• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.3C
• /
• pp.336-342
• /
• 2003

Noise predictive maximum likelihood(NPML) detector embeds noise prediction/whitening process in branch metric calculation of Viterbi detector and improves the reliability of branch metric computation. Therefore, PRML detector with a noise predictor achieves some performance improvement and has an advantage of low complexity. This thesis random sequences are applied to linear channel. In perpendicular magnetic recording density KP=2.5, NP(121)ML and NP(1221)ML detection system which is based on a noise predictive PR-equalized signal are evaluated by the Performance through a computing simulation. Therefore, NPML systems are implemented and are verified by VHDL.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.9
• /
• pp.1-9
• /
• 2016

Recent embedded processors employ set-associative L1 instruction cache to improve the performance. The energy consumption in the set-associative L1 instruction cache accounts for considerable portion in the embedded processor. When an instruction is required from the processor, all ways in the set-associative instruction cache are accessed in parallel. In this paper, we propose the technique to reduce the energy consumption in the set-associative L1 instruction cache effectively by accessing only one way. Gshare branch predictor is employed to predict the instruction flow and determine the way to fetch the instruction. When the branch prediction is untaken, next instruction in a sequential order can be fetched from the instruction cache by accessing only one way. According to our simulations with SPEC2006 benchmarks, the proposed technique requires negligible hardware overhead and shows 20% energy reduction on average in 4-way L1 instruction cache.

• Journal of the Korean Chemical Society
• /
• v.60 no.4
• /
• pp.225-234
• /
• 2016

The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

• Nuclear Engineering and Technology
• /
• v.53 no.3
• /
• pp.804-811
• /
• 2021

The T-junction exists widely in industrial engineering, especially in nuclear power plants, which plays an important part in nuclear power reactor thermal-hydraulics. However, the existing prediction models of the liquid entrainment are mainly based on the small branches or small breaks while there are a few researches for large branches (d/D > 0.2). Referring to the classical models about the onset of liquid entrainment of the T-junction, most of previous models regard liquid as ideal working fluid and ignore surface tension. This paper aims to study the effect of surface tension on the liquid entrainment, and develops an improved model based on the reasonable assumption. The establishment of new model employs the methods of force analysis, dimensional analysis. Besides, the dimensionless Weber number is adopted innovatively into the model to show the effect of surface tension. What is more, in order to validate the new model, three kinds of working fluids with different surface tensions are creatively adopted in the experiments: water, silicone oil and ethyl alcohol. The final results show that surface tension has a nonnegligible effect on the onset of liquid entrainment in large branch T-junction. The new model is well matched with the experimental data.

• Journal of the Korean Society of Industry Convergence
• /
• v.24 no.3
• /
• pp.323-331
• /
• 2021

With global warming and pollution problems, accurate forecasting of the harmful gases would be an essential alarm in our life. In this paper, we forecast the emission of the five gases(SOx, NO₂, NH₃, H₂S, CH₄) using the time series model of ARIMA, the learning algorithms of Random forest, and LSTM. We find that the gas emission data depends on the short-term memory and behaves like a random walk. As a result, we compare the RMSE, MAE, and MAPE as the measure of the prediction performance under the same conditions given to three models. We find that ARIMA forecasts the gas emissions more precisely than the other two learning-based methods. Besides, the ARIMA model is more suitable for the real-time forecasts of gas emissions because it is faster for modeling than the two learning algorithms.

• Geomechanics and Engineering
• /
• v.31 no.3
• /
• pp.291-304
• /
• 2022

Maximum shear modulus (G_max or G₀) is an important soil property useful for many engineering applications, such as the analysis of soil-structure interactions, soil stability, liquefaction evaluation, ground deformation and performance of seismic design. In the current study, bender element (BE) tests are used to evaluate the effect of the void ratio, effective confining pressure, grading characteristics (D₅₀, C_u and C_c), anisotropic consolidation and initial fabric anisotropy produced during specimen preparation on the G_max of sand-gravel mixtures. Based on the tests results, an empirical equation is proposed to predict G_max in granular soils, evaluated by the experimental data. The artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were also applied. Coefficient of determination (R²) and Root Mean Square Error (RMSE) between predicted and measured values of G_max were calculated for the empirical equation, ANN and ANFIS. The results indicate that all methods accuracy is high; however, ANFIS achieves the highest accuracy amongst the presented methods.

• Journal of the Korea Concrete Institute
• /
• v.26 no.2
• /
• pp.159-169
• /
• 2014

This study generalizes the lateral load-displacement relationship of reinforced concrete shear walls from the section analysis for moment-curvature response to straightforwardly evaluate the flexural capacity and ductility of such members. Moment and curvature at different selected points including the first flexural crack, yielding of tensile reinforcing bar, maximum strength, 80% of the maximum strength at descending branch, and fracture of tensile reinforcing bar are calculated based on the strain compatibility and equilibrium of internal forces. The strain at extreme compressive fiber to determine the curvature at the descending branch is formulated as a function of reduction factor of maximum stress of concrete and volumetric index of lateral reinforcement using the stress-strain model of confined concrete proposed by Razvi and Saatcioglu. The moment prediction models are simply formulated as a function of tensile reinforcement index, vertical reinforcement index, and axial load index from an extensive parametric study. Lateral displacement is calculated by using the moment area method of idealized curvature distribution along the wall height. The generalized lateral load-displacement relationship is in good agreement with test result, even at the descending branch after ultimate strength of shear walls.

• Journal of Korean Society of Steel Construction
• /
• v.13 no.1
• /
• pp.9-18
• /
• 2001

many steel tubular truss as roof structures are used of the large span structures Steel tubular sectioned truss has the structural merits in compared with other sections such as H, L-shape sections However it occurs local buckling at the joint of branch in truss and it makes the deterioration of loading capacity Loading capacity and deformation characteristics of truss joints are very complicate so it is very hard to predict exact solution of them Therefore this thesis dealt with T-type joints of steel circular hollow sectioned truss. A series of experimental scheme were planned and mainly experimental parameters were : ratio of diameter of branch-diameter of main chord(d/D). diameter-thickness(T/D) of main chord. In this paper predicted yield load capacity using by closed ring analysis method additionally compared with that of suggested by closed ring analysis method additionally compared with that of suggested by other countries.

• Journal of computational fluids engineering
• /
• v.18 no.3
• /
• pp.67-71
• /
• 2013

Recently, the rapid development of the semiconductor industry induces the prompt technical progress in the area of device integration and the application of large diameter wafers for the price competitiveness. As a result of the usage of large wafers in the semiconductor industry, the silicon carbide components which have layers of silicon carbide on graphite or RBSC substrates is getting widely used due to the advantages of SiC such as high hardness and strength, chemical and ionic resistant to all the environments superior than other ceramic materials. For the uniform and homogeneous deposition of silicon carbide on these huge components, it needs to know about the gas flow in the CVD reactor, not only for the delicate adjustment of the process variables but more essentially for the cost reduction for the shape change of specimens and their holders on the stage of reactor. In this research, the CFD simulation is challenged for the prediction of the inner distribution of the gas velocity. Chemical reaction simulation is used to predict the distribution of concentration of the reacting gas with the rotating velocity of the stage. With the increase of the rotating speed, more uniform distribution of the reacting gas on the surface of the stage was obtained.

• Journal of Digital Convergence
• /
• v.12 no.3
• /
• pp.237-242
• /
• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.