• Journal of Distribution Science
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• v.21 no.7
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• pp.65-71
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• 2023

Purpose: This study attempted to analyze the impacts of the backward linkage and output multipliers, and investigate the price fluctuation and the price forecast amongst the manufacturing sectors associated with food processing industrial output of Thailand. Research design, data and methodology: The Thailand Input-Output table with a size of 180 x 180 sectors from 2005, 2010, and 2015 was utilized while the secondary data of the time series from January 2002 to December 2021 were processed via a multiplicative model and Box-Jenkins model. Results: The backward linkage analysis indicates that canning and preserving of the meat sector majorly utilized the factors of production from the slaughtering sector; canning and preservation of fish and other seafoods sector largely used those factors from the ocean and coastal fishing sector; and the sugar sector used those of the sugarcane sector. Notably, the output multiplier analysis indicated that output multipliers of those 3 manufacturing sectors were highly increased; meanwhile the price fluctuation continually existed in all forms. Besides, the price forecast suggested that prices of chicken and sugarcane tended to be higher; whereas, the price of shrimp was unstable. Conclusions: Food processing industry contains the favorable components to be one of the industries of the future of Thailand.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.53-58
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• 2001

In this paper, a low price ultrasound image processing system is developed using DSP and PC. Ultrasound for image is generated by the 32-channel transducer. Ultrasound image is captured by DSP instead of the private image grabber board. Display of image and image processing algorithms are performed by PC. The image processing algorithms based on GUI are realized by software. So users without knowledge of image processing can perform the image enhancement more easily.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Journal of the korean Society of Automotive Engineers
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• v.11 no.6
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• pp.89-96
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• 1989

Engine analyzer is developed with the aids of personal computer, A/D converter, interfacing signal transducer and data processing computer programs. The objective of this development are that it should firstly be produced at the resonable low price compared with imported one taking advantage of using existing personal computer and printer, and it should also give good quality of performance. For the attainment of this objective, A/D converter should have been developed to meet the price limit of the equipment. The experiment is performed in a 4 cycle 4 cylinder gasoline engine by this analyzer, and all the information which are necessary for the combustion analysis can be obtained through the processing of the pressure data that are stored in the computer. These are pressure-volume curve, pressure-crank angle curve, the rate of pressure rise and heat release versus crank angle curve etc. With this developed experimental system of resonable price, it will be considered that more easy way of engine data pick-up and processing is possible.

• Culinary science and hospitality research
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• v.13 no.2
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• pp.123-135
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• 2007

This study intended to determine how price-ending types used for menu products affected consumers' price evaluation and value perception and such behavioral intentions as purchase intention and search intention. The collected data went through statistical processing, including frequency analysis, factor analysis, T-test and ANOVA using an $SPSS/PC^+$ 12.0 statistical package. The results can be summarized as follows: First, as for differences in internal reference price by ending, internal reference price was lower if menu ending was an odd number than it was an even number, which indicates that consumers tend to consider the price of the menu products to be lower if the ending of menu products is an odd number. Second, menu price-ending was found to have significant differences on consumers' purchase intention, search intention, and value perception. But the results of two-way ANOVA showed that price ending by restaurant types had no effect on consumers' search intention and restaurant type. This study suggested a desirable external reference price-ending type for menu products as one of sales promotion strategies to menu products planners and drew up a concrete plan to determine which price-ending type is useful by consumers' personal properties.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.93-104
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• 2024

China is playing more predominant role in the liquefied natural gas (LNG) market worldwide and LNG import price is subject to various factors both at home and abroad. Nevertheless, previous studies rarely heed a multiple of factors. A time-varying parameter factor augmented vector auto-regression (TVP-FAVAR) model is adopted to discover the determinants of China's LNG import price and their dynamic impacts from January 2012 to December 2021. According to the findings, market fundamentals have a greater impact on the import price of natural gas in China than overall economic demand, financial considerations, and world oil prices. The primary determinants include domestic gas consumption, consumer confidence and other demand-side information. Then, there are diverse and time-varying spillover effects of the four common determinants on the volatility of China's LNG import price at different intervals and time nodes. The price volatility is more sensitive and long-lasting to domestic natural gas pricing reform than other negative shocks such as the Sino-US trade war and the COVID-19 pandemic. The results in this study further proves the importance of domestic natural gas market liberalization. China ought to do more to support the further marketization of natural gas prices while working harder to guarantee natural gas supplies.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.131-144
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• 2020

As the blockchain technology attracts attention, interest in cryptocurrency that is received as a reward is also increasing. Currently, investments and transactions are continuing with the expectation and increasing value of cryptocurrency. Accordingly, prediction for cryptocurrency price has been attempted through artificial intelligence technology and social sentiment analysis. The purpose of this paper is to develop a deep learning ensemble model for predicting the price fluctuations and one-day lag price of cryptocurrency based on the design science research method. This paper intends to perform predictive modeling on Ethereum among cryptocurrencies to make predictions more efficiently and accurately than existing models. Therefore, it collects data for five years related to Ethereum price and performs pre-processing through customized functions. In the model development stage, four LSTM models, which are efficient for time series data processing, are utilized to build an ensemble model with the optimal combination of hyperparameters found in the experimental process. Then, based on the performance evaluation scale, the superiority of the model is evaluated through comparison with other deep learning models. The results of this paper have a practical contribution that can be used as a model that shows high performance and predictive rate for cryptocurrency price prediction and price fluctuations. Besides, it shows academic contribution in that it improves the quality of research by following scientific design research procedures that solve scientific problems and create and evaluate new and innovative products in the field of information systems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.12
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• pp.568-574
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• 2001

The variations of real time electric power price in competitive electricity markets have influence on electric power demands of the consumers. The effects of the consumers for electric power price can be expressed the price elasticity coefficient of the power demand as a measurement. Residential, commercial, and industrial consumers with different characteristics cause the different price elasticity of the power demand due to changing the pattern of consumption. It is necessary that the effects of electric power demands as a function of elasticity coefficient for each loads should be analyzed in Korea which is processing deregulated electric market. Therefore, this paper calculate the elasticity coefficient of each loads and analysis the effects of electric power demands as a function of elasticity coefficient of inflexible and flexible consumers in competitive electricity market.

• The Journal of Fisheries Business Administration
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• v.47 no.1
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• pp.47-61
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• 2016

This study is aimed to analyze economical meaning and problems on the industrial differentiation of Korean laver industry. Based on the surveyed data, the export value of korean laver has increased over 28 times for last 20 years($10 million to $300 million) and the separation of farming and processing was an important success factor of rapid growth of korean laver industry. However, the result of the survey shows that the farming profit is 534.1 won out of the total price for a bunch of dried laver, 3,566.3 won. So, farming profit counts for just 15 percent of total price. In contrast, the processing profit is 1,143.5 won and it is 32.1 percent of total price. This means that laver farmers are not being guaranteed their profit properly. This phenomenon is occurred due to lower status of first-hand processors(which produce dried laver) to second-hand processors(which produce seasoned laver) due to advanced payment given by second-hand processors. So, fist-hand processors should provide their product in the price which was designated by second-hand processors. Besides, despite of many business risks caused from climate change and environmental pollution, the market price of raw laver has steadily decreased. For sustainable prosperity of korean laver industry, imbalance on korean laver industry concerning profit sharing is need to be changed. In future, self-processing of dried laver in fishery household and enhancing the role of The Fisheries Cooperative Union in laver industry can be considered.