Although the structured light system that uses digital fringe projection has been widely implemented in three-dimensional surface profile measurement, the measurement system is susceptible to non-linear error. In this...Although the structured light system that uses digital fringe projection has been widely implemented in three-dimensional surface profile measurement, the measurement system is susceptible to non-linear error. In this work, we propose a convenient look-up-table-based （LUT-based） method to compensate for the non-linear error in captured fringe patterns. Without extra calibration, this LUT-based method completely utilizes the captured fringe pattern by recording the full-field differences. Then, a phase compensation map is established to revise the measured phase. Experimental results demonstrate that this method works effectively.展开更多
Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring,optimal control,and fault diagnosis.However,the op...Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring,optimal control,and fault diagnosis.However,the operating environment of acid production with flue gas is complex and there is much equipment.The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers.Therefore,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is proposed.Firstly,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the dataset.Secondly,an improved random forest regression model is established.Genetic algorithm is used to optimize the hyperparameters of the random forest regression model.Then the optimal parameter combination is found in the search space and the trend of data is predicted.Finally,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is realized.Results show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue gas.The method improves the accuracy of compensation for missing data.With the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature control.The conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.展开更多
Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,whi...Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.展开更多
随着电力系统中可再生能源(renewable energy sources,RESs)比例不断增加,新能源参与能量市场和备用市场在技术上和经济上的重要性不断凸显。研究了含有风、光、水、储资源的RES聚合商在日前市场、备用市场及实时平衡市场等多个市场的...随着电力系统中可再生能源(renewable energy sources,RESs)比例不断增加,新能源参与能量市场和备用市场在技术上和经济上的重要性不断凸显。研究了含有风、光、水、储资源的RES聚合商在日前市场、备用市场及实时平衡市场等多个市场的综合竞价策略。针对北欧顺序交易市场框架,提出了一套考虑备用资源和波动补偿耦合的数据驱动信息间隙理论(data-driven information gap theory,DIGDT)决策模型解决RES聚合商的多阶段竞价优化问题。在DIGDT中采用基于置信区间的模糊集构造方法(confidence interval-based ambiguity set,CIAS)估计风、光的预测误差,通过机会约束对水电和储能(batteryenergy storage,BES)补偿出力偏差的可能性进行建模,并考虑备用资源与补偿容量的多时间尺度耦合。在备用市场中,利用基于备用调用场景的随机优化确保日前备用计划的可行性。通过案例分析验证了所提出模型的有效性。展开更多
On the basis of an analysis of the error sources in multibeam echosounding system,a data processing method for compensating systematic errors in multibeam survey is proposed.In order to improve the accuracy of overall...On the basis of an analysis of the error sources in multibeam echosounding system,a data processing method for compensating systematic errors in multibeam survey is proposed.In order to improve the accuracy of overall swath,a data fusion technique using single beam survey data as control information for single beam and multibeam echosounding is then presented.Some questions involved in solving the adjustment problem,such as its feasibility and the numerical stability,are discussed in detail,and a two_step adjustment method is suggested.Finally,a practical survey data set is used as a case study to prove the efficiency and reliability of the proposed methods.展开更多
According to the "theoretical admittance " and the "observation admittance" of the actual data,the theoretical value of effective elastic thickness in the study area was 10 km.Combining the gravity...According to the "theoretical admittance " and the "observation admittance" of the actual data,the theoretical value of effective elastic thickness in the study area was 10 km.Combining the gravity anomalies and vertical gravity gradient anomalies,the admittance function is used to construct the 1′×1′ bathymetry model over the Philippine Sea by using the adaptive weighting technique.It is found that the accuracy of the bathymetry model constructed is the highest when the ratio of inversion result of vertical gravity gradient anomalies and inversion result of gravity anomalies is 2∶3.At the same time,using multi-source gravity data to predict bathymetry could synthesize the superiority of gravity anomalies and vertical gravity gradient anomalies on the different seafloor topography,and the accuracy is better than bathymetry model that only used gravity anomalies or vertical gravity gradient anomalies.Taking the ship test data as the checking condition,the accuracy of predicting model is slightly lower than that of V18.1 model and improved by 27.17% and 39.02% respectively compared with the ETOPO1 model and the DTU10 model.Check points which the absolute value of the relative error of the predicting model is in the range of 5% accounted for 94.25% of the total.展开更多
基金the financial support provided by the National Natural Science Foundation of China(11472267 and 11372182)the National Basic Research Program of China(2012CB937504)
文摘Although the structured light system that uses digital fringe projection has been widely implemented in three-dimensional surface profile measurement, the measurement system is susceptible to non-linear error. In this work, we propose a convenient look-up-table-based （LUT-based） method to compensate for the non-linear error in captured fringe patterns. Without extra calibration, this LUT-based method completely utilizes the captured fringe pattern by recording the full-field differences. Then, a phase compensation map is established to revise the measured phase. Experimental results demonstrate that this method works effectively.
基金supported by the National Natural Science Foundation of China(61873006)Beijing Natural Science Foundation(4204087,4212040).
文摘Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring,optimal control,and fault diagnosis.However,the operating environment of acid production with flue gas is complex and there is much equipment.The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers.Therefore,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is proposed.Firstly,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the dataset.Secondly,an improved random forest regression model is established.Genetic algorithm is used to optimize the hyperparameters of the random forest regression model.Then the optimal parameter combination is found in the search space and the trend of data is predicted.Finally,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is realized.Results show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue gas.The method improves the accuracy of compensation for missing data.With the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature control.The conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.
基金supported in part by the 14th Five-Year Project of Ministry of Science and Technology of China(2021YFD2000304)Fundamental Research Funds for the Central Universities(531118010509)Natural Science Foundation of Hunan Province,China(2021JJ40114)。
文摘Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.
文摘随着电力系统中可再生能源(renewable energy sources,RESs)比例不断增加,新能源参与能量市场和备用市场在技术上和经济上的重要性不断凸显。研究了含有风、光、水、储资源的RES聚合商在日前市场、备用市场及实时平衡市场等多个市场的综合竞价策略。针对北欧顺序交易市场框架,提出了一套考虑备用资源和波动补偿耦合的数据驱动信息间隙理论(data-driven information gap theory,DIGDT)决策模型解决RES聚合商的多阶段竞价优化问题。在DIGDT中采用基于置信区间的模糊集构造方法(confidence interval-based ambiguity set,CIAS)估计风、光的预测误差,通过机会约束对水电和储能(batteryenergy storage,BES)补偿出力偏差的可能性进行建模,并考虑备用资源与补偿容量的多时间尺度耦合。在备用市场中,利用基于备用调用场景的随机优化确保日前备用计划的可行性。通过案例分析验证了所提出模型的有效性。
文摘On the basis of an analysis of the error sources in multibeam echosounding system,a data processing method for compensating systematic errors in multibeam survey is proposed.In order to improve the accuracy of overall swath,a data fusion technique using single beam survey data as control information for single beam and multibeam echosounding is then presented.Some questions involved in solving the adjustment problem,such as its feasibility and the numerical stability,are discussed in detail,and a two_step adjustment method is suggested.Finally,a practical survey data set is used as a case study to prove the efficiency and reliability of the proposed methods.
基金The National Natural Science Foundation of China(41774021,41404020,41774018,41674082,41504018,41674026)The State Key Laboratory of Geo-Information Engineering(SKLGIE2016-M-3-2)The School Project(2017503902,2018222).
文摘According to the "theoretical admittance " and the "observation admittance" of the actual data,the theoretical value of effective elastic thickness in the study area was 10 km.Combining the gravity anomalies and vertical gravity gradient anomalies,the admittance function is used to construct the 1′×1′ bathymetry model over the Philippine Sea by using the adaptive weighting technique.It is found that the accuracy of the bathymetry model constructed is the highest when the ratio of inversion result of vertical gravity gradient anomalies and inversion result of gravity anomalies is 2∶3.At the same time,using multi-source gravity data to predict bathymetry could synthesize the superiority of gravity anomalies and vertical gravity gradient anomalies on the different seafloor topography,and the accuracy is better than bathymetry model that only used gravity anomalies or vertical gravity gradient anomalies.Taking the ship test data as the checking condition,the accuracy of predicting model is slightly lower than that of V18.1 model and improved by 27.17% and 39.02% respectively compared with the ETOPO1 model and the DTU10 model.Check points which the absolute value of the relative error of the predicting model is in the range of 5% accounted for 94.25% of the total.
