Square loop and square slot are the simplest and commonly used elements of frequency selective surfaces (FSSs) providing bandstop and bandpass responses, respectively. Despite it is already known that employment of a double square slot structure presents a tunable stopband between two passbands, it is not clear which parameters of the structure are crucial for tunning the inbetween stopband. We propose an analysis of a double square slot FSS considering its element as a parallel connection of square slot and square loop predicting two geometrical parameters of the constituent square loop are crucial for tunning the inbetween stopband. The stopband is tunable by changing geometrical parameters of the constituent square loop and does not depend on geometries of the constituent square slot. The validity of this analytical prediction is verified by full wave EM simulation results.

The selection of layers in the transfer learning fine-tuning process ensures a pre-trained model’s accuracy and adaptation in a new target domain. However, the selection process is still manual and without clearly defined criteria. If the wrong layers in a neural network are selected and used, it could lead to poor accuracy and model generalisation in the target domain. This paper introduces the use of Kullback-Leibler divergence on the weight correlations of the model’s convolutional neural network layers. The approach identifies the positive and negative weights in the ImageNet initial weights selecting the best-suited layers of the network depending on the correlation divergence. We experiment on four publicly available datasets and four ImageNet pre-trained models that have been used in past studies for results comparisons. This proposed approach method yields better accuracies than the standard fine-tuning baselines with a margin accuracy rate of 10.8% to 24%, thereby leading to better model adaptation for target transfer learning tasks.

A method to reduce crosstalk using TL-shaped defect microstrip structure (DMS) is proposed to solve the far-end crosstalk between microstrip lines. This method optimizes the ratio of the capacitive coupling and the inductive coupling between the coupled microstrip lines by etching the TL-shaped DMS on the microstrip line and reduces the strength of the electromagnetic (EM) coupling, which can achieve crosstalk suppression. The equivalent circuit model, S-parameters and full-wave EM simulations are used to analyze the crosstalk between the microstrip lines etched with and without the TL-shaped DMS. High Frequency Structure Simulator (HFSS) software simulation and samples test results show that the TL-shaped DMS can effectively reduce the far-end crosstalk while guaranteeing the transmission ability of microstrip line to the signal. The maximum far-end crosstalk can be reduced by 42dB in the frequency range of 0–8 GHz and the test results of the samples are in good agreement with the simulation results.

Expansive soil is a kind of special soil with soil particles mainly composed of hydrophilic minerals, and has significant deformation characteristics of water absorption expansion and water loss shrinkage. This characteristic often brings great harm to engineering construction. Relying on the foundation engineering of the bridge on the expansive soil slope of the Jiangxi-Huaihe River, this paper studies the effects of slope excavation and rainfall on the horizontal deformation of the bridge foundation by using temperature simulation and humidity fields and numerical simulation methods. The research results show that during the excavation process, the bridge foundation will produce a horizontal displacement pointing to the top of the slope, and the rainfall after excavation will cause the horizontal displacement of the bridge foundation to decrease and gradually develop in the opposite direction. During the excavation process, the rebound of slope excavation is the main factor affecting the horizontal displacement of the bridge foundation. Under short-term rainfall conditions, the hygroscopic expansion of the slope soil becomes the main influencing factor. Under extreme rainfall conditions, the main cause of the horizontal deformation of the bridge foundation is The influencing factors are transformed into the reduction of the soil weight of the slope and the strength of the soil material.

Time series classification (TSC) is an important and challenging problem in data mining. Time series data sets are an important basis for this research and are widely used in baseline verification of various algorithm models. Aiming at the problem that there are few domestic data sets and the current TSC data set is relatively old, a new data set for TSC task is established based on the average price data of concrete in major cities in China, which provides new data support for the research of TSC algorithm. We made use of the data center of Oriental Fortune to disclose the sample data of the average price of concrete from 2013-10-23 to 2021-01-20, created 730 autoregression-based series data sets by using sliding windows of different lengths, and then selected the appropriate sliding window length through machine learning model verification, finally, convolutional neural network (CNN) and long and short memory (LSTM) network, which are good at processing temporal features, are used to verify the data and prove the validity of the dataset. The dataset is freely available at https://gitee.com/lq2012/tsc-dataset.

This paper presents a thorough methodology for the voltage assessment of pilot offshore wind project in Shandong province, China. The results presented in this paper can be considered as the milestone of the offshore wind research in Shandong since there is still no grid codes for offshore wind power plant grid connection in the local electrical power grid. It mainly consists of three parts. In the first part, a detailed model of the offshore wind farm created in the DIgSILENT PowerFacotry simulation platform is presented, including wind turbines, power converters, transformers, submarine cables and relevant control schemes. In the second part, nonlinear time-domain simulations were performed to analyse the wind farm’s active power, reactive power, and voltage conditions under different wind scenarios. Based on the simulations results, a dynamic reactive power compensation system was proposed, and the consequence of the reactive power compensation was also demonstrated using nonlinear time-domain simulations.

While many structural damage detection methods have been developed in recent decades, few data-driven methods in unsupervised learning mode have been developed to solve the practical difficulties in data acquisition for civil infrastructures in different scenarios. To address such a challenge, this paper proposes a number of improved unsupervised novelty detection methods and conducts extensive comparative studies on a laboratory scale steel bridge to examine their performances of damage detection. The key concept behind unsupervised novelty detection in this paper is that only normal data from undamaged structural scenarios are required to train statistical models with these methods. Then, these trained models are used to identify abnormal testing data from damaged scenarios. To detect structural damage in the form of loosening bolts in the steel bridge, four machine-learning methods (i.e., K-nearest neighbors method, Gaussian mixture models, One-class support vector machines, Density peaks-based fast clustering method) and one deep learning method using a deep auto-encoder are selected. Meanwhile, some modifications and improvements are made to enable these methods to detect structural damage in unsupervised novelty detection mode. In their comparative studies, the advantages and disadvantages of these methods are analyzed based on their results of structural damage detection.

Graph representation learning has attracted increasing attention in a variety of applications that involve learning on non-Euclidean data. Recently, generative adversarial networks(GAN) have been increasingly applied to the field of graph representation learning, and large progress has been made. However, most GAN-based graph representation learning methods use adversarial learning strategies directly on the update of the vector representation instead of the embedding mechanism, which does not make full use of the essential advantages of GAN. The essential advantage of GAN is the final embedding mechanism rather than the embedding representation itself. To address this problem, we propose to use adversarial idea on the reconstruction mechanism of deep autoencoders. Specifically, the generator and the discriminator are the two basic components of the GAN structure. We use the deep autoencoder as the discriminator, which can capture the highly non-linear structure of the graph. In addition, the generator another generative model is introduced into the adversarial learning system as a competitor. A series of empirical results proved the effectiveness of the new approach.

This paper presents the enhancement of Legendre Optimum low pass filters in terms of reusability and bandwidth, based on the variable or programmable memristance of memristors. Two low pass filters, of third and fifth order, operating in the radio frequency range, and designed using the insertion loss method are presented. At 600 KHz and at 110 MHz, two MS memristor models, of the non-linear ion drift class is incorporated into the filter circuits in turn and their memristances varied such that R_off- R_on decreases monotonically and R_off- R_on>0. Results show a bandwidth enhancement of up to 100 KHz at 600 KHz, and up to 19MHz at 110MHz. This study also examines the effect of the simultaneous versus asynchronous variation of the memristance of the pair of memristors introduced into the filter circuits, as well as increase in filter order.

Mergers and acquisitions operations continue to be one of the most explored growth strategies in all markets, and this is indeed the case in the elevator industry. The volume of investments grows year after year. It is the fastest way to grow in international and domestic markets, but the reality is that a high percentage of the operations carried out do not meet the expectations of the investors once the integration phase has been completed. There are several causes of failures in mergers and acquisitions processes, such as lack of commitment from the management, an unrealistic business plan, cultural shock, etc. But the most common one, and the one with the highest risk in an acquisition decision, is information asymmetry. During the negotiation phase a large amount of data is collected, and subsequently analyzed during the due diligence period, but it may not correspond to the reality during the integration phase. In this article we will propose how information asymmetry can be avoided through the application of information and communication technologies (ICTs) via internet of things (hereinafter IoT) devices in the elevator industry. This can also be applied to other industries.

With the growing demand for rectangular and square hollow steel sections in the last few decades, the cold roll forming process has become a widely acknowledged hollow sections manufacturing method; however, residual stress generated during the roll forming process is one of the primary concerns on roll-formed products. In this regard, several researchers have conducted numerical and experimental investigations of residual stress distributions on roll-formed steel sections. However, most of the studies found in the literature have been confined to the measurement of residual surface stresses. On the other hand, experimental studies conducted on fatigue and load-carrying capacity of hollow structural steels have shown that there is indeed a simple relation between the through-thickness residual stress distributions and mechanical properties of structures. Thus, this paper employed a proper numerical modelling procedure using LS-DYNA’s finite element code to explore through-thickness residual stress distributions generated during the roll forming process of rectangular and square hollow steel sections from different material grades. Moreover, a small-scale parametric study was conducted to explore the effects of the partial heating roll forming method on through-the-thickness residual stress distributions to satisfy the growing demand for residual stress-free roll-formed products.

The stability of relaxation techniques has been studied for strongly coupled fluid-structure interaction (FSI) with application to a cantilever immersed in channel flow. The fluid is governed by Navier-Stokes equations for incompressible flow condition using turbulence modelling and the solid is governed by the equation of motion with compressible material modelling. The applied kinematic description is Lagrangian for the solid and Eulerian for the fluid. The coupling of the state solvers is achieved by the Arbitrary Lagrange-Euler procedure which involves a mesh motion solver and the FSI procedure is stabilised by relaxation. It is shown that the stability can be related to the frequency shift caused by FSI and they follow the same rate for the shape factor of the structure with an offset. This correlates well to theoretical results but also show that for given mesh resolution, all relaxations fail for sufficient high-frequency shift. We also propose a continuation technique to stabilise the solution near the instability region, which also improves the efficiency and can be integrated easily for the black-box FSI solution procedure.

The purpose of this article is to introduce an application to draw the asymptotes of Bode diagram module and phase from each constituent elementary factors of any transfer function for minimum and non-minimal phase systems without transport delay. The Bode diagram is the most used tool in the frequency response method. Python was used to program the application to perform the operations as well as the Qt5 Design for the simple graphical interface for the application and all this in the Linux operating system. The application purpose is to assist students in learning the concept and drawing of Bode diagram. For students the non-minimum phase system Bode diagram is more difficult to draw than a minimum phase system due to the presence of zeros and/or poles on right half of s-plane. The phase asymptotes of a quadratic factor was closest to the real phase curve around the corresponding undamped natural frequency and this can be observed in the example showed in this article. This example must be used as a help and not a simply to solve a problem.

In this paper, the advantage of reusing scrap tires in Scrap Rubber Block (SRB) to improve thermal insulation in buildings was examined experimentally. By testing the use of SRB in black and white colours as external wall insulators and comparing their performance with walls without insulation. The results indicated that a wall with scrap tire blocks gave the best thermal insulation results when the outer face was painted white. The decrement factor (f) and the rate of heat loss increase, while the rate of heat gain decreases. This was done through the mechanisms of heat transfer by conduction through the layers of the wall and the effect of adding the rubber block on its thermal properties. The results showed that the use of rubber blocks reduces the temperature of the inner surface of the wall by 3-4oC lower than the traditional wall. The thermal diffusion inside the wall was determined effectively in the case of a wall with the rubber block, where the temperature of the inner surface reaches its maximum value by about 0.5-hour difference from the traditional wall in the case of the wall with the rubber block in black colour, and 9.5-hour in the case with the white block.

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

Aiming at the problem of huge energy consumption in the Fog Wireless Access Networks (F-RANs), the resource allocation scheme of the F-RAN architecture under the cooperation of renewable energy is studied in this paper. Firstly, the transmission model and Energy Harvesting (EH) model are established, the solar energy harvester is installed on each Fog Access Point (F-AP), and each F-AP is connected to the smart grid. Secondly, the optimization problem is established according to the constraints of Signal to Noise Ratio (SNR), available bandwidth and energy harvesting, so as to maximize the average throughput of F-RAN architecture with hybrid energy sources. Finally, the dynamic power allocation scheme in the network is studied by using Q-learning and Deep Q Network (DQN) respectively. Simulation results show that the proposed two algorithms can improve the average throughput of the whole network compared with other traditional algorithms.

Despite all the importance of dams, dam failure, always threatens downstream areas by both natural and man-made sources and caused great human and financial losses. Vulnerability analysis is one of the main assessments to achieve a risk map and help policymakers reduce the losses and consequences of the failure. Therefore, this study aims to develop a new integrated hybrid model based on the security vulnerability assessment (SVA) and hydraulic analysis of flood and Source-Pathway-Receptor–Consequences (SPRC) approach. For SVA forms, data were collected by questionnaires survey, and for flood mapping, the open-source software, HEC-RAS was used. Geographical Information System (GIS) was applied to a combination of layers based on the SPRC approach. Five different scenarios were modeled to obtain the flood map due to a dam break. Then, the output data from the HEC-RAS software were transferred to the GIS software to be merged with other data achieved from the survey based on the SVA approach. Finally, the risk map for a case study was developed by this new hybrid approach. The results of the modeling showed that the highest vulnerability was achieved by residential areas and the lowest vulnerability is achieved by agricultural land due to floods by the dam failure.

This study aimed at investigating the variation of heat transfer and velocity changes of the fluid flow along the vertical line on a surface drawn from both sides. In the beginning, the several parameters such as Prandtl number and viscoelastic effect evaluated for heat transfer and fluid velocity by variation Iteration method. The results were compared with the numerical method. The second part of the description relates to the use RSM method in the Design Expert software. In this paper by using the RSM method, optimized the fluid velocity and heat transfer passing from the stretching sheet. By increasing the Prandtl number, the convection heat transfer 43 % increased ratio the minimum Prandtl number. In accordance with balanced modes for Prandtl number and viscoelastic parameter and wall temperature, the best optimization occurred for fluid velocity and fluid temperature with f=0.67 and θ=0.606. The results of variation iteration method are accurate for the nonlinear solution. As the value of k increases, the value of fluid velocity indicates an increase and by increase Prandtl number, the value of Temperature decreases.