• Safety and Health at Work
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• v.10 no.3
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• pp.321-326
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• 2019

Background: Performing cognitive tasks and muscular fatigue have been shown to increase muscle activity of the lower extremity during quiet standing. A common intervention to reduce muscular fatigue is to provide a softer shoe-surface interface. However, little is known regarding how muscle activity is affected by softer shoe-surface interfaces during static standing. The purpose of this study was to assess lower extremity muscular activity during erect standing on three different standing surfaces, before and after an acute workload and during cognitive tasks. Methods: Surface electromyography was collected on ankle dorsiflexors and plantarflexors, and knee flexors and extensors of fifteen male participants. Dependent electromyography variables of mean, peak, root mean square, and cocontraction index were calculated and analyzed with a $2{\times}2{\times}3$ within-subject repeated measures analysis of variance. Results: Pre-workload muscle activity did not differ between surfaces and cognitive task conditions. However, greater muscle activity during post-workload balance assessment was found, specifically during the cognitive task. Cognitive task errors did not differ between surface and workload. Conclusions: The cognitive task after workload increased lower extremity muscular activity compared to quite standing, irrespective of the surface condition, suggesting an increased demand was placed on the postural control system as the result of both fatigue and cognitive task.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.61-73
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• 2021

Recently, the deterioration of water quality in Wonju stream has been reported due to the increase in diverse pollution sources along with community development and urbanization. Various types of attempts with a huge budget were made for better water quality so far, but its effectiveness is still doubted. In order to establish site-oriented measures for water quality improvement, the topographic and hydrologic factors were evaluated based on site inspection and survey. As the major streams merged into the Wonju stream, the Hwa and Heungyang streams were found to have higher pollution loads and contributions compared to other streams due to the scattered livestock farms and industries, and vulnerable land use. Notably, the discharge water from the Wonju Public Sewage Treatment Plant had the highest level of pollution load, impacting on the water quality of Wonju Stream. According to the SWAT model as water quality measures, the improvement effect of water quality in this treatment plant can be reached to the reductions in BOD 11.06%, T-N 23.56%, T-P 10.60% when the proper managements applied, whereas the improvement of water quality would be 3.89%, 1.23%, and 3.32% for BOD, T-N, T-P, respectively, for the industries. The reduction of the livestock industry was generally very high as a pollution source, but it was not much higher at the end of Wonju Stream than other measures. These results recommended that the water q uality improvement measures should be designated for each upper-middle-lower section in Wonju stream.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.89-102
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• 2021

Soils in agricultural lands contain large amount of organic matter and nutrients due to the injected fertilizers and manure. During heavy rain, surface water and base runoff pollutants flows into a nearby stream or lake with eroded soil from agricultural lands. On the other hands, agricultural lands near the lake are inundated due to the increase of the water level in the lake, leading to organic matter and nutrient release from the inundated soil. In this study, releasing rates of nutrient salts and organic substances were analyzed for the soil in the agricultural land, where cultivation activities has been carried out and periodically flooded, to account for the possibility of contamination from the inundated agricultural land in reservoir areas The experiment results have shown that COD was released from the soil in anaerobic conditions, and T-P was released in both anaerobic and aerobic conditions. However, in the case of T-N, it was found that the runoff by soil was not made before the rainfall occurred, and when the soil was impound due to rainfall, the elution occurred under the aerobic conditions. Through the results of this study, it was possible to account for the effect of flooded agricultural lands on the water quality in the lake, and this could be reflected in an efficient agricultural non-point pollution management policy. In order to determine the precise releasing rate for each agricultural land, it is believed that the leaching experiment for paddy fields and grasslands are needed.

• Journal of Hydrogen and New Energy
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• v.33 no.3
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• pp.209-218
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• 2022

Recently, research on the development of anion exchange membranes (AEMs) has received considerable attention from the scientific community around the world. Here, we fabricated a series of AEMs with branched structures with different alkyl spacers and conducted comparative evaluations. The introduction of these branched structures is an attempt to overcome the low ionic conductivity and stability problems that AEMs are currently facing. To this end, branched polymers with different spacer lengths were synthesized and properties of each membrane prepared according to the branched structure were compared. The chemical structure of the polymer was investigated by proton nuclear magnetic resonance, Fourier transform infrared, and gel permeation chromatography, and the thermal properties were investigated using thermogravimetric analysis. The branched anion exchange membrane with (CH₂)₃ and (CH₂)₆ spacers exhibited ionic conductivities of 8.9 mS cm^-1 and 22 mS cm^-1 at 90℃, respectively. This means that the length of the spacer affects the ionic conductivity. Therefore, this study showing the effect of the spacer length on the ionic conductivity of the membrane in the polymer structure constituting the ion exchange membrane is judged to be very useful for future application studies of AEM fuel cells.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.179-184
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• 2022

As public data opened its doors in line with the era of the 4th Industrial Revolution, agricultural public data also increased. Currently, the majority of farmers are writing farming diaries due to eco-friendly certification and basic public interest direct payment projects. However, it is a difficult task for busy farmhouses in the aging agricultural community to write farming diaries. Therefore, there have been cases where farming diaries have been filled out on behalf of the farmhouses. However, one may get disadvantaged in terms of receiving eco-friendly certification and public interest direct payment projects. In succession, this paper proposes a system to conveniently write farming diaries by checking the farming diary data stored in the server via categories of crops and dates and finding farming diary public data suitable for the user to automatically fill out the diary.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.59-67
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• 2022

The purpose of this study is to provide essential data necessary to assess ecological flow requirements by understanding habitat conditions for fish species through monitoring an ecological environment in the Korea stream (Dal Stream) and building related database. On-site surveys were conducted for identifying ecological and habitat conditions at the four monitoring sites. Fish sampling was carried out at the selected four sites (St.) during the period ranging from September, 2008 to September, 2009. At the four sampling sites, we measured water surface elevation, depth and velocity at the cross-sections. Optimal Ecological Flowrates (OEFs) were estimated using the Habitat Suitability Index (HSI) established for four fish species Zacco koreanus (St.1), Pungtungia herzi (St.2), Coreoleuciscus splendidus (St.3), and Zacco platypus (St.4) selected as icon species using the Physical HABitat SIMulation system (PHABSIM). Eighteen species (56.3%) including Odontobutis interrupta, Coreoperca herzi and C. splendidus were found endemic out of the 32 species in eight families sampled during this study period. The endangered species was collected Acheilognathus signifier, Pseudopungtungia tenuicorpa and Gobiobotia macrocephala, and this relative abundance was 9.4%. The most frequently found one was Z. platypus (31.3%) followed by C. splendidus (17.6%) and Z. koreanus (15.7%). The estimated IBI values ranged from 27.3 to 34.3 with average being 30.3 out of 50, rendering the site ecologically poor to fair health conditions. For C. splendidus (St.3), the dominant fish species in the stream, the favored habitat conditions were estimated to be 0.3-0.5 m for water depth, 0.4-0.7 m/s for flow velocity and sand-cobbles for substrate size, respectively. An OEFs of 8.5 m³/s was recommended for the representative fish species at the St.3.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.3
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• pp.87-95
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• 2021

The Doam Lake Watershed is one of Gangwon-do's non-point source management areas. This area has a lot of snowfall in winter, and it is expected that there will be a lot of soil erosion in early spring due to snow melting. In this study, snow melting was monitored in the Doam Lake watershed from February to 3, 2020. It was conducted to analyze the water quality changes by calculating the concentration of non-point source pollution caused by snowmelt, and to compare the concentration of water quality during snowmelt event with rainfall and non-rainfall event. As a result of water quality analysis, Event Mean Concentration (EMC) at the first monitoring was SS 33.9 mg/L, TP 0.13 mg/L, TN 4.33 mg/L, BOD 1.35 mg/L, TOC 1.84 mg/L. At the second monitoring, EMC were SS 81.3 mg/L, TP 0.15 mg/L, TN 3.12 mg/L, BOD 1.32 mg/L, TOC 3.46 mg/L. In parameter except SS, it showed good water quality. It is necessary to establish management measures through continuous monitoring.