• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.174-182
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• 2020

The permissible limit of ammonia concentration in drinking water recommended by the World Health Organization (WHO) is 1.5 mg/L. However, in the case of groundwater in Kathmandu, Nepal, the concentration of ammonia fluctuates dramatically from 0 to 120 mg/L at different locations and groundwater depths (Chapagain et al., 2010). Such a high concentration of ammonia causes aesthetic problems in drinking water, such as bad taste and odor; hence, prior treatment is required. In Kathmandu, half of the population utilizes groundwater, which is also employed for drinking water, but owing to a lack of knowledge of household water filters, residents of Kathmandu tend to depend greatly on commercially available jar water than on the installation of a proper household filtration method. Thus, in our study, we employed adsorption and reverse osmosis (RO) as two of the most viable decentralized/household treatment options to address the issue of high contamination of ammonia in drinking water. We evaluated their performances from technical and the economic perspectives using synthetically prepared groundwater at varying ammonia concentrations (50 mg/L and 15 mg/L). Consequently, it was found that adsorption via ion exchange (IE) resin was a comparatively better ammonia removal technology than RO, with 100% ammonia removal even after regeneration; the removal by RO was limited to up to 90%. Furthermore, our study suggests that IE is the most suitable ammonia removal technology for places with lower water consumption (< 50 L/day), whereas RO seemed to be a cost-effective technology for places with higher water consumption, where the daily water demand exceeds 50 L/day. Lastly, these assessments suggest that installing a suitable household treatment system would be more efficient and sustainable from both technical and economic points of view than purchasing commercially bottled water.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.59-68
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• 1998

Digital hearing aids offer many advantages over conventional analog hearing aids. With the advent of high speed digital signal processing chips, new digital techniques have been introduced to digital hearing aids. In addition, the evaluation of new ideas in hearing aids is necessarily accompanied by intensive subject-based clinical tests which requires much time and cost. In this paper, we present an objective method to evaluate and predict the performance of hearing aid systems without the help of such subject-based tests. In the hearing impairment simulation(HIS) algorithm, a sensorineural hearing impairment medel is established from auditory test data of the impaired subject being simulated. Also, the nonlinear behavior of the loudness recruitment is defined using hearing loss functions generated from the measurements. To transform the natural input sound into the impaired one, a frequency sampling filter is designed. The filter is continuously refreshed with the level-dependent frequency response function provided by the impairment model. To assess the performance, the HIS algorithm was implemented in real-time using a floating-point DSP. Signals processed with the real-time system were presented to normal subjects and their auditory data modified by the system was measured. The sensorineural hearing impairment was simulated and tested. The threshold of hearing and the speech discrimination tests exhibited the efficiency of the system in its use for the hearing impairment simulation. Using the HIS system we evaluated three typical hearing aid algorithms.

• Journal of IKEEE
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• v.15 no.2
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• pp.179-189
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• 2011

This paper presents an SVC based multi-channel transmission technique. Transmission of high definition multimedia and its service efficiency can be significantly improved by the proposed method. In this method, the HD stream is divided into the two layer streams, i.e., a base layer stream and an enhancement layer stream. The divided streams are transmitted through a primary channel and an auxiliary channel, respectively. The proposed technique provides a noble mode switching technique which enables a seamless service of HD multimedia even under the conditions of abrupt and intermittent deterioration of the auxiliary channel. When the enhancement layer stream is disrupted by the channel monitoring in the mode switching algorithm, the algorithm works further to maintain the spatial and time resolution of the HD multimedia by upsampling and interpolating the base layer stream, consequently serving for the non disrupted play of the media. Moreover, the adoption of an adaptive switching algorithm significantly reduces the frequency of channel disruption avoiding the unnecessary switching for the short period variations of the channel. The feasibility of the proposed technique is verified through the simulation study with an example application to the simultaneous utilization of both Ku and Ka bands for HD multimedia broadcasting service. The rainfall modeling and the analysis of the satellite channel attenuation characteristics are performed to simulate the quality of service performance of the proposed HD broadcasting method. The simulation results obtained under a relatively poor channel (weather) situations show that the average lasting period of enhancement layer service is extended from 9.48[min] to 23.12[min] and the average switching frequency is reduced from 3.84[times/hour] to 1.68[times/hour]. It is verified in the satellite example that the proposed SVC based transmission technique best utilizes the Ka band channel for the service of HD broadcasting, although it is characterized by its inherent weather related poor reliability causing severe limitations in its independent application.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.455-464
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• 2014

After launching the Geostationary Ocean Color Imager (GOCI) on June 2010, field campaigns were performed routinely around Korean peninsula to collect in-situ data for calibration and validation. Key measurements in the campaigns are radiometric ones with field radiometers such as Analytical Spectral Devices FieldSpec3 or TriOS RAMSES. The field radiometers must be regularly calibrated. We, in the paper, introduce the optical laboratory built in KOSC and the relative calibration method for in-situ measurement spectroradiometer. The laboratory is equipped with a 20-inch integrating sphere (USS-2000S, LabSphere) in 98% uniformity, a reference spectrometer (MCPD9800, Photal) covering wavelengths from 360 nm to 1100 nm with 1.6 nm spectral resolution, and an optical table ($3600{\times}1500{\times}800mm^3$) having a flatness of ${\pm}0.1mm$. Under constant temperature and humidity maintainance in the room, the reference spectrometer and the in-situ measurement instrument are checked with the same light source in the same distance. From the test of FieldSpec3, we figured out a slight difference among in-situ instruments in blue band range, and also confirmed the sensor spectral performance was changed about 4.41% during 1 year. These results show that the regular calibrations are needed to maintain the field measurement accuracy and thus GOCI data reliability.

• Journal of the Korean Applied Science and Technology
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• v.35 no.1
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• pp.235-246
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• 2018

Paeony has pharmacological activities such as anti-inflammatory, anti-allergic, anti-bacterial, central inhibitory, gastric secretion inhibition, and antispasmodic activities. In addition, its antioxidant activity and whitening effect being reported, thus it is being explored as raw materials for cosmetics. We compared the changes in the contents of paeoniflorin and paeonol in Peony extracts, depending on the changes of extracting solvents, temperature and time. The HPLC method was set up for simultaneous analysis, the system suitabilities were confirmed by using the calibration curves and the QC samples for each assay batch. Paeonol was detected only in roots, and paeoniflorin was higher in leaf and flower than root. Higher concentrations of both ingredients were extracted when the root was used after grinding to a suitable size, and when 30% 1,3-butylene glycol was used as the extraction solvent. Also the concentrations tended to increase at higher temperature and longer time, but the increase was gradual at over $75^{\circ}C$ and 4 hours. The ratio of root, leaf and flower was determined to be 2+2+1g/0.5kg of batch, reaching the contents criteria of paeoniflorin and paeonol. Finally, we selected as the best extraction condition when the raw materials are mixed with 2+2+1g/0.5kg and extracted with 30% 1,3-butylene glycol as an extraction solvent at $75^{\circ}C$ for 4 hours, considering both the concentrations of two components and the cost of raw materials and manufacturing process, The extraction units were scaled up to 10 kg under this condition.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Korean Journal of Environmental Agriculture
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• v.37 no.1
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• pp.49-56
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• 2018

BACKGROUND: The mobile vortex wet scrubber was developed to remove the harmful chemicals from accidental releases. However, there was a disadvantage that it was limitedly used for volatile organic compounds (VOCs) such as toluene according to the physicochemical properties. This study compared the removal efficiencies of an improved mobile scrubber on toluene and ammonia by applying diverse adsorption and absorption methods. METHODS AND RESULTS: The removal efficiencies on harmful chemicals were examined using various adsorption and absorption methods of water vortex process (C), phosphoric acid-impregnated activated carbon adsorption (PCA), pH-controlled water (pH 2.5) vortex process absorption with sulfuric acid (SWA) after ammonia exposure, granular activated carbon adsorption (GCA), and activated carbon mat adsorption (CMA) after toluene exposure. As a result, the best removal efficiency was shown in the SWA for ammonia and GCA for toluene. Also, the SWA and GCA methods were compared with different concentration levels. In the case of ammonia exposure (5, 10 and 25%), there was no difference by concentration levels, and the concentration in the outlet gradually increased, with pH change from acid to base. In the case of toluene exposure (50, 75 and 100%), the outlet concentration was higher relative to the exposure concentration in the initial 10 min, but the outlet concentration was remained steady after 10 min. CONCLUSION: The newly improved mobile scrubber was also effective in removing VOCs through adsorption techniques (activated carbon, activated carbon fiber, carbon mat filter etc.), as well as removing acid-base harmful chemicals by neutralization reaction.

• Journal of the Korean Society of Food Science and Nutrition
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• v.32 no.2
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• pp.295-301
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• 2003

This study was carried out to investigate perception about body image, dietary attitude, nutrient intake and dietary self-efficacy of high school students in Pusan. A questionaire survey was distributed among 491 high school students. The survey was conducted from April 8 to April 22 in 2002. The results are summarized as follows. Forty percents of the underweight group, 53.9% of the normal weight group, 61.8% of the overweight group and 48.2% of obesity group have correct perception about their body image. Most of the students were concerned with their body image and weight control. Obesity of parents was significantly correlated with obesity of the subjects (p<0.01). The higher obesity rate, the lower dietary self-efficacy. The higher dietary self-efficacy, the higher dietary attitude. There was a significant positive correlation between the education level of their parents and dietary attitude of the subjects (p<0.01) and a significant negative correlation between obesity rate of their mothers and dietary attitude of the subjects (p<0.01). Dietary attitude scores showed no significant difference among the groups. Intakes of most nutrients, except protein, niacin and vitamin C, were lower than those of the recommended dietary allowances for Koreans. Therefore, proper nutrition education is required to improve their nutritional status and dietary self-efficacy.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.