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The objective of this study is to develop a wireless ultrasonic structural health monitoring (SHM) system for aircraft wing inspection. In part I of the study (Zhao et al 2007 Smart Mater. Struct. 16 1208–17), small, low cost and light weight piezoelectric (PZT) disc transducers were bonded to various parts of an aircraft wing for detection, localization and growth monitoring of defects. In this part, two approaches for wirelessly interrogating the sensor/actuator network were developed and tested. The first one utilizes a pair of reactive coupling monopoles to deliver 350 kHz RF tone-burst interrogation pulses directly to the PZT transducers for generating ultrasonic guided waves and to receive the response signals from the PZTs. It couples enough energy to and from the PZT transducers for the wing panel inspection, but the signal is quite noisy and the monopoles need to be in close proximity to each other for efficient coupling. In the second approach, a small local diagnostic device was developed that can be embedded into the wing and transmit the digital signals FM-modulated on a 915 MHz carrier. The device has an ultrasonic pulser that can generate 350 kHz, 70 V tone-burst signals, a multiplexed A/D board with a programmable gain amplifier for multi-channel data acquisition, a microprocessor for circuit control and data processing, and a wireless module for data transmission. Power to the electronics is delivered wirelessly at X-band with an antenna–rectifier (rectenna) array conformed to the aircraft body, eliminating the need for batteries and their replacement. It can effectively deliver at least 100 mW of DC power continuously from a transmitter at a range of 1 m. The wireless system was tested with the PZT sensor array on the wing panel and compared well with the wire connection case.

Landslides are a common type of natural disaster in mountainous areas. As a result of the comprehensive influences of geology, geomorphology and climatic conditions, the susceptibility to landslide hazards in mountainous areas shows obvious regionalism. The evaluation of regional landslide susceptibility can help reduce the risk to the lives of mountain residents. In this paper, the Shannon entropy theory, a fuzzy comprehensive method and an analytic hierarchy process (AHP) have been used to demonstrate a variable type of weighting for landslide susceptibility evaluation modeling, combining subjective and objective weights. Further, based on a single factor sensitivity analysis, we established a strict criterion for landslide susceptibility assessments. Eight influencing factors have been selected for the study of Zhen'an County, Shan'xi Province: the lithology, relief amplitude, slope, aspect, slope morphology, altitude, annual mean rainfall and distance to the river. In order to verify the advantages of the proposed method, the landslide index, prediction accuracy P, the R-index and the area under the curve were used in this paper. The results show that the proposed model of landslide hazard susceptibility can help to produce more objective and accurate landslide susceptibility maps, which not only take advantage of the information from the original data, but also reflect an expert's knowledge and the opinions of decision-makers.

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

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We first present two GPU implementations of the standard Inverse Distance Weighting (IDW) interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP). Then we evaluate the power of GPU acceleration for IDW interpolation algorithm by comparing the performance of CPU implementation with three GPU implementations, that is, the naive version, the tiled version, and the CDP version. Experimental results show that the tilted version has the speedups of 120x and 670x over the CPU version when the power parameter p is set to 2 and 3.0, respectively. In addition, compared to the naive GPU implementation, the tiled version is about two times faster. However, the CDP version is 4.8x∼6.0x slower than the naive GPU version, and therefore does not have any potential advantages in practical applications.

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This paper presents a novel bird monitoring and recognition system in noisy environments. The project objective is to avoid bird strikes to aircraft. First, a cost-effective microphone dish concept (microphone array with many concentric rings) is presented that can provide directional and accurate acquisition of bird sounds and can simultaneously pick up bird sounds from different directions. Second, direction-of-arrival (DOA) and beamforming algorithms have been developed for the circular array. Third, an efficient recognition algorithm is proposed which uses Gaussian mixture models (GMMs). The overall system is suitable for monitoring and recognition for a large number of birds. Fourth, a hardware prototype has been built and initial experiments demonstrated that the array can acquire and classify birds accurately.