低压电力线载波通信技术研究与应用

在详细分析了低压电力线载波通信特点的基础上，系统地阐述了电力线载波通信技术的发展概况，并针对传统的电力线载波通信技术，扩频载波通信技术以及正交频分复用技术阐述了其性能特点以及适用性。同时，结合国内电力线通信技术的发展，阐述了电力线载波通信技术应用现状。文章最后阐述了低压电力线载波通信的国内外发展现状。并提出了电力线载波通信的应用前景。     

低压电力线载波通信中的抗干扰问题

讨论了低压电力线载波通信中干扰来源，提出了电力线载波通信与扩频通信相结合的一种新的抗干扰方案，论证了利用扩频技术实现电力线数据传输的可靠性和可行性。     

电力线扩频载波通信技术及其应用

分析了配电网电力线通信信道存在的高噪声、变化的低阻抗、高衰减等一般性问题,阐述了电力线扩频载波通信的基本原理及其抗时变衰减、抗随机干扰的优越性;从应用角度总结了配电网电力线扩频载波通信技术的国内外现状以及在配电网自动化、楼宇自动化以及公用通信领域的典型应用。     

低压电力线载波通信的应用与发展

低压电力线载波通信以其覆盖范围广、连接方便、应用潜力巨大的显著特点,而被日益关注,成为近年来研究的热点。在介绍低压电力线载波通信特征的基础上,分析了正交频分复用和扩频技术在低压电力线载波通信中的应用,探讨了低压电力线载波通信在远程抄表和接入Internet的应用。     

载波数字脉冲间隔调制电力线通信技术与收发器

提出了一种扩频载波或窄带载波数字脉冲间隔调制(C—DPIM)电力线载波通信系统。采用线性扫频扩频载波对DPIM符号进行编码，引入脉冲前导码作为传输数据块的同步信号以防止差错传播，井采用单片机实现了该系统。研制了电力线载波通信接口收发控制器，使其在至时变、高信号衰减特性并存在复杂干扰的电力线网络上实现理想的数字通信，巳成功地用于基于列车380V电力线载波通信的列车信息集中监控系统。     

城市路灯节能控制管理系统的应用

目前试用于松江沪松公路的城市路灯节能控制管理系统,是一套基于扩频电力载波通信及无线通信技术的城市路灯节能及管理的地理信息系统,通过计算机、扩频电力载波通信、数据传输等技术的运用,可以实现对发射功率范围内的路灯进行实时监控,从而提高城市路灯监控和管理水平,确保城市道路照明系统功能的正常发挥。     

扩频技术在电力线载波通信中的应用分析

简要分析了实现电力线扩展频谱载波通信系统的技术依据,包括对扩频通信和种方法的介绍和优化的电力线直扩系统技术特点的叙述,从而说明扩频技术能有效地应用于配电线等恶劣通道特性的载波通信中。     

低压载波自动抄表系统中通信方式的研究

介绍了低压载波通信模块的组成结构及实现方式,分析了扩频载波通信方式、窄带通信方式以及相对应的控制芯片特点,并从自动抄表系统的可靠性、抗干扰能力、经济性上达到实用标准的角度出发,阐述了采用窄带通信方式的观点     

基于SSCP200的自动抄表系统

简述了扩频载波通信的原理,从应用的角度介绍了一个基于SSCP200的自动抄表系统,实际中取得了较为满意的结果。     

电力线载波通信技术的发展与应用前景

介绍了电力线载波通信的发展、特点和应用前景,并针对其特点介绍了实现电力线载波通信的几种方案:QAM&TCM、OFDM和扩频,然后介绍了同步、自适应信道均衡、自动增益控制和信道编码等其他重要的相关技术,并简要说明了这些技术的实现方法.     

汽轮发电机转子绕组不稳定接地的寻找与消除方法

本文论述了怎样判定汽轮发电机励磁回路接地的区段以及将不稳定接地变成稳定接地和取护环前计算确定接地点具体位置的方法,并均以实例验证。     

新型H级绕组线在电机上应用的探讨

本文介绍了新型H级单玻HS(HF)薄膜绕包绕组线的性能特征和耐热老化寿命,绕组线在海洋石油开采驱动用直流电机和干式变压器上的应用,以及对绕组线广泛应用的可能性加以探讨。     

国产200MW发电机油膜振荡问题的分析

本文论述了油膜振荡的机理和特点,指出引起国产200MW发电机油膜振荡的主要原因是三油楔轴承稳定性裕度偏低和运行过程中5瓦标高变化引起的6瓦载荷下降,并指出用椭圆瓦替代三油楔瓦是解决这个问题的一条简单易行的途径。     

混流式水轮机转轮内部流场直接三维有限元计算

本文从变分原理出发导出了以势函数为未知数的混流式水轮机内部流场直接三维有限元求解方程及各边界方程,论述了求解过程,编写了程度,并给出了计算实例,结果表明,混流式水轮机内部流场直接三维有限元求解是可行的。     

用分离变量法求解水轮发电机端部三维磁场

本文以分离变量法对水轮发电机端区定子线棒附近的三维磁场进行求解,用所谓“气隙虚拟电流”代替凸极电机气隙的作用,较之汽轮发电机作了改进。本文还对边界条件作了合理的简化,使整个模型较好地反映实际情况。将TS854/156-40型水轮发电机的端部磁场实测值与计算值相比,结果表明,符合程度较好。     

轴流式水力机械S2流面杂交命题的变分原理与广义变分原理

本文建立了轴流式水力机械(如轴流式水轮机)S_2流面A、B、C三类杂交命题的变分原理与广义变分原理,旨在为水力机械内的三维流动分析、转轮设计和改型等提供理论依据。     

水轮机座环与导水机构流态研究的边界元法

本文用直接边界元法数值模拟水轮机座环和导水机构的双排环列叶栅内的不可压流体的流动。建立了双排叶栅绕流的边界元方程,把周期性边界条件和Kutta条件引入到方程中,实现了Kutta条件的非迭代计算的自动满足,节省了计算时间,计算结果与有关解析解及试验数据吻合较好。     

An Approach to Speech Emotion Classification Using k-NN and SVMs

The interaction between humans and machines has become an issue of concern in recent years. Besides facial ex-pressions or gestures, speech has been evidenced as one of the foremost promising modalities for automatic emotion recognition. Effective computing means to support HCI (Human-Computer Interaction) at a psychological level, al-lowing PCs to adjust their reactions as per human requirements. Therefore, the recognition of emotion is pivotal in High-level interactions. Each Emotion has distinctive properties that form us to recognize them. The acoustic signal produced for identical expression or sentence changes is essentially a direct result of biophysical changes, (for example, the stress instigated narrowing of the larynx) set off by emotions. This connection between acoustic cues and emotions made Speech Emotion Recognition one of the moving subjects of the emotive computing area. The most motivation behind a Speech Emotion Recognition algorithm is to observe the emotional condition of a speaker from recorded Speech signals. The results from the application of k-NN and OVA-SVM for MFCC features without and with a feature selection approach are presented in this research. The MFCC features from the audio signal were initially extracted to characterize the properties of emotional speech. Secondly, nine basic statistical measures were calculated from MFCC and 117-dimensional features were consequently obtained to train the classifiers for seven different classes (Anger, Happiness, Disgust, Fear, Sadness, Disgust, Boredom and Neutral) of emotions. Next, Classification was done in four steps. First, all the 117-features are classified using both classifiers. Second, the best classifier was found and then features were scaled to [-1, 1] and classified. In the third step, the with or without feature scaling which gives better performance was derived from the results of the second step and the classification was done for each of the basic sta-tistical measures separately. Finally, in the fourth step, the combination of statistical measures which gives better per-formance was derived using the forward feature selection method Experiments were carried out using k-NN with different k values and a linear OVA-based SVM classifier with different optimal values. Berlin emotional speech da-tabase for the German language was utilized for testing the planned methodology and recognition rates as high as 60% accomplished for the recognition of emotion from voice signal for the set of statistical measures (median, maximum, mean, Inter-quartile range, skewness). OVA-SVM performs better than k-NN and the use of the feature selection technique gives a high rate.     

Compressive sensing‐based adaptive sparse predistorter design for power amplifier linearization

Greedy algorithms in the compressive sensing theory have been formed the essential method for pruning power amplifier (PA) behavioral models and digital predistorters (DPDs). However, the inherent batch mode of these algorithms limits their application in adaptive digital predistortion framework. In this paper, a powerful subspace pursuit greedy scheme combined with stochastic gradient descent adaptive algorithm is proposed to design a class of adaptive sparse DPDs. According to the given sparsity level, the proposed approach can obtain the sparse terms of DPDs and extract the corresponding coefficients adaptively. Performance improvement of the proposed method is validated by simulation results on the adaptive DPD excited by 15‐MHz 3‐carrier Long‐Term Evolution signals and 50‐MHz 16 amplitude/phase‐shift keying signals. Meanwhile, measurement results on a Doherty PA excited by 30‐MHz 3‐carrier Long‐Term Evolution signals are also performed to verify the advantage of the proposed approach. Simulation and experimental results show that proposed algorithm can efficiently construct the adaptive sparse DPD models with only a small number of parameters; both nonlinear distortions and memory effects in the PA can be almost completely removed. A comparison with the nonsparsity aware DPD techniques and batch mode compressive sensing pruning techniques has been demonstrated that the proposed method exhibit faster convergence, improving tracking capabilities and reduced computational complexity.     

一种改进PWM控制半桥ZVS变换器研究

本文分析了一种半桥软开关直流变换器。与传统半桥电路相比,该电路增加了一个由辅助开关管和一个二级管组成的支路。采用一种改进的PWM控制方法。主开关管不仅工作在对称状态,而且能很好地实现软开关。辅助开关开关管在主开关管关闭期间实现ZVS和ZCS导通,辅助开关管不仅为主开关管实现ZVS创造了条件,而且大大减轻了变压器漏感和主开关管结电容之间的振荡。文中详细分析了该变换器的工作原理,仿真和试验研究表明,该变换器具有优良的性能。