Caseymedlin4062

The correction based on nighttime profiles is recommended as the primary method as it captures dark measurements with the largest dynamic range of temperature. Surprisingly, more than 28% of daytime profiles (130,674 in total) were found to record significant downwelling irradiance at 240-250 dbar. The correction is shown to be small relative to near-surface radiance and thus most useful for studies investigating light fields in the twilight zone and the impacts of radiance on deep organisms. Based on these findings, we recommend that BGC-Argo floats profile occasionally at night and to depths greater than 250 dbar. We provide codes to perform the dark corrections.In this study, a measurement system for gas generation of coal-rock under temperature-pressure coupling was developed by adding gas extraction, collection, and flow-monitoring devices to the original stainless-steel liquid seepage pipeline of an MTS-815 rock triaxial testing machine, which can be used to study the production mechanism of coalbed methane in a real geological environment. The system has the functions of axial loading, confining pressure loading, continuous heating, gas gathering, etc., and has the advantages of good air tightness, high accuracy and stability, long-term loading and heating, and controllable single variables. The preliminary test for the gas production of anthracite in the Shaanxi Formation of the Qinshui Basin under temperature-pressure coupling was carried out by the developed test system. The results show that the test system can provide accurate and effective measurement means for the study of gas production by coal-rock deformation and is expected to provide effective help for the control and exploitation of coalbed methane.Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired filters were applied to the color channels of an input image to enhance those statistical regularities in the image to which the human visual system is sensitive. From the obtained feature maps, the statistics of a wide range of local feature descriptors were extracted to compile quality-aware features since they treat images from the human visual system's point of view. To prove the efficiency of the proposed method, it was compared to 16 state-of-the-art NR-IQA techniques on five large benchmark databases, i.e., CLIVE, KonIQ-10k, SPAQ, TID2013, and KADID-10k. It was demonstrated that the proposed method is superior to the state-of-the-art in terms of three different performance indices.Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive.Data receiving frontends using avalanche photodiodes are used in optical free-space communications for their effective sensitivity, large detection area, and uncomplex operation. SAR405838 molecular weight Precise control of the high voltage necessary to trigger the avalanche effect inside the photodiode depends on the semiconductor's excess noise factor, temperature, received signal power, background light, and also the subsequent thermal noise behavior of the transimpedance amplifier. Several prerequisites must be regarded and are explained in this document. We focus on the application of using avalanche photodiodes as data receivers for the on/off-keying of modulated bit streams with a 50% duty cycle. Also, experimental verification of the performance of the receiver with background light is demonstrated.Insight into, and measurements of, muscle contraction during movement may help improve the assessment of muscle function, quantification of athletic performance, and understanding of muscle behavior, prior to and during rehabilitation following neuromusculoskeletal injury. A self-adhesive, elastic fabric, nanocomposite, skin-strain sensor was developed and validated for human movement monitoring. We hypothesized that skin-strain measurements from these wearables would reveal different degrees of muscle engagement during functional movements. To test this hypothesis, the strain sensing properties of the elastic fabric sensors, especially their linearity, stability, repeatability, and sensitivity, were first verified using load frame tests. Human subject tests conducted in parallel with optical motion capture confirmed that they can reliably measure tensile and compressive skin-strains across the calf and tibialis anterior. Then, a pilot study was conducted to assess the correlation of skin-strain measurements with surface electromyography (sEMG) signals. Subjects did biceps curls with different weights, and the responses of the elastic fabric sensors worn over the biceps brachii and flexor carpi radialis (i.e., forearm) were well-correlated with sEMG muscle engagement measures. These nanocomposite fabric sensors were validated for monitoring muscle engagement during functional activities and did not suffer from the motion artifacts typically observed when using sEMGs in free-living community settings.This paper is concerned with individual identification by late fusion of two-stream deep networks from Electromyogram (EMG) signals. EMG signal has more advantages on security compared to other biosignals exposed visually, such as the face, iris, and fingerprints, when used for biometrics, at least in the aspect of visual exposure, because it is measured through contact without any visual exposure. Thus, we propose an ensemble deep learning model by late information fusion of convolutional neural networks (CNN) and long short-term memory (LSTM) from EMG signals for robust and discriminative biometrics. For this purpose, in the ensemble model's first stream, one-dimensional EMG signals were converted into time-frequency representation to train a two-dimensional convolutional neural network (EmgCNN). In the second stream, statistical features were extracted from one-dimensional EMG signals to train a long short-term memory (EmgLSTM) that uses sequence input. Here, the EMG signals were divided into fixed lengths, and feature values were calculated for each interval. A late information fusion is performed by the output scores of two deep learning models to obtain a final classification result. To confirm the superiority of the proposed method, we use an EMG database constructed at Chosun University and a public EMG database. The experimental results revealed that the proposed method showed performance improvement by 10.76% on average compared to a single stream and the previous methods.The compound fault acoustic signal of a rolling bearing has the characteristics of a varying noise mixture, a low signal-to-noise ratio (SNR), and nonlinearity, which makes it difficult to separate and extract exactly the fault features of compound fault signals. A fault feature extraction approach combining adaptive variational modal decomposition (AVMD) and improved multiverse optimization (IMVO) algorithm parameterized maximum correlated kurtosis deconvolution (MCKD)-named AVMD-IMVO-MCKD-is proposed. In order to adaptively select the parameters of VMD and MCKD, an adaptive optimization method of VMD is proposed, and an improved multiverse optimization (IMVO) algorithm is proposed to determine the parameters of MCKD. Firstly, the acoustic signal of bearing compound faults is decomposed by AVMD to generate several modal components, and the optimal modal component is selected as the reconstruction signal depending on the minimum information entropy of the modal components. Secondly, IMVO is utilized to select the parameters of MCKD, and then MCKD processing is performed on the reconstructed signal. Finally, the compound fault features of the bearing are extracted by the envelope spectrum. Both simulation analysis and acoustic signal experimental data analysis show that the proposed approach can efficiently extract the acoustic signal fault features of bearing compound faults.Transmitter-receiver (T-R) probes are widely used in the eddy-current testing of carbon fibre reinforced plastics (CFRP). However, T-R probes have the disadvantage of being highly sensitive to lift-off. On this basis, lift-off interference can be eliminated by differential structure. However, due to the electrical anisotropy of CFRP, the detection sensitivity of the side-by-side T-R probe and traditional R-T-R differential probe are greatly affected by the scanning angle, and the probe often needs to scan the sample along a specific path to achieve the ideal required detection effect. To solve these problems, a symmetrical dual-transmit-dual-receive (TR-TR) differential probe is designed in this paper. The detection performance of the TR-TR probe was verified by simulation and experiments. Results show that the TR-TR probe is less affected by the scanning angle and lift-off when used in CFRP defect detection, and has high detection sensitivity. However, the imaging results of the TR-TR probe do not show the defect characteristics straightforwardly. To solve this problem, a defect feature extraction algorithm is proposed in this paper. The results show that the defect feature extraction algorithm can locate and size the defect more accurately and improve the signal-to-noise ratio.Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m.