• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.34-46
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• 2020

In order to supply stable natural gas in the future, it is necessary to forecast the demand in advance and secure the quantity of supply. In this paper, we propose a method of estimating the demand function of industrial natural gas, which is the core of the increase of domestic natural gas demand in the future. The cross-sectional data of 304 domestic industries were used to estimate the demand function of the industrial natural gas, and the effect of industry specific characteristics such as capital investment, manufacturing cost. Finally, the least absolute deviation estimation method which is robust to outliers and does not assume the homogeneity of the error term and the normality, And the results were derived. In addition, the economic value of industrial city gas was estimated using the price elasticity of industrial city gas. Therefore, it can be seen that the continuous expansion and supply of city gas to the industrial sector is beneficial at the national level, and the government needs to promote expansion through the industrial city gas support policy.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1053-1060
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• 2021

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.337-345
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• 2022

For data such as rainfall and soil moisture, it is important to obtain the values of all points required as geostatistical data. Spatial interpolation is generally performed in this process, and commercial software such as ArcGIS is often used. However, commercial software has fatal drawbacks due to its high expertise and cost. In this study, R, an open source-based environment with ArcGIS, a commercial software, was used to compare the differences according to the processing environment when performing spatial interpolation. The data for spatial interpolation was weather forecast data calculated through Land-Atmosphere Modeling Package (LAMP)-WRF model, and soil moisture data calculated for each cumulative rainfall scenario. There was no difference in the output value in the two environments, but there was a difference in user interface and calculation time. The results of spatial interpolation work in the test bed showed that the average time required for R was 5 hours and 1 minute, and for ArcGIS, the average time required was 4 hours and 40 minutes, respectively, showing a difference of 7.5%. The results of this study are meaningful in that researchers can derive the same results in a commercial software environment and an open source-based environment, and can choose according to the researcher's environment and level.

• Steel and Composite Structures
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• v.50 no.5
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• pp.595-608
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• 2024

In this study, aluminum lathe waste was used by replacing aggregates in certain proportions in order to obtain expansive concrete using recycled materials. For this reason, five different aluminum wastes of 1%, 2%, 3%, 4% and 5% were selected and also reference without aluminum waste was produced. Based on the mechanical tests conducted, which included slump, compression, splitting tensile, and flexural tests, it was evident that the workability of the material declined dramatically once the volume ratio of aluminum exceeded 2%. As determined by the compressive strength test (CST), the CS of concrete (1% aluminum lathe wastes replaced with aggregate) was 11% reducer than that of reference concrete. It was noted that the reference concrete's CS values, which did not include aluminum waste, were greater than those of the concrete that contained 5% aluminum. When comparing for splitting tensile strength (STS), it was observed that the results of STS generally follow the parallel inclination as the CS. The reduction in these strengths when 1% aluminum is utilized is less than 10%. These ratios modified 18% when flexural strength (FS) is considered. Therefore, 1% of aluminum waste is recommended to obtain expansive concrete with recycled materials considering minimum loss of strength. Moreover, Scanning Electron Microscope (SEM) analysis was performed and the results also confirm that there was expansion in the aluminum added concrete. The presence of pores throughout the concrete leads to the formation of gaps, resulting in its expansion. Additionally, for practical applications, basic equations were developed to forecast the CS, STS, and FS of the concrete with aluminum lathe waste using the data already available in the literature and the findings of the current study. In conclusion, this study establishes that aluminum lathe wastes are suitable, readily available in significant quantities, locally sourced eco-materials, cost-effective, and might be selected for construction using concrete, striking a balance among financially and ecological considerations.

• The Journal of Fisheries Business Administration
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• v.34 no.2
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• pp.75-90
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• 2003

When we think of fundamental problems of the aquaculture industry, there are several strict conditions, and consequently the aquaculture industry is forced to change. Fish aquaculture has a structural supply surplus in production, aggravation of fishing grounds, stagnant low price due to recent recession, and drastic change of distribution circumstances. It is requested for us to initiate discussion on such issue as “how fish aquaculture establishes its status in the coastal fishery\ulcorner, will fish aquaculture grow in the future\ulcorner, and if so “how it will be restructured\ulcorner” The above issues can be observed in the mariculture of yellow tail, sea scallop and eel. But there have not been studied concerning seabream even though the production is over 30% of the total production of fish aquaculture in resent and it occupied an important status in the fish aquaculture. The objectives of this study is to forecast the future movement of sea bream aquaculture. The first goal of the study is to contribute to managerial and economic studies on the aquaculture industry. The second goal is to identify the factors influencing the competition between production areas and to identify the mechanisms involved. This study will examine the competitive power in individual producing area, its behavior, and its compulsory factors based on case study. Producing areas will be categorized according to following parameters : distance to market and availability of transportation, natural environment, the time of formation of producing areas (leaderㆍfollower), major production items, scale of business and producing areas, degree of organization in production and sales. As a factor in shaping the production area of sea bream aquaculture, natural conditions especially the water temperature is very important. Sea bream shows more active feeding and faster growth in areas located where the water temperature does not go below 13∼14$^{\circ}C$ during the winter. Also fish aquaculture is constrained by the transporting distance. Aquacultured yellowtail is a mass-produced and a mass-distributed item. It is sold a unit of cage and transported by ship. On the other hand, sea bream is sold in small amount in markets and transported by truck; so, the transportation cost is higher than yellow tail. Aquacultured sea bream has different product characteristics due to transport distance. We need to study live fish and fresh fish markets separately. Live fish was the original product form of aquacultured sea bream. Transportation of live fish has more constraints than the transportation of fresh fish. Death rate and distance are highly correlated. In addition, loading capacity of live fish is less than fresh fish. In the case of a 10 ton truck, live fish can only be loaded up to 1.5 tons. But, fresh fish which can be placed in a box can be loaded up to 5 to 6 tons. Because of this characteristics, live fish requires closer location to consumption area than fresh fish. In the consumption markets, the size of fresh fish is mainly 0.8 to 2kg.Live fish usually goes through auction, and quality is graded. Main purchaser comes from many small-sized restaurants, so a relatively small farmer and distributer can sell it. Aquacultured sea bream has been transacted as a fresh fish in GMS ,since 1993 when the price plummeted. Economies of scale works in case of fresh fish. The characteristics of fresh fish is as follows : As a large scale demander, General Merchandise Stores are the main purchasers of sea bream and the size of the fish is around 1.3kg. It mainly goes through negotiation. Aquacultured sea bream has been established as a representative food in General Merchandise Stores. GMS require stable and mass supply, consistent size, and low price. And Distribution of fresh fish is undertook by the large scale distributers, which can satisfy requirements of GMS. The market share in Tokyo Central Wholesale Market shows Mie Pref. is dominating in live fish. And Ehime Pref. is dominating in fresh fish. Ehime Pref. showed remarkable growth in 1990s. At present, the dealings of live fish is decreasing. However, the dealings of fresh fish is increasing in Tokyo Central Wholesale Market. The price of live fish is decreasing more than one of fresh fish. Even though Ehime Pref. has an ideal natural environment for sea bream aquaculture, its entry into sea bream aquaculture was late, because it was located at a further distance to consumers than the competing producing areas. However, Ehime Pref. became the number one producing areas through the sales of fresh fish in the 1990s. The production volume is almost 3 times the production volume of Mie Pref. which is the number two production area. More conversion from yellow tail aquaculture to sea bream aquaculture is taking place in Ehime Pref., because Kagosima Pref. has a better natural environment for yellow tail aquaculture. Transportation is worse than Mie Pref., but this region as a far-flung producing area makes up by increasing the business scale. Ehime Pref. increases the market share for fresh fish by creating demand from GMS. Ehime Pref. has developed market strategies such as a quick return at a small profit, a stable and mass supply and standardization in size. Ehime Pref. increases the market power by the capital of a large scale commission agent. Secondly Mie Pref. is close to markets and composed of small scale farmers. Mie Pref. switched to sea bream aquaculture early, because of the price decrease in aquacultured yellou tail and natural environmental problems. Mie Pref. had not changed until 1993 when the price of the sea bream plummeted. Because it had better natural environment and transportation. Mie Pref. has a suitable water temperature range required for sea bream aquaculture. However, the price of live sea bream continued to decline due to excessive production and economic recession. As a consequence, small scale farmers are faced with a market price below the average production cost in 1993. In such kind of situation, the small-sized and inefficient manager in Mie Pref. was obliged to withdraw from sea bream aquaculture. Kumamoto Pref. is located further from market sites and has an unsuitable nature environmental condition required for sea bream aquaculture. Although Kumamoto Pref. is trying to convert to the puffer fish aquaculture which requires different rearing techniques, aquaculture technique for puffer fish is not established yet.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.77-88
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• 2008

There has been growing demands to manage the water quality of west coastal region due to the large scale urbanization along the coastal zone, the possibility of application of TMDL(Total Maximum Daily Loadings) to Han river, and the natural disaster such as oil spill incident in Taean, Chungnam. However, no system has been developed for such purposes. In this background, the demand of GIS based effective water quality management has been increased to monitor water quality environment and propose best management alternatives for Han river and Kyeonggi bay. This study mainly focused on the development of integrated water quality management system for Han river bas in and its estuary are a connected to Kyeonggi bay to support integrated water quality management and its plan. Integration was made based on GIS by spatial linking between water quality attributes and location information. A GIS DB was built to estimate the amount of generated and discharged water pollutants according to TMDL technical guide and it included input data to use two different water quality models--W ASP7 for Han river and EFDC for coastal area--to forecast water quality and to suggest BMP(Best management Practices). The results of BOD, TN, and TP from WASP7 were used as the input to run EFDC. Based on the study results, some critical areas which have relatively higher pollutant loadings were identified, and it was also identified that the locations discharging water pollutant loadings to river and seasonal factor affected water quality. And the relationship of water quality between river and its estuary area was quantitatively verified. The results showed that GIS based integrated system could be used as a tool for estimating status-quo of water quality and proposing economically effective BMPs to mitigate water pollution. Further studies need to be made for improving system's capabilities such as adding decision making function as well as cost-benefit analysis, etc. Also, the concrete methodology for water quality management using the system need to be developed.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.