太湖地区防汛抗洪工作的实践与思考

2020年汛期,江苏省太湖地区发生流域性大洪水,省太湖地区水利工程管理处提前部署,细化预案,科学预降太湖及区域河网水位.在发生超标准洪水后,通过及时启动应急响应,精准调度,错峰运行,部署处属枢纽工程全力投入排水运行,在精准调度、安全运行和信息化等方面的经验和做法对同类工程的调度运用有一定的参考作用.     

水管单位安全生产标准化创建动态评价与关键因素分析

近年来我国经济快速发展,水利基础设施发挥了巨大的作用,尤其是安全生产标准化的推行,促进了水利工程平稳投产及运营.水管单位安全生产标准化评审涉及体系建设、教育培训、现场运管、应急管理等方面,考核事项众多,所以明确创建关键因素、动态评价创建成效是标准化创建的重要任务.基于层次分析法建立标准化评价指标体系,分析创建各阶段中具有主导作用的关键因素,以江苏水管单位的标准化创建实例,探讨了动态评价方法的应用.     

小浪底细砂岩单轴压缩声发射特征研究

采用岩石伺服试验机和声发射仪,对小浪底三叠系细砂岩进行单轴压缩声发射试验。基于试验结果,将试样变形过程划分为压密、弹性、塑性和破坏四个阶段,分析了不同阶段试样声发射幅值、峰值频率、振铃计数和能量的变化特征,在此基础上对比分析了试样声发射ｂ值以及累计振铃计数的演化规律,得出了岩石的破坏时间前兆特征。研究结果表明:压密和弹性阶段试样声发射幅值、振铃计数和能量处于较低水平、峰值频率频带数量依次增多;塑性阶段试样幅值增大、振铃计数与能量上升且整体处于较高水平、峰值频率数值增大且呈现多频带化,临近破坏阶段试样产生大量100ｄＢ的高幅值信号,振铃计数与能量持续急增,峰值频率降低,频带减少;声发射ｂ值的变化规律可以间接反映岩体内部微裂隙萌生、扩展的演化特征。     

聚丙烯玄武岩混杂纤维混凝土氯盐侵蚀耐久性试验研究

玄武岩纤维和聚丙烯纤维可在混凝土中产生互补和协同作用,大幅提升混凝土性能.文章通过试验研究的方法,探讨了玄武岩聚丙烯纤维混凝土的抗氯盐侵蚀性能.从试验结果来看,纤维混掺有助于提高混凝土的抗氯盐侵蚀性能,而玄武岩纤维掺量0.15％、聚丙烯纤维掺量0.5％的混杂纤维混凝土抗氯盐侵蚀性能最佳,建议在工程设计和施工中采用.     

Instability of diatomic deuterium in fcc palladium

To clarify some of the solid-state aspects of cold fusion in deuterated transition metal electrodes, we have carried out first-principles self-consistent total energy calculations for various configurations of atomic and diatomic deuterium inside fcc palladium. We find that the stability of the Pd+D system is controlled by the relative position of the deuterium-inducedantibonding level with respect to the Fermi energy. The equilibrium D-D distance in dense PdD up to =3 is found to be much larger than the free space value. The calculated Born-Oppenheimer energy surface of diatomic D₂ in crystalline palladiuim is shown to have but metastable local minima whose internuclear separation is at least 0.2 Ålarger than that of the isolated D₂ molecule. We conclude that D₂ incrystalline Pd will have a substantially lower tunneling probability than hitherto thought and that explanation for fusion mechanisms should be sought elsewhere.     

Data stream classification with novel class detection: a review,comparison and challenges

Knowledge and Information Systems - Developing effective and efficient data stream classifiers is challenging for the machine learning community because of the dynamic nature of data streams. As a...     

Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.

     

Layout Optimization for Greenhouse WSN Based on Path Loss Analysis

When wireless sensor networks (WSN) are deployed in the vegetable greenhouse with dynamic connectivity and interference environment, it is necessary to increase the node transmit power to ensure the communication quality, which leads to serious network interference. To offset the negative impact, the transmit power of other nodes must also be increased. The result is that the network becomes worse and worse, and node energy is wasted a lot. Taking into account the irregular connection range in the cucumber greenhouse WSN, we measured the transmission characteristics of wireless signals under the 2.4 Ghz operating frequency. For improving network layout in the greenhouse, a semi-empirical prediction model of signal loss is then studied based on the measured data. Compared with other models, the average relative error of this semi-empirical signal loss model is only 2.3%. Finally, by combining the improved network topology algorithm and tabu search, this paper studies a greenhouse WSN layout that can reduce path loss, save energy, and ensure communication quality. Given the limitation of node-degree constraint in traditional network layout algorithms, the improved algorithm applies the forwarding constraint to balance network energy consumption and constructs asymmetric network communication links. Experimental results show that this research can realize the energy consumption optimization of WSN layout in the greenhouse.     

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.     

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.