基于混沌免疫网络的镍氢电池建模研究

针对电动汽车用镍氢电池组,提出利用计算智能算法训练的RBF网络实现电池组建模;首先采用免疫聚类的方法,通过对样本径基进行成分提取,并与并行免疫进化规划(PCEIP)相结合,形成一种更有效的RBF网络构造策略,然后基于PCIEP设计了改进的RBF网络的训练步骤;最后,在镍氢电池恒流放电和变功率放电工况下,以改进的RBF网络实现镍氢电池建模,验证建模的精度。     

基于无线通信技术的FFU风机群控系统

针对大型洁净工程实现自动化控制的需要,利用数字无线通信技术,设计了一种新型网络化的多风机过滤器单元(FFU)群控系统,详细介绍了系统的组成实现原理和软硬件设计方法;系统间采用基于射频收发芯片nRF2401实现数据的无线收发,有效地减少了系统布线和设计成本,提高了系统的组网灵活性和适用性;对设计开发的系统进行了测试实验,实验结果表明该系统数据通信正确可靠,系统具有结构简单、组网灵活、监控效果好和可扩展性强等优点。     

机载塔康设备综合检查仪设计

使用市场上成熟的模块和技术研制机载塔康设备综合检查仪,以降低成本和提高设备的可靠性;根据设备的测试需求,选用嵌入式工业平板电脑、USB-ARINC429通讯卡、USB-数字IO卡、串行接口数字表和自研的塔康信号模拟器等构成检查仪硬件,使用CVI9.0编写了测试程序;应用表明:检查仪集成度高,使用方便,能够完成对塔康设备的自动及手动测试。     

Detection of sudden pedestrian crossings for driving assistance systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.     

Generalized extreme learning machine acting on a metric space

Functional data learning is an extension of traditional data learning, that is, learning the data chosen from the Euclidean space ${\mathbb{R}^{n}}$ to a metric space. This paper focuses on the functional data learning with generalized single-hidden layer feedforward neural networks (GSLFNs) acting on some metric spaces. In addition, three learning algorithms, named Hilbert parallel overrelaxation backpropagation (H-PORBP) algorithm, ν-generalized support vector regression (ν-GSVR) and generalized extreme learning machine (G-ELM) are proposed to train the GSLFNs acting on some metric spaces. The experimental results on some metric spaces indicate that GELM with additive/RBF hidden-nodes has a faster learning speed, a better accuracy, and a better stability than HPORBP algorithm and ν-GSVR for training the functional data. The idea of GELM can be used to extend those improved extreme learning machines (ELMs) that act on the Euclidean space ${\mathbb{R}^{n}, }$ such as online sequential ELM, incremental ELM, pruning ELM and so on, to some metric spaces.     

Least squares approach in measurement-dependent filtering for selective medical images

An image-processing method called measurement-dependent filtering has been introduced to improve the SNR (signal-to-noise ratio) of selective images produced by various medical imaging systems. The basic algorithm involves the combination of the low-frequency information of the selective image with the high-frequency information of a nonselective image. A spatially variant control function modulates the amount of high frequency to be added at each point. A least-mean-square (LMS) control function formed from two basis images, namely the high-passed versions of the nonselective image (M(b)) and the selective image (S(b)), is introduced. The original algorithm is now viewed as a two-stage filtering method, including the low-pass filtering noise reduction and least squares filtering for the edge restoration. An appropriate linear transformation is used to convert the original basis images M(b) and S(b) into a new pair with orthogonal noise. This allows the implementation of the LMS and control function with practically obtainable a priori knowledge.     

基于STM32的逆变弧焊电源控制器设计

数字化是弧焊电源的发展趋势,其关键在于控制器的数字化。目前市场上的大多数数字化产品价格相对高昂,采用STM32系列单片机进行经济型数字化弧焊电源控制器的设计和实现。该控制器采用数字式PI方法进行输出电流控制,并具有按键操作、液晶显示、过流保护、过热保护等功能。该控制器还具有良好的扩展I}生,可以通过修改程序增强系统功能。     

Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

Efficiently performing high-resolution direction of arrival (DOA) estimation under low signal-to-noise ratio (SNR) conditions has always been a challenge task in the literatures. Obvi-ously, in order to address this problem, the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs. However, it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all. Therefore, this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators. Specifically, these basic spatial spec-trums are employed as the input of multi-source information fusion model. And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines, namely neural networks (NN), support vector machine (SVM), and random forests (RF). The final meta-spec-trum can be obtained by performing a final decision-making method. Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators. Even under low SNR conditions, promising DOA estimation performance can be achieved.     

基于现代学徒制的校企双元育人机制构建与实施路径

以现代学徒制为载体,加快构建校企双元育人机制是我国职业教育改革的新要求。基于现代学徒制的发展趋势,保障校企双元育人的有效落实,需要将校企共建命运共同体、共建高水平专业和课程标准、共建育人队伍、共建共享教学资源作为双元育人机制的建设主线。在此基础上提出注重顶层设计、坚持立德树人目标、完善职教师资培养体系、打造高水平实训基地等校企双元育人的实施路径。