• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.973-979
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• 2024

In the framework of the Generation IV research and development project, in which the French Commission of Alternative and Atomic Energies (CEA) is involved, a main objective for the design of Sodium-cooled Fast Reactor (SFR) is to meet the safety goals for severe accidents. Among the severe ones, the Unprotected Transient OverPower (UTOP) accidents can lead very quickly to a global melting of the core. UTOP accidents can be considered either as slow during a Control Rod Withdrawal (CRW) or as fast. The paper focuses on fast UTOP accidents, which occur in a few milliseconds, and three different scenarios are considered: rupture of the core support plate, uncontrolled passage of a gas bubble inside the core and core mechanical distortion such as a core flowering/compaction during an earthquake. Several levels and rates of reactivity insertions are also considered and the thermal-mechanical behavior of an ASTRID fuel pin from the ASTRID CFV core is simulated with the GERMINAL code. Two types of fuel pins are simulated, inner and outer core pins, and three different burn-up are considered. Moreover, the feedback from the CABRI programs on these type of transients is used in order to evaluate the failure mechanism in terms of kinetics of energy injection and fuel melting. The CABRI experiments complete the analysis made with GERMINAL calculations and have shown that three dominant mechanisms can be considered as responsible for pin failure or onset of pin degradation during ULOF/UTOP accident: molten cavity pressure loading, fuel-cladding mechanical interaction (FCMI) and fuel break-up. The study is one of the first step in fast UTOP accidents modelling with GERMINAL and it has shown that the code can already succeed in modelling these type of scenarios up to the sodium boiling point. The modeling of the radial propagation of the melting front, validated by comparison with CABRI tests, is already very efficient.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.89-98
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• 2009

In this study, the YLPODS (Yonsei Laser-ranging Precision Orbit Determination System) is developed for POD using SLR (Satellite Laser Ranging) NP (Normal Point) observations. The performance of YLPODS is tested using SLR NP observations of TOPEX/POSEIDON and CHAMP satellite. JPL's POE (Precision Orbit Ephemeris) is assumed to be true orbit, the measurement residual RMS (Root Mean Square) and the orbit accuracy (radial, along-track, cross-track) are investigated. The validation of POD using GPS (Global Positioning System) raw data is achieved by YLPODS performance and highly accurate SLR NP observations. YGPODS (Yonsei GPS-based Precision Orbit Determination System) is used for generating GPS based precise orbits for TOPEX/POSEIDON. The initial orbit for YLPODS is derived from the YGPODS results. To validate the YGPODS results the range residual of the first adjustment of YLPODS is investigated. The YLPODS results using SLR NP observations of TOPEX/POSEIDON and CHAMP satellite show that the range residual is less than 10 cm and the orbit accuracy is about 1 m level. The validation results of the YGPODS orbits using SLR NP observations of the TOPEX/POSEIDON satellite show that the range residual is less than 10 cm. This result predicts that the accuracy of this GPS based orbits is about 1m level and it is compared with JPL's POE. Thus this result presents that the YLPODS can be used for POD validation using SLR NP observations such as STSAT-2 and KOMPSAT-5.

• Clinics in Shoulder and Elbow
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• v.12 no.2
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• pp.159-166
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• 2009

Purpose: We wanted to evaluate the surgical results of early mobilization after rigid fixation of small coronoid fracture using the tension band technique Materials and Methods: Eight cases of coronoid fracture were fixed with the tension band technique and using K-wire and wire through the medial approach. All the cases were Regan-Morrey type 2. According to O'Driscoll, they were classified as 5 cases of the tip type (subtype 2) and 3 cases of the anteromedial type (1 case of subtype 2, and 2 case of subtype 3). The associated collateral ligament injuries (6 cases) and radial head/neck fractures (4 cases) were managed simultaneously. After immobilization for 5~7 days, active ROM exercise with a fitted hinge brace started and continued till postoperative 6 weeks. The patients were assessed for pain, ROM and functional disability using the Mayo elbow performance score (MEPS) at an average of 11 months (range: 6~28 months). The ulnar nerve symptoms were also investigated. Results: We observed solid union in all the coronoid fractures without hardware failure. An average of 2.2 wires (range: 2~4) were used. The mean extension was $3^{\circ}$(range: $0^{\circ}\sim25^{\circ}$), the mean flexion was $137^{\circ}$(range: $130^{\circ}\sim140^{\circ}$), the mean pronation was $69^{\circ}$(range: $45^{\circ}\sim90^{\circ}$) and the mean supination was $78^{\circ}$(range: $45^{\circ}\sim90^{\circ}$). The mean MEPS was 96 (range: 65~100). Ulnar nerve symptoms occurred at postoperative one day and persisted in one patient with the terrible triad of taking radial head excision and residual medial instability. Conclusion: The tension band technique uses easily obtained, economic K-wires and the wire was strong enough to permit early elbow ROM exercise and the technique might improve the elbow function. It was especially useful for fixation of multiple small fragments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.1
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• pp.73-79
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• 2016

Higher circuit breaker safety standards can be obtained by increasing the sustaining time of the latching section. This time increase is achieved through velocity reduction after contacting when the closing mechanism operates. The potential for the re-closing phenomenon to occur is also reduced by obtaining time to return open latch. In this study, the sustaining time for the latching section was increased through cam profile optimization based on the displacement response of the moving parts. In addition, the existing performance velocity was also satisfied. A multibody dynamics model of the circuit breaker was developed using ADAMS. To validate the model, simulation results were compared to experiment results. Then, cam profile optimization was carried out using an optimal design program PIAnO. Design variables selected included the radial direction of the cam. Design sensitivity analysis was carried out by design section as well. As a result of optimization, the sustaining time for the latching section was increased.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.373-380
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• 2003

Most of low cost digital cameras reveal relatively high lens distortion. The purpose of this research is to compensate the degradation of image quality due to the geometrical distortion of a lens system. The proposed method consists of two stages : calculation of a lens distortion coefficient by a simplified version of Tsai´s camera calibration and subsequent image warping of the original distorted image to remove geometrical distortion based on the calculated lens distortion coefficient. In the lens distortion coefficient calculation stage, a practical method for handling scale factor ratio and image center is proposed, after which its feasibility is shown by measuring the performance of distortion correction using a quantitative image quality measure. On the other hand, in order to apply image warping via inverse spatial mapping using the result of the lens distortion coefficient calculation stage, a cubic polynomial derived from an adopted radial distortion lens model must be solved. In this paper, for the purpose of real-time operation, which is essential for embedding into an information device, an approximated solution to the cubic polynomial is proposed in the form of a solution to a quadratic equation. In the experiment, potential for real-time implementation and equivalence in performance as compared with that from cubic polynomial solution are shown.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

• The Korean Journal of Nuclear Medicine
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• v.30 no.4
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• pp.548-559
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• 1996

A series of performance measurements of positron emission tomography (PET) were performed following the recommendations of the Computer and Instrumentation Council of the Society of Nuclear Medicine and the National Electrical Manufacturers Association. We investigated the performance of the General Electric $Advance^{TM}$ PET. The measurements include the basic intrinsic tests of spatial resolution, scatter fraction, sensitivity, and count rate losses and randoms. They also include the tests of the accuracy of corrections: count rate linearity correction, uniformity correction, scatter correction and attenuation correction. GE $Advance^{TM}$ PET has bismuth germanate oxide crystals (4.0mm transaxial ${\times}$ 8.1mm axial ${\times}$ 30.0mm radial) in 18 rings, which form 35 imaging planes spaced by 4.25mm. The system has retractable tungsten septa 1mm thick and 12cm long. Transaxial resolution was 4.92mm FWHM in 2D and 5.14mm FWHM in 3D at the center. Average axial resolution in 2D decreased from 3.91mm FWHM at the center to 6.49mm FWHM at R=20cm. Average scatter fraction of direct and cross slices was 9.57%. Dead-time losses of 50% corresponded to a radioactivity concentration of $4.86{\mu}Ci/cc$ and a true count rate of 519 kcps in 2D. The accuracy of count rate linearity correction was 1.84% at the activity of $4.50{\mu}Ci/cc$. Non-uniformity was 2.06% in 2D and 2.93% in 3D. Remnant errors after scatter correction were 0.55% in 2D and 4.12% in 3D. The errors of attenuation correction were 6.21% (air), 0.20% (water), -6.32% (teflon) in 2D and 5.00% (air), 6.94% (water), 3.01% (teflon) in 3D. The results indicate the performance of GE $Advance^{TM}$ PET scanner to be well suited for clinical and research applications.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.