To study the influence of support timing and support strength on the mechanical properties and deformation damage characteristics of a single-sided unloaded rock mass,a true triaxial perturbation unloaded rock testing...To study the influence of support timing and support strength on the mechanical properties and deformation damage characteristics of a single-sided unloaded rock mass,a true triaxial perturbation unloaded rock testing system was used to conduct rock damage tests on sandstone with different support timing and strength paths.Based on the acoustic emission monitoring system,the spatial and temporal evolution characteristics of the whole process of rock body loaded instability under two stress paths were studied,and the mechanism of the reinforcing effect of stress support on the unloaded rock mass was analyzed.The results show that,within the scope of this study,both earlier applications of shoring and an increase in shoring strength can effectively improve the ultimate bearing capacity of the unloaded rock,which increases the ultimate bearing capacity of the unloaded rock mass by 60.31% and 54.96%,respectively;There is a phenomenon of rebound deformation of the rock mass during sudden changes in stress(single-sided unloading,stress support),which shows opposite expansion and compression platforms on the stress−strain curve;The crack evolution of unloaded rock under different stress support conditions shows the state law of"initial crack activation→middle steady state expansion→late main crack penetration",and the lagging support significantly accelerates the crack evolution from local activation to main penetration;The single-sided unloading and stress-supporting stages have less influence on the unloading deformationsσ_(1u),σ_(2u) and support deformationsσ_(1) t,σ_(2t) in theσ_(1) andσ_(2)directions,while they show significant response characteristics toσ_(3u),σ_(vu) and σ_(3) t,σ_(vt),and with the increase of the support strength,the stress-supporting stagesσ_(3) t,σ_(vt) gradually increase and exceed the deformations generated by the unloading stagesσ_(3u),σ_(vu);The increase of support strength can effectively compensate for the rock stress loss caused by unloading,which makes the maximum,minimum,and volumetric strain support coefficients during the loading and unloading of the rock body increase gradually while the effect on the intermediate principal strain support coefficient is small;During loading,the support strength of rock masses seeks a new bearing area by regulating stress equilibrium states.This process primarily manifests as a shift in the locations of the crushing zone and the main bearing area,accompanied by a corresponding transformation in failure patterns.Consequently,the rock mass transitions from asymmetric three-zone damage under no or weak support to approximate symmetric three-zone damage under strong support.Simultaneously,the main load-bearing area of the rock mass shifts from deep bearing in the unsupported to middle bearing under strong support as the support strength increases.展开更多
The methods combined by test, field monitoring and theoretical analysis were adopted to do the systemic research on the rock mass from micro-structure to macro-deformation, and rheological model of Jinchuan rock mass ...The methods combined by test, field monitoring and theoretical analysis were adopted to do the systemic research on the rock mass from micro-structure to macro-deformation, and rheological model of Jinchuan rock mass was established to discuss the reasonable supporting time. Resuhs show that supporting after suitable stress and displacement release can benefit for the long-term stability of surrounding rock.展开更多
Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel i...Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel in SVM is drawn in a more natural way by using the fuzzy logic method proposed in this paper. This method provides easy hardware implementation and straightforward interpretability. Experiments on two typical chaotic time series predictions have been carried out and the obtained results show that the average CPU time can be reduced significantly at the cost of a small decrease in prediction accuracy, which is favourable for the hardware implementation for chaotic time series prediction.展开更多
This paper has put forward a concept of optimal supporting time through analysing the influence of the supporting time in the heading face on the supporting result of surrounding rock.The method Of the optimal Support...This paper has put forward a concept of optimal supporting time through analysing the influence of the supporting time in the heading face on the supporting result of surrounding rock.The method Of the optimal Supporting time determined by graphical method is discussed, and the calculating formula for determining the optimal supporting time through the analysis method is derived.展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved...This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are complicated since each operation may be done by several machining centers. The system design approach is built around the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in operation assignment and scheduling of multi-purpose machining centers which have different tools with their own efficiency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real time event handler for implementing inter-process synchronization. The controller’s performance is validated by benchmark test problem.展开更多
To keep coal workers away from the hazardous area with frequent accidents such as the roof fall and rib spalling in an underground coalmine,we put forward the solution with robotized self-moving anchor-supporting unit...To keep coal workers away from the hazardous area with frequent accidents such as the roof fall and rib spalling in an underground coalmine,we put forward the solution with robotized self-moving anchor-supporting unit.The existing research shows that the surrounding rock of the roadway has self-stability,and the early or late support is not conducive to the safe and reliable support of the roadway,so there is a problem of support opportunity.In order to study the supporting effect and the optimal supporting time of the above solution,we established the mechanical coupling model of surrounding rock and advance support,and investigated the surrounding rock deformation and advance support pressure distribution under different reserved roof subsidence by using the numerical simulation software FLAC3D.The results show that the deformation of surrounding rock increases and finally tends to a stable level with the increase of pre settlement of roadway roof,and when the pre settlement of roof is between 8-15 mm,the vertical pressure of the top beam of advance support reaches the minimum value,about 0.58 MPa.Based on the above research,we put forward the optimum supporting time in roadway excavation,and summarized the evaluation method based on the mechanical coupling model of surrounding rock-advance support.展开更多
This study explores the least square support vector and wavelet technique (WLSSVM) in the monthly stream flow forecasting. This is a new hybrid technique. The 30 days periodic predicting statistics used in this study ...This study explores the least square support vector and wavelet technique (WLSSVM) in the monthly stream flow forecasting. This is a new hybrid technique. The 30 days periodic predicting statistics used in this study are derived from the subjection of this model to the river flow data of the Jhelum and Chenab rivers. The root mean square error (RMSE), mean absolute error (RME) and correlation (R) statistics are used for evaluating the accuracy of the WLSSVM and WR models. The accuracy of the WLSSVM model is compared with LSSVM, WR and LR models. The two rivers surveyed are in the Republic of Pakistan and cover an area encompassing 39,200 km2 for the Jhelum River and 67,515 km2 for the Chenab River. Using discrete wavelets, the observed data has been decomposed into sub-series. These have then appropriately been used as inputs in the least square support vector machines for forecasting the hydrological variables. The resultant observation from this comparison indicates the WLSSVM is more accurate than the LSSVM, WR and LR models in river flow forecasting.展开更多
In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to e...In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to extract important information of data at different levels and enhances the forecasting ability of the model. After wavelet transform different components are forecasted by their corresponding SVR predictors. The final prediction result is obtained by the summation of the predicted results for each component. The proposed hybrid model is examined by the data of bus route No.550 in Nanjing, China. The performance of WT-SVR model is evaluated by mean absolute error(MAE), mean absolute percent error(MAPE) and relative mean square error(RMSE), and also compared to regular SVR and ANN models. The results show that the prediction method based on wavelet transform and SVR has better tracking ability and dynamic behavior than regular SVR and ANN models. The forecasting performance is remarkably improved to obtain within 6% MAPE for testing section Ⅰ and 8% MAPE for testing section Ⅱ, which proves that the suggested approach is feasible and applicable in bus travel time prediction.展开更多
Based on discussion on the theories of support vector machines （SVM）, an one-step prediction model for time series prediction is presented, wherein the chaos theory is incorporated. Chaotic character of the time ser...Based on discussion on the theories of support vector machines （SVM）, an one-step prediction model for time series prediction is presented, wherein the chaos theory is incorporated. Chaotic character of the time series is taken into account in the prediction procedure; parameters of reconstruction-detay and embedding-dimension for phase-space reconstruction are calculated in light of mutual-information and false-nearest-neighbor method, respectively. Precision and functionality have been demonstrated by the experimental results on the basis of the prediction of Lorenz chaotic time series.展开更多
Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed...Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed, with a small review of the previous work. A model for the travel-time prediction on freeways based on wavelet packet decomposition and support vector regression (WDSVR) is proposed, which used the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better (with selected wavelets only), when compared with the support vector regression model (without wavelet decomposition) with a prediction horizon of 45 minutes and more. The data used in this paper was taken from the California Department of Transportation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5-minute intervals over a distance of 9.13 miles. The results indicated MAPE ranging from 12.35% to 14.75% against the classical SVR method with MAPE ranging from 12.57% to 15.84% with a prediction horizon of 45 minutes to 1 hour. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is presented with interchangeable prediction methods.展开更多
To improve the diagnosis accuracy and self-adaptability of fatigue crack in ulterior place of the supporting shaft, time series and neural network are attempted to be applied in research on diag-nosing the fatigue cr...To improve the diagnosis accuracy and self-adaptability of fatigue crack in ulterior place of the supporting shaft, time series and neural network are attempted to be applied in research on diag-nosing the fatigue crack’s degree based on analyzing the vibration characteristics of the supporting shaft. By analyzing the characteristic parameter which is easy to be detected from the supporting shaft’s exterior, the time series model parameter which is hypersensitive to the situation of fatigue crack in ulterior place of the supporting shaft is the target input of neural network, and the fatigue crack’s degree value of supporting shaft is the output. The BP network model can be built and net-work can be trained after the structural parameters of network are selected. Furthermore, choosing the other two different group data can test the network. The test result will verify the validity of the BP network model. The result of experiment shows that the method of time series and neural network are effective to diagnose the occurrence and the development of the fatigue crack’s degree in ulterior place of the supporting shaft.展开更多
Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the ...Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.展开更多
基金Projects(2023 YFC 2907602,2022 YFF 1303302)supported by the National Key Research and Development Project of ChinaProject(52342404)supported by the National Natural Science Foundation of China+2 种基金Project(GXXT-2021-075)supported by the University Synergy Innovation Program of Anhui Province,ChinaProject(2022AH010053)supported by Excellent Scientific Research and Innovation Team of Universities in Anhui Province,ChinaProject(2022xscx080)supported by Anhui Provincial Department of Education Graduate Student Academic Innovation Fund,China。
文摘To study the influence of support timing and support strength on the mechanical properties and deformation damage characteristics of a single-sided unloaded rock mass,a true triaxial perturbation unloaded rock testing system was used to conduct rock damage tests on sandstone with different support timing and strength paths.Based on the acoustic emission monitoring system,the spatial and temporal evolution characteristics of the whole process of rock body loaded instability under two stress paths were studied,and the mechanism of the reinforcing effect of stress support on the unloaded rock mass was analyzed.The results show that,within the scope of this study,both earlier applications of shoring and an increase in shoring strength can effectively improve the ultimate bearing capacity of the unloaded rock,which increases the ultimate bearing capacity of the unloaded rock mass by 60.31% and 54.96%,respectively;There is a phenomenon of rebound deformation of the rock mass during sudden changes in stress(single-sided unloading,stress support),which shows opposite expansion and compression platforms on the stress−strain curve;The crack evolution of unloaded rock under different stress support conditions shows the state law of"initial crack activation→middle steady state expansion→late main crack penetration",and the lagging support significantly accelerates the crack evolution from local activation to main penetration;The single-sided unloading and stress-supporting stages have less influence on the unloading deformationsσ_(1u),σ_(2u) and support deformationsσ_(1) t,σ_(2t) in theσ_(1) andσ_(2)directions,while they show significant response characteristics toσ_(3u),σ_(vu) and σ_(3) t,σ_(vt),and with the increase of the support strength,the stress-supporting stagesσ_(3) t,σ_(vt) gradually increase and exceed the deformations generated by the unloading stagesσ_(3u),σ_(vu);The increase of support strength can effectively compensate for the rock stress loss caused by unloading,which makes the maximum,minimum,and volumetric strain support coefficients during the loading and unloading of the rock body increase gradually while the effect on the intermediate principal strain support coefficient is small;During loading,the support strength of rock masses seeks a new bearing area by regulating stress equilibrium states.This process primarily manifests as a shift in the locations of the crushing zone and the main bearing area,accompanied by a corresponding transformation in failure patterns.Consequently,the rock mass transitions from asymmetric three-zone damage under no or weak support to approximate symmetric three-zone damage under strong support.Simultaneously,the main load-bearing area of the rock mass shifts from deep bearing in the unsupported to middle bearing under strong support as the support strength increases.
文摘The methods combined by test, field monitoring and theoretical analysis were adopted to do the systemic research on the rock mass from micro-structure to macro-deformation, and rheological model of Jinchuan rock mass was established to discuss the reasonable supporting time. Resuhs show that supporting after suitable stress and displacement release can benefit for the long-term stability of surrounding rock.
文摘Support vector machines （SVM） have been widely used in chaotic time series predictions in recent years. In order to enhance the prediction efficiency of this method and implement it in hardware, the sigmoid kernel in SVM is drawn in a more natural way by using the fuzzy logic method proposed in this paper. This method provides easy hardware implementation and straightforward interpretability. Experiments on two typical chaotic time series predictions have been carried out and the obtained results show that the average CPU time can be reduced significantly at the cost of a small decrease in prediction accuracy, which is favourable for the hardware implementation for chaotic time series prediction.
文摘This paper has put forward a concept of optimal supporting time through analysing the influence of the supporting time in the heading face on the supporting result of surrounding rock.The method Of the optimal Supporting time determined by graphical method is discussed, and the calculating formula for determining the optimal supporting time through the analysis method is derived.
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
文摘This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are complicated since each operation may be done by several machining centers. The system design approach is built around the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in operation assignment and scheduling of multi-purpose machining centers which have different tools with their own efficiency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real time event handler for implementing inter-process synchronization. The controller’s performance is validated by benchmark test problem.
基金National Key Basic Research and Development Program Fund project(Grant No.2014CB046306)the Central University Funding Project for Basic Scientific Research Operations(Grant No.2009QJ16)
文摘To keep coal workers away from the hazardous area with frequent accidents such as the roof fall and rib spalling in an underground coalmine,we put forward the solution with robotized self-moving anchor-supporting unit.The existing research shows that the surrounding rock of the roadway has self-stability,and the early or late support is not conducive to the safe and reliable support of the roadway,so there is a problem of support opportunity.In order to study the supporting effect and the optimal supporting time of the above solution,we established the mechanical coupling model of surrounding rock and advance support,and investigated the surrounding rock deformation and advance support pressure distribution under different reserved roof subsidence by using the numerical simulation software FLAC3D.The results show that the deformation of surrounding rock increases and finally tends to a stable level with the increase of pre settlement of roadway roof,and when the pre settlement of roof is between 8-15 mm,the vertical pressure of the top beam of advance support reaches the minimum value,about 0.58 MPa.Based on the above research,we put forward the optimum supporting time in roadway excavation,and summarized the evaluation method based on the mechanical coupling model of surrounding rock-advance support.
文摘This study explores the least square support vector and wavelet technique (WLSSVM) in the monthly stream flow forecasting. This is a new hybrid technique. The 30 days periodic predicting statistics used in this study are derived from the subjection of this model to the river flow data of the Jhelum and Chenab rivers. The root mean square error (RMSE), mean absolute error (RME) and correlation (R) statistics are used for evaluating the accuracy of the WLSSVM and WR models. The accuracy of the WLSSVM model is compared with LSSVM, WR and LR models. The two rivers surveyed are in the Republic of Pakistan and cover an area encompassing 39,200 km2 for the Jhelum River and 67,515 km2 for the Chenab River. Using discrete wavelets, the observed data has been decomposed into sub-series. These have then appropriately been used as inputs in the least square support vector machines for forecasting the hydrological variables. The resultant observation from this comparison indicates the WLSSVM is more accurate than the LSSVM, WR and LR models in river flow forecasting.
基金Sponsored by the Projects of International Cooperation and Exchange of the National Natural Science Foundation of China(Grant No.51561135003)the Scientific Research Foundation of Graduated School of Southeast University(Grant No.YBJJ1842)
文摘In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to extract important information of data at different levels and enhances the forecasting ability of the model. After wavelet transform different components are forecasted by their corresponding SVR predictors. The final prediction result is obtained by the summation of the predicted results for each component. The proposed hybrid model is examined by the data of bus route No.550 in Nanjing, China. The performance of WT-SVR model is evaluated by mean absolute error(MAE), mean absolute percent error(MAPE) and relative mean square error(RMSE), and also compared to regular SVR and ANN models. The results show that the prediction method based on wavelet transform and SVR has better tracking ability and dynamic behavior than regular SVR and ANN models. The forecasting performance is remarkably improved to obtain within 6% MAPE for testing section Ⅰ and 8% MAPE for testing section Ⅱ, which proves that the suggested approach is feasible and applicable in bus travel time prediction.
文摘Based on discussion on the theories of support vector machines （SVM）, an one-step prediction model for time series prediction is presented, wherein the chaos theory is incorporated. Chaotic character of the time series is taken into account in the prediction procedure; parameters of reconstruction-detay and embedding-dimension for phase-space reconstruction are calculated in light of mutual-information and false-nearest-neighbor method, respectively. Precision and functionality have been demonstrated by the experimental results on the basis of the prediction of Lorenz chaotic time series.
文摘Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed, with a small review of the previous work. A model for the travel-time prediction on freeways based on wavelet packet decomposition and support vector regression (WDSVR) is proposed, which used the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better (with selected wavelets only), when compared with the support vector regression model (without wavelet decomposition) with a prediction horizon of 45 minutes and more. The data used in this paper was taken from the California Department of Transportation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5-minute intervals over a distance of 9.13 miles. The results indicated MAPE ranging from 12.35% to 14.75% against the classical SVR method with MAPE ranging from 12.57% to 15.84% with a prediction horizon of 45 minutes to 1 hour. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is presented with interchangeable prediction methods.
基金This project is supported by National Natural Science Fundation of China (No. 50675066)Provincial Key Technologies R&D of Hunan, China (No. 05FJ2001)China Postdoctoral Science Foundation (No. 2005038006).
文摘To improve the diagnosis accuracy and self-adaptability of fatigue crack in ulterior place of the supporting shaft, time series and neural network are attempted to be applied in research on diag-nosing the fatigue crack’s degree based on analyzing the vibration characteristics of the supporting shaft. By analyzing the characteristic parameter which is easy to be detected from the supporting shaft’s exterior, the time series model parameter which is hypersensitive to the situation of fatigue crack in ulterior place of the supporting shaft is the target input of neural network, and the fatigue crack’s degree value of supporting shaft is the output. The BP network model can be built and net-work can be trained after the structural parameters of network are selected. Furthermore, choosing the other two different group data can test the network. The test result will verify the validity of the BP network model. The result of experiment shows that the method of time series and neural network are effective to diagnose the occurrence and the development of the fatigue crack’s degree in ulterior place of the supporting shaft.
文摘Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.
