DETECTION OF LONGITUDINAL DEFECT IN PIPES USING TORSIONAL MODES

The multi-modes and disperse characteristics of torsional modes in pipes are investigated theoretically and experimentally. At all frequencies, both phase velocity and group velocity of the lowest torsional mode T（0,1） are constant and equal to shear wave velocity. T（0,1） mode at all frequencies is the fastest torsional mode. In the experiments, T（0,1） mode is excited and received in pipes using 9 thickness shear vibration mode piezoelectric ceramic elements. Furthermore, an artificial longitudinal defect of a 4 m long pipe is detected using T（0,1） mode at 50 kHz. Experimental results show that it is feasible for longitudinal defect detection in pipes using T（0,1） mode of ultrasonic guided waves.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

Physical Activity Calculation by Transport Modes Detection Using Fuzzy Logic

This research presents a methodology,to calculate the amount of physical activity during the transportation.It contains the following steps:(1)trip and activity detection(2)speed calculation(3)splitting trips into trip-leg(4)transportation mode detection and(5)physical activity calculation.The Global Positioning System is used to record the transport activities,either single mode or multimode.During the trip execution,the travel behaviour and the travel mode are also observed to obtain the physical activity levels.The physical activity levels are calculated by taking the ratio of the Total Energy Expenditure and the Basal Metabolic Rate.To obtain the results,an automated system is presented which calculates the speed and also detects the mode of each trip-leg.It also calculates the amount of physical activity.The obtained physical activity levels for the recorded 1750 trips are unit less and range from 1.10 to 2.00.By using the motorized transportation mode,the physical activity levels stay low and the subject failed to achieve the recommended health guideline.The minimum value for the moderate level of physical activity is 1.6.The requirement can be fully achieved when the transportation mode is active i.e.walking,cycling,and performed at moderate intensity level for at least 30 min a day.

Review and evaluation of methods in transport mode detection based on GPS tracking data

Mobility data,based on global positioning system(GPS)tracking,have been widely used in many areas,such as analyzing travel patterns,investigating transport safety and efficiency,and evaluating travel impacts.Transport modes are essential factors in understanding mobility within the transport system.Therefore,in this study,a significant number of algorithms were tested for transport mode detection.However,no conclusive recommendations can be drawn regarding which method should be used.The evaluation of the performance of the algorithms was not discussed systematically either in current literature.This paper aims to provide an in-depth review of the methods applied in transport mode detection based on GPS tracking data.The performances of the reviewed methods are then compared and evaluated to provide guidance in choosing algorithms for transport mode detection based on GPS tracking data.The results indicate that the majority of current studies are based on a supervised learning method for transport mode detection.Many of the reviewed methods first require manual dataset labeling,which can produce major drawbacks,such as inefficiency and human errors.It was also found that deep learning approaches have the potential to deal with large amounts of unlabeled raw GPS datasets and increase the accuracy and efficiency of transport mode detection.

Improved Strategies for CRISPR-Cas12-based Nucleic Acids Detection

The COVID-19 pandemic has brought great challenges to traditional nucleic acid detection technology.Thus,it is urgent to develop a more simple and efficient nucleic acid detection technology.CRISPR-Cas12 has signal amplification ability,high sensitivity and high nucleic acid recognition specificity,so it is considered as a nucleic acid detection tool with broad development prospects and high application value.This review paper discusses recent advances in CRISPR-Cas12-based nucleic acid detection,with an emphasis on the new research methods and means to improve the nucleic acid detection capability of CRISPR-Cas12.Strategies for improving sensitivity,optimization of integrated detection,development of sim-plified detection mode and improvement of quantitative detection capabilities are included.Finally,the future development of CRISPR-Cas12-based nucleic acids detection is prospected.

Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system,which conducts natural and close cooperation with the human by recognizing human locomotion timely.Requiring subject-specific training is the main challenge of the existing approaches,and most methods have the problem of insufficient recognition.This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition(LMR)method based on GA-CNN for a lower limb exoskeleton system.The LMR method is a combination of Convolutional Neural Networks(CNN)and Genetic Algorithm(GA)-based multi-sensor information selection.To improve network performance,the hyper-parameters are optimized by Bayesian optimization.An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method.Twelve locomotion modes,which composed an integral locomotion system for the daily application of the exoskeleton,can be recognized by the proposed method.According to a series of experiments,the recognizer shows strong comprehensive abilities including high accuracy,low delay,and sufficient adaption to different subjects.

Observations of the Singlets of Higher-Degree Modes Based on the OSE

In this study,we selected 18 SG（superconducting gravimeter）records from 15 GGP stations with 99 vertical and 69 horizontal components of IRIS broad-band seismograms during 2004 Sumatra Earthquake to detect the splitting of higher-degree Earth’s free oscillations modes（0S4,0S7〈sub〉0S10,2S4,1S5,2S5,1S6）and 12 inner-core sensitive modes（25S2,27S2,6S3,9S3,13S3,15S3,11S4,18S4,8S5,11S5,23S5,16S6）by using OSE（optimal sequence estimation）method which only considers self-coupling.Results indicate that OSE can completely isolate singlets of high-degree modes in time-domain,effectively resolve the coupled multiplets independently,and reduce the possibility of mode mixing and end effect,showing that OSE could improve some signals’signal-to-noise ratio.Comparing the results of SG records with seismic data sets suggests that the number of SG records is inadequate to detect all singlets of higher modes.Hence we mainly selected plentiful seismograms of IRIS to observe the multiplets of higher modes.We estimate frequencies of the singlets using AR method and evaluate the measurement error using bootstrap method.Besides,we compared the observations with the predictions of PREM-tidal model.This study demonstrates that OSE is effective in isolating singlets of Earth’s free oscillations with higher modes.The experimental results may provide constraints to the construction of 3D Earth model.

Transmission of 200 Tb/s （375 × 3 × 178.125 Gb/s） PDM-DFTS-OFDM-32QAM super channel over lkm FMF

A few-mode fiber （FMF） is designed to support three spatial modes （LP01, LP 11a, and LP 11 b） and fabricated through plasma chemical vapor deposition （PCVD）and rod-in-tube （RIT） method. Using PDM-DFTS-OFDM- 32QAM modulation, wavelength division multiplexing, mode multiplexing, and coherent detection, we successfully demonstrated 200Tb/s （375× 3 × 178.125Gb/s） signal over 1 km FMF using C and L bands with 25 GHz channel spacing. After 1 km FMF transmission, all the tested bit error rates （BERs） are below 20% forward error correction （FEC） threshold （2.0 × 10-2）. Within each sub-channel, we achieved a spectral efficiency of 21.375 bits/Hz in the C and L bands.