Identification of nonlinear Wiener-Hammerstein systems by a novel adaptive algorithm based on cost function framework

This paper investigates parameter identification of nonlinear Wiener-Hammerstein systems by using filter gain and novel cost function. Taking into account the system information is corrupted by noise, the filter gain is exploited to extract the system data. By using several auxiliary filtered variables, an extended estimation error vector is developed. Then, based on the discount term of the extended estimation error and the penalty term on the initial estimate, a novel cost function is developed to obtain the optimal parameter adaptive law. Compared with the conventional cost function which is composed of the square sum of output error, the proposed algorithm based on the cost function of this paper can provide faster convergence rate and higher estimation accuracy. Furthermore, the convergence analysis of the proposed scheme indicates that the parameter estimation error can converge to zero. The effectiveness and practicality of the proposed scheme are validated through the simulation example and experiment on the turntable servo system.     

Random-valued impulse noise removal using adaptive dual threshold median filter

Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.     

基于深度神经网络和加权隐反馈的个性化推荐

改进的矩阵分解(SVD++)将用户和物品特征向量的内积作为用户对物品的评分,而内积无法捕捉用户与物品之间复杂的高阶非线性关系.此外,SVD++在融入用户隐式反馈时,未区分不同交互物品对于用户特征表达的贡献.针对上述问题,文中提出基于深度神经网络和加权隐反馈的推荐算法(DeepNASVD++),采用深度神经网络建模用户与物品之间的关系,使用注意力机制计算历史交互物品在建模用户隐式反馈时的权重.在公开数据集上的实验验证文中算法的有效性.     

An improvement of the Space-Time Image Velocimetry combined with a new denoising method for estimating river discharge

The Space-Time Image Velocimetry (STIV) is a time-averaged velocity measurement method, which takes river surface images as the analysis object, and detects the Main Orientation of Texture (MOT) in a generated Space-Time Image (STI) to obtain one-dimensional velocities on the water surface. The STIV has great potential in real-time monitoring of river flow owing to its high spatial resolution and low time complexity. However, the generated STI contains a lot of noise and interference texture, which is inevitable in practical applications. The practicality of the STIV is severely limited by the low-quality STI. To solve this problem, a denoising method based on the filtering technology is proposed and combined with different texture detection algorithms in this paper. The accuracy of this method is verified through a comparative field experiment with an impellor-style current meter. The experimental results show: (1) By using this new denoising method, the robustness and accuracy of the STIV are significantly improved no matter what kind of texture detection algorithm is adopted; (2) Among all the texture detection algorithms, the FFT-based STIV combined with the new denoising method performs best. The relative errors of the surface velocities are controlled within 6%, and the relative errors of the discharges are controlled within ±4%.     

电源系统的谐波干扰分析与抑制技术

谐波源分电流型和电压型2类.电流型的抑制采用滤波器分流,即通过滤波器电感和电容的谐振,形成低阻抗并为谐波电流提供通路,以降低进入电网中的谐波电流.而电压型谐波源的抑制则采用并联型滤波器分压,通过滤波器电感和电容的并联谐振,形成高阻抗,抑制降低电路中谐波电流在电网中所形成的谐波电压.2种谐波都可采用无源和有源滤波两种形式抑制.     

基于热扩散影响力传播的社交网络个性化推荐算法

有针对性地为用户提供推荐,提高互联网信息利用率是个性化推荐系统的主要目标.文中基于热扩散传播概率模型,结合用户在社交网络中隐含的跟随关系,提出基于热扩散影响力传播的社交网络个性化推荐算法.首先,算法将现实生活中人与人的朋友关系转化为购物网络中用户与用户的跟随关系,构建异构信息网络图,计算用户之间的复合相似度.然后,利用基于热扩散概率模型模拟社会网络中影响力的传播过程,计算社交网络中用户的跟随概率分数并精确排序,筛选与目标用户相似的邻近用户.最后,根据目标邻近用户对各个产品的评分,将评分较高、具有潜在兴趣的产品推荐给目标用户,实现个性化的用户推荐.在公开数据集上与现有的个性化推荐算法进行对比,实验表明,文中算法具有较好的精确度和多样化的推荐效果.     

Time-of-Flight camera based 3D point cloud reconstruction of a car

Modern car wash systems are generally controlled by light barriers and power measurement sensors. These sensors are directly mounted on the movable parts of the system which result in a slow regulation, leading to non-optimal performance, e.g. in terms of energy, water and detergent consumption or regarding the quality of the result.     

Estimation of optimum number of cycles for combustion analysis using measured in-cylinder pressure signal in conventional CI engine

Analysis of measured in-cylinder pressure data provides various parameters that characterize engine combustion process. Advanced engine control technologies use cylinder pressure based combustion parameters for closed loop control. Four step signal processing (i) absolute pressure correction, (ii) crank angle position referencing, (iii) cycle averaging and (iv) filtering is typically applied to get sufficiently accurate cylinder pressure data for an engine cycle. This paper focuses on cycle averaging and filtering of in-cylinder pressure signal from a conventional compression ignition (CI) engine. Experiments are conducted at different engine load and compression ratios at 1500 rpm. The in-cylinder pressure trace of 2500 consecutive engine cycles is recorded and analyzed. Effect of in-cylinder pressure signal noise and cyclic variation on combustion analysis is investigated. A method based on standard deviation of pressure and pressure rise rate is used to find sufficient minimum number of engine cycles to be recorded for averaging to get reasonably accurate pressure data independent of cyclic variability.     

Bayesian changepoint and time-varying parameter learning in regime switching volatility models

This paper proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based volatility models with an unknown number of changepoints. Modern auxiliary particle filtering techniques are used to calculate the posterior densities and online forecasts. This approach also automatically deals with the common ancestral path dependence problem faced in these type volatility models. The model is tested on Borsa Istanbul (BIST) formerly known as Istanbul Stock Exchange (ISE) market data using daily log returns. A full structural changepoint specification is defined in which all parameters of the conditional variance of the volatility models are dynamic. Finally, it is shown with simulation experiments that the proposed approach partitions the series into several regimes and learns the parameters of each regime's volatility model in parallel with the multiple changepoint detection process.     

Integrated design of filtering antenna with high selectivity and improved performance for L-band applications

An integrated design of compact filtering antenna having high selectivity and suppressed unwanted harmonics is proposed in this paper. The proposed filtering antenna is obtained by integrating a modified elliptic-shaped monopole antenna with a modified interdigital bandpass filter (IBPF). At first, a modified elliptic-shaped monopole antenna is designed and investigated through numerical simulation and experimentally. Further, the modified IBPF reported in the literature is integrated with the proposed monopole antenna to achieve good passband selectivity and suppressed unwanted harmonics for the proposed filtering antenna. The proposed filtering antenna is analysed through numerical simulation software. The simulated −10 dB reflection coefficient bandwidth of the proposed filtering antenna covers the frequency range 1.01–1.96 GHz with improved band-edge selectivity and unwanted harmonic suppression up to 8 GHz (= 5.4 f₀, where f₀ is the centre frequency of passband). It has nearly stable omnidirectional radiation patterns over the whole frequency band. The proposed filtering antenna was fabricated and experimentally tested. The experimental results are nearly in agreement with corresponding numerical simulation results. The proposed filtering antenna can be a suitable candidate for various L-band wireless communication applications.