• ETRI Journal
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• v.40 no.6
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• pp.759-773
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• 2018

Uncore components such as on-chip memory systems and on-chip interconnects consume a large amount of energy in emerging embedded applications. Few studies have focused on next-generation analytical models for future chip-multiprocessors (CMPs) that simultaneously consider the impacts of the power consumption of core and uncore components. In this paper, we propose a convex-optimization approach to design heterogeneous uncore architectures for embedded CMPs. Our convex approach optimizes the number and placement of memory banks with different technologies on the memory layer. In parallel with hybrid memory architecting, optimizing the number and placement of through silicon vias as a viable solution in building three-dimensional (3D) CMPs is another important target of the proposed approach. Experimental results show that the proposed method outperforms 3D CMP designs with hybrid and traditional memory architectures in terms of both energy delay products (EDPs) and performance parameters. The proposed method improves the EDPs by an average of about 43% compared with SRAM design. In addition, it improves the throughput by about 7% compared with dynamic RAM (DRAM) design.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1729-1737
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• 2019

This paper presents neutronics design of VVER-1000 fuel assembly using burnable poison particles (BPPs) for controlling excess reactivity and pin-wise power distribution. The advantage of using BPPs is that the thermal conductivity of BPP-dispersed fuel pin could be improved. Numerical calculations have been conducted for optimizing the BPP parameters using the MVP code and the JENDL-3.3 data library. The results show that by using $Gd_2O_3$ particles with the diameter of $60{\mu}m$ and the packing fraction of 5%, the burnup reactivity curve and pin-wise power distribution are obtained approximately that of the reference design. To minimize power peaking factor (PPF), total BP amount has been distributed in a larger number of fuel rods. Optimization has been conducted for the number of BPP-dispersed rods, their distribution, BPP diameter and packing fraction. Two models of assembly consisting of 18 BPP-dispersed rods have been selected. The diameter of $300{\mu}m$ and the packing fraction of 3.33% were determined so that the burnup reactivity curve is approximate that of the reference one, while the PPF can be decreased from 1.167 to 1.105 and 1.113, respectively. Application of BPPs for compensating the reduction of soluble boron content to 50% and 0% is also investigated.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1060-1067
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• 2018

A feasibility of reusing option of spent nuclear fuel in a fusion-fission hybrid system without partitioning was checked as an alternative option of pyro-processing with critical reactor system. Neutronic study was performed with MCNP 6.1 for this option, direct reuse of spent PWR fuel (DRUP). Various options with DRUP fuel were compared with the reference design concept; transmutation purpose blanket with (U-TRU)Zr fuel loading connected with pyro-processing. Performance parameters to be compared are transmutation performance of transuranic (TRU) nuclides, required fusion power and tritium breeding ratio (TBR). When blanket part is loaded only with DRUP, initial $k_{eff}$ level becomes too low to maintain a practical subcritical system, increasing the required fusion power. In this case, production rate of TRU nuclides exceeds the incineration rate. Design optimization is done for combining DRUP fuel with (U-TRU)Zr fuel. Reactivity swing is reduced to about 2447 pcm through fissile breeding compared to (U-TRU)Zr fuel option. Therefore, a required fusion power is reduced and tritium breeding performance is improved. However, transmutation performance with TRU nuclides especially $^{241}Am$ is degraded because of softening effect of spectrum. It is known that partitioning and transmutation should be accompanied with fusion-fission hybrid system for the effective transmutation of TRU.

• Journal of Environmental Science International
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• v.30 no.5
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• pp.399-406
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• 2021

The dissolution of ionized gas in dielectric barrier plasma, similar to the principle of ozone generation, is a major performance-affecting factor. In this study, the plasma gas dissolving performance of a gas mixing-circulation plasma process was evaluated using an experimental design methodology. The plasma reaction is a function of four parameters [electric current (X₁), gas flow rate (X₂), liquid flow rate (X₃) and reaction time (X₄)] modeled by the Box-Behnken design. RNO (N, N-Dimethyl-4-nitrosoaniline), an indictor of OH radical formation, was evaluated using a quadratic response surface model. The model prediction equation derived for RNO degradation was shown as a second-order polynomial. By pooling the terms with poor explanatory power as error terms and performing ANOVA, results showed high significance, with an adjusted R² value of 0.9386; this indicate that the model adequately satisfies the polynomial fit. For the RNO degradation, the measured value and the predicted values by the model equation agreed relatively well. The optimum current, gas flow rate, liquid flow rate and reaction time were obtained for the highest desirability for RNO degradation at 0.21 A, 2.65 L/min, 0.75 L/min and 6.5 min, respectively.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.178-183
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• 2021

This paper describes a dynamic spectroscopic ellipsometry based on dual-wavelength calibration. DSE provides ellipsometric parameters at rates above 20 Hz, but the interferometer's sensitivity to temperature makes it difficult for that proposed system to maintain stable 𝜟k over long periods of time. To solve this problem, we set up an additional path in the DSE to perform simulations of the polarization phase calibration method using dual wavelengths. Through simulation, we were able to eliminate most of the polarization phase error and maintain a stable 𝜟k in the long-term stability experiment for 10 hours. This is the result that the 𝜟k stability of the proposed system is improved tens of times compared to the existing system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• Smart Structures and Systems
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• v.26 no.6
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• pp.753-763
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• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• Current Optics and Photonics
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• v.5 no.3
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• pp.213-219
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• 2021

We describe a single-channel rubidium (Rb) radio-frequency atomic magnetometer (RFAM) as a receiver that takes magnetic signal resonating with Zeeman splitting of the ground state of Rb. We optimize the performance of the RFAM by recording the response signal and signal-to-noise ratio (SNR) in various parameters and obtain a noise level of 159 $fT{\sqrt{Hz}}$ around 30 kHz. When a resonant radiofrequency magnetic field with a peak amplitude of 8.0 nT is applied, the bandwidth and signal-to-noise ratio are about 650 Hz and 88 dB, respectively. It is a good agreement that RFAM using alkali atoms is suitable for receiving signals in the very low frequency (VLF) carrier band, ranging from 3 kHz to 30 kHz. This study shows the new capabilities of the RFAM in communications applications based on magnetic signals with the VLF carrier band. Such communication can be expected to expand the communication space by overcoming obstacles through the high magnetic sensitive RFAM.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.519-528
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• 2021

One of the most efficient designs of solar trackers for photovoltaic panels is the single-axis tracker, which holds the panels along a torque tube that is driven by a motor at the central section. These trackers have evolved to become extremely slender structures due to mechanical optimization against static load and the need of cost reduction in a very competitive market. Owing to the corresponding decrease in mechanical resistance, some of these trackers have suffered aeroelastic instability even at moderate wind speeds, leading to catastrophic failures. In the present work, an analytical and experimental approach has been developed to study that phenomenon. The analytical study has led to identify the dimensionless parameters that govern the motion of the panel-tracker structure. Also, systematic wind tunnel experiments have been carried out on a 3D aeroelastic scale model. The tests have been successful in reproducing the aeroelastic phenomena arising in real-scale cases and have allowed the identification and a close characterization of the phenomenon. The main results have been the determination of the critical velocity for torsional galloping as a function of tilt angle and a calculation methodology for the optimal sizing of solar tracker shafts.