Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Intelligent monitor for typhoon in IoT system of smart city

The Journal of Supercomputing - Accidents often occur in the earth—typhoons, floods, earthquakes, traffic accidents and so on. Whether these accidents can be timely and effectively responded...     

Incorporating Actor-Critic in Monte Carlo tree search for symbolic regression

Neural Computing and Applications - Most traditional genetic programming methods that handle symbolic regression are random algorithms without memory and direction. They repeatedly search for the...     

基于Multipath的煤矿巷道三维模型构建方法

针对现有煤矿巷道三维模型存在灵活性和可移植性差、数据更新维护困难等问题,提出了一种基于Multipath的煤矿巷道三维模型构建方法。该方法通过定义点弧拓扑数据模型,将煤矿CAD格式数据转换为Geodatabase中的要素类,利用开发的ArcGIS Engine程序、通过插入节点坐标生成巷道三维模型,实现了煤矿巷道自动建模,有效减少了巷道三维建模的工作量。     

航空发动机变结构全局快速Terminal滑动模态控制方法研究

针对航空发动机这个具有时变不确定性的非线性系统,提出了一种新型变结构全局快速最终滑动模态控制(Variable Structure Global Fast Terminal Sliding Mode Control)的航空发动机控制方法;通过对利用VSGFTSMC理论设计航空发动机最终滑动模态控制器的方法进行了深入研究,设计了航空发动机变结构全局快速滑动模态控制器;仿真结果表明,所设计的控制器的控制效果良好,对外界干扰有很强的抑制能力,使被控系统在整个控制阶段都具有较强的鲁棒性.     

加油站库存信息采集与管理系统

为了提高石油公司对下属加油站库存的管理问题,设计了加油站库存信息采集与管理系统,系统分别由油罐液位仪、加油站管理主机和上级石油运营公司三级组成;油罐液位仪负责采集加油站各油罐内的油位并通过CAN总线发送给管理主机;管理主机接收来自各油罐监液位的数据,经过处理、统计分析、显示并存储到数据库ACCESS2003里;石油运营公司与加油站主机通过TCP/IP网络连接进行数据交互,获取各加油站的库存和销售速度等信息;经过对某加油站内8个椭圆型储油罐进行实验,得出了各油罐内传感器的输出电压、液位高度和剩余油量等数据,并且根据销售油速预测出了断油时间,能够达到及时油品配送的目的,避免断油事故的发生。     

基于FPGA的MEMS陀螺仪SAR150实时数据采集系统设计

针对传统MEMS陀螺仪输出信息为模拟量且易受干扰、噪声较大的问题,结合SAR150陀螺仪可以直接输出数字信号的特点,提出了一种新型的数字陀螺仪实时数据采集系统;该系统以FPGA为主控制器,通过SPI接口完成对MEMS陀螺仪SAR150的控制与数据的传输,实现了对陀螺仪输出信号的采集、实时传输及存储;通过将所研制的数据采集系统应用于SAR150陀螺仪测量角速率实验,并对测得数据进行误差分析,实验结果表明:数据采集系统采集输出的角速率值与理论值的绝对误差标准差均小于0.2°/s;且该系统的可靠性高、实时性好,为后续的陀螺仪测试及标定奠定了基础,有一定的工程应用价值。     

STM32F207和DM9161A的以太网实现方案

电力抄表系统常通过网络采集和传输电网中的谐波等信息。本文提出了一种适合电力系统的网络设计方案。在STM32F207和DM9161A为核心的硬件平台上,完成了LwIP协议栈的移植,实现了远程终端和上位机通信。使电力系统更具实时性与交互性,并保证了通信的可靠性。     

Example-based automatic music-driven conventional dance motion synthesis

We introduce a novel method for synthesizing dance motions that follow the emotions and contents of a piece of music. Our method employs a learning-based approach to model the music to motion mapping relationship embodied in example dance motions along with those motions' accompanying background music. A key step in our method is to train a music to motion matching quality rating function through learning the music to motion mapping relationship exhibited in synchronized music and dance motion data, which were captured from professional human dance performance. To generate an optimal sequence of dance motion segments to match with a piece of music, we introduce a constraint-based dynamic programming procedure. This procedure considers both music to motion matching quality and visual smoothness of a resultant dance motion sequence. We also introduce a two-way evaluation strategy, coupled with a GPU-based implementation, through which we can execute the dynamic programming process in parallel, resulting in significant speedup. To evaluate the effectiveness of our method, we quantitatively compare the dance motions synthesized by our method with motion synthesis results by several peer methods using the motions captured from professional human dancers' performance as the gold standard. We also conducted several medium-scale user studies to explore how perceptually our dance motion synthesis method can outperform existing methods in synthesizing dance motions to match with a piece of music. These user studies produced very positive results on our music-driven dance motion synthesis experiments for several Asian dance genres, confirming the advantages of our method.