SINOCES将在青岛举行

日前获悉,一年一度的SINOCES(中国国际消费电子博览会)将于2008年7月10日～13日在青岛拉开帷幕.据组委会人士介绍,今年参展商招募情况十分乐观,松下、惠普、甲骨文、GE、杜比实验室、东芝、蓝光、海尔、TCL、海信、长虹、闪联等企业和机构均表示参加,并有望展出与互联网有关的最新消费电子产品;沃尔玛、百思买、家乐福,百思买、国美、苏宁等届时也将到会采购.     

以环保品质打造绿色厨房概念——海尔橱柜亮相“中国广州国际厨房家具和电器展览会”

首届《中国广州国际厨房家具和电器展览会》(简称:广州厨房展)将于2008年3月27日在广州中国进出口商品交易会琶洲展馆隆重登场。该展会是由德国科隆国际展览有限公司(Koelnmesse GmbH)     

电缆附件国产高压电缆走向市场的最大障碍

"电缆附件是个高利润率的产品,它的利润远高于电力电缆.现在正有企业逐渐挤入这个行业,但这一行业有门槛,技术难度大,投资高.国内基本还处于仿制阶段." 北京电力电缆公司副经理李华春告诉记者.在高压电缆这一利润较高产品引起业内众多媒体关注时,高压电缆附件似乎被很多人忽视了.而这一与电缆相比用量少的多的附件却已经影响到国内高压电缆的销售.……     

改革开放焕发STI英姿与时俱进再创STI辉煌

2005年夏天,花繁叶茂,STI迎来了45华诞. 45年,弹指一挥间,STI已由一株幼苗长成为一棵参天大树.为了STI的今天,我国德高望重的著名变压器专家汤明奇、周茂培、王文铮、周仲民、史习禁、朱英浩、于海年、贺以燕、陈道辉、张文相、李文海等白发染鬓的老一辈奉献了他们的一生心血,任重道远的中年人奉献了他们的青春年华,意气风发年轻人正在将他们的满腔热血奉献给一代又一代STI人开创的变压器事业.     

"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     

流水线结构RS译码器的超大规模集成电路设计

RS码已经广泛地应用于通信系统、数字电视和计算机存储系统中,用来提高数据可靠性.以数字电视广播(DVB)标准中定义的RS(204,188)译码器为例,详细介绍了改进的欧几里德(ME)算法及以此算法为基础的RS译码器的超大规模集成电路VLSI实现,采用了流水线结构,对译码器的各个模块进行了分析和建模.通过对流程的仿真和综合验证,发现这些模块能较好的满足设计要求,能纠正不大于8个的误码.     

假名牌市场的存在机理及其对策分析

假名牌市场的普遍存在,严重扰乱了我国社会主义市场经济秩序,影响了企业间的公平竞争,同时也反映出目前我国消费市场上,市场运行机制不完善,政府执法监督不严等诸多问题.结合对假名牌市场相关信息的调查数据,本文通过对假名牌市场的现状,以及给我国市场经济所带来的危害分析,从消费者,制造商、政府三方面探讨了假名牌市场存在的原因,再针对我国目前在打击假名牌方面存在的问题,参考国外的打假经验,提出了几点打假建议.     

核2、3级承压设备制造过程中的成型工艺评定——RCCM标准和ASME规范的应用体会

随着我国核电建设高潮的到来,越来越多的核级设备制造将实现国产化,目前,2个主要的核电技术标准RCCM和ASME也正得到或将得到广泛的应用.通过对这2个标准在核2、3级承压设备制造过程中的要求和成型工艺评定方面的比较,列出了2个标准要求的侧重点和按2个标准实施成型工艺评定的不同之处,以期给读者有益的启迪及参考.     

将并购进行到底国美控股三联商社

现场参与竞拍的有4位竞买人,每家缴纳1000万元保证金,牌号分别为11、18、77、88。上午九时,拍卖会正式开始。当每股竞价很快达到6元以上后,竞拍人只剩下77号与11号竞买方。此后,双方进行了一场堪称"疯狂"的竞价,拍卖师以不足一秒一价的速度提高着喊价。当每股竞拍价格升至16.8元时,代表三联集团的77号中年男子稍有迟疑,接了个电话。随后双方继续加价,竞拍价到19.9元时,77号竞拍人停下来打了一个电话,随后无奈地放弃了竞价……     

天正：登高望远 借势突围——访天正集团销售总公司总经理高天放

2004年对于天正集团而言是不同寻常的一年。不论是参与南京“三联动”企业改制成功收购南京市机电产业集团下属的耐特机电集团公司和容光达电子有限公司,还是在南京江宁区投资1.5亿元打造变压器产业基地,或者是收购内蒙古电线电缆厂,都引起了业内外人士的广泛关注。天正,正在发     

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