• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.20-29
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• 2021

This study developed a smartwatch and a wind speed system in accordance with the necessity of a device that provides the information required in marine leisure. Based on a marine leisure smartwatch with a multi-sensor, a gyro box, and a wind speed system, external data such as GPS, motion, humidity, temperature, air pressure, and heart rate can be collected. In addition, the collected external environment data can be managed through an application on a smartphone, which is an Android-based mobile device. The developed smartwatch and wind speed system are expected to contribute to increasing accessibility and revitalization of the marine leisure industry. In addition, in terms of safety and education, the need for a device that provides marine information is large, so it is expected to increase the possibility of entering the high value-added market and improve the product localization rate.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.82-89
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• 2023

Due to the characteristics of ship structure, the compartment structure is complicated and narrow, so safety accidents frequently occur during the work process. The main causes of accidents include structural collisions, falling objects, toxic substance leaks, fires, explosions, asphyxiation, and more. Understanding the on-site conditions of workers during accidents is crucial for mitigating damages. In order to ensure safety, the on-site situation is monitored using CCTV in the ship, but it is difficult to prevent accidents with the existing method. To address this issue, a smart safety helmet equipped with location identification and voice/video communication capabilities is being developed as a safety technology. Additionally, the smart safety helmet incorporates environmental sensors for temperature, humidity, vibration, noise, tilt (gyro sensor), and gas detection within the work area. These sensors can notify workers wearing the smart safety helmet of hazardous situations. By utilizing the smart safety helmet and environmental sensors, the safety of workers aboard ships can be enhanced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5B
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• pp.794-800
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• 2010

It is necessary to accurate and efficient management for measured digital data from various sensors in digital marine vessel. It is not efficient that sensor network process input stream data of mass storage stored in database the same time. In this paper, We propose to improve the processing performance of multidimensional stream data continuous incoming from multiple sensor. We propose that we arrange some sensors (temperature, humidity, lighting, voice) and process query based on sliding window for efficient input stream and found multiple query plan to Mjoin method and we reduce stored data using SVM algorithm. We automatically delete that it isn't necessary to the data from the database and we used to ship diagnosis system for available data. As a result, we obtained to efficient result about 18.3% reduction rate of database using 35,912 data sets.

• Journal of the Korean Wood Science and Technology
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• v.52 no.1
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• pp.1-12
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• 2024

Betung bamboo (Dendrocalamus asper) is used as a building and handicraft material in Indonesia; however, bamboo needs to be dried to increase its stability. This study aimed to evaluate the efficiency of drying bamboo using solar energy and different drying oven designs. The betung bamboo pieces were dried using a direct solar dryer (direct drying) and an indirect solar dryer (indirect drying) and then the decrease in levels that occurred based on the relative humidity (RH) and temperature values achieved in the two dryers were compared. The highest average temperature in the direct indirect drying oven compartment was 60.1 ± 13.1℃ with 19.9 ± 16.4% RH and 60.2 ± 11.9℃ with 19.5 ± 15.5% RH, respectively. The drying defect in indirect drying was lower than that in direct drying, and indirect drying had a 61.7% greater average water loss than direct drying with significant difference (95%, analysis of variance) based on water loss/compartment volume parameters. Thus, the solar drying oven can be used to air-dry bamboo (14%) for 7 d from an initial moisture content of 70%-80% in bamboo strips. The results of this research can be used for small-scale bamboo processing industries that have limited use of electrical energy with quite good results.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.73-78
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• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.67-78
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• 2019

Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.5
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• pp.397-402
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• 2010

In this paper, we studied WVTR(water vapor transmission rate) properties of $Si_3N_4$ thin film that was deposited using TCP-CVD (transformer coupled plasma chemical vapor deposition) method for the possibility of OLED(organic light emitting diode) encapsulation. Considering the conventional OLED processing temperature limit of below $80^{\circ}C$, the $Si_3N_4$ thin films were deposited at room temperature. The $Si_3N_4$ thin films were prepared with the process conditions: $SiH_4$ and $N_2$, as reactive gases; working pressure below 15 mTorr; RF power for TCP below 500 W. Through MOCON test for WVTR, we analyzed water vapor permeation per day. We obtained that WVTR property below 6~0.05 gm/$m^2$/day at process conditions. The best preparation condition for $Si_3N_4$ thin film to get the best WVTR property of 0.05 gm/$m^2$/day were $SiH_4:N_2$ gas flow rate of 10:200 sccm, working pressure of 10 mTorr, working distance of 70 mm, TCP power of 500 W and film thickness of 200 nm. respectively. The proposed results indicates that the $Si_3N_4$ thin film could replace metal or glass as encapsulation for flexible OLED.

• Journal of Climate Change Research
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• v.2 no.4
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• pp.297-307
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• 2011

This study investigates relation of food safety incidents with climate. Therefore food safety incidents and climate data during 1999 to 2009 have been analyzed. In situ observations of monthly mean temperature, maximum temperature, minimum temperature, precipitation, and relative humidity in 60 observation stations of Korean Meteorological Administration (KMA) have been used in this study. Food safety incidents data have been constructed by searching media reports following Park's method (2009) during the same period. According to the Park's method, 729 events were collected. To analyze its relations, food safety incidents data have been classified into chemical, biological, and physical hazards. Pearson product-moment correlation coefficients have been applied to analyze the relations. The correlation of food safety incidents has negative one with precipitation (-0.48), and positive one with minimum temperature(0.45). Precipitation has been correlated with biological and physical hazards more than chemical hazard. Temperatures (mean temperature, maximum temperature, and minimum temperature) have been correlated closely with chemical hazard than others. Food safety incidents data has been interblended with human behavior factor through decision-making processes in food manufacturing, processing, and consumption phases of "farm-totable" food processing. Act in the preventing damage will be obvious if the hazard were apparent. Therefore abnormal condition could be more dangerous than that of apparent extreme events because apparent events or extreme events become one of alarm over hazards. Therefore, human behavior should be considered as one of the important factors for analysis of food safety incidents. The result of this study can be used as a better case study for food safety researches related to climate change.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.177-182
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• 2021

The fourth industrial revolution based on information and communication technology (ICT) becomes the center of society, and the overall industrial structure is also changing significantly. ICT refers to the hardware of information devices and the software technologies required for the operation and information management of these devices, and any means of collecting, producing, processing, preserving, communicating and utilizing them. ICT is integrated into industries and services or combined with new technologies in various fields such as robotics and nanotechnology to connect all products and services to the network. The development of ICT, which continuously creates new products and services, has spread to all sectors of the industry, affecting not only daily life but also the livestock sector recently. In agriculture, ICT technology can reduce production costs by efficiently managing labor and energy because it can improve quality and yield based on data on environmental and growth information such as temperature, humidity, light and soil. In particular, smart livestock is considered suitable for achieving livestock management goals because it can reduce labor force and improve productivity by remotely and automatically managing accurate information necessary for raising and breeding livestock with ICT devices. The purpose of this study is to propose the need for ICT technology by comparing farm productivity before and after ICT is introduced. The method of the study is to compare the productivity before and after the introduction of ICT in Korean beef farms, pig farms, and poultry farms. The effectiveness of the study proved the excellence of ICT technology through the production results before ICT introduction and the productivity improvement case of livestock farms that efficiently operated manpower management and reduced labor force after ICT introduction. The conclusion of this paper is to present the need for smart livestock through ICT adoption through case study results.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.83-95
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• 2019

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.