• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.49-58
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• 2014

Variability factors of transship cargo in the container transportation market analysis short term factors. In the past, studies on the factor of variability in container cargo volume have focused on long term volume forecast and increase in investment and competitiveness from strategic perspectives. Unlike previous studies, this paper analyzes factors of variability in transshipment volume rapidly varying in short term and seeks measures. Since it was identified that transshipment volume depends on vessel operation cost and port volume in long term but effectively on special strategies launched by port authorities in short term, the port authority experienced rapid drop in volume should continue to observe strategies of competition ports and to make use of strategies seeking appropriate countermeasures.

• Journal of Environmental Science International
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• v.16 no.8
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• pp.1001-1009
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• 2007

Passive sampler is a simple and cost-effective measuring equipment for ambient and indoor air pollution. We studied the performance of a short term (1 hour mean concentration) ozone passive sampler which was coated with a colorant (indigo carmine) to a filter substrate. Acetone and sulfamic acid added ozone passive sampler was investigated to measure short term mean ozone concentration. Ozone response and interference of criteria air pollutant($SO_2,\;NO_2$, CO) on a short term ozone passive sampler was tested through experimental chamber. The results show sulfamic acid added passive ozone sampler have good response in ozone exposure. Interference of $NO_2$ gas is larger than other two criteria gases.

• IE interfaces
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• v.16 no.4
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• pp.400-410
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• 2003

Most of the demand forecasting which have been studied is about long-term time series over 15 years demand forecasting. In this paper, we set up the most optimal ARIMA model for the short-term time series demand forecasting and suggest demand forecasting system for short-term time series by appraising suitability and predictability. We are going to use the univariate ARIMA model in parallel with the bivariate transfer function model to improve the accuracy of forecasting. We also analyze the effect of advertisement cost, scale of branch stores, and number of clerk on the establishment of marketing policy by applying statistical methods. After then we are going to show you customer's needs, which are number of buying products. We have applied this method to forecast the annual sales of refrigerator in four branch stores of A company.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.149-154
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• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.1940-1949
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• 2015

This paper presents an Enhanced Particle Swarm Optimization (EPSO) to solve short-term hydrothermal scheduling (STHS) problem with non-convex fuel cost function and a variety of operational constraints related to hydro and thermal units. The operators of the conventional PSO are dynamically controlled using exponential functions for better exploration and exploitation of the search space. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement in the conventional PSO. The effectiveness of the proposed method has been tested for STHS of two standard test generating systems while considering several operational constraints like system power balance constraints, power generation limit constraints, reservoir storage volume limit constraints, water discharge rate limit constraints, water dynamic balance constraints, initial and end reservoir storage volume limit constraints, valve-point loading effect, etc. The application results show that the proposed EPSO method is capable to solve the hard combinatorial constraint optimization problems very efficiently.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.139-144
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• 2024

In this study, four types of fall detection systems - designed with YOLOPose, principal component analysis (PCA), convolutional neural network (CNN), and long short-term memory (LSTM) architectures - were developed and compared in the detection of everyday falls. The experimental dataset encompassed seven types of activities: walking, lying, jumping, jumping in activities of daily living, falling backward, falling forward, and falling sideways. Keypoints extracted from YOLOPose were entered into the following architectures: RAW-LSTM, PCA-LSTM, RAW-PCA-LSTM, and PCA-CNN-LSTM. For the PCA architectures, the reduced input size stemming from a dimensionality reduction enhanced the operational efficiency in terms of computational time and memory at the cost of decreased accuracy. In contrast, the addition of a CNN resulted in higher complexity and lower accuracy. The RAW-LSTM architecture, which did not include either PCA or CNN, had the least number of parameters, which resulted in the best computational time and memory while also achieving the highest accuracy.

• Korean Journal of Agricultural Science
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• v.38 no.3
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• pp.583-591
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• 2011

Status survey on short-term agricultural machinery rental business was conducted to provide basic data for effective and sustainable implementation of the rental system. Selected survey samples were 34 rental management institutions such as city and county level government offices and agricultural technology development centers, and Primary Agricultural Cooperatives. Survey was conducted through mailing of questionnaire papers and direct interviews with the officers in charge of the agricultural machinery short-term rental management. Number of agricultural machinery retained by the 34 management institutions for the machinery rental business was 3,699, and numbers of the machinery were 1630 for upland crops, 929 for rice, 542 for orchard farming, 274 for animal husbandry, and 324 for common use. Regarding size of warehouse for rental agricultural machinery, 50% of the institutes were less than 660 $m^2$, 26.5% were greater than 993 $m^2$, and 23.5% were between 663 and 990 $m^2$. Institutes maintaining machinery washing facilities were only 10 (29%) among the 34 rental management institutions. Agricultural machinery rental business was advertised to farmers by 91% of the institutes, and the methods were leaflet (35.2%), village broadcasting (26.5%), call-up education (23.6%), and TV and radio (14.7%). Major contents of the advertisement were rental procedure (52.9%), rental machinery (26.5%), and rental cost (20.6%).

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.178-185
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• 2015

Purpose: This research suggests a method of establishing criteria for working area and operating cost for a long-term lease of agricultural machinery. Methods: Eight crops were selected-three food crops and five open-field vegetables-and agricultural machines used for sowing, transplanting, and cultivation in dry-field farming were analyzed. Results: The break-even acreage for agricultural machinery under a long-term lease was found to differ by agricultural machine, ranging from 1.0 to 5.8 ha. In terms of arable land area, the break-even acreages for harvesting machinery and transplanters were 15.6 to 26.1 ha and 6.1 to 8.6 ha, respectively. The working area lessees should secure was divided into two cases: (1) 2.0 to 11.6 ha when leasing individual agricultural machines (sowing and transplanting) for a long-term period, and (2) more than 10 ha when farmers who cultivate beans, potatoes, garlic, onions, and so on lease sowing and transplanting machines as a set. When agricultural machinery was leased for a long term, the operating cost and working time were reduced by 27.6 to 74.4% and 2.5 to 21.6%, respectively, indicating considerable effect. Conclusions: A long-term lease project needs to be promoted to overcome the limitation of short-term leases of agricultural machinery. The local government should lead this project and facilitate the mechanization of dry-field farming. The department in charge of agricultural machinery lease projects needs to set the working area to cover the rate and maintenance cost for farmers who lease agricultural machinery for the long term.

• The Journal of Information Technology
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• v.9 no.4
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• pp.75-85
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• 2006

This study is to test the influence of stock option granting information on the firm's performance. The important issue in stock option is that agent cost is the important determinant factor for the long term performance. The agent cost arises between the manager and shareholders. So many study are concentrated in diminishing the agent cost, and develop some substitute tools to measure the agent cost. The event study about stock option analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Announcements about stock option are generally associated with positive abnormal returns in short term period, but not showing positive effect in long term period. It is important to investigate the responses of stocks to new information contained in the announcements of stock option. Therefore it is important to study the long term performance in the case of stock option. The event time portfolio approach exists the CAR model, BHAR model and WR model. And the calendar time portfolio approach has the 3 factor model, 4 factor model, CTAR model, and RATS model. This study is forced to develop and arrange two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach.