• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.43-57
• /
• 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 Radiology
• /
• v.19 no.6
• /
• pp.1187-1195
• /
• 2018

Objective: To compare correlations between pulmonary function test (PFT) results and different reconstruction algorithms and to suggest the optimal reconstruction protocol for computed tomography (CT) quantification of low lung attenuation areas and airways in healthy individuals. Materials and Methods: A total of 259 subjects with normal PFT and chest CT results were included. CT scans were reconstructed using filtered back projection, hybrid-iterative reconstruction, and model-based IR (MIR). For quantitative analysis, the emphysema index (EI) and wall area percentage (WA%) were determined. Subgroup analysis according to smoking history was also performed. Results: The EIs of all the reconstruction algorithms correlated significantly with the forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) (all p < 0.001). The EI of MIR showed the strongest correlation with FEV1/FVC (r = -0.437). WA% showed a significant correlation with FEV1 in all the reconstruction algorithms (all p < 0.05) correlated significantly with FEV1/FVC for MIR only (p < 0.001). The WA% of MIR showed the strongest correlations with FEV1 (r = -0.205) and FEV1/FVC (r = -0.250). In subgroup analysis, the EI of MIR had the strongest correlation with PFT in both eversmoker and never-smoker subgroups, although there was no significant difference in the EI between the reconstruction algorithms. WA% of MIR showed a significantly thinner airway thickness than the other algorithms ($49.7{\pm}7.6$ in ever-smokers and $49.5{\pm}7.5$ in never-smokers, all p < 0.001), and also showed the strongest correlation with PFT in both ever-smoker and never-smoker subgroups. Conclusion: CT quantification of low lung attenuation areas and airways by means of MIR showed the strongest correlation with PFT results among the algorithms used, in normal subjects.

• Clinical and Experimental Reproductive Medicine
• /
• v.27 no.1
• /
• pp.47-57
• /
• 2000

Objective: The effects of cryopreservation with or without coculture on the in vitro development of blastomere-biopsied 8-cell mouse embryos were investigated. This experimental study was originally designed for the setup of a preclinical mouse model for the preimplantation genetic diagnosis (PGD) in human. Methods: Eight-cell embryos were obtained after in vitro fertilization (IVF) from F1 hybrid mice (C57BL(표현불가)/CBA(표현불가)). Using micromanipulation, one to four blastomeres were aspirated through a hole made in the zona pellucida by zona drilling (ZD) with acid Tyrode's solution (ATS). A slow-freezing and rapid-thawing protocol with 1.5M dimethyl sulfoxide (DMSO) and 0.1M sucrose as cryoprotectant was used for the cryopreservation of blastomere- biopsied 8-cell mouse embryos. After thawing, embryos were cultured for 110 hours in Ham's F-10 supplemented with 0.4% bovine serum albumin (BSA). In the coculture group, embryos were cultured for 110 hours on the monolayer of Vero cells in the same medium. The blastocyst formation was recorded, and the embryos developed beyond blastocyst stage were stained with 10% Giemsa to count the total number of nuclei in each embryo. Results: The survival rate of embryos after cryopreservation was significantly lower in the blastomere-biopsied (7/8, 6/8, 5/8, and 4/8 embryos) groups than in the non-biopsied, zona intact (ZI) group. Without the coculture, the blastocyst formation rate of embryos after cryopreservation was not significantly different among ZI, the zona drilling only (ZD), and the balstomere-biopsied groups, but it was significantly lower than in the non-cryopreserved control group. The mean number of cells in embryos beyond blastocyst stage was significantly higher in the control group ($50.2{\pm}14.0$) than in 6/8 ($26.5{\pm}6.2$), 5/8 ($25.0{\pm}5.5$), and 4/8 ($17.8{\pm}7.8$) groups. With the coculture using Vero cells, the blastocyst formation rate of embryos after cryopreservation was significantly lower in 5/8 and 4/8 groups, compared with the control, 7/8, and 6/8 groups. The mean number of cells in embryos beyond blastocyst stage was also significantly lower in 4/8 group ($25.9{\pm}10.2$), compared with the control ($50.2{\pm}14.0$), 7/8 ($56.0{\pm}22.2$), and 6/8 ($55.3{\pm}25.5$) groups. Conclusion: After cryopreservation, blastomere-biopsied mouse embryos have a significantly impaired developmental competence in vitro, but this detrimental effect might be prevented by the coculture with Vero cells in 8-cell mouse embryos biopsied one or two blastomeres. Biopsy of mouse embryos after ZD with ATS is a safe and highly efficient preclinical model for PGD of human embryos.