• Nuclear Engineering and Technology
• /
• v.52 no.6
• /
• pp.1110-1119
• /
• 2020

The Floating Absorber for Safety at Transient (FAST) is a safety device used in the innovative Sodium-cooled Fast Reactor (iSFR). The FAST insert negative reactivity under transient or accident conditions. However, behavior of the FAST is still unclear under transient conditions. Therefore, the existing Floating Absorber for Safety at Transient Analysis Code (FASTAC) is improved to analyze the FAST movement by considering the reactivity and temperature distribution within the reactor core. The current FAST system is simulated under a single control rod withdrawal accident condition. In this investigation, the reactor thermal power does not return to its initial thermal power even if the FAST inserts negative reactivity. Only a 9 K of coolant temperature margin, in the hottest fuel assembly at EOL, can lead to unnecessary insertion of the negative reactivity. On the other hand, the FASTs cannot contribute to controlling the reactivity when normalized radial power is less than 0.889 at BOL and 0.972 at EOL. These simulation results suggest that the current FAST design needs to be optimized depending on its installed location. Meanwhile, the FAST system keeps the fuel, cladding and coolant temperatures below their limit temperatures with given conditions.

• Journal of the Korean earth science society
• /
• v.36 no.5
• /
• pp.437-446
• /
• 2015

Many edge detection methods, based on horizontal and vertical derivatives, have been introduced to provide us with intuitive information about the horizontal distribution of a subsurface anomalous body. Understanding the characteristics of each edge detection method is important for selecting an optimized method. In order to compare the characteristics of the individual methods, this study applied each method to synthetic magnetic data created using homogeneous prisms with different sizes, the numbers of bodies, and spacings between them. Seven edge detection methods were comprehensively and quantitatively analyzed: the total horizontal derivative (HD), the vertical derivative (VD), the 3D analytic signal (AS), the title derivative (TD), the theta map (TM), the horizontal derivative of tilt angle (HTD), and the normalized total horizontal derivative (NHD). HD and VD showed average good performance for a single-body model, but failed to detect multiple bodies. AS traced the edge for a single-body model comparatively well, but it was unable to detect an angulated corner and multiple bodies at the same time. TD and TM performed well in delineating the edges of shallower and larger bodies, but they showed relatively poor performance for deeper and smaller bodies. In contrast, they had a significant advantage in detecting the edges of multiple bodies. HTD showed poor performance in tracing close bodies since it was sensitive to an interference effect. NHD showed great performance under an appropriate window.

• Genomics & Informatics
• /
• v.1 no.2
• /
• pp.94-100
• /
• 2003

Motivation: Many have observed a nonlinear relationship between the signal intensity and the transcript abundance in microarray data. The first step in analyzing the data is to normalize it properly, and this should include a correction for the nonlinearity. The commonly used linear normalization schemes do not address this problem. Results: Nonlinearity is present in both cDNA and oligonucleotide arrays, but we concentrate on the latter in this paper. Across a set of chips, we identify those genes whose within-chip ranks are relatively constant compared to other genes of similar intensity. For each gene, we compute the sum of the squares of the differences in its within-chip ranks between every pair of chips as our statistic and we select a small fraction of the genes with the minimal changes in ranks at each intensity level. These genes are most likely to be non-differentially expressed and are subsequently used in the normalization procedure. This method is a generalization of the rank-invariant normalization (Li and Wong, 2001), using all available chips rather than two at a time to gather more information, while using the chip that is least likely to be affected by nonlinear effects as the reference chip. The assumption in our method is that there are at least a small number of non­differentially expressed genes across the intensity range. The normalized expression values can be substantially different from the unnormalized values and may result in altered down-stream analysis.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1408-1414
• /
• 2019

Deep artificial neural network with transfer learning is applied to compressed sensing cardiovascular MRI. Transfer learning is a method that utilizes structure, filter kernels, and weights of the network used in prior learning for current learning or application. The transfer learning is useful in accelerating learning speed, and in generalization of the neural network when learning data is limited. From a cardiac MRI experiment, with 8 healthy volunteers, the neural network with transfer learning was able to reduce learning time by a factor of more than five compared to that with standalone learning. Using test data set, reconstructed images with transfer learning showed lower normalized mean square error and better image quality compared to those without transfer learning.

• Journal of electromagnetic engineering and science
• /
• v.9 no.2
• /
• pp.71-77
• /
• 2009

OFDM(Orthogonal Frequency Division Multiplexing) signals sutler serious nonlinear distortion in the nonlinear HPA(High Power Amplifier) because of high PAPR(Peak Average Power Ratio). Nonlinear distortion can be improved by a pre-distorter, but this pre-distorter is insufficient when the PAPR is very high in an OPFDM system. In this paper, a DFT(Discrete Fourier Transform) transform technique is introduced for PAPR reduction. It is especially important to consider the memory effect of HPA for more precise predistortion. Therefore, in this paper, we consider two models, the TWTA(Traveling-Wave Tube Amplifier) model of Saleh without a memory effect and the HPA memory polynomial model that has a memory effect. We design a pre-distorter and an adaptive pre-distorter that uses the NLMS(Normalized Least Mean Square) algorithm for the compensation of this nonlinear distortion. Without the consideration of a memory effect, the system performance would be degraded, even if the pre-distorter is used for the compensation of the nonlinear distortion. From the simulation results, we can confirm that the proposed system shows an improvement in performance.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.26 no.4
• /
• pp.515-523
• /
• 2002

Effects of rotary oscillation on unsteady laminar flow past a circular cylinder have been investigated in this study. Numerical simulations are performed for the flow at Re=100 in the range of 0.2<$\Omega$<2.5 and 0.02<$St_f$<0.8, where $\Omega$ and $St_f$ are, respectively, the maximum rotation velocity and rotation frequency normalized by the free-stream velocity and cylinder diameter. Results show that rotary oscillation has significant effects on the flow. When the rotation frequency is near the natural vortex-shedding frequency, lock-on occurs and the lock-on frequency range becomes wider as the rotation velocity increases. In a certain range of the rotation frequency and velocity, modulations in the velocity, lift and drag signals occur and this modulation frequency is expressed as a linear combination of the rotation frequency and vortex-shedding frequency. The mean drag and amplitude of the lift fluctuations show local minima near the boundary between the lock-on non and lock-on regions.

• Composites Research
• /
• v.30 no.3
• /
• pp.188-192
• /
• 2017

The mechanical properties of composites are significantly affected by external environment. It is essential to understand the degradation of material performance and judge the material's lifetime in advance. In the current research, changes in mechanical properties of glass fiber and unsaturated polyester composite materials (GFRP, Glass fiber reinforced plastic) were investigated under different bending stress and submerged in hot water at a temperature of $80^{\circ}C$. Loading time of 100 H (hours), 200 H, 400 H, 600 H, 800 H for testing under stresses equal to 0% (stress-free state), 30%, 50% and 70% of the ultimate strength was applied on the GFRP specimens. From the values of bending stress, obtained from three-point bending test, fracture energy, failure time, and life curve were analysed. Moreover, a normalized strength degradation model for this condition was also developed. It was observed that within 100 H, the decline rate of the bending strength was proportional to the pressure.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.3 no.2
• /
• pp.212-216
• /
• 2000

Subacute necrotizing lymphadenitis was first reported by Kikuchi and Fujimoto in 1972. Young females no more than 30 years of age are mainly affected. It usually manifests as fever and cervical lymphadenopathy. We experienced one case of subacute necrotizing lymphadenitis with hepatic complication in an 11-year-old boy. Symptoms presented were URI signs, diarrhea, headache, and weight loss along with fever and cervical lymphadenopathy. Elevated serum AST/ALT levels were also noted up to 682/1560 (IU/L) and were normalized within one month. We performed aspiration biopsy of the liver twice (at admission and 5 months thereafter). The hepatic histopathologic findings were nonspecific.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2612-2633
• /
• 2020

Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years. In this paper, we proposed an encrypted speech retrieval scheme based on long short-term memory (LSTM) neural network and deep hashing. This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage. Firstly, a novel speech encryption algorithm based on 4D quadratic autonomous hyperchaotic system is proposed to realize the privacy and security of speech data in the cloud. Secondly, the integrated LSTM network model and deep hashing algorithm are used to extract high-level features of speech data. It is used to solve the high dimensional and temporality problems of speech data, and increase the retrieval efficiency and retrieval accuracy of the proposed scheme. Finally, the normalized Hamming distance algorithm is used to achieve matching. Compared with the existing algorithms, the proposed scheme has good discrimination and robustness and it has high recall, precision and retrieval efficiency under various content preserving operations. Meanwhile, the proposed speech encryption algorithm has high key space and can effectively resist exhaustive attacks.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.6
• /
• pp.238-245
• /
• 2013

Gesture recognition has been widely one of the research areas for natural user interface. This paper presents a novel gesture recognition method using tree classification and multiclass SVM(Support Vector Machine). In the learning step, 3D trajectory of human gesture obtained by a Kinect sensor is classified into the tree nodes according to their distributions. The gestures are resampled and we obtain the histogram of the chain code from the normalized data. Then multiclass SVM is applied to the classified gestures in the node. The input gesture classified using the constructed tree is recognized with multiclass SVM.