• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.6
• /
• pp.509-523
• /
• 2012

The sound quality based on design optimization, throughout the development process of various electronic office equipments, needs to be considered in order to respond the increased needs for the emotional satisfaction of customers in terms of psycho-acoustics. This paper focuses on how to describe the characteristics of operating sound radiated from laser printers by using various sound attributes, and to model the sound quality index that can properly evaluate the subjective preference on modification conditions in the improvement study quantitatively. Especially, the proposed verification process, in the form of combining the correlation based method and the decision error based method, was applied to improve the generality and reliability of a group of participants in the jury evaluation. The modified Aures tonality model was also proposed to improve the correlation coefficient with the mean response of participants by optimizing some parameters. As a result, the loudness, articulation index, roughness, tonality, fluctuation strength were used to model the sound quality index for laser printers by using the multiple-linear regression method. Through the improvement study, it was confirmed that replacing the absorbing materials is effective to reduce the tonalness radiated from the side of a reference printer model. Based on above results, it can be concluded that the proposed model has enough usefulness as quantitative evaluation index to evaluate the difference between modification conditions in the improvement study.

• Journal of Korean Neurosurgical Society
• /
• v.39 no.4
• /
• pp.256-259
• /
• 2006

Objective : Having a reliable and reproducible measurement technique to measure the sagittal contour in vertebral fractures is paramount to clinical decision making. This study is designed to determine the most reliable measurement technique in osteoporotic vertebral compression fracture. Methods : Fifteen lateral radiographs of thoracic and lumbar fractures were selected and measured on two separate occasions by three spine surgeons using six different measurement techniques [Centroid, Harrison Posterior Tangent Methods and 4 different types of modified Cobb method]. The radiograph quality was assessed and the center beam location was determined. Statistical analysis including ANOVA for repeated measures was carried out using the SAS software [v 8.0]. Results : The inter and intraobserver variance of the Cobb method 4 and Harrison posterior tangent method were significantly lower than the other four methods. The intraobserver correlation coefficients were the most consistent using the Cobb method 4 [0.982]. which was followed by the Harrison posterior tangent [0.953] and Cobb methods 1 [0.874]. The intraobserver agreement [% of repeated measures within 5 degrees of the original measurement] ranged from 42% to 98% for each technique for all three observers, with the Cobb method 4 showing the best agreement [97.8%] followed by the Harrison posterior tangent method [937%]. Conclusion : The Cobb method-4 and Harrison posterior tangent methods, when applied to measuring the kyphosis, are reliable and have a similar small error range. The Cobb method 4 shows the best overall reliability. However, the centroid method and Cobb method using a fractured endplate do not produce an accurate result due to inter and intraobserver differences in determining the baseline.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.10
• /
• pp.486-492
• /
• 2019

The system-based approach is needed to select an optimal mix of weapon systems and ship platform among a variety of design alternatives with the uncertainties of the initial required operational capability. In the early-phase design, which included a feasibility study and concept design, it is possible to cause problems when a review of the operational concept, database development, and systematic design are not done, thereby producing uncertain and unstable requirements. To select the best solution without trial-and-error, the U.S. navy has applied the set-based method for the early-phase design of a new ship-to-shore connector. The ship synthesis model plays an important role in applying the set-based method, but only a few countries possess this model and have prohibited this model from being transferred to other countries. This paper suggests a set-based method using a genetic algorithm and decision-making theory through benchmarking existing ship data. The algorithm was verified using the DDG-51 class ship synthesis model to optimize the weapon system design, which has been released for research purposes.

• Journal of the Korea Institute of Building Construction
• /
• v.21 no.5
• /
• pp.495-506
• /
• 2021

A Conceptual cost estimate, which is computed in the preliminary step of a project, is important for decision-making by a contractor in terms of the project budget, economic feasibility and validity analysis, and alternative comparisons. Therefore, a high error rate of a prediction model for a conceptual cost estimate can lead to various problems including excessive project expenditures and a delayed break-even point. this study proposed optimal impact factors by configuring quantitative impact factors computable in a preliminary step in various cases(combinations of impact factors). subsequently, the accuracy of different cases was comparatively analyzed by using the cases as input values of a prediction model using regression analysis. when the optimal combination of impact factors proposed in this study and other combination of impact factors were applied to the prediction model, the regression analysis-based prediction model exhibited 0.2-4.7% improvements in accuracy, respectively. the optimal combination of impact factors proposed in this study improved the accuracy of the prediction model of a conceptual cost estimate by removing unnecessary impact factor.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.1
• /
• pp.83-91
• /
• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• Journal of Korean Society of Water Science and Technology
• /
• v.26 no.6
• /
• pp.133-142
• /
• 2018

Increasing localized torrential rainfall caused by abnormal climate are making higher damage to human and property through urban inundation So The need of preventive measures is being highlighted. In this study, the methodology for calculating flood depth in domestic water map using an interpolation method in order to utilizing the results of flood analysis provided only in the form of a report is suggested. In the Incheon Metropolitan City S area as the test-bed, the flood depth was calculated using the interpolating the actual flood analysis by image and verification was performed. Verification results showed that the error rate was 5.2% for the maximum flooding depth, and that the water depth value was compared to 10 random points, which showed a difference of less than 0.030 m. Also, as the results of the flood analysis were presented in various ways, the flood depth was extracted from the image of the result of the flood analysis, which changed the presentation method, and then compared and analyzed. The results of this study could be available for the use of basic data from the research on the urban penetration of domestic consumption and for decision-making of policy.

• Journal of Biomedical Engineering Research
• /
• v.41 no.6
• /
• pp.247-255
• /
• 2020

Accurate diagnosis of movement disorders is important for providing right patient care at right time. In general, assessment of motor impairment relies on clinical ratings conducted by experienced clinicians. However, this may introduce subjective opinions into scoring the severity of motor impairment. Digital devices such as table PC and smart band with accelerometer can be used for more accurate and objective assessment and possibly helpful for clinicians to make right decision of patient's states. In this study, we introduce quantification algorithms of motor impairment which uses the digital data acquired during four clinical motor tests (Line drawing, Spiral drawing, Nose to finger and Hand flip tests). The step by step procedure of quantifying metrics (Tremor Frequency, Tremor Magnitude, Error Distance, Time, Velocity, Count and Period) are provided with flowchart. The effectiveness of the proposed algorithm is presented with the result from simulated data (normal, normal with tremor and slowness, poor with tremor, poor with tremor and slowness).

• Clinical and Experimental Reproductive Medicine
• /
• v.48 no.2
• /
• pp.163-173
• /
• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• Journal of Broadcast Engineering
• /
• v.27 no.5
• /
• pp.694-702
• /
• 2022

This paper is the UX-based technology strategy research which is a solution to how conversational AI can be ethically evolved in the Metaverse environment. Since conversational AI influences people's on-offline decision-making factors through interaction with people, the Metaverse AI ethics must be reflected. In the machine learning process of conversational AI, cultural codes along with user's personal experience data must be included and considered to reduce the error value of user experience. Through this, the super-personalized Metaverse service can evolve ethically with social values. With above hypothesis as a result of the study, a conceptual model of a forward-looking perspective was developed and proposed by adding user experience data to the machine learning (ML) process for context-based interactive AI in the Metaverse service environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.3
• /
• pp.125-132
• /
• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.