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

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

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

Multiple-symbol Differential Sphere Decoding for Network Coding

In order to shorten 3dB performance gap between the conventional differential detection and correlation detection in network coding, we consider multiple-symbol differential detection (MSDD) for two-way relay channel (TWRC) model. MSDD, which makes use of continuously N symbols to jointly detect N-1 symbols. However, the complexity of the maximum likelihood differential detection increases exponentially with the detection group length and the modulation constellation points. In this paper, we propose multiple-symbol differential sphere decoding (MSDSD) to circumvent this excessive computational complexity. Simulation results show that the combination of MSDSD and differential network coding can not only reduce the computational complexity, but also overcome error platform caused by High-Doppler frequency offset at high signal-to-noise ratio, and obtain the optimum detection performance simultaneously. Hence, MSDSD can be regarded as a low complexity detection algorithm in differential network coding scheme. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.367

Steady flow around an inclined torus at low Reynolds numbers: Lift and drag coefficients

The steady flow around an inclined torus has received little attention in the hydrodynamics literature, despite being relevant to many engineering and biological processes, such as the sedimentation of fluidized particles and the motion of natural micro-swimmers. In this study, we perform three-dimensional direct numerical simulations of the flow around an inclined torus over a range of aspect ratios Image 1, inclination angles (0 ⩽ θ ⩽ 90°) and Reynolds numbers (10 ⩽ Re ⩽ 50), with a focus on the steady flow regime preceding the onset of vortex shedding. For a fixed Re, we find that as the torus inclines from a flow-normal orientation (θ=0∘) to a flow-parallel orientation (θ=90∘), the drag coefficient (CD) decreases monotonically, while the lift coefficient (CL) first increases from zero, reaches a maximum at 40° ⩽ θ ⩽ 50° and then returns to zero owing to top-down symmetry at full inclination. The decrease in CD with θ is caused by a decrease in the pressure drag, with almost no change in the viscous drag. The variation in CL with θ is caused by the pressure lift dominating the viscous lift. With increasing Re, the overall trends in CD and CL remain qualitatively unchanged but their quantitative values decrease. Compared with the effects of θ and Re, those of Image 2 are relatively weak for the specific flow conditions examined here. We conclude by performing a nonlinear regression analysis to generate curve fits for CD and CL in terms of Image 2, θ and Re. © 2018 Elsevier Lt