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Vikrant Guleria,Vivek Kumar,Pradeep K. Singh 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.6
The abrupt changes in tool-workpiece interaction during machining process induce variation in the surface quality of work material. These interactions include built-up edge formation and their break-off, environmental conditions (use of coolant, rise of temperature etc.), material imperfections, improper structural fitness of machine & tool components, etc. This study presents prediction of surface roughness in turning of EN353 steel implementing the variational mode decomposition (VMD) for processing the vibration data, followed by estimation of the surface roughness using the relevance vector regression (RVR) optimized by particle swarm optimization (PSO). The raw vibration data has been decomposed in five discrete sets of frequency components known as variational mode functions (VMFs). A set of twenty-one statistical features in each three axes have been extracted for raw data and each VMF. The RVR has been trained using these 21×3 = 63 features and 3 cutting parameters – cutting speed, feed depth of cut. The RVR has also been trained separately using top 5 features selected through RreliefF algorithm. The optimal decomposition level has been determined to minimize the noise and predict the surface finish accurately. The results obtained in 1st VMF (high frequency, low amplitude) using its top 5 features for prediction have been found to be reliable with higher prediction accuracy.
( Su Young Park ),( Youn Jae Lee ),( Eun Uk Jung ),( Sung Jae Park ),( Sang Heon Lee ),( Ji Hyun Kim ),( Jung Sik Choi ),( Sam Ryong Jee ),( Sang Young Seol ) 대한간학회 2012 춘·추계 학술대회 (KASL) Vol.2012 No.-
Background: Rapid virologic response (RVR) is a strong on-treatment predictor of SVR. However the relevance of viral clearance at week 2 for SVR remained unclear. The aim of this study was to investigate the predictive value of viral clearance after 2 weeks of treatment on SVR in HCV patients treated with peg-interferon-α and ribavirin. Methods: 314 patients chronically infected with the HCV were treated with peg-interferon-α and ribavirin. Patients with genotype 1 were treated for 48weeks and patients with genotype 2 or 3 were treated for 24 weeks. HCV RNA was assessed by qualitative PCR at pretreatment, at week 2, 4 and 12 during treatment, and at week 24 of follow-up. The effects of virological response rates at different weeks on SVR were analyzed. Results: Among the 314 patients, 159 (50.6%) were genotype 1, 147 (46.8%) were genotype 2, and 8 (2.5%) were genotype 3. Overall, 144 (45.9%) achieved viral clearance at week 2 (26.4% in genotype 1 and 65.8% in genotype non-1, p<0.001), 240 (76.4%) RVR (50.3% in genotype 1 and 90.3% in genotype non-1, p<0.001), and 239 (76.1%) SVR ( 66.7% in genotype 1 and 85.8% in genotype non-1, p<0.001). There was no significant difference in positive predictive value and negative predictive value between RVR and viral clearance at week 2. In univariate analysis, genotype (p <0.001), viral clearance at week 2 (p<0.001), RVR (p<0.001) and early virological response (EVR) (p<0.001) were associated with SVR. However, in stepwise regression analysis, the independent predictive factors were RVR (p<0.001), EVR (p<0.001) and genotype (p = 0.016). Conclusions: Viral clearance at week 2 is not superior predictive factor for SVR compared the established parameters RVR and EVR.
( Su Young Park ),( Youn Jae Lee ),( Eun Uk Jung ),( Sung Jae Park ),( Sang Heon Lee ),( Ji Hyun Kim ),( Jung Sik Choi ),( Sam Ryong Jee ),( Sang Young Seol ) 대한간학회 2012 춘·추계 학술대회 (KASL) Vol.2012 No.1
Background: Rapid virologic response (RVR) is a strong on-treatment predictor of SVR. However the relevance of viral clearance at week 2 for SVR remained unclear. The aim of this study was to investigate the predictive value of viral clearance after 2 weeks of treatment on SVR in HCV patients treated with peg-interferon-α and ribavirin. Methods: 314 patients chronically infected with the HCV were treated with peg-interferon-α and ribavirin. Patients with genotype 1 were treated for 48weeks and patients with genotype 2 or 3 were treated for 24 weeks. HCV RNA was assessed by qualitative PCR at pretreatment, at week 2, 4 and 12 during treatment, and at week 24 of follow-up. The effects of virological response rates at different weeks on SVR were analyzed. Results: Among the 314 patients, 159 (50.6%) were genotype 1, 147 (46.8%) were genotype 2, and 8 (2.5%) were genotype 3. Overall, 144 (45.9%) achieved viral clearance at week 2 (26.4% in genotype 1 and 65.8% in genotype non-1, p<0.001), 240 (76.4%) RVR (50.3% in genotype 1 and 90.3% in genotype non-1, p<0.001), and 239 (76.1%) SVR ( 66.7% in genotype 1 and 85.8% in genotype non-1, p<0.001). There was no significant difference in positive predictive value and negative predictive value between RVR and viral clearance at week 2. In univariate analysis, genotype (p <0.001), viral clearance at week 2 (p<0.001), RVR (p<0.001) and early virological response (EVR) (p<0.001) were associated with SVR. However, in stepwise regression analysis, the independent predictive factors were RVR (p<0.001), EVR (p<0.001) and genotype (p = 0.016). Conclusions: Viral clearance at week 2 is not superior predictive factor for SVR compared the established parameters RVR and EVR.
Hybrid CSA optimization with seasonal RVR in traffic flow forecasting
( Zhangguo Shen ),( Wanliang Wang ),( Qing Shen ),( Zechao Li ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.10
Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.
Ivan Litvinov,Daniil Suslov,Mikhail Tsoy,Evgeny Gorelikov,Sergey Shtork,Sergey Alekseenko,Kilian Oberleithner 한국유체기계학회 2023 International journal of fluid machinery and syste Vol.16 No.4
This paper presents an active method to control the pressure fluctuations induced by the rotating vortex rope (RVR) in a Francis hydro turbine model under part load conditions. The control method is based on the injection of axial or radial jets through a stagnant crown attached to the hydro turbine runner. A wide range of injection strategies are com-pared, and the effectiveness of suppressing pressure fluctuations is analyzed in terms of the spatial distribution of the jets and the flow rate required to suppress the oscillations. The experiments are performed on a fully automated aerody-namic test rig. The pressure fluctuations are quantified using data from the four acoustic sensors placed at a cross sec-tion in the cone of the hydro turbine draft tube. The best suppression of pressure fluctuations is achieved with a radial actuator. At a control flow rate of 2% of the main flow, the pressure fluctuations at the vortex rope frequency are re-duced by 80% in terms of PSD compared to the baseline case without control. The presented control method will be useful for extending the operating range of Francis hydro turbines.