基于特征融合的室外天气图像分类

天气状况对室外视频设备的成像效果有很大影响。为实现成像设备在恶劣天气下的自适应调整,从而提升智能监控系统的效果,同时针对传统的天气图像判别方法分类效果差且对相近天气现象不易分类的不足,以及深度学习方法识别天气准确率不高的问题,提出了一个将传统方法与深度学习方法相结合的特征融合模型。融合模型采用4种人工设计算法提取传统特征,采用AlexNet提取深层特征,利用融合后的特征向量进行图像天气状况的判别。融合模型在多背景数据集上的准确率达到93.90%,优于对比的3种常用方法,并且在平均精准率（AP）和平均召回率（AR）指标上也表现良好;在单背景数据集上的准确率达到96.97%,AP和AR均优于其他模型,且能很好识别特征相近的天气图像。实验结果表明提出的特征融合模型可以结合传统方法和深度学习方法的优势,提升现有天气图像分类方法的准确度,同时提高在特征相近的天气现象下的识别率。     

高精度太阳能跟踪控制器设计与实现

针对目前采用传感器检测实现的太阳能跟踪控制器抗干扰性差,跟踪误差大的缺点,介绍了采用软件算法和传感器检测控制结合的高精度太阳能跟踪摔制器设计与实现的方法。软件算法是根据天体的运行规律来计算太阳的高度角和方位角,控制太阳能跟踪器的水平角和俯仰角的范围。传感器检测控制是由精密的四象限传感器检测电路来实现,在软件算法计算的水平角和俯仰角移动的范围内搜索,精确的跟踪太阳光信号的最强点,提高太阳光能的利用率。     

PLC在CPV太阳能跟踪系统上应用

第三代太阳能电池采用聚光光伏技术（Concentrated Photovoltaic,cPv）,与第一代晶硅电池和第二代薄膜电池相比,它具有更高的转换效率、更好的耐高温性能和更环保的资源利用优势.太阳能跟踪系统是CPV技术关键的组成部分,它的追日效果直接影响着CPV电池的转换效率.本文从太阳能跟踪系统的原理出发,针对CPV系统追日的高精度要求和国内光伏产业的0EM市场现况,着重阐述了一种以SIMAT1C S7-1200 PLC为核心的低成本解决方案在具体项目中的应用.     

面向小运动目标的压缩域跟踪方法

压缩域跟踪是直接从压缩码流中提取运动矢量和块编码模式来实现目标对象的跟踪.针对现有压缩域跟踪方法对小运动目标跟踪性能较差的问题,本文提出了一种面向小运动目标的压缩域跟踪算法.在分析现有算法不足原因的基础上,本文从起始帧掩模的获取、离群值边界的设置和预测跟踪小目标的边缘控制三个方面提升小目标跟踪的性能,并通过数据驱动的方法寻找到块编码感知的系统参数优化.所提算法在三个小目标视频序列上进行了测试,实验结果表明,与其它压缩域跟踪算法相比,本文算法可以有效地提高小运动目标跟踪的准确率和F度量.     

基于STM32F103VC的简易太阳能充电控制系统的研制

为了提高太阳能光伏组件的充电效率以及适应外界气候变化,设计了一套具有自适应四种充电模式且具备最大功率点跟踪的太阳能充电控制系统。该系统采用意法半导体公司的STM32FL03VC作为控制系统的核心,监控整个系统的正常工作,具有浮充、防过充功能。硬件设计采用高精度的集成芯片,使得系统设计简易精确、集成度更高。测试结果表明,该控制器能实时跟踪最大功率点,正确监控蓄电池各充电模式,充电效率高,性能可靠。     

基于图像重构的MNIST对抗样本防御算法

随着深度学习的应用普及,其安全问题越来越受重视,对抗样本是在原有图像中添加较小的扰动,即可造成深度学习模型对图像进行错误分类,这严重影响深度学习技术的发展。针对该问题,分析现有对抗样本的攻击形式和危害,由于现有防御算法存在缺点,提出一种基于图像重构的对抗样本防御方法,以达到有效防御对抗样本的目的。该防御方法以MNIST为测试数据集,核心思路是图像重构,包括中心方差最小化和图像缝合优化,中心方差最小化只针对图像中心区域进行处理;图像缝合优化将重叠区域纳入补丁块选取的考量,并以补丁块的1/2大小作为重叠区域。使用FGSM、BIM、DeepFool以及C&W攻击方式生成对抗样本来测试两种方式的防御性能,并与现有的3种图像重构防御方式（裁剪与缩放、位深度压缩和JPEG压缩）效果对比。实验结果表明,所提中心方差最小化和图像缝合优化算法,对现有常见对抗样本的攻击起到了较好的防御效果。图像缝合优化对4种攻击算法生成的样本分类正确率都达到了75%以上,中心方差最小化的防御效果在70%左右。而用作对比的3种图像重构算法则对不同攻击算法的防御效果不稳定,整体分类正确率不足60%。所提中心方差最小化...     

基于TLD框架的快速目标跟踪方法

现实中目标在被长期跟踪时容易发生形变、遮挡、光照干扰以及其它问题,现有跟踪算法虽能解决该系列问题但算法计算量巨大导致跟踪系统实时性能较差,很难应用于实际场合。因此准确快速跟踪目标成为近年来非常有挑战的热点课题。以国外学者Zdenek Kalal等人提出的TLD(Tracking-Learning-Detection)框架为基础,提出了三点改进方法。一根据目标所占整幅图像的面积大小动态调整被处理图像的分辨率,从总体上减少样本数量;二在目标邻近区域扫描生成样本,缩小检测器的检测范围;三更换检测部分中分类器模板匹配方法,实现快速匹配,提高算法运行速度。针对与不同的场景,实验表明上述问题在改进后的算法中得到了较大的改善,算法的计算量有效降低,系统运行速度得到提高。且对于实时摄像头监控,改进后算法在保证目标跟踪准确率的同时拥有较好的实时性。     

基于GPS定位的太阳能板自动追光系统设计

为了使太阳能电池板更好地吸收太阳光,提出了一种基于GPS定位的太阳能电池板自动追光系统设计。通过使用GPS定位功能为跟踪装置所在位置提供精确的经纬度和时间,通过视日运动轨迹法计算当前时间太阳的高度角和方位角,以及利用高度/方位传感器获得当前太阳能电池板转动的仰角和旋转角度,根据间歇跟踪法计算出当前跟踪偏差值,并通过主控制器输出相应的电脉冲信号。以控制步进电机的跟踪装置驱动太阳能电池板。综上所述,通过实现主要参数包括地理位置与授时,跟踪偏差以及太阳的位置,从而能够准确提高太阳光的高精度自动跟踪,以及也增加了太阳能电池板自动追光系统的稳定性,且实用性更强。     

Swarm-Based Nature-Inspired Algorithm and Genetic Algorithms for Optimizing a Sun Tracker Trajectory

Over the last 10 years, the popularity of solar panels for catching solar energy has reduced development and manufacturing costs. Nevertheless, costs per watt are still high when compared to other less-clean energy sources such as wind energy. Therefore, the goal of the sun tracker is to maximize the energy generation of solar cells, thus giving a competitive advantage to solar energy. However, finding the optimal position is a very complex task and different algorithms such as genetic algorithms or swarm-based optimization algorithms have been used to improve the results. This article shows the design and implementation of two optimal sun tracker algorithms. The first method presented is genetic algorithms, which allow finding the position of the sun tracker based on an offline solution. When genetic algorithms find the solution offline, the results can be programmed in a simple lookup table. This approximation decreases the computational cost, and it is effective for geographical climes where conditions are constant. However, there are places with nonconstant climate conditions that need online optization algorithms. In this case, a newly developed intelligent water drop algorithm is proposed for running an online solution. Both methods were designed for the sun tracker problem and were implemented. The power and energy analytics show that the algorithms increase the efficiency of the sun tracker, compared to a static solar cell, by at least 40% in some cases. The sun tracker presented gives an excellent solution for obtaining energy from the sun during diverse weather conditions. This work also introduces a novel derivation of the intelligent water drop algorithm for sun trackers based on a nonconventional trajectory and conventional genetic algorithms adjusted for sun tracker needs. The experimental results are shown in order to validate the methodologies proposed.     

Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks

Concentrating Photovoltaic (CPV) technology attempts to optimize the efficiency of solar energy production systems. As conventional Photovoltaic (PV) technology, suffers from variability in its production and needs models for determining the exact module performance. There are several problems when analyzing CPV systems performance with traditional techniques due to absence of standardization. In this sense it is remarkable the importance for the emerging CPV technology, of the existence of models which allow the prediction of modules performance from initial atmospheric conditions. In this paper, a CPV module is studied by means of atmospheric conditions obtained using an automatic test and measuring system developed by the authors. The characterization of the CPV module is carried out considering incident normal irradiance, ambient temperature, spectral irradiance distribution and wind speed. CO²RBFN, a cooperative-competitive algorithm for the design of radial basis neural networks, is adapted and applied to these data obtaining a model with a good level of accuracy on test data, improving the results obtained by other methods considered in the experimental comparison. These results are promising and the obtained model could be used to work out the maximum power at the CPV reporting conditions and to analyze the performance of the module under any conditions and at any moment.     

On-road vehicle detection in varying weather conditions using faster R-CNN with several region proposal networks

Developing automated systems to detect and track on-road vehicles is a demanding research area in Intelligent Transportation System (ITS). This article proposes a method for on-road vehicle detection and tracking in varying weather conditions using several region proposal networks (RPNs) of Faster R-CNN. The use of several RPNs in Faster R-CNN is still unexplored in this area of research. The conventional Faster R-CNN produces regions-of-interest (ROIs) through a single fixed sized RPN and therefore cannot detect varying sized vehicles, whereas the present investigation proposes an end-to-end method of on-road vehicle detection where ROIs are generated using several varying sized RPNs and therefore it is able to detect varying sized vehicles. The novelty of the proposed method lies in proposing several varying sized RPNs in conventional Faster R-CNN. The vehicles have been detected in varying weather conditions. Three different public datasets, namely DAWN, CDNet 2014, and LISA datasets have been used to evaluate the performance of the proposed system and it has provided 89.48%, 91.20%, and 95.16% average precision on DAWN, CDNet 2014, and LISA datasets respectively. The proposed system outperforms the existing methods in this regard.

     

均衡化特征匹配的非刚体细胞形态跟踪

提出利用均衡化特征匹配来进行非刚性细胞形体跟踪的方法。采用重启动的随机游走方法建立并求解特征匹配概率模型,利用双向均衡方法对匹配邻接矩阵进行均衡化处理,得到指定目标与待跟踪目标之间的精确匹配,以获得目标的定位跟踪结果。同时利用特征匹配结果进行目标的自动标定,并应用图像分割方法进行目标的精确轮廓跟踪。实验结果表明,将该方法应用于视频中动态背景下的运动细胞形态跟踪时,在背景相似度较高及目标迅速移动的条件下,表现出了良好的性能,与同类方法相比可获得较高的定位精度以及更为准确的目标轮廓。     

Leader–follower tracking control with guaranteed consensus performance for interconnected systems with linear dynamic uncertain coupling

This paper considers the leader–follower tracking control problem for linear interconnected systems with undirected topology and linear dynamic coupling. Interactions between the systems are treated as linear dynamic uncertainty and are described in terms of integral quadratic constraints (IQCs). A consensus-type tracking control protocol is proposed for each system based on its state relative to its neighbours. In addition, a selected set of subsystems is used to control their relative states with respect to the leader. Two methods are proposed for the design of this control protocol. One method uses a coordinate transformation to recast the protocol design problem as a decentralised robust control problem for an auxiliary interconnected large-scale system. Another method is direct; it does not employ coordinate transformation, rather it also allows for more general linear uncertain interactions. Using these methods, sufficient conditions are obtained which guarantee that the system tracks the leader. These conditions guarantee a suboptimal bound on the system consensus and tracking performance. The proposed methods are compared using a simulation example, and their effectiveness is discussed. Also, algorithms are proposed for computing suboptimal controllers.     

Incremental Learning for Robust Visual Tracking

Visual tracking, in essence, deals with non-stationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object’s appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a forgetting factor to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large changes in pose, scale, and illumination.     

A predictive algorithm for multimedia data compression

In lossless image compression, many prediction methods are proposed so far to achieve better compression performance/complexity trade off. In this paper, we concentrate on some well-known and widely used low-complexity algorithms exploited in many modern compression systems, including MED, GAP, Graham, Ljpeg, DARC, and GBSW. This paper proposes a new gradient-based tracking and adapting technique that outperforms some existing methods. This paper aims to design an efficient highly adaptive predictor that can be incorporated in modeling step of image compression systems. This claim is proved by testing the proposed method upon a wide variety of images with different characteristics. Six special sets of images including face, sport, texture, sea, text, and medical constitute our dataset.     

New enhanced performance robust control design scheme for grid-connected VSI

In this paper, a new method for intelligent robust control design is presented that achieves the best possible convergence rate of the system, utilizing the knowledge on the range of uncertain parameter. Thus resulting in enhanced stability and performance. The proposed method is applied to the grid-connected voltage source inverter (VSI) system with uncertainties in grid-impedance. Simulation and experimental results illustrate the efficacy of the proposed scheme. Comparison with existing methods shows that the proposed scheme can provide better reference tracking, stability for a wider uncertainty range, and improved transient and steady-state performance with low implementation cost.     

Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells

The emerging non-crystalline silicon (c-Si) solar cell technologies are starting to make significant inroads into solar cell markets. Most of the researchers have focused on c-Si solar cell in maximum power points tracking applications of photovoltaic (PV) systems. However, the characteristics of non-c-Si solar cell technologies at maximum power point (MPP) have different trends in current-voltage characteristics. For this reason, determining the optimum operating point is very important for different solar cell technologies to increase the efficiency of PV systems. In this paper, it has been shown that the use of fuzzy system coupled with a discrete wavelet network in Takagi-Sugeno type model structure is capable of identifying the MPP voltage of different non-c-Si solar cells with very high accuracy. The performance of the fuzzy-wavelet network (FWN) method has been compared with other ANN structures, such as radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN). The simulation results show that the single FWN architecture has superior approximation accuracy over the other methods and a very good generalization capability for different operating conditions and different technologies.     

A lattice-based MRF model for dynamic near-regular texture tracking

A near-regular texture (NRT) is a geometric and photometric deformation from its regular origin - a congruent wallpaper pattern formed by 2D translations of a single tile. A dynamic NRT is an NRT under motion. Although NRTs are pervasive in man-made and natural environments, effective computational algorithms for NRTs are few. This paper addresses specific computational challenges in modeling and tracking dynamic NRTs, including ambiguous correspondences, occlusions, and drastic illumination and appearance variations. We propose a lattice-based Markov-random-field (MRF) model for dynamic NRTs in a 3D spatiotemporal space. Our model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations. Based on the proposed MRF model, we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic NRT tracking without any assumption on the motion types or lighting conditions. We provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing     

Image denoising using new pixon representation based on fuzzy filtering and partial differential equations

In this paper, we have proposed two extensions to pixon-based image modeling. The first one is using bicubic interpolation instead of bilinear interpolation and the second one is using fuzzy filtering method, aiming to improve the quality of the pixonal image. Finally, partial differential equations (PDEs) are applied on the pixonal image for noise removing. The proposed algorithm has been examined on variety of standard images and their performance compared with the existing algorithms. Experimental results show that in comparison with the other existing methods, the proposed algorithm has a better performance in denoising and preserving image edges.     

ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks

This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach.