• International Journal of Computer Science & Network Security
• /
• v.21 no.12spc
• /
• pp.375-382
• /
• 2021

The paper introduces a model for evaluating and prioritizing mobile quality attributes and refactoring techniques through the examination of their effectiveness during the mobile application development process. The astonishing evolution of software and hardware has increased the demand for techniques and best practices to overcome the many challenges related to mobile devices, such as those concerning device storage, network bandwidth, and energy consumption. A number of studies have investigated the influence of refactoring, leading to the enhancement of mobile applications and the overcoming of code issues as well as hardware issues. Furthermore, rapid and continuous mobile developments make it necessary for teams to apply effective techniques to produce reliable mobile applications and reduce time to market. Thus, we investigated the influence of various refactoring techniques on mobile applications to understand their effectiveness in terms of quality attributes. First, we extracted the most important mobile refactoring techniques and a set of quality attributes from the literature. Then, mobile application developers from nine mobile application teams were recruited to evaluate and prioritize these quality attributes and refactoring techniques for their projects. A prioritization-based model is examined that integrates the lightweight multi-criteria decision making method, called the best-worst method, with the process of refactoring within mobile applications. The results prove the applicability and suitability of adopting the model for the mobile development process in order to expedite application production while using well-defined procedures to select the best refactoring techniques. Finally, a variety of quality attributes are shown to be influenced by the adoption of various refactoring techniques.

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.22-28
• /
• 2019

This paper presents the monitoring method of machining error and quality check to improve the productivity of boring manufacturing process. Machining error usually appears as the offset of spatial location of actual cutting path compared to ideal cutting path. In order to monitor an error of workpiece, multiple factors affecting quality of boring, such as distortion of workpiece, clamping error, radial rotation error of the spindle and motion error of machine tools, were took into account. To verify the productive quality, we propose the quality check system. The system based on IT convergence analyzes the process error rate and saves the analyzed data in memory. Also, these play important roles in detecting an inferior production goods and can decrease the production cost and loss of bearing.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.5
• /
• pp.773-787
• /
• 2020

In the injection molding process, the controlling stability of products quality is a very important factor in terms of productivity. Even when the optimum process conditions for the desired product quality are applied, uncontrollable external factors such as ambient temperature and humidity cause inevitable changes in the state of the melt resin, mold temperature. etc. Therefore, it is very difficult to maintain prodcut quality. In this study, a system that learns the correlation between process variables and product weight through artificial neural networks and predicts process conditions for the target weight was established. Then, when a disturbance occurs in the injection molding process and fluctuations in the weight of the product occur, the stability control of the product quality was performed by ANN predicting a new process condition for the change of weight. In order to artificially generate disturbance in the injection molding process, controllable factors were selected and changed among factors not learned in the ANN model. Initially, injection molding was performed with a polypropylene having a melt flow index of 10 g/10min, and then the resin was replaced with a polypropylene having a melt floiw index of 33 g/10min to apply disturbance. As a result, when the disturbance occurred, the deviation of the weight was -0.57 g, resulting in an error of -1.37%. Using the control method proposed in the study, through a total of 11 control processes, 41.57 g with an error of 0.00% in the range of 0.5% deviation of the target weight was measured, and the weight was stably maintained with 0.15±0.07% error afterwards.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.10a
• /
• pp.754-758
• /
• 2005

Optimization of process variables such as arc current, welding voltage and welding speed in terms of the weld characteristics desired is the key step in achieving high quality and improving performance characteristics without increasing the cost. Consequently, incorrect settings of those process variables give rise to deviations in the welding characteristics from the desired bead geometry. Therefore, trainee welders are referred to the tabulated information relating different metal types and thickness as to recommend the desired values of process variables. Basically, the bead geometry plays an important role in determining the mechanical properties of the weld. So that it is very important to select the process variables for obtaining optimal bead geometry. However, it is difficult for the traditional identification methods to provide an accurate model because the optimized welding process is non-linear and time-dependent. In this paper, the possibilities of the Infra-red sensor in sensing and control of the bead geometry in the automated welding process are presented. Infra-red sensor is a well-known method to deal with the problems with a high degree of fuzziness so that the sensor is employed to build the relationship between process variables and the quality characteristic the proposed above respectively. Based on several neural networks, the mathematical models are derived from extensive experiments with different welding parameters and complex geometrical features. The developed system enables to select the optimal welding parameters and control the desired weld dimensions during arc welding process.

• The Korea Journal of Herbology
• /
• v.33 no.1
• /
• pp.85-91
• /
• 2018

Objectives : This study was carried out to evaluate the quality of different drying methods and different roots(primary root and lateral root) of Angelica gigas Nakai. Methods : The experimental method was performed according to the Korea Pharmacopoea Eleventh edition (KP11). Loss on drying, ash, acid insoluble ash, ethanol extract, nodakenin and total decursin contents were tested to evaluate the quality of root tissue of Angelica gigas Nakai. In addition, the treatment of different root parts were prepared in two groups of washing dry process and natural dry process. Results : In comparison of dry processing methods, total contents of nodakenin and total decursin in the primary root and lateral root through washing dry process were ranged from 3.55 to 4.09% and from 5.18 to 6.13%, respectively. And also, those of roots from the natural dry process were from 4.36 to 6.22% and from 6.28 to 8.34%, respectively. In the washing dry process and natural dry process methods, 47.9% and 22.3% higher amount of nodakenin and total decursin were measured in lateral root compared to primary root. In common, lateral roots accumulated higher contents of nodakenin and total decursin compared to primary roots, and samples drying processed with natural dry process compared to washing dry process method contained higher amount of compounds. Conclusions : We sincerely hope that this study will be contributed to the standardization and quality control of Angelica Gigas Root.

• Journal of Korean Society for Quality Management
• /
• v.52 no.1
• /
• pp.43-56
• /
• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

• Journal of Korean Society for Quality Management
• /
• v.52 no.2
• /
• pp.377-394
• /
• 2024

Purpose: To derive the optimal conditions for the Rubber based colloidal suspension manufacturing process, which made using a stirrer, to apply the mixture design method. Methods: We used two process component and one process variable Mixture design to derive the optimal conditions for the process. The response variables were selected for rotational viscometer measures which can represent Rubber based colloidal suspension quality. The input variables were selected as the values of rubber-organic solvent expressed in proportions as process components and stirring amount as a process variable which are controllable factors in the process. Results: Based on the results of the experiment, rubber and organic solvent and the interaction between stirring amount and rubber and the interaction between stirring amount and rubber and organic solvent were significant. Reproducibility of the regression model was confirmed by the observation that the values obtained from the reproducibility experiment fell within the confidence interval. Additionally, the model predictions were found to be in close agreement with the field measurements. Conclusion: In this study, a regression model was developed to predict the viscosity change of colloidal suspensions based on the proportion of rubber based colloidal suspension. The developed regression model can lead to improved product quality.

• Information Systems Review
• /
• v.13 no.1
• /
• pp.1-11
• /
• 2011

This study empirically investigated the effect of firm-level data quality on economic performance in the Korean financial industry during 2008~2009. The data quality was measured by data quality management process index and data quality criteria by Korea Database Agency, and financial firm performance data was acquired from Financial Statistics Information System of the Financial Supervisory Service. The result showed that the data quality has statistically significant impacts on financial firm performance such as sales, operating profit, and value added. If the data quality management process index increases by one, the value added can increase by 2.3 percent. Moreover, the data quality criteria increase by one, the value added can increase by 72.6 percent.

• Journal of Korean Society for Quality Management
• /
• v.42 no.2
• /
• pp.179-187
• /
• 2014

Purpose: Since several problems were found when present non-conducting metal coating process was applied to mass production, we study and develop to improve those problems. Methods: In this paper, a couple of analysis methods such as surface hardness, XPS spectrum analysis, morphology, and reflection ratio were used. Results: This paper suggest a new possibility of Non-conducting thin metal coating method that has quality of mass production phase without UV coating process. Conclusion: By the result of analysis, we can set optimized process conditions of the electro deposition coating using electron beam.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2003.05a
• /
• pp.421-426
• /
• 2003

Micro-suing that used on micro mechanism should be equal to distance between pitch and correct of shape. Therefore, micro spring must make by super-precision working. But, current step of super-precision processing depends on special quality of work piece and is ineffective the aspect of cost and productivity yet. Also, to use as demandable length shearing process perform but even if make precision spring, in the aspect of quality of coil spring make difficult that produce product of good quality. Therefore, purpose of this study presented proposed process that extract the point of processing factor after perform finite element analysis applying existing sheet shearing process to suing shearing process consider cost and productivity after evaluate.