Advances have been made in understanding the interactions of composition, molecular weight, liquid crystallinity,orientation, and three-dimensional crystallinity on the properties of injection-molded and melt-spun liq...Advances have been made in understanding the interactions of composition, molecular weight, liquid crystallinity,orientation, and three-dimensional crystallinity on the properties of injection-molded and melt-spun liquid crystalline polyesters (LCP's). Two classes of potentially low-cost LCP's were compared:(1) semiflexible LCP's prepared from 1,6-hexanediol and the dimethyl ester of either trans-4, 4'stilbenedicarboxylic acid or 4.4 ' biphenyldicarboxylic acid and (2) all-aromatic LCP's prepared from terephthalic acid, 2, 6-naphthalenedicarboxylic acid, the diacetate of hydroquinone, and the acetate of p-hydroxybenzoic acid. The effects of composition on the plastic properties of the 4-component all-aromatic LCP's were determined with the aid of a 3 x 3 factorial statistically designed experiment, the generation of equations with a computer program, and the plotting of three-dimensional figures and contour diagrams. The effects of absolute molecular weight (M_w) on the tensile strengths of the semiflexible LCP's and one of the all-aromatic LCP's having an excellent balance of plastic properties were also compared, and it was observed that the semiflexible LCP's required M_w's about 4 times higher than the all-aromatic LCP to attain a given strength. Persistence lengths and molecular modeling were used to explain these differences.展开更多
Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec as...Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec asting model. However, the conventional methods of making sell and buy decision based on human forecast or conventional moving average and exponential smoothing methods is no longer be sufficient to meet the future need. Furthermore, the un derlying statistics of the market information change from time to time due to a number of reasons such as change of global economic environment, government poli cies and business risks. This demands for highly adaptive forecasting model which is robust enough to response and adapt well to the fast changes in the dat a characteristics, in other words, the trajectory of the "dynamic characteristic s" of the data. In this paper, an adaptive time-series modelling method was proposed for short -term dynamic forecasting. The method employs an autoregressive (AR) time-seri es model to carry out the forecasting process. A modified least mean square (MLM S) adaptive filter algorithm was established for adjusting the AR model coeffici ents so as to minimise the sum of squared of forecasting errors. A prototype dyn amic forecasting system was built based on the adaptive time-series modelling m ethod. Basically, the dynamic forecasting system can be divided into two phases, i.e. the Learning Phase and the Application Phase. The learning procedures star t with the determination of upper limit of the adaptation gain based on the conv ergence in the mean square criterion. Hence, the optimum ELMS filter parameters are determined using an iteration algorithm which changes each filter parameter i.e. the order, the adaptation gain andthe values initial coefficient vector on e by one inside a predetermined iteration range. The set of parameters which giv es the minimum value for sum of squared errors within the iteration range is sel ected as the optimum set of filter parameters. In the Application Phase, the sys tem is operated under a real-time environment. The sampled data is processed by the optimised ELMS filter and the forecasted data are calculated based on the a daptive time-series model. The error of forecasting is continuously monitored w ithin the predefined tolerance. When the system detects excessive forecasting er ror, a feedback alarm signal was issued for system re-calibration. Experimental results indicated that the convergence rate and sum of squared erro rs during initial adaptation could be significantly improved using the MLMS algorithm. The performance of the system was verified through a series of experi ments conducted on the forecast of materials demand and costing in productio n logistics. Satisfactory results were achieved with the forecast errors confini ng within in most instances. Further applications of the system can be found i n sales demand forecast, inventory management as well as collaborative planning, forecast and replenishment (CPFR) in logistics engineering.展开更多
Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive B...Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.展开更多
Conclusion is that the soft threshold de-noising effect is better than hard threshold de-noising though the signal to noise ratio and Root Mean Square error. After analyzing, de-noising and reconstructing signal throu...Conclusion is that the soft threshold de-noising effect is better than hard threshold de-noising though the signal to noise ratio and Root Mean Square error. After analyzing, de-noising and reconstructing signal through wavelet packet from Matlab software, the average value of peak extracted from signal reconstruction is gotten to provide data for dynamic balance. Making use of the influence coefficient method to adjust drum of CTP dynamic balance, the program of Matlab is used to find phase position and weight of the mass block quickly, which can provide evidence for software for dynamic balance developed.展开更多
The measurement of <sup>36</sup>Cl in the nature is of importance in geoscience. The <sup>36</sup>Cl reaction induced by cosmicray neutrons in the atmosphere has been considered as a significan...The measurement of <sup>36</sup>Cl in the nature is of importance in geoscience. The <sup>36</sup>Cl reaction induced by cosmicray neutrons in the atmosphere has been considered as a significant source for the formation of <sup>36</sup>Cl. In 1968, Onufriev calculated the fallout rate of <sup>36</sup>展开更多
Structural design for a drum of a CTP device and modal analysis by means of ANSYS workbench have been done to get the first four natural frequencies and vibration models.Natural frequency and maximum deformation of dr...Structural design for a drum of a CTP device and modal analysis by means of ANSYS workbench have been done to get the first four natural frequencies and vibration models.Natural frequency and maximum deformation of drum as the optimization goal,three kinds of proposals are put forward based on the weak area of the drum.By means of adjusting static balance and adding thickness,an optimal project 3 is determined the best in the three kinds of proposals.The first natural frequency of the drum optimized is increased by 33.1% than that before optimization,the maximum displacement is reduced by 23.2%.Testing the result of simulation meets image high quality requirements,dynamics analysis and structural optimization are realized.展开更多
The advance of the Internet in the past decade has radically changed the way people communicate and col- laborate with each other. Physical distance is no more a barrier in online social networks, but cultural differe...The advance of the Internet in the past decade has radically changed the way people communicate and col- laborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences （at the individual, community, as well as societal levels） still govern human-human interactions and must be con- sidered and leveraged in the online world. The rapid deployment of high-speed lnternet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ＇connected multimedia＇, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.展开更多
文摘Advances have been made in understanding the interactions of composition, molecular weight, liquid crystallinity,orientation, and three-dimensional crystallinity on the properties of injection-molded and melt-spun liquid crystalline polyesters (LCP's). Two classes of potentially low-cost LCP's were compared:(1) semiflexible LCP's prepared from 1,6-hexanediol and the dimethyl ester of either trans-4, 4'stilbenedicarboxylic acid or 4.4 ' biphenyldicarboxylic acid and (2) all-aromatic LCP's prepared from terephthalic acid, 2, 6-naphthalenedicarboxylic acid, the diacetate of hydroquinone, and the acetate of p-hydroxybenzoic acid. The effects of composition on the plastic properties of the 4-component all-aromatic LCP's were determined with the aid of a 3 x 3 factorial statistically designed experiment, the generation of equations with a computer program, and the plotting of three-dimensional figures and contour diagrams. The effects of absolute molecular weight (M_w) on the tensile strengths of the semiflexible LCP's and one of the all-aromatic LCP's having an excellent balance of plastic properties were also compared, and it was observed that the semiflexible LCP's required M_w's about 4 times higher than the all-aromatic LCP to attain a given strength. Persistence lengths and molecular modeling were used to explain these differences.
文摘Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec asting model. However, the conventional methods of making sell and buy decision based on human forecast or conventional moving average and exponential smoothing methods is no longer be sufficient to meet the future need. Furthermore, the un derlying statistics of the market information change from time to time due to a number of reasons such as change of global economic environment, government poli cies and business risks. This demands for highly adaptive forecasting model which is robust enough to response and adapt well to the fast changes in the dat a characteristics, in other words, the trajectory of the "dynamic characteristic s" of the data. In this paper, an adaptive time-series modelling method was proposed for short -term dynamic forecasting. The method employs an autoregressive (AR) time-seri es model to carry out the forecasting process. A modified least mean square (MLM S) adaptive filter algorithm was established for adjusting the AR model coeffici ents so as to minimise the sum of squared of forecasting errors. A prototype dyn amic forecasting system was built based on the adaptive time-series modelling m ethod. Basically, the dynamic forecasting system can be divided into two phases, i.e. the Learning Phase and the Application Phase. The learning procedures star t with the determination of upper limit of the adaptation gain based on the conv ergence in the mean square criterion. Hence, the optimum ELMS filter parameters are determined using an iteration algorithm which changes each filter parameter i.e. the order, the adaptation gain andthe values initial coefficient vector on e by one inside a predetermined iteration range. The set of parameters which giv es the minimum value for sum of squared errors within the iteration range is sel ected as the optimum set of filter parameters. In the Application Phase, the sys tem is operated under a real-time environment. The sampled data is processed by the optimised ELMS filter and the forecasted data are calculated based on the a daptive time-series model. The error of forecasting is continuously monitored w ithin the predefined tolerance. When the system detects excessive forecasting er ror, a feedback alarm signal was issued for system re-calibration. Experimental results indicated that the convergence rate and sum of squared erro rs during initial adaptation could be significantly improved using the MLMS algorithm. The performance of the system was verified through a series of experi ments conducted on the forecast of materials demand and costing in productio n logistics. Satisfactory results were achieved with the forecast errors confini ng within in most instances. Further applications of the system can be found i n sales demand forecast, inventory management as well as collaborative planning, forecast and replenishment (CPFR) in logistics engineering.
文摘Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.
文摘Conclusion is that the soft threshold de-noising effect is better than hard threshold de-noising though the signal to noise ratio and Root Mean Square error. After analyzing, de-noising and reconstructing signal through wavelet packet from Matlab software, the average value of peak extracted from signal reconstruction is gotten to provide data for dynamic balance. Making use of the influence coefficient method to adjust drum of CTP dynamic balance, the program of Matlab is used to find phase position and weight of the mass block quickly, which can provide evidence for software for dynamic balance developed.
文摘The measurement of <sup>36</sup>Cl in the nature is of importance in geoscience. The <sup>36</sup>Cl reaction induced by cosmicray neutrons in the atmosphere has been considered as a significant source for the formation of <sup>36</sup>Cl. In 1968, Onufriev calculated the fallout rate of <sup>36</sup>
文摘Structural design for a drum of a CTP device and modal analysis by means of ANSYS workbench have been done to get the first four natural frequencies and vibration models.Natural frequency and maximum deformation of drum as the optimization goal,three kinds of proposals are put forward based on the weak area of the drum.By means of adjusting static balance and adding thickness,an optimal project 3 is determined the best in the three kinds of proposals.The first natural frequency of the drum optimized is increased by 33.1% than that before optimization,the maximum displacement is reduced by 23.2%.Testing the result of simulation meets image high quality requirements,dynamics analysis and structural optimization are realized.
基金supported in part by US National Science Foundation through grant IIS-0956924College of Computer Science and Technology of Zhejiang University, China+2 种基金The follow-up workshop in 2010 held in Florence was supported in part by ACM and Microsoft ResearchZhongfei ZHANG is also supported in part by the National Basic ResearchProgram of China (No. 2012CB316400)ZJU-Alibaba Financial Joint Lab, Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis, and US NSF (Nos. IIS-0812114 and CCF-1017828)
文摘The advance of the Internet in the past decade has radically changed the way people communicate and col- laborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences （at the individual, community, as well as societal levels） still govern human-human interactions and must be con- sidered and leveraged in the online world. The rapid deployment of high-speed lnternet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ＇connected multimedia＇, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.
