• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1508-1515
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• 2022

In the present study, the cavity module of the MELCOR code is used for the simulation of molten corium concrete interaction (MCCI) during the late phase of postulated large break loss of coolant (LB-LOCA) accident in the APR1400 reactor design. Using the molten corium composition data from previous MELCOR Simulation of APR1400 under LB-LOCA accident, the ex-vessel phases of the accident sequences with long-term MCCI are recalculated with stand-alone cavity package of the MELCOR code to investigate the impact of water ingression and melt eruption models which were hitherto absent in MELCOR code. Significant changes in the MCCI behaviors in terms of the heat transfer rates, amount of gases released, and maximum cavity ablation depths are observed and reported in this study. Most especially, the incorporation of these models in the new release of MELCOR code has led to the reduction of the maximum ablation depth in radial and axial directions by ~38% and ~32%, respectively. These impacts are substantial enough to change the conclusions earlier reached by researchers who had used the older versions of the MELCOR code for their studies. and it could also impact the estimated cost of the severe accident mitigation system in the APR1400 reactor.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2198-2203
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• 2022

As robotics become more sophisticated, there are a growing number of generic systems being used for routine tasks in nuclear environments to reduce risk to radiation workers. The nuclear sector has called for more commercial-off-the-shelf (COTS) devices and components to be used in preference to nuclear specific hardware, enabling robotic operations to become more affordable, reliable, and abundant. To ensure reliable operation in nuclear environments, particularly in high-gamma facilities, it is important to quantify the tolerance of electronic systems to ionizing radiation. To deliver their full potential to end-users, mobile robots require sophisticated autonomous behaviors and sensing, which requires significant computational power. A popular choice of computing system, used in low-cost mobile robots for nuclear environments, is the UP Core single board computer. This work presents estimates of the total ionizing dose that the UP Core running the Robot Operating System (ROS) can withstand, through gamma irradiation testing using a Co-60 source. The units were found to fail on average after 111.1 ± 5.5 Gy, due to faults in the on-board power management circuitry. Its small size and reasonable radiation tolerance make it a suitable candidate for robots in nuclear environments, with scope to use shielding to enhance operational lifetime.

• Applied Microscopy
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• v.50
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• pp.17.1-17.9
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• 2020

Recent advances in fabrication have enabled radial-junction architectures for cost-effective and high-performance optoelectronic devices. Unlike a planar PN junction, a radial-junction geometry maximizes the optical interaction in the three-dimensional (3D) structures, while effectively extracting the generated carriers via the conformal PN junction. In this paper, we report characterizations of radial PN junctions that consist of p-type Si micropillars created by deep reactive-ion etching (DRIE) and an n-type layer formed by phosphorus gas diffusion. We use electron-beam induced current (EBIC) microscopy to access the 3D junction profile from the sidewall of the pillars. Our EBIC images reveal uniform PN junctions conformally constructed on the 3D pillar array. Based on Monte-Carlo simulations and EBIC modeling, we estimate local carrier separation/collection efficiency that reflects the quality of the PN junction. We find the EBIC efficiency of the pillar array increases with the incident electron beam energy, consistent with the EBIC behaviors observed in a high-quality planar PN junction. The magnitude of the EBIC efficiency of our pillar array is about 70% at 10 kV, slightly lower than that of the planar device (≈ 81%). We suggest that this reduction could be attributed to the unpassivated pillar surface and the unintended recombination centers in the pillar cores introduced during the DRIE processes. Our results support that the depth-dependent EBIC approach is ideally suitable for evaluating PN junctions formed on micro/nanostructured semiconductors with various geometry.

• Journal of Powder Materials
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• v.30 no.3
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• pp.223-232
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• 2023

Metal additive manufacturing (AM) has transformed conventional manufacturing processes by offering unprecedented opportunities for design innovation, reduced lead times, and cost-effective production. Aluminum alloy, a material used in metal 3D printing, is a representative lightweight structural material known for its high specific strength and corrosion resistance. Consequently, there is an increasing demand for 3D printed aluminum alloy components across industries, including aerospace, transportation, and consumer goods. To meet this demand, research on alloys and process conditions that satisfy the specific requirement of each industry is necessary. However, 3D printing processes exhibit different behaviors of alloy elements owing to rapid thermal dynamics, making it challenging to predict the microstructure and properties. In this study, we gathered published data on the relationship between alloy composition, processing conditions, and properties. Furthermore, we conducted a sensitivity analysis on the effects of the process variables on the density and hardness of aluminum alloys used in additive manufacturing.

• Journal of Yeungnam Medical Science
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• v.40 no.2
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• pp.218-222
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• 2023

A holistic approach to diabetes considers patient preferences, emotional health, living conditions, and other contextual factors, in addition to medication selection. Human and social factors influence treatment adherence and clinical outcomes. Social issues, cost of care, out-of-pocket expenses, pill burden (number and frequency), and injectable drugs such as insulin, can affect adherence. Clinicians can ask about these contextual factors when discussing treatment options with patients. Patients' emotional health can also affect diabetes self-care. Social stressors such as family issues may impair self-care behaviors. Diabetes can also lead to emotional stress. Diabetes distress correlates with worse glycemic control and lower overall well-being. Patient-centered communication can build the foundation of a trusting relationship with the clinician. Respect for patient preferences and fears can build trust. Relevant communication skills include asking open-ended questions, expressing empathy, active listening, and exploring the patient's perspective. Glycemic goals must be personalized based on frailty, the risk of hypoglycemia, and healthy life expectancy. Lifestyle counseling requires a nonjudgmental approach and tactfulness. The art of diabetes care rests on clinicians perceiving a patient's emotional state. Tailoring the level of advice and diabetes targets based on a patient's personal and contextual factors requires mindfulness by clinicians.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2157-2177
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• 2024

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.6
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• pp.103-112
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• 2007

The purpose of this study is to evaluate the characteristics of the structural behavior in precast segmental prestressed concrete girders, which consist of five precast segments. These girders were developed to save labor and cost in construction field reducing a term of work. Therefore, four different types of specimens of 25m in length were built, and they were tested and analyzed for observing flexural behavior. The analysis included the investigation of the flexural behaviors in varying tendon amount and at joints using the relationship between moment and deflection. Moreover, nonlinear finite element analysis was utilized to verify the experimental result.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.

• Management & Information Systems Review
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• v.38 no.2
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• pp.105-118
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• 2019

In order for a strategy established by a company to be implemented efficiently, it must be supported by an appropriate organizational culture. This means that if a firm has an organizational culture suitable for strategy implementation, performance can be enhanced. This study divides competitive strategy into cost leadership strategy and product differentiation strategy, and organizational culture into hierarchical/rational culture and consensual/development culture. Based on 122 questionnaires collected through KOSPI listed manufacturing companies, the results of the empirical analysis on the effect of suitability between competitive strategy and organizational culture on performance by balanced scorecard perspective are summarized as follows. First, it shows that the cost leadership strategy and the hierarchical/rational culture are more fit. Specifically, The high suitability between the cost leadership strategy and the hierarchical/rational culture has a positive effect on the performance of the balanced scorecard perspective(excluding performance by learning and growth perspective). Second, The high suitability between the product differentiation strategy and the consensual/development culture has a positive effect on the performance of the balanced scorecard perspective. The results of this study suggest that it is important to form a corporate culture that can lead to changes in the beliefs and behaviors of organizational members in accordance with the competitive strategy in order to successfully implement the strategies established by the company.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.