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Pre-training technique has gained tremendous success in enhancing model performance on various tasks, but found to perform worse than training from scratch in some uni-modal situations. This inspires us to think: are the pre-trained models always effective in the more complex multi-modal scenario, especially for the heterogeneous modalities such as audio and visual ones? We find that the answer is No. Specifically, we explore the effects of pre-trained models on two audio-visual learning scenarios: cross-modal initialization and multi-modal joint learning. When cross-modal initialization is applied, the phenomena of "dead channel" caused by abnormal Batchnorm parameters hinders the utilization of model capacity. Thus, we propose Adaptive Batchnorm Re-initialization (ABRi) to better exploit the capacity of pre-trained models for target tasks. In multi-modal joint learning, we find a strong pre-trained uni-modal encoder would bring negative effects on the encoder of another modality. To alleviate such problem, we introduce a two-stage Fusion Tuning strategy, taking better advantage of the pre-trained knowledge while making the uni-modal encoders cooperate with an adaptive masking method. The experiment results show that our methods could further exploit pre-trained models' potential and boost performance in audio-visual learning.

Inspired by increasing demand of advanced pulsed power capacitors, the development of lead-free dielectric ceramic capacitors with high energy storage density and temperature-insensitive performance are extremely crucial. Herein, the lead-free relaxor ferroelectric ceramics based on (1-x) (Na0.5Bi0.5)0.6Sr0.4TiO3- xSr0.7La0.2ZrO3 [abbreviated as (1-x)NBST-xSLZ] are prepared by the solid-state reaction route. The large recoverable energy density (Wrec) of 3.45 J/cm3 and efficiency (η) of 90.1% are simultaneously realized in 0.86NBST-0.14SLZ ceramic due to increased breakdown strength. Furthermore, both the Wrec and η of 0.86NBST-0.14SLZ ceramic display superior of thermal stability (20–180 °C), frequency stability (1–1000 Hz), and cycle stability (104) within a satisfactory range of variation. In addition, the 0.86NBST-0.14SLZ ceramic can also achieve a large current density (CD) of 625 A/cm2, an ultrahigh power density (PD) of 50 MW/cm3 and a fast discharge rate (τ0.90) of 160.8 ns at 160 kV/cm. These results demonstrate that the 0.86NBST-0.14SLZ ceramic could be a highly competitive and eco-friendly relaxor ferroelectric material for next-generation pulsed power capacitors.

Low-field nuclear magnetic resonance (NMR) technology was used to perform the experiments of transverse relaxation time (T2), pore size distribution, and water absorption rate for wood-plastic composites (WPC) with different contents of added slag powder, exploring the water movement and the dynamic changes of pore size during the moisture absorption process of the material under immersion condition. The experimental results were as follows: (1) According to the T2 of H proton and its inversion pattern, the measured porosity had a relatively small difference from that of the weighing method. (2) The pore size distribution graph showed the following: (i) when the immersion time of composite materials was different, the changing law of volume of pores with different radius was different.; (ii) when the material’s immersion time was greater than 216 h, the pore radius and its distribution characteristics showed large differences; (iii) slag powder changed the pore structure of the WPC but did not change the water absorption characteristics of the wheat straw. (3) The changes of water absorption and expansion rate showed that the slag powder changed the time for the materials’ pores to absorb water until saturation and reduced the water absorption and expansion rate. The measurement results were consistent with changing trend in the pore size obtained by low-field NMR relaxometry.

Multimodal learning helps to comprehensively understand the world, by integrating different senses. Accordingly, multiple input modalities are expected to boost model performance, but we actually find that they are not fully exploited even when the multimodal model outperforms its uni-modal counterpart. Specifically, in this paper we point out that existing multimodal discriminative models, in which uniform objective is designed for all modalities, could remain under-optimized uni-modal representations, caused by another dominated modality in some scenarios, e.g., sound in blowing wind event, vision in drawing picture event, etc. To alleviate this optimization imbalance, we propose on-the-fly gradient modulation to adaptively control the optimization of each modality, via monitoring the discrepancy of their contribution towards the learning objective. Further, an extra Gaussian noise that changes dynamically is introduced to avoid possible generalization drop caused by gradient modulation. As a result, we achieve considerable improvement over common fusion methods on different multimodal tasks, and this simple strategy can also boost existing multimodal methods, which illustrates its efficacy and versatility. The source code is available at \url{https://github.com/GeWu-Lab/OGM-GE_CVPR2022}., Comment: Accepted by CVPR 2022 (ORAL)

Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing research focuses primarily on utilizing the labeled set at the methodological level, with less emphasis on the analysis of the labeled set itself. Thus, in this paper, we rethink novel class discovery from the labeled set and focus on two core questions: (i) Given a specific unlabeled set, what kind of labeled set can best support novel class discovery? (ii) A fundamental premise of NCD is that the labeled set must be related to the unlabeled set, but how can we measure this relation? For (i), we propose and substantiate the hypothesis that NCD could benefit more from a labeled set with a large degree of semantic similarity to the unlabeled set. Specifically, we establish an extensive and large-scale benchmark with varying degrees of semantic similarity between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical class structure. As a sharp contrast, the existing NCD benchmarks are developed based on labeled sets with different number of categories and images, and completely ignore the semantic relation. For (ii), we introduce a mathematical definition for quantifying the semantic similarity between labeled and unlabeled sets. In addition, we use this metric to confirm the validity of our proposed benchmark and demonstrate that it highly correlates with NCD performance. Furthermore, without quantitative analysis, previous works commonly believe that label information is always beneficial. However, counterintuitively, our experimental results show that using labels may lead to sub-optimal outcomes in low-similarity settings., Comment: Workshop on Distribution Shifts, 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under adverse conditions. For example, label noise usually leads to less reliable models due to undesired memorization \cite{zhang2017understanding,arpit2017closer}. Wrong knowledge misleads the learning rather than helps. This problem can be handled by two aspects: (i) improving the reliability of a model where the knowledge is from (i.e., knowledge source's reliability); (ii) selecting reliable knowledge for distillation. In the literature, making a model more reliable is widely studied while selective MKD receives little attention. Therefore, we focus on studying selective MKD. Concretely, a generic MKD framework, \underline{C}onfident knowledge selection followed by \underline{M}utual \underline{D}istillation (CMD), is designed. The key component of CMD is a generic knowledge selection formulation, making the selection threshold either static (CMD-S) or progressive (CMD-P). Additionally, CMD covers two special cases: zero-knowledge and all knowledge, leading to a unified MKD framework. Extensive experiments are present to demonstrate the effectiveness of CMD and thoroughly justify the design of CMD. For example, CMD-P obtains new state-of-the-art results in robustness against label noise., Comment: NeurIPS 2022 Workshop(Trustworthy and Socially Responsible Machine Learning) paper

Additional file 1: Figure S1. Musculoskeletal structure of ankle plantar-flexors and hip flexors. Figure S2. Exoskeleton structure parameters. Table S1. Net metabolic rates of walking and running on the treadmill. Table S2. Net metabolic rates of walking and running at different speeds. Table S3. Net metabolic rates of walking and running with common optimal stiffness spring. Table S4. Exoskeleton actuation parameters during walking. Table S5. Exoskeleton actuation parameters during running. Table S6. Biomechanical parameters during walking. Table S7. Biomechanical parameters during running.

Model web services provide an approach for implementing and facilitating the sharing of geographic models. The description and acquisition of inputs and outputs (IO) of geographic models is a key issue in constructing and using model web services. These approaches for describing and acquiring the data formats of the IO of geographic models can be classified into two categories, i.e., intermediate-data-format-based and native-data-format-based. Nonetheless, these two categories mainly consider the description of the IO of geographical models but relatively pay little attention to the acquisition. To address this issue, this paper proposes an approach for automatically parsing data formats of the IO utilizing the relationship between the IO and source codes. This proposed approach can utilize such a strict and coupling relationship and the expression form of the data formats in the source codes to retrospectively derive the IO data format and automatically generate data format documentation. The feasibility of the proposed approach has been verified via a geographical model coded in the FORTRAN language, which shows that it significantly improves the efficiency of writing data format specifications and promotes sharing geographic models as model web services., Comment: 22 pages, 7 figures

Many current deep learning approaches make extensive use of backbone networks pre-trained on large datasets like ImageNet, which are then fine-tuned to perform a certain task. In remote sensing, the lack of comparable large annotated datasets and the wide diversity of sensing platforms impedes similar developments. In order to contribute towards the availability of pre-trained backbone networks in remote sensing, we devise a self-supervised approach for pre-training deep neural networks. By exploiting the correspondence between geo-tagged audio recordings and remote sensing imagery, this is done in a completely label-free manner, eliminating the need for laborious manual annotation. For this purpose, we introduce the SoundingEarth dataset, which consists of co-located aerial imagery and audio samples all around the world. Using this dataset, we then pre-train ResNet models to map samples from both modalities into a common embedding space, which encourages the models to understand key properties of a scene that influence both visual and auditory appearance. To validate the usefulness of the proposed approach, we evaluate the transfer learning performance of pre-trained weights obtained against weights obtained through other means. By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery. The dataset, code and pre-trained model weights will be available at https://github.com/khdlr/SoundingEarth., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Talk@VALSE Webinar:http://valser.org/article-391-1.html

This paper provides an analysis of the trend in autonomous driving traffic and the development of infrastructural support, whereas the requirements on the infrastructural support will be analyzed. Then selected traffic scenes will be implemented in an autonomous driving simulator tool in order to figure out the required parameters to assist the autonomous vehicle from the infrastructure.

Temporal relational modeling in video is essential for human action understanding, such as action recognition and action segmentation. Although Graph Convolution Networks (GCNs) have shown promising advantages in relation reasoning on many tasks, it is still a challenge to apply graph convolution networks on long video sequences effectively. The main reason is that large number of nodes (i.e., video frames) makes GCNs hard to capture and model temporal relations in videos. To tackle this problem, in this paper, we introduce an effective GCN module, Dilated Temporal Graph Reasoning Module (DTGRM), designed to model temporal relations and dependencies between video frames at various time spans. In particular, we capture and model temporal relations via constructing multi-level dilated temporal graphs where the nodes represent frames from different moments in video. Moreover, to enhance temporal reasoning ability of the proposed model, an auxiliary self-supervised task is proposed to encourage the dilated temporal graph reasoning module to find and correct wrong temporal relations in videos. Our DTGRM model outperforms state-of-the-art action segmentation models on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset. The code is available at https://github.com/redwang/DTGRM., Comment: Accepted by the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)

We present a modified fiber model based on the nested hollow core anti-resonant fiber that enables the stable transmission of the orbital-angular-momentum mode HE21. By replacing a pair of nested anti-resonant tubes in the horizontal axis with resonant tubes, the coupling between core mode and cladding mode has been increased. Therefore, the relative strength of fundamental mode HE11 and the first higher mode HE21 has been modified. The numerical simulation results indicate that the loss ratio of the lowest loss HE11 to HE21 can be optimized to more than 187, while the HE21 still maintains a low confinement loss as 0.0027 dB/m. Our research has brought about a solution of low loss hollow core mode propagation in optical fiber. Those properties will make this fiber an ideal medium for blue-detuned atomic guidance.

In order to explore the operation and maintenance characteristics of important auxiliary machines in large-scale power plant coal-fired boilers, a running state assessment model for auxiliary equipment is established. In this paper, taking the complex variability of the operating conditions of thermal power equipment into consider, auto encoder model combined with fuzzy synthetic is proposed. Based on the residual of the model results and the actual power plant operation data, combined with the fuzzy evaluation model to establish a state assessment model, and analyze the actual situation of the induced draft fan of the power plant, to make a real-time assessment of operation status. The evaluation results show the advantages of the state assessment strategy proposed in this paper, and it can reflect the deterioration of the induced draft fan status in time, providing guidance for the operation and maintenance of the equipment.

Since only the molecules that are in direct contact with the TiO2 surface undergo photosensitization, it is challenging to regenerate the TiO2-impregnated chitosan (TIC) adsorbent beads under visible light. This study focused on the role of chitosan swelling properties. It was found that dye-loaded TIC adsorbent exhibited a pH-dependent swelling owing to protonation/deprotonation of free amino groups on chitosan chains. In the acidic medium (pH

In this study, a novel method was developed for chemo-enzymatic synthesis of hydrophilic phytosterol derivatives, phytosteryl polyethylene glycol succinate (PPGS), through an intermediate phytosteryl hemisuccinate (PSHS), which was first chemically prepared and subsequently coupled with polyethylene glycol (PEG) through lipase-catalyzed esterification. The chemical structure of intermediate and goal product were finally confirmed to be PSHS and PPGS by FT-IR, MS and NMR, suggesting that hydrophilic phytosterol derivatives were successfully synthesized. The effects of various parameters on the conversion of PSHS to PPGS were investigated and the highest conversion (>78%) was obtained under the selected conditions: 75 mmol/L PSHS, 1:2M ratio of PSHS to PEG, 50 g/L Novozym 435, 120 g/L 3 A molecular sieves in tert-butanol, 55 °C, 96 h and 200 rpm. The solubility of phytosterols in water was significantly improved by coupling with PEG, facilitating the incorporation into a variety of foods containing water.

The structure design, simulation, fabrication, signal measurement and control of a symmetrical decoupled dual-mass micro-gyroscope (SDDMG) are presented in the paper. The two masses of the SDDMG are linked elastically not only in the drive mode but also in the sense mode and can be resonant driven simultaneously in in-phase and anti-phase mode even if there is fabrication error. The micro-gyroscope structure is designed and the operating principle of the improved lever support system is described detailedly. The modes of the SDDMG are controlled to optimize the acceleration sensitivity. The micro-gyroscope structure is simulated by Ansys software to optimize modes and the structure parameters. The DDSOG process is adopted for the fabrication of the micro-gyroscope structure. The frequency sweep results indicate that the modes have an in-phase drive frequency of 3328.8 Hz, an anti-phase drive frequency of 3530.8 Hz, an in-phase sense frequency of 3380.0 Hz and an anti-phase sense frequency of 3579.1 Hz. The signal processing circuit based on FPGA is implemented. The rotation rate sensitivity experiment exhibits a rate sensitivity of 15.13 mV/°/s. Comparing the acceleration sensitivities with that of the prevenient micro-gyroscope, the maximum acceleration sensitivity decreases by 3.18 times, which indicates that the mode optimization is viable. Finally, the Allan variance analysis shows that the device has a bias instability of 2.38o/h under the vacuum environment.

This study explores the non-ideal dynamics of a dual-mass silicon micro-gyroscope (DSMG). The operating principle and description of the DSMG are interpreted. Under the in-phase, anti-phase, and single-mass excitation conditions, the motion characteristics of the drive mode and sense mode are deduced. The non-ideal dynamics of the drive and sense modes with stiffness errors are analyzed. Then, the finite element simulation of the DSMG with a stiffness error is implemented to verify the non-ideal dynamics. Simulation results demonstrate that the left and right masses share the same natural frequencies in the in-phase or anti-phase drive modes even with the existence of stiffness errors. However, a significant amplitude difference between the left and right proof masses can be observed in the in-phase and anti-phase drive modes. The harmonic response analysis proves that the in-phase drive mode is activated even under anti-phase excitation conditions. Finally, sweep frequency experiments are performed on the fabricated DSMG to verify the correctness of the theoretical analysis. Experimental results indicate that the in-phase and anti-phase modes are excited simultaneously under both the anti-phase and in-phase excitation conditions because of the stiffness errors resulting from fabrication errors. This outcome is consistent with the theoretical analysis and simulation.

Arcing fault is an important factor of electrical fire, which cause it very hard to be detected by the traditional protection appliances, as a result, a new method is urgent needed to detect arcing fault accurately. At present, methods of detecting arcing fault is based on Fourier transform have disadvantages such as high rate of misjudgment and limited range of load. In this paper, we purposed a new method that based on db3 wavelet decomposition to detect arcing fault. By analyzing the changing rate of current energy in normal and fault conditions, using db3 wavelet to analysis its current characteristic of frequency band energy, we can extract approximation signals a5 and detail signal $\mathrm {d}\mathrm {l}\sim \mathrm {d}5$. By comparing the energy ratio during each frequency band, we can determine whether the arcing fault occurred or not. Both the theoretical and experimental results show that the method has good reliability and correctness of fault arc detection.

In this study, resveratrol was successfully encapsulated using zein-chitosan complex coacervation. The encapsulation efficiency was markedly improved (51.4%) after chitosan coating at 1:2.5 zein/chitosan ratio, compared with 38.6% using native zein. Analysis of multi-model frequency ultrasound treatment effects on resveratrol encapsulation using zein-chitosan complex coacervation showed that 28/40 kHz dual-frequency ultrasound led to the highest encapsulation efficiency (65.2%; 31.9% increase) and loading capacity (5.9%; 31.1% increase) of resveratrol, followed by multi-frequency ultrasound at 20/28/40 kHz (17.8% encapsulation efficiency increase; 17.8% loading capacity increase). Dual-frequency ultrasound treatment significantly reduced the zein-chitosan complex coacervation particle size and reduced their distribution, however, did not change the zeta potential. Fourier transform infrared spectroscopy and fluorescence spectroscopy analysis demonstrated that ultrasound treatment had no effect on secondary structure of zein-chitosan complex but markedly decreased the fluorescence emission intensity. Differential scanning calorimetry and X-ray diffraction results indicated that Dual-frequency ultrasound treatment improved the thermal stability of zein-chitosan complex coacervation but had no effect on the crystal structure. Atomic force microscopy and scanning electron microscopy images revealed uniform distribution of zein-chitosan complex coacervation followed by ultrasonic treatment.

Multiple modalities can provide more valuable information than single one by describing the same contents in various ways. Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities. However, previous methods mainly focus on fusing the shallow features or high-level representations generated by unimodal deep networks, which only capture part of the hierarchical correlations across modalities. In this paper, we propose to densely integrate the representations by greedily stacking multiple shared layers between different modality-specific networks, which is named as Dense Multimodal Fusion (DMF). The joint representations in different shared layers can capture the correlations in different levels, and the connection between shared layers also provides an efficient way to learn the dependence among hierarchical correlations. These two properties jointly contribute to the multiple learning paths in DMF, which results in faster convergence, lower training loss, and better performance. We evaluate our model on three typical multimodal learning tasks, including audiovisual speech recognition, cross-modal retrieval, and multimodal classification. The noticeable performance in the experiments demonstrates that our model can learn more effective joint representation., Comment: 10 pages, 4 figures

Mesenchymal stem/stromal cells (MSCs) possess some characteristics of immune cells, including a pro-inflammatory phenotype, an immunosuppressive phenotype, antibacterial properties and the expression of Toll-like receptor proteins. Here we show that, similar to immune cells, MSCs retain information from danger signals or environmental stimuli for a period of time. When treated with the pro-inflammatory factors lipopolysaccharide (LPS) or tumor necrosis factor-α (TNF-α), MSCs display increased expression of IL-6, IL-8 and MCP-1. Following re-plating and several rounds of cell division in the absence of stimulating factors, the expression of IL-6, IL-8 and MCP-1 remained higher than in untreated cells for over 7 days. A spike in cytokine secretion occurred when cells were exposed to a second round of stimulation. We primed MSCs with LPS and LPS-primed MSCs had better therapeutic efficacy at promoting skin flap survival in a diabetic rat model than did unprimed MSCs. Finally, we found that several microRNAs, including miR146a, miR150 and miR155, along with the modification of DNA by 5-hydroxymethylcytosine (5hmC), mediate the MSC response to LPS and TNF-α stimulation. Collectively, our data suggest that MSCs have a short-term memory of environmental signals, which may impact their therapeutic potential.

Bacteria use type VI secretion systems (T6SSs) to manipulate host cells during pathogenesis or to kill competing bacteria, which, in some cases, increases horizontal gene transfer. These functions largely depend on T6SS regulation, dynamics, and the set of effectors that the system delivers into the target cells. Here, we show that Acinetobacter baylyi ADP1 assembles a highly dynamic T6SS capable of killing and lysing bacterial cells. T6SS function depends on conserved T6SS components as well as Acinetobacter-specific genes of unknown function. Five different effectors, encoded next to VgrG or PAAR proteins and their cognate immunity proteins, cause distinct changes in the prey cells, resulting in various degrees of their lysis. Prey lysis correlates with the rate of DNA transfer from prey to predator, suggesting that lytic effectors are required for efficient T6SS-dependent horizontal gene transfer in naturally competent bacteria.

For fractional order control systems, fast and accurate numerical algorithm is the key step in real-time control and simulation. Although the short memory method (SMM) and the constant weight memory method (CWMM) accelerate the computation speed through the choice and approximation of the historical information of the fractional differential operator, the computation precision decreases with the increase of time. The CWMM is affected by input function and the error range is large. Considering the limitation of fixed step size discretization (FSSD) of fractional differential operator, a variable step size discretization (VSSD) scheme is utilized. According to the local error obtained by the step-doubling technique, the iterative step size is adjusted adaptively. The algorithm not only can achieve good accuracy, uniform error, but also fast calculation speed, which provides a feasible way for the numerical calculation of state space description of fractional order control system. Finally, the reliability of the algorithm is verified by an example.

The soft electrical properties are limiting Proton Exchange Membrane Fuel Cell (PEMFC) to be directly applied. To overcome such shortcoming, the high efficiency structure Four Switch Buck-Boost (FSBB) converter, which combines with Fractional Order PID (FOPID) controller, is used as pre-stage power converter of PEMFC to achieve regulated output. Also, Particle Swarm Optimization (PSO) based on the optimization of similar ITAE performance index is used to optimize FOPID controller parameters. And in order to reduce the probability of PSO falling into local optimum prematurely, the fitness variance is introduced to adjust the inertia weight change rule. Finally, compared with the traditional PID controller, the FOPID controller can make FSBB converter have a more excellent dynamic and steady performance, especially in the system parameters even the structural change, which shows a stronger robustness.

A fully distributed multi-parameter acoustic sensing technology is proposed. Current fully distributed sensing techniques are exclusively based on intrinsic scatterings in optical fibers. They demonstrate long sensing span, but their limited applicable parameters (temperature and strain) and costly interrogation systems have prevented their widespread applications. A novel concept of acoustic fiber Bragg grating (AFBG) is conceived with inspiration from optical fiber Bragg grating (FBG). This AFBG structure exploits periodic spatial perturbations on an elongated waveguide to sense variations in the spectrum of an acoustic wave. It achieves ten times higher sensitivity than the traditional time-of-flight measurement system using acoustic pulses. A fast interrogation method is developed to avoid frequency scan, reducing both the system response time (from 3min to

In this paper, the phenomenon of stochastic resonance (SR) in a prototype fluctuating damping harmonic oscillator with trichotomous Markovian noise is investigated. The exact expression of output amplitude gain has been calculated using the well-known Shapiro–Loginov formula. The phenomenon of SR has been found in a broad sense—that is, the non-monotonic behavior of output amplitude gain as a function of noise parameters. Then the influences of noise amplitude, noise switching rate, and noise flatness on the output amplitude gain have also been discussed. Finally, the reverse resonance phenomenon has been presented.

In this paper, we present the improved performance of silicon micro-gyroscope under closed-loop correction and frequency tuning. First, this paper analyzes the source of the quadrature error in the micro-gyroscope. Second, the mode-matching state of the silicon vibratory gyroscope is studied. The sense mode resonant frequency is tuned to be closed to the drive mode resonant frequency under the mode-matched condition. Then, the closed-loop system correction is realized under the mode-matched condition and the proportion-integral differential correction with a passive impedance network. Finally, the experiment results demonstrate the feasibility of the closed-loop system correction scheme under the mode-matched condition.

The 3D geometric model was established based on working principle of a new keyway broaching machine. The horizontal deformation of drawing knife blade in working stroke is analyzed by the finite element software ABAQUS. The research which effect law of the feed, structural sizes of cutter rod and sizes of cutter guiding rod on position deformation of broaching blade in working stroke given quantitative relationship between parameters of processing craft and structure and cutting precision. The selection method and way of processing parameters and structural parameters to improve broaching precision of keyway broaching machine are obtained. The research work provides relevant technological basis for the design of high precision keyway broaching machine.

The influences of silica fume and aluminum sulfate on hydration process of sulfoaluminate cement were carried out by ring flow, setting time, hydration heat, XRD and DTG analyses. In addition, mortar mixtures with different functional additives have been studied through compressive strength, flexural strength, volume stability at early age and porosity characterization tests. The results show that the addition of silica fume and aluminum sulfate reduces the fluidity and shortens the setting time of sulfoaluminate cement paste, promoting hydration process and increasing hydration products at early age. In the case of appropriate proportion of mortar, the inclusion of hydroxy propyl methyl cellulose, dispersible polypropylene fiber and organic silicon kind of defoamer can control segregation and bleeding, improve mechanical strength and volume stability at early age, and modify the pore distribution of sulfoaluminate cement mortar, respectively. The sulfoaluminate cement mortar can carry out gravitational grouting in the absence of outside force, the compressive strength of 2 hours and 24 hours have reached 26 and 58 MPa respectively, and have good microexpansion and tiny pore distribution characterization.

A testing system of the strength of the ceramic sintering which can achieve a 20Mpa testing liquid pressure is presented in this paper. The system is in accordance with IEC60335-1:2006 Safety household and similar electrical appliance Part 1: General requirement ". The experiments show that the system can resolve the security issues of high pressure and explosive liquid in the test process.

This study provided the first spatial distribution of perfluorinated compounds (PFCs) in Hanjiang River in Wuhan, China (HR). Surface water samples, collected from 23 sites in HR were analyzed for eight PFCs. The total concentrations of PFCs ranged from 8.90 to 568 ng L(-1), while perfluoropentanoic acid (PFOA

A two-step improved particle swarm optimization (TIPSO) algorithm was recently proposed and used for the optimization of flexible satellite-control parameters. It was found to be much more stable and much less complex than other evolutionary algorithms. In this article the efficacy of the TIPSO algorithm is investigated for multidisciplinary optimization of aerothermodynamic parameters of performance, cowl deflection angle and shock–boundary layer separation on a cone-derived, wedge-integrated hypersonic (waverider) compression system. This algorithm uses an aggregate objective function. Optimization results from the TIPSO algorithm are compared with those obtained from a hybrid genetic algorithm, particle swarm optimization using an inertial weight approach and a multi-objective genetic algorithm. Since optimality of the forebody configuration is the basic requirement, the optimization variables selected are the isolator exit Mach number, static pressure ratio across the forebody–inlet configuration, cyc...

The structure design of an improved dual-mass decoupled micro-gyroscope(DDMG) was presented in the paper. The non-ideal decoupled error was suppressed by the improved drive decoupled beam. The improved drive decoupled beam reduced the drive decoupled torque by decreasing the moment arm, which was beneficial to suppress the rotational displacement error of sense frame in drive mode. At the same time, the quadrature error correction electrode was adopted to suppress the quadrature error further in the DDMG. The mode distribution of the micro-gyroscope was optimized by Ansys simulation. The simulation results indicated that the improved drive decoupled beam has suppressed effectively the rotation movement of sense frame resulting from the non-ideal decoupling error. The DDSOG process was adopted for the fabrication of the micro-gyroscope structure. The mechanical coupling error of the improved DDMG was measured to assess the non-ideal decoupled characteristics. The experiment results demonstrated that, comparing to the first generation DDMG, the quadrature error in the improved DDMG was decreased by 9.66 times and the offset error was decreased by 6.36 times, which indicated that the non-ideal decoupled error was suppressed significantly in the optimization DDMG.

Supplementary figures, tables and notes

We investigate stochastic resonance in an underdamped linear system subjected to multiplicative trichotomous noise. We carry out the Shapiro–Loginov formula to find the exact expression of output amplitude gain, and the impacts of the input signal frequency and noise parameters will be observed, such as noise switching rate or noise correlation time, noise amplitude and noise flatness. Then one can find the stochastic resonance for the proposed linear system.

This paper presents the structure design, working principles and heat transformation of the on-chip temperature-control for dual-mass micro-gyroscope. The dedicated temperature sensor and heater provide real-time monitored and controlled working environment for the micro-gyroscope, thermal stabilization is thus achieved by maintaining the gyroscope temperature larger than the highest temperature of the surroundings. Moreover, thin glass membrane is formed and hermetic vacuum encapsulation is implemented help improve the heat insulation and reduce the power consumption. Finally, FEM analyses are performed and results from the simulations validate the practicability of the on-chip temperature-control system.

This paper presents the structure design of a dual-mass decoupled tuning fork micro-gyroscope(DDTFG). Two proof masses of the DDTFG are linked elastically not only in the drive mode but also in the sense mode and can be resonant driven simultaneously in in-phase and inverted-phase mode even if there is fabrication error. The mode distribution of the DDTFG is adjusted flexibly to optimize the acceleration sensitivity. The micro-gyroscope structure is designed and the operating principle is described detailedly. The structure parameters and the mode distribution of the DDTFG are optimized by Ansys simulation. The simulation results show that the improved lever support mechanism has suppressed effectively the rotation movement of the proof mass comparing to the general lever support mechanism. The DDTFG process is adopted for the fabrication of the micro-gyroscope structure. The prototype of the DDTFG with the discrete electronic components is implemented. Comparing the acceleration sensitivities with that of the prevenient micro-gyroscope, the maximum acceleration sensitivity decreases 3.18 times, which indicates that the mode optimization is viable.

The quadrature error analysis, structure design and simulation of a symmetric decoupled silicon micro-gyroscope with high decoupling performance are present in the paper. The improvements of straight beam, the anchor and the balance beam greatly restrained the mechanical error. In the drive mode, the maximum displacement of the sense framework along the sense axis is 0.088% of the vibration amplitude of the proof mass. It almost decreases 15 times relative to the early structure. In the sense mode, the maximum displacement of the proof mass along the drive axis is 0.041% of the vibration amplitude of the proof mass. It almost decreases 20 times relative to the early structure.

Influence of fabrication errors on modal amplitude in a dual-mass silicon micro-gyroscope is present in this paper. Firstly, the structure and operation principle a dual-mass silicon microgyroscope is introduced. Then the equation of amplitude ratio between left and right proof mass is deduced. Finally, the simulation analysis of fabrication errors on modal amplitude is implemented. The simulation results show the amplitude ratio is decreased to -1.2741 when the left proof mass error is 5% in anti-phase driving mode. The amplitude ratio is increased to -0.800 when the left stiffness error is 5% in anti-phase sensing mode. Introduction Silicon micro-gyroscopes are sensors using MEMS technology. Because of their features such as small size, intelligence and suitable for mass production, silicon micro-gyroscopes have been widely applied in military, industrial and other fields. The biggest difference between silicon microgyroscopes and traditional gyroscopes is that silicon micro-gyroscopes are fabricated by micromachining process. Although the precision of the micromachining process is quite high, there are inevitable errors during processing. Two proof masses and stiffnesses in the left and right structures will be unequal if fabrication error occurs in the dual-mass micro-gyroscope [1]. The unequal proof masses will change the gyroscope mechanical properties. Therefore, it is important to analyze the impact of fabrication errors on mechanical properties in dual-mass micro-gyroscopes. In this paper, we present the analysis and simulation of fabrication errors on modal amplitude in a decoupled dual-mass silicon micro-gyroscope. Principle of decoupled dual-mass silicon micro-gyroscope A decoupled dual-mass silicon micro-gyroscope is shown in Fig.1. This gyroscope is composed of two identical single-mass gyroscopes. There are two sets of driving frames on the left and right side of the gyroscope. Driving frames are connected to proof masses by sensing decoupling springs. There are two sets of driving sensing frames between two proof masses and two sets of sensing frames on the top and bottom of the proof masses. Sensing frames are connected to proof masses by driving decoupling springs. On the top and bottom of the gyroscope, there are two lever mechanisms. The lever mechanism is used to reduce the in-phase sensing modal and amplify the anti-phase sensing modal. When driving voltage is applied on driving electrodes, the driving frames will be excited to Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015) © 2015. The authors Published by Atlantis Press 1199 resonant vibration by the driving combs. The vibration will be translated to proof masses by sensing decoupling springs, whereas driving decoupling springs in driving direction is flexible, leading to a drive decoupling with respect to the sensing frames. If there is an external angular rate along the Zaxis, the proof masses are driven into a vibrating motion by the Coriolis forces. This motion is translated to the sensing frames via driving decoupling springs, whereas sensing decoupling springs in driving direction is flexible, leading to a sense decoupling with respect to the driving frames. Sensing Cmbo Driving Decoupling Spring Sensing Frame Sensing Spring Driving Sensing Frame Driving Coupling Spring Base beam Sensing Decoupling Spring Anchor Sensing Coupling Spring Lever beam Driving Sensing Comb

Direct current (DC) characterization of high temperature superconducting (HTS) coils is important for HTS applications, such as electric machines, superconducting magnetic energy storage (SMES) and transformers. In this paper, DC characterization of a circular, epoxy-impregnated HTS coil made from YBCO coated conductor for use as a prototype axial flux HTS electric machine is presented. Multiple voltage taps were utilized within the coil during measurement to help provide further detailed information on its DC behavior as a function of length. Based on the experimental results, there exist regions of non-uniformity along the length of superconductor in the coil, resulting in non-ideal superconducting properties of the coil. By studying the current-voltage (I-V) curves across different regions, it is found that a decreasing n-value and critical current exists in the non-uniform parts of the HTS coil.

The development of cryogenic technology and high temperature superconducting (HTS) materials has seen continued interest worldwide in the development of HTS machines since the late 1980s. In this paper, the authors present a conceptual design of a 2.5 MW class synchronous motor. The structure of the motor is specified and the motor performance is analyzed via a three-dimensional model using the finite element method (FEM). Rotor optimization is carried out to decrease the harmonic components in the air gap field generated by HTS tapes. Based on the results of this 3D simulation, the determination of the operating conditions and load angle is discussed with consideration to the HTS material properties. The economic viability of air-core and iron-core designs is compared. The results show that this type of HTS machine has the potential to achieve an economic, efficient and effective machine design, which operates at a low load angle, and this design process provides a practical way to simulate and analyze the performance of such machines.

Concentrations and spatial distribution of PFOS and PFOA in surface water from East Lake in Wuhan, Hubei, China were studied. The total concentration of PFCs ranged from 31.1 to 237 ng x L(-1), and the mean value was 115 ng x L(-1), revealing lake-wide contamination in East Lake. PFOS and PFOA were detected in all samples with maximum values of 132 ng x L(-1) and 158 ng x L(-1), respectively, whereas the mean values of PFOS and PFOA were 60.4 and 55.0 ng x L(-1), respectively. The highest concentrations of PFOS and PFOA were found in the eastern area of lake, followed by the southern area of lake, and the western and northern areas of East Lake were the least contaminated regions. The specific distribution and composition profile of PFOS and PFOA, and the lack of significant correlation between PFOS and PFOA, suggested that there were various sources of PFCs. The PFOS concentrations at 30 sample sites (63%) were greater than 43 ng x L(-1), which was used to estimate an avian wildlife value for PFOS. The PFOA concentrations at 20 sample sites (42%) were greater than 40 ng x L(-1), which was a health-based guidance level recommended by the State of New Jersey for PFOA.

A new hair sensor for multi-physical signal measurements, including acceleration, angular velocity and air flow, is presented in this paper. The entire structure consists of a hair post, a torsional frame and a resonant signal transducer. The hair post is utilized to sense and deliver the physical signals of the acceleration and the air flow rate. The physical signals are converted into frequency signals by the resonant transducer. The structure is optimized through finite element analysis. The simulation results demonstrate that the hair sensor has a frequency of 240 Hz in the first mode for the acceleration or the air flow sense, 3115 Hz in the third and fourth modes for the resonant conversion, and 3467 Hz in the fifth and sixth modes for the angular velocity transformation, respectively. All the above frequencies present in a reasonable modal distribution and are separated from interference modes. The input-output analysis of the new hair sensor demonstrates that the scale factor of the acceleration is 12.35 Hz/g, the scale factor of the angular velocity is 0.404 nm/deg/s and the sensitivity of the air flow is 1.075 Hz/(m/s)², which verifies the multifunction sensitive characteristics of the hair sensor. Besides, the structural optimization of the hair post is used to improve the sensitivity of the air flow rate and the acceleration. The analysis results illustrate that the hollow circular hair post can increase the sensitivity of the air flow and the II-shape hair post can increase the sensitivity of the acceleration. Moreover, the thermal analysis confirms the scheme of the frequency difference for the resonant transducer can prominently eliminate the temperature influences on the measurement accuracy. The air flow analysis indicates that the surface area increase of hair post is significantly beneficial for the efficiency improvement of the signal transmission. In summary, the structure of the new hair sensor is proved to be feasible by comprehensive simulation and analysis.

The influence of reaction atmosphere and TiO2 on photochemical decomposition of perfluorooctanoic acid (PFOA) in aqueous periodate was investigated using a type of low-pressure mercury lamps emitted at 254 nm. PFOA photolysis was slight with 254 nm light irradiation under nitrogen, whereas significant decomposition PFOA obtained with the addition of IO4-. In addition, oxygen restrained photochemical decomposition of PFOA. In UV/TiO2/IO4- system, PFOA degradation ratio was 54%, 15% lower than that for UV/IO4- system. *OH radicals generated from UV/TiO2 system exhausted a lot of IO4-, resulting in lower degree of IO3* production. IO3* was high reactive radical which great excitated PFOA decomposition. The accumulation of short-chain perfluorocarbonxylic acids (PFCAs) as products were identified with HPLC/MS. PFCAs bearing shorter perfluoroalkyl groups were formed in a stepwise way from PFCAs that bear longer perfluoroalkyl groups.