Underwater dual manipulators-Part II: Kinematics analysis and numerical simulation

1104-1112This paper introduces dual-arm underwater manipulators mounted on an autonomous underwater vehicle (AUV), which can accomplish the underwater handling task. Firstly, the mechanical structure of the dual-arm system is briefly introduced, wherein each 4-DOF manipulator has an additional grasping function. In addition, the kinematics model of the manipulator is derived by using the improved D-H method. Secondly, the working space of the underwater dual-arm system is analyzed, which is obtained by using Monte Carlo method. The cubic polynomial interpolation and the five polynomial interpolation trajectory planning methods are compared in the joint space. Finally, with the help of the Robotics Toolbox software, the numerical test is conducted to verify the functions of the underwater dual-arm manipulator system

Numerical study of the fluid fracturing mechanism of granite at the mineral grain scale

Hydraulic fracturing is an essential technique for reservoir stimulation in the process of deep energy exploitation. Granite is composed of different rock-forming minerals and exhibits obvious heterogeneity at the mesoscale, which affects the strength and deformation characteristics of rocks and controls the damage and failure processes. Therefore, in this paper, based on the discrete element fluid-solid coupling algorithm and multiple parallel bond-grain based model (Multi Pb-GBM), a numerical model of a granite hydraulic fracturing test is established to study the evolution of hydraulic fractures in crystalline granite under different ground stress conditions. The main conclusions are as follows. The crack propagation of hydraulic fractures in granite is determined by the in situ stress state, crystal size, and mineral distribution, and the ground stress is the main controlling factor. The final fracture mode affects the maximum principal stress and shear stress, and the generation of cracks changes the distribution of the stress field. The hydraulic fracturing initiation pressure decreases with decreasing crystal size. The influence of the crystal size on the crack inclination angle is mainly reflected in local areas, and the general trend of the fissure dip angle distribution is along the direction of the maximum in situ stress. This study not only has important theoretical significance for clarifying the propagation mechanism of hydraulic fractures but also provides a theoretical basis for deep reservoir reconstruction and energy extraction

Multimodal Medical Image Fusion by Adaptive Manifold Filter

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images

BiTCAN: An emotion recognition network based on saliency in brain cognition

In recent years, with the continuous development of artificial intelligence and brain-computer interfaces, emotion recognition based on electroencephalogram (EEG) signals has become a prosperous research direction. Due to saliency in brain cognition, we construct a new spatio-temporal convolutional attention network for emotion recognition named BiTCAN. First, in the proposed method, the original EEG signals are de-baselined, and the two-dimensional mapping matrix sequence of EEG signals is constructed by combining the electrode position. Second, on the basis of the two-dimensional mapping matrix sequence, the features of saliency in brain cognition are extracted by using the Bi-hemisphere discrepancy module, and the spatio-temporal features of EEG signals are captured by using the 3-D convolution module. Finally, the saliency features and spatio-temporal features are fused into the attention module to further obtain the internal spatial relationships between brain regions, and which are input into the classifier for emotion recognition. Many experiments on DEAP and SEED (two public datasets) show that the accuracies of the proposed algorithm on both are higher than 97%, which is superior to most existing emotion recognition algorithms

Data-Driven Distributed Optical Vibration Sensors: A Review

Distributed optical vibration sensors (DOVS) have attracted much attention recently since it can be used to monitor mechanical vibrations or acoustic waves with long reach and high sensitivity. Phase-sensitive optical time domain reflectometry (Φ-OTDR) is one of the most commonly used DOVS schemes. For Φ-OTDR, the whole length of fiber under test (FUT) works as the sensing instrument and continuously generates sensing data during measurement. Researchers have made great efforts to try to extract external intrusions from the redundant data. High signal-to-noise ratio (SNR) is necessary in order to accurately locate and identify external intrusions in Φ-OTDR systems. Improvement in SNR is normally limited by the properties of light source, photodetector and FUT. But this limitation can also be overcome by post-processing of the received optical signals. In this context, detailed methodologies of SNR enhancement post-processing algorithms in Φ-OTDR systems have been described in this paper. Furthermore, after successfully locating the external vibrations, it is also important to identify the types of source of the vibrations. Pattern classification is a powerful tool in recognizing the intrusion types from the vibration signals in practical applications. Recent reports of Φ-OTDR systems employed with pattern classification algorithms are subsequently reviewed and discussed. This thorough review will provide a design pathway for improving the performance of Φ-OTDR while maintaining the cost of the system as no additional hardware is required

Study on optimization of nano-coatings for ultra-sensitive biosensors based on long-period fiber grating

Bio-chemical sensors are expected to offer high sensitivity and specificity towards the detection of an analyte. It has been found that optical sensors based on long period fiber gratings (LPFGs) meet most of these requirements, particularly when coated with thin and high-refractive index overlays with proper bio-functionalization. In this paper, the influence of properties of the overlay material on the sensitivity of LPFG sensors to bio-analytes is analyzed. It has been observed that the sensitivity of a particular cladding mode of LPFG can be changed drastically with the adhesion of few tens of ‘nm’ of bio-layers to the surface of LPFG. “Volume refractive index sensitivity” and “add-layer sensitivity” of a particular cladding mode, dynamic range, and limit of detection of the sensors have been investigated in the context of overlay materials, bio-functionalization steps, and surrounding buffer medium. The selection criteria of the thin-film deposition technique are discussed with the aim of designing highly sensitive sensors for biological and chemical applications. Concept of optimum overlay thickness has been redefined and an effective case-specific design methodology is proposed

Comprehensive analysis of the association between inflammation indexes and complications in patients undergoing pancreaticoduodenectomy

BackgroundDuring clinical practice, routine blood tests are commonly performed following pancreaticoduodenectomy (PD). However, the relationship between blood cell counts, inflammation-related indices, and postoperative complications remains unclear.MethodWe conducted a retrospective study, including patients who underwent PD from October 2018 to July 2023 at the First Hospital of Chongqing Medical University, and compared baseline characteristics and clinical outcomes among different groups. Neutrophil count (NC), platelet count (PLT), lymphocyte count (LC), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and the product of platelet count and neutrophil count (PPN) were derived from postoperative blood test results. We investigated the association between these indicators and outcomes using multivariable logistic regression and restricted cubic spline analysis. The predictive performance of these indicators was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).ResultA total of 232 patients were included in this study. Multivariate logistic regression and restricted cubic spline analysis showed that all indicators, except for PLT, were associated with clinical postoperative pancreatic fistula (POPF). SII, NLR, and NC were linked to surgical site infection (SSI), while SII, NLR, and PLR were correlated with CD3 complication. PLT levels were related to postoperative hemorrhage. SII (AUC: 0.729), NLR (AUC: 0.713), and NC (AUC: 0.706) effectively predicted clinical POPF.ConclusionIn patients undergoing PD, postoperative inflammation-related indices and blood cell counts are associated with various complications. NLR and PLT can serve as primary indicators post-surgery for monitoring complications