• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.349-359
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• 2022

The aim of this study is to analyze the dynamic characteristics of proportional control valves according to various working conditions as a basic study for developing proportional control valves for auto-steering tractors. The dynamic characteristics of proportional valves were measured using hydraulic characteristics measurement system, and the power was analyzed using measured flow rate and pressure data. As the experimental conditions, the tractor engine speed and steering angle was selected as the main variables, and the experiment was performed on urethane road conditions. As a result, it was found that the flow rate, pressure, and power of the proportional control valve increased as the tractor engine speed and steering angle increased. In particular, as the steering angle increased at the same engine speed, the flow rate, pressure, and power tended to increase by up to 190%, 172%, and 273%, respectively. Similarly, as the engine speed increased at the same steering angle, the flow rate, pressure, and power tended to increase up to 161%, 122%, and 168%, respectively. Therefore, it can be seen that the steering angle has a higher influence on the dynamic characteristics of the proportional control valve than the engine speed.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• Journal of People, Plants, and Environment
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• v.23 no.6
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• pp.655-665
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• 2020

Background and objective: This study was conducted to compare the cultivation environment, growth of cut flowers, and management performance of conventional farms and smart farms growing the standard cut chrysanthemum, 'Jinba'. Methods: Conventional and smart farms were selected, and facility information, cultivation environment, cut flower growth, and management performance were investigated. Results: The conventional and smart farms were located in Muan, Jeollanam-do, and conventional farming involved cultivating with soil culture in a plastic greenhouse, while the smart farm was cultivating with hydroponics in a plastic greenhouse. The conventional farm did not have sensors for environmental measurement such as light intensity and temperature and pH and EC sensors for fertigation, and all systems, including roof window, side window, thermal screen, and shading curtain, were operated manually. On the other hand, the smart farm was equipped with sensors for measuring the environment and nutrient solution, and was automatically controlled. The day and night mean temperatures, relative humidity, and solar radiation in the facilities of the conventional and the smart farm were managed similarly. But in the floral differentiation stage, the floral differentiation was delayed, as the night temperature of conventional farm was managed as low as 17.7℃ which was lower than smart farm. Accordingly, the harvest of cut flowers by the conventional farm was delayed to 35 days later than that of the smart farm. Also, soil moisture and EC of the conventional farm were unnecessarily kept higher than those of the smart farm in the early growth stage, and then were maintained relatively low during the period after floral differentiation, when a lot of water and nutrients were required. Therefore, growth of cut flower, cut flower length, number of leaves, flower diameter, and weight were poorer in the conventional farm than in the smart farm. In terms of management performance, yield and sales price were 10% and 38% higher for the smart farm than for the conventional farm, respectively. Also, the net income was 2,298 thousand won more for the smart farm than for the conventional farm. Conclusion: It was suggested that the improved growth of cut flowers and high management performance of the smart farm were due to precise environment management for growth by the automatic control and sensor.

• 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.

• Journal of Conservation Science
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• v.37 no.6
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• pp.626-637
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• 2021

The tomb complex of the royal family from the period of the Ungjin Baekje Kingdom (475 to 538 AD) in Gongju, Korea, contains the tomb of King Muryeong and other royal tombs. After the excavation of the tomb of King Muryeong in 1971, these tombs were opened up to the public, without the establishment of systems for their safety, conservation and management. The tombs have consequently experienced rapid environmental changes and suffered various damages. In this study, specific vulnerable parts inside the tombs were selected for deviation analysis using 3D scanning, and 3D image models were constructed on this basis. Progressive displacement was identified in tomb No. 5, and basic data for future investigations was acquired from tomb No. 6 and the tomb of King Muryeong. In the deviation analysis for the southern plastered wall of tomb No. 5, the damage was not found to exceed the ranges of ±18 mm and ±2 mm. However, the lintel stone was found to be sagging by 0.32 mm on average, and the distance between the walls to have increased by 0.36 mm on average. Direct water seepage occurring in tomb No. 5 is considered to be increasing the damage within the tomb, such as the dropping and sagging of the lintel. The 3D image models constructed in this study will play an important role as baseline data for future research, and can be used to discuss a secure conservation scheme for the tombs through cross-validation with precise measurement monitoring.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.6
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• pp.219-224
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• 2022

CaF₂ single crystal has a large band gap (12 eV), and it is used for optical windows, prisms, and lenses due to its excellent transmittance in a wide wavelength range and low refractive index. Moreover, it is expected to be one of the materials for ultraviolet transmissive laser optical components. CaF₂ belongs to the fluoride compounds and has a face-centered cubic (FCC) structure with three sub-lattices. The representative method for CaF₂ single crystal growth is Czochralski, which method has the advantages of high production efficiency and the ability to make large crystals. In this study, X-ray diffraction (XRD), X-ray rocking curves (XRC) measurement, and chemical etching were performed to analyze the crystallinity and defect density of the CaF₂ single crystals, grown by the Czochralski method. Fourier-transform infrared spectroscopy (FT-IR) and UV-VIS-NIR spectroscopy systems were used to investigate the optical properties of the CaF₂ crystal. The provability of various applications, including UV application, was systematically investigated with various analysis results.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.366-375
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• 2022

The monitoring of electricity consumption using Internet of Things (IoT) technology is attracting attention as a technology to reduce operation costs of smart farms. In this study, we propose a method to apply a real-time electrical consumption monitoring system (the e-Gauge system) and utilization of the collected data real-time while a melon-producing smart farm is in operation. For this purpose, the electrical consumption data for the individual smart-farm facilities such as boilers, nutrient distribution systems, automatic controllers, circulation fans, boiler controllers, and other IoT-related utilities were collected during three months of melon cultivation period. By using the monitoring results, the electrical energy consumption pattern was analyzed as an example, and necessary considerations needed to optimally utilize the measurement data were suggested. This paper will be useful in lowering the technological implementation barriers for new researchers to build a electrical consumption monitoring system and reducing trial and errors in the usage of the generated data.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.71-76
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• 2023

Recently, in order to store and manage big data, research and development of a high-performance storage system capable of stably accessing large data have been actively conducted. In particular, storage systems in data centers and enterprise environments use large amounts of SSD (solid state disk) to manage large amounts of data. In general, SSD uses FTL(flash transfer layer) to hide the characteristics of NAND flash memory, which is a medium, and to efficiently manage data. However, FTL's algorithm has a limitation in using DRAM more to manage the location information of NAND where data is stored as the capacity of SSD increases. Therefore, this paper introduces FTL policies that apply virtual memory to reduce DRAM resources used in FTL. The virtual memory-based FTL policy proposed in this paper manages the map data by using LRU (least recently used) policy to load the mapping information of the recently used data into the DRAM space and store the previously used information in NAND. Finally, through experiments, performance and resource usage consumed during data write processing of virtual memory-based FTL and general FTL are measured and analyzed.

• Journal of Environmental Impact Assessment
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• v.32 no.4
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• pp.214-229
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• 2023

This study was aimed to derive the following regional characteristics and implications by reviewing the effects of local communities and overseas cases through agricultural heritage and related systems to prepare rural regeneration measures using agricultural and rural heritage. First, The study was examined to improve the awareness to improve awareness of the value and preservation of heritage through the designation of agricultural heritage. However, it was found that it was necessary to prepare for social problems such as the aging population in the future. Second, most of the residents' perceptions showed a positive perception of the designation of agricultural heritage, but they were somewhat less recognized in terms of economics, so it was found that regeneration measures were needed to compensate for this. Third, as a result of applying the effect measurement model, the preservation and management effect that meets the purpose of the system is high, and the effect varies depending on projects such as local governments and residents' councils. Fourth, as a result of examining rural regeneration measures through overseas cases, it was found that rather than large-scale development, various cultural and natural resources and activation measures were prepared by expanding the scope to surrounding areas. This study was conducted only on agricultural heritage areas, but it is meaningful that agricultural and rural heritage should be reviewed from various perspectives suitable for the current trend, and it is meaningful in that it considers not only local residents' perception but also regional effects and revitalization measures.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.