Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

Binaural Speech Separation Algorithm Based on Long and Short Time Memory Networks

Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System

In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other

Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus

A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.

Portable Dynamic Positioning Control System on A Barge in Short-Crested Waves Using the Neural Network Algorithm

This paper develops a nonlinear mathematical model to simulate the dynamic motion behavior of the barge equipped with the portable outboard Dynamic Positioning （DP） system in short-crested waves. The self-tuning Proportional- Derivative （PD） controller based on the neural network algorithm is applied to control the thrusters for optimal adjustment of the barge position in waves. In addition to the wave, the current, the wind and the nonlinear drift force are also considered in the calculations. The time domain simulations for the six-degree-of-freedom motions of the barge with the DP system are solved by the 4th order Runge-Kutta method which can compromise the efficiency and the accuracy of the simulations. The technique of the portable alternative DP system developed here can serve as a practical tool to assist those ships without being equipped with the DP facility while the dynamic positioning missions are needed.

Short-Term Load Forecasting Using Radial Basis Function Neural Network

An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.

Short-term load forecasting based on fuzzy neural network

The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory.

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

基于改进LSTM的数码雷管模组印刷质量预测

由于数码雷管模组印刷过程中生产工艺复杂、强时序性等特点,其质量的精准预测已成为提高产品质量管理水平的关键。基于此提出一种改进长短期记忆(long short-term memory,LSTM)网络的数码雷管模组印刷质量预测模型。首先根据数码雷管模组印刷过程提炼机器运行参数、环境参数与检测参数作为印刷产品质量的原始特征,并对关键检测参数进行时序特征重构以增强特征表达能力;其次基于改进的LSTM网络建立数码雷管模组印刷特征提取框架,采用卷积神经网络提取空间特征避免LSTM挖掘高维印刷特征时隐含关系的不足,通过全局注意力机制自适应学习不同时刻印刷特征对印刷产品质量的贡献度,为LSTM提取的深层时序特征分配不同权值;最后以深层特征作为输入,通过全连接网络实现数码雷管模组印刷产品的质量预测。实验结果表明,相较于BP神经网络、门控循环单元网络、LSTM等预测方法,改进的LSTM网络有效提高了数码雷管模组印刷产品质量的预测精度。

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

基于IWOA-LSTM算法的预应力钢筋混凝土梁损伤识别

为准确识别桥梁结构的损伤程度,制作了桥梁的关键构件——预应力钢筋混凝土梁,进行三点弯曲加载试验.收集了损伤破坏全过程的声发射(AE)信号,通过AE信号参数分析,将梁的损伤破坏过程划分为4个典型阶段.构建了长短时记忆神经网络(LSTM)模型,根据经验设置LSTM模型的超参数容易导致网络陷入局部最优而影响了分类结果,提出采用Sine混沌映射和自适应权重来改进鲸鱼优化算法(WOA),对LSTM进行超参数寻优.设计了IWOA-LSTM算法模型,训练识别试验梁各损伤阶段的AE信号特征参数.定型网络结构,并识别同种工况下其他梁的AE信号.结果表明:IWOA-LSTM算法模型识别准确率均超过或接近92%,相较于普通LSTM模型,IWOA-LSTM模型识别准确率提高了约7%.

拟南芥根的辐射形态相关基因SHORT-ROOT研究进展

从模式植物拟南芥中克隆得到的SHORT-ROOT基因(SHR)被证明参与根部形态建成途径。目前已知SHR是与根辐射形态直接相关的重要调控因子,同时也参与维持根尖分生组织的活性。SHR既作为转录因子启动下游基因的表达,又作为短程信号调节根的发育。本文综述了SHR相关研究进展,并展望其研究前景。

基于多模态唇部状态识别的语音导航抗干扰系统

针对现有车载语音导航设备易受到车内外噪声干扰、无法准确判定声音信号来源的问题,提出了一种基于唇部状态识别的语音导航抗干扰系统.通过摄像头实时识别驾驶员唇部状态,准确判定驾驶员声音信号的起止时间端点,进而控制语音导航输入信号开启和关闭,增强驾驶员对语音导航的控制权限,减少车内外的噪声干扰.为保证唇部状态识别的准确性和鲁棒性,提出了一种基于关键点-外观短时特征融合的多模态唇部状态识别网络,进行了关键点短时特征有效性试验、多模态特征融合的唇部状态识别消融试验、实验室模拟环境和真实车载环境下的语音导航抗干扰试验.结果表明,文中提出的关键点短时特征算子可增强唇部状态变化表征能力14%以上,关键点-外观特征融合的唇部状态识别网络通过特征互补提升识别准确性8.98%以上.基于该网络的语音导航抗干扰系统准确性高(92.6%)、实时性好(检测速度为35帧/s);在驾驶员左、右侧面超过70°的大幅度头部姿态变化下,能有效减少车内外噪声对导航语音控制的干扰,表现出较高的鲁棒性.

短视频过度使用及注意力不集中与初中生拒学行为的关系

目的:探讨初中生短视频过度使用、注意力不集中与拒学行为的显要表现及内在关联。方法:选取1 106名在校初中生进行调查,采用短视频APP过度使用量表、注意力缺陷多动障碍评定量表的注意力不集中维度、拒绝上学行为问卷评估短视频过度使用程度、注意力不集中、拒学行为。采用R语言网络分析工具包评估单个网络的核心节点和共存网络的桥梁节点,比较不同性别和年级的网络结构差异。结果:短视频过度使用、注意力不集中、拒学行为网络的核心节点分别为“使用短视频会增强与他人的联系”(预期影响值=1.03)、“容易分心”(预期影响值=1.99)、“学校疏离”(预期影响值=0.83)。共存网络的桥梁节点为“违抗行为”(桥预期影响值=0.69)、“难以从事持续性的脑力活动”(桥预期影响值=0.47)。网络结构差异分析发现,初一、初二、初三学生共存网络的核心节点分别为“因使用短视频而停止做其他事”“违抗行为”“学习能力”。结论:初中生短视频过度使用、注意力不集中、拒学行为的显要表现分别为使用短视频会增强与他人的联系、容易分心、学校疏离;三者共存时最重要的影响因素为违抗行为、难以从事持续性的脑力活动。

冻融环境下玄武岩纤维增强泡沫混凝土的单轴压缩特性

为了研究密度、纤维掺量及冻融循环对玄武岩纤维增强泡沫混凝土单轴压缩特性的影响,借助X-CT技术以及Avizo软件对试件的孔隙构造进行了研究,对不同冻融环境下的多种密度等级(800和1000kg/m3)及纤维体积掺量(0%、0.15%、0.30%、0.45%)的玄武岩纤维增强泡沫混凝土进行了单轴压缩试验,分析了不同影响因素下玄武岩纤维增强泡沫混凝土的单轴压缩特性,对各因素与抗压强度的关系进行了灰关联度分析,并使用机器学习算法模型对抗压强度进行预测.结果表明:掺入玄武岩纤维能有效提高泡沫混凝土抵抗冻融的能力,体积掺入0.15%、0.30%、0.45%的试件冻融80次后强度损失率分别为75.2%、46.2%、37.8%.密度等级与抗压强度关联度最大,灰关联度为0.799;其次是纤维掺量,关联系数为0.723;灰狼算法优化下的长短期记忆网络模型对玄武岩纤维增强泡沫混凝土抗压强度的预测性能良好.

融合BiLSTM与CNN的推特黑灰产分类模型

当前推特等国外社交平台,已成为从事网络黑灰产犯罪不可或缺的工具,对推特上黑灰产账号进行发现、检测和分类对于打击网络犯罪、维护社会稳定具有重大意义。现有的推文分类模型双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)可以学习推文的上下文信息,却无法学习局部关键信息,卷积神经网络(convolution neural network,CNN)模型可以学习推文的局部关键信息,却无法学习推文的上下文信息。结合BiLSTM与CNN两种模型的优势,提出了BiLSTM-CNN推文分类模型,该模型将推文进行向量化后,输入BiLSTM模型学习推文的上下文信息,再在BiLSTM模型后引入CNN层,进行局部特征的提取,最后使用全连接层将经过池化的特征连接在一起,并应用softmax函数进行四分类。模型在自主构建的中文推特黑灰产推文数据集上进行实验,并使用TextCNN、TextRNN、TextRCNN三种分类模型作为对比实验,实验结果显示,所提的BiLSTM-CNN推文分类模型在对四类推文进行分类的宏准确率为98.32%,明显高于TextCNN、TextRNN和TextRCNN三种模型的准确率。