• 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.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Korean Journal of Agricultural Science
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• v.14 no.1
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• pp.164-185
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• 1987

The Korean economy has been developed rapidly in the course of implementing the five year economic development plans since 1962. Accordingly the industrial and employment structure have been changed from the traditional agriculture to modem industrial economy. In the course of implementing export oriented industrialization policies, rural farm economy has been encountered labour shortage owing to rural farm population drain to urban areas, rural wage hike and pressure on farm operation costs, and possibility of farm productivity decrease. To cope with the above problems the Korean government has supplied farm machinery such as power tillers, tractors, transplanters, binders, combines, dryers and etc. by means of the favorable credit support and subsidies. The main objectives of this study are to identify the investment effects of farm mechanization such as B/C and Internal Rate of Return by machinery and operation patterns, changes of labour requirement per 10a for rice culture since 1965, partial farm budget of rice with and without mechanization, and estimation labour input with full mechanization. To achieve the objectives Saemaeul farm mechanization groups, common ownership and operation, and farms with private ownership and operation were surveyed mainly in Nonsan granary area, Chungnam province. The results of this study are as follows 1. The national average of labor input per 10a of paddy has decreased from 150.1Hr in 1965 to 87.2Hr in 1985 which showes 42% decrease of labour inputs. On the other hand the hours of labour input in Nonsan area have also decreased from 150.1Hr to 92.8Hr, 38% of that in 1965, during the same periods. 2. The possible labor saving hours per 10a of Paddy was estimated at 60 hours by substituting machine power for labor forces in the works of plowing, puddling, transplanting, harvesting and threshing, transporting and drying The labor savings were derived from 92.8 hours in 1986 deducting 30 hours of labor input with full mechanization in Nonsan area. 3. Social benefits of farm mechanization were estimated at 124,734won/10a including increment of rice (10%): 34,064won,labour saving: 65,800won,savings of conventional farm implements: 18,000 won and savings of animal power: 6,870won. 4. Rental charges by works prevailing in the area were 12,000won for land preparation, 15,000won for transplanting with seedlings, 19,500won for combine works and 6,000won for drying paddy. 5. Farm income per 10a of paddy with and without mechanization were amounted to 247,278won and 224,768won respectively. 6. Social rate of return of the machinery were estimated at more than 50% in all operation patterns. On the other hand internal rate of return of the machinery except tractors were also more than 50% but IRR of tractors by operation patterns were equivalent to 0 to 9%. From the view point of farmers financial status, private owner-operation of tractors is considered uneconomical. Tractor operation by Saemaeul mechanization groups would be economical considering the government subsidy, 40% of tractor price. 7. Farmers recommendations for the government that gained through field operation of farm machinery are to train maintenance technology for rural youth, to standardize the necessary parts of machinery, to implement price tag system, to intercede spare parts and provide marketing information to farmers by rural institutions as RDA,NACF,GUN office and FLIA.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.3-7
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• 2012

Purpose : In myocardial perfusion SPECT, the type of orbit (circular vs. body contouring) that affect the image quality is still on the debate. Presently in the nuclear medicine field, the body contouring orbit acquisition is widely used to improve the image quality on the myocardial perfusion SPECT. But in case of body contouring acquisition using the vertical method with dual detect machine, there is a tendency of increasing the radius. In this research, we compared body contouring orbit acquisition with circular orbit acquisition, so we suggest ideal method that reduces the radius for improving image quality. Materials and Methods : Phantom and clinical studies were performed. The anthropomorphic torso phantom was made on equally with counts from patient's body. The study was performed under six different conditions. To compare image quality according to the radius, we increased radius sequentially per step during circular orbit acquisition. On the other hand, sensors that protect a collision and reduce the radius automatically were used to acquire image during body contouring orbit acquisition. So we compared FWHM value of apex. In clinical studies, we analyzed the 40 patients who were examined by Tl-201 gated myocardial perfusion SPECT in department of nuclear medicine at Asan Medical Center in August 2011. To acknowledge the differences according to the radius, we acquired the results two times using circular orbit acquisition and body contouring orbit acquisition. Results : In phantom study, we analyzed that increase of radius resulted in changes of FWHM value. It was 5.41, 6.24, 6.33, 6.42, 6.93 mm. On the other hand, using the body contouring orbit acquisition, FWHM value was 6.23 mm. In clinical study, difference of average radius between two methods was 2.5 cm (circular orbit acquisition was more close to patients). Conclusion : Through the experiments using Anthropomorphic torso phantom and patients data, we found that FWHM value of circular orbit acquisition was lower than body contouring orbit acquisition. As a result, if the difference of average radius exists approximately 3 cm, circular orbit type acquisition is better than body contouring type acquisition. But clinical investigation is only aimed to average radius, so it needs more investigation in comparison of patient's image.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Asian Journal of Turfgrass Science
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• v.24 no.2
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• pp.211-217
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• 2010

This study was conducted to investigate a proper method of ball mark repair by comparing the creeping bentgrass recovery speed on golf course green treated by various methods of ball mark repair. Nine general repairing methods were tested and compared; control (no repair, A type), two common methods of USGA (B type) and GCSAA (C type), three methods with fork shaped hand set performing at Korean golf courses (Ansung Benest, D; Sky72, E; Lakeside, F type), and three methods using the repair machine with 6, 8, or 14 teeth (G, H, I type, respectively). Three creeping bentgrass cultivar of 'Penncross', 'T-1', and 'CY-2' were tested in this field experiment. This test was carried out from September to November in 2009 at the nursery on the Seoul Lakeside Golf course. The average speed of turfgrass recovery after various ball mark repairing methods have been ranked as in the order of E, D, C, B, F, I, H, G, and A. The methods of hand practise showed more effective results than repair method using machines. The ball mark recovery speeds of 'Penncross' were in the order of E, D, B, C, F, I, H, and A. In the case of 'T1' and 'CY-2', similar orders were showed as D, E, B, F, C, H, I, A, G and the order of D, E, C, F, B, H, G, I, A, respectively. The ball mark recovery speed among creeping bentgrass cultivar resulted in the order of 'CY-2', 'Penncross', and 'T-1'. The most proper method of ball mark repair was repair method using a hand set tool especially the method of the Sky72 Golf course (E type). At the first, remove a damaged grass area with fork and tap. And then gather the side grasses into the center area with pulling the grasses with fork. After that, make harden and flat on the turf surface by pounding and rolling with the round wooden stick. The final Nstep, water the repaired grass surface. This ball mark repairing practise showed a most rapid and proper recovery method on creeping bentgrass green.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.583-603
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• 2023

Recently, the advancement of mechanical tunnel boring machine (TBM) technology and the characteristics of subsea railway tunnels subjected to hydrostatic pressure have led to the widespread application of shield TBM methods in the design and construction of subsea railway tunnels. Subsea railway tunnels are exposed in a constant pore water pressure and are influenced by the amplification of seismic waves during earthquake. In particular, seismic loads acting on subsea railway tunnels under various ground conditions such as soft ground, soft soil-rock composite ground, and fractured zones can cause significant changes in tunnel displacement and stress, thereby affecting tunnel safety. Additionally, the dynamic response of the ground and tunnel varies based on seismic load parameters such as frequency characteristics, seismic waveform, and peak acceleration, adding complexity to the behavior of the ground-tunnel structure system. In this study, a finite difference method is employed to model the entire ground-tunnel structure system, considering hydrostatic pressure, for the investigation of dynamic behavior of subsea railway tunnel during earthquake. Since the key factors influencing the dynamic behavior during seismic events are ground conditions and seismic waves, six analysis cases are established based on virtual ground conditions: Case-1 with weathered soil, Case-2 with hard rock, Case-3 with a composite ground of soil and hard rock in the tunnel longitudinal direction, Case-4 with the tunnel passing through a narrow fault zone, Case-5 with a composite ground of soft soil and hard rock in the tunnel longitudinal direction, and Case-6 with the tunnel passing through a wide fractured zone. As a result, horizontal displacements due to earthquakes tend to increase with an increase in ground stiffness, however, the displacements tend to be restrained due to the confining effects of the ground and the rigid shield segments. On the contrary, peak compressive stress of segment significantly increases with weaker ground stiffness and the effects of displacement restrain contribute the increase of peak compressive stress of segment.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.53-62
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• 2023

This paper has suggested the necessity of introducing training contents using XR(Extended reality) technology as a way to lower the high rate of nursing accidents among unskilled technical personnel in domestic shipbuilding industry, focusing on cable pulling using winch. The occurrence rate of nursing accidents in the domestic shipbuilding industry was almost double(197.4%) (2017~2020) when compared with other manufacturing industries. In particular, it is worth noting that more than 31.8% of nursing accidents in the shipbuilding industry occurred among workers whose job experience is no more than 6 months. Most of new workers are seen to have hard time due to several factors such as lack of work information, inexperience, and unfamiliarity with the working environments. This indicates that it is essential to incorporate more effective training method that could help new workers become familiar with technical skills as well as working environments in a short period of time. Currently, education/training at the domestic shipyard is biased toward technical skills such as welding, painting, machine installation, and electrical installation. Contrary, even more important training required to get new workers used to the working environment has remained at a superficial level such as explaining ship building processes using 2D drawings. This may be the reason why it is inevitable to repeat similar training at OJT (On-the-Job Training) even at the leading domestic companies. Domestic shipbuilding industries have been attracting a lot of new workers thanks to recent economic recovery, which is very likely to increase the occurrence of disasters. In this paper, the introduction of training using XR technology was proposed, and as a specific example, the process of pulling cables using winches on ships was implemented as XR-based training content by using Unity. Using the developed content, it demonstrated that new workers can experience the actual work process in advance through simulation in a virtual space, thereby becoming more effective training content that can help new workers become familiar with the work environment.