• Korean Journal of Applied Biomechanics
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• v.31 no.1
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• pp.72-78
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• 2021

Objective: The purpose of this study was to investigate relations and effectiveness about mountain climbling exercise with different level of support surfaces by analyzing heart rate and EMG data. A total of 10 male college students with no musculoskeltal disorder were recruited for this study. Method: The biomechanical analysis was performed using heart rate monitor (Polar V800, Polar Electro Oy, Finland), step-box, exercise mat, and EMG device (QEMG8, Laxtha Inc. Korea, sampling frequency = 1,024 Hz, gain = 1,000, input impedance > 1012 Ω, CMRR > 100 dB). In this research, step-box were used to create different surface levels on the upper body (flat surface, 10% of subject's height, 20% of subject's height, and 30% of subject's hight). Based on these different conditions, data was collected by performing mountain climbing exercise during 30 seconds. Subjects were given 5 minutes of break to prevent muscular fatigue after each exercise. For each dependent variable, a one-way analysis of variance with repeated measures was conducted to find significant differences and Bonferroni post-hoc test was performed. Results: The results of this study showed that exercise intensity was reduced statistically as increased surface level on the upper body. Muscle activity of the upper rectus abdominis and biceps femoris for 30% of surface level was significantly higher than the corresponding values for flat surface. However, the opposite was found in the rectus femoris. In general, muscle activity of the lower rectus abdominis, erector spinae, external oblique abdominis, and gluteus maximus increased when surface level increased, but the differences were not significant. Conclusion: As a result, the increase in surface level of the body would change muscle activity of the upper body, indicating that different surface level of the upper body may cause significant effect on particular muscles to be more active during mountain climbing exercise. Based on results of this study, it is suggested to set up an appropriate surface level to target particular muscle to expect an effective training. It is also important to set adequate surface levels to create an effective training condition for preventing exercise injuries.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.969-983
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• 2021

Evapotranspiration, one of the hydrometeorological components, is considered an important variable for water resource planning and management and is primarily used as input data for hydrological models such as water balance models. The FAO56 PM method has been recommended as a standard approach to estimate the reference evapotranspiration with relatively high accuracy. However, the FAO56 PM method is often challenging to apply because it requires considerable hydrometeorological variables. In this perspective, the Hargreaves equation has been widely adopted to estimate the reference evapotranspiration. In this study, a set of parameters of the Hargreaves equation was calibrated with relatively long-term data within a Bayesian framework. Statistical index (CC, RMSE, IoA) is used to validate the model. RMSE for monthly results reduced from 7.94 ~ 24.91 mm/month to 7.94 ~ 24.91 mm/month for the validation period. The results confirmed that the accuracy was significantly improved compared to the existing Hargreaves equation. Further, the evaporative demand drought index (EDDI) based on the evaporative demand (E₀) was proposed. To confirm the effectiveness of the EDDI, this study evaluated the estimated EDDI for the recent drought events from 2014 to 2015 and 2018, along with precipitation and SPI. As a result of the evaluation of the Han-river watershed in 2018, the weekly EDDI increased to more than 2 and it was confirmed that EDDI more effectively detects the onset of drought caused by heatwaves. EDDI can be used as a drought index, particularly for heatwave-driven flash drought monitoring and along with SPI.

• Informatization Policy
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• v.28 no.4
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• pp.76-92
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• 2021

The value of personal information is increasing with the digital transformation of the 4th Industrial Revolution. The purpose of this study is to analyze the efficiency of personal information protection efforts of 2,000 private companies. It uses a stochastic frontier approach (SFA), a parametric estimation method that measures the absolute efficiency of protective activities. In particular, the personal information activity index is used as an output variable for efficiency analysis, with the personal information protection budget and number of personnel utilized as input variables. As a result of the analysis, efficiency is found to range from a minimum of 0.466 to a maximum of 0.949, and overall average efficiency is 0.818 (81.8%). The main causes of inefficiency include non-fulfillment of personal information management measures, lack of system for promoting personal information protection education, and non-fulfillment of obligations related to CCTV. Policy support is needed to implement safety measures and perform personal information encryption, especially customized support for small and medium-sized enterprises.

• Land and Housing Review
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• v.13 no.3
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• pp.125-140
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• 2022

Road Deposited Sediment (RDS) is the solids formed from the wear of road, wear of vehicles, exhausts, and the input of the emissions from various sources out of the roads. RDS is seriously polluted by organic matter, nutrients, and metals. RDS plays an important role as the sink and the transport medium of the associated pollutants because RDS can be carried to the adjacent water system via stormwater runoff. In this regard, the heavy metals in RDS were investigated based on the publications. The contents of the metals in RDS were highly variable. The concentration of Cr, Ni, Cu, Fe, Zn, As, Cd, and Pb in urban RDS in various regions was in a range of 3.16-3,410, 1.15-1,382, 20.2-9,069, 2,980-124,853, 81-2,550, 2.3-214, 0.19-21.3, and 15.21-1,125 mg/kg, respectively. The anthropogenic enrichment of the metals in RDS was confirmed by the high concentration of Cu, Zn, Cd, and Pb. The contents of the metals were higher in industrial and traffic areas than in residential areas, while they were generally increased with decreasing particle size. It is believed that this study's results would contribute to quantifying the metals' load via RDS and establishing control strategies.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• Journal of the Korean Wood Science and Technology
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• v.31 no.5
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• pp.72-79
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• 2003

In reliability based design(RBD) method, the distribution characteristics of mechanical properties of material are basic input variable. Therefore, distribution type and parameters of mechanical properties should be determined accurately. Until now, the properties were derived from tests with small, clear specimens. However, the test conditions should emulate as nearly as possible the way in which the timber would be used in practice and the test results should, as closely as possible, reflect the structural end use conditions to which the timber products would be subjected. In this study, structural timbers (38mm by 140mm, 3.0m long) were graded by visual assessment of growth characteristics and defects. And then bending tests were conducted on 498 structural size timbers. For each grade, the distribution type and the parameters of mechanical properties were determined for each grade. For the determination of best-fit distribution type, comparing of square error between distribution types and KS test were conducted. Best-fit distribution type of bending strength(MOR) is weibull distribution for all grade. In case of MOE, normal distribution is best-fit.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.135-140
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• 2022

In addition to the stack that directly generates electricity by the reaction of hydrogen and oxygen, the fuel cell power generation system has a reformer that generates hydrogen from various fuels such as methanol and natural gas. It also consists of a power converter that converts the DC voltage generated in the stack into a stable AC voltage. The fuel cell output of such a system is direct current, and in order to be used at home, an inverter device that converts it into alternating current through a power converter is required. In addition, a DC-DC step-up converter is used to boost the fuel cell voltage to about 30~70V, which is the inverter operating voltage, to about 380V. The DC-DC step-up converter is a DC voltage variable device that exists between the fuel cell output and the inverter. Accordingly, since a constant output voltage of the converter is generated in response to a change in the output voltage of the fuel cell, the inverter can receive constant power regardless of the voltage change of the fuel cell. Therefore, in this paper, we discuss the detailed hardware design of the full-bridge converter, which is the main power source of the inverter that receives the fuel cell output voltage (30~70V) as an input and is applied to the grid among the members of the fuel cell power generation system.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.135-140
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• 2022

A study to estimate the appropriate size for the quota of civil servants during the period of change of government is required. Therefore, in this study, we would like to introduce a study that uses DEA to estimate the appropriate size of public officials. The department of a public institution is DMU, and the number of employees in each department is applied as an input variable, and the number of electronic approval production documents and the number of electronic approval expenditures are applied as output variables. MaxDEA 8 was used as an analysis program for this purpose. As a result of the analysis, when the efficiency level was 1.00 (100%), 3 out of 14 departments showed the optimal level by satisfying the efficiency, and 10 of the remaining departments scored 0.50 (50%) with a score of 0.50 (50%), confirmed to be relatively inefficient. In other words, it was confirmed that most departments had inefficient surplus staff. As an additional analysis, we calculated the number of possible staff reductions using the efficiency level. Using this, it is expected that the field of manpower reduction can be discovered in advance through an analysis of manpower efficiency by department, and based on this, it can be used to relocate manpower by department according to future response strategies.