• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.384-391
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• 2023

Among the elderly, fatal injuries and deaths are significantly attributed to falls. Therefore, a pre-impact fall detection system is necessary for injury prevention. In this study, a robust threshold-based algorithm was proposed for pre-impact fall detection, reducing false positives in highly dynamic daily-living movements. The algorithm was validated using public datasets (KFall and FARSEEING) that include the real-world elderly fall. A 6-axis IMU sensor (Movella Dot, Movella, Netherlands) was attached to S2 of 20 healthy adults (aged 22.0±1.9years, height 164.9±5.9cm, weight 61.4±17.1kg) to measure 14 activities of daily living and 11 fall movements at a sampling frequency of 60Hz. A 5Hz low-pass filter was applied to the IMU data to remove high-frequency noise. Sum vector magnitude of acceleration and angular velocity, roll, pitch, and vertical velocity were extracted as feature vector. The proposed algorithm showed an accuracy 98.3%, a sensitivity 100%, a specificity 97.0%, and an average lead-time 311±99ms with our experimental data. When evaluated using the KFall public dataset, an accuracy in adult data improved to 99.5% compared to recent studies, and for the elderly data, a specificity of 100% was achieved. When evaluated using FARSEEING real-world elderly fall data without separate segmentation, it showed a sensitivity of 71.4% (5/7).

• Journal of the Korean Recycled Construction Resources Institute
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• v.12 no.2
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• pp.162-168
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• 2024

This study investigated the setting characteristics of cement paste with varying proportions of fly ash replacement using the electro-mechanical impedance (EMI) sensing technique. Cement paste samples were prepared with a water-to-binder ratio of 40 %, substituting fly ash for 10 %, 20 %, and 30 % of the cement weight. Piezoelectric (PZT) sensors were embedded in the center of each cement paste sample to continuously monitor the EMI signals. Vicat needle test and semi-adiabatic calorimetry test were conducted to validate the reliability of the EMI sensing technique in monitoring the setting of cement paste. Experimental results revealed notable changes in the magnitude and resonant frequency of the EMI resonant peaks during the setting time. It was confirmed that the setting times measured through the EMI sensing technique were correlated with those determined by the Vicat needle test and semi-adiabatic calorimetry test.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.

• Journal of Preventive Medicine and Public Health
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• v.25 no.2 s.38
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• pp.141-147
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• 1992

This study was carried out not only to determine blood alcohol levels by time but also to examine the changes of working ability and reaction speed after ingestion of alcohol. Fifteen healthy students aged from 21 to 27 volunteered as subjects for this study, Liguor (Sojoo) in concentration of 25% ethyl alcohol was administrated with the amount of 1ml of ethyl alcohol per kg of body weight to the subjects. The concentration of alcohol in the blood were determined by the 'Alcohol Sensor 100' at 5, 30, 60 and 90 minutes after the administration of alcohol. Also, the choice reactiontest, the eye-hand coordination test and kraepelin test were examined at the same time after checking of alcohol concentration in the blood. The results of this study can be summarized as follows. 1. Mean blood alcohol level changes resulting from administration of 1ml of ethyl alcohol per kg of body weight were $0.16%(160{\pm}57mg/100ml,\;0.10%(100{\pm}42mg/100ml),\;0.08%(80{\pm}36mg/100ml)\;and\;0.03%(30{\pm}24mg/100ml)$ at the 3, 30, 60 and 90 minutes after the administration respectively The peak in the concentration of blood alcohol was 5 miniutes after the ingestion according to alcohol examination by the respiration. 2. As for choice reaction test, reaction times became prolonged as blood alcohol levels increased. The reaction time showed a significant changes when the blood alcohol concentration reached 0.08% or more after alcohol ingestion. 3. In eye and hand coordination test, the accuracy of the performance became decreased as blood alcohol levels increased. The difference of accuracy of the test was significantly shown when alcohol levels in the blood reached 0.08% or more after alcohol intake. 4. As for kraepelin test, the abilities of calculation also became lowered as blood alcohol levels increased. The abilities of calculation differed signigicantly from control group when alchool levels of 0.08% and more.

• Journal of Mushroom
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• v.17 no.4
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• pp.211-217
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• 2019

Currently, cultivation of mushrooms using the Information and Communication Technology (ICT)-based smart farming technique is increasing rapidly. The main environmental factors for growth of mushrooms are temperature, humidity, carbon dioxide (CO₂), and light. Among all the mentioned factors, currently, only temperature has been maintained under automatic control. However, humidity and ventilation are controlled using a timer, based on technical experience.Therefore, in this study, a Pleurotus eryngii first-generation smart farm model was set up that can automatically control temperature, humidity, and ventilation. After installing the environmental control system and the monitoring device, the environmental condition of the mushroom cultivation room and the growth of the fruiting bodies were studied. The data thus obtained was compared to that obtained using the conventional cultivation method.In farm A, the temperature during the primordia formation stage was about 17℃, and was maintained at approximately 16℃ during the fruiting stage. The humidity was initially maintained at 95%, and the farm was not humidified after the primordia formation stage. There was no sensor for CO₂ management, and the system was ventilated as required by observing the shape of the pileus and the stipe. It was observed that, the concentration of CO₂ was between 700 and 2,500 ppm during the growth period. The average weight of the mushrooms produced in farm A was 125 g, and the quality was between that of the premium and the first grade.In farm B. The CO₂ sensor was in use for measurement purposes only; the system was ventilated as required by observing the shape of the pileus and the stipe. During the growth period, the CO₂ concentration was observed to be between 640 and 4,500 ppm. The average weight of the mushrooms produced in farm B was 102 g.These results indicate that the quality of the king oyster mushroom is determined by the environmental conditions, especially by the concentration of CO₂. Thus, the data obtained in this study can be used as an optimal smart farm model, where, by improving the environmental control method of farm A, better quality mushrooms were obtained.

• Economic and Environmental Geology
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• v.54 no.3
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• pp.399-408
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• 2021

Natural soil was artificially contaminated with heavy metals (Cd, Pb, and Zn), and the movement of earthworm was characterized in real time using the ViSSET system composed of vibration sensor and the other components. The manifestation mechanism of ecological toxicity of heavy metals was interpreted based on the accumulative frequency of earthworm movement obtained from the real-time monitoring as well as the conventional indices of earthworm behavior, such as the change in body weight before and after tests and biocumulative concentrations of each contaminant. The results showed the difference in the earthworm movement according to the species of heavy metal contaminants. In the case of Cd, the earthworm movement was decreased with increasing its concentration and then tended to be increased. The activity of earthworm was severely increased with increasing Pb concentration, but the movement of earthworm was gradually decreased with increasing Zn concentration. The body weight of earthworm was proved to be greatly decreased in the Zn-contaminated soil, but it was similarly decreased in Cd- and Pb-contaminated soils. The bioaccumulation factor (BAF) was higher in the sequence of Cd > Zn > Pb, and particularly the biocumulative concentration of Pb did not show a clear tendency according to the Pb concentrations in soil. It was speculated that Cd is accumulated as a metallothionein-bound form in the interior of earthworm for a long time. In particular, Cd has a bad influence on the earthworm through the critical effect at its higher concentrations. Pb was likely to reveal its ecotoxicity via skin irritation or injury of sensory organs rather than ingestion pathway. The ecotoxicity of Zn seemed to be manifested by damaging the cell membranes of digestive organs or inordinately activating metabolism. Based on the results of real-time monitoring of earthworm movement, the half maximal effective concentration (EC50) of Pb was estimated to be 751.2 mg/kg, and it was similar to previously-reported ones. The study confirmed that if the conventional indices of earthworm behavior are combined with the results of newly-proposed method, the mechanism of toxicity manifestation of heavy metal contaminants in soils is more clearly interpreted.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.954-961
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• 2010

Ground-based remote sensing can be used as one of the non-destructive, fast, and real-time diagnostic tools for quantifying yield, biomass, and nitrogen (N) stress during growing season. This study was conducted to assess biomass and nitrogen (N) status of paddy rice (Oryza sativa L.) plants under N stress using passive and active ground-based remote sensors. Nitrogen application rates were 0, 70, 100, and 130 kg N $ha^{-1}$. At each growth stage, reflectance indices measured with active sensor showed higher correlation with DW, N uptake and N concentration than those with the passive sensor. NIR/Red and NIR/Amber indices measured with Crop Circle active sensors generally had a better correlation with dry weight (DW), N uptake and N content than vegetation indices from Crop Circle passive sensor and NDVIs from active sensors. Especially NIR/Red and NIR/amber ratios at the panicle initiation stage were most closely correlated with DW, N content, and N uptake. Rice grain yield, DW, N content and N uptake at harvest were highly positively correlated with canopy reflectance indices measured with active sensors at all sampling dates. N application rate explains about 91~92% of the variability in the SI calculated from NIR/Red or NIR/Amber indices measured with Crop Circle active sensors on 12 July. Therefore, the in-season sufficiency index (SI) by NIR/Red or NIR/Amber index from Crop Circle active sensors can be used for determination of N application rate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• Journal of Mushroom
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• v.16 no.3
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• pp.155-161
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• 2018

In this study, we analyze the growth environment using smart farm technology in order to develop the optimal growth model for the precision cultivation of the bottle-grown oyster mushroom 'Suhan'. Experimental farmers used $88m^2$ of bed area, 2 rows and 5 columns of shelf shape, 5 hp refrigerator, 100T of sandwich panel for insulation, 2 ultrasonic humidifiers, 12 kW of heating, and 5,000 bottles for cultivation. Data on parameters such as temperature, humidity, carbon dioxide concentration, and illumination, which directly affect mushroom growth, were collected from the environmental sensor part installed at the oyster mushroom cultivator and analyzed. It was found that the initial temperature at the time of granulation was $22^{\circ}C$ after the scraping, and the mushroom was produced and maintained at about $25^{\circ}C$ until the bottle was flipped. On fruiting body formation, mushrooms were harvested while maintaining the temperature between $13^{\circ}C$ and $15^{\circ}C$. Humidity was approximately 100% throughout the growth stage. Carbon dioxide concentration gradually increased until 3 days after the beginning of cultivation, and then increased rapidly to approximately 2,600 ppm. From the 6th day, $CO_2$ concentration was gradually decreased through ventilation and maintained at 1,000 ppm during the harvest. Light was not provided at the initial stage of oyster mushroom cultivation. On the $3^{rd}$ and $4^{th}$ day, mushrooms were irradiated by 17 lux light. Subsequently, the light intensity was increased to 115-120 lux as the growth progressed. Fruiting body characteristics of 'Suhan' cultivated in a farmhouse were as follows: Pileus diameter was 30.9 mm and thickness was 4.5 mm; stipe thickness was 11.0 mm and length was 76.0 mm; stipe and pileus hardness was 0.8 g/mm and 2.8 g/mm, respectively; L values of the stipe and pileus were 79.9 and 52.3, respectively. The fruiting body yield was 160.2 g/850 ml, and the individual weight was 12.8 g/10 unit.

• Journal of Mushroom
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• v.17 no.3
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• pp.119-125
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• 2019

This study aims to report the results for the analysis of the growth environment by applying smart farm technology to "Chunchu No 2" farmers in order to develop an optimal growth model for precision cultivation of bottle-grown oyster mushrooms. The temperature, humidity, carbon dioxide concentration, and illumination data were collected and analyzed using an environmental sensor installed to obtain growth environment data from the oyster mushroom cultivator. Analysis of the collected temperature data revealed that the temperature at the time of granulation was $19.5^{\circ}C$ after scraping, and the mushroom was generated and maintained at about $21^{\circ}C$ until the bottle was flipped. When the fruiting body grew and approached harvest time, mushrooms were harvested while maintaining the temperature between $14^{\circ}C$ and $18^{\circ}C$. The humidity was maintained at almost 100% during the complete growth stage. Carbon dioxide concentration gradually increased until 3 days after the beginning of cultivation, and then increased rapidly to almost 5,500 ppm. From the 6th day, carbon dioxide concentration was gradually decreased through ventilation and was maintained at 1,600 ppm during harvest. Light intensity of 8 lux was irradiated up to day 6 after seeding, and growth was then continued while periodically irradiating 4 lux light. The fruiting body characteristics of "Chunchu No 2" cultivated in the farmhouse were as follows: pileus diameter of 26.5 mm and thickness of 4.9 mm, stipe thickness of 8.9 mm, and length of 68.7 mm. The fruiting body yield was 166.8 g/850 ml, and the individual weight was 12.8 g/10 units.