes (ISEs). The ISE application to water monitoring has several advantages, such as direct measurement methodology, high sensitivity, wide measurement range, low cost, and portability. However, the ISE methods may yield inconsistent results where there was a difference in temperature between the calibration and measurement solutions, which is associated with the temperature dependence of ionic activity coefficients in solution. In this study, to investigate the potential of using the combination of a temperature sensor and nitrate ISEs for minimizing the effect of temperature on real-time nitrate sensing in natural water, a prototype of on-site water monitoring system was built, mainly consisting of a sensor chamber, an array of 3 ISEs, an waterproof temperature sensor, an automatic sampling system, and an arduino MCU board. The analog signals of ISEs were obtained using the second-order Sallen-key filter for performing voltage following, differential amplification, and low pass filtering. The performance test of the developed water nitrate sensing system was conducted in a monitoring station of drinking water located in Jeongseon, Kangwon. A temperature compensation method based on two-point normalization was proposed, which incorporated the determination of temperature coefficient values using regression equations relating solution temperature and electrode signal determined in our previous studies.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.2
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• pp.168-174
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• 2008

Diagnosis on setteability based on human operator's experimental knowledge, which could be established by long term operation, is a limit factor to construction of automation control system in wastewater treatment plant. On-line SVI(Sludge Volume Index) analyzer was developed which can measure SV30 automatically by image capture and image analysis method. In this paper, information got by settling process was studied using On-line SVI analyzer for better operation & management of WWTPs. First, SV30 detection algorithm was developed using image capture and image analysis for settling test and it showed that automatic detection is feasible even if deflocculation and bulking was occurred. Second, turbidity assessment model was developed using image analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.226-231
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• 2013

In The matching algorithm for automatic packaging of drugs is essential to determine whether the canister can exactly refill the suitable medicine. In this paper, we propose a hierarchical neural network with the upper and lower layers which can perform real-time processing and classification of many types of medicine bottles to prevent accidental medicine disaster. A few number of low-dimensional feature vector are extracted from the label images presenting medicine-bottle information. By using the extracted feature vectors, the lower layer of MLP(Multi-layer Perceptron) neural networks is learned. Then, the output of the learned middle layer of the MLP is used as the input to the upper layer of the MLP learning. The proposed hierarchical neural network shows good classification performance and real- time operation in the test of up to 30 degrees rotated to the left and right images of 100 different medicine bottles.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.5
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• pp.34-40
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• 2005

Most analyses and researches on Micro Aerial Vehicle(MAV) have focused upon propulsion, automatic control, aerodynamic configuration in low Reynolds number region, and miniaturization of telemetric parts. In the present study, a structural concept for MAV is designed by using the composite material suitable for light flight structures. In order to study the load path and stress state of the MAV, the load and structural analyses are simultaneously performed by the aeroelasticity module of MSC/NASTRAN. The stability derivatives of the MAV are obtained for three symmetric, two antisymmetric, and four unsymmetric maneuvering conditions. Although the aerodynamic theory in MSC/NASTRAN could not be proper for MAV analysis, it provides an traditional and effective tool for trim and load analyses and may be corrected with the results by more accurate theory or test. The results show that the inertial load due to payloads has a more effect on stress rather than the aerodynamic load.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.613-622
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• 2000

Watering operation for oyster mushroom growing houses is regarded as drudgery and time consuming farm operation for growers. Most of mushroom growing beds in oyster mushroom growing houses are designed as two-row with four floor beds, therefore the watering and ventilation between the bed floors are much difficult for farmers because of its structural design. The study aimed to reduce the watering operation and improve the mushroom growing environment through the humidification and air supply on mushroom growing beds. Results showed that appropriate size of nozzle is between 0.8~0.5ml/s for the humidification and higher than the 2.0ml/s for the watering. The optimum water supply pressure was regarded as between 1.0~2.0MPa and the uniform distribution of droplet on the bed showed on air flow speed of 14m/s. The prototype was equipped with twin nozzle with. the humidification nozzle of 0.85ml/s and watering nozzle of 5.0ml/s, and the air blast fan with the air speed of 10m/sec in each air spout. In the field test in a practical scale mushroom growing house, it was well operated dependant on the set desire by a electric control unit. The machine can be practically used as air blast watering and air blast humidification for oyster mushroom growing farms without manual.