• Radiation Oncology Journal
• /
• v.10 no.1
• /
• pp.95-100
• /
• 1992

Since LINAC-based stereotactic radiosurgery uses multiple noncoplanar arcs, three-dimensional dose evaluation and many beam parameters, a lengthy computation time is required to optimize even the simplest case by a trial and error. The basic approach presented in this paper is to show promising methods using an experimental optimization and an analytic optimization The purpose of this paper is not to describe the detailed methods, but introduce briefly, proceeding research done currently or in near future. A more detailed description will be shown in ongoing published papers. Experimental optimization is based on two approaches. One is shaping the target volumes through the use of multiple isocenters determined from dose experience and testing. The other method is conformal therapy using a beam's eye view technique and field shaping. The analytic approach is to adapt computer-aided design optimization in finding optimum irradiation parameters automatically.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.15 no.3
• /
• pp.8-14
• /
• 2016

Medical bed head consoles (BHC) are generally used to increase the efficiency of medical equipment and speed the medical treatment response time. The BHC design has been consistently improved including a movable shelf unit that is embedded to mount stably medical instruments on the lower part of the main console. The cost of a BHC can be reduced through design optimization to limit the overall weight. However, as the size of a head console might decrease due to design optimization, the BHC deflection could be increased. In this study, multi-objective optimal design was adopted to consider this BHC design problem. In order to reduce the cost of optimization planning, an approximate model was applied for the design optimization. In the context of approximate optimization, we used the response surface method and non-dominant sorting genetic algorithm developed from various fields. Multi-objective optimal solutions were also compared with a single objective optimal design.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.73-82
• /
• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Nuclear Engineering and Technology
• /
• v.54 no.4
• /
• pp.1414-1420
• /
• 2022

High dose rate (HDR) brachytherapy treatment planning usually involves optimization methods to deliver uniform dose to the target volume and minimize dose to the healthy tissues. Four optimizations were used to evaluate the high-risk clinical target volume (HRCTV) coverage and organ at risk (OAR). Dose-volume histogram (DVH) and dosimetric parameters were analyzed and evaluated. Better coverage was achieved with PGO (mean CI = 0.95), but there were no significant mean CI differences than GrO (p = 0.03322). Mean EQD₂ doses to HRCTV (D₉₀) were also superior for PGO with no significant mean EQD₂ doses than GrO (p = 0.9410). The mean EQD₂ doses to bladder, rectum, and sigmoid were significantly higher for NO plan than PO, GrO, and PGO. PO significantly reduced the mean EQD₂ doses to bladder, rectum, and sigmoid but compromising the conformity index to HRCTV. PGO was superior in conformity index (CI) and mean EQD₂ doses to HRCTV compared with the GrO plan but not statistically significant. The mean EQD₂ doses to the rectum by PGO plan slightly exceeded the limit from ABS recommendation (mean EQD₂ dose = 78.08 Gy EQD₂). However, PGO can shorten the treatment planning process without compromising the CI and keeping the OARs dose below the tolerance limit.

• Journal of Microbiology and Biotechnology
• /
• v.25 no.11
• /
• pp.1920-1927
• /
• 2015

This study aimed to optimize four factors affecting biomass accumulation and 5-aminolevulinic acid (ALA) yield together with pollutants removal in Rhodobacter sphaeroides wastewater treatment. Results showed that it was feasible to produce biomass and ALA in R. sphaeroides wastewater treatment. Microaerobic, 1,000-3,000 lux, and pH 7.0 were optimal conditions for the highest ALA yield of 4.5 ± 0.5 mg/g-biomass. Under these conditions, COD removal and biomass production rate were 93.3 ± 0.9% and 31.8 ± 0.5 mg/l/h, respectively. In addition, trace elements Fe²⁺, Mg²⁺, Ni²⁺, and Zn²⁺ further improved the ALA yield, COD removal, and biomass production rate. Specifically, the highest ALA yield (12.5 ± 0.6 mg/g-biomass) was achieved with Fe²⁺ addition.

• Tribology and Lubricants
• /
• v.31 no.6
• /
• pp.239-244
• /
• 2015

Chemical mechanical polishing (CMP) is one of the most important processes for enabling sub-14 nm semiconductor manufacturing. Moreover, post-CMP defect control is a key process parameter for the purpose of yield enhancement and device reliability. Due to the complexity of device with sub-14 nm node structure, CMP-induced defects need to be fixed in the CMP in-situ cleaning module instead of during post ex-situ wet cleaning. Therefore, post-CMP in-situ cleaning optimization and cleaning efficiency improvement play a pivotal role in post-CMP defect control. CMP in-situ cleaning module normally consists of megasonic and brush scrubber processes. And there has been an increasing effort for the optimization of cleaning chemistry and brush scrubber cleaning in the CMP cleaning module. Although there have been many studies conducted on improving particle removal efficiency by brush cleaning, these studies do not consider the effects of brush contamination. Depending on the process condition and brush condition, brush cross contamination effects significantly influence post-CMP cleaning defects. This study investigates brush cross contamination effects in the CMP in-situ cleaning module by conducting experiments using 300mm tetraethyl orthosilicate (TEOS) blanket wafers. This study also explores brush pre-treatment in the CMP tool and proposes recipe effects, and critical process parameters for optimized CMP in-situ cleaning process through experimental results.

• Journal of Electrochemical Science and Technology
• /
• v.14 no.2
• /
• pp.174-183
• /
• 2023

The dye wastewater discharge from the textile industries mainly affects the aquatic environment. Hence, the treatment of this wastewater is essential for a pollutant-free environment. The purpose of this research is to optimize the dye removal efficiency for process influencing independent variables such as pH, electrolysis time (ET), and current density (CD) by using Box-Behnken design (BBD) optimization and Genetic Algorithm (GA) modelling. The electrocoagulation treatment technique was used to treat the synthetically prepared Reactive Black dye solution under batch mode potentiometric operations. The percentage of error for the BBD optimization was significantly greater than for the GA modelling results. The optimum factors determined by GA modelling were CD-59.11 mA/cm², ET-24.17 minutes, and pH-8.4. At this moment, the experimental and predicted dye removal efficiencies were found to be 96.25% and 98.26%, respectively. The most and least predominant factors found by the beta coefficient were ET and pH respectively. The outcome of this research shows GA modeling is a better tool for predicting dye removal efficiencies as well as process influencing factors.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.34-39
• /
• 1991

In this paper, we provide a treatment of the $H^{\infty}$-mixed sensitivity optimization approach to feedback system design. With compromising between the effect of a disturbance at the plant output and the effect of plant perturbations, we propose an algorithm to design robust controller. A $H^{\infty}$-optimization problem is to be equivalent to a Hankel-approximation, this enables the problem to be solved using state-space methods based on balanced realizations.s.

• Proceedings of the Korean Society of Medical Physics Conference
• /
• 2006.08a
• /
• pp.90-90
• /
• 2006

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2001.10a
• /
• pp.203-206
• /
• 2001

Finding the optimal solution in the river water quality management system is very hard with the non-linearity of the water quality model. Many suggested methods for that using the linear programming, non-linear programming and dynamic programming, are failed to give an optimal solution of sufficient accuracy and satisfaction. We studied a method to find a solution optimizing the river water quality management in the aspect of the efficiency and the cost of the waste water treatment facilities satisfying the water Quality goals. In the suggested method, we use the QUAL2E water quality model and the genetic algorithm. A brief result of the project to optimize the water quality management in the Youngsan river is presented.