• Clinics in Shoulder and Elbow
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• v.12 no.2
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• pp.159-166
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• 2009

Purpose: We wanted to evaluate the surgical results of early mobilization after rigid fixation of small coronoid fracture using the tension band technique Materials and Methods: Eight cases of coronoid fracture were fixed with the tension band technique and using K-wire and wire through the medial approach. All the cases were Regan-Morrey type 2. According to O'Driscoll, they were classified as 5 cases of the tip type (subtype 2) and 3 cases of the anteromedial type (1 case of subtype 2, and 2 case of subtype 3). The associated collateral ligament injuries (6 cases) and radial head/neck fractures (4 cases) were managed simultaneously. After immobilization for 5~7 days, active ROM exercise with a fitted hinge brace started and continued till postoperative 6 weeks. The patients were assessed for pain, ROM and functional disability using the Mayo elbow performance score (MEPS) at an average of 11 months (range: 6~28 months). The ulnar nerve symptoms were also investigated. Results: We observed solid union in all the coronoid fractures without hardware failure. An average of 2.2 wires (range: 2~4) were used. The mean extension was $3^{\circ}$(range: $0^{\circ}\sim25^{\circ}$), the mean flexion was $137^{\circ}$(range: $130^{\circ}\sim140^{\circ}$), the mean pronation was $69^{\circ}$(range: $45^{\circ}\sim90^{\circ}$) and the mean supination was $78^{\circ}$(range: $45^{\circ}\sim90^{\circ}$). The mean MEPS was 96 (range: 65~100). Ulnar nerve symptoms occurred at postoperative one day and persisted in one patient with the terrible triad of taking radial head excision and residual medial instability. Conclusion: The tension band technique uses easily obtained, economic K-wires and the wire was strong enough to permit early elbow ROM exercise and the technique might improve the elbow function. It was especially useful for fixation of multiple small fragments.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.788-818
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• 2009

Renewable energy refers to solar energy, biomass energy, hydrogen energy, wind power, fuel cell, coal liquefaction and vaporization, marine energy, waste energy, and liquidity fuel made out of byproduct of geothermal heat, hydrogen and coal; it excludes energy based on coal, oil, nuclear energy and natural gas. Developed countries have recognized the importance of these energies and thus have set the mid to long term plans to develop and commercialize the technology and supported them with drastic political and financial measures. Considering the growing recognition to the field, it is necessary to analysis up-to-now achievement of the government's related projects, in the standards of type of renewable energy, management of sectional goals, and its commercialization. Korean government is chiefly following suit the USA and British policies of developing and distributing renewable energy. However, unlike Japan which is in the lead role in solar rays industry, it still lacks in state-directed support, participation of enterprises and social recognition. The research regarding renewable energy has mainly examinedthe state of supply of each technology and suitability of specific region for applying the technology. The evaluation shows that the research has been focused on supply and demand of renewable as well as general energy and solution for the enhancement of supply capacity in certain area. However, in-depth study for commercialization and the increase of capacity in industry followed by development of the technology is still inadequate. 'Cost-benefit model for each energy source' is used in analysis of technology development of renewable energy and quantitative and macro economical effects of its commercialization in order to foresee following expand in related industries and increase in added value. First, Investment on the renewable energy technology development is in direct proportion both to the product and growth, but product shows slightly higher index under the same amount of R&D investment than growth. It indicates that advance in technology greatly influences the final product, the energy growth. Moreover, while R&D investment on renewable energy product as well as the government funds included in the investment have proportionate influence on the renewable energy growth, private investment in the total amount invested has reciprocal influence. This statistic shows that research and development is mainly driven by government funds rather than private investment. Finally, while R&D investment on renewable energy growth affects proportionately, government funds and private investment shows no direct relations, which indicates that the effects of research and development on renewable energy do not affect government funds or private investment. All of the results signify that although it is important to have government policy in technology development and commercialization, private investment and active participation of enterprises are the key to the success in the industry.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.2
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• pp.97-104
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• 2019

This experiment was conducted to investigate the effect of whole crop rice (WCR) based TMR on growth performance and carcass characteristics of Hanwoo steers. WCR "Yeongwoo"was harvested at yellow ripen stage and ensiled for 60 days. The crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), in vitro dry matter digestibility (IVDMD) and total digestible nutrient (TDN) content was 8.4 %, 28.0 %, 53.8 %, 72.4 % and 66.8 %, respectively. For silage quality, pH was 4.37 and lactic and butyric acid content were 2.84 and 0.04 % in DM. Sixteen Hanwoo steers (8-mon-old) were allocated into either a control (commercial TMR) and WCR-TMR (WCR-based TMR) group. The TMR were fed according to the feeding stage phase: growing (Initiate~14 month), early fattening (15 month~21 month) and late fattening (22 month~30 month). The body weight of control group increased (P<0.05) until early fattening stage, but late growing stage of WCR-TMR group was higher than that of control (P<0.05). Average daily gain (ADG) was significantly greater (P<0.05) in WCR-TMR group (total 0.78 kg/head) compared to control (total 0.66 kg/head) except for late fattening stage. The marketing weight and carcass weight were higher in WCR-TMR group (726 vs 765 kg; 417.8 vs 450.4 kg). The back fat thickness (11.75 vs 13.00 mm), Longissimus dorsi area (88.00 vs $89.88cm^2$) and yield index (65.87 vs 64.30) were not different between the two groups (P>0.05) and also no difference in meat yield grade (A : B : C = 2 : 4 : 2). Marbling score (4.00 vs 4.13), meat color (4.75 vs 4.75), fat color (3.13 vs 2.88), texture (1.25 vs 1.50) and maturity (2.00 vs 2.00) were not significant difference between the two groups and meat quality grade ($1^{{+}{+}}:1^+:1:2:3=0:2:4:2:0$) was also not different. In conclusion, TMR feeding based on WCR silage showed superiority in carcass yield and ADG compared to control TMR. It is considered that the use of WCR for feed is a necessary option for the substitution of the imported forages and the government's policy for rice production adjustment.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.194-203
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• 2022

Cultivation of tomatoes in Korea grown in soil covers 89% of the total area for tomato cultivation. Tomatoes grown in soil often encounter various environment stresses including not only salt stress and soil-borne diseases but also cold stress in the winter season. This study was conducted to comparatively analyze the performance of rootstocks with cold stress by measuring the growth, yield, and photosynthetic efficiency in tomatoes grown in soil. The rootstocks were used 'Powerguard', 'IT173773', and '20LM' for the domestic rootstock cultivars and 'B-blocking' for a control cultivar. The tomato cultivar 'Red250' was used as the scion and the non-grafted tomatoes. Stem diameter, flowering position, leaf length, and leaf width were investigated for the growth parameters. The stem diameter of the non-grafted tomatoes decreased by 15% compared to the grafted tomatoes at 80 days after transplanting when exposed to low temperatures of 9-14℃ for 14 days. The leaf length and width of the non-grafted tomatoes were the lowest with 42.4 cm and 41.8 cm at 80 days after transplanting. The total yield per plant was the highest in tomato plants grafted on 'Powerguard' with 1,615 g and lowest in non-grafted tomatoes with 1,299 g. As the result of measuring the chlorophyll fluorescence parameters, PI_ABS and DI₀/RC, which mean the performance index and dissipated energy flux, 'Powerguard' was the highest with 3.73 in PI_ABS and the lowest with 0.34 in DI₀/RC, whereas non-grafted tomatoes was the lowest with 2.62 in PI_ABS and the highest with 0.41 in DI₀/RC at 80 days after transplanting. The stem diameter has positive correlation with PI_ABS, while it has negative correlation with DI₀/RC. The results indicate that can be analyzed by chlorophyll fluorescence parameters can be used for analyzing the differences in the growth of tomato plants grafted on different rootstocks when exposed to cold stress.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Geophysics and Geophysical Exploration
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• v.2 no.1
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• pp.26-32
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• 1999

Among the various seismic data processing sequences, the velocity analysis is the most time consuming and man-hour intensive processing steps. For the production seismic data processing, a good velocity analysis tool as well as the high performance computer is required. The tool must give fast and accurate velocity analysis. There are two different approches in the velocity analysis, batch and interactive. In the batch processing, a velocity plot is made at every analysis point. Generally, the plot consisted of a semblance contour, super gather, and a stack pannel. The interpreter chooses the velocity function by analyzing the velocity plot. The technique is highly dependent on the interpreters skill and requires human efforts. As the high speed graphic workstations are becoming more popular, various interactive velocity analysis programs are developed. Although, the programs enabled faster picking of the velocity nodes using mouse, the main improvement of these programs is simply the replacement of the paper plot by the graphic screen. The velocity spectrum is highly sensitive to the presence of the noise, especially the coherent noise often found in the shallow region of the marine seismic data. For the accurate velocity analysis, these noise must be removed before the spectrum is computed. Also, the velocity analysis must be carried out by carefully choosing the location of the analysis point and accuarate computation of the spectrum. The analyzed velocity function must be verified by the mute and stack, and the sequence must be repeated most time. Therefore an iterative, interactive, and unified velocity analysis tool is highly required. An interactive velocity analysis program, xva(X-Window based Velocity Analysis) was invented. The program handles all processes required in the velocity analysis such as composing the super gather, computing the velocity spectrum, NMO correction, mute, and stack. Most of the parameter changes give the final stack via a few mouse clicks thereby enabling the iterative and interactive processing. A simple trace indexing scheme is introduced and a program to nike the index of the Geobit seismic disk file was invented. The index is used to reference the original input, i.e., CDP sort, directly A transformation techinique of the mute function between the T-X domain and NMOC domain is introduced and adopted to the program. The result of the transform is simliar to the remove-NMO technique in suppressing the shallow noise such as direct wave and refracted wave. However, it has two improvements, i.e., no interpolation error and very high speed computing time. By the introduction of the technique, the mute times can be easily designed from the NMOC domain and applied to the super gather in the T-X domain, thereby producing more accurate velocity spectrum interactively. The xva program consists of 28 files, 12,029 lines, 34,990 words and 304,073 characters. The program references Geobit utility libraries and can be installed under Geobit preinstalled environment. The program runs on X-Window/Motif environment. The program menu is designed according to the Motif style guide. A brief usage of the program has been discussed. The program allows fast and accurate seismic velocity analysis, which is necessary computing the AVO (Amplitude Versus Offset) based DHI (Direct Hydrocarn Indicator), and making the high quality seismic sections.

• The Journal of Korean Society for Radiation Therapy
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• v.32
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• pp.93-109
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• 2020

Purpose: The radiochromic film (Gafchromic EBT3, Ashland Advanced Materials, USA) and 3-dimensional analysis system dosimetry checkTM (DC, MathResolutions, USA) were evaluated for patient-specific quality assurance (QA) of helical tomotherapy. Materials and Methods: Depending on the tumors' positions, three types of targets, which are the abdominal tumor (130.6㎤), retroperitoneal tumor (849.0㎤), and the whole abdominal metastasis tumor (3131.0㎤) applied to the humanoid phantom (Anderson Rando Phantom, USA). We established a total of 12 comparative treatment plans by the four geometric conditions of the beam irradiation, which are the different field widths (FW) of 2.5-cm, 5.0-cm, and pitches of 0.287, 0.43. Ionization measurements (1D) with EBT3 by inserting the cheese phantom (2D) were compared to DC measurements of the 3D dose reconstruction on CT images from beam fluence log information. For the clinical feasibility evaluation of the DC, dose reconstruction has been performed using the same cheese phantom with the EBT3 method. Recalculated dose distributions revealed the dose error information during the actual irradiation on the same CT images quantitatively compared to the treatment plan. The Thread effect, which might appear in the Helical Tomotherapy, was analyzed by ripple amplitude (%). We also performed gamma index analysis (DD: 3mm/ DTA: 3%, pass threshold limit: 95%) for pattern check of the dose distribution. Results: Ripple amplitude measurement resulted in the highest average of 23.1% in the peritoneum tumor. In the radiochromic film analysis, the absolute dose was on average 0.9±0.4%, and gamma index analysis was on average 96.4±2.2% (Passing rate: >95%), which could be limited to the large target sizes such as the whole abdominal metastasis tumor. In the DC analysis with the humanoid phantom for FW of 5.0-cm, the three regions' average was 91.8±6.4% in the 2D and 3D plan. The three planes (axial, coronal, and sagittal) and dose profile could be analyzed with the entire peritoneum tumor and the whole abdominal metastasis target, with planned dose distributions. The dose errors based on the dose-volume histogram in the DC evaluations increased depending on FW and pitch. Conclusion: The DC method could implement a dose error analysis on the 3D patient image data by the measured beam fluence log information only without any dosimetry tools for patient-specific quality assurance. Also, there may be no limit to apply for the tumor location and size; therefore, the DC could be useful in patient-specific QAl during the treatment of Helical Tomotherapy of large and irregular tumors.

• Journal of Animal Science and Technology
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• v.44 no.3
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• pp.327-340
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• 2002

This study was conducted to investigate the effects of domestic clay minerals on feed efficiency, meat quantity, meat quality and economic traits in 24 head of Hanwoo steers(166.1kg in body weight) for 540 days from six to 24 months in age. Feeding trial was conducted with 4 treatment(six heads/treatment) which were T1(Control), T2(Control+Kaolinite), T3(Control+Bentonite), T4(Control+Illite). The results obtained are summarized as follows; The range of average daily gains were 0.682 to 0.713, 0.669 to 0.714, 0.690 to 0.840 and 0.699 to 0.756kg in growing, fattening, finishing and over-all period, respectively, and the gains were high in T1 for growing and fattening period but in clay mineral groups for finishing and over-all period, especially it was high in Illite and Bentonite groups. Concentrates and TDN intakes per unit of kg gains were lower in clay mineral groups than in control and was lower especially in Bentonite groups. In carcass characteristics, dressed carcass and fresh meat and retailed cut percent were not apparently difference by treatments, and yield index was 69.3, 68.9, 68.8 and 68.6 in T3, T2, T4 and T1, respectively. Marbling scores were 5.1, 4.6, 4.4 and 3.3 in T3, T2, T4 and T1, respectively, and the range of shear force by treatment was from 3.51 to 6.02kg/cm2. and were improved with significant difference(P<0.05) in clay mineral groups than in control. Also in palatability traits, panel test scores of juiciness, tenderness and flavor were improved in clay mineral feeding groups, especially the flavor was improved with highly significant difference(P<0.01) in clay mineral groups than in control. In total fatty acid contents, the rate of SFA(saturated fatty acid) in longissimus muscle of beef was higher in the order of T2, T3, T1 and T4 while the rate of MUFA(monounsaturated fatty acid) was high in the order of T4, T3, T1 and T2. The content of oleic acid which is major influential factor at the flavor of beef was higher in Illite groups than in any other groups. In composition of amino acids in longissimus muscles of beef, the rate of essential amino acids was high in the order of T1, T2, T3 and T4. and the rate of amino acids in clay mineral groups was smaller than in control.In chemical component in Gom-Tang(soup of bone) made by Hanwoo steer’s leg-bone, the ranges of crude protein, ether extract, and crude ash was 0.81 to 1.24, 0.17 to 0.35 and 0.07 to 0.09%, respectively. In mineral composition, the ranges of Ca, P, Na and Mg was 14.01 to 15.77, 11.45 to 16.40, 37.92 to 49.99 and 0.26 to 0.46ppm, respectively. Chemical composition were not apparently different but mineral composition was increased in clay mineral groups than in control. Income by treatments was 967,096 to 1,524,055 Won per head for 540 days and income of clay mineral groups in comparison with control’s increased by 23.7 to 57.6 percent, and especially it was higher in bentonite and(or) Illite groups than others. According to the above results it may be concluded that clay mineral to growing-fattening Hanwoo steers can be improved the meat quantity, meat quality and income. Especially the effect of bentonite and illite is large and can be recommended for usage to improve animal performance as feed additives of growing-fattening Hanwoo steers.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.