Topological design of freely vibrating bi-material structures to achieve the maximum band gap centering at a specified frequency

The topological design of structures to avoid vibration resonance for a certain external excitation frequency is often desired. This paper considers the topology optimization of freely vibrating bi-material structures with fixed/varying attached mass positions, targeting at maximizing the frequency band gap centering at a specified frequency. A band gap measure index is proposed to measure the size of the band gap with a specified center frequency. Aiming at maximizing this measure index, the topology optimization problem is formulated on the basis of the material-field series-expansion (MFSE) method, which greatly reduces the number of design variables and at the same time keeps the capability to describe relatively complex structural topologies with clear boundaries. As the considered optimization problem is highly non-linear and may yield multiple local minima, a sequential Kriging-based optimization solution strategy is employed to effectively solve the optimization problem. This solution strategy exhibits a relatively strong global search capability and requires no sensitivity information. With the present topology optimization model and the gradient-free algorithm, relative large band gaps with specified center frequencies have been obtained for 2D beams and 3D plates, without specifying the frequency orders between which the desired band gap occurs in prior.

Investigating the origin of the Belgian second SARS-CoV-2 wave by using (pre)admission screening samples

The goal of this study was to estimate rates of SARS-CoV-2 carriership and viral loads in the general Antwerp population and to compare the estimated prevalences and incidences with governmental data (numbers of detected positive cases, stringency measure index) in order to evaluate the dynamics leading to the second wave. We used (pre)admission screening results from the major Antwerp hospitals for estimating community prevalences and incidences. 43.545 samples were included (April – November 2020). High SARS-CoV-2 carriership rates (mean week prevalence of 1.3%) were found in the general Antwerp population. 35.4% of positive cases carried high viral loads. Only a small proportion (15.3%) of the viral circulation was detected by the nationally implemented testing policy. In the weeks before the second Belgian wave, increasing prevalences and incidences were found, together with country-wide easing of restriction measures. In our opinion these findings have led to origin of the second viral wave.

Two Improved Topic Word Detection Algorithms

Topic word extraction is the task of identifying single or multi-word expressions that represent the main topics of a document. In this paper, two improved algorithms for extracting and discovering topic words are proposed in the Rapid Topic word Detection (RTD) Algorithm and CategoryTextRank (CTextRank) Algorithm, which can effectively obtain information by extracting and filtering the topic words in the text. The algorithms overcome the shortcomings of traditional topic words discovering algorithms that require deep linguistic knowledge, domain or language specific annotated corpora. The two algorithms we proposed can process both short and long text. The biggest advantage of the algorithms is that they are unsupervised machine learning algorithms. They need not be trained to process text directly to get topic words. The Accuracy rate, recall rate and F-measure index have been greatly improved when using the two algorithms which show that the results obtained compare favorably with previously published results on datasets Inspec and SemEval. The first algorithm Rapid Topicword Detection improves the metrics compared to PositionRank and TextRank, the second algorithm CategoryTextRank improves the metrics compared to TextRank, SingleRank and TF-IDF.

Two-Round Diagnosability Measures for Multiprocessor Systems

In a multiprocessor system, as a key measure index for evaluating its reliability, diagnosability has attracted lots of attentions. Traditional diagnosability and conditional diagnosability have already been widely discussed. However, the existing diagnosability measures are not sufficiently comprehensive to address a large number of faulty nodes in a system. This article introduces a novel concept of diagnosability, called two-round diagnosability, which means that all faulty nodes can be identified by at most a one-round replacement (repairing the faulty nodes). The characterization of two-round t-diagnosable systems is provided; moreover, several important properties are also presented. Based on the abovementioned theories, for the n-dimensional hypercube Qn, we show that its two-round diagnosability is n2+n/2, which is n+1/2 times its classic diagnosability. Furthermore, a fault diagnosis algorithm is proposed to identify each node in the system under the PMC model. For Qn, we prove that the proposed algorithm is the time complexity of On2n.

Capturing driving behavior Heterogeneity based on trajectory data

Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time, gender, driving years and so on. Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior, and the drivers are generally divided into two classes (including aggressive drivers and careful drivers) or three classes (including aggressive drivers, normal drivers and careful drivers). Nevertheless, the classification approaches have not been verified, and the rationality of the classifications has not been confirmed as well. In this study, the trajectory data of drivers is extracted from the NGSIM datasets. By combining the K-Means method and Silhouette measure index, the drivers are classified into four clusters (named as clusters A, B, C and D, respectively) in accordance with the acceleration and time headway. The two-dimensional approach is applied to analyze the characteristics of different clusters. Here, one dimension consists of “Cautious” and “Aggressive” behaviors in terms of velocity and acceleration, and the other dimension consists of “Sensitive” and “Insensitive” behaviors in terms of reaction time. Finally, the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model. A surprising result indicates that overly “cautious” and “sensitive” behaviors may result in more fuel consumption and emissions. Therefore, it is necessary to find the balance between the driving characteristics.

Modelling and Simulation of Cognitive Electronic Attack under the Condition of System of systems Combat

From the height of system-of-systems combat and operational perspective, the operations of cognitive electronic warfare (CEW) was analysed, and its main process and links were described. Secondly, the jamming effectiveness evaluation (JEE) model of cognitive electronic attack (CEA) operations was established based on the interference side, in which the change of threat degree was used as the measure index of jamming effectiveness. Then, based on the Q-learning model, an intelligent countermeasure strategy generation (ICSG) model was established, and the main steps in the model were given. Finally, on the basis the JEE model and the ICSG model, the simulation experiment was carried out for CEA operations. The result showed that combining the JEE model with the ICSG model can express the main process of the operations of CEW, as well as proved the validity of these models.