• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.118-125
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• 2012

When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called 'adjacent point operation', becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.123-130
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• 2022

In this paper is one of the measures to prevent concrete collapse accidents at construction sites in advance. Analyzed based on accumulated Meteorological Agency data. It is a reliable model that confirms the prediction of the decrease rate occurrence interval, and the verification items such as p_value is 0.5 or less and ecof appears in one direction through the SARIMA model, which is suitable for regular and clear time series data models, ensure reliability. Significant results were obtained. As a result of analyzing the temperature change by time zone and the water reduce ratio by section using the data secured based on such trust, the water reduce ratio is the highest in the 29-31 ℃ section from 12:00 to 13:00 from July to August. found to show. If a factor in the research result interval occurs using the research results, it is expected that the batch plant will produce Ready-mixed concrete that reflects the water reduce ratio at the time of designing the water-cement mixture, and prevent the decrease in concrete compressive strength due to the water reduce ratio.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• Clinical Endoscopy
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• v.56 no.1
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• pp.75-82
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• 2023

Background/Aims: The etiology of superficial non-ampullary duodenal epithelial tumors (SNADETs) remains unclear. Recent studies have reported conflicting associations between duodenal tumor development and Helicobacter pylori infection or endoscopic gastric mucosal atrophy. As such, the present study aimed to clarify the relationship between SNADETs and H. pylori infection and/or endoscopic gastric mucosal atrophy. Methods: This retrospective case-control study reviewed data from 177 consecutive patients with SNADETs who underwent endoscopic or surgical resection at seven institutions in Japan over a three-year period. The prevalence of endoscopic gastric mucosal atrophy and the status of H. pylori infection were compared in 531 sex- and age-matched controls selected from screening endoscopies at two of the seven participating institutions. Results: For H. pylori infection, 85 of 177 (48.0%) patients exhibited SNADETs and 112 of 531 (21.1%) control patients were non-infected (p<0.001). Non-atrophic mucosa (C0 to C1) was observed in 96 of 177 (54.2%) patients with SNADETs and 112 of 531 (21.1%) control patients (p<0.001). Conditional logistic regression analysis revealed that non-atrophic gastric mucosa was an independent risk factor for SNADETs (odds ratio, 5.10; 95% confidence interval, 2.44-8.40; p<0.001). Conclusions: Non-atrophic gastric mucosa, regardless of H. pylori infection status, was a factor independently associated with SNADETs.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.99-107
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• 2015

In this era of smart convergent environment wherein all industries are converged on ICT infrastructure and industries and cultures come together, the information and communication construction business is becoming more important. For the information and communication construction business to continue growing, it is very important to ensure that technical manpower is stably supplied. To date, however, there has been no theoretically methodical analysis of manpower supply and demand in the information and communications construction business. The need for the analysis of manpower supply and demand has become even more important after the government announced the road map for the development of construction business in December 2014 to seek measures to strengthen the human resources capacity based on the mid- to long-term manpower supply and demand analysis. As such, this study developed the manpower supply and demand forecast model for the information and communications construction business and presented the result of manpower supply and demand analysis. The analysis suggested that an overdemand situation would arise since the number of graduates of technical colleges decreased beginning 2007 because of fewer students entering technical colleges and due to the restructuring and reform of departments. In conclusion, it cited the need for the reeducation of existing manpower, continuous upgrading of professional development in the information and communications construction business, and provision of various policy incentives.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.3
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• pp.274-284
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• 2022

The purpose of this study was to analyze the trend of the number of births in Gwangju and the number of outpatients in Pediatric Dentistry at Chonnam National University Dental Hospital over the past 10 years (2010 - 2019) and predict the next year using time series analysis. The number of births showed an unstable downward trend with monthly variations, with the highest in January and the lowest in December. The average number of births in 2020 was predicted to be 682 (595 to 782, 95% CI), and the actual number of births was an average of 610. The number of outpatients was relatively stable, showing a month-to-month variation, with highest in August and the lowest in June. The average number of patients in 2020 was predicted to be 603 (505 to 701, 95% CI), and the average number of actual visits was 587. Despite the decrease in the number of births, the number of outpatients was expected to increase somewhat. Due to the special situation of COVID-19, the actual number of births and patients was to be slightly lower than the predicted values, but it was that they were within the predicted confidence interval. Time series analysis can be used as a basic tool to prepare for the low fertility era in the field of pediatric dentistry.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Korean Journal of Construction Engineering and Management
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• v.14 no.4
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• pp.24-34
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• 2013

Since 2004, the government decided to gradually introduce Actual Cost Data into cost estimate for improving problems of below-cost tendering and to reflect fair market price through competition and carry contract efficiently. However, there are many concerns that Actual Cost Data has not reflected real market price, even that has contributed to reduce the government's budget. General construction firm's burden for labor cost is imputed to specialty contractors and eventually it becomes construction worker's burden. Therefore, realization of Actual Cost Data is very important factor to settle this system. To understand realization level and make short term forecast, this paper drew construction group of which labor cost constitutes more than 95% of direct cost, and compares their Actual Cost Data with relevant skilled workers's unit wage and predicts using time series analysis. The bid price which is not be reflected market price accelerates work environment changes and leads to directly affect such as late disbursement of wages, bankruptcy to workers. Therefore this paper is expected to be used to the preliminary data for solving the problem and establishing improvement of Actual Cost Data.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.