• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1712-1725
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• 2022

For improving the seismic performance of the nuclear power plant (NPP) piping system, attempts have been made to apply a dynamic absorber (DA). However, the current piping DA design method is limited because it cannot provide the globally optimum values for the target design seismic loading. Therefore, this study proposes a seismic time history analysis-based DA optimal design method for piping. To this end, the Kriging approach is introduced to reduce the numerical cost required for seismic time history analyses. The appropriate design of the experiment method is used to increase the efficiency in securing response data. A gradient-based method is used to efficiently deal with the multi-dimensional unconstrained optimization problem of the DA optimal design. As a result, the proposed method showed an excellent response reduction effect in several responses compared to other optimal design methods. The proposed method showed that the average response reduction rate was about 9% less at the maximum acceleration, about 5% less at the maximum value of the response spectrum, about 9% less at the maximum relative displacement, and about 4% less at the maximum combined stress compared to existing optimal design methods. Therefore, the proposed method enables an effective optimal DA design method for mitigating seismic response in NPP piping in the future.

• Advances in nano research
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• v.12 no.4
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• pp.375-386
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• 2022

Synthesis of acrylate-based dispersion resins involves many parameters including temperature, ingredients concentrations, and rate of adding ingredients. Proper controlling of these parameters results in a uniform nano-size chain of polymer on one side and elimination of hazardous residual monomer on the other side. In this study, we aim to screen the process parameters via Internet of Things (IoT) to ensure that, first, the nano-size polymeric chains are in an acceptable range to acquire high adhesion property and second, the remaining hazardous substance concentration is under the minimum value for safety of public and personnel health. In this regard, a set of experiments is conducted to observe the influences of the process parameters on the size and dispersity of polymer chain and residual monomer concentration. The obtained dataset is further used to train an Adaptive Neural network Fuzzy Inference System (ANFIS) to achieve a model that predicts these two output parameters based on the input parameters. Finally, the ANFIS will return values to the automation system for further decisions on parameter adjustment or halting the process to preserve the health of the personnel and final product consumers as well.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.8
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• pp.112-119
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• 1996

Among the various type of accelerometer, the servo rebalancing type accelermoter can be suitable for Inertial Navigation System, because of its high sensitivity and good response in low frequency. In this paper, we proposed a new technology to control inductive tuype accelerometer utilizing digital PWM method. The new developed digital PWM control has special design scheme for transmitting measurement value to outer device in its servo ollp. So it has no quantized error of transforming outputs of sensors to digital domain. The quantized error may make serious problem in INS system, because outputs of sensor are integrated once or twice by digital computer and it happens every sensor reading times. Therefore, in order to get the accurate information such as displacement, it is necessary to measure accurately the input current. In addition, Digital Signal Processing needs digital data transmission, digital PWM method is adaptive for this purpose. We realized a practical circuit for digital PWM control, analyzed the stability of the circuit, and designed the controller etc. In this study, we solved many practical problem for this application, and got out good results.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.289-299
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• 2024

In the Pedestrian Dead Reckoning (PDR)/Global Positioning System (GPS)/Wi-Fi-integrated navigation system for indoor/outdoor continuous positioning of pedestrians, the process of detecting outliers in measurements is very important. When accurate location information from measurements is used, reliable correction data can be generated during the fusion filtering process. However, abnormal measurements may occur in certain situations, such as indoor/outdoor transitions, which can degrade filter performance and lead to significant errors in the estimated position. To address this issue, this paper proposes a method for detecting outliers in measurements based on Rényi Divergence (RD). When the deviation of the RD value is large, the measurements are considered outliers, and positioning is performed using only pure PDR. Based on experiments conducted with real data, it was confirmed that outliers were effectively detected for abnormal measurements, leading to an improvement in the performance of pedestrian navigation.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.179-186
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• 2016

Purpose : To figure out if the treatment plan for rectum, bladder and prostate that have a lot of interfraction errors satisfies dosimetric limits without adaptive plan by analyzing MR image. Materials and Methods : This study was based on 5 prostate cancer patients who had IMRT(total dose: 70Gy) Using ViewRay MRIdian System(ViewRay, ViewRay Inc., Cleveland, OH, USA) The treatment plans were made on the same CT images to compare with the plan quality according to adaptive plan, and the Eclipse(Ver 10.0.42, Varian, USA) was used. After registrate the 5 treatment MR images to the CT images for treatment plan to analyze the interfraction changes of organ, we measured the dose volume histogram and the changes of the absolute volume for each organ by appling the first treatment plan to each image. Over 5 fractions, the total dose for PTV was $V_{36.25}$ Gy $${\geq_-}$$ 95%. To confirm that the prescription dose satisfies the SBRT dose limit for prostate, we measured $V_{100%}$, $V_{95%}$, $V_{90%}$ for CTV and $V_{100%}$, $V_{90%}$, $V_{80%}$ $V_{50%}$ of rectum and bladder. Results : All dose average value of CTV, rectum and bladder satisfied dose limit, but there was a case that exceeded dose limit more than one after analyzing the each image of treatment. After measuring the changes of absolute volume comparing the MR image of the first treatment plan with the one of the interfraction treatment, the difference values were maximum 1.72 times at rectum and maximum 2.0 times at bladder. In case of rectum, the expected values were planned under the dose limit, on average, $V_{100%}=0.32%$, $V_{90%}=3.33%$, $V_{80%}=7.71%$, $V_{50%}=23.55%$ in the first treatment plan. In case of rectum, the average of absolute volume in first plan was 117.9 cc. However, the average of really treated volume was 79.2 cc. In case of CTV, the 100% prescription dose area didn't satisfy even though the margin for PTV was 5 mm because of the variation of rectal and bladder volume. Conclusion : There was no case that the value from average of five fractions is over the dosimetric limits. However, dosimetric errors of rectum and bladder in each fraction was significant. Therefore, the precise delivery is needed in case of prostate SBRT. The real-time tracking and adaptive plan is necessary to meet the precision delivery.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.2028-2042
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• 1994

Properly defining signal and noise in a self-organizing system like ART(Adaptive Resonance Theory) neural network model raises a number of subtle issues. Pattern context must enter the definition so that input features, treated as irrelevant noise when they are embedded in a given input pattern, may be treated as informative signals when they are embedded in a different input pattern. The ATR automatically self-scales their computational units to embody context and learning dependent definitions of a signal and noise and there is no problem in categorizing input pattern that have features similar in nature. However, when we have imput patterns that have features that are different in size and nature, the use of only one vigilance parameter is not enough to differentiate a signal from noise for a good categorization. For example, if the value fo vigilance parameter is large, then noise may be processed as an informative signal and unnecessary categories are generated: and if the value of vigilance parameter is small, an informative signal may be ignored and treated as noise. Hence it is no easy to achieve a good pattern categorization. To overcome such problems, a Coupled-ART neural network capable of modularized categorization of patterns is proposed. The Coupled-ART has two layer of tightly coupled modules. the upper and the lower. The lower layer processes the global features of a pattern and the structural features, separately in parallel. The upper layer combines the categorized outputs from the lower layer and categorizes the combined output, Hence, due to the modularized categorization of patterns, the Coupled-ART classifies patterns more efficiently than the ART1 model.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.33-56
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• 2010

The current business environment has been characterized as less munificent, highly uncertain and constantly evolving. In this environment, the company with dynamic capability is reported to be more successful than others in building competitive advantage. Dynamic capability focuses on the link between a dynamically changing environment, strategic agility, architectural reconfiguration, and value creation. Being characterized to be flexible and adaptive to market circumstance changes, an organization with dynamic capability is described to have high resource fluidity, which represents business process, resource allocation, human resource management and incentives that make business transformation faster and easier. Successful redeployment of the resources for dynamic adaptation requires organizational forms and reward systems to be well aligned with firm's technological infrastructures and business process. The alignment is considered to be an executive level commitment. Building dynamic capability is knowledge driven; relying on new knowledge to reconfigure firm's resources. Past studies established the link between the effective execution of a knowledge-focused strategy and relevant setting of architectural elements such as human resources, structure, process and information systems. They do not, however, describe in detail the underlying processes by which architectural elements are adjusted in coordinated manners to build knowledge-driven dynamic capability. In fact, understandings of these processes are one of the top issues in IT management. This study analyzed how a Korean corporation with a knowledge-focused strategy aligned its architectural elements to develop the dynamic capability and thus create value in the dynamically changing markets. When the Korean economy was in crisis, the company implemented a knowledge-focused strategy, restructured the organization's architecture by which human and knowledge resources are identified, structured, integrated and coordinated to identify and seize market opportunity. Specifically, the following architectural elements were reconfigured: human resource, decision rights, reward and evaluation systems, process, and IT infrastructure. As indicated by sales growth, the reconfiguration helped the company create value under an extremely turbulent environment. According to Ancona et al. (2001), depending on the types of lenses the organization uses, different types of architecture will emerge. For example, if an organization uses political lenses focusing on power, influence, and conflict. the architecture that leverage power and negotiate across multiple interest groups would emerge. Similarly, if an organization uses economic lenses focusing on the rational behavior of organizational actors making choices based on the costs and benefits of action, organizational architecture should be designed to motivate and provide incentives for the actors (Smith, 2001). Compared to this view, information processing perspectives consider architecture to be designed to maximize the capacity of information processing by the actors. Using knowledge lenses, the company studied in this research established architectural elements in a manner that allows the firm to effectively structure knowledge resources to form dynamic capability. This study is revelatory single case with a historic perspective. As a result of this study, a set of propositions and a framework are derived, which can be used for architectural alignment.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.73-80
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• 2010

A wireless sensor network which is deployed in hostile environment can be easily compromised by attackers. The selective forwarding attack can jam the packet or drop a sensitive packet such as the movement of the enemy on data flow path through the compromised node. Xiao, Yu and Gao proposed the checkpoint-based multi-hop acknowledgement scheme(CHEMAS). In CHEMAS, each path node enable to be the checkpoint node according to the pre-defined probability and then can detect the area where the selective forwarding attacks is generated through the checkpoint nodes. In this scheme, the number of hops is very important because this parameter may trade off between energy conservation and detection capacity. In this paper, we used the fuzzy rule system to determine adaptive threshold value which is the number of hops for the ACK packets. In every period, the base station determines threshold value while using fuzzy logic. The energy level, the number of compromised node, and the distance to each node from base station are used to determine threshold value in fuzzy logic.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.66-74
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• 2010

Recently distributed video coding (DVC) is spotlighted for the environment which has restriction in computing resource at encoder. Wyner-Ziv (WZ) coding is a representative scheme of DVC. The WZ encoder independently encodes key frame and WZ frame respectively by conventional intra coding and channel code. WZ decoder generates side information from reconstructed two key frames (t-1, t+1) based on temporal correlation. The side information is regarded as a noisy version of original WZ frame. Virtual channel noise can be removed by channel decoding process. So the performance of WZ coding greatly depends on the performance of channel code. Among existing channel codes, Turbo code and LDPC code have the most powerful error correction capability. These channel codes use stochastically iterative decoding process. However the iterative decoding process is quite time-consuming, so complexity of WZ decoder is considerably increased. Analysis of the complexity of LPDCA with real video data shows that the portion of complexity of LDPCA decoding is higher than 60% in total WZ decoding complexity. Using the HDA (Hard Decision Aided) method proposed in channel code area, channel decoding complexity can be much reduced. But considerable RD performance loss is possible according to different thresholds and its proper value is different for each sequence. In this paper, we propose an adaptive HDA method which sets up a proper threshold according to sequence. The proposed method shows about 62% and 32% of time saving, respectively in LDPCA and WZ decoding process, while RD performance is not that decreased.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.73-81
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• 2016

In general, infrared images have less sharpness and image details than visible images. So, the prior image upscaling methods are not effective in the infrared images. In order to solve this problem, this paper proposes an algorithm which initially up-scales an input infrared (IR) image by using adaptive dynamic range encoding (ADRC)-based super-resolution (SR) method, and then fuses the result with the corresponding visible images. The proposed algorithm consists of a up-scaling phase and a fusion phase. First, an input IR image is up-scaled by the proposed ADRC-based SR algorithm. In the dictionary learning stage of this up-scaling phase, so-called 'pre-emphasis' processing is applied to training-purpose high-resolution images, hence better sharpness is achieved. In the following fusion phase, high-frequency information is extracted from the visible image corresponding to the IR image, and it is adaptively weighted according to the complexity of the IR image. Finally, a up-scaled IR image is obtained by adding the processed high-frequency information to the up-scaled IR image. The experimental results show than the proposed algorithm provides better results than the state-of-the-art SR, i.e., anchored neighborhood regression (A+) algorithm. For example, in terms of just noticeable blur (JNB), the proposed algorithm shows higher value by 0.2184 than the A+. Also, the proposed algorithm outperforms the previous works even in terms of subjective visual quality.