基带芯片 智能手机不能没有你

我们在买手机时总是重点关注它的CPU和GPU,但又有多少人晓得基带芯片的重要性?前不久曾传出德州仪器(TI)有意退出智能移动终端处理器市场的消息。虽然随后TI又表示OMAP5系列处理器还是会如期面世,但无论如何,德州仪器确实面临着市场份额减少、需求萎缩的问题,而这与其缺乏完整的解决方案有着密切的关联。其中最核心的问题是TI缺乏3G/4G网络基带芯片,因此越来越多的智能移动终端厂商更为青睐有着完整解决方案的高通公司。那么,基带芯片的影响真有那么大吗?     

襄阳市"市域乡镇打捆模式"污水治理案例分析及总结

襄阳市在乡镇污水治理方面加大投入,创新实施"市域乡镇打捆模式",实行"四个统一",在厂网一体化建设、厂网智慧化运维等方面摸索出了优势经验.对襄阳"市域乡镇打捆模式"污水治理案例进行分析,总结出一条乡镇污水治理的"襄阳路径".     

公共建筑用水现状分析与节水策略研究

近年来我国公共建筑虽已开始采取节约用水的措施,但仍存在人均月用水单耗超定额现象,以及因超压出流、管网漏损等引起的水资源浪费问题.公共建筑节水活动的开展和节水技术的应用有待进一步加强.基于此现状,分别从供水系统、用水系统,以及设计、施工、运行维护阶段,分析公共建筑节水主要节点.通过对公共建筑用水问题和节水潜力的分析,从技术、管理、行为方面提出公共建筑节水技术体系.     

电力工程中的地质灾害危险性评估

我国幅员辽阔,地质环境条件多样且复杂,导致了地质灾害发育强烈,种类繁多、危害较大。地质灾害与工程建设活动密切相关,既相互作用,相互依存,又相互影响[1]。电力工程项目遍布全国,特别是输电线路大多为人烟稀少的山区分布,线路长,跨越的地质地貌区域复杂,存在的地质灾害多种多样,或多或少面临着各种地质灾害的严重威胁[2]。通过电力工程地质灾害危险性评估工作的现状及评估工作流程的介绍,进一步分析电力工程地质灾害的危险性评估工作中存在的问题并提出了相应的看法。     

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.