Prager-structures: Archgrids and cable networks of optimal layout

This paper is concerned with a new class of optimal structures termed ‘Prager-structures’ which satisfy the following design criteria: all members have the same constant stress throughout the system and the total structural weight is minimized with respect to the member layout as well as the location of external loads along their line of action. In contrast to Michell frames, Prager structures (i) must have member forces of the same sign throughout; and (ii) have been found to constitute ‘surface structures’ whose middle surface contains the centroidal axis of all members. Using Prager's theories, global optimality is established for broad classes of loading and boundary conditions. The results are compared with those for Michell structures and optimal grillages.     

Support vector machines for spatiotemporal tornado prediction

The use of support vector machines (SVMs) for predicting the location and time of tornadoes is presented. In this paper, we extend the work by Lakshmanan et al. (Proceedings of 2005 IEEE international joint conference on neural networks (Montreal, Canada), 3, 2005a, 1642–1647) to use a set of 33 storm days and introduce some variations that improve the results. The goal is to estimate the probability of a tornado event at a particular spatial location within a given time window. We utilize a least-squares methodology to estimate shear, quality control of radar reflectivity, morphological image processing to estimate gradients, fuzzy logic to generate compact measures of tornado possibility and SVM classification to generate the final spatiotemporal probability field. On the independent test set, this method achieves a Heidke's skill score of 0.60 and a critical success index of 0.45.     

输电塔架参数化三维建模方法研究与实现

快速建立输电塔架三维仿真模型对电网安全运行与信息管理有重要作用。根据输电塔架的结构特点及结构相似性进行模块化划分,并用参数化方法为相同结构型式的模块建立原始三维模型;在此基础上根据塔架型号与呼高,对各模块进行选择与组装,赋值参数驱动各模块的原始模型,实现各种塔型与呼高的塔架三维快速建模。以某酒杯型直线塔为例,输入塔架型号与呼高即实现其三维模型快速建立,验证了该参数化三维建模方法的正确性与实用性。     

Learning networks for tornado detection

In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and minimax probability machines (MPMs) are applied for tornado detection. The last two approaches utilize kernel methods to address non-linearity of the data in the input space. All methods are applied to detect when tornadoes occur, using variables based on radar derived velocity data and month number. Computational results indicate that BNNs are more accurate for tornado detection over a suite of forecast evaluation indices.     

Secure Transmission Lines Monitoring and Efficient Electricity Management in Ultra-Reliable Low Latency Industrial Internet of Things

Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure security of the resource. This power transmission monitoring is a good example of ultra reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take the preventive measures. In this paper, we present an Internet of Things enabled real-time transmission line monitoring system comprising of wireless, wired, and cellular technologies. The objective is to minimize the time delay at minimum installation cost of the network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that proposed model provides efficient solutions and takes less time for data transmission and is more energy efficient.     

Context enabled decision aids to support the situation awareness and performance of risk engineers carrying out loss prevention surveys

Routine inspection by insurance companies at their clients’ facility, also known as loss prevention survey, help identify the best strategies to minimize damages when there is a high-speed wind event. More specifically, wind vulnerabilities associated with a building are evaluated using a process known as windstorm risk inspection. This routine inspection helps clients reduce the extent of damages caused by high-speed wind events including hurricane and tornado. Risk engineers make use of their subjective and analytical deduction skills to successfully carry out the inspection tasks. In this research the researchers investigated the effect of context-based visualization strategies on situation awareness and their understanding of the situation. The study examined how different types of information contribute towards the three levels of situation awareness. Following a between-subjects study design, 65 participants completed the study. Each session lasted 90–120 min. A checklist based and predictive display-based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of risk engineers. However, the predictive display only helped with certain tasks such as understanding the interaction among different components on the rooftop. For remaining tasks such as perceiving obvious issues like membrane tear, clogged drains and vegetation growth, checklist alone was sufficient. This study helped the understanding of the advantages and disadvantages of the decision aids tested. More specifically, these decision aids can improve the mental model of novice risk engineers. Additionally, this study provided insights that could help design training materials for infrastructure inspectors.     

Turbulent modeling of a vortex boundary layer flow

The boundary layer flow of an atmospheric vortex was analyzed by utilizing a two-equation model of turbulence together with the conventional boundary layer equations. Steady-state solutions were obtained from the time-dependent solutions by using a predictor-corrector, multiple iteration method. Surprisingly complicated flow patterns with four circulating regions (4-cell vortex) were predicted in a meridional plane of the boundary layer. The intrinsic nature of sharp-turning, large-shear, and high-swirl flow characteristics may provide explanations for the tremendous roars and extremely intensive damage to ground structures associated with tornadoes. The present results are in good agreement with the tornado dust clouds simulated in our laboratory as well as those observed in natural cases.     

基于门控循环单元网络的输电杆塔螺栓紧固检测

输电塔杆螺栓紧固检测是保障高压电网安全的重要依据,传统的人工检测方法需要员工爬上输电杆塔检测操作,通常伴有一定程度的风险,而采用无人机巡检受许多外在的因素的影响,其检测效果并不理想.因此,本文提出一种基于门控循环单元网络的输电杆塔螺栓紧固检测方法,利用振动传感器和传感分析仪构建一套采集输电铁塔声波数据的作业流程,提取训练样本中声波数据的线性预测倒谱系数LPCC构成特征向量;训练门控循环单元网络(Gated Recurrent Unit,GRU)分类模型从而检测未知紧固状态的声波样本,实验结果达到实用分析性能.通过本算法的应用,解决了在检测输电铁塔螺栓紧固问题上传统方法上的人力和方法性能问题.     

Agent-based tool to reduce the maintenance cost of energy distribution networks

There has been continuous research in the energy distribution sector because of its huge impact on modern societies. Nonetheless, aerial high voltage power lines are still supported by old transmission towers which involve some serious risks. Those risks may be avoided with periodic and expensive reviews. The main objective of this work is to reduce the number of these periodic reviews so that the maintenance cost of power lines is also reduced. More specifically, the work is focused on reducing the number of periodic reviews of transmission towers to avoid step and touch potentials, which are very dangerous for humans. A virtual organization-based multi-agent system is proposed in conjunction with different artificial intelligence methods and algorithms. The developed system is able to propose a sample of transmission towers from a selected set to be reviewed. The system ensures that the whole set will have similar values without needing to review all the transmission towers. As a result of this work, a website application is provided to manage all the review processes and all the transmission towers of Spain. It allows the companies that review the transmission towers to initiate a new review process for a whole line or area, while the system indicates the transmission towers to review. The system is also able to recommend the best place to locate a new transmission tower or the best type of structure to use when a new transmission tower must be used.     

基于COMSOL和MATLAB的输电铁塔力学仿真界面设计

输电线路覆冰严重时,可能导致线路断线、线间闪络、铁塔倒塌等事故,严重影响电网的稳定运行。为了更深入地了解输电铁塔关键构件的力学特性,提高其运行质量并降低运维成本,本文利用COMSOL和MATLAB设计了一个具有交互功能的输电铁塔力学仿真界面。用户可以根据需要输入不同的仿真参数,并通过点击不同电压等级下的输电铁塔类型节点进行关键构件的力学仿真和参数计算。该仿真界面实现了输电铁塔的几何建模、网格剖分、仿真结果云图展示以及关键构件的最大节点位移、最大轴向应力等力学参数的动态输出。同时,根据输出参数对输电铁塔的力学失效状态进行评估。界面操作简单直观,用户可以快速获取仿真结果,有助于加深对输电铁塔关键构件力学仿真的理解。这对于开展输电铁塔关键构件的运行维护工作,提高输电铁塔的运行质量具有重要意义。     

基于MXA2500G/GSM的远程电塔倾斜测量仪设计

针对偏远地区的电力传输高塔巡检周期长和因无法及时地获知倾斜、倒塌等突发情况而造成巨大损失等问题,设计了一种基于MEMS加速度传感器+GSM+C8051F021构架的远程电塔倾斜测量仪。利用C8051F021和MEMS加速度传感器组成的倾角测量仪安装在电力传输高塔上,实时地获取倾斜角度。通过GSM子模块将时间、倾角、位置等信息发送至短信平台为电力监控部门提供巡检维修依据。实验表明:本系统可以实时地测量和发送倾斜角度信息,并具有体积小、功耗低等特点。     

Visual language and graph-based structures in conceptual design

The paper deals with using design knowledge encoded in a visual language and graph-based structures to support the conceptual phase of designing. The visual language is based on the design conceptualization and composed of design diagrams being configurations of geometric primitives. A symbolic representation of design structures in terms of objects specified by the conceptualization and relations between them is defined, and then mapped into diagrams by a given realization. Diagrams are automatically transformed into the corresponding graph-based data structures. The knowledge stored in the graph representations of diagrams is translated into first-order logic formulas which describe generated design configurations. A proposed logic-based reasoning mechanism allows the design supporting system to check the compatibility of designs with the given requirements and constraints. The approach is illustrated by examples of designing configurations of transmission truss towers.     

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.     

Individuals’ religiosity and emotional coping in response to disasters

Providing information to help individuals cope physically and psychologically with a disaster is critical in crisis communication. However, how individuals cope is relatively understudied. In particular, researchers have examined how people emotionally cope during different types of crises, but not in a natural disaster context and not religiosity. Yet, religiosity can be important during disasters, given that about 89% of adults in the United States believe in God (Pew Research Center, 2014). Through ten focus groups (N = 77) and a survey (N = 1,484), this study examines how residents of the Southeast United States cope in response to tornadoes. Findings indicate that participants experience anxiety and fear during a tornado, but fear and hope trigger physical action taking (e.g., sheltering in place or collecting supplies). Prayer during a tornado does not significantly predict action taking. Religiosity significantly predicts physical action taking.     

Nonlinear analysis of reinforced concrete hyperboloid cooling towers—II. Parametric study and results

In this second and final part of this series of papers the details of parametric studies conducted to assess the influence of various geometric and material parameters on the load-displacement response of three reinforced concrete hyperboloid cooling towers are presented. The material model adopted for the nonlinear finite element analysis is described in part I of this paper.     

外力破坏影响下的输电线路综合监测与预警系统

针对现有输电线路需要人工巡查盯守且监测效率低的缺陷,提出新型的方案.采用MSP430F5438单片机芯片计算,通过杆塔监测子系统中的六种传感器检测出输电线路受外力破坏情况,利用云存储智能摄像头采集输电线路现场视频,经由光纤通信网络传送至后台主机管理子系统.后台主机管理子系统通过高斯背景模型法,判断外力破坏情况是否符合输电线路杆塔安全距离.测试结果表明,该系统可稳定监测不同电压等级输电线路受外力破坏情况并及时预警.     

Predicting the probability of ice storm damages to electricity transmission facilities based on ELM and Copula function

In this paper, a novel method based on extreme learning machine (ELM) and Copula function is proposed to predict the damages to electricity transmission facilities during ice storms. The ELM is firstly trained based on the historical data of wind speed, freezing precipitation, temperature, as well as the distribution parameters of wind and ice loads. The ELM can then be employed to predict the distributions of the real-time wind and ice loads on electricity transmission facilities. Furthermore, the correlation between wind load and ice load is modeled with Copula functions. On the basis of ELM and Copula function, the joint probability distribution of wind and ice loads can be finally formulated and applied to predict the potential damages to electricity transmission facilities such as transmission lines and towers. The proposed method is tested with a real dataset to demonstrate its effectiveness.     

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning

Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.     

Why does site visit matter in global software development: A knowledge-based perspective

ContextFace-to-Face (F2F) interaction is a strong means to foster social relationships and effective knowledge sharing within a team. However, communication in Global Software Development (GSD) teams is usually restricted to computer-mediated conversation that is perceived to be less effective and interpersonal. Temporary collocation of dispersed members of a software development team is a well-known practice in GSD. Despite broad realization of the benefits of visits, there is lack of empirical evidence that explores how temporary F2F interactions are organized in practice and how they can impact knowledge sharing between sites.ObjectiveThis study aimed at empirically investigating activities that take place during temporary collocation of dispersed members and analyzing the outcomes of the visit for supporting and improving knowledge sharing.MethodWe report a longitudinal case study of a GSD team distributed between Denmark and Pakistan. We have explored a particular visit organized for a group of offshore team members visiting onshore site for two weeks. Our findings are based on a systematic and rigorous analysis of the calendar entries of the visitors during the studied visit, several observations of a selected set of the team members’ activities during the visit and 13 semi-structured interviews.ResultsLooking through the lens of knowledge-based theory of the firm, we have found that social and professional activities organized during the visit, facilitated knowledge sharing between team members from both sites. The findings are expected to contribute to building a common knowledge and understanding about the role and usefulness of the site visits for supporting and improving knowledge sharing in GSD teams by establishing and sustaining social and professional ties.