• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.1-23
• /
• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• KDI Journal of Economic Policy
• /
• v.40 no.4
• /
• pp.47-63
• /
• 2018

Government R&D grants for SMEs have risen to three trillion Korean won a year, placing Korea second among OECD nations. Indeed, analysis results have revealed that government support has not only expanded corporate R&D investment and the registration of intellectual property rights but has also increased investment in tangible and human assets and marketing. However, value added, sales and operating profit have lacked improvement owing to an ineffective recipient selection system that relies solely on qualitative assessments by technology experts. Nevertheless, if a predictive model is properly applied to the system, the causal effect on value added could increase by more than two fold. Accordingly, it is important to focus on economic performance rather than technical achievements to develop such a model.

• Structural Engineering and Mechanics
• /
• v.53 no.6
• /
• pp.1183-1200
• /
• 2015

Reinforcement corrosion can cause serious safety deterioration to aging concrete structures exposed in aggressive environments. This paper presents an approach for reliability analyses of deteriorating reinforced concrete structures affected by reinforcement corrosion on the basis of the representative symptoms identified during the deterioration process. The concrete cracking growth and rebar bond strength evolution due to reinforcement corrosion are chosen as key symptoms for the performance deterioration of concrete structures. The crack width at concrete cover surface largely depends on the corrosion penetration of rebar due to the expansive rust layer at the bond interface generated by reinforcement corrosion. The bond strength of rebar in the concrete correlates well with concrete crack width and decays steadily with crack width growth. The estimates of cracking development and bond strength deterioration are examined by experimental data available from various sources, and then matched with symptom-based lifetime Weibull model. The symptom reliability and remaining useful life are predicted from the predictive lifetime Weibull model for deteriorating concrete structures. Finally, a numerical example is provided to demonstrate the applicability of the proposed approach for forecasting the performance of concrete structures subject to reinforcement corrosion. The results show that the corrosion rate has significant impact on the reliability associated with serviceability and load bearing capacity of reinforced concrete structures during their service life.

• Journal of Integrative Natural Science
• /
• v.10 no.3
• /
• pp.141-147
• /
• 2017

Vascular endothelial growth factor (VEGF) is an important signaling protein involved in angiogenesis, which is the formation of new blood vessels from pre-existing vessels. Consequently, blocking of the vascular endothelial growth factor receptor (VEGFR-2) by small molecule inhibitors leads to the inhibition of cancer induced angiogenesis. In this study, we performed a two dimensional quantitative structure activity relationship (2D-QSAR) study of 38 N-Phenyl-N'-{4-(4-quinolyloxy) phenyl} urea derivatives as VEGFR-2 inhibitors based on hologram quantitative structure-activity (HQSAR). The model developed showed reasonable $q^2=0.521$ and $r^2=0.932$ values indicating good predictive ability and reliability. The atomic contribution map analysis of most active compound (compound 7) indicates that hydrogen and oxygen atoms in the side chain of ring A and oxygen atom in side chain of ring C contributes positively to the activity of the compounds. The HQSAR model developed and the atomic contribution map can serve as a guideline in designing new compounds for VEGFR-2 inhibition.

• Journal of Environmental Science International
• /
• v.28 no.8
• /
• pp.701-707
• /
• 2019

Highland farming is agriculture that takes place 400 m above sea level and typically involves both low temperatures and long sunshine hours. Most highland Chinese cabbages are harvested in the Gangwon province. The Ubiquitous Sensor Network (USN) has been deployed to observe Chinese cabbages growth because of the lack of installed weather stations in the highlands. Five representative Chinese cabbage cultivation spots were selected for USN and meteorological data collection between 2015 and 2017. The purpose of this study is to develop a weight prediction model for Chinese cabbages using the meteorological and growth data that were collected one week prior. Both a regression and random forest model were considered for this study, with the regression assumptions being satisfied. The Root Mean Square Error (RMSE) was used to evaluate the predictive performance of the models. The variables influencing the weight of cabbage were the number of cabbage leaves, wind speed, precipitation and soil electrical conductivity in the regression model. In the random forest model, cabbage width, the number of cabbage leaves, soil temperature, precipitation, temperature, soil moisture at a depth of 30 cm, cabbage leaf width, soil electrical conductivity, humidity, and cabbage leaf length were screened. The RMSE of the random forest model was 265.478, a value that was relatively lower than that of the regression model (404.493); this is because the random forest model could explain nonlinearity.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.9
• /
• pp.3735-3740
• /
• 2015

Context: Insulin-like growth factor peptides play important roles in regulating cell growth, cell differentiation, and apoptosis, and have been demonstrated to promote the development of colorectal cancer (CRC). Objective: To examine the association of insulin-related biomarkers including insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein-3 (IGFBP-3) and C-peptide with CRC risk and assess their relevance in predictive models. Materials and Methods: The odds ratios of colorectal cancer for serum levels of IGF-1, IGFBP-3 and C-peptide were estimated using unconditional logistic regression models in 100 colorectal cancer cases and 100 control subjects. Areas under the receiving curve (AUC) and integrated discrimination improvement (IDI) statistics were used to assess the discriminatory potential of the models. Results: Serum levels of IGF-1 and IGFBP-3 were negatively associated with colorectal cancer risk (OR=0.07, 95%CI: 0.03-0.16, P for trend <.01, OR=0.06, 95%CI: 0.03-0.15, P for trend <.01 respectively) and serum C-peptide was positively associated with risk of colorectal cancer (OR=4.38, 95%CI: 2.13-9.06, P for trend <.01). Compared to the risk model, prediction for the risk of colorectal cancer had substantially improved when all selected biomarkers IGF-1, IGFBP-3 and inverse value of C-peptide were simultaneously included inthe reference model [P for AUC improvement was 0.02 and the combined IDI reached 0.166% (95 % CI; 0.114-0.219)]. Conclusions: The results provide evidence for an association of insulin-related biomarkers with colorectal cancer risk and point to consideration as candidate predictor markers.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.1
• /
• pp.40-49
• /
• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Journal of Fisheries Business Administration
• /
• v.47 no.4
• /
• pp.65-75
• /
• 2016

The purpose of this study was to analyze the production and marketing control effects of aqua-cultured flounder required for stable income growth of aqua-cultured household. We analyzed the supply and demand structure of cultured flounder using the partial equilibrium model approach. And we estimated the optimal yield of cultured flounder and analyzed the effect of marketing control through constructed model. The main results of this study are summarized as follows. First, the fitness and predictive power of the estimated model showed that the RMSPE and MAPE values were less than 5% and Theil's inequality coefficient was very close to 0 rather than 1. It was evaluated that the prediction ability of the aqua-cultured flounder supply and demand model by dynamic simulation was excellent. Second, dynamic simulation based on policy simulation was conducted to analyze the price increase effect of production and shipment control of cultured flounder. As a result, if the annual production volume is reduced by 1%, 5%, and 10% among 32,852~37,520 tons, it is analyzed that the price increase effect is from 1.2% to 12.5%. Finally, this study suggests that the production and marketing control can increase the price of aqua-cultured flounder in the market. In this paper, we propose a policy implementation of the total supply system instead of conclusions.

• Smart Structures and Systems
• /
• v.12 no.2
• /
• pp.97-119
• /
• 2013

This paper presents numerical and experimental results on the use of guided waves for structural health monitoring (SHM) of crack growth during a fatigue test in a thick steel plate used for civil engineering application. Numerical simulation, analytical modeling, and experimental tests are used to prove that piezoelectric wafer active sensor (PWAS) can perform active SHM using guided wave pitch-catch method and passive SHM using acoustic emission (AE). AE simulation was performed with the multi-physic FEM (MP-FEM) approach. The MP-FEM approach permits that the output variables to be expressed directly in electric terms while the two-ways electromechanical conversion is done internally in the MP-FEM formulation. The AE event was simulated as a pulse of defined duration and amplitude. The electrical signal measured at a PWAS receiver was simulated. Experimental tests were performed with PWAS transducers acting as passive receivers of AE signals. An AE source was simulated using 0.5-mm pencil lead breaks. The PWAS transducers were able to pick up AE signal with good strength. Subsequently, PWAS transducers and traditional AE transducer were applied to a 12.7-mm CT specimen subjected to accelerated fatigue testing. Active sensing in pitch catch mode on the CT specimen was applied between the PWAS transducers pairs. Damage indexes were calculated and correlated with actual crack growth. The paper finishes with conclusions and suggestions for further work.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.3
• /
• pp.341-346
• /
• 2011

A vacant technology forecasting is an important issue in management of technology. The forecast of vacant technology leads to the growth of nation and company. So, we need the results of technology developments until now to predict the vacant technology. Patent is an objective thing of the results in research and development of technology. We study a predictive method for forecasting the vacant technology quantitatively using patent data in this paper. We propose an ensemble model that is to vote some clustering criteria because we can't guarantee a model is optimal. Therefore, an objective and accurate forecasting model of vacant technology is researched in our paper. This model combines statistical analysis methods with machine learning algorithms. To verify our performance evaluation objectively, we make experiments using patent documents of diverse technology fields.