• Journal of the East Asian Society of Dietary Life
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• v.3 no.2
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• pp.83-97
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• 1993

The purpose of this study is to serve as the basic data for the possible effort of succeeding to traditional culture. The major findings of this study are as follows : On the question about [the importance of Special Days] was shown to average score of 3.8 On the question about [helpfulness degree of Seasonally Special Days] was shown to average score of 3,4 Regarding to the kinds of Seasonally Special days that people celebrate most, [The New Year's Day], [Chusok(Chinese Thanks-giving day)], [Dried Vegetables and mixed bowl of five-sort grains(Chusok:The 1st Full-Moon Day)], [Red beans Gruel (The Winter Solstice)] were shown to enjoy most. In preparation of dishes for Seasonally Special Days, about 58% of the respendants answered that they prepared them at their own homes. [Rice Cake] was shown to the highest among the kinds of ready-made deshes for Seasonally Special Days. On the hand down to foods for Seasonally Special Days, about 38% of respondants answered that they do want to their daughters, The significant variable on family environment for this if family religion. On the prospect for succession of the Seasonally Special Days' customs including the dishes, about 80% of respondants answered that a part of them would be handed down to next generations. The significant variable on family environment for this is subjects' religion. On the degree of recognition of the Seasonally Special Days, mothers's group was predominent(compared with daughters')

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.53-59
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• 2004

In case of load forcasting the most important problem is to deal with the load of special days. According this paper presents forecasting method for speaial days peak load by neural networks model. by means of neural networks mothod using the historical past special- days load data, special-days load was directly forecasted, and forecasting % error showed good result as 1∼2% except vacation season in summer Consequently, it is capable of directly special days load, With the models, precision of forecasting was brought satisfactory result. When neural networks was compared with the orthogonal polynomials models at a view of the results of special-days load forecasting, neural networks model which used pattern conversion ratio was more effective on forecasting for special-days load. On the other hand, in case of short special-days load forecasting, both were valid.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.98-103
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• 2002

In case of power demand forecasting, the most important problems are to deal with the load of special-days. Accordingly, this paper presents the method that forecasting long (the Lunar New Year, the Full Moon Festival) and short(the Planting Trees Day, the Memorial Day, etc) special-days peak load using neural networks and regression models. long and short special-days peak load forecast by neural networks models uses pattern conversion ratio and four-order orthogonal polynomials regression models. There are using that special-days peak load data during ten years(1985∼1994). In the result of special-days peak load forecasting, forecasting % error shows good results as about 1 ∼2[%] both neural networks models and four-order orthogonal polynomials regression models. Besides, from the result of analysis of adjusted coefficient of determination and F-test, the significance of the are convinced four-order orthogonal polynomials regression models. When the neural networks models are compared with the four-order orthogonal polynomials regression models at a view of the results of special-days peak load forecasting, the neural networks models which uses pattern conversion ratio are more effective on forecasting long special-days peak load. On the other hand, in case of forecasting short special-days peak load, both are valid.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1115-1120
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• 2016

This paper presents the daily load forecasting for special days considering the rate of operation of industrial consumers. The authors analyzed the power consumption pattern for both the special and ordinary days according to the contract power classification of industrial consumers, and selected 400~600 specific consumers for which the rates of operation during special days are needed. Load forecasting for 2014 special days considering the rate of operation of industrial consumers showed a noticeable improvement on forecasting error of daily peak demand, which proved the effectiveness of the survey for the rates of operation during special days of industrial consumers.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.295-297
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• 2000

In case of power demand forecasting the most important problem is to deal with the load of special-days, Accordingly, this paper presents a method that forecasting special-days load with regression models and neural networks. Special-days load in summer season was forecasted by the multiple regression models using weekday change ratio Neural networks models uses pattern conversion ratio, and orthogonal polynomial models was directly forecasted using past special-days load data. forecasting result obtains % forecast error of about $1{\sim}2[%]$. Therefore, it is possible to forecast long and short special-days load.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.73-78
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• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.482-488
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• 2013

Load forecasting is essential to the economical and the stable power system operations. In general, the forecasting days can be classified into weekdays, weekends, special days and special light-load periods in short-term load forecast. Special light-load periods are the consecutive holidays such as Lunar New Years holidays, Korean Thanksgiving holidays and summer special light-load period. For the weekdays and the weekends forecast, the conventional methods based on the statistics are mainly used and show excellent results for the most part. The forecast algorithms for special days yield good results also but its forecast error is relatively high than the results of the weekdays and the weekends forecast methods. For summer special light-load period, none of the previous studies have been performed ever before so if the conventional methods are applied to this period, forecasting errors of the conventional methods are considerably high. Therefore, short-term load forecast for summer special light-load period have mainly relied on the experience of power system operation experts. In this study, the trends of load profiles during summer special light-load period are classified into three patterns and new forecast algorithms for each pattern are suggested. The proposed method was tested with the last ten years' summer special light-load periods. The simulation results show the excellent average forecast error near 2%.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.118-127
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• 2022

This study analyzes the characteristics of occupational accidents of food delivery via motorcycle in terms of accident probability and work-days. Depending on their employment status, food-delivery workers were divided into "employed" workers (who work for restaurants) and "special-type" workers (who work for delivery platforms). Collected data include occupational accident-information during the last two years (1,468 cases for employed workers and 4,899 cases for special-type workers) and their risk information such as age, work experience, accident location, season of the accident, and weather conditions. The study finds that special-type workers had a significantly higher accident probability for the younger age group (80.8%), while employed workers had more accidents in both 20's or younger (34.9%) and 50's or older (25.4%). The number of work-days-lost was higher for special-type workers with less work experience, and it decreased with increasing work experience. Moreover, the chance for accidents was higher at night time (55%) than for day time (45%) for special-type workers as well as for employed workers. The number of work-days-lost was higher in foreign workers (180.79 days) than in Korean workers (121.44 days). Accident probability (30.7%) and work-days-lost (136.2 days) was higher in winter than in other seasons. In addition, accidents-per-day was higher on snowy days (12.7 cases per day) than rainy (8.1) and windy days (7.1). In addition, it was found that deadly accidents mainly caused injuries to face, head, and chest, while non-deadly accidents affected mainly the legs and feet. This study enables the development of better policies to prevent accidents of food delivery workers.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.94-102
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• 2005

This paper performs analysis the load patterns for the all the special days and studies the change of the load patterns for the last 15 years using Expert system based on the load record and the weather condition record. The Expert system is one of the four major load forecasting methods of the power system And it is used for forecasting. loads of the special days based on the Know-how of the load forecasting Experts. After the author simulates the load forecasting using hourly relative factors of the load patterns based on the past load records, there is considerable improved effect. The average errors of past 5 days load forecasting of lunar New Year's Day (year 2002 and 2003) is $3.23{[\%]}$. Using the new method the author forecast loads of the lunar new year's days (the year 2005) and it shows only $1.78{[\%]}$ error. A field manual for the load forecast can be made using proposed method. The authors expect this article could give a guidance to those who wish to be load forecast expert.

• Journal of Pharmacopuncture
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• v.8 no.3
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• pp.107-113
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• 2005

Objectives : The purpose of this study is to report the effect of oriental medicine about two patients with oligomenorrhea. Methods : Two patients had no special abnormality in BC, CBC, UA, ultra-sono, hormonal test in this study are 25-years-old and 26 years-old female who have over 40-days menstrual cycle. They had treated for 49-days, 21-days each by oriental medicine method. Their herb medicine is On-kyung-tang and Gwa-gi-eum. Main acupuncture points are Hapkok(LI4), Kihae(CV6), Gwanwon(CV3), Choksamni(ST36), Samumgyo(SP6) and moxibuation Gwanwon(CV3) and treated Carthami flos of Aqua-Acupuncture 0.05cc at Samumgyo(SP6). Results : After treatment, their menstrual cycle decreased 33-days, 36-days each. And improved dyspepsia, menstrual pain. Conclusions : Oriental medical methods are effective in two patients with oligomenorrhea had no special abnormality in BC, CBC, UA, ultra-sono, hormonal test.