"延保服务"离我们有多远?

有关数据显示:早在2005年底,我国的电视机、电冰箱、洗衣机的社会保有量就分别达到3.5亿台、1.3亿台和1.7亿台;吸油烟机达到8000万台;空调1.17亿台;电脑和手机保有量超过2000万台     

高压加热器的运行故障分析

归纳近年全国各电厂发生的高压加热器故障情况,主要由管束爆管、水位失控、配套件发生故障及操作不当所引发.现电厂对高加的正常运行,其重视程度前所未有.这不仅因为高加投运与否直接与电厂出力和经济效益有关,而且会直接影响整个机组的安全性.因此,提出各种保障措施,确保高压加热器能高效正常地运行.     

传统家电企业图谋小家电市场——访广东志高空调有限公司家电营销公司总经理蔡吉强

在2007年顺德国际家电博览会上,小家电产品的展示占到极大的比重,而且值得注意的是,在众多的小家电产品企业中,不乏志高、长虹、新飞、奥克斯等以空调、彩电著称的传统家电制造商。对此,这些大家电企业都自信地认为,他们能够凭借各自的品牌效应与渠道在未来的3～5年内占领部分市场。业内人士认为,这些传统家电企业的进入能够对小家电行业的发展起到一定的作用,不过这一产业仍需要引导、提高。     

2008华南锂电高层论坛(国际年)

2008年3月4日,第三届华南锂电高层论坛(国际年)在深圳会展中心隆重开幕.本次大会由信息产业部电信研究院、信息产业部数据通信产品质量监督检验中心和中国威尔克实验室主办,由深圳邦凯电子、IDG、深圳六方展览等联合策划承办.本届论坛以"中国锂电--从制造走向创造"为主题,同时强调"新技术、新材料、新应用",针对锂电的生存现状,通过专家的"视角"探讨今后市场的发展趋势.     

希望海信科龙是我退休前最后一个事业——访海信科龙电器股份有限公司副总裁周小天

4月17日上午九点三十分,他首次以海信科龙电器股份有限公司(以下简称海信科龙)副总裁的身份出席了在上海举办的中国家电博览会(以下简称家博会)开幕式;下午,在简单看完家博会后,他又赶赴距上     

空调、洗衣机用电机漆包线"铝代铜"进行时?

"我们正在研究并尝试使用电机漆包线铝代铜方案,现已进入小批量试产."江苏三江电器集团副总经理环剑斌称;"我们已经开发出空调用铝漆包线电机."威灵电机有关负责人表示;"我们也想在洗衣机用电机上尝试铜铝替代."无锡小天鹅华印电器有限公司有关技术人员称.     

标准促进行业发展企业仍需有所担当

产品标准是一个行业发展的基础,而标准背后则是企业强大的核心竞争力和自觉为社会、人民利益严格要求自身的决心和毅力。在国民经济发展的大环境下,与生活生产相关的每一个产业都有无数规则控制着中国产业的发展,电信,网络,钢材,饮料,物流,背后是一长窜名字摩托罗拉,英特尔,可口可乐,沃尔玛,他们就是规则的制定者。规则是什么?规则就是标准,就是不战屈人之兵。同样在中国的家电市场上,面临标     

中国火电建设发展史(2)

"中国火电建设发展史"原系"中华人民共和国电力工业史"的一部分,该书由10多位资深专家分工撰编多年,但迄今未能付梓出版。现将其中"火电建设"部分单独刊出,本刊以连载方式发表,以飨读者。写史要求实事求是,秉笔直抒,总结经验教训,探讨发展规律,以史为鉴,鉴往知来。火力发电在我国电力工业史中始终处于主导地位,在21世纪的电力发展中,火电建设仍将处于主导地位,愿全国火电建设者,不懈努力,为全面建成小康社会和实现四个现代化做出自己的贡献。     

高性价比之选日立P42E101C等离子电视

一直以来,等离子电视与液晶电视的纷争几乎从未停息过,从最初的分辨率高低对比,响应时间和屏幕拖影问题到现在的色彩画质效果、健康护眼等方面的新说法,两大平板主流极尽所能地宣传各自的优势特点。但是,随着今年以来液晶面板产能的增加,大屏幕特别是40英寸以上的高清液晶电视的价格如雪崩般降下了很多,不少品牌的产品纷纷降到了万元以内,对消费者产生了极大吸引力;而相比之下,几乎能够兼容高清信号的等离子都在万元以上,等离子电视在大屏幕领域的优势越发被削弱了。日立P42E101C等离子电视的推出无疑为现在的等离子阵营注入了一针强心剂,它一方面继承了等离子电视优异的画质显示性能,更可贵的是不到9000元人民币的价格,令这款产品成为了众多平板电视中的高性价比之选。     

关于加强湖南电力调度管理工作的思考

在进行地区电力调度系统安全性评价、安全生产检查、班组建设座谈会和问卷调查等调研的基础上,根据电力系统安全优质经济运行的客观要求,指出电力调度应完善统一调度、分级管理的原则,实施"统一制度,统一标准,统一平台,科学考核"的管理模式.     

基于图信号处理的智能电表功率信号分解

智能电表的大规模部署,使得对电表采集的低频信号进行数据分析成为一个研究热点。以非侵入式负荷监测为背景,研究基于图信号处理(GSP)的低频功率信号分解算法。首先,将功率信号分解定义为最小化求解问题,并引入基于图转移矩阵的全局变化量作为正则项。然后,分两步对该优化问题求解:第1步最小化正则项得到满足图信号全局变化量最小的近似解;第2步以该解为基础,利用模拟退火算法对目标函数和约束条件迭代寻优。最后利用开源数据库REDD进行仿真,验证了该算法在分类准确率上的优势,且与其他算法相比对训练数据的依赖性较小。     

Unsupervised Domain Adaptation Learning Algorithm for RGB-D Stairway Recognition

Detection and recognition of a stairway as upstairs, downstairs and negative (e.g., ladder, level ground) are the fundamentals of assisting the visually impaired to travel independently in unfamiliar environments. Previous studies have focused on using massive amounts of RGB-D scene data to train traditional machine learning (ML)-based models to detect and recognize stationary stairway and escalator stairway separately. Nevertheless, none of them consider jointly training these two similar but different datasets to achieve better performance. This paper applies an adversarial learning algorithm on the indicated unsupervised domain adaptation scenario to transfer knowledge learned from the labeled RGB-D escalator stairway dataset to the unlabeled RGB-D stationary dataset. By utilizing the developed method, a feedforward convolutional neural network (CNN)-based feature extractor with five convolution layers can achieve 100% classification accuracy on testing the labeled escalator stairway data distributions and 80.6% classification accuracy on testing the unlabeled stationary data distributions. The success of the developed approach is demonstrated for classifying stairway on these two domains with a limited amount of data. To further demonstrate the effectiveness of the proposed method, the same CNN model is evaluated without domain adaptation and the results are compared with those of the presented architecture.     

多场景下的人体跌倒检测方法及应用

针对可穿戴MEMS传感器检测多场景下的人体摔倒行为时,单一采用加速度阈值判断存在表征不完全的问题,提出了改进麻雀搜索算法(ISSA)优化SVM(SVM)的人体跌倒检测识别方法。首先通过可穿戴MEMS传感器采集人体离散化姿态数据,然后通过时间滑动窗口找出加速度阈值与角速度阈值特征向量并进行一级判定;同时构建ISSA-SVM跌倒状态检测模型,即利用改进的麻雀搜索算法对SVM的核参数和惩罚因子进行自适应优化,获得最优分类模型;最后根据SVM分类模型,对一级判定的数据进行分析,判断是否真正跌倒。实验仿真和产品应用结果表明:对于人体在不同场景下意外跌倒的检测,所提出的ISSA-SVM识别检测模型测试正确率达98%以上,同时降低了漏报率。经过多次测试,跌倒检测器表现出较好的鲁棒性。     

基于优化神经网络的采集终端软件可靠性预测

采集终端软件的可靠性是评价软件系统生命周期的一个重要指标。针对多种神经网络和支持向量机等方法在软件系统可靠性评价中存在的参数优化困难、软件系统预测模型的低准确率问题,提出基于SAGFA-BPNN的建模方法。该方法采用PCA对实验数据降维处理,剔除影响模型准确率的冗余和干扰样本;在优化SA和GA的基础上,给出退火遗传融合优化算法(SAGFA),并发挥其全局寻优能力,以及BPNN非线性映射能力,提出SAGFA-BPNN网络,及基于它的建模方法,以提高训练速度、全局寻优能力及准确度。文章还应用该方法对采集终端软件的可靠性进行了预测,预测结果表明,该方法可以有效地提高模型的准确度。     

基于实测数据的电力系统过电压分类识别

构造了一个有效的基于实测数据的过电压自动分类识别树。首先抽取过电压信号的时域特征量,将过电压类别集合分为2个子集。其次对信号进行离散小波变换,抽取小波变换域特征量。为使小波变换域特征量更具区别性,对2个子集内的过电压信号采用不同的采样频率和小波分解层数。最后在分类树的各节点构造一个支持向量机二值分类器,采用实测过电压数据进行交叉验证。总识别率达95%,验证了分类树的有效性。     

Particle identification in terms of acoustic partial discharge measurements in transformer oils

Acoustic measurements of partial discharge (PD) are employed to classify particles in transformer mineral oil according to material and size. Wavelet multi-resolution analysis data of the acoustic signals together with higher order statistics of the particle intercollision times and magnitudes comprise the input features to a Support Vector Machine (SVM) classifier. The training and validation measurement data, which are contaminated by time varying noise, are first filtered using wavelet decomposition. Results indicate that the SVM algorithm with the selected features provides a remarkably high success rate when classifying particles by size and material type. A potentially significant conclusion is that acoustic measurements alone are by themselves effective in classifying discharged particles in terms of the foregoing selected features. The proposed algorithm can be employed to enhance quality control procedures based on acoustic measurements of partial discharge.     

离散差分模块在癫痫脑电分类中的应用

针对癫痫脑电信号多分类的精度提升问题,提出了一种基于信号转差分模块与卷积模块结合的分类算法。信号转差分模块对原始脑电信号进行多阶差分运算,得到描述其波动特征的差分表示;然后卷积模块动态学习的方式将差分脑电信号转换为图片,利用预训练的卷积神经网络来提取信号特征并实现自动分类。分类结果表明,与现有研究相比,所提出的方法的最高提升了8.1%的分类准确率。在两分类问题上达到了99.8%的分类准确率,在三分类问题上获得了92.8%的准确率,在五分类问题上取得了86.7%的准确率。说明信号转差分模块对于脑电信号分类问题有积极作用。     

基于优化空间金字塔匹配模型的图像分类

基于空间金字塔匹配模型(SPM)的图像分类中,构建视觉词直方图时对图像中所有特征都是同等对待,没有考虑到图像中不同区域特征的影响因子.显然,图像中目标区域比背景区域的特征重要性要大,为了避免图像中不重要区域的特征给图像分类带来干扰,提出了一种优化空间金字塔模型的图像分类方法.首先利用模拟退火算法与遗传算法相结合的聚类算法(SAGA)构造视觉词典,然后利用视觉注意机制构造加权的视觉词直方图.该方法在不丢失图像的全局信息的情况下,还考虑到了图像中各个区域对图像分类的重要性.最后将图像的表示向量使用SVM训练和分类.实验表明,本方法能够提高图像分类的准确率.     

Classification of multiple partial discharge sources in dielectric insulation material using Cepstrum analysis–artificial neural network

In high‐voltage equipment insulation, multiple partial discharge (PD) sources may exist at the same time. Therefore, it is important to identify PDs from different PD sources under noisy condition in insulations, with the highest accuracy. Although many studies on classifying different PD types in insulation have been performed, some signal processing methods have not been used in the past for this application. Thus, in this work, Cepstrum analysis on PD signals combined with artificial neural network (ANN) is proposed to classify the PD types from different PD sources simultaneously under noisy condition. Measurement data from different sources of artificial PD signals were recorded from insulation materials. Feature extractions were performed on the recorded signals, including Cepstrum analysis, discrete wavelet transform, discrete Fourier transform, and wavelet packet transform for comparison between the different methods. The features extracted were used to train the ANN. To investigate the classification accuracy under noisy signals, the remaining data were corrupted with artificial noise. The noisy data were classified using the ANN, which had been trained by noise‐free PD signals. It is found that Cepstrum–ANN yields the highest classification accuracy for noisy PD signals than the other methods tested. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Modeling of power system dynamics using neural network

This paper examines the modeling of power system dynamics through the direct approximation of power system input/output mapping by neural network (NN) instead of through the conventional method by differential equations. The NN used is of multilayer type with delayed signals, which is suitable for dealing with time series data, and it is trained by the error back-propagation algorithm. Two sample systems are modeled by the NN; one is a numerical simulation model, and the other is an experimental system, both of which are a one-machine infinite-bus system. The input signal and the output signal to the NN are the reference value of the generator terminal voltage and the terminal voltage itself, respectively. The parameters in the learning algorithm are adjusted so that the training develops smoothly and converges in 30,000 times for the numerical simulation model. Thus obtained values of parameters are used for the identification of the experimental system, and the development of training is evaluated. NNs with different input structure are trained for both sample systems, and high approximation accuracy was demonstrated. The performance of the NN trained by the experimental system data is compared with that of the conventional differential equation model, and the possibility of the power system dynamics modeling by NN is shown. © 1998 Scripta Technica, Electr Eng Jpn, 125(2): 10–18, 1998