• Journal of radiological science and technology
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• v.44 no.2
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• pp.101-107
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• 2021

The purpose of this study was to evaluate optimal CT scan parameters to minimize patient dose to the irradiation and maintain satisfactory image quality in low-dose chest computed tomography (CT) scans. In a chest anthropomorphic phantom, chest CT scans were performed at different kVp and mA within reference of 3.4mGy in volume CT Dose Index (CTDI_vol). The following quantitative parameters had been statistically evaluated: image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and figure of merit (FOM). Nine radiographers conducted the blind test to select the optimal kVp-mA combination. Results indicated that the kVp-mA combination of 80kVp-90mA, 100kVp-50mA, 120kVp-30mA and 140kVp-30mA were obtained high SNR and CNR. The 120kVp-30mA combination offered good compromise in the FOM, which showed the quality and dose performance. In the blind test, an image of 80kVp-90mA obtained a high score with 4.7 points, and 120kVp-10mA or 140kVp-10mA with a low tube current were observed severe noise and poor image quality, thus resulting in decreased diagnostic accuracy. On the other hand, in the combination of high kVp and high mA(140kVp-90mA), the image quality was improved, but the radiation dose was also increased. the FOM value of 140kVp-90mA was lower than 120kVp-30mA. The application of appropriate scan parameters in low-dose chest CT scans produced satisfactory results in dose and image quality for the accuracy of the clinical diagnosis.

• Fire Science and Engineering
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• v.32 no.6
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• pp.40-45
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• 2018

The omnidirectional surveillance camera uses the object detection algorithm to level the object by unit so that broadband surveillance can be performed using a fisheye lens and then, it was a field experiment with a system composed of an omnidirectional surveillance camera and a tracking (PTZ) camera. The omnidirectional surveillance camera accurately detects the moving object, displays the squarely, and tracks it in close cooperation with the tracking camera. In the field test of flame detection and temperature of the sensing camera, when the flame is detected during the auto scan, the detection camera stops and the temperature is displayed by moving the corresponding spot part to the central part of the screen. It is also possible to measure the distance of the flame from the distance of 1.5 km, which exceeds the standard of calorific value of 1 km 2,340 kcal. In the performance test of detecting the flame along the distance, it is possible to be 1.5 km in width exceeding $56cm{\times}90cm$ at a distance of 1km, and so it is also adaptable to forest fire. The system is expected to be very useful for safety such as prevention of intrinsic or surrounding fire and intrusion monitoring if it is installed in a petroleum gas storage facility or a storing place for oil in the future.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• Geophysics and Geophysical Exploration
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• v.24 no.1
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• pp.6-19
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• 2021

Subsurface physical properties can be obtained and imaged by seismic exploration, and various algorithms have been developed for this purpose. In this regard, root mean square error (RMSE) has been widely used to quantitatively evaluate the accuracy of the developed algorithms. Although RMSE has the advantage of being numerically simple, it has limitations in assessing structural similarity. To supplement this, full-reference image quality assessment (FR-IQA) techniques, which reflect the human visual system, are being investigated. Therefore, we selected six FR-IQA techniques that could evaluate the obtained physical properties. In this paper, we used the full-waveform inversion, because the algorithm can provide the physical properties. The inversion results were applied to the six selected FR-IQA techniques using three benchmark models. Using salt models, it was confirmed that the inversion results were not satisfactory in some aspects, but the value of RMSE decreased. On the other hand, some FR-IQA techniques could definitely improve the evaluation.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.1
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• pp.3-14
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• 2018

An apartment complex is a building use with great potential to contribute to solving problems related to urban ecological environment and climate change. The first goal of this study is to grasp the current situation of application and limitations of the ecological area rate, which is a representative evaluation index used to evaluate the environmental performance of the external space of an apartment complex in Green Standard for Energy and Environmental Design (G-SEED). The second goal is to propose a prototype of the evaluation index for evaluating greenhouse gas (GHG) reduction performance in order to supplement the evaluation index for the environmental performance of the external space in terms of response to climate change. We analyzed 43 cases of apartment complexes certified according to G-SEED, which was enforced since July 1, 2010, and found application characteristics of each space type and the limitations of ecological area rate. We analyzed overseas green building certification systems such as LEED and BREEAM that derived implications for supplementing the limitations of ecological area rate, which is focused on the evaluation of soil and water circulation function, and set up a development direction of complementary measures. Through analysis of previous studies, relevant regulations and standards, and technical documents of the manufacturer, the heat island mitigation performance of the pavement and roof surfaces of the apartment complex and the carbon uptake performance of the trees in the apartment complex was selected as parameters to yield the GHG reduction performance of the external space of the apartment complex. Finally, a quantitative evaluation method for each parameter and a prototype of the evaluation index for the GHG reduction performance were proposed. As a result of applying the prototype to an apartment complex case, the possibility of adoption and applicability as an evaluation index of G-SEED were proved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.1-9
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• 2020

As pet dogs rapidly increase in number, abandoned and lost dogs are also increasing in number. In Korea, animal registration has been in force since 2014, but the registration rate is not high owing to safety and effectiveness issues. Biometrics is attracting attention as an alternative. In order to increase the recognition rate from biometrics, it is necessary to collect biometric images in the same form as much as possible-from the face. This paper proposes a method to determine whether a dog is facing front or not in a real-time video. The proposed method detects the dog's eyes and nose using deep learning, and extracts five types of directional face information through the relative size and position of the detected face. Then, a machine learning classifier determines whether the dog is facing front or not. We used 2,000 dog images for learning, verification, and testing. YOLOv3 and YOLOv4 were used to detect the eyes and nose, and Multi-layer Perceptron (MLP), Random Forest (RF), and the Support Vector Machine (SVM) were used as classifiers. When YOLOv4 and the RF classifier were used with all five types of the proposed face orientation information, the face recognition rate was best, at 95.25%, and we found that real-time processing is possible.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.45-54
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• 2022

This paper is about designing and implementing the simulation training equipment (simulator) for the CBRN Reconnaissance Vehicle-II (armor type). The simulation training equipment (simulator) is a military training equipment in a virtual environment that analyzes the training using various CBRN equipment according to the CBRN situation and make a professional report. The controller or training instructor can construct a scenario using the instructor control system for a possible CBRN situation, spread the situation, and observe the process of the trainee performing the propagated situation appropriately. All process can be monitored and analyzed by the system, and it can be recorded, so it is also used for AAR (After Action Review). To implement CBRN situation training in a virtual environment, instructor control (IOS), host (HOS), video (IGS), input/output device (IOC), and sound (ACS) were implemented, a long-range chemical automatic detector (LCA), a combined chemical detector (CAD), a control (MCC) and an operation (OCC) computer were developed as simulators. In this paper, the design and development of simulation training equipment for CBRN Reconnaissance Vehicle-II (armor type) was conducted, and the performance was verified through integrated tests and acceptance tests.

• Journal of the Korean Geotechnical Society
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• v.38 no.5
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• pp.47-59
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• 2022

A series of experiments were performed to investigate the design and application of a rubble-ground modification method with grout injection. A small-sized injection machine was designed, and the grouts with various mix proportions were injected into 25 mm aggregate using the designed small-sized injection machine. With the compressive strength of the grout ranging from 20 to 80 MPa, the uniaxial compressive strength of the grout-filling bodies with clean gravels was higher than 1/6th of the strength of grouts themselves. However, this fraction may reduce depending on the interface conditions. The erosion resistance of the hardened grout was evaluated, and it was determined that the grout with a strength greater than 15 MPa did not require erosion consideration. Moreover, a full-scale injection test was performed for 25 cm-sized rubbles in cages with a diameter greater than 1 m and a height of 1.2 m to evaluate the filling characteristics of the grout. Results from this test indicated that the grout flowability sensitively influenced the filling characteristics.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.8
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• pp.671-680
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• 2021

This paper deals with the contents of designing and producing reconnaissance hardware and software, and verifying the functions after being installed on the CanSat platform and ground stations. The main reconnaissance mission is largely composed of two things: terrain search that renders the surrounding terrain in 3D using radar, GPS, and IMU sensors, and real-time detection of major objects through optical camera image analysis. In addition, data analysis efficiency was improved through GUI software to enhance the completeness of the CanSat system. Specifically, software that can check terrain information and object detection information in real time at the ground station was produced, and mission failure was prevented through abnormal packet exception processing and system initialization functions. Communication through LTE and AWS server was used as the main channel, and ZigBee was used as the auxiliary channel. The completed CanSat was tested for air fall using a rocket launch method and a drone mount method. In experimental results, the terrain search and object detection performance was excellent, and all the results were processed in real-time and then successfully displayed on the ground station software.