Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.
The study aim was to derive a stem taper equation for Phyllostachys pubescens, a type of bamboo in South Korea, and to develop a stem volume table. To derive the stem taper equation, three stem taper models (Max & Burkhart, Kozak, and Lee) were used. Since bamboo stalks are hollow because of its woody characteristics, the outer and inner diameters of the tree were calculated, and connecting them enabled estimating the tree curves. The results of the three equations for estimating the outer and inner diameters led to selection of the Kozak model for determining the optimal stem taper because it had the highest fitness index and lowest error and bias. We used the Kozak model to estimate the diameter of Phyllostachys pubescens by stem height, which proved optimal, and drew the stem curve. After checking the residual degree in the stem taper equation, all residuals were distributed around "0", which proved the suitability of the equation. To calculate the stem volume of Phyllostachys pubescens, a rotating cube was created by rotating the stem curve with the outer diameter at 360°, and the volume was calculated by applying Smalian's method. The volume of Phyllostachys pubescens was calculated by deducting the inner diameter calculated volume from the outer diameter calculated volume. The volume of Phyllostachys pubescens was only 20~30% of the volume of Larix kaempferi, which is a general species. However, considering the current trees/ha of Phyllostachys pubescens and the amount of bamboo shoots generated every year, the individual tree volume was predicted to be small, but the volume/ha was not very different or perhaps more. The significance of this study is the stem taper equation and stem volume table for Phyllostachys pubescens developed for the first time in South Korea. The results are expected to be used as basic data for bamboo trading that is in increasing public and industrial demand and carbon absorption estimation.
One of the applications of geomagnetic paleo-secular variation (PSV) is the age dating of archeological remains (i.e., the archeomagnetic dating technique). This application requires the local model of PSV that reflects non-dipole fields with regional differences. Until now, the tentative Korean paleosecular variation (t-KPSV) calculated based on JPSV (SW Japanese PSV) has been applied as a reference curve for individual archeomagnetic directions in Korea. However, it is less reliable due to regional differences in the non-dipole magnetic field. Here, we present PSV curves for AD 1 to 600, corresponding to the Korean Three Kingdoms (including the Proto Three Kingdoms) Period, using the results of archeomagnetic studies in the Korean Peninsula and published research data. Then we compare our PSV with the global geomagnetic prediction model and t-KPSV. A total of 49 reliable archeomagnetic directional data from 16 regions were compiled for our PSV. In detail, each data showed statistical consistency (N > 6, 𝛼95 < 7.8°, and k > 57.8) and had radiocarbon or archeological ages in the range of AD 1 to 600 years with less than ±200 years error range. The compiled PSV for the initial six centuries (KPSV0.6k) showed declination and inclination in the range of 341.7° to 20.1° and 43.5° to 60.3°, respectively. Compared to the t-KPSV, our curve revealed different variation patterns both in declination and inclination. On the other hand, KPSV0.6k and global geomagnetic prediction models (ARCH3K.1, CALS3K.4, and SED3K.1) revealed consistent variation trends during the first six centennials. In particular, the ARCH3K.1 showed the best fitting with our KPSV0.6k. These results indicate that contribution of the non-dipole field to Korea and Japan is quite different, despite their geographical proximity. Moreover, the compilation of archeomagnetic data from the Korea territory is essential to build a reliable PSV curve for an age dating tool. Lastly, we double-check the reliability of our KPSV0.6k by showing a good fitting of newly acquired age-controlled archeomagnetic data on our curve.
In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (108 or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 108. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.
In this paper, a control system for a complex microbial incubator was proposed. The proposed control system consists of a control unit, a communication unit, a power supply unit, and a control system of the complex microbial incubator. The controller of the complex microbial incubator is designed and manufactured to convert analog signals and digital signals, and control signals of sensors such as displays using LCD panels, water level sensors, temperature sensors, and pH concentration sensors. The water level sensor used is designed and manufactured to enable accurate water level measurement by using the IR laser method with excellent linearity in order to solve the problem that existing water level sensors are difficult to measure due to foreign substances such as bubbles. The temperature sensor is designed and used so that it has high accuracy and no cumulative resistance error by measuring using the thermal resistance principle. The communication unit consists of two LAN ports and one RS-232 port, and is designed and manufactured to transmit signals such as LCD panel, PCT panel, and load cell controller used in the complex microbial incubator to the control unit. The power supply unit is designed and manufactured to supply power by configuring it with three voltage supply terminals such as 24V, 12V and 5V so that the control unit and communication unit can operate smoothly. The control system of the complex microbial incubator uses PLC to control sensor values such as pH concentration sensor, temperature sensor, and water level sensor, and the operation of circulation pump, circulation valve, rotary pump, and inverter load cell used for cultivation. In order to evaluate the performance of the control system of the proposed complex microbial incubator, the result of the experiment conducted by the accredited certification body showed that the range of water level measurement sensitivity was -0.41mm~1.59mm, and the range of change in water temperature was ±0.41℃, which is currently commercially available. It was confirmed that the product operates with better performance than the performance of the products. Therefore, the effectiveness of the control system of the complex microbial incubator proposed in this paper was demonstrated.
Controlled pollination (CP) is an important method in tree breeding programs because CP quickly generates desirable genotypes and rapidly maximizes genetic gains. However, few studies have evaluated the efficiency and success rate of CP in the breeding program of Pinus densiflora. To evaluate CP and the management of control pollinated progenies, we used 159 individuals in CB2 × KW40 or KW40 × CB2 populations that were established in 2015. After genotyping microsatellite loci, we estimated whether the number of primers was sufficient or not. Then, we performed pedigree analysis. The result showed that the number of primers was sufficient. By pedigree analysis, we found out that 60 of 159 individuals had been generated by the mating between CB2 and KW40. In the maternity analysis, there was evidence to indicate the possibility of management problems. Therefore, we excluded 54 individuals and repeated the pedigree analysis. In the second analysis, 47 of 105 individuals were generated by the mating between CB2 and KW40. To increase the efficiency of CP in tree breeding programs, several precautions are required. It is necessary to identify the exact clone names of the mother and father trees. In addition, CP processes should be performed properly, including deciding on the schedule of CP and the isolation of female strobili or flowers. Finally, the monitoring of hybrid progenies management after mating is important. Molecular markers should be used to identify the clone names of the mother and father trees and for monitoring post hoc management. This study provides a reference for tree breeding programs for the future control pollination of pine species.
In this study, analytical methodology for several organic fatty acids (OFA: propionic acid (PA), butyric acid (BA), isovaleric acid (IA), and valeric acid (VA)) designated as new offensive odorants in Korea (as of year 2010) was investigated along with some odorous VOCs (styrene, toluene, xylene, methyl ethyl ketone, methyl isobutyl ketone, butyl acetate, and isobutyl alcohol). For this purpose, working standards (WS) containing all of these 13 compounds were loaded into adsorption tube filled with Tenax TA, and analyzed by gas chromatography (GC) system thermal desorber interfaced with. The analytical sensitivities of organic fatty acids expressed in terms of detection limit (both in absolute mass (ng) and concentration (ppb)) were lower by 1.5-2 times than other compounds (PA: 0.24 ng (0.16 ppb), BA: 0.19 ng (0.11 ppb), IA: 0.15 ng (0.07 ppb), and VA: 0.28 ng (0.13 ppb)). The precision of BA, IA, and VA, if assessed in terms of relative standard error (RSE), maintained above 5%, while the precison of other compounds were below 5%. The reproducibility of analysis improved with the aid of internal standard calibration (PA: $1.1{\pm}0.4%$, BA: $10{\pm}0.46$, IA; $12{\pm}0.3%$, VA: $4{\pm}0.1%$), respectively. The results of this study showed that organic fatty acid can be analyzed using adsorption tube and thermal desorber in a more reliable way to replace alkali absorption method introduced in the odor prevention law of the Korea Ministry of Environment (KMOE).
The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.
This study concentrated on a report on the results of smelting experiments conducted eight times by the Jungwon National Research Institute of Cultural Heritage, put together the goals and results of the operation, and examined changes in the content of experiments and in the experimental results. First, changes related to operation, such as the ratio of raw materials to fuel and the presence or absence of additives, were reviewed depending on the operation goal. In addition, the results of metallurgical analysis of raw materials, formations, and byproducts were summarized and reviewed by comparing them with materials excavated from the ruins. The operation method varied up to the eighth smelting experiment in terms of iron ore roasting, additives, and raw material/fuel ratio. After reviewing the results again, pure iron with a low carbon content began to be confirmed through metallurgical analysis. As a result, it was confirmed that the charging ratio of raw materials and fuel plays an important role depending on the purpose of production. In addition, most of the products are gray cast iron, and it was deemed that this is due to changes in the internal structure of the pig iron while it was left in the furnace for a long time. The iron was an ingot that was in a molten state even though the carbon content did not reach 4.3%, where the process reaction takes place, and it was deemed to have been caused by excessive operating temperature. Based on the previously reviewed results and the structure and shape of the experimental furnace used in other ironmaking technology restoration experiments, this study finally attempted to restore the structure of an ancient iron smelting furnace, including the furnace's upper structure. By comprehensively referring to the remaining conditions of the excavated iron smelting furnace and the characteristics of the blow pipe, the form of the ancient iron smelting furnace was subdivided into six categories: furnace wall thickness, furnace height, blower height, blow pipe size, furnace inner wall shape, and top shape, and a restoration plan was proposed. To improve the problems of the restoration plan and the Jungwon National Research Institute of Cultural Heritage's experiments that have been conducted through continuous trial and error, an experiment that reflects changes in operating methods by lowering the furnace height and controlling the blowing volume is necessary.
Purpose In a cyclosporine experiment using a robotic liquid handing system has found a deviation of its standard curve and low reproducibility of patients's results. The difference of the test is that methanol is mixed with samples and the extractions are used for the test. Therefore, we assumed that the abnormal test results came from using methanol and conducted this test. In a manual of a robotic liquid handling system mentions that we can choose several setting parameters depending on the viscosity of the liquids being used, the size of the sampling tips and the motor speeds that you elect to use but there's no exact order. This study was undertaken to confirm pipetting ability depending on types of liquids and investigate proper setting parameters for the optimum dispensing ability. Materials and Methods 4types of liquids(water, serum, methanol, PEG 6000(25%)) and $TSH^{125}I$ tracer(515 kBq) are used to confirm pipetting ability. 29 specimens for Cyclosporine test are used to compare results. Prepare 8 plastic tubes for each of the liquids and with multi pipette $400{\mu}l$ of each liquid is dispensed to 8 tubes and $100{\mu}l$ of $TSH^{125}I$ tracer are dispensed to all of the tubes. From the prepared samples, $100{\mu}l$ of liquids are dispensed using a robotic liquid handing system, counted and calculated its CV(%) depending on types of liquids. And then by adjusting several setting parameters(air gap, dispense time, delay time) the change of the CV(%)are calcutated and finds optimum setting parameters. 29 specimens are tested with 3 methods. The first(A) is manual method and the second(B) is used robotic liquid handling system with existing parameters. The third(C) is used robotic liquid handling system with adjusted parameters. Pipetting ability depending on types of liquids is assessed with CV(%). On the basis of (A), patients's test results are compared (A)and(B), (A)and(C) and they are assessed with %RE(%Relative error) and %Diff(%Difference). Results The CV(%) of the CPM depending on liquid types were water 0.88, serum 0.95, methanol 10.22 and PEG 0.68. As expected dispensing of methanol using a liquid handling system was the problem and others were good. The methanol's dispensing were conducted by adjusting several setting parameters. When transport air gap 0 was adjusted to 2 and 5, CV(%) were 20.16, 12.54 and when system air gap 0 was adjusted to 2 and 5, CV(%) were 8.94, 1.36. When adjusted to system air gap 2, transport air gap 2 was 12.96 and adjusted to system air gap 5, Transport air gap 5 was 1.33. When dispense speed was adjusted 300 to 100, CV(%) was 13.32 and when dispense delay was adjusted 200 to 100 was 13.55. When compared (B) to (A), the result increased 99.44% and %RE was 93.59%. When compared (C-system air gap was adjusted 0 to 5) to (A), the result increased 6.75% and %RE was 5.10%. Conclusion Adjusting speed and delay time of aspiration and dispense was meaningless but changing system air gap was effective. By adjusting several parameters proper value was found and it affected the practical result of the experiment. To optimize the system active efforts are needed through the test and in case of dispensing new types of liquids proper test is required to check the liquid is suitable for using the equipment.
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