Micro-expression recognition algorithm based on the combination of spatial and temporal domains

Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN) to extract spatial features of micro-expressions,and long short-term memory network(LSTM) to extract time domain features.CNN and LSTM are combined as the basis of micro-expression recognition.In many CNN structures,the visual geometry group(VGG) using a small convolution kernel is finally selected as the pre-network through comparison.Due to the difficulty of deep learning training and over-fitting,the dropout method and batch normalization method are used to solve the problem in the VGG network.Two data sets CASME and CASME II are used for test comparison,in order to solve the problem of insufficient data sets,randomly determine the starting frame,and a fixedlength frame sequence is used as the standard,and repeatedly read all sample frames of the entire data set to achieve trayersal and data amplification.Finallv.a hieh recognition rate of 67.48% is achieved.     

A New Method of Portfolio Optimization Under Cumulative Prospect Theory

In this paper,the portfolio selection problem under Cumulative Prospect Theory(CPT) is investigated and a model of portfolio optimization is presented.This model is solved by coupling scenario generation techniques with a genetic algorithm.Moreover,an Adaptive Real-Coded Genetic Algorithm(ARCGA) is developed to find the optimal solution for the proposed model.Computational results show that the proposed method solves the portfolio selection model and that ARCGA is an effective and stable algorithm.We compare the portfolio choices of CPT investors based on various bootstrap techniques for scenario generation and empirically examine the effect of reference points on investment behavior.     

BotGuard: Lightweight real-time botnet detection in software defined networks

The distributed detection of botnets may induce heavy computation and communication costs to network devices. Each device in related scheme only has a regional view of Internet, so it is hard to detect botnet comprehensively. In this paper, we propose a lightweight real-time botnet detection framework called Bot-Guard, which uses the global landscape and flexible configurability of software defined network (SDN) to identify botnets promptly. SDN, as a new network framework, can make centralized control in botnet detection, but there are still some challenges in such detections. We give a convex lens imaging graph (CLI-graph) to depict the topology characteristics of botnet, which allows SDN controller to locate attacks separately and mitigate the burden of network devices. The theoretical and experimental results prove that our scheme is capable of timely botnet detecting in SDNs with the accuracy higher than 90% and the delay less than 56 ms.     

超声雾化液体除湿系统对室内空气品质的影响

研究了超声雾化液体除湿空调系统对室内空气品质的影响,得到室内空气中氯化锂质量浓度的安全阈值为70μg/m~3.通过实验分析了入口溶液中氯化锂的质量分数(w_i)、除雾器孔隙率等对空气带液量的影响.当w_i由31.5%上升至38.8%时,空气带液量也随之上升,最高为26.93μg/m~3.除雾器的孔隙率显著影响空气带液量,应使用孔隙率低于93.5%的丝网除雾器.在最不利工况下,该系统空气带液量未超过安全阈值,表明对室内空气品质没有影响.研究成果对超声雾化液体除湿系统的推广有一定指导意义.     

基于C++的高效内存池的设计与实现

为了高效、安全地利用计算机内存资源,在大型的软件设计中,往往要进行大量的内存分配与回收操作,为此,C++专门提供了malloc等相关函数进行操作,这些函数能够满足一般的使用,但由于它们调用了操作系统API,所以实际使用时会在操作系统中产生大量的内存碎片,让内存分配成为效率瓶颈,从而降低系统性能.基于此,通过对循环首次适应算法进行改进,设计并实现了基于C++的高效内存池,大幅提升了内存分配与回收的效率.同时,还为内存池编写了相关的分配子,使其能与C++标准库无缝对接,提供了若干具有垃圾回收功能的智能指针,提高了内存管理与程序运行的效率.     

油藏流场定量表征方法及应用

复杂断块油藏经过长期注水开发,导致油藏流场分布愈加复杂,逐渐形成优势流场和弱势流场。优势流场区无效注水循环严重,弱势流场区注水却难以波及,油藏开发效果差,开发调整困难。为此,以黄骅拗陷南部的沧东凹陷枣南孔一段枣Z断块为例,基于真实油藏建立概念模型,开展了复杂断块高含水油藏流场定量化研究。根据油藏静、动态参数对油藏流场的影响程度,优选出渗透率、地层压力梯度、含油饱和度、面通量4个参数作为油藏流场的主要表征参数。在此基础上,基于模糊数学理论建立油藏流场强度计算模型,从而确定了目标油藏的不同类型流场强度划分界限,并针对不同流场类型制定了流场调整技术对策。目标油藏的流场调整实践结果表明,应用油藏流场定量表征方法能有效描述高含水期油藏流场特征,有利于指导油藏层系组合调整、注采井网重构,进而改善油藏注水开发效果。     

分离式微通道热管低风量下换热性能模拟研究

为了降低数据中心的运行能耗,分离式热管冷却系统会根据实际负载降低风扇转速,造成换热性能的衰减. 为了对低风量和低负载下分离式微通道热管的换热性能进行研究和分析,建立了适用于预测低风量下分离式微通道热管换热性能的一维稳态模型,并与试验结果对比,验证了模型的准确性,其最大预测平均偏差为6.3%. 利用该模型研究了运行参数对分离式微通道热管的换热性能和数据中心热安全的影响. 不同风量下,服务器排风温度从27 ℃上升至39 ℃时,系统换热量均有超过60%的提升;冷冻水供水温度从6 ℃提升至18 ℃会导致系统换热量最高下降41.8%. 在200～1 400 m3/h风量下,增加蒸发器和冷凝器的高度差可有效提升系统换热量和制冷剂质量流量,风量越大,换热量增长率越高. 但在相同风量下,随着高度差的逐渐增大,换热量增长率逐渐降低. 研究结果对分离式微通道热管在数据中心的设计优化和节能运行有一定促进作用.     

A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving

Railway transportation plays an important role in modern society. As China's massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters(e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging – REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.     

基于星座图统计的盲载波同步算法

卫星通信信号中90%以上采用相位或幅相联合调制方案,包括多进制相位键控（multiple phase shift keying,MPSK）,多进制正交幅度调制（multiple quadrature amplitude modulation,MQAM）,多进制幅相键控（multiple amplitude phase shift keying,MAPSK）等,此外短波通信、移动通信中MPSK也是最常用的信号类型之一。载波同步是信号解调中十分关键与核心的部分。针对非合作接收条件下幅相调制类信号星座图分布特性进行了深入研究,提出了一种基于星座图密度统计的载波同步算法,实现了一定信噪比条件下盲频偏消除与星座图恢复,此方案不需要预先进行调制识别,对数据量要求不高,复杂度低,抗噪性能好,可广泛应用于常规信号以及未知、猝发信号解调。仿真表明,在接近或高于解调信噪比的条件下,该方法频偏估计性能接近克拉美罗限,可应用于工程实践。     

论消费动机在室内设计中的类型及影响

通过对室内设计中的消费动机进行研究,可以更加理性与系统地将其作为室内设计的依据之一。同时,室内设计师只有深入系统地了解了有关室内设计中消费者的消费动机,才能在设计中有所侧重,从而做到有的放矢、胸中有数。