NDM-536: EFFECT OF WIND SPEED AND TERRAIN EXPOSURE ON THE WIND PRESSURES FOR ELEVATED STEEL CONICAL TANKS

Steel liquid storage tanks in the form of truncated cones are commonly used as containment vessels for water supply or storing chemicals. A number of failures have been recorded in the past few decades for steel liquid tanks and silos under wind loading. A steel conical tank vessel will have a relatively small thickness making it susceptible to buckling under wind loads especially when they are not fully-filled. In this study, a wind tunnel pressure test is performed on an elevated conical tank in order to estimate the external wind pressures when immersed into a boundary layer. The tested tank configuration represents combined conical tanks where the cone is capped with a cylinder. In addition, the effect of terrain exposure and wind speed on the pressure values and wind forces is assessed. The mean and rms pressure coefficients are presented for different test cases in addition to the mean and rms total drag forces that are obtained by integrating the pressure coefficient over the tank model’s surface. It is found that the total mean and rms drag forces are highly-dependent on Reynolds number which is a function of wind speed and they have a maximum value at mid-height for the lower cylinder, at top for the conical part, and at bottom for the upper cylindrical part

Deep learning for cancer tumor classification using transfer learning and feature concatenation

Deep convolutional neural networks (CNNs) represent one of the state-of-the-art methods for image classification in a variety of fields. Because the number of training dataset images in biomedical image classification is limited, transfer learning with CNNs is frequently applied. Breast cancer is one of most common types of cancer that causes death in women. Early detection and treatment of breast cancer are vital for improving survival rates. In this paper, we propose a deep neural network framework based on the transfer learning concept for detecting and classifying breast cancer histopathology images. In the proposed framework, we extract features from images using three pre-trained CNN architectures: VGG-16, ResNet50, and Inception-v3, and concatenate their extracted features, and then feed them into a fully connected (FC) layer to classify benign and malignant tumor cells in the histopathology images of the breast cancer. In comparison to the other CNN architectures that use a single CNN and many conventional classification methods, the proposed framework outperformed all other deep learning architectures and achieved an average accuracy of 98.76%

NDM-529: NUMERICAL EVALUATION OF WIND LOADS ON A TALL BUILDING LOCATED IN A CITY CENTRE

Estimation of wind-induced loads and responses is an essential step in tall building design process. Wind load for super tall buildings is commonly evaluated using boundary layer wind tunnel (BLWT) tests. However, the recent development in computational power and techniques is encouraging designers to explore numerical wind load evaluations using a Computational Fluid Dynamics (CFD) approaches. CFD can provide a faster estimation for building loads and responses with lower cost and satisfactory accuracy for preliminary design stages. The current study investigates the accuracy of evaluating wind pressure and building responses of a typical tall building (CAARC building). Two configurations are investigated, which are (1) standalone building and (2) located in a city center. Large Eddy Simulation (LES) numerical model is utilized adopting a newly developed synthesizing turbulence generator named Consistent Discrete Random Flow Generator (CDRFG). The adopted inflow technique is believed to provide good representation of wind statistics (i.e. velocity and turbulence profiles, spectra and coherency). Pressure distributions and building responses from the current study match with those obtained from boundary layer wind tunnel tests. The average difference between the pressure values between the current model and the BLWT is 4%. While the difference in building responses resulted from the LES model to those from BLWT is 6%. It was found that utilizing CDRFG in LES models provides an accurate estimation for building aerodynamic performance in an efficient computational time owing to its capability of supporting parallel processing

NDM-538: WIND TUNNEL TESTING OF A MULTIPLE SPAN AEROELASTIC TRANSMISSION LINE SUBJECTED TO DOWNBURST WIN

A 1:50 scale aeroelastic wind tunnel test for a multi-span transmission line system is conducted at the WindEEE dome under downburst wind. WindEEE is a novel three-dimensional wind testing facility capable of simulating downbursts and tornadoes. This test simulates a transmission line consisting of v-shaped guyed towers holding three conductor bundles. Details about the model design, the wind field and the test setup are provided. A downburst loading case that is critical for the line design and causes unbalanced tension load on the conductors is investigated in the current study. Resulting line responses obtained from the test are compared with a previously developed finite element model by the research group at the University of Western Ontario. The comparison shows a good agreement which validates the finite element models. Results obtained from this test will be very useful to understand the behavior of the lines under downburst wind

NDM-534: SENSITIVITY OF WIND INDUCED DYNAMIC RESPONSE OF A TRANSMISSION LINE TO VARIATIONS IN WIND SPEED

This paper studies the dynamic behavior of a multi-span transmission line system under synoptic wind considering various speeds to determine the range of wind speeds in which the system experiences resonance. A finite element numerical model was developed for the purpose of this study. This model is employed to assess the dynamic behavior of a self-supported lattice tower line under various wind speeds. Dynamic Amplification Factor (DAF), defined as the ratio between the peak total response to the peak quasi-static response, is evaluated. It is found that conductors’ responses exhibit large DAF compared to the towers especially at low wind speeds (v ≤ 25 m/s). This results from the low natural frequency of the conductors (0.19 Hz) which is close to the wind load frequency while the natural frequency of the tower is equal to 2.36 Hz. In addition, the conductors’ aerodynamic damping decreases with the decrease of wind speed which leads to higher dynamic effect while the tower’s aerodynamic damping plays a minor role. The results of the dynamic analysis conducted in this study are also used to compare the gust response factors (GFT), defined as the ratio between peak total response to the mean response, to those obtained from the ASCE code (GFT-ASCE). It has been noticed that the gust response factors obtained from the ASCE code lead to conservative peak responses for both towers and conductors of the chosen line

Employing a Recurrent Linear Model to Guide Mobile Robots

Controller adaptation is always a major concern. A controller that meets certain performance design objectives can not be satisfactory unless it can preserve them in the presence of system parameters fluctuation over a wide range of operation conditions. For autonomous robots, a controller that preserves stability and robustness dynamically is especially desirable. This paper presents, therefore, a design of an adaptive controller using recurrent linear model (RLM) to guide a robot driven by multi-references. Moreover, it shows how the artificial neural networks (ANN) span from their classical pattern recognition to the adaptive behaviour learning. The main objective of that is to maintain the stability and to reinforce the robustness of the overall control system by which the transition among different set points will be smoothed. The RLM is systematic, methodical and easy to design

Adaptive Navigation und Bahnregelung für autonome mobile Roboter

Exploring autonomy in robotics is a meaningful task. The intuitive definition of autonomy is the capability of a robot to make a decision based on its own knowledge, acquired by its distributed sensors, without any human interference. Throughout this framework we discuss some algorithms and techniques underlying the subjects of adaptive navigation and motion planning for autonomous mobile robots. Mobile Robots will play an important role in many future applications, such as personal and service robots, handicapped aid, entertainment, space exploration, medical applications, nuclear industry, surveillance or autonomous transportation. It is the mobility that distinguishes a mobile platform from robot manipulators and gives the possibility to actively and adaptively interact with the environment and humans. In real world environments actual mobile robot platforms will need increased adaptability and autonomy with better techniques for navigation safety, map building, obstacle avoidance and path planning. This study will first focus on sensor integration and adapted interaction that is considered one of the major basic concepts for mobile platforms. The contributions of this study arise from a formulation of new methods and techniques for sensor integration, mapping, motion planning and adaptive navigation instead of previously dominant approaches. By manipulating the manner in which feature information of sensor data is incorporated into processing, it can be shown that significant improvements in the performance of the presented algorithms can be attained. Moreover, the simplicity and the efficiency of the applied techniques succeeded to reinforce the robustness of the overall system in static and dynamic environments. The key idea of that is to achieve the following tasks improvement of system autonomy, reinforcement of the overall stability, enhancement of precision, increment of flexibility, reduction of energy consumption and more adaptation.In dieser Arbeit wurde die Pfadplanung und die adaptive Regelung mobiler Roboter diskutiert. Die Robustheit des Regelungssystems wurde durch Sensorintegration verstärkt, z.B. Sonarsensoren, Laserscanner, CCD-Kameras und einen geomagnetischen Kompass. Dabei wurde die Einsatzumgebung abgebildet, die Kollision vermieden und die Geschwindigkeit bei der Objektverfolgung geregelt

Adaptive visual guidance of mobile robots based on takagi-sugeno model

In industrial applications arises the need for a high level cognition system to guide mobile robots robustly. This article addresses an integrated solution to achieve this task. The proposed system exhibits robust tracking of a visual guide and provides an adaptive interaction, which maintains the stability of the overall system against fluctuations of internal or external system parameters. Moreover, the proposed guidance system counteracts the noise influence regarding vision and sensory entries. This system relies on a biologically inspired sensor integration, this means that outcomes of both of vision system and distributed sensors are fused to obtain a high precision guidance. This study has been implemented on the B21-RWI robot platform (laboratory for autonomous mobile robots, university of Tübingen)