Modern architectures are developing in the direction of tall buildings and complex structures,and the theoretical analysis and the design experience have seriously lagged behind the construction of super high-rise str...Modern architectures are developing in the direction of tall buildings and complex structures,and the theoretical analysis and the design experience have seriously lagged behind the construction of super high-rise structures.Structural form selection,especially the case based reasoning (CBR) based structural form selection,is a promising tool for the construction of high-rise structures.In view of the limit of cognitive ability of domain experts,a BP (back propagation)-PSO (particle swarm optimization)-based intelligence case retrieval method for high-rise structural form selection is proposed.The CBR-based case retrieval method and the construction of the BP-PSO neutral network are introduced.And then the BP-PSO-based case retrieval method is validated by some engineering cases.The results of training and prediction indicate that the proposed method has good ability to retrieve the cases of high-rise structures.展开更多
The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is...The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is proposed in this paper, which can be applied to battle damage assessment (BDA). This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine (UCI) data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.展开更多
Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to ...Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes,such as brain atrophy after brain damage and surgery.The non-trivial evaluation of the consciousness level,along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made.We studied 32 secondary mild hydrocephalus patients with different consciousness levels,who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage.We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages.Then,we built a regression model to regress the JFK Coma Recovery Scale-Revised(CRS-R) scores to quantify the level of consciousness.The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients.The regression model has high potential for the evaluation of consciousness in clinical practice.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 61040031)the Technological Project of Henan Province (Grant No. 082102210066)
文摘Modern architectures are developing in the direction of tall buildings and complex structures,and the theoretical analysis and the design experience have seriously lagged behind the construction of super high-rise structures.Structural form selection,especially the case based reasoning (CBR) based structural form selection,is a promising tool for the construction of high-rise structures.In view of the limit of cognitive ability of domain experts,a BP (back propagation)-PSO (particle swarm optimization)-based intelligence case retrieval method for high-rise structural form selection is proposed.The CBR-based case retrieval method and the construction of the BP-PSO neutral network are introduced.And then the BP-PSO-based case retrieval method is validated by some engineering cases.The results of training and prediction indicate that the proposed method has good ability to retrieve the cases of high-rise structures.
基金Project supported by the National Basic Research Program (973) of China (No. 61331903) and the National Natural Science Foundation of China (Nos. 61175008 and 61673265)
文摘The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is proposed in this paper, which can be applied to battle damage assessment (BDA). This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine (UCI) data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.
基金supported by the National Natural Science Foundation of China (81571025 and 81702461)the National Key Research and Development Program of China (2018YFC0116400)+6 种基金the International Cooperation Project from Shanghai Science Foundation (18410711300)Shanghai Science and Technology Development Funds (16JC1420100)the Shanghai Sailing Program (17YF1426600)STCSM (19QC1400600, 17411953300)the Shanghai Pujiang Program (19PJ1406800)the Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZJlabthe Interdisciplinary Program of Shanghai Jiao Tong University。
文摘Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes,such as brain atrophy after brain damage and surgery.The non-trivial evaluation of the consciousness level,along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made.We studied 32 secondary mild hydrocephalus patients with different consciousness levels,who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage.We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages.Then,we built a regression model to regress the JFK Coma Recovery Scale-Revised(CRS-R) scores to quantify the level of consciousness.The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients.The regression model has high potential for the evaluation of consciousness in clinical practice.