Structural optimization with an automatic mode identification method for tracking the local vibration mode

Traditional optimization methods, which take a specific order of modal frequency as the design constraint, could fail to obtain the desired solution because of modal substitution. An improved optimization model with continuous sizing variables is established to solve this problem, in which the minimum weight and a given local modal frequency are considered as the objective and the constraint. To capture accurately the expected mode of vibration, a local mode identification technique is proposed based on the strain energy ratio between the local area and the whole structure. With that scheme, an optimization system is developed, in which the local mode can be effectively identified and the constraint can be updated with it in the iteration process. Two numerical examples, of a reinforced plate and a satellite structure, are applied to illustrate the effectiveness and efficacy of the proposed method.     

Joint tracking coefficients and the time delay of a nonstationary linear system,preceded by a static nonlinearity

The paper considers a block-oriented nonlinear Hammerstein system that consists of a linear block with real-valued time-varying unknown coefficients and a pure time delay, preceded by the static nonlinearity. The joint tracking of linear block coefficients and the time delay while processing observation samples is under consideration. The difficulty of the tracking process consists in the initial nonconvexity of the objective function to be minimized, in respect of the time delay. To obtain current estimates of the true coefficients and the time delay it is necessary to overcome the multiextremality of the objective function. A global optimization problem could be solved by changing a form of the nonconvex objective function into a convex one. The optimization problem has been subdivided into two problems that are analyzed in this article, as well. The first one consists of removal of the objective function multiextremality in respect of the time delay, while hunting for the global extremum. Therefore, the well-known approach based on the method of corrective operators, which is used to transform the multiextremal criterion into a unimodal function for the nonstationary Wiener system, is developed here for the Hammerstein one. The other problem consists of the applicability of unified adaptive algorithms, used for Wiener systems in the case of Hammerstein systems. The recursive parametric identification technique, based on the adaptive algorithm procedures, is developed here for the nonstationary block-oriented Hammerstein systems, too. The applicability of algorithms is supported by various simulation tests solved by computer for distinct inputs and for two static nonlinearities.     

扩建工程对原泵房结构安全性影响分析

介绍了扩建工程是否影响原泵房结构安全以及沉降缝处裂缝是否会继续扩展和检测方法,并得出安全性鉴定结论,并给出处理意见。     

电动汽车动力锂电池模型参数辨识

针对纯电动汽车上动力锂电池等效模型参数辨识的问题,以某纯电动汽车的由87个单体串联的84 Ah的镍钴锰三元锂电池组为研究对象,基于市区行驶的电池数据,选用了二阶RC电池等效模型,辨识了等效模型的参数。基于整体电池数据,选取出了8段在连续的12个(及12个以上)采样周期内相邻两个采样点的电流变化绝对值超过0.2 C的电池数据段,分别对初始开路电压最大和最小的单体进行了参数辨识。以最小二乘法对电流连续变化最长的一段电池数据段作为参数辨识的结果,并结合整体电池数据对辨识得的结果进行了参数的验证。研究结果表明,初始开路电压最大单体的绝对误差平均值为3.62%,初始开路电压最小单体的绝对误差平均值为3.24%,满足工程要求,可运用于工程实践中。     

Particle filter with one-step randomly delayed measurements and unknown latency probability

In this paper, a new particle filter is proposed to solve the nonlinear and non-Gaussian filtering problem when measurements are randomly delayed by one sampling time and the latency probability of the delay is unknown. In the proposed method, particles and their weights are updated in Bayesian filtering framework by considering the randomly delayed measurement model, and the latency probability is identified by maximum likelihood criterion. The superior performance of the proposed particle filter as compared with existing methods and the effectiveness of the proposed identification method of latency probability are both illustrated in two numerical examples concerning univariate non-stationary growth model and bearing only tracking.     

A study of bicycle dynamics via system identification approaches

This study investigates bicycle dynamic properties by using system identification approaches. The non-linear bicycle model with configuration parameters from a previously developed benchmark model is studied. The roll angle of the bicycle is controlled at different speeds to generate input–output data including steering torque, roll, and steering angles. The collected data are then used to identify the one-input two-output linear model by a prediction-error identification method using parameterization in canonical state-space form derived as Whipple's model. Simulations are used to verify the accuracy of the generated linear model. Numerous properties for various speed ranges are discussed from the pole and zero locations of the identified linear model. The system stability, limit-cycle phase portraits of the roll and steering angles, and the non-minimum phase property of the non-linear system are further investigated and compared with the corresponding linearized results from previous studies in the literature.     

Multinational vehicle license plate detection in complex backgrounds

Many methods for multinational License Plate Detection (LPD) have been proposed in recent times but most of them are not sophisticated enough to handle complex backgrounds. Moreover, their ability to handle various environmental and illumination conditions has been limited and still needs improvement. In this paper, we propose a novel technique to detect license plates of vehicles regardless of their color, size, and content. As the rear vehicle lights are an essential part of any vehicle, we reduce the image processing area to eliminate the complex background by detecting the rear-lights as the license plates are in a certain range of these lights. Heuristic Energy Map (HEM) of the vertical edge information in the Region of Interest (ROI) is calculated and area with the dense edges is selected using a unique histogram approach which is considered to be the license plate. The proposed algorithm is tested on 855 images from various countries including China, Pakistan, Serbia, Italy and various states of America. Experimental results show that the proposed method is able to detect license plates 90.4% of times despite of complex backgrounds in 0.25 s on average that can achieve real time performance.     

RF fingerprint measurements for the identification of devices in wireless communication networks based on feature reduction and subspace transformation

This document proposes a radiofrequency (RF) fingerprinting strategy for the proper identification of wireless devices in mobile and wireless networks. The proposed identification methods are based on the extraction of the preamble RF fingerprint of a device and its comparison with a set of already known device RF fingerprints. The identification method combines techniques for feature reduction such as Principal Component Analysis (PCA) and Partial Least Squares regression (PLS), both based on subspace transformation, along with a similarity-based analysis. In this work, a complete procedure for RF fingerprint data extraction and analysis is provided. In addition, some experimentation with commercial Wi-Fi devices is carried out for the methods validation.     

Identification of tuna species by a real-time polymerase chain reaction technique

Tuna are highly priced fishes that are often used in processed products. For effective fishery management and protection of consumers’ rights, it is important to develop a molecular method to identify the species of the tuna products. In this study we have developed a molecular method based on real-time polymerase chain reaction (real-time PCR) technology for the rapid identification of four tuna species. Four species-specific TaqMan probes were designed to identify bigeye tuna (Thunnus obesus), Pacific bluefin tuna (Thunnus orientalis), southern bluefin tuna (Thunnus maccoyii), and yellowfin tuna (Thunnus albacares). A SYBR green system was also designed to enhance the authentication of T. obesus. Both systems can distinguish target species from others in an efficient and high-throughput manner and can be applied to species identification of tuna products.