Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

On approximating multifractal traffic burstiness with Markov modulated Poisson processes

We investigate the approximating capability of Markov modulated Poisson processes (MMPP) for modeling multifractal Internet traffic. The choice of MMPP is motivated by its ability to capture the variability and correlation in moderate time scales while being analytically tractable. Important statistics of traffic burstiness are described and a customized moment-based fitting procedure of MMPP to traffic traces is presented. Our methodology of doing this is to examine whether the MMPP can be used to predict the performance of a queue to which MMPP sample paths and measured traffic traces are fed for comparison respectively, in addition to the goodness-of-fit test of MMPP. Numerical results and simulations show that the fitted MMPP can approximate multifractal traffic quite well, i.e. accurately predict the queueing performance.

Modeling of Mass Transfer in Cavity Limited by a Semi Permeable Membrane （Simulation of Spiral Wound Module）

The reverse osmosis process has been applied in large industrial fields （water treatment, food industry, biotechnology, and ect.）. Despite, this progress more investigation are required to optimize the reverse osmosis process. The present paper deals the modeling of mass transfer in a cavity limited by a semi-permeable membrane. Mass conservation and momentum balances are developed, dimensionless and control volume method has been applied. The velocity and concentration profiles versus the Reynolds number and Sherwood are studied. The results show that the permeability of the membrane decreases as function of the transversal （radial） component of the velocity. The axial （tangential） component of the velocity presents a good stability along the thickness of the cavity; this phenomenon can be attributed to the zero gradient of the tangential velocity. These preliminary results show that the phenomenon of the concentration polarization affects the mass transfer coefficient in a channel. Current study has considered the cavity without a promoter of the turbulence; whereas, the design of the spacer has an important role on mass transfer coefficient in the reverse osmosis module. Our next interest is the integration of the spacer in the cavity, and the study of the effect of its design on the concentration and velocity profiles and the mass transfer coefficient through the reverse osmosis membrane.

Adaptive and Intelligent Digital Signal Processing for Improved Optical Interconnection

In recent years, explosively increasing data traffic has been boosting the con?tinuous demand of high speed optical interconnection inside or among data centers, high performance computers and even consumer electronics. To pursue the improved intercon?nection performance of capacity, energy efficiency and simplicity, effective approaches are demonstrated including particularly advanced digital signal processing (DSP) meth?ods. In this paper, we present a review about the enabling adaptive DSP methods for opti?cal interconnection applications, and a detailed summary of our recent and ongoing works in this field. In brief, our works focus on dealing with the specific issues for short-reach interconnection scenarios with adaptive operation, including signal-to-noise-ratio (SNR) limitation, level nonlinearity distortion, energy efficiency consideration and the de?cision precision.

Preclinical Verification of Modulated Electro-Hyperthermia —Part I. In Vitro Research

Modulated electro-hyperthermia (mEHT) targets tissue’s natural electric and thermal heterogeneities to heat the cancer cells selectively. The applied 13.56 MHz radiofrequency (RF) is a carrier of the low-frequency modulation. The high-frequency part was chosen to select the malignant lesion using the specialties of the tumor: the higher conductivity and dielectric constant of the tumor than its host. The electric field selects the tumor, and the low-frequency amplitude modulation polarizes and excites the transmembrane proteins of the malignant cells. The dominant absorption of the energy by the microscopic clusters of the membrane rafts acts like nanoparticle heating. Exciting the membrane produces various apoptotic signals. The processes were modeled using silico and phantom experiments, which proved the concept. The preclinical verification was made in vitro and in vivo, and in the end, clinical proofs validated the method. Our objective is to follow all the development steps from the laboratory to the clinics in a trilogy of articles. This present is the first part, which deals with in silico, phantom, and in vitro research.

Modulation classification of MPSK signals based on nonparametric Bayesian inference

A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart （NIW） distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain （MCMC） random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR 〉5 dB and 1 600 symbols are used in this method.

A Semi-Blind Method to Estimate the I/Q Imbalanc of THz Orthogonal Modulator

In terahertz communication,the direct frequency conversion structure in which orthogonal mixer is the main frequency conversion unit,makes engineers get into trouble of in-phase(I)branch and quadrature(Q)branch imbalance,carrier wave leakage,etc.These damages result in system performance tremendous degrades.We proposed a semiblind method to estimate the I/Q imbalance of THz orthogonal modulator,based on predefined preamble and pilot symbols for quadrature amplitude modulation(QAM).In this paper,a transmitter with Y band quadrature mixer and 20Gbps base-band signal has been tested.The bandwidth of the baseband signal was 7GHz,and the modulation type was 16QAM.By this method,7dB improvement of the system’s symbol Mean Square Error(MSE)has been got.That means the proposed method can be used to eliminate the I/Q imbalance effectively.

Modulation classification based on spectrogram

The aim of modulation classification （MC） is to identify the modulation type of a commtmication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-based modulation classification methods are proposed. Their recognition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment-like features is robust to frequency offset while the other two, which make use of principal component analysis （PCA） with different transformation inputs, can achieve satisfactory accuracy even at low SNR （as low as 2 dB）. Due to the properties of spectrogram, the statistical pattern recognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.

Study on the model of an insurer's solvency ratio in Markov-modulated Brownian markets

In this paper the insurer＇s solvency ratio model with or without jump diffusion process in the presence of financial distress cost is constructed, where an insurer＇s solvency ratio is characterized by a Markov-modulated dynamics. By Girsanov＇s theorem and the option pricing formula, the expected present value of shareholders＇ terminal payoff is provided.

Research of Hybrid Modulation Radar Signal

Based on the advantage of phase coded signal and stepped frequency signal,a new hybrid modulation signal is introduced in this paper. It combines phase code modulation during the pulse with stepped frequency modulation between the pulses, which is named as phase-coded stepped-frequency ( PCSF ) signal. By analyzing its waveform and ambiguity function,the comparison between Stepped-Frequency ( SF) signal and PCSF signal is given,which shows that the PCSF signal is better than SF signal. Finally,the signal processing method with two stage compressed processing is presented. The simulation results show that this new hybrid modulation radar signal can get a higher stepped frequency than ordinary SF signal,realize the same equivalent bandwidth with less pulse number,and solve the conflict among the stepped frequency,the number of pulse, and transmit average power. Under the premises of a certain range resolution,this new hybrid modulation radar signal not only raises the data rate of radar system,but also reduces Doppler sensitivity with a good prospect, and the effect of one-dimensional range profile is much better than that of traditional SF signal. Therefore,this new hybrid modulation radar signal can be widely used in application.

Modulation Index Estimation of Frequency and Phase Modulated Signals

Modulation index estimation is important in the demodulation and recognition of Angle Modulation (AM) signals which include Frequency Modulation (FM) and Phase Modulation (PM) signals. In this paper, we firstly analyzed the AM signals with baseband modulation types, such as monotone, PSK, FSK, in the time and frequency domain. Then, we established a unified mathematical representation for the AM signals. Finally, we derived a blind estimation algorithm for the modulation index without using any prior knowledge. Simulation results verify the capabilities of the proposed algorithm.

Introducing 3D-potting:a novel production process for artificial membrane lungs with superior blood flow design

Currently,artificial-membrane lungs consist of thousands of hollow fiber membranes where blood flows around the fibers and gas flows inside the fibers,achieving diffusive gas exchange.At both ends of the fibers,the interspaces between the hollow fiber membranes and the plastic housing are filled with glue to separate the gas from the blood phase.During a uniaxial centrifugation process,the glue forms the“potting.”The shape of the cured potting is then determined by the centrifugation process,limiting design possibilities and leading to unfavorable stagnation zones associated with blood clotting.In this study,a new multiaxial centrifugation process was developed,expanding the possible shapes of the potting and allowing for completely new module designs with potentially superior blood flow guidance within the potting margins.Two-phase simulations of the process in conceptual artificial lungs were performed to explore the possibilities of a biaxial centrifugation process and determine suitable parameter sets.A corresponding biaxial centrifugation setup was built to prove feasibility and experimentally validate four conceptual designs,resulting in good agreement with the simulations.In summary,this study shows the feasibility of a multiaxial centrifugation process allowing greater variety in potting shapes,eliminating inefficient stagnation zones and more favorable blood flow conditions in artificial lungs.

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,etc.The recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation signals.In this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)approach.Moreover,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

充电桩充电模块功率器件故障诊断研究综述

大功率直流充电桩是未来电动汽车充电设施的发展方向,充电模块是直流充电桩最重要以及故障率最高的部件,其中功率器件开路故障较为常见。为保证充电模块安全可靠运行,需要对充电模块功率器件开路故障进行准确识别和定位。文中首先对充电模块拓扑结构和故障类型进行分析,然后分别对基于解析模型、基于信号处理和基于知识的功率器件开路故障诊断方法进行总结,分别介绍了各类方法的基本思想、研究进展和优缺点,最后总结并展望充电模块功率器件开路故障诊断方法未来的研究和发展方向。

Research on Feature Extraction of Composite Pseudocode Phase Modulation-Carrier Frequency Modulation Signal Based on PWD Transform

The identification features of composite pseudocode phase modulation and carry frequency modulation signal include pseudocode and modulation frequency. In this paper,PWD is used to extract these features. First,the feature of pseudocode is extracted using the amplitude output of PWD and the correlation filter technology. Then the feature of frequency modulation is extracted by way of PWD analysis on the signal processed by anti-phase operation according to the extracted feature of pseudo code,i.e. position information of changed abruptly point of phase. The simulation result shows that both the features of frequency modulation and phase change position caused by the pseudocode phase modulation can be extracted effectively for SNR=3 dB.

Ultrashort optical pulse shaper based on complex-modulated long-period-grating coupler

In this paper,we present a novel ultrashort pulse shaper based on complex-modulated long-period-grating coupler（CM-LPGC）.Temporal rectangular waveform with 2-ps full width at half maximum（FWHM） is obtained by transforming the input Gaussian pulse.Tolerances of the CM-LPGC-based shaper to various non-ideal excitation conditions and fabricating errors are investigated.Results confirm that CM-LPGC is stable and suitable for optical pulse shaping operation.

专业群“模块递进、项目贯穿”工作过程导向的实践教学体系改革与实践——以电气自动化技术专业群为例

高职院校专业群建设的实质,就是依据产业和人才培养逻辑对群内专业进行有效整合,实现资源共享,凸显群效应,服务产业对人才的需求。明确专业群服务产业的人才培养定位,理清专业群内各专业能力培养的自身特色以及服务产业的逻辑关联。以课程为本搭建实践能力递进模块,以工作过程为导向开发课程,项目贯穿设计专业群实践教学体系。建设工程化的实践环境,利用信息化管理手段,推动过程评价和增值评价,持续改进培养质量,融“三教”改革于项目化教学,实现专业群高水平服务学生成长成才,高质量服务区域经济发展,适应产业发展新形势和新业态的要求。

多轴线运输车的模块化设计方法研究

目前多轴线运输车存在设计周期长、生产制造困难等问题。为了对此类轴线车进行快速开发,在分析现有模块划分方法的基础上,引入复杂网络理论,开展针对多轴线运输车的模块化设计方法研究。首先从零部件间的几何、功能及设计变更三个角度分析评价多轴线运输车零部件之间的关联强度,然后采用基于七标度法则的区间层析分析法(IAHP)确定三个相关属性权重大小,并建立产品零部件综合复杂网络模型。在此基础上,采用Louvain算法进行该网络的社区发现,从而得到多轴线运输车模块划分方案,与GN算法、LPA算法、FCM算法相比,采用Louvain算法能更有效地对轴线车进行模块划分。基于模块化理论对多轴线运输车的设计进行研究,能够实现此轴线车的快速设计,为轴线车的优化升级奠定了基础。

基于STM32的智能航行系统设计

为解决航行器智能确定航向并航行的问题,本文采取模块化的思想设计了一款基于STM32的红外导引的智能航行控制系统。该智能航行系统利用红外接收模块进行红外导引信号的接收,采取线性加权法进行航向与导引信号之间的偏离角确定,利用比例-积分-微分(proportion-integration-differentiation,PID)控制算法对控制舵机转角的脉冲宽度调制(pulse width modulation,PWM)信号的占空比进行确定,从而对螺旋桨的转速与舵桨的转角进行可靠控制。根据水池测试结果,该智能航行控制系统能够对红外接收信号进行快速处理,在保持较高航速的条件下进行可靠的航行控制。

调频信号宽带采集与解调实验系统设计

针对射频实验系统成本昂贵问题,提出了一种基于共享数据采集硬件,利用互联网向实验终端分发数据的信号处理实验教学系统结构。基于此结构,以无线信号宽带采集和调频广播信号解调为目标,设计并实现了用于信号处理类课程的实验教学系统,同时给出了实验内容设计和扩展阅读材料。