An Improved Feature Parameter Extraction Algorithm of Composite Detection Method Based on the Fusion Theory

An improved feature parameter extraction algorithm is proposed in this study to solve the problem of quantitative detection of subsurface defects. Firstly, the common feature parameters from the differential signal of pulsed eddy current and ultrasonic are extracted in time domain and frequency domain. Then, the dispersion model and ReliefF model are established to determine the weights of each parameter. Finally, the weights from the two different algorithms are fused by the D-S evidence theory to determine feature parameters. Compared with the PCA feature parameter algorithm from the pulsed eddy current or ultrasonic, the experiment results show the feature parameters extracted by the algorithm proposed in this paper are more effective in quantitative detection of subsurface defects. It will lead to high accuracy in the subsurface defections

Review of Bolted Connection Monitoring

This paper reviews the research of monitoring technologies for bolted structural connections. The acoustoelastic effect based method, the piezoelectric active sensing method, and the piezoelectric impedance method are the three commonly used to monitor bolted connections. The basic principle and the applications of these three methods are discussed in detail in this paper. In addition, this paper presents a comparison of these methods and discusses their suitability for in situ or real-time bolt connection monitoring

Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND

A Smart Washer for Bolt Looseness Monitoring Based on Piezoelectric Active Sensing Method

Piezoceramic based active sensing methods have been researched to monitor preload on bolt connections. However, there is a saturation problem involved with this type of method. The transmitted energy is sometimes saturated before the maximum preload which is due to it coming into contact with flat surfaces. When it comes to flat contact surfaces, the true contact area will easily saturate with the preload. The design of a new type of bolt looseness monitoring sensor, a smart washer, is to mitigate the saturation problem. The smart washer is composed of two annular disks with contact surfaces that are machined into convex and concave respectively, to eliminate the complete flat contact surfaces and to reduce the saturation effect. One piezoelectric patch is bonded on the non-contact surface of each annular disk. These two mating annular disks form a smart washer. One of the two piezoelectric patches serves as an actuator to generate an ultrasonic wave that propagates through the contact surface; the other one serves as a sensor to detect the propagated waves. The wave energy propagated through the contact surface is proportional to the true contact area which is determined by the bolt preload. The time reversal method is used to extract the peak of the focused signal as the index of the transmission wave energy; then, the relationship between the signal peak and bolt preload is obtained. Experimental results show that the focused signal peak value changes with the bolt preload and presents an approximate linear relationship when the saturation problem is experienced. The proposed smart washer can monitor the full range of the rated preload

Performance Analysis and Working Fluid Selection of a Supercritical Organic Rankine Cycle for Low Grade Waste Heat Recovery

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On improving the accuracy of Visible Light Positioning system based PAPR reduction schemes

International audienceDC-biased optical Orthogonal Frequency Division Multiplexing (DCO-OFDM)-based visible light positioning (VLP) technology along with the Received Signal Strength(RSS) positioning algorithm is widely used to achieve centimeter level positioning accuracy for incoming 5G era, especially indoor environment. However, the DCO-OFDM has the peak-to-average power ratio (PAPR) issue which imposes the nonlinear distortion and it will directly affect the positioning accuracy in the VLP system. The PAPR reduction scheme is urgently needed. Therefore , in this paper, the impact of PAPR reduction scheme on positioning accuracy is investigated. The positioning accuracy with and without the selected mapping (SLM)-based PAPR reduction method are compared. The preliminary simulation results show that the positioning accuracy has been improved by 7.07 cm after using the SLM-based PAPR reduction method. Index Terms-5G, Visible Light Positioning(VLP), Visible Light Communication(VLC), Received Signal Strength, Localiza-tion, DC-biased optical orthogonal frequency division multiplex-ing (DCO-OFDM), peak-to-average power ratio (PAPR), selected mapping (SLM