For the three-phase squirrel cage asynchronous motor sensorless fast Fourier transformation(FFT)speed measurement method,the number of rotor slots needs to be known in advance,and the test accuracy is easily affected by factors such as noise and acquisition time.Therefore,a sensorless high-precision speed detection method based on singular value decomposition(SVD)and the Prony algorithm is proposed.Based on the study of the mechanism of rotor slot harmonic speed measurement,the calculation method of integer multiple rotor slot number is given.The SVD method based on the singular value growth rate curve is used to filter the stator current and the Prony algorithm is used to identify the rotor slot harmonics to calculate the motor speed.By using finite element and numerical simulation methods,the adaptability of the proposed algorithm and analog-to-digital(A/D)acquisition is analyzed and studied under different operating conditions of the motor.The physical test platform is constructed and the YE90S-2 motor is measured.Simulation and experiment results show that under the same acquisition condition,the absolute error of the proposed method is only a fraction or even one tenth of the FFT speed measurement,and the test accuracy is greatly improved,which proves the correctness and accuracy of the proposed method.
The short pulse laser is a research focus due to its high precision,non-contact and high controllability advantages.In this paper,nanosecond laser pulse ablation experiments on the titanium alloy surface were conducted to explore the interaction between nanosecond laser and titanium alloy.The formation mechanism of a volcano-like pit under the combined influence of surface tension,the Marangoni effect and recoil pressure was clarified and verified.Thereafter,a multi-pulse laser parameter experiment was carried out.Ablation morphology,quality and feature sizes of the volcano-like pit and micro-groove were studied under different laser parameters.Thus,the correlation between laser parameters and feature sizes was built.This study contributes to micromachining technology with nanosecond laser,which suggests that micro-pit and micro-groove with specific sizes and high processing quality can be obtained.
Microalgae are a type of photosynthetic cellular factory capable of efficient CO2 capture,biofuel production and wastewater treatment.However,the large-scale harvesting of microalgae still faces technological bottlenecks,and microbial flocculation has distinct advantages.In this study,a highly efficient flocculating bacterium named EbZL-1 is screened from the sediment samples of Jingyue Lake at Donghua University.It is identified as Enterobacter through 16S rDNA sequencing and a strain identification kit.The flocculation ability of EbZL-1 is tested through single-factor and multifactorial analysis experiments by using Kaolin suspension as standard flocculating liquid.The results show that the flocculation rate of EbZL-1 can reach 96.6%within 10 min,which is superior to that of the commercial polyaluminum chloride(95.0%,120 min).The optimal culture conditions for the bacterium to achieve the above flocculation rate are a carbon source of 11.69 g/L glucose,a nitrogen source of 1.04 g/L ammonium nitrate,a medium pH value of 7.8,a shaker rotation speed of 160 r/min,an inoculation amount of 45 μL(1.2×105 CFU/mL),and a culture time of 12 h to reach the logarithmic growth phase.The optimal flocculation conditions are Ca2+ as a flocculant aid with the pH value of the Kaolin suspension being 7.0.In the flocculation and sedimentation experiment of chlorella,the flocculation rate of EbZL-1 is higher than 95%,and the chlorella cells after flocculation are found to be undamaged upon inspection,demonstrating the advantages of the microbial flocculation technology.Further investigation into the mechanism of flocculation reveals that EbZL-1 can complete the rapid sedimentation of large particles by tight adsorption.In summary,the flocculating bacterium EbZL-1 has application value in the large-scale harvesting of microalgae and wastewater treatment.
To reduce slippage between the sensing module mounted on the tight-fitting clothing and the human skin during body movement,the clothing was optimized by numerical simulation.Firstly,solid models of the upper body and the sleeveless tight-fitting clothing were established using the reverse engineering software Geomagic Studio.Secondly,the sleeveless tight-fitting clothing was cut and segmented by the structural style of the tight-fitting clothing and the range of the skin deformation in each area.Finally,taking the running movement of the body as an example,the finite element analysis platform ANSYS Workbench was used to study the position change of sensing module mounting points relative to the human skin when different elastic fabrics were combined into the clothing.The results showed that when fabrics with higher elasticity were selected for front and back pieces and fabrics with lower elasticity were selected for shoulder and side pieces,the slippage between the sensing module and the human skin was smaller.
Monitoring muscle fatigue is a hot research topic in rehabilitation medicine and sports science.However,the previous research on monitoring muscle fatigue is limited by the size and price of equipment and is not applicable to the field of sports.In this study,a wearable smart band powered by a resistive fabric strain sensor was implemented to measure thickness changes and combined with a portable electromyography(EMG)sensor to monitor bicep fatigue.A dumbbell curl training scheme was designed,and based on muscle physiology,fatigue-related features of the muscle thickness were proposed.The Pearson correlation coefficient between the median frequency(MDF)of EMG and the MDF of resistance was 0.781 5,the Pearson correlation coefficient between the zero crossing rate(ZCR)of EMG and the enclosed area of the resistance was 0.874 7,and the significant level P-values were 0.022 0 and 0.004 5,respectively.The results indicated that the muscle thickness characteristic indices were significantly correlated with common EMG fatigue indices.This study proves the feasibility of muscle fatigue monitoring based on muscle thickness characteristic indices and flexible fabric strain sensors as a supplementary method in the study of muscle fatigue.The methodology of this study has broad development prospects in the field of muscle fatigue research.
This paper proposed a new structural design method for multi-layer honeycomb-filled woven fabrics,and the impact resistance of rope-like core supporting fabrics was studied on multi-layer honeycomb woven fabrics.The effects of the rope-like core filling yarn fineness,the rope-like core surface structure and the multi-layer fabric surface structure on the impact resistance of the filled fabric were analyzed.Nine samples were woven with different factors in accordance with the orthogonal test scheme.The effects of the above factors on the peak transmitted force of the filled fabric were discussed.The study demonstrates that all three factors are significant,and the primary order of influence with the optimal level in the parentheses is rope-like core filling yarn fineness(22.22 tex)>multi-layer fabric surface structure(interweave 2/2)>rope-like core surface structure(interweave 3/1).
Mobile crowd sensing(MCS)systems,which offer a great opportunity to take full advantage of the wisdom of the crowd,naturally benefit from low deployment cost and wide spatial coverage.Due to failure or risk caused by a central server,constructing an efficient MCS system with untrustworthy participants in a decentralized manner is investigated.An efficient and practical decentralized MCS system based on a distributed auction process and the blockchain system is proposed.The proposed method achieves the optimal social profit satisfying individual rationality and protecting their privacy,through a neutral,public and trustful platform.Both theoretical analysis and numerical experiments show the effectiveness of the proposed approach.
In order to further improve the bearing stiffness,the porous aerostatic bearings can obtain bidirectional stiffness without additional preload guideway by combining vacuum preloaded(VPL)technology with the bearings.To explore the influence of working conditions and structural parameters on VPL porous aerostatic bearing performance,a numerical analysis of static and dynamic characteristics was performed based on the computational fluid dynamics(CFD)method.The influences of the air supply pressure,the vacuum degree,the permeability,the vacuum suction port diameter,the perturbation amplitude and the perturbation frequency were thoroughly investigated.The analysis shows that the stiffness of VPL porous aerostatic bearings increases by 22.34%compared with that of ordinary porous bearings,and working conditions and structural parameters have a significant influence on static and dynamic characteristics.It is expected that the results can guide performance research and optimization design for the high-stiffness aerostatic bearings.
Great progress has been made in the development of liquid-metal-based wearable devices.However,the sensitivities of the liquid-metal-based strain sensors are still limited in an inadequate range.Herein,a highly sensitive and stretchable strain sensor constructed with an aligned electrospun thermoplastic polyurethane(TPU)fibrous base and a patterned biphasic metal sensing layer was developed.The patterned biphasic metal sensing layer was composed of liquid metal(LM)Galinstan as"islands"and solid metal silver(Ag)as"sea".Ag"sea"prevented the formation of a continuous LM conductive pathway.The sensitivity was enhanced significantly based on the crack propagation mechanism of the Ag conductive region.Additionally,the alignment of fibers(horizontally or vertically aligned)weakened fiber rearrangement,and enlarged the deformation of the sensing layer.Results showed that,the sensitivity of the sensors composed of a single LM layer or a pure Ag layer with a vertically aligned fibrous base increased by 1.09 and 33.19 times compared to that with the randomly oriented fibrous base,respectively.The final patterned biphasic metal strain sensor with a vertically aligned fibrous base could achieve an ultrahigh sensitivity(gauge factor up to 952.20)and a wide sensing range(up to 59.33%).This design shows a great potential for a wide variety of wearable devices.
In the task of sales forecasting of new clothing products,the lack of historical sales data often necessitates the full utilization of data from other modalities as a supplement.However,multi-modal clothing data are usually redundant and heterogeneous.To solve the problems,a hierarchical multi-modal attention based recurrent neural network(HMA-RNN)including three main elements is proposed.The hierarchical structure separates high-level semantic information from low-level semantic information to avoid information redundancy.The multi-modal attention(MMA)is introduced in the fusion stage to mitigate inherent data non-alignment.The shared attention mechanism is utilized to build the dependencies across the multi-modal data.Experimental results on the Visuelle 2.0 dataset show that the proposed approach achieves promising results with 72.07 on the weighted average percentage error(WAPE)and 0.80 on the mean absolute error(MAE),outperforming existing works significantly,which indicates the effectiveness of the proposed approach.
Computer vision-based virtual try-on(VITON)technology refers to warping and composing the try-on clothing according to the model image features into the model image to replace the original clothing parts.Current VITON methods have two main challenges:insufficient preservation of original features such as the head,bottom,and background of the model image;poor matching of the warped try-on clothing to the model image.To solve these two problems,an original feature preserving virtual try-on network(OFP-VTON)is proposed,which consists of semantic segmentation map generation,try-on clothing warping,and try-on image synthesis.In the try-on clothing warping phase,the network learns the mapping of warping of the clothing worn in the model image to better constrain the try-on warping.In the try-on image synthesis phase,the original features of the model image are extracted and preserved,and a receptive field block(RFB)is introduced to preserve the features of try-on clothing as much as possible.Qualitative and quantitative experiments on the publicly available VITON dataset show that the proposed OFP-VTON better preserves the original features and that the warped try-on clothing matches the model images better than the baseline method.
An efficient,redox-neutral and visible-light-induced three-component tandem trifluoromethylation/gem-difluoroallylation reaction of electron-rich alkenes is described.Trifluoromethyl(—CF3)radical is generated from commercially available sodium trifluorome-thanesulfinate(CF3SO2Na),which is matched with the electron-rich alkene and α-trifluoromethyl alkene in turn,and finally,the polyfluoroolefin is obtained by β-F elimination.The advantages of this method are mild reaction conditions,excellent functional-group tolerance,broad substrate scope,and high reaction efficiency,which provides an opportunity for the late-stage modification of pharmaceuticals and natural product derivatives.
There is an urgent need for small-diameter artificial blood vessels in clinic.Physical,chemical and biological factors should be integrated to avoid thrombosis and intimal hyperplasia after implantation and to promote successful fabrication of small-diameter artificial blood vessels.From a physical perspective,the internal oriented structures of natural blood vessels plays an important role in guiding the directional growth of cells,improving the blood flow environment,and promoting the regeneration of vascular tissue.In this review,the effects of the oriented structures on cells,including endothelial cells(ECs),smooth muscle cells(SMCs)and stem cells,as well as the effect of the oriented structures on hemodynamics and vascular tissue remodeling and regeneration are introduced.Various forms of oriented structures(fibers,grooves,channels,etc.)and their construction methods are also reviewed.Conclusions and future perspectives are given.It is expected to give some references to relevant researches.
The quality of printed circuit board(PCB)micro-hole processing directly determines the stability of the inner and outer circuit connections.Micro-hole drilling technology is a typical method for PCB micro-hole processing.The problem of optimal control of its drilling force is one of the main factors affecting the quality of micro-hole machining.To address this problem,the thrust forces and torques in PCB drilling were first modeled and analyzed,and the corresponding prediction models were established.The drilling force analysis was carried out through the micro-hole drilling experiment,the specific cutting energy under different feed rates was calculated,the influence of the size effect was clarified,and the accuracy of the prediction model was verified.The result shows that during the drilling of glass fiber cloth,changes in the material removal mechanism are induced as the feed per revolution is varied.When the feed per revolution is less than the tool edge radius,the glass fiber is not cut by the main cutting edge,but is crushed and broken.When the feed per revolution is greater than the radius of the tool edge,the glass fiber is cut by the main cutting edge.At the same time,the established analytical model can accurately reflect the influence of the size effect on the drilling torque in PCB micro-hole drilling,and the error is within 10%.This method has certain practical application value in controlling PCB micro hole processing quality.
Deep convolutional neural networks(DCNNs)are widely used in content-based image retrieval(CBIR)because of the advantages in image feature extraction.However,the training of deep neural networks requires a large number of labeled data,which limits the application.Self-supervised learning is a more general approach in unlabeled scenarios.A method of fine-tuning feature extraction networks based on masked learning is proposed.Masked autoencoders(MAE)are used in the fine-tune vision transformer(ViT)model.In addition,the scheme of extracting image descriptors is discussed.The encoder of the MAE uses the ViT to extract global features and performs self-supervised fine-tuning by reconstructing masked area pixels.The method works well on category-level image retrieval datasets with marked improvements in instance-level datasets.For the instance-level datasets Oxford5k and Paris6k,the retrieval accuracy of the base model is improved by 7%and 17%compared to that of the original model,respectively.
Traditional studies of evaluating breast displacement included cup fabrics,shoulder straps,and under-bands restricted by own features of sports bras(SBs).The effects of pads on breast support during movement were investigated by conducting trials with the motion capture experiment.The three-dimensional breast displacements at five breast positions were collected and values in the vertical direction were evaluated.Results indicate that the effectiveness of the cup pad depends on the pad diameter and the thickness.SBs with larger pads and thicker pads made of soft polyurethane cause less overall breast displacement during vertical jumping compared to unpadded bras(p<0.05).The effectiveness of bra oscillation reduction was characterized by the total reduced percentage of the breast displacement.These findings enlarge scopes in terms of the current breast displacement analysis and understanding of selecting SBs associated with changes in the specifications of pads,thereby providing objective methods for bra cup evaluation and a design basis for breast protection.
The natural language to SQL(NL2SQL)task is an emerging research area that aims to transform a natural language with a given database into an SQL query.The earlier approaches were to process the input into a heterogeneous graph.However,previous models failed to distinguish the types of multi-hop connections of the heterogeneous graph,which tended to ignore crucial semantic path information.To this end,a two-layer attention network is presented to focus on essential neighbor nodes and mine enlightening semantic paths for feature encoding.The weighted edge is introduced for schema linking to connect the nodes with semantic similarity.In the decoding phase,a rule-based pruning strategy is offered to refine the generated SQL queries.From the experimental results,the approach is shown to learn a good encoding representation and decode the representation to generate results with practical meaning.
The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.
The given unstable hybrid stochastic differential equation is stabilized in the sense of pth-moment exponential stability.We achieve the results by feedback controls based on the discrete-time state and mode observations.The upper bound on the duration between two consecutive observations is obtained as well.Finally,a numerical example is given to verify the validity of the theoretical conclusions.
In order to make the synthetic route of Thiamidol more efficient and easier to scale up,a new method was developed.The compound 2,4-dihydroxyacetophenone was dissolved in ethyl acetate and brominated with cupric bromide.After bromination,the mixture was filtered to remove cuprous bromide.The obtained ethyl acetate solution containing 2-bromo-1-(2,4-dihydroxyphenyl)ethanone was directly reacted with(2-methylpropanoyl)thiourea to obtain the precipitate of Thiamidol hydrobromide.The precipitate was neutralized by deacidification and recrystallized to afford Thiamidol with high purity.In addition,in the screening of fertility-promoting substances,Thiamidol was found to be effective in protecting human sperm from motility loss in vitro.This suggests that it may have potential application in fertility protection and promotion.
