Monte Carlo Localization for an Autonomous Underwater Vehicle with a Low-Cost Sonar

This paper proposes a Monto Carlo based localization (MCL) algorithm for autonomous underwater vehicle (AUV) with a low-cost mechanical scanning imaging sonar (MSIS). As MSIS has a slow-sampling characteristic, its scan is distorted by the vehicle motion during the scan interval and the sonar readings are sparse. Our contribution is introducing this two-stage approach to overcome the shortages of MSIS to achieve accurate localization: 1) the scan formation module is devised to eliminate the motion induced distortion of sonar scan; 2) MCL is applied to estimate the AUV pose accurately by the Dead Reckoning (DR) result and the formed sonar scan. Results of simulation verify that the proposed algorithm performs well in terms of effectiveness and accuracy

Effects of driver behavior style differences and individual differences on driver sleepiness detection

Driving sleepiness is still a major causes of traffic accidents. Individual drivers, under various conditions, act and respond in different manners. This article presents the attempt of a straight-line driving simulator study that examined the effects of driver behavior style differences and individual differences on driver sleepiness detection which is based on driving performance measures. A total of 15 drivers who were classified into two categories through subjective assessment based on a Driver Behavior Questionnaire participated in driving simulator experiments. A total of 18 detection models, including 15 SE models for each subject, an A model for the aggressive drivers, an NA model for the non-aggressive drivers, and a G model for all experiment participants, were developed using support vector machine method based on driving performance characteristic parameters. The results show that the G model is not suitable for all drivers due to its lower mean accuracy of 69.88% (standard deviation = 7.70%) and higher standard deviation. The SE models for each subject show the best detection accuracy performance of 84.26% (standard deviation = 5.38%); however, it is impossible to set up a special detection model for every individual driver. The SD models on different style categories show an accuracy value of 77.54% (standard deviation = 5.78%). The results demonstrate that driver style differences as well as individual differences have great effects on driver sleepiness detection ( F  = 19.148, p  < 0.000)

Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks

Abstract To solve the synchronisation problem associated with fractional‐order hyperchaotic systems, in this study, a new dual‐neural network finite‐time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional‐order hyperchaotic system was realised in a finite time; moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method

Combined Feature Extraction and Random Forest for Laser Self-Mixing Vibration Measurement without Determining Feedback Intensity

To measure the vibration of a target by laser self-mixing interference (SMI), we propose a method that combines feature extraction and random forest (RF) without determining the feedback strength (C). First, the temporal, spectral, and statistical features of the SMI signal are extracted to characterize the original SMI signal. Secondly, these interpretable features are fed into the pretrained RF model to directly predict the amplitude and frequency (A and f) of the vibrating target, recovering the periodic vibration of the target. The results show that the combination of RF and feature extraction yields a fit of more than 0.94 for simple and quick measurement of A and f of unsmooth planar vibrations, regardless of the feedback intensity and the misalignment of the retromirror. Without a complex optical stage, this method can quickly recover arbitrary periodic vibrations from SMI signals without C, which provides a novel method for quickly implementing vibration measurements

An in-source helical membrane inlet single photon ionization time-of-flight mass spectrometer for automatic monitoring of trace VOCs in water

An in-source, helical membrane inlet single photon ionization time-of-flight mass spectrometry (SPI-TOFMS) has been developed to improve the detection sensitivity of trace volatile organic compounds (VOCs) in water. A helical winding membrane and a four-stage differential pumping system of TOFMS was designed to improve and maintain the vapor pressure of analyte, which is linearly associated with the sensitivity of SPI. The helical winding increased the length of the hollow fiber membrane (HFM) from 7 cm to 100 cm and the pressure inside of SPI source was elevated from 3.6 Pa to 28 Pa, and then the sensitivity was increased by 16, 34.7, 32.3, 17.9 and 13.9 times for benzene, ethyl tert-butyl ether (ETBE), aniline, p-xylene, and chlorobenzene (MCBz) respectively. The limits of quantitation (LOQs) of benzene, ETBE, aniline, p-xylene and MCBz were 0.014, 0.143, 0.556, 0.036, 0.025 mu g L-1 respectively with a measurement time of 50 s, which were enhanced by more than one order of magnitude compared to our previous work (reference [32]). The in-source design of helical winding membrane i.e. putting the membrane inside the SPI source dramatically reduced the response time to 1.33 min. This system has been evaluated for VOCs in sewage water of different laboratory buildings and automatic monitoring the pollutants in sewage water from a biological laboratory building. The automatic continuous analysis of organic pollutants in water has very important significance and broad application prospect for online assessment of water quality

Research on Blast Furnace Tuyere Image Anomaly Detection, Based on the Local Channel Attention Residual Mechanism

The channel attention mechanism is widely used in deep learning. However, the existing channel attention mechanism directly performs the global average pooling and then full connection for all channels, which causes the local information to be ignored and the feature information cannot be reasonably assigned with the proper weights. This paper proposed a local channel attention module, based on the channel attention. This module focuses on the local information of the feature image, obtains the weight of each regional channel through convolution, and then integrates the information, so that the regional information can be fully utilized. Moreover, the local channel attention module is combined with the residual module, and the local channel attention residual network LSERNet is constructed to detect the abnormal state of the blast furnace tuyere image. With sufficient experiments on the collected datasets of the blast furnace tuyere, the results show that the proposed method can efficiently extract the feature information, and the recognition accuracy of the LSERNet model reached 98.59%. Further, our model achieved the highest accuracy, compared with SE-ResNet50, ResNet50, LSE-ResNeXt, SE-ResNeXt, and ResNeXt models

Single photon ionization time-of-flight mass spectrometry with a windowless RF-discharge lamp for high temporal resolution monitoring of the initial stage of methanol-to-olefins reaction

Methanol-to-olefins (MTO) is a very important industrial catalysis technique for the production of light olefins, which is of great economic value and strategic significance. However, it is a great challenge for the traditional analytical methods to obtain the real-time information of product variation during MTO reaction process, which is vital for the conversion process research and mechanism explanation. In this study, a single photon ionization time-of-flight mass spectrometry (SPI-TOFMS) based on a windowless RF-discharge (WLRF) lamp was developed for real-time measurement of catalytic product during the initial stage of MTO reaction. The vacuum ultraviolet (VUV) photon energy was easily adjusted by changing the discharge gas. Argon (Ar) gas was eventually adopted as the discharge gas, since it produces photons with appropriate energy of 11.6 eV and 11.8 eV for ionization of light olefin molecules. The detection sensitivities of ethylene and propylene were largely improved to a substantially similar level with limits of detection (LODs) down to 16.98 and 9.64 ppbv, respectively. The initial stage of MTO reaction was real-time monitored with a high temporal resolution of 0.5 s, revealing that ethylene was the first olefin product followed by propylene. The successful application of WLRF-SPI-TOFMS in the monitoring of MTO catalytic process indicated broad application prospects of this instrument in the industrial reaction process monitoring