智能建筑弱电技术的研究

通过对智能建筑弱电技术中的电线/电缆导管的敷设、电气照明安装、工程中的冲突以及弱电系统的施工计划等问题的研究,总结了智能建筑弱电技术中的综合布线系统、计算机网络系统、子系统间接口方面、母线的安装、弱电设备功能以及配电装置等技术问题的解决要点。所述内容对智能建筑整体运行和安全管理以及电气施工安装的质量监控等具有较强的参考价值。     

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。     

大坝安全监测智能感知与智慧管理技术研究及应用

水库大坝安全监测是工程安全的重要保障措施,亟需结合新一代信息技术,提升大坝安全监测能力。系统总结了长江科学院近年来在大坝安全监测智能感知与智慧管理技术方面的研究及应用工作,通过研发系列化智能传感器、智能采集单元和物联网感知平台,建设统一的大坝安全监测数据资源池,开发通用化安全监测云服务系统,搭建专业数据挖掘平台和综合可视化应用,实现了大坝安全监测数据感知、传输、管理、分析及展示全链路应用,形成了大坝安全监测全生命周期智慧解决方案。研究成果已在乌东德、溪洛渡、向家坝、大藤峡等100余项水利水电工程中成功应用,为保障工程建设及运行安全发挥了重要的支撑作用,具有很好的推广应用前景。     

Poetry I: Teaching verse with CAI

The teaching of literature through CAI raises problems of both a linguistic and instructional nature; student involvement and creativity in studying literature, and especially poetry, is difficult to build into a computer-based lesson. We have confronted these difficulties in the lessonPoetry I, which introduces undergraduates to basic concepts of poetic verse in a design using screen display, speech synthesis, and verse processing to maximize interactivity and student involvement.The lesson contains instructional modules which include the student's composition of a limerick. Computational processing of the limerick's text enables the program to offer guidance as the student composes and revises the verse and gains first-hand experience with metrical language. The significant problems inherent in processing verse are addressed by adapting Digital Equipment Corporation's DECTalk speech synthesizer as an engine converting natural language verse text into accessible strings of phonemic symbols. Although the verse processor cannot scan verse, it can interpret DECTalk's symbols reliably enough to successfully stimulate students' thinking about their verse compositions.Preliminary responses to the program have been favorable and demonstrate its effectiveness in involving students more deeply in learning about poetic verse. This suggests that the techniques of verse processing prototyped inPoetry I might be usefully extended to other types of verse and levels of study.W. Webster Newbold is an Assistant Professor of English at Ball State University, Muncie, Indiana, USA, who has recently served as English Department Computer Coordinator. His research interests include computer-assisted instruction and computer-based composition.Herbert F. W Stahlke is a Professor of English, specializing in linguistics, and currently serves as Associate Director for Academic Computing, Ball State University Computing Services. His research interests include computer-assisted instruction, natural language processing, and lexical databases.     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

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.     

融合知识图谱的网络安全违法行为识别系统

《网络安全法》是我国第一部关于网络安全的综合立法,与大众的互联网生活息息相关.因此,一款面向大众的《网络安全法》智能违法行为识别系统有助于规范互联网行为.然而,现有智能违法行为识别系统构建方法难以适应《网络安全法》,这是由于:首先,现有方法需要专业司法语言进行交互,不适应普通大众的语言体系.其次,现有方法需要大量的案例训练模型,不适应案例匮乏的《网络安全法》.针对这些问题,本文提出了一个面向《网络安全法》的智能违法行为识别系统.该系统主要利用知识图谱技术解决上述问题,在构建网络安全法知识图谱的基础上,通过将普通用户的自然语言与知识图谱中的违法事件实体和违法主体实体进行实体链接的方式获得更强的特征,提高违法行为识别系统在训练集较为匮乏的条件下的准确度.通过在真实数据集上的实验,表明了提出的系统的准确度有明显提高.     

智能力敏传感器的研制

针对传统的差压密度计和压力式液位计分别存在可靠性不高、不适用于介质密度经常变化的场合的问题,设计了一种基于Freescale MC9S08AW60微控制器的智能力敏传感器,详细介绍了该传感器主要电路的设计。该智能传感器实现了数据采集、信号处理、数据通信和电流环输出式数模转换功能。实际应用表明,在-20～+80℃的工作温度范围内,密度测量精度可达±5kg/m3,液位测量不受悬浮液回流和液位波动的影响,很好地满足了工业现场的应用。     

基于改进的MeanShift算法的选煤厂人员目标跟踪方法

针对采用传统的MeanShift算法进行智能视频监控易受背景干扰而丢失目标的问题,提出了一种将MeanShift算法与卡尔曼滤波算法相结合的选煤厂人员目标跟踪方法。该方法首先通过运动检测方法分割出跟踪目标区域,然后通过卡尔曼滤波算法预测下一帧跟踪窗口的起点,在此基础上采用MeanShift算法跟踪目标区域;由于选煤厂环境较复杂,为了防止跟踪失败,采用跟踪与检测相结合的方法来进一步保证跟踪的鲁棒性。实验结果表明,该方法能很好地消除背景中相似颜色区域的影响,具有较好的跟踪效果。     

新型矿用智能水压传感器的设计

介绍了一种基于STM32F103RBT6微控制器的新型矿用智能水压传感器的总体结构、主要硬件电路及软件设计方案。该传感器采用低功耗电路设计方式以及分时上电的供电方式,在空闲状态下功耗仅为0.006 mA,在采集发送状态下功耗约为0.5 mA。