• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.49-55
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• 2021

Recently, a small tracking radar requires the development of a small millimeter wave tracking radar having a high range resolution that can acquire and track a target in various environments and disable the target system with a single blow. Small millimeter wave tracking radar with high range resolution needs to implement a signal processor that can process wide bandwidth signals in real time and meet the requirements of small tracking radar. In this paper, we designed a signal processor that can perform the role and function of a signal processor for a small millimeter wave tracking radar. The signal processor for the small millimeter wave tracking radar requires the real-time processing of input signal of OOOMHz center frequency and OOOMHz bandwidth from 8 channels. In order to satisfy the requirements of the signal processor, the signal processor was designed by applying the high-performance FPGA (Field Programmable Gate Array) and ADC (Analog-to-digital converter) for pre-processing operations, such as DDC (Digital Down Converter) and FFT (Fast Fourier Transform). Finally, the signal processor of the small millimeter wave tracking radar was verified via performance test.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1329-1338
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• 2021

Rain radar provides high spatio-temporal radar rainfall that can be used as input data to short-term precipitation forecasting models. Korea Institute of Civil Engineering and Building Technology (KICT) has developed a flash flood forecasting system that is providing flash flood forecasting based on short-term rainfall forecasts estimated by the radar rainfall. Accuracy of the radar rainfall as well as the short-term rainfall forecasts, however, can deteriorate when radar polarimetric variables have error. In this study, we develope real-time program that can correct the error inherent in the radar polarimetric variables. First, effect according to the correction of the error was verified using 363 rainfall events on non real-time. The accuracy (1-NE) of the radar rainfall was approximately 70% and correlation coefficient was higher than 0.8 after correcting the error on non real-time. The accuracy (1-NE) using the real-time program was also approximately 70% after correcting the error.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.31 no.4
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• pp.317-330
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• 2021

Objectives: By law, companies in Korea must periodically measure workers' exposure to harmful chemicals (the system is called the Work Environment Monitoring Program (WMP)[a]) and report the results to the government. The government also measures exposure to monitor the WMP's reliability (called Reliability Assessment (RA) for WMP[b]). The issue is that measured data from these two sources are so different that the objectivity of WMP needs to be confirmed by comparing the results using the European Centre for Ecotoxicology and Toxicology of Chemicals' Targeted Risk Assessment (ECETOC TRA). Methods: Step 1: Data collection from WMP reports submitted by companies (n=586) and RA for WMP written by the government (n=33). Step 2: Data Standardization by key information included. Step 3: Data conversion to input-variables required to run the ECETOC TRA model, and run the model with specific data (n=514) which meet the predetermined exposure scenario. Step 4: Statistical data analysis by process category (PROC) and ventilation type from each source ([A] and [B]). Step 5: Additional analysis of any unexpected results. Results: The process categories of the production and handling of Dichloromethane were classified into 12 PROCs, and ten of them were selected to run ECETOC TRA. Modeled values tended to be higher than measured values from both sources. For the measured values from WMP, RCR distribution by PROC was narrow (0.197-0.267, 95% CI) and did not have a relationship with ventilation type, which differs from the tendency of the modeling result. Meanwhile, the measured values from RA for WMP were relatively widely distributed (0.301-1.177, 95% CI) by PROC. In particular PROCs (13,19) were high enough to exceed 1. Also, they become low with better ventilation types and appear differently depending on the ventilation type, similar to the model result. Conclusions: This study revealed that ECETOC TRA might have the potential to serve as a screening tool for exposure assessment and to be used as assistive method for WMP to estimate exposure. Further empirical study is required to confirm its availability as a screening tool.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.3-12
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• 2019

Despite the importance and function of productivity information, in the Korean construction industry, the method of collecting and analyzing productivity data has not been organized. Also, in most cases, productivity management is reliant on the experience and intuitions of field managers, and productivity data are rarely being utilized in planning and management. Accordingly, this study intends to develop a prediction model for interior finishes of apartment using deep learning techniques, so as to provide a foundation for analyzing the productivity impacting factors and predicting productivity. The result of the study, productivity prediction model for interior finishes of apartment using deep learning techniques, can be a basic module of apartment project management system by applying deep learning to reliable productivity data and developing as data is accumulated in the future. It can also be used in project engineering processes such as estimating work, calculating work days for process planning, and calculating input labor based on productivity data from similar projects in the past. Further, when productivity diverging from predicted productivity is discovered during construction, it is expected that it will be possible to analyze the cause(s) thereof and implement prompt response and preventive measures.

• Clean Technology
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• v.24 no.4
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• pp.269-274
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• 2018

In this study, Life Cycle Assessment (LCA) of wet tissue manufacturing process was performed. The wet tissue manufacturing process consists of preparation of wetting agent (chemical liquid), impregnation of nonwoven fabric into wetting agent and primary and secondary packaging. Data and information were collected on the input and output of the actual process from a certain company and the database of the Korea Ministry of Environment and some foreign countries (when Korean unavailable) were employed to connect the upper and the lower process flow. Based on the above and the potential environmental impacts of the wet tissue manufacturing process were calculated. As a result of the characterization, Ozone Layer Depletion (OD) is 3.46.E-06 kg $CFC_{11}$, Acidification (AD) is 5.11.E-01 kg $SO_2$, Abiotic Resource Depletion (ARD) is $3.52.E+00\;1yr^{-1}$, Global Warming (GW) is 1.04.E+02 kg $CO_2$, Eutrophication (EUT) is 2.31.E-02 kg ${PO_4}^{3-}$, Photochemical Oxide Creation (POC) was 2.22.E-02 kg $C_2H_4$, Human Toxicity (HT) was 1.55.E+00 kg 1,4 DCB and Terrestrial Ecotoxicity (ET) was 5.82.E-04 kg 1,4 DCB. In order to reduce the environmental impact of the manufacturing process, it is necessary to improve the overall process as other general cases and change the raw materials including packaging materials with less environmental impact. Conclusively, the energy consumed in the manufacturing process has emerged as a major issue, and this needs to be reconsidered other options such as alternative energy. Therefore, it is recommended that a process system should be redesigned to improve energy efficiency and to change to an energy source with lower environmental impact. Due to the nature of LCA, the final results of this study can be varied to some extent depending on the type of LCI DB employed and may not represent of all wet tissue manufacturing processes in the current industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Journal of the Korea Organic Resources Recycling Association
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• v.27 no.2
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• pp.5-12
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• 2019

The study is carried out to survey the proper management and to propose an eco - friendly separation system through efficient screening and resource recovery of excavated materials containing waste from various excavating fields such as reconstruction of landfill sites for reuse, reclamation of unsanitary landfill and residential land development of waste dumping sites. The current status and screening process and analytical characteristics of the excavated materials containing waste were reviewed. Through the analysis of the samples such as separated combustibles, recyclable soils and residues collected from the on-site visits we were able to understand the characteristics of separated materials and excavated materials containing waste such as calorific value, elementary composition, TOC, foreign material content and LOI. It has been found that elimination of the moisture of excavations, removal of attached soil from the surfaces of the excavated combustibles and the quantitative supply method of the input devices are the main operating factors as essential factors for the optimal separation of excavated materials containing waste. For efficient management and recycling of excavated materials containing, it is necessary to set criteria of ash content in separated combustibles and criteria organic matter content in separated soils.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.201-209
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• 2019

Recently various start-up competitions have been held in the government, public and private sectors. There is a need to improve the business model of the majority of preliminary founders and early founders through the start - up contest, while improving the possibility of commercialization. The purpose of this study is to analyze the present status of various start - up contests based on the actual survey results of the major start - up contest operators and major participants in Korea. The main results of this study are as follows: First, in the run - up contest, there is a tendency to break out of the event personality, to prevent the opening of the business model of the entrepreneur in the competition, to reduce the formal procedure considering the input time, Improvement of the use of presentation materials, and the purpose of the contest and precise specification of the object of the recruitment. Secondly, it is necessary to establish a juror and a mentor pool with expertise. It is necessary to establish the judges and the mentor pool with expertise in each field, allocate the region according to the regional composition, entrust the judges with entrepreneurial experience, and introduce the post evaluation system for the judges after the competition. Third, most of the contest winners are manufacturing / technology-based businesses.

• Journal of Climate Change Research
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• v.1 no.2
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• pp.147-161
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• 2010

The temporal and spatial distributions of surface solar radiation were calculated by the one layer solar radiative transfer model(GWNU) which was corrected by multi layer Line-by-Line(LBL) model during 2009 in South Korea. The aerosol optical thickness, ozone amount, cloud fraction and total precipitable water were used as the input data for GWNU model run and they were retrieved from Moderate Resolution Imaging Spectrometer(MODIS), Ozone Monitoring Instrument(OMI), MTSAT-1R satellite data and the Regional Data Assimilation Prediction System(RDAPS) model result, respectively. The surface solar radiation was calculated with 4 km spatial resolution in South Korea region using the GWNU model and the results were compared with surface measurement(by pyranometer) data of 22 KMA solar sites. The maximum values(more than $5,400MJ/m^2$) of model calculated annual solar radiation were found in Andong, Daegu and Jinju regions and these results were corresponded with the MTSAT-1R cloud amount data. However, the spatial distribution of surface measurement data was comparatively different from the model calculation because of the insufficient correction and management problems for the sites instruments(pyranometer).

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1427-1443
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• 2018

Object-based image analysis (OBIA) allows higher computation efficiency and usability of information inherent in the image, as it reduces the complexity of the image while maintaining the image properties. Superpixel methods oversegment the image with a smaller image unit than an ordinary object segment and well preserve the edges of the image. SLIC (Simple linear iterative clustering) is known for outperforming the previous superpixel methods with high image segmentation quality. Although the input parameter for SLIC, number of superpixels has considerable influence on image segmentation results, impact analysis for SLIC parameter has not been investigated enough. In this study, we performed optimal parameter analysis and evaluation of change detection for SLIC-based superpixel techniques using KOMPSAT data. Forsuperpixel generation, three superpixel methods (SLIC; SLIC0, zero parameter version of SLIC; SNIC, simple non-iterative clustering) were used with superpixel sizes in ranges of $5{\times}5$ (pixels) to $50{\times}50$ (pixels). Then, the image segmentation results were analyzed for how well they preserve the edges of the change detection reference data. Based on the optimal parameter analysis, image segmentation boundaries were obtained from difference image of the bi-temporal images. Then, DBSCAN (Density-based spatial clustering of applications with noise) was applied to cluster the superpixels to a certain size of objects for change detection. The changes of features were detected for each superpixel and compared with reference data for evaluation. From the change detection results, it proved that better change detection can be achieved even with bigger superpixel size if the superpixels were generated with high regularity of size and shape.