• Journal of IKEEE
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• v.18 no.4
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• pp.552-558
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• 2014

This paper suggests how to design a ZigBee-chip-based communication module to remotely measure radiation level. The suggested communication module consists of two control processors for the chip as generally required to configure a ZigBee system, and one chip module to configure a ZigBee RF device. The ZigBee-chip-based communication module for remote radiation measurement consists of a wireless communication controller; sensor and high-voltage generator; charger and power supply circuit; wired communication part; and RF circuit and antenna. The wireless communication controller is to control wireless communication for ZigBee and to measure radiation level remotely. The sensor and high-voltage generator generates 500 V in two consecutive series to amplify and filter pulses of radiation detected by G-M Tube. The charger and power supply circuit part is to charge lithium-ion battery and supply power to one-chip processors. The wired communication part serves as a RS-485/422 interface to enable USB interface and wired remote communication for interfacing with PC and debugging. RF circuit and antenna applies an RLC passive component for chip antenna to configure BALUN and antenna impedance matching circuit, allowing wireless communication. After configuring the ZigBee-chip-based communication module, tests were conducted to measure radiation level remotely: data were successfully transmitted in 10-meter and 100-meter distances, measuring radiation level in a remote condition. The communication module allows an environment where radiation level can be remotely measured in an economically beneficial way as it not only consumes less electricity but also costs less. By securing linearity of a radiation measuring device and by minimizing the device itself, it is possible to set up an environment where radiation can be measured in a reliable manner, and radiation level is monitored real-time.

• Horticultural Science & Technology
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• v.34 no.6
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• pp.871-877
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• 2016

Recycling nutrient solutions in closed hydroponic production systems is usually accompanied by an imbalance of nutrient solutions when concentration is controlled according to electrical conductivity (EC) levels. This study investigated whether it was possible to automatically control the concentrations of five essential elements nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg) using only N, K and Ca ion sensors. N, P, K, Ca, and Mg uptake was measured in the nutrient solution, and relationships between absorbed ions were analyzed through twice-repeated experiments in lettuce. Results confirmed that the pattern of $PO_4$ ion uptake was similar that of N, and the pattern of Mg ion uptake was similar that of Ca. $PO_4$ ion uptake was most highly correlated with N, and Mg was most highly correlated with Ca. Regression coefficients of N and $PO_4$ were significantly different at 1.04 and 0.55, respectively, but were similar between Ca and Mg at 0.35 and 0.40, respectively. Additional experiments were conducted to measure nutrient uptake in pak choi and rose plants, both to confirm the results from the first experiment in lettuce, and to assess possible application to other crops. Coefficients of determination both for N and $PO_4$, and Ca and Mg were considerably high ($R^2=0.86$) in cultured pak choi, and similar results were observed in cultured rose ($R^2=0.87$ and 0.73, respectively). Regression coefficients for cultured pak choi were 0.56 and 0.24, respectively, and for rose were 0.51 and 0.16, respectively. Although the results obtained for N and $PO_4$ were not consistent between the lettuce experiments, N and $PO_4$ have similar regression coefficients for all crops. No common coefficient was found between Ca and Mg.

• 한국지구물리탐사학회:학술대회논문집
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• 2002.09a
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• pp.101-125
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• 2002

A lake seismic survey was carried out to investigate possible geohazards for construction of the underground LPG storage at Namyang Lake. The proposed survey site has a land-lake combined geography and furthermore water depth of the lake is shallow. Therefore, various seismic methods such as marine single channel high resolution seismic reflection survey, sonobuoy refraction survey, land refraction survey and land-lake combined refraction survey were applied. Total survey amounts are 34 line-km of high resolution lake seismic survey, 14 lines of sonobuoy refraction survey, 890 m of land refraction survey and 8 lines of land-lake combined refraction survey. During the reflection survey, there were severe water reverberations from the lake bottom obscured subsurface profiling. These strong multiple events appeared in most of the survey area except the northern and southern area near the embankment where seems to be accumulated mainly mud dominated depositions. The sonobuoy refraction profiles also showed the same Phenomena as those of reflection survey. Meanwhile the results of the land-lake combined refraction survey showed relatively better qualities. However, the land refraction survey did not so due to low velocity soil layer and electrical noise. Summarized results from the lake seismic survey are that acoustic basement with relatively flat pattern appeared 30m below water level and showed three types of bedrock such as fresh, moderately weathered and weathered type. According to the results of the combined refraction survey, a velocity distribution pattern of the lake bottom shows three types of seismic velocity zone such as >4.5 km/s, 4.5-4.0km/s and <4.0km/s. The major fault lineament in the area showed NW-SE trend which was different from the Landsat image interpretation. A drilling was confirmed estimated faults by seismic survey.

• The Journal of Engineering Geology
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• v.16 no.4 s.50
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• pp.411-427
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• 2006

This study aimed to recognize characteristics of groundwater flow in fractured bedrocks based on zonal pump-ing tests, slug tests, water quality logs and borehole TV camera logs conducted on two boreholes (NJ-11 and SJ-8) in the city of Naju. Especially, the zonal pumping tests using sin91e Packer were executed to reveal groundwater flow characteristics in the fractured bedrocks with depth. On borehole NJ-11, the zonal pumping tests resulted in a flow dimension of 1.6 with a packer depth of 56.9 meters. It also resulted in lower flow dimensions as moving to shallower packer depths, reaching a flow dimension of 1 at a 24 meter packer depth. This fact indicates that uniform permissive fractures take place in deeper zones at the borehole. On borehole SJ-8, a flow dimension of 1.7 was determined at the deepest packer level (50 m). Next, a dimension of 1.8 was obtained at 32 meters of packer depth, and lastly a dimension of 1.4 at 19 meters of packer depth. The variation of flow dimension with different packer depths is interpreted by the variability of permissive fractures with depth. Zonal pumping tests led to the utilization of the Moench (1984) dual-porosity model because hydraulic characteristics in the test holes were most suitable to the fractured bedrocks. Water quality logs displayed a tendency to increase geothermal temperature, to increase pH and to decrease dissolved oxygen. In addition, there was an increasing tendency towards electrical conductance and a decreasing tendency towards dissolved oxygen at most fracture zones.

• The Journal of Engineering Geology
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• v.25 no.4
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• pp.533-545
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• 2015

Spatial and seasonal variations in hydrogeochemical characteristics and the factors affecting the deterioration in quality of shallow portable groundwater in an agricultural area are examined. The aquifer consists of (from the surface to depth) agricultural soil, weathered soil, weathered rock, and bedrock. The geochemical signatures of the shallow groundwater are mostly affected by the NO₃⁻ and Cl⁻ contaminants that show a gradual downward increase in concentration from the upper area, due to the irregular distribution of contamination sources. The concentrations of the major cations do not varied with the elapsed time and the NO₃⁻ and Cl⁻ ions, when compared with concentrations in background groundwater, increase gradually with the distance from the upper area. This result suggests that the water quality in shallow groundwater deteriorates due to contaminant sources at the surface. The contaminations of the major contaminants in groundwater show a positive linear relationship with electrical conductivity, indicating the deterioration in water quality is related to the effects of the contaminants. The relationships between contaminant concentrations, as inferred from the ternary plots, show the contaminant concentrations in organic fertilizer are positively related to concentrations of NO₃⁻, Cl⁻, and SO₄²⁻ ions in the shallow portable groundwaters, which means the fertilizer is the main contaminant source. The results also show that the deterioration in shallow groundwater quality is caused mainly by NO₃⁻ and Cl⁻ derived from organic fertilizer with additional SO₄²⁻ contaminant from livestock wastes. Even though the concentrations of the contaminants within the shallow groundwaters and the contaminant sources are largely variable, it is useful to consider the ratio of contaminant concentrations and the relationship between contaminants in groundwater samples and in the contaminant source when analyzing deterioration in water quality.

• Journal of the Korean Society of Radiology
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• v.4 no.1
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• pp.5-9
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• 2010

In recent, a digital x-ray detector attracted worldwide attention and there are many studies to commercialize. There are two methods in digital x-ray detector. This method is an Indirect method and Direct method. This study is to see the differences between the digital x-ray detector based on a-Se used in the existing indirect conversion method and an x-ray conversion material that has better SNR(Signal-to-noise ratio) and property than the a-Se. To solve the problem that is difficult to make a large area film using Screen-Print method, we used a Screen-Print method. In this study, we used a polyclystal $HgI_2$ as x-ray conversion material and a sample thickness is $150{\mu}m$ and an area is $3cm{\times}3cm$. ITO(Indium-Tin-Oxide) electrode was used as top electrode using a Magnetron Sputtering System and each area is $3cm{\times}3cm$, $2cm{\times}2cm$ and $1cm{\times}1cm$ and then we evaluated darkcurrent, sensitivity and SNR of the $HgI_2$ film are measured, then we evaluated the electrical properties. And we used a current integration mode when I-V test. This experiment shows that the sensitivity increases in accordance with the area of the electrode but the SNR is decreased because of the high darkcurrent. Through fabricating of various thicknesses and optimal electrodes, we will optimize SNR in the future work.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of IKEEE
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• v.25 no.1
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• pp.156-167
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• 2021

In this paper, we propose a digital transceiver unit design for in-building of 5G optical repeaters that extends the coverage of 5G mobile communication network services and connects to a stable wireless network in a building. The digital transceiver unit for driving the proposed 5G optical repeater is composed of 4 blocks: a signal processing unit, an RF transceiver unit, an optical input/output unit, and a clock generation unit. The signal processing unit plays an important role, such as a combination of a basic operation of the CPRI interface, a 4-channel antenna signal, and response to external control commands. It also transmits and receives high-quality IQ data through the JESD204B interface. CFR and DPD blocks operate to protect the power amplifier. The RF transmitter/receiver converts the RF signal received from the antenna to AD, is transmitted to the signal processing unit through the JESD204B interface, and DA converts the digital signal transmitted from the signal processing unit to the JESD204B interface and transmits the RF signal to the antenna. The optical input/output unit converts an electric signal into an optical signal and transmits it, and converts the optical signal into an electric signal and receives it. The clock generator suppresses jitter of the synchronous clock supplied from the CPRI interface of the optical input/output unit, and supplies a stable synchronous clock to the signal processing unit and the RF transceiver. Before CPRI connection, a local clock is supplied to operate in a CPRI connection ready state. XCZU9CG-2FFVC900I of Xilinx's MPSoC series was used to evaluate the accuracy of the digital transceiver unit for driving the 5G optical repeater proposed in this paper, and Vivado 2018.3 was used as the design tool. The 5G optical repeater digital transceiver unit proposed in this paper converts the 5G RF signal input to the ADC into digital and transmits it to the JIG through CPRI and outputs the downlink data signal received from the JIG through the CPRI to the DAC. And evaluated the performance. The experimental results showed that flatness, Return Loss, Channel Power, ACLR, EVM, Frequency Error, etc. exceeded the target set value.

• Journal of IKEEE
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• v.26 no.3
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• pp.408-415
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• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

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.