Failure mechanism and practical load-carrying capacity calculation method of welded hollow spherical joints connected with circular steel tubes

p. 2679-2691According to the ultimate load-carrying capacity obtained from finite element analysis, data point is designed based on orthogonal method, utilizing F-inspection from mathematical statistics to perform multi-parameter and single-factor significance analysis of compressive load capacity. The result indicates that yield strength of spherical material fy are the critical factor that influence the load carrying capacity of hollow spherical joint, as well as wall thickness t, outer diameter of sphere D and outer diameter of steel tube d. Comparatively destructive experiments on 8 typical full-scale joints made from two different graded material, Q235B and Q345B, were conducted to understand directly the structural behavior and the collapse mechanism of the joint, and also to validate the finite element analysis and parameter study. Finally, the simplified theoretical solution is also derived for the loading-carrying capacity of the joint based on the punching shear failure model, and the basic form for the design equation is obtained. By applying the results from the simplified theoretical solution, finite element analysis and experimental study, and utilizing the theory of mathematic statistics and regression analysis, the practical calculation method is established for the load-carrying capacity of the joints subjected to axial compressive forces. By the check of large amount of experiment data, the calculation result obtained from this formula is consistent with experiment result, and the practical formula has safety reserve meeting the regulation in national codes. The achievements from this study can be applied for direct design , and also provide a reference for the revision of relevant design codes.Xue, W.; Yang, L.; Zhang, Q.; Wang, P. (2009). Failure mechanism and practical load-carrying capacity calculation method of welded hollow spherical joints connected with circular steel tubes. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/660

Memory-aware embedded control systems design

Control applications are often implemented on highly cost-sensitive and resource-constrained embedded platforms, such as microcontrollers with a small on-chip memory. Typically, control algorithms are designed using model-based approaches, where the details of the implementation platform are completely ignored. As a result, optimizations that integrate platform-level characteristics into the control algorithms design are largely missing. With the emergence of cyber-physical systems (CPS)-oriented thinking, there has lately been a strong interest in co-design of control algorithms and their implementation platforms, leading to work on networked control systems and computation-aware control algorithms design. However, there has so far been no work on integrating the characteristics of a memory architecture into the design of control algorithms. In this paper we, for the first time, show that accounting for the impact of on-chip memory (or cache) reuse on the performance of control applications motivates new techniques for control algorithms design. This leads to significant improvement in quality of control for given resource availability, or more efficient implementations of embedded control applications. We believe that this paper opens up a variety of possibilities for memory-related optimizations of embedded control systems, that will be pursued by researchers working on computer-aided design for CPS

A multi-view CNN-based acoustic classification system for automatic animal species identification

Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer from several disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based on cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the recognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that the proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement and deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of the proposed acoustic classification system in distinguishing species of animals