• International Journal of Automotive Technology
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• v.6 no.1
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• pp.37-44
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• 2005

The creation of automotive systems of active safety with intelligent functions needs the use of new control principles for the wheel and automobile. One of such directions is the pre-extreme control strategy. Its aim is the ensuring of wheel's work in pre-extreme, stable area of tire grip wheel slip dependence. The simplest realization of pre-extreme control in automotive anti-lock brake systems consists in the threshold and gradient algorithms. A comparative analysis of these algorithms, which has been made on 'hardware in-the-loop' simulation results of the braking for bus with various anti-lock brake systems (ABS), indicated their high efficiency.

• 한국균학회소식:학술대회논문집
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• 2018.05a
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• pp.41-41
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• 2018

There has been an impetus in the development of biocontrol agents (BCAs) with the removal of a number of chemical compounds in the market, especially in the European Union. This has been a major driver in the development of Integrated Pest Management systems (IPM) for both pest and disease control. For control of mycotoxigenic fungi, there is interest in both control of colonization and more importantly toxin contamination of staple food commodities. Thus the relative inoculum potential of biocontrol agent vs the toxigenic specie sis important. The major bottlenecks in the production and development of formulations of biocontrol agents are the resilience of the strains, inoculum quality and formulation with effective field efficacy. It was recently been shown for mycotoxigenic fungi such as Aspergillus flavus, under extreme climate change conditions, growth is not affected although there may be a stimulation of aflatoxin production. Thus, the development of resilient biocontrol strains which can may have conserved control efficacy but have the necessary resilience becomes critical form a food security point of view. Indeed, under predicted climate change scenarios the diversity of pests and fungal diseases are expected to have profound impacts on food security. Thus, when examining the identification of potential biocontrol strains, production and formulation it is critical that the resilience to CC environmental factors are included and quantified. The problems in relation to the physiological competence and the relative humidity range over which efficacy can occur, especially pre-harvest may be increase under climate change conditions. We have examined the efficacy of atoxigenic strains of A. flavus and Clanostachys rosea and other candidates for control of A. flavus and aflatoxin contamination of maize, and for Fusarium verticillioides and fumonisin toxin control. We have also examined the potential use of fluidized-bed drying, nanoparticles/nanospheres and encapsulation approaches to enhance the potential for the production of resilient biocontrol formulations. The objective being the delivery of biocontrol efficacy under extreme interacting climatic conditions. The potential impact of climate change factors on the efficacy of biocontrol of fungal diseases and mycotoxins are discussed.

• Journal of the Korean Society of Food Culture
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• v.10 no.1
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• pp.67-74
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• 1995

This study researched microbial change of quality according to the various phases of product flow of cooked pea and rice, cold cucumber and seaweed soup, soybean sprouts japchae feeding urban type of a commissary school and a satellite school in Daejeon area, also it suggested the possibility that the central commissary foodservice system can be established and utilized more developmental to identify its food of variation of temperature and state of safety unitl 3 hours after cooking for the case of delay of distribution and holding because of the satellite school of geographical location and traffic problem. The critical Control Points identified for each category of menu items were: Boiled pea and rice: inadequate distribution, holding and storing before assembly; Cold cucumber and seaweed soup: pre-preparation and post-preparation after cooking; Soybean sprouts japchae: Pre-preparation, post-preparation and storing. As the result of observation of the variation of temperature and microbial safety according to the delay of distribution and holding for each food, all of them were relatively safe until 3 hours after cooking, but cold cucumber and seaweed soup being stored for 3 hours, the value of E. coli is $10^3$ CFU/g. The variation of temperature was more extreme in soybean sprouts japchae than cooked pea and rice and cold cucumber and seaweed soup. It was proved that the stainless container was excellent and that adequate holding container should be used.

• Atmosphere
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• v.24 no.3
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• Journal of Oriental Neuropsychiatry
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• v.25 no.4
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• pp.319-332
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• 2014

Objectives: This study was conducted to evaluate the clinical effects of group therapy for Panic disorder patients based on Mindfulness & Li-Gyeung-Byun-Qi therapy. Methods: The FFMQ, BDI, STAI, STAXI, Panic attack, Anticipatory anxiety and subjective improvement of three Panic disorder patients were compared pre- and post-treatment when given Mindfulness & Li-Gyeung-Byun-Qi therapy. Results: 1) After the patient in case 1 underwent 5 weeks of group therapy for Panic disorder, the Mindfulness meditation score was slightly improved, anxiety and depression were significantly decreased, and expression of anger was also improved. In addition, the Panic attack and Anticipatory anxiety became more stable in the objective evaluation, while 'Extreme improvement' was shown in the subjective evaluation. 2) After the patients in case 2 and 3 underwent 5 weeks of group therapy for Panic disorder, Mindfulness meditation scores were slightly improved, anxiety and depression were significantly decreased, and expression of anger was also improved. In addition, the Panic attack and Anticipatory anxiety became more stable in the objective evaluation, while 'Moderate improvements' were shown in the subjective evaluation. Conclusions: As per the results in these cases, it was shown that group therapy for Panic disorder utilizing Mindfulness & Li-Gyeung-Byun-Qi therapy was effective to maintain meditation and control the emotions of anxiety, depression, anger and so on. Therefore, it was considered that expansion of clinical utilization through the standardization of a group therapy program for Panic disorder is needed. Furthermore, it was also considered that a comparative study of the effects of previous cognitive programs for Panic disorder according to the objectified and standardized manual is needed in the future.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.