HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images

We propose a robust and accurate method for reconstructing 3D hand mesh from monocular images. This is a very challenging problem, as hands are often severely occluded by objects. Previous works often have disregarded 2D hand pose information, which contains hand prior knowledge that is strongly correlated with occluded regions. Thus, in this work, we propose a novel 3D hand mesh reconstruction network HandGCAT, that can fully exploit hand prior as compensation information to enhance occluded region features. Specifically, we designed the Knowledge-Guided Graph Convolution (KGC) module and the Cross-Attention Transformer (CAT) module. KGC extracts hand prior information from 2D hand pose by graph convolution. CAT fuses hand prior into occluded regions by considering their high correlation. Extensive experiments on popular datasets with challenging hand-object occlusions, such as HO3D v2, HO3D v3, and DexYCB demonstrate that our HandGCAT reaches state-of-the-art performance. The code is available at https://github.com/heartStrive/HandGCAT.Comment: 6 pages, 4 figures, ICME-2023 conference pape

Measurement and Interpretation of the Effect of Electrical Sliding Speed on Contact Characteristics of On-Load Tap Changers

This paper analyzes the effect of sliding speed on the electrical conductivity and friction properties of the contact pair of an on-load tap changer (OLTC). Reciprocating current-carrying tribological tests were carried out on a rod–plate–copper–tin–copper contact galvanic couple at different sliding speeds in air and insulating oil media. The results show that as the sliding speed increases from 24 mm/s to 119 mm/s, the average contact resistance in air increases from 0.2 Ω to 0.276 Ω, and the average contact resistance in insulating oil also increases from 0.2 Ω to 0.267 Ω. At 119 mm/s, the maximum contact resistance in insulating oil reaches 0.3 Ω. The micro-topography images obtained by scanning electron microscopy show that with the increase in sliding speed, the wear mechanisms in the air are mainly abrasive wear and adhesive wear, and the wear mechanisms in oil are mainly layered wear and erosion craters; high sliding speed and arcing promote contact surface fatigue and crack generation. X-ray photoelectron spectroscopy was used to analyze the surface. The copper oxide in the air and the cuprous sulfide in the insulating oil cause the surface film resistance, and the total contact resistance increases accordingly. In addition, the test shows that 119 mm/s in air and 95 mm/s in insulating oil are the speed thresholds. Below these speed thresholds, the increase in contact resistance is mainly caused by mechanical wear. Above these thresholds, the increase in contact resistance is mainly caused by arc erosion and chemical oxidation processes. Non-mechanical factors exacerbate the deterioration of the contact surface and become the main factor for the increase in contact resistance

Programming hydrogel adhesion with engineered polymer network topology

Hydrogel adhesion that can be easily modulated in magnitude, space, and time is desirable in many emerging applications ranging from tissue engineering, and soft robotics, to wearable devices. In synthetic materials, these complex adhesion behaviors are often achieved individually with mechanisms and apparatus that are difficult to integrate. Here, we report a universal strategy to embody multifaceted adhesion programmability in synthetic hydrogels. By designing the surface network topology of a hydrogel, supramolecular linkages that result in contrasting adhesion behaviors are formed on the hydrogel interface. The incorporation of different topological linkages leads to dynamically tunable adhesion with high-resolution spatial programmability without alteration of bulk mechanics and chemistry. Further, the association of linkages enables stable and tunable adhesion kinetics that can be tailored to suit different applications. We rationalize the physics of chain slippage, rupture, and diffusion that underpins emergent programmable behaviors. We then incorporate the strategy into the designs of various devices such as smart wound patches, fluidic channels, drug-eluting devices, and reconfigurable soft robotics. Our study presents a simple and robust platform in which adhesion controllability in multiple aspects can be easily integrated into a single design of a hydrogel network

Construction of a prognostic model of lung adenocarcinoma based on machine learning

In order to more accurately predict the prognosis and survival of lung adenocarcinoma patients, this paper used the gene expression and clinical information data of lung adenocarcinoma patients in the open database of TCGA to jointly construct a prognosis model of lung adenocarcinoma. Three difference analysis methods and univariate cox regression analysis were used as the preliminary screening method. By comparing the variable selection ability of lasso regression and random survival forest, comparing the performance of cox proportional risk regression model and random survival forest model, and integrating clinical data, a model that can more accurately predict the prognosis of lung adenocarcinoma patients was constructed. After comparison and selection, lasso regression was used to select variables and cox proportional risk model was used as the prediction model. The consistency index of the model reached 0.712. The AUC for 1-year, 3-year and 5-year survival of lung adenocarcinoma patients in the validation set were 0.808, 0.816 and 0.754, respectively. After the fusion of clinical data, the 1-year, 3-year and 5-year survival prediction AUC in the validation set were 0.840, 0.836 and 0.865, respectively, indicating that the model had good predictive performance

Gazelle: A Low Latency Framework for Secure Neural Network Inference

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time

A Single Dose of Baicalin Has No Clinically Significant Effect on the Pharmacokinetics of Cyclosporine A in Healthy Chinese Volunteers

Despite its narrow therapeutic window and large interindividual variability, cyclosporine A (CsA) is the first-line therapy following organ transplantation. Metabolized mainly by CYP3A and being a substrate of P-glycoprotein (P-gp), CsA is susceptible to drug–drug interactions. Baicalin (BG) is a drug used for adjuvant therapy of hepatitis in traditional Chinese medicine. Since its aglycone baicalein (B) inhibits CYP3A and P-gP, co-administration might affect CsA pharmacokinetics. This study investigated the effect of BG on CsA pharmacokinetics. In a two-period study, 16 healthy volunteers received a single 200 mg oral CsA dose alone (reference period) or in combination with 500 mg BG (test period). Pharmacokinetic evaluation of CsA was carried out using non-compartmental analysis (NCA) and population pharmacokinetics (popPK). Treatments were compared using the standard bioequivalence method. Based on NCA, 90% CIs of AUC and Cmax test-to-reference ratios were within bioequivalence boundaries. In the popPK analysis, a two-compartment model (clearance/F 62.8 L/h, central and peripheral volume of distribution/F 254 L and 388 L) with transit compartments for absorption appropriately described CsA concentrations. No clinically relevant effect of 500 mg BG co-administration on CsA pharmacokinetics was identified and both treatments were well tolerated

Voltage Stability Analysis of Front-End Speed Controlled Wind Turbine Integrated into Regional Power Grid Based on Bifurcation Theory

Since wind power has characteristics such as intermittent and fluctuation, the integration of large-scale wind turbines into the power grid will bring a great impact on the voltage stability of the system. In this paper, the influence of the front-end speed controlled wind turbine (FSCWT) on the system voltage stability is studied. An actual model of the wind turbines, including the FSCWTs, connected to a regional power grid in Zhangye, Gansu Province, is established. Firstly, differential-algebraic equations (DAEs) describing the dynamic characteristics of the wind turbine are given and the mathematical model of the system includes FSCWT is established. The continuation method is used to track the balance solution of the DEAs within given parameter intervals. Based on that, the influence of the reactive power variation and wind speed fluctuation on the stability of system voltage is analyzed through both the bifurcation theory and the time-domain simulation. Results show that the Hopf bifurcation (HB) and the saddle-node bifurcation (SNB) are inherited for the system, indicating that such bifurcations are the essence of nonlinear dynamics that lead to voltage instability. The greater the disturbance of the bifurcation parameter Q1, the shorter the time of voltage collapse and the smaller the stable operation area of the system. With the increase of wind speed, the amplitude of system voltage will increase slightly, but the HB point will appear in advance, which is more likely to lead to voltage instability and further reduce the stable operation area of system voltage

On Variable-Universe Fuzzy Control for Drive Chain of Front-End Speed Regulated Wind Generator

The rapid development of wind generation technology has boosted types of the new topology wind turbines. Among the recently invented new wind turbines, the front-end speed regulated (FSR) wind turbine has attracted a lot of attention. Unlike conventional wind turbine, the speed regulation of the FSR machines is realized by adjusting the guide vane angle of a hydraulic torque converter, which is converterless and much more grid-friendly as the electrically excited synchronous generator (EESG) is also adopted. Therefore, the drive chain control of the wind turbine owns the top priority. To ensure that the FSR wind turbine performs as a general synchronous generator, this paper firstly modeled the drive chain and then proposed to use the variable-universe fuzzy approach for the drive chain control. It helps the wind generator operate in a synchronous speed and outperform other types of wind turbines. The multipopulation genetic algorithm (MPGA) is adopted to intelligently optimize the parameters of the expansion factor of the designed variable-universe fuzzy controller (VUFC). The optimized VUFC is applied to the speed control of the drive chain of the FSR wind turbine, which effectively solves the contradiction between the low precision of the fuzzy controller and the number of rules in the fuzzy control and the control accuracy. Finally, the main shaft speed of the FSR wind turbine can reach a steady-state value around 1500 rpm. The response time of the results derived using VUFC, compared with that derived from a neural network controller, is only less than 0.5 second and there is no overshoot. The case study with the real machine parameter verifies the effectiveness of the proposal and results compared with conventional neural network controller, proving its outperformance

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

As vital equipment in high-speed train power supply systems, the failure of onboard traction transformers affect the safe and stable operation of the trains. To diagnose faults in onboard traction transformers, this paper proposes a hybrid optimization method based on quickly and accurately using support vector machines (SVMs) as fault diagnosis systems for onboard traction transformers, which can accurately locate and analyze faults. Considering the limitations of traditional transformers for identifying faults, this study used kernel principal component analysis (KPCA) to analyze the feature quantity of dissolved gas analysis (DGA) data, electrical test data, and oil quality test data. The improved seagull optimization algorithm (ISOA) was used to optimize the SVM, and a Henon chaotic map was introduced to initialize the population. Combined with differential evolution (DE) based on the adaptive formula, the foraging formula of the seagull optimization algorithm (SOA) was improved to increase the diversity of the algorithm and enhance its ability to find the optimal parameters of SVM, which made the simulation results more accurate. Finally, the KPCA–ADESOA–SVM model was constructed and applied to fault diagnosis for the traction transformer. The example analysis compared the diagnosis results of the proposed diagnosis model with those of the traditional diagnosis model, showing further optimization of the feature quantity and improvements in the diagnosis accuracy. This proves that the proposed diagnosis model has high generalization performance and can effectively increase the fault diagnosis accuracy and speed of traction transformers