Evaluating Visual Properties via Robust HodgeRank

International Journal of Computer Vision - Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the...     

基于WSN的粮情监测系统LEACH改进协议研究与应用

首先介绍了基于WSN的粮情仓储监测系统的结构,针对粮仓温度监控的特殊环境,分析了经典LEACHE协议中簇头节点能量消耗过大和簇头节点与网关通信距离长的不足,提出以节点综合信息作为簇头节点选取的标准,同时通过限制簇内节点数目,在传输中结合簇内单跳、簇间与网关单跳与多跳结合的数据传输方式;最后将提出的改进算法在布设50个节点的粮仓中运行半年,经测量回收节点电池电压,90%节点均有效减少消耗,延长网络整体生存时间,较好地满足实际应用要求,为大型粮库设施现代化管理奠定了基础。     

基于PCA-BP神经网络的装甲车辆柴油机状态评估

针对装甲车辆柴油机摩托小时不能真实反映实际技术状况的问题,通过提取气缸压缩压力、加速时间、减速时间、供油提前角、振动能量和平均燃油流量等状态参数,运用主成分分析和BP神经网络相结合的方法构建了一种柴油机状态评估模型;该模型首先利用主成分分析方法将多个参数简化为两个综合参数,并根据综合参数的散点图对柴油机的状态进行初步划分,得到BP神经网络的训练数据;最后通过建立BP神经网络进行状态评估;评估结果表明,该模型准确度高,具有较好的应用和推广价值。     

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。     

基于多Agent的协同制造平台的研究与设计

随着企业间的集成与协作越来越紧密,知识也越来越高度密集,本文基于多Agent技术,提出一种支持以知识为基础的协同制造开发平台——基于多Agent的可伸缩的多智能协同制造平台。详细设计了多Agent协同开发平台的体系结构、协作框架,以及多agent系统基于本体的通信机制。该平台凭借包装服务和代理商提供的通信能力,通过有效地支持制造业知识的表示、获取、检索和重利用,不仅有利于多学科的个体和团队的组织和协作,而且提出的基于本体库的广义知识库能使设计人员有效地、不受限制地交换信息和知识。     

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.     

Interactive genetic algorithms with large population and semi-supervised learning

Interactive genetic algorithms are effective methods of solving optimization problems with implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional evolution mechanisms. The heavy evaluation burden of the user, however, is crucial and limits their applications in complex optimization problems. We focus on reducing the evaluation burden by presenting a semi-supervised learning assisted interactive genetic algorithm with large population. In this algorithm, a population with many individuals is adopted to efficiently explore the search space. A surrogate model built with an improved semi-supervised learning method is employed to evaluate a part of individuals instead of the user to alleviate his/her burden in evaluation. Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes. We quantitatively analyze the performance of the proposed algorithm and apply it to the design of sunglasses lenses, a representative optimization problem with one qualitative criterion. The empirical results demonstrate the strength of our algorithm in searching for satisfactory solutions and easing the evaluation burden of the user.