• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.827-836
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• 2004

The design parameter of the heat loss for the pressurized water reactor has been studied. The heat loss from the reactor vessel through the air gap. insulation are analysed by using the computational fluid dynamics code, FLUENT. Parametric study has been performed on the air gap width between the reactor vessel wall and the inner surface of the insulation, and on the insulation thickness. Also evaluated is the performance degradation due to the chimney effect due to gaps left between the panels during the installation of the insulation system. From the analysis results, the optimal with of air gap and insulation thickness and the value of heat loss are obtained The results show how the heat loss varies with the air gap width and insulation thickness. The temperature and the velocity distributions are also presented. From the results of the evaluation. the optimal air gap width and the optimal insulation thickness are obtained. As the difference between the predicted heat loss and measured heat loss from the reactor vessel is construed Primarily as losses due to chimney effect. the contribution of the chimney effect to the total heat loss is quantitatively indicated.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1025-1035
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• 2016

Luenberger observer (LO)-based sensorless multi-loop control of a converter requires an iterative trial-and-error design process, considering that many parameters should be determined, and loop gains are indirectly related to the closed-loop characteristics. Robust H∞ control adopts a compact sensorless controller. The algebraic Riccati equation (ARE)-based and linear matrix inequality (LMI)-based H∞ approaches need an exhaustive procedure, particularly for a low-order controller. Therefore, in this study, a novel robust H∞ synthesis approach is proposed to design a low-order sensorless controller for boost converters, which need not solve any ARE or LMI, and to parameterize the controller by an adjustable parameter behaving like a "knob" on the closed-loop characteristics. Simulation results show the straightforward closed-loop characteristics evaluation and better dynamic performance by the proposed H∞ approach, compared with the LO-based sensorless multi-loop control. Practical experiments on a digital processor confirmed the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2458-2482
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• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_2
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• pp.897-908
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• 2024

Evaluating the adequacy of facility safety inspection and diagnosis services performed by private enterprises is a time-consuming and administratively complex process. This study aims to analyze the determinants that could influence the rating of these safety inspection and diagnosis services using data analytics approach. Through a comparative analysis of several machine learning algorithms suitable for multi-class classification, we selected the model with the best performance (Random Forest) and identified the main determinants using the permutation importance technique. Among the variables examined, "contract value," "days of service performed" and "adherence to fair market value" were found to be strongly correlated with the rating assessments. Furthermore, we discovered that the skills and expertise of service performing personnel significantly impacted the rating. The results of this study can contribute to the enhancement of the current post-evaluation administrative processes and offer valuable insights into rating assessments by incorporating previously unexplored variables pertaining to both service providers and the services itself.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.3 s.234
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• pp.418-424
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• 2005

We present the design, fabrication and experimental results of 4-bit digital microinjectors, whose ejected droplet volumes are adjusted by the digital operation of a 4-bit microheater array. We design the reference microinjectors as well as its comparative test structures. In the fabrication process, we use a five-mask micromachining process and the total chip size of the fabricated microinjector is $7,640{\mu}m{\times}5,260{\mu}m.$ We measure the ejected droplet volumes and velocities, which are adjusted from $12.1{\pm}1.0~55.6{\pm}14.7pl\;and\;2.3{\pm}0.1~15.7{\pm}0.8m/s.$ respectively, depending on the 15 possible combinations of 4-bit microheater array. We also experimentally characterize the effect of geometric variation including the microheater size, inter-microheater gap, microchannel width and sequential operation of microheater array on the ejected droplet volume and velocity. Among these parameters, we find that the microheater size is the most dominant parameter affected to the ejected droplet volumes and velocities. Thus, the present microinjector has a potential for application to the high-resolution inkjet printers with multiple gray levels or high-precision fluid injectors with variable volume control.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.462-472
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• 1999

De-NOx facility using Selective Catalytic Reduction method is the most widely applied one that removes NOx from flue gas emitted from combustion facility such as boiler for power generation engine incinerator etc. Reductant $NH_3\;or\;NH_4OH$ is sprayed into flue gas to convert NOx into $H_2O$ and $N_2.$ Good mixing between flue gas and $NH_3$ is the most important factor to increase reduction in catalytic layer and to reduce unreacted NH3 slip. Therefore the development of mixer device for mixing effect is one of the important part for SCR facility. Objectives of this study are to investigate the relation between flow and concentration field by observation at the wake of delta-wing type mixer. At the first stage qualitative measurement of flow field is conducted by flow visualization using laser light sheet in lab. scale wind tunnel. Also we have conducted the quantitative analysis by comparing flow field measurement using LDV with numerical simulation. On the basis of qualitative and quantitative analysis we investigate the dis-tribution of flow and concentration in flow model facility. The results of an experimental and compu-tational examination of the vortex structures shed from delta wing type vortex generator having $40^{\circ}$ angle of attack are presented, The effects of vortex structure on the gas mixing is discussed, too.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.566-577
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• 2012

In randomly deployed networks, such as sensor networks, an important problem for each node is to discover its neighbor nodes so that the connectivity amongst nodes can be established. In this paper, we consider this problem by incorporating the physical layer parameters in contrast to the most of the previous work which assumed a collision channel. Specifically, the pilot signals that nodes transmit are successfully decoded if the strength of the received signal relative to the interference is sufficiently high. Thus, each node must extract signal parameter information from the superposition of an unknown number of received signals. This problem falls naturally in the purview of random set theory (RST) which generalizes standard probability theory by assigning sets, rather than values, to random outcomes. The contributions in the paper are twofold: First, we introduce the realistic effect of physical layer considerations in the evaluation of the performance of logical discovery algorithms; such an introduction is necessary for the accurate assessment of how an algorithm performs. Secondly, given the double uncertainty of the environment (that is, the lack of knowledge of the number of neighbors along with the lack of knowledge of the individual signal parameters), we adopt the viewpoint of RST and demonstrate its advantage relative to classical matched filter detection method.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.725-734
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• 2016

For multi-mode radar signals in complex electromagnetic environment, different modes of one emitter tend to be deinterleaved into several emitters, called as "extension", when processing received signals by use of existing sorting methods. The "extension" problem inevitably deteriorates the sorting performance of multi-mode radar signals. In this paper, a novel method based on spatial data mining is presented to address above challenge. Based on theories of data field, we describe the distribution information of feature parameters using potential field, and makes partition clustering of parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud model membership is established to measure the relevance between different cluster-classes, which provides important spatial knowledge for the solution of the "extension" problem. It is shown through numerical simulations that the proposed method is effective on solving the "extension" problem in multi-mode radar signal sorting, and can achieve higher correct sorting rate.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.700_701
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• 2009

In comparison with conventional motors, Superconducting Homopolar Synchronous Motors (SHSMs) have advantages that it generates high magnetic field by superconducting winding. Therefore, superconducting coil used in SHSM can reduce the motor size and enhance the motor efficiency for high torque applications under the space constraints for propulsion system. During the design process of SHSM, it is required to evaluate the performance of initial design model, that is accurately analyzed using 3D magnetic field modeling large air-gap and flux distribution of axial direction is properly taken into account. In this paper, we analyze magnetic field of a homopolar motor with a 4-pole homopolar rotor and a stator of 3 phase windings. The field analysis is done using 3D finite element analysis which can reflect the end effect and overhang winding. And we extract mutual inductances between a rotor wind and the 3 stator windings. The extracted inductances are used for evaluation of overall motor performances that are calculated with generalized circuit theory of electrical machines.