The Okiep Copper District is the oldest mining district in South Africa with a legacy of more than 150 years of mining.This legacy can be felt in the presence of large tailings dams scattered throughout the area.These...The Okiep Copper District is the oldest mining district in South Africa with a legacy of more than 150 years of mining.This legacy can be felt in the presence of large tailings dams scattered throughout the area.These tailings have a deleterious impact on the surrounding environment.To use geochemical methods in determining the scale of the impact, pre-mining background levels need to be determined. This is especially difficult in areas for which展开更多
The paper has introduced the journals on electrical engineering in China in detail, presented its publication year, the distributions for all elec. eng. journals and its core journals, the position and level of the jo...The paper has introduced the journals on electrical engineering in China in detail, presented its publication year, the distributions for all elec. eng. journals and its core journals, the position and level of the journals, its main journal evaluation indexes, its core journal proportion and inclusion info. by famous international database. The paper offers much information about the journals on electrical engineering in China to readers, and also points out present problems and gives suggestion.展开更多
Cables that have been in service for over 20 years in Shanghai, a city with abundant surface water, failed more frequently and induced different cable accidents. This necessitates researches on the insulation aging st...Cables that have been in service for over 20 years in Shanghai, a city with abundant surface water, failed more frequently and induced different cable accidents. This necessitates researches on the insulation aging state of cables working in special circumstances. We performed multi-parameter tests with samples from about 300 cable lines in Shanghai. The tests included water tree investigation, tensile test, dielectric spectroscopy test, thermogravimetric analysis (TGA), fourier transform infrared spectroscopy (FTIR), and electrical aging test. Then, we carried out regression analysis between every two test parameters. Moreover, through two-sample t-Test and analysis of va- riance (ANOVA) of each test parameter, we analyzed the influences of cable-laying method and sampling section on the degradation of cable insulation respectively. Furthermore, the test parameters which have strong correlation in the regression analysis or significant differ- ences in the t-Test or ANOVA analysis were determined to be the ones identifying the XLPE cable insulation aging state. The thresholds for distinguishing insulation aging states had been also obtained with the aid of statistical analysis and fuzzy clustering. Based on the fuzzy in- ference, we established a cable insulation aging diagnosis model using the intensity transfer method. The results of regression analysis indicate that the degradation of cable insulation accelerates as the degree of in-service aging increases. This validates the rule that the in- crease of microscopic imperfections in solid material enhances the dielectric breakdown strength. The results of the two-sample t-Test and the ANOVA indicate that the direct-buried cables are more sensitive to insulation degradation than duct cables. This confirms that the tensile strength and breakdown strength are reliable functional parameters in cable insulation evaluations. A case study further indicates that the proposed diagnosis model based on the fuzzy inference can reflect the comprehensive aging state of cable insulation well, and that the cable service time has no correlation with the insulation aging state.展开更多
Statistical energy analysis (SEA) is an effective method for predicting high frequency vibro-acoustic performance of automobiles. A full vehicle SEA model is presented for interior noise reduction. It is composed of a...Statistical energy analysis (SEA) is an effective method for predicting high frequency vibro-acoustic performance of automobiles. A full vehicle SEA model is presented for interior noise reduction. It is composed of a number of subsystems based on a 3D model with all parameters for each subsystem. The excitation inputs are measured through road tests in different conditions,including inputs from the engine vibration and the sound pressure of the engine bay. The accuracy in high frequency of SEA model is validated,by comparing the analysis results with the testing pressure level data at driver's right ear. Noise contribution and sensitivity of key subsystems are analyzed. Finally,the effectiveness of noise reduction is verified. Based on the SEA model,an approach combining test and simulation is proposed for the noise vibration and harshness (NVH) design in vehicle development. It contains building the SEA model,testing for subsystem parameter identification,validating the simulation model,identifying subsystem power inputs,analyzing the design sensitivity. An example is given to demonstrate the interior noise reduction in high frequency.展开更多
The paper presented the statistics and analysis on papers published on the journal 'Advanced Technology of Electrical Engineering and Energy' from 1996 to 2008: the paper acceptance rate,the paper category,the...The paper presented the statistics and analysis on papers published on the journal 'Advanced Technology of Electrical Engineering and Energy' from 1996 to 2008: the paper acceptance rate,the paper category,the first author's affiliations,the top 7 first authors,the top 10 coauthors and also the journal evaluation indexes of the journal.It offers details of the journal to anyone interested,especially to our editorial board and our broad readers.展开更多
Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effect...In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.展开更多
文摘The Okiep Copper District is the oldest mining district in South Africa with a legacy of more than 150 years of mining.This legacy can be felt in the presence of large tailings dams scattered throughout the area.These tailings have a deleterious impact on the surrounding environment.To use geochemical methods in determining the scale of the impact, pre-mining background levels need to be determined. This is especially difficult in areas for which
文摘The paper has introduced the journals on electrical engineering in China in detail, presented its publication year, the distributions for all elec. eng. journals and its core journals, the position and level of the journals, its main journal evaluation indexes, its core journal proportion and inclusion info. by famous international database. The paper offers much information about the journals on electrical engineering in China to readers, and also points out present problems and gives suggestion.
基金Project supported by National Natural Science Foundation of China（51277117）, Shang- hai Science and Technology Comrmssion（11 DZ2283000）.
文摘Cables that have been in service for over 20 years in Shanghai, a city with abundant surface water, failed more frequently and induced different cable accidents. This necessitates researches on the insulation aging state of cables working in special circumstances. We performed multi-parameter tests with samples from about 300 cable lines in Shanghai. The tests included water tree investigation, tensile test, dielectric spectroscopy test, thermogravimetric analysis (TGA), fourier transform infrared spectroscopy (FTIR), and electrical aging test. Then, we carried out regression analysis between every two test parameters. Moreover, through two-sample t-Test and analysis of va- riance (ANOVA) of each test parameter, we analyzed the influences of cable-laying method and sampling section on the degradation of cable insulation respectively. Furthermore, the test parameters which have strong correlation in the regression analysis or significant differ- ences in the t-Test or ANOVA analysis were determined to be the ones identifying the XLPE cable insulation aging state. The thresholds for distinguishing insulation aging states had been also obtained with the aid of statistical analysis and fuzzy clustering. Based on the fuzzy in- ference, we established a cable insulation aging diagnosis model using the intensity transfer method. The results of regression analysis indicate that the degradation of cable insulation accelerates as the degree of in-service aging increases. This validates the rule that the in- crease of microscopic imperfections in solid material enhances the dielectric breakdown strength. The results of the two-sample t-Test and the ANOVA indicate that the direct-buried cables are more sensitive to insulation degradation than duct cables. This confirms that the tensile strength and breakdown strength are reliable functional parameters in cable insulation evaluations. A case study further indicates that the proposed diagnosis model based on the fuzzy inference can reflect the comprehensive aging state of cable insulation well, and that the cable service time has no correlation with the insulation aging state.
基金Sponsored by the Key Project of the Development of Science and Technology of Jilin Province (20040332-1)the National"863"Project(2006AA110102-3)
文摘Statistical energy analysis (SEA) is an effective method for predicting high frequency vibro-acoustic performance of automobiles. A full vehicle SEA model is presented for interior noise reduction. It is composed of a number of subsystems based on a 3D model with all parameters for each subsystem. The excitation inputs are measured through road tests in different conditions,including inputs from the engine vibration and the sound pressure of the engine bay. The accuracy in high frequency of SEA model is validated,by comparing the analysis results with the testing pressure level data at driver's right ear. Noise contribution and sensitivity of key subsystems are analyzed. Finally,the effectiveness of noise reduction is verified. Based on the SEA model,an approach combining test and simulation is proposed for the noise vibration and harshness (NVH) design in vehicle development. It contains building the SEA model,testing for subsystem parameter identification,validating the simulation model,identifying subsystem power inputs,analyzing the design sensitivity. An example is given to demonstrate the interior noise reduction in high frequency.
文摘The paper presented the statistics and analysis on papers published on the journal 'Advanced Technology of Electrical Engineering and Energy' from 1996 to 2008: the paper acceptance rate,the paper category,the first author's affiliations,the top 7 first authors,the top 10 coauthors and also the journal evaluation indexes of the journal.It offers details of the journal to anyone interested,especially to our editorial board and our broad readers.
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
基金Project(2003AA430200) supported by the National High-Tech Research and Development Program of China
文摘In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.
