Identification of Structural Parameters Based on HHT and NExT

Signal processing approaches are widely used in the field of earthquake engineering, especially in the identification of structural modal parameters. Hilbert-Huang Transformation (HHT) is one new signal processing approach, which can be used to identify the modal frequency, damping ratio, mode shape, even the interlayer stiffness of the shear-type structure, incorporating with Natural Excitation Technique (NExT) method to take information from the response records of the structure. The stiffness of the structure is of great importance to judge the loss of its bearing capacity after earthquake. However, all of modal parameters are required to calculate the stiffness of the structure by use of HHT and NExT, which means that the response records shall contain all of modal information. However, it has been found that the responses of the structure recorded only contain the former order modal information; even it is excited by earthquake. Therefore, it is necessary to found a formula (formulas) to calculate the stiffness only using limited modal parameters. In this paper, the calculation formulas of the interlayer stiffness of shear-type structure are derived by using of the flexibility method, which indicate that all of interlayer stiffnesses could be worked out as long as any one set of modal parameters is obtained. After that, Taking Sheraton-Universal Hotel subjected to North Bridge earthquake in 1994 as an example, HHT and NExT are used to identify its modal parameters, the derived formulas are used to calculate the interlayer stiffnesses, and their applicability and accuracy are verified

Empirical and human response studies of personalized ventilation combined with underfloor air distribution system

Ph.DNUS-DTU JOINT PH.D

Discrimination of approved drugs from experimental drugs by learning methods

<p>Abstract</p> <p>Background</p> <p>To assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods.</p> <p>Results</p> <p>Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.</p> <p>Conclusion</p> <p>The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.</p

Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors

Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT). In this paper, we propose a UAV-aided ISAC framework and investigate three robust power allocation schemes. First, we derive an explicit expression of the Cram\'er-Rao bound (CRB) based on time-of-arrival (ToA) estimation, which serves as the performance metric for location sensing. Then, we analyze the impact of the location sensing error (LSE) on communications, revealing the inherent coupling relationship between communication and sensing. Moreover, we formulate three robust communication and sensing power allocation problems by respectively characterizing the LSE as an ellipsoidal distributed model, a Gaussian distributed model, and an arbitrary distributed model. Notably, the optimization problems seek to minimize the CRB, subject to data rate and total power constraints. However, these problems are non-convex and intractable. To address the challenges related to the three aforementioned LSE models, we respectively propose to use the ${\cal{S}}$-Procedure and alternating optimization (${\cal{S}}$-AO) method, Bernstein-type inequality and successive convex approximation (BI-SCA) method, and conditional value-at-risk (CVaR) and AO (CVaR-AO) method to solve these problems. Finally, simulation results demonstrate the robustness of our proposed UAV-aided ISAC system against the LSE by comparing with the non-robust design, and evaluate the trade-off between communication and sensing in the ISAC system