This paper studies the state-of-art of Internet of Things (IoT). By enabling new forms of communication between people and things, and between things themselves, IoT would add a new dimension to the world of information and communication just as Internet once did. In this paper, IoT definitions from different perspective in academic communities are described and compared. The main enabling technologies in IoT are summarized such like RFID systems, sensor networks, and intelligence in smart objects, etc. The effects of their potential applications are reviewed. Finally the major research issues remaining open for academic communities are analyzed.
A crack propagation criterion for a rock–concrete interface is employed to investigate the evolution of the fracture process zone (FPZ) in rock–concrete composite beams under three‐point bending (TPB). According to the criterion, cracking initiates along the interface when the difference between the mode I stress intensity factor at the crack tip caused by external loading and the one caused by the cohesive stress acting on the fictitious crack surfaces reaches the initial fracture toughness of a rock–concrete interface. From the experimental results of the composite beams with various initial crack lengths but equal depths under TPB, the interface fracture parameters are determined. In addition, the FPZ evolution in a TPB specimen is investigated by using a digital image correlation technique. Thus, the fracture processes of the rock–concrete composite beams can be simulated by introducing the initial fracture criterion to determine the crack propagation. By comparing the load versus crack mouth opening displacement curves and FPZ evolution, the numerical and experimental results show a reasonable agreement, which verifies the numerical method developed in this study for analysing the crack propagation along the rock–concrete interface. Finally, based on the numerical results, the effect of ligament length on the FPZ evolution and the variations of the fracture model during crack propagation are discussed for the rock–concrete interface fracture under TPB. The results indicate that ligament length significantly affects the FPZ evolution at the rock–concrete interface under TPB and the stress intensity factor ratio of modes II to I is influenced by the specimen size during the propagation of the interfacial crack.
This paper aims to analyze and understand the irregularity and complexity of earthquake ground motions from the perspective of nonlinear dynamics. Chaotic dynamics theory and chaotic time series analysis are suggested to examine the nonlinear dynamical characteristic of strong earthquake ground motions. Based on the power spectral analysis, principal component analysis and modified false nearest neighbors method, it is illustrated qualitatively that the acceleration time series of earthquake ground motions exhibit chaotic property. Next, the chaotic time series analysis is proposed to calculate quantitatively the nonlinear characteristic parameters of acceleration time histories of near-fault ground motions. Numerical results show that the correlation dimension of these ground motions is fractal dimension. Their Kolmogorov entropy is a limited positive value, and their maximal Lyapunov exponent is larger than 0. It is demonstrated that the strong earthquake ground motions present the chaotic property rather than the pure random signals, and the severe irregularity and complexity of ground motions are the reflection of high nonlinearity of earthquake physical process.
<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">Recruitment prediction is a key element for management decisions in many fisheries. A new approach using neural network is developed as a tool to produce a formula for forecasting fish stock recruitment. In order to deal with the local minimum problem in training neural network with back-propagation algorithm and to enhance forecasting precision, neural network’s weights are adjusted by optimization algorithm. It is demonstrated that a well trained artificial neural network reveals an extremely fast convergence and a high degree of accuracy in the prediction of fish stock recruitment.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>
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