Log volume inspection is very important in forestry research and paper making engineering. This paper proposed a novel approach based on computer vision technology to cope with log volume inspection. The needed hardwa...Log volume inspection is very important in forestry research and paper making engineering. This paper proposed a novel approach based on computer vision technology to cope with log volume inspection. The needed hardware system was analyzed and the details of the inspection algorithms were given. A fuzzy entropy based on image enhancement algorithm was presented for enhancing the image of the cross-section of log. In many practical applications the cross-section is often partially invisible, and this is the major obstacle for correct inspection. To solve this problem, a robust Hausdorff distance method was proposed to recover the whole cross-section. Experiment results showed that this method was efficient.展开更多
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele...To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.展开更多
文摘Log volume inspection is very important in forestry research and paper making engineering. This paper proposed a novel approach based on computer vision technology to cope with log volume inspection. The needed hardware system was analyzed and the details of the inspection algorithms were given. A fuzzy entropy based on image enhancement algorithm was presented for enhancing the image of the cross-section of log. In many practical applications the cross-section is often partially invisible, and this is the major obstacle for correct inspection. To solve this problem, a robust Hausdorff distance method was proposed to recover the whole cross-section. Experiment results showed that this method was efficient.
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.
