• Journal of the Microelectronics and Packaging Society
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• v.31 no.3
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• pp.38-41
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• 2024

In semiconductor manufacturing, the alignment process is fundamental to all manufacturing steps, and alignment errors are inevitably introduced. These alignment errors can lead to issues such as increased resistance, signal delay, and degradation. This study systematically analyzes the changes in the electrical characteristics of the bonding interface when alignment errors occur in metal interconnect and bonding structures. The results show that current density tends to concentrate at the edges of the bonding interface, with the middle part of the interface being particularly vulnerable. As alignment errors increase, the current path redistributes, causing previously concentrated current areas to disappear and an effect similar to an increase in contact area, resulting in a decrease in resistance in certain vulnerable parts. These findings suggest that proposing structural improvements to eliminate the vulnerable parts of the bonding interface could lead to interconnect with significantly improved resistance performance compared to existing structure. This study clarifies the impact of alignment errors on electrical characteristics, which is expected to play a crucial role in optimizing the electrical performance of semiconductor devices and enhancing the efficiency of the manufacturing process.

• Journal of KIBIM
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• v.14 no.3
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• pp.1-12
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• 2024

Many efforts are being made to enhance user productivity and promote collaboration while ensuring the economic efficiency of office buildings. Analyzing space utilization, indicating how users utilize spaces, has been a crucial factor in these efforts. Appropriate space utilization enhances building maintenance and space layout design, reducing unnecessary energy waste and under-occupied spaces. Recognizing the importance of space utilization, there have been several studies to predict space utilization using information about users, activities, and spaces. These studies suggested an ontology of the information and implemented automated activity-space mapping as part of space utilization prediction. Despite the existing studies, there remains a gap in integrating space utilization prediction with automated space layout design. As a foundational study to bridge this gap, our study proposes a novel methodology that automatically generates office rooms based on space utilization optimization. This methodology consists of three modules: Activity-space mapping, Space utilization calculation, and Room generation. The first two modules use data on space types and user activity types as input to calculate and optimize space utilization through requirement-based activity-space mapping. After optimizing the space utilization value within an appropriate range, the number and area of each space type are determined. The Room generation module then automatically generates rooms with optimized areas and numbers. The practical application of the developed methodology is demonstrated, highlighting its effectiveness in fabricated case scenario. By automatically generating rooms with optimal space utilization, our methodology shows potential for expanding to automated generation of optimized space layout design based on space utilization.

• Applied Chemistry for Engineering
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• v.35 no.5
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• pp.361-371
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• 2024

This study reviews recent advancements in Ni-based catalysts for CO₂ methanation, emphasizing high thermal stability and catalytic performance at elevated temperatures. Ni catalysts are preferred for their strong hydrogen adsorption, high activity, and methane selectivity. Strategies such as optimizing metal loading, using efficient supports, and introducing promoters enhance thermal stability by preventing sintering and carbon deposition. The produced methane serves as a valuable feedstock for synthetic fuels and chemicals, improving the economic feasibility of the CO₂ methanation process. These findings underscore the importance of thermal stability in developing effective Ni catalysts for large-scale CO₂ methanation.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.2
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• pp.137-147
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• 2012

Purpose: Analyze the Effectiveness of Radiation Treatment Planning by dose calculation and optimization algorithm, apply consideration of actual treatment planning, and then suggest the best way to treatment planning protocol. Materials and Methods: The treatment planning system use Eclipse 10.0. (Varian, USA). PBC (Pencil Beam Convolution) and AAA (Anisotropic Analytical Algorithm) Apply to Dose calculation, DVO (Dose Volume Optimizer 10.0.28) used for optimized algorithm of Intensity Modulated Radiation Therapy (IMRT), PRO II (Progressive Resolution Optimizer V 8.9.17) and PRO III (Progressive Resolution Optimizer V 10.0.28) used for optimized algorithm of VAMT. A phantom for experiment virtually created at treatment planning system, $30{\times}30{\times}30$ cm sized, homogeneous density (HU: 0) and heterogeneous density that inserted air assumed material (HU: -1,000). Apply to clinical treatment planning on the basis of general treatment planning feature analyzed with Phantom planning. Results: In homogeneous density phantom, PBC and AAA show 65.2% PDD (6 MV, 10 cm) both, In heterogeneous density phantom, also show similar PDD value before meet with low density material, but they show different dose curve in air territory, PDD 10 cm showed 75%, 73% each after penetrate phantom. 3D treatment plan in same MU, AAA treatment planning shows low dose at Lung included area. 2D POP treatment plan with 15 MV of cervical vertebral region include trachea and lung area, Conformity Index (ICRU 62) is 0.95 in PBC calculation and 0.93 in AAA. DVO DVH and Dose calculation DVH are showed equal value in IMRT treatment plan. But AAA calculation shows lack of dose compared with DVO result which is satisfactory condition. Optimizing VMAT treatment plans using PRO II obtained results were satisfactory, but lower density area showed lack of dose in dose calculations. PRO III, but optimizing the dose calculation results were similar with optimized the same conditions once more. Conclusion: In this study, do not judge the rightness of the dose calculation algorithm. However, analyzing the characteristics of the dose distribution represented by each algorithm, especially, a method for the optimal treatment plan can be presented when make a treatment plan. by considering optimized algorithm factors of the IMRT or VMAT that needs to optimization make a treatment plan.

• The Journal of Korean Society for Radiation Therapy
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• v.20 no.1
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• pp.1-9
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• 2008

Purpose: For head and neck cancer patients treated with radiation therapy, proper immobilization of intra-oral structures is crucial in reproducing treatment positions and optimizing dose distribution. We produced a man-made tongue immobilization device for each patient subjected to this study. Reproducibility of treatment positions and dose distributions at air-and-tissue interface were compared using man-made tongue immobilization devices and conventional tongue-bites. Materials and Methods: Dental alginate and putty were used in producing man-made tongue immobilization devices. In order to evaluate reproducibility of treatment positions, all patients were CT-simulated, and linac-gram was repeated 5 times with each patient in the treatment position. An acrylic phantom was devised in order to evaluate safety of man-made tongue immobilization devices. Air, water, alginate and putty were placed in the phantom and dose distributions at air-and-tissue interface were calculated using Pinnacle (version 7.6c, Phillips, USA) and measured with EBT film. Two different field sizes (3$\times$3 cm and 5$\times$5 cm) were used for comparison. Results: Evaluation of linac grams showed reproducibility of a treatment position was 4 times more accurate with man-made tongue immobilization devices compared with conventional tongue bites. Patients felt more comfortable using customized tongue immobilization devices during radiation treatment. Air-and-tissue interface dose distributions calculated using Pinnacle were 7.78% and 0.56% for 3$\times$3 cm field and 5$\times$5 cm field respectively. Dose distributions measured with EBT (international specialty products, USA) film were 36.5% and 11.8% for 3$\times$3 cm field and 5$\times$5 cm field respectively. Values from EBT film were higher. Conclusion: Using man-made tongue immobilization devices made of dental alginate and putty in treatment of head and neck cancer patients showed higher reproducibility of treatment position compared with using conventional mouth pieces. Man-made immobilization devices can help optimizing air-and-tissue interface dose distributions and compensating limited accuracy of radiotherapy planning systems in calculating air-tissue interface dose distributions.

• Journal of radiological science and technology
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• v.35 no.4
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• pp.299-308
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• 2012

To perform patient dose surveys in major interventional radiography procedures as a mean of inter-institutional comparison and of establishing reference dose levels with the ultimate goal of optimizing patient doses in the field of interventional radiography. We reviewed international patient dose survey data in the literature and measured patient dose in major interventional radiography procedures (TACE, AVF, PTBD, TFCA, GDC embolization). ESD(Entrance Skin Dose) was measured using TLD chips attached to the patient skin and ED(Effective Dose) was calculated using angiography unit-derived DAP. A survey of patient dose in interventional radiography procedures were also performed with a questionnaire for interventional radiologists and we proposed a guideline for optimizing patient doses in the field of interventional radiology. The patient dose survey data in interventional radiography procedures were very rare in literature compared with those in diagnostic radiography procedures. In TACE, the mean ED was 25.43 mSv and the mean ESD was 511.75 mGy. The mean ED of TACE was not high, but the cumulative dose should be checked, due to longer procedure TACE. In TFCA, the mean ED was 22.6 mSv and it was relatively high compared with data of other countries. In GDC embolization, the mean ED was not available, because GDC embolization was performed with old Image-Intensifier-type unit and there has no unit-installed ionization chamber. Also, the mean ESD of GDC embolization was up to 2,264 mGy and further studies are needed to calculate the net ED of GDC embolization. Patient dose occurred during interventional radiography procedures are high related with the difficulty of the procedure, fluoroscopy time, the number of angiographies and the treatment protocol. Therefore, continuous education and efforts should be made to optimize the patient dose in the field of interventional radiology.

• Journal of the Korean Geotechnical Society
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• v.15 no.2
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• pp.153-164
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• 1999

The solubilization of BTEX was evaluated in aqueous surfactant solutions with and without several additives. Anionic surfactant(Sodium Dodecyl Sulfate, SDS) and nonionic surfactants (NEODOL(equation omitted)25-3 and $SOFTANOL\circledR-90$ were used as test surfactants. The effects of surfactant HLB(Hydrophile-Lipophile Balance) Number and hydrocarbon molar volume and polarity of BTEX on the MSR(Molar Solubilization Ratio), micelle-water partition coefficient of BTEX, and CMC(C,itical Micelle Concentration) were investigated. Optimizing treatment conditions applicable to enhanced solubilization was also studied by manupulating salinity or electrolyte control with additives of ethyl alcohol, hydrotrope, and electrolyte solution. The most effective surfactant for solubilization was found $SOFTANOL\circledR-90$, since HLB number of 13.6 is similar to those values of BTEX ranging between 11.4 and 12.2, which was also proved experimentally. Ethyl alchohol of 3% was the most effective additives in reducing CMC and improving solubilization among the conditions using SDS, NEODOL(equation omitted)25-3, and $SOFTANOL\circledR-90$ with three additives. The partitioning of BTEX between surfactant micelles and aqueous solutions was characterized by a mole fraction micelle-phase/aqueous phase partion coefficient, $K_m$. Values of log $K_m$. for BTEX compounds in surfactant solutions of this study range from 2.95 to 3.76(100mM SDS) and 2.95 to 3.49(117mM $SOFTANOL\circledR-90$. Log $K_m$ appears to be a linear function of log $K_{ow}$ for SDS and $SOFTANOL\circledR-90$. A knowledge of partitioning of BTEX in aqueous surfactant system can be a prerequisite for the understanding of the behavior of hydrophobic organic compounds in soil-water systems in which surfactants play a role in remediation of contaminated soil and facilitated transport.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.942-955
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• 2000

This paper proposes enhanced designs of global service logic and information flow for the mass-call based IN service, which increase call completion rates and optimize the resource in joint wired and wireless networks. In order to hanve this logic implemented, we design a Dynamic Queue Manager(DQM) applied to the call queuing service feature in the Service Control Point(SCP). In order to apply this logic to wireless service subscribers as well as wired service subscribers, the service registration flags between the Home Location Register(HLR) and the SCP are managed to notify the DQM of the corresponding service subscribers’ mobility. Hence, we present a dynamic queue management mechanism, which dynamically manages the service group and the queue size based on M/M/c/K queueing model as the wireless subscribers roam the service groups due to their mobility characteristics. In order to determine the queue size allocated by the DQM, we simulator and analyze the relationship between the number of the subscriber’s terminals and the drop rate by considering the service increment rate. The appropriate waiting time in the queue as required is simulated according to the above relationship. Moreover, we design and implement the DQM that includes internal service logic interacting with SIBs(Service Independent building Blocks) and its data structure.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.603-612
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• 2014

Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network.