Mid-long Term Optimal Dispatching Method of Hydro-thermal Power System Considering Scheduled Maintenance

在中长期水火发电调度中考虑检修计划的影响是目前中长期水火发电调度面临的难题。利用现代整数代数建模技术，建立发电计划和检修计划协调优化的多场景调度模型。在该模型中，鉴于设备检修计划的连续性，在预测场景树的基础上，将场景节点划分成不同的场景，通过节点和场景关联矩阵，实现多场景下设备检修模型的构建。同时，鉴于中长期调度计划中发电计划和检修计划对时段间隔要求的不同，分别设置电量相关节点和电力相关节点，实现中长期发电计划和检修计划的协调。上述模型是一个大规模混合整数线性规划（mixed integer linear programming，MILP）问题，采用商用MILP求解器进行求解。大规模实际水火电系统的实例分析结果表明，所提模型和方法是可行、有效的。

Design of Low-Power Data Logger of Deep Sea for Long-Term Field Observation

This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.

Design and Implementation of Long-Term Single-Lead ECG Monitor

Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which could acquire and store patient’s ECG data for 7 days continuously. This device is available for long-term wearing with a small volume. Also, it could detect user’s motion status with an acceleration sensor and supports Bluetooth 4.0 protocol. So it could be expanded to be a dynamic heart rate monitor and/or sleep quality monitor combined with smart phone. The device has huge potential of application for health care of human daily life.

Short-TermWind Power Prediction Based on Combinatorial Neural Networks

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

The Influence of Culture and Customs on International Business Communications

This paper discusses culture. Scholars have not been able to agree on a definition of culture but most include values and norms as components of their definition. Intercultural communication is vital that skill managers must possess in order to effectively do business in foreign countries. Hofstede＇s theory of national cultural dimensions is employed to provide a framework for defining intercultural communication. Subsequently, several countries are examined using Hofstede＇s scoring system for cultural dimensions. This is followed by a discussion of ways to achieve intercultural competence.

基于KCR-Informer的长期风电功率预测研究

准确的长期风电功率预测对电网系统稳定运行至关重要,传统预测方法在处理长序列预测时效果并不理想,近期研究表明Informer模型在长序列预测领域取得良好效果。然而,该模型在捕捉数据的局部特征以及处理网络层数堆叠问题上还有待改进。文章提出一种基于卡尔曼滤波器-卷积神经网络-残差网络-Informer(Kalman filter-convolutional neural network-residual network-informer,KCR-Informer)模型的长期风电功率预测方法,首先分析气象数据对风电功率的影响,使用卡尔曼滤波器对风电气象数据进行数据平滑处理,以减轻噪声对数据的影响,然后基于Informer模型建立风电功率预测模型,根据气象数据以及历史功率数据进行长期功率预测;在此基础上,引入卷积神经网络和残差连接模块,使模型能够更好的捕捉到局部特征,同时加快模型收敛,解决模型网络退化问题。算例的结果表明,与长短期记忆网络(long short-term memory,LSTM)算法、Transformer算法、Informer算法相比,文章方法在不同预测步长下的平均绝对误差(mean absolute error,MAE)降低5.7%~30%,均方误差(mean square error,MSE)降低8.3%~35%,长期风功率预测的精度得到提升,验证了模型的有效性。

Improved Method of Contention-Based Random Access in LTE System

Random access is the necessary process to establish the wireless link between the user equipment （UE） and network. The performance of the random access directly affects the performance of the network. In this work, we propose a method on the basis of the existing alternatives. In this method, we estimate the system load in advance to adjust the number of terminals. An access threshold is set to control the number of terminals which want to access the base station at an acceptable level. At the same time, we havean improvement on the existing power climbing strategy. We suppose that the power ramping is not always necessary for the re-access. And the selection ofpower ramping steps is studied in this paper. Simulations based on MATLAB are employed to evaluate the effectiveness of the proposed solution and to make comparisons with existing alternatives.

A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.

基于长短期记忆及自适应Kriging子集模拟优化的风电功率预测方法

风电功率的准确预测是目前践行各类监管和运营战略,发展智能电网和先进控制系统等重要技术的前提。提出一种基于长短期记忆及自适应Kriging子集模拟优化的风电功率预测方法。在风电场数据特征提取的基础上,以长短期记忆预测均方根误差为目标,超参数为设计变量,通过基于期望改善的深度耦合自适应Kriging子集模拟优化,进行超参数的高效优化设计,输出最优预测功率。最后,通过案例验证所提方法的预测性能。

基于Kriging和长短期记忆网络的风电功率预测方法

为提高风电功率预测的精确度,提出一种基于Kriging和长短期记忆网络的风电功率组合预测模型。首先,将风速、风向、空气密度、转速、偏航角和桨距角作为输入向量,并利用偏互信息理论对这些向量进行加权处理,建立基于Kriging的风电功率线性分量预测模型。然后,将预测出的线性分量加前述加权监测量作为输入,使用长短期记忆网络预测出功率的非线性分量。最后,将两者的预测结果相结合,得出风电功率的最终预测值。实例结果表明,该模型能够利用Kriging和长短期记忆网络的优势,预测性能指标得到提高。

基于DFT-S-OFDM传输方式的物理层双矩阵密钥加密算法

现有基于OFDM调制的物理层加密算法的安全性普遍依赖于较大的子载波个数,且不能抵抗明文密文攻击,当子载波个数比较少时,其密钥空间快速变小,安全性急剧下降,因此,很难适应资源受限终端的安全通信需求.针对这一共性问题,文中利用LTE上行链路采用的DFT-S-OFDM传输方式以及资源块划分方式,提出了一种基于双矩阵变换的物理层加密算法.该算法主要包含两个步骤:一是通过AES计数器模式控制产生两个对角密钥矩阵;二是通过密钥矩阵控制N点DFT变换前后的数据,使得密文、明文和密钥之间形成非线性方程组关系.通过这个加密过程,实现两个目的,一是利用DFT-S-OFDM传输方式的特点,实现对输入的明文和输出的密文分别加密的目的,保证算法具备抵抗明文密文攻击能力;二是实现明文、密文和密钥三者之间的非线性关系,保证算法的安全.算法设置了两种密钥工作模式,第一种为每加密N-1组明文就改变一次子密钥,第二种为每加密大于等于N组明文才改变一次子密钥.在无噪的理想情况和有噪的非理想情况下,详细分析了两种密钥工作模式的安全性.理论分析结果表明,在子载波数大于等于12的情况下,第一种密钥工作模式无论在理想条件还是有扰信道条件下,均可以抵抗穷举攻击和明文密文攻击,保证算法的安全性,第二种密钥工作模式只有在有扰信道条件下才可以抵抗穷举攻击和明文密文攻击,保证算法安全;在两种密钥工作模式下,算法均不会改变DFT-S-OFDM系统中的峰均功率比.分别仿真分析了加密前后系统的峰均比、误码率等参数的变化,仿真数据证实了理论分析所得的结论,表明算法对系统的峰均比、功率以及误码特性等固有性能影响较小,能够在子载波数比较小的情况下,很好的保证通信数据的安全性,满足资源受限终端的安全通信需求.

基于VMD-Informer-BiLSTM模型的超短期光伏功率预测

由于光伏出力的波动性和随机性给电网的规划和运行带来了挑战,因此提高光伏功率预测的精度对提升新能源系统的稳定性具有重要意义。为此提出了一种结合模态分解、多维特征建模、Informer、双向长短期神经网络(bidirectional long short-term memory network,BiLSTM)的超短期光伏组合预测模型。首先通过变分模态分解将光伏功率序列分解成不同频率的本征模态函数(intrinsic mode function,IMF),降低光伏功率信号的非平稳性与复杂度;随后使用离散小波变换提取天气因素中的细节分量,实现不同分解算法的优势互补,并用随机森林算法为每个IMF筛选冗余特征,然后将特征矩阵送入Informer进行建模,提取不同时间步中关键时刻的信息,提高对长时间序列的预测效率;最后为进一步提高模型预测精度,分析误差序列特性,利用BiLSTM进行误差校正。采用实际光伏数据进行算例分析,结果表明所提方法提高了超短期光伏功率预测精度。

基于Informer的长序列时间序列电力负荷预测

针对长时间序列电力负荷的预测精度低的问题,应用了基于Informer长时间序列模型的电力负荷预测方法.该方法通过Informer模型中的自注意力蒸馏机制,使得每层的解码器都将输入序列的长度缩短一半,从而极大地节约了Encoder内存开销,并在编码器结构中使用生成式结构,使得预测解码时间极大的缩短;以澳大利亚的电力负荷数据作为测试用例,并与长短时记忆神经网络(long-short term memory,LSTM)和卷积神经网络(convolutional neural network,CNN)模型方法进行对比,结果表明,Informer模型的预测精度更高,Pearson相关系数可以达到91.30%,有效提高了负荷预测精度.

UEs Power Reduction Evolution with Adaptive Mechanism over LTE Wireless Networks

At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).

结合太阳辐射量计算与CNN-LSTM组合的光伏功率预测方法研究

为了提高模型预测性能,提出一种综合太阳辐射模型及深度学习的光伏功率预测模型。首先,利用太阳辐射机理建立太阳辐射模型(SRM),估算出水平面上总辐射值,再由斜面辐照度转换方法计算出光伏组件所接收的斜面辐射值。其次,通过皮尔逊相关分析法筛选出对光伏功率影响较大的主要因素,将斜面辐射计算值及主要影响因素作为输入,采用卷积神经网络(CNN)和长短期记忆网络(LSTM)建立光伏功率SRM-CNN-LSTM预测模型。分别利用春夏秋冬四季典型日的数据开展对比实验,结果表明:与几种其他方法相比,该文方法具有更好的预测效果。

新型电力系统长时储能技术综述

随着未来我国电力系统中可再生能源渗透率的提高,系统稳定性将应对更加严峻的挑战。长时储能技术在电力系统中起到平衡电网需求、提高电网稳定性、促进可再生能源的消纳、推动绿色低碳发展等作用。长时储能在系统的电源侧、电网侧、负荷侧都有着广泛的应用,对我国新型电力系统的发展意义重大。首先,介绍了目前新型电力系统的特点以及发展趋势,分析长时储能技术在新型电力系统中所具备的支撑作用;其次,分别总结了压缩空气储能、锂离子电池储能、液流电池储能、熔盐储能、氢储能5种长时储能的技术原理及路线、现有的示范应用项目及未来发展趋势,分析了各种长时储能技术的优势和不足;最后,对新型电力系统中长时储能技术未来的应用前景进行了展望。

Childhood Leukemia Not Linked with ELF Magnetic Fields

The discussion whether extremely low frequency (ELF) magnetic fields (MF) are causally linked with childhood leukemia is ongoing for almost four decades. Results of epidemiologic studies have indicated such an association might exist and led to IARC’s classification of ELF MF as possibly carcinogenic (class 2B). Although in the meanwhile many epidemiologic studies and meta-analyses of selected studies are available, this did not change the situation. By a new approach of pooling all epidemiologic data, this paper shows that it is possible to come to a convincing conclusion which explains controversial results and reports dose-response relationship, and provides answers to striking facts such as that epidemiologic results on childhood leukemia are independent from field source or exposure metric of whatever kind with no specific favorite. The analysis revealed that the assumption of a causal link between ELF MF exposure and childhood leukemia is no longer plausible and hence that ELF MF’s classification as possibly carcinogenic needs revision.

基于CEEMD-SE的CNN&LSTM-GRU短期风电功率预测

为进一步提升短期风电功率的预测精度,提出了一种基于互补集合经验模态分解-样本熵(complementary ensemble empirical mode decomposition-sample entropy,CEEMD-SE)的卷积神经网络(convolutional neural network,CNN)和长短期记忆-门控循环单元(longshorttermmemory-gatedrecurrentunit,LSTM-GRU)的短期风电功率预测模型。首先,利用互补集合经验模态分解将原始风电功率序列分解为若干本征模态函数(intrinsic mode function,IMF)分量和一个残差(residual,RES)分量,利用样本熵算法将相近的分量进行重构;其次,搭建卷积神经网络和长短期记忆网络的并行网络结构,提取数据的局部特征和时序特征,并将特征融合后输入门控循环单元网络中进行学习预测;最后,通过算例进行验证,结果表明采用该模型后预测精度得到了有效提升,其均方根误差降低了15.06%、平均绝对误差降低了15.22%、决定系数提高了1.91%。

基于聚类的HPO-BILSTM光伏功率短期预测

考虑到光伏发电功率在不同天气类型下的波动性和不确定性,对此提出一种基于模糊C均值聚类算法(FCM)和猎食者优化算法(HPO)优化双向长短期记忆网络(BILSTM)的光伏发电短期功率预测模型。首先对光伏发电数据进行处理和分析,再进行主成分分析(PCA)降维和FCM聚类算法将数据按天气类型分为阴、晴、雨;最后通过HPO筛选得出BILSTM神经网络的最佳超参数,避免因超参数设置不佳对实验带来的影响,进一步提高实验的准确性和模型的泛化能力。最后通过预测和对比实验进行分析,验证所提方法的优越性。

基于VMD-BiLSTM-WOA的短期风电功率预测

风力发电对于解决全球能源短缺问题有重要意义,准确预测风电功率有助于风电并网的合理调度和可靠的电网运行.文章提出了一种基于变分模态分解(Variational Mode Decomposition, VMD)、双向长短期记忆网络(Bidirectional Long Short-term Memory Network, BiLSTM)以及鲸鱼优化算法(Whale Optimization Algorithm, WOA)的混合深度学习模型,以用于短期风电功率预测.首先,VMD将原始风电功率分解为多个子模态,有效减少了序列的波动性;然后对每个子模态分别建立BiLSTM模型,使用WOA对BiLSTM中的参数进行优化,以提高混合模型的效率和预测性能;最后将各个子模型的结果叠加得到最终预测结果.在实验中通过建立不同的比较模型来说明改进策略的有效性和优越性,结果表明所提的混合模型在风电功率预测中具有较高的预测精度.