• The Journal of Korean Academy of Prosthodontics
• /
• v.61 no.3
• /
• pp.179-188
• /
• 2023

Purpose. The purpose of this study was to evaluate the effect of surface treatments on the shear bond strength of two types of zirconia (3-TZP and 5Y-PSZ) with resin cement. Materials and methods. Two different types of zirconia specimens with a fully sintered size of 14.0×14.0×2.0 mm³ were prepared, polished with 400, 600, and 800 grit silicon carbide paper, and buried in epoxy resin. They were classified into four groups each control, sandblasting, primer, and sandblasting & primer. Cylindrical resin adhered to the surface-treated zirconia with resin cement. It was stored in distilled water (37℃) for 24 hours, and a shear bond strength test was performed. The normality of the experimental group was confirmed with the Kolmogorov-Smirnov & Shapiro-Wilk test. The interaction and statistical difference were analyzed using a two-way ANOVA. A post-hoc analysis was performed using Dunnett T3. Results. As a result of two-way ANOVA, there was no significant difference in shear bonding strength between zirconia types (P > .05), but there was a significant correlation in the sandblasting, primer, and alumina sandblasting & primer group (P < .05). Dunnett T3 post-test showed that, regardless of the type of zirconia, shear bonding strength was sandblasting & primer > Primer > sandblasting > control group (P < .05). Conclusion. There was no difference in shear bond strength between the types of zirconia. The highest shear bond strength was shown when the mechanical and chemical treatments of the zirconia surface was performed simultaneously.

• Journal of Korean Philosophical Society
• /
• no.125
• /
• pp.63-89
• /
• 2019

The aim of this paper is to examine the so-called theory of automatic action attributed to Plotinus, according to which the sage can act automatically without deliberation or reasoning. Concerns were raised that such a theory runs the risk of turning the agent into an automaton by reducing action to mechanical reflexes to external stimuli. I attempt to show that Plotinus does not hold a theory of automatic action by arguing that the Plotinian sage's non-deliberative action is not automatic at all. For this purpose, I first draw attention to the non-deliberative action of the World-Reason (i.e. the reason of the World-Soul), which is supposed to present an ideal model of action. Indeed, Plotinus mentions that the World-Reason rules the world "as if automatically". This is, however, meant to indicate the spontaneous and natural manner in which the World-Reason rules. In this respect, the way the World-Reason works is compared to the way nature (i.e. the productive power of the World-Soul) works. But Plotinus points out that the World-Reason knows what to do, whereas nature works without knowing. In this connection, Plotinus makes it clear that the World-Reason does not calculate or deliberate about what to do because it already knows it. To clarify this point, I turn to Plotinus' analogy of practical wisdom (phronêsis) and skill, according to which the World-Reason is compared to an accomplished craftsman or artist, who confidently works without any doubt, hesitation or difficulty, thereby expressing her intelligence, unmediated by deliberation. From this perspective, non-deliberative action according to practical wisdom turns out to be superior to deliberative action. Plotinus admits that there are difficult circumstances in which even the skilled craftsman, unlike the World-Reason who always controls the whole situation, needs to deliberate or calculate, but he is nevertheless confident that the craftsman easily finds the solution. This suggests that the sage, who possesses practical wisdom, can act normally like a great master or virtuoso without deliberation, but in an emergency situation he also employs deliberation, but resourcefully and creatively responds to challenge. The attempt is made to elucidate the Plotinian model of sage's action with the help of Csikzentmihalyi's concept of 'flow' and Annas' application of it to the analogy of virtue and skill. Finally, it is shown that the sage's virtuous action, in spite of being a habituated action, is not a passive, routinized, automatic action, but an active, flexible, intelligent action.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.3 no.1
• /
• pp.142-157
• /
• 2001

This study was conducted to get the basic information for promoting farm machinery productivity by surveying the regular maintenance and repair status of major farm machinery such as power tiller, farm tractor, rice transplanter and combine harvester. The survey was carried out through 9 provinces including Cheju province by direct visiting farmers with prepared questionnaire. The results of this study can be summarized as follows : 1. The average farming carrier of the surveyed farmers was 25.3 years, and 21-30 years of farming carrier showed the highest portion as 40.7%. The average carrier of using farm machinery was 9.4 years, and that was 14.9 years for power tiller, 8.3 years for farm tractor, 9.0 years for rice transplanter, 7.9 years for combine harvester, 7.5 years for mini tiller, 9.7 years for power sprayer, and 8.2 years for binder etc. 2. The regular maintenance for farm machinery was conducted mainly at repair shop (49.5%) or dealer agency (12.0%) as 61.5%, and 34.9% of farmers conducted the regular maintenance by themselves at their house. 3. The reasons for not-fully recognizing operation manual and insufficient before-, during-, after-maintenance of farm machinery were insufficient time for them (45.8%), troublesome (22.9%), unknown maintenance method (16.3%), unknown the necessity for maintenance (12.4%), and others (2.6%) in order. 4. For the annual exchange of engine oil, 3.2 times is necessary but actually 1.7 times was exchanged for power tiller, 4.3 times is necessary but actually 1.9 times was exchanged for farm tractor, 2.7 times is necessary but actually 1.7 times was exchanged for rice transplanter, 2.2 times is necessary but actually 2.3 times was exchanged combine harvester. 5. For the annual cleanness or exchange of fuel filter, 3.2 times is necessary but actually 1.1 times was done for power tiller, 4.3 times is necessary but actually 1.6 times was done for farm tractor, 2.7 times is necessary but actually 1.7 times was done for rice transplanter, 1.9 times is necessary but actually 0.8 times was done for combine harvester. 6. For the annual cleanness or exchange of air filter, 3.2 times is necessary but actually 1.4 times was done for power tiller, 4.2 times is necessary but actually 2.4 times was done for farm tractor, 2.6 times is necessary but actually 1.6 times was done for rice transplanter, 3.9 times is necessary but actually 7.0 times was done for combine harvester. 7. For the experience of breakdown related to maintenance, 5.3% of farmers experienced breakdown due to the insufficient exchange of engine oil, 7.7% of farmers experienced breakdown due to the insufficient cleanness or exchange of fuel filter, and 2.9% of farmers experienced breakdown due to the insufficient cleanness or exchange of air filter. 8. Most farmers (76.1%) recognized the necessity for agricultural machinery training or education, and most farmers preferred about one week for the training period, simple or ease maintenance for the training level, agricultural technical center or agricultural machinery manufacturer for the training agency. 9. Complete recognition of operation manual and sufficient before-, during-, and after-maintenance for farm machinery can minimize the breakdown as well as conduct suitable period farming, enlarge the endurance, prevent the safety accidents, and promote productivity of farm machinery. Therefore, these can be accomplished by the thorough training or education for agricultural machinery.

• Journal of the Korean Society of International Agriculture
• /
• v.31 no.4
• /
• pp.428-436
• /
• 2019

As the main objective of rice breeding programs, rice eating quality is one of critical factors directly determining the market price and the consumer preference. However, the genetic complexity of eating quality and the difficulty in accurate evaluation often constrain improvement of the eating quality in rice breeding programs. In addition, given that the rice eating quality of current cultivars has already reached some high-level, diversifying of genetic resources are demanded more than ever to improve the rice eating quality. In this study, we developed a recombinant inbred lines (RILs) population derive from Wandoaengmi6, a japonica-type Korean weedy rice with high eating quality and a high degree of glossiness of cooked rice. Year-to-year correlations between the traits in three years were shown normal distribution for major agronomic traits and physicochemical characteristics. After evaluating tested traits related to eating quality procedure, a total of ten lines were ultimately selected from the population. Increasement of the taste of cooked rice (TA) and the overall eating quality (OE) were confirmed in the selected lines, which are caused by alleles derived from Wandoaengmi6 without any linkage drag. These results indicate that the utility of wide genomic resources in Korean landrace could be of application in various rice breeding programs and countermeasure to contribute to properly response to climate change.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.20 no.2
• /
• pp.57-72
• /
• 2018

In order to extract meaningful information from the excellent farming settlement cases of young farmers published by KNCAF, we studied the key words with text mining and created a word cloud for visualization. First, in the text mining results for the entire sample, the words 'CEO', 'corporate executive', 'think', 'self', 'start', 'mind', and 'effort' are the words with high frequency among the top 50 core words. Their ability to think, judge and push ahead with themselves is a result of showing that they have ability of to be managers or managers. And it is a expression of how they manages to achieve their dream without giving up their dream. The high frequency of words such as "father" and "parent" is due to the high ratio of parents' cooperation and succession. Also 'KNCAF', 'university', 'graduation' and 'study' are the results of their high educational awareness, and 'organic farming' and 'eco-friendly' are the result of the interest in eco-friendly agriculture. In addition, words related to the 6th industry such as 'sales' and 'experience' represent their efforts to revitalize farming and fishing villages. Meanwhile, 'internet', 'blog', 'online', 'SNS', 'ICT', 'composite' and 'smart' were not included in the top 50. However, the fact that these words were extracted without omission shows that young farmers are increasingly interested in the scientificization and high-tech of agriculture and fisheries Next, as a result of grouping the top 50 key words by crop, the words 'facilities' in livestock, vegetables and aquatic crops, the words 'equipment' and 'machine' in food crops were extracted as main words. 'Eco-friendly' and 'organic' appeared in vegetable crops and food crops, and 'organic' appeared in fruit crops. The 'worm' of eco-friendly farming method appeared in the food crops, and the 'certification', which means excellent agricultural and marine products, appeared only in the fishery crops. 'Production', which is related to '6th industry', appeared in all crops, 'processing' and 'distribution' appeared in the fruit crops, and 'experience' appeared in the vegetable crops, food crops and fruit crops. To visualize the extracted words by text mining, we created a word cloud with the entire samples and each crop sample. As a result, we were able to judge the meaning of excellent practices, which are unstructured text, by character size.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.427-435
• /
• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.521-535
• /
• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.669-681
• /
• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.6
• /
• pp.53-62
• /
• 2023

This paper has suggested the necessity of introducing training contents using XR(Extended reality) technology as a way to lower the high rate of nursing accidents among unskilled technical personnel in domestic shipbuilding industry, focusing on cable pulling using winch. The occurrence rate of nursing accidents in the domestic shipbuilding industry was almost double(197.4%) (2017~2020) when compared with other manufacturing industries. In particular, it is worth noting that more than 31.8% of nursing accidents in the shipbuilding industry occurred among workers whose job experience is no more than 6 months. Most of new workers are seen to have hard time due to several factors such as lack of work information, inexperience, and unfamiliarity with the working environments. This indicates that it is essential to incorporate more effective training method that could help new workers become familiar with technical skills as well as working environments in a short period of time. Currently, education/training at the domestic shipyard is biased toward technical skills such as welding, painting, machine installation, and electrical installation. Contrary, even more important training required to get new workers used to the working environment has remained at a superficial level such as explaining ship building processes using 2D drawings. This may be the reason why it is inevitable to repeat similar training at OJT (On-the-Job Training) even at the leading domestic companies. Domestic shipbuilding industries have been attracting a lot of new workers thanks to recent economic recovery, which is very likely to increase the occurrence of disasters. In this paper, the introduction of training using XR technology was proposed, and as a specific example, the process of pulling cables using winches on ships was implemented as XR-based training content by using Unity. Using the developed content, it demonstrated that new workers can experience the actual work process in advance through simulation in a virtual space, thereby becoming more effective training content that can help new workers become familiar with the work environment.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.979-995
• /
• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.