In this paper, a novel method, relying on Markov Transition Fields (MTF) and deep learning, is proposed to classify the vibration-events and measure vibration-frequency, for $ {\varphi } $ -OTDR based fiber-optic distributed vibration sensor. The normalized time series of a signal detected by ${\varphi }$ -OTDR are converted into an image of MTF, serving as a sample for supervised learning. Next, the MTF image is classified using a convolutional neural network (CNN) and a fully connected neural network. Initially, five different vibration-events including blowing, rain, direct and indirect knocking, and false vibration caused by noises, are classified. Furthermore, approximate single-frequency vibrations (with different central-frequency) are regarded as different classifications, where vibration frequency can be obtained by classification tasks. Compared to conventional method based on phase-demodulation with complex techniques and high cost, the proposed method demonstrates a low-cost and fast-time method. Moreover, learning algorithm is trained and tested through the data sets generated by experiments. To evaluate feasibility and validate the performance of classification, we analyze the test accuracy of classification and relevant receiver operating characteristic curves. The results indicate that our method is effective for the classification of both vibration-events and single-frequency vibrations. We believe that this work provides not only a technical reference for the application of deep learning in measuring the vibration frequency of ${\varphi } $ -OTDR using software-technique, but also an impressive inspiration for event-classification of vibrations by a combination of image-processing methods.

Vibrational energy harvesters, which are generally based on a single cantilever beam, are mainly effective for excitations perpendicular to the beam. However, typical ambient vibrations are of multifrequency and are multidirectional. To achieve full harvesting of multifrequency vibrational energy in multiple directions, this letter proposes a multidirectional vibrational energy harvester utilizing multimodal vibrations of a double-branched beam that has four natural frequencies below 22 Hz. Comparison experiments in different directions demonstrate an improvement of energy harvesting when this double-branched beam is used. Under horizontal excitation, the maximum open-circuit voltage is increased to 1845%. Under vertical excitation, the average power density is increased over 3800 times. The experimental results indicate that this structural design significantly improves the energy collection efficiency in both horizontal and vertical directions, even with a lighter structural weight.

This paper presents an innovative seat suspension with variable stiffness and variable damping (VSVD) via magnetorheological fluid dampers to enhance ride comfort. The VSVD seat suspension is developed based on an innovative structure with springs in series and damping units, and its principles of VSVD and vibration attenuation are evaluated theoretically. The proposed VSVD seat suspension is then manufactured and tested. The test results illustrate that the equivalent stiffness of the seat suspension with power on ( I _s = 2.0 A current) increased to 186% and the equivalent damping ( I _d = 1.0 A) raised to 520% compared to the seat suspension with no current applied. The evaluation of vibration-attenuation capability of the seat suspension is conducted by employing Fuzzy logic controls and both harmonic and random signals are considered as vibration excitations. The experimental data show that the proposed seat suspension with controlled stiffness and damping results in lower seat acceleration compared to a passive seat or one with traditional damping control.

This paper presents a magnetorheological (MR) automobile damper with a self-sensing function. The self-sensing structure is designed based on the principle of triboelectric nanogeneration. The self-sensing performance of the MR automobile damper is verified from the theoretical analysis and experimental results. A vehicle suspension vibration control system composed of 1/4 vehicle suspension, fuzzy control algorithm, and vibration excitation platform is established to test the vibration control performance of self-sensing MR automobile damper (SMRAD). The experimental results show that the fuzzy control system reduces the body acceleration of the vehicle suspension compared with the passive control. The root mean square value of vehicle suspension body acceleration is reduced by 28.8 % compared with the vehicle body acceleration under passive control. This verifies the effectiveness of the self-sensing performance of the speed self-sensing structure of the MR automobile damper. The application of the SMRAD in the vehicle suspension improves the vibration reduction performance of the vehicle suspension.

The limited load capacity and power resources of small-scale fixed-wing drones mean that it is difficult to employ internal high-precision inertial navigation devices to assist with the landing procedure. As an alternative, this paper proposes an attitude measurement system based on a monocular camera. The attitude angles are obtained from a single captured image containing five coded landmark points using the radial constraint method and three-dimensional coordinate transformations. The landing procedure is simulated for pitch angles from −15 ${}^{\circ }$ to −40 ${}^{\circ }$ , roll angles from −15 ${}^{\circ }$ to +15 ${}^{\circ }$ and yaw angles from −15 ${}^{\circ }$ to +15 ${}^{\circ }$ . For roll and pitch angles of approximately 0 ${}^{\circ }$ and −25 ${}^{\circ }$ , respectively, the accuracy of the method reaches 0.01 ${}^{\circ }$ and 0.04 ${}^{\circ }$ . This UAV attitude measurement system obtains an attitude angle by a single captured image, which has great potential for assisting with the landing of small-scale fixed-wing UAVs.