Helmholtz共振腔气动噪声数值仿真

为探究出一套完整、准确的气动噪声仿真方法,用FLUENT和Actran仿真Helmholtz共振腔旁接管道系统模型.针对流场仿真,采用六面体网格建模,分析选择合适的网格密度,明确网格及边界条件的影响,以获得准确的声源信息;运用Lighthill声类比方法对声场进行仿真,采用数值计算、传声损失仿真和气动噪声仿真计算等3种方法提取管道内部场点声压级频谱曲线,分析曲线峰值频率特征,包括共振频率分析和声模态分析等.采用CFD软件与声学仿真软件相结合的方法,可以有效进行流场和声场的仿真.     

中医方剂与图集的转换方法

中药复方数据挖掘研究是将我国丰富的中药信息资源和现代最新信息技术相结合的重要内容,它意味着基于中医理论,利用知识发现技术,对传统中药新药、中医组方理论及规律、中药作用机制、有效成分构效关系等多个方面进行全面、系统的研究。图作为一种经典的数据结构,被广泛用于复杂数据结构分析与建模。文章探讨了图建模技术并实现了中医方剂与图集的转换。     

基于APCNN和BiGRU-Att的单词DGA域名检测方法

为了提高对基于单词的域名生成算法（domain generation algorithm,DGA）生成的恶意域名的检测准确率,提出了一种结合改进的并行卷积神经网络（APCNN）和融合简化注意力机制的双向门控循环单元（BiGRU-Att）的网络模型,该模型能充分学习单词特征、单词之间的组合关系和关键字符信息。实验结果表明,相比Bilbo和CL模型,APCNN-BiGRU-Att模型的分类准确率和F1值更高,表明该模型具有更好的检测效果、多分类效果和稳定性。     

基于智能垃圾桶的垃圾分类动态收运路径优化问题研究

针对城市生活垃圾分类收运过程中存在的环境二次污染和垃圾产生量不确定性等问题,提出了一种基于智能垃圾桶的动态收运车辆路径优化方法。建立以最小化碳排放成本、燃油消耗成本、固定成本和车辆延迟到达惩罚成本为目标的动态车辆路径优化模型。采用滚动时域的方式将动态问题转换为一系列静态问题,并设计两阶段算法进行求解。首先采用粒子群算法对收运车辆路径进行规划,而后在每个时域末,综合考虑待清运垃圾桶的位置和垃圾量、垃圾收运车辆的位置和装载量以动态调整现有车辆路径。研究结果表明,相较于传统的静态收运方案,动态垃圾收运方案能够在降低车辆运输成本和碳排放成本的同时,显著降低由于清运不及时造成的环境二次污染的风险。     

基于自适应哈夫曼编码的密文可逆信息隐藏算法

随着云存储和隐私保护的发展,密文域可逆信息隐藏作为一种可以在密文中嵌入秘密信息,保证嵌入后的信息可以无错误提取,并能无损恢复原始明文图像的技术,越来越受到人们的关注.本文提出了一种基于自适应哈夫曼编码的密文域可逆信息隐藏算法,对不同的图像采用不同的哈夫曼码字编码腾出空间来嵌入秘密信息.首先利用自然图像相邻像素间的相关性对原始明文图像进行像素值预测,从最高有效位到最低有效位,对原始像素值和预测像素值的相同比特位进行自适应的哈夫曼编码标记.然后,利用流密码对原始明文图像进行加密.最后在腾出的空间,通过位替换来自适应的嵌入秘密信息.由于哈夫曼编码和解码的可逆性,合法接收者可以对原始明文图像和秘密信息实现分离的无损恢复和提取.实验结果表明,与现有的几种方法相比,本文提出的方法具有更好的安全性和更高的嵌入率,在BOSSBase、BOWS-2和UCID三个数据集上的平均嵌入率比MPHC算法分别提高了0.09bpp、0.062 bpp和0.06bpp,在最佳情况下比MPHC算法能分别高出0.958 bpp、0.797 bpp和0.320 bpp,最差情况下的嵌入率比MPHC算法也分别高出了 0.01 bpp、0.039 bpp和0.061 bpp.     

Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods.     

基于等级保护的网络改造方法及异常情况分析与处理

本文介绍了在电力企业信息网中基于等级保护的网络安全防护改造方法。在具体案例中,针对网络改造过程中遇到的网络异常情况,技术人员通过逐一分析、排查,最终恢复正常网络,达到企业信息网络二级域改造的目的。网络安全防护改造方法以及异常情况分析排查案例对同类型企业在网络改造及异常排查工作中具有一定借鉴和指导意义。     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

基于CMOS模拟开关实现平衡混频器

详细介绍了有源单平衡混频器的电路组成,分析了有源平衡混频器的工作原理。基于CMOS模拟开关设计实现了一种(低功耗、高线性度的)开关平衡混频器,最后对混频器的指标进行了测量和分析。测试结果表明(在3.3V电源电压下,消耗电流小于10mA,射频输入信号)在60～110MHz频带范围内,(本振信号+10dBm时)插入损耗小于7dB,波动小于1dB,输入P1dB压缩点大于13dBm。     

用Word邮件合并功能套打固定格式的毕业证书

邮件合并是Word提供的一种非常重要的实用功能,如能巧妙应用将大大减轻工作量。本文通过毕业证书的套打,根据Word邮件合并功能和Excel函数域功能,以解决问题的思路为顺序,论述了毕业证书套打设计工作的方法和步骤,详细介绍了邮件合并的使用,对实现批量打印非常有用。