• Education of Primary School Mathematics
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• v.26 no.2
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• pp.99-113
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• 2023

In this study, I explored the possibility of using ChatGPT math education. For this purpose, students' problem-solving outputs and conversation data between pre-service teachers and a student were selected as an analysis case. A case was analyzed using ChatGPT and compared with the results of mathematics education experts. The results that ChatGPT analyzed students' problem-solving strategies and mathematical thinking skills were similar to those of math education experts. ChatGPT was able to analyze teacher questions with evaluation criteria, and the results were similar to those of math education experts. ChatGPT could also respond with mathematical theory as a source of evaluation criteria. These results demonstrate the potential of ChatGPT to analyze students' thinking and teachers' practice in mathematics education. However, there are limitations in properly applying the evaluation criteria or providing inaccurate information, so the further review of the derived information is required.

• Journal of Fashion Business
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• v.26 no.1
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• pp.1-15
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• 2022

Digital transformation has induced changes in human life patterns; consumption patterns are also changing to digitalization. Entering the era of industry 4.0 with the 4th industrial revolution, it is important to pay attention to a new paradigm in the fashion industry, the shift from developer-centered to user-centered in the era of the 3rd industrial revolution. The meaning of storing users' changing life and consumption patterns and analyzing stored big data are linked to consumer sentiment. It is more valuable to read emotions, then develop and distribute products based on them, rather than developer-centered processes that previously started in the fashion market. An AI(Artificial Intelligence) deep learning algorithm that analyzes user emotion big data from user experience(UX) to emotion and uses the analyzed data as a source has become possible. By combining AI technology, the fashion industry can develop various new products and technologies that meet the functional and emotional aspects required by consumers and expect a sustainable user experience structure. This study analyzes clear and useful user experience in the fashion industry to derive the characteristics of AI algorithms that combine emotions and technologies reflecting users' needs and proposes methods that can be used in the fashion industry. The purpose of the study is to utilize information analysis using big data and AI algorithms so that structures that can interact with users and developers can lead to a sustainable ecosystem. Ultimately, it is meaningful to identify the direction of the optimized fashion industry through user experienced emotional fashion technology algorithms.

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.131-139
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• 2024

This study investigated the chemical properties of Organic Soil Amendments (OSAs) made from organic waste. It also assessed the effectiveness of using these OSAs in the soil layer of Green Infrastructure (GI) to reduce stormwater runoff and non-point source pollutants. The goal was to improve the national environmental value through resource recycling and contribute to the circular economy transformation and carbon neutrality of urban GI. The OSAs used in this study consisted of spent coffee grounds and food waste compost. They were found to be nutrient-rich and stable as artificial soils, indicating their potential use in the soil layer of GI facilities. Applying OSAs to bio-retention cells and permeable pavement resulted in a reduction of approximately 11-17% in stormwater runoff and a decrease of about 16-18% in Total Phosphorus (TP) discharge in the target area. Increasing the proportion of food waste compost in the OSAs had a positive impact on reducing stormwater runoff and pollutant emissions. This study highlights the importance of utilizing recycled resources and can serve as a foundation for future research, such as establishing parameters for assessing the effectiveness of GI facilities through experiments. To enable more accurate analysis, it is recommended to conduct studies that consider both the chemical and biological aspects of substance transfer in OSAs.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.111-115
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• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.

• Korean Journal of Poultry Science
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• v.18 no.3
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• pp.151-159
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• 1991

As a part of investigation on causes of drop in egg production in domestic chicken breeding farm, bacteriological contamination on air, feed, drinking water and artificial insemination instruments of randomly selected three farms was surveyed. Total bacterial population in the air was very high in all of the chicken houses tested and was not significantly different among these farms . However, total bacterial counts in the air of the problem house having egg drop problem and colibacillosis was higher than normal house within the problem farm. Bacterial population in the assorted feed was low before or after administration on the normal farm while it was much more increased after administration than before administration on the problem farm. Bacterial population of the drinking water in the source of water supply was very low and has no differences among farms tested. Also, bacterial population in the normal farm was not significantly different between source of water supply and after administration. However, population of total bacteria and coliform bacteria after administration was increased. Bacterial population was much higher in the artificial insemination instrument of problem farm than normal farm. However, this bacterial population in the problem farm was decreased to those of normal farm after these instruments were sanitized.

• Korean Journal of Environmental Agriculture
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• v.34 no.1
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• pp.38-45
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• 2015

BACKGROUND: For commercial production of greenhouse crops under shorter day length condition, supplementary radiation has been usually achieved by the artificial light source with higher electric consumption such as high-pressure sodium, metal halide, or incandescent lamps. Light-Emitting Diodes (LEDs) with several characteristics, however, have been considered as a novel light source for plant production. Effects of supplementary lighting provided by the artificial light sources on growth of Kale seedlings during shorter day length were discussed in this experiment. METHODS AND RESULTS: Kale seedlings were grown under greenhouse under the three wave lamps (3 W), sodium lamps (Na), and red LEDs (peak at 630 nm) during six months, and leaf growth was observed at intervals of about 30 days after light exposure for 6 hours per day at sunrise and sunset. Photosynthetic photon flux (PPF) of supplementary red LEDs on the plant canopy was maintained at 0.1 (RL), 0.6 (RM), and $1.2(RH){\mu}mol/m^2/s$ PPF. PPF in 3 W and Na treatments was measured at $12{\mu}mol/m^2/s$. Natural light (NL) was considered as a control. Leaf fresh weight of the seedlings was more than 100% increased under the 3 W, Na and RH treatment compared to natural light considering as a conventional condition. Sugar synthesis in Kale leaves was significantly promoted by the RM or RH treatment. Leaf yield per $3.3m^2$ exposed by red LEDs of $1.2{\mu}mol/m^2/s$ PPF was 9% and 16% greater than in 3W or Na with a higher PPF, respectively. CONCLUSION: Growth of the leafy Kale seedlings were significantly affected by the supplementary radiation provided by three wave lamp, sodium lamp, and red LEDs with different light intensities during the shorter day length under greenhouse conditions. From this study, it was suggested that the leaf growth and secondary metabolism of Kale seedlings can be controlled by supplementary radiation using red LEDs of $1.2{\mu}mol/m^2/s$ PPF as well as three wave or sodium lamps in the experiment.

• Journal of Bio-Environment Control
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• v.22 no.4
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• pp.392-399
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• 2013

This study aimed to investigate responses of photosynthesis, plant growth, and phytochemical contents to different artificial light sources for 'Seneca RZ' and 'Gaugin RZ' two butterhead lettuce (Lactuca sativa L.). In this study, fluorescent lamps (FL), three colors LEDs (red, blue and white, 5 : 4 : 1; RBW) and metalhalide lamps (MH) were used as artificial lighting sources. Photoperiod, air temperature, relative humidity, EC, and pH in a cultivation system were maintained at 16/8 h, $25/15^{\circ}C$, 60~70%, $1.4{\pm}0.2dS{\cdot}m^{-1}$, and $6.0{\pm}0.5$, respectively. The photosynthetic rate of both two butterhead lettuce were the highest under RBW in middle growth stage. However, in late growth stage, the photosynthetic rate of both two butterhead lettuce were higher under RBW and MH than FL. The light sources showed significant results for plant growth but those effects were different to variety. Fresh and dry weight of 'Gaugin RZ' butterhead lettuce under MH were heavier than other lights in all growth stages. Growth of 'Seneca RZ' butterhead lettuce was maximized highest under MH in middle growth stage and FL in late growth stage. In the leaf tissue of 'Seneca RZ' butterhead lettuce, tipburn symptom occurred under all light sources and in the leaf tissue of 'Gaugin RZ' butterhead lettuce, it occurred under two light sources except for fluorescent lamps in late growth stage. kinds of lamp affect plant growth more than plant quality. Relative growth rate of both two butterhead lettuce was faster in middle growth stage than late stage. Growth of 'Gaugin RZ' was shown by kinds of lamp in middle growth stage and but it was not significantly affected by light sources and variety in late stage. Most of the phytochemical contents of two butterhead lettuce were significantly affected by different light sources. Contents of all vitamins showed higher than other light sources on RBW for both two lettuce, especially ${\beta}$-Carotene content of 'Gaugin RZ' was the highest. Plant growth, photosynthesis, and phytochemical contents were observed significant effects by different light sources for two butterhead lettuce but those effects were highly different between variety and kinds of phytochemicals. Therefore, the selection of optimum light source should be considered by variety and kinds of phytochemicals in the plant factory.

• Journal of Korean Society of Forest Science
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• v.64 no.1
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• pp.33-41
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• 1984

This study was conducted to examine the physiology of pine mushroom mycelia cultured with various media for artificial culture of pine mushroom. The results obtained were as follows: 1) Among the various media, the medium composed of honey, boiled pine mushroom and soil extract fluid, fibrous root extract fluid, dry yeast, $KH_2PO_4$ inositol, folic acid, and biotin was the best for the growth of pine mushroom mycelium. 2) The optimum temperature for germinating pine mushroom spore and for culturing pine mushroom mycelium, was $24^{\circ}C$ and the optimum pH was 4.5. 3) There was no significant difference in growth between the mycelium separated from the tissue of pine mushroom sporophore and that separated from the spore. 4) No noticeable effect was found on the growth if such salts as $ZnSO_4$, $MnSO_4$, $MgSO_4$, $CaCl_2$ and ferric citrate were added to the Hamada's medium. 5) The addition of fibrous root extract promoted the growth of pine mushroom mycelium. 6) As a carbon source of artificial media, honey was more effective than glucose. 7) The culture infiltration of Mortierlla growing often in Fairy Ring was good for the growth of mycelium compared with the control. 8) The addition of fibrous root extract, inositol, biotin, and folic acid to artificial culture media was greatly effective in growth. When the temperature was lowered $19^{\circ}C$ after mycelium has appeared, the formation of primordium was observed.

• Analytical Science and Technology
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• v.28 no.6
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• pp.377-384
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• 2015

A survey of the artificial radionuclides in rivers and lakes was conducted to investigate their levels in surface water. Water samples were collected at 60 points and analyzed by gamma-ray spectrometry with a measurement time of 10,000 seconds for ¹³⁴Cs, ¹³⁷Cs, and ¹³¹I. The obained values were lower than MDA for all points, except one point for ¹³¹I that was 0.533±0.058 Bq/L. ¹³¹I is known as a radioactive material that occurs frequently in sewage treatment plants. Because it is often used for medical treatments and subject to spreading into the environment due to the excretion from the patients. For the point where ¹³¹I was detected, we conducted additional investigation on the upstream river point and the effluent points of nearby sewage treatment plant to find the source of ¹³¹I. ¹³¹I was not detected at the upstream points of one of the upstream sewage treatment plants but found at the downstream points with the level being 0.257±0.034 to 0.799±0.051 Bq/L, proving the sewage treatment plant was the ¹³¹Isource.