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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Journal of Shanghai Jiaotong University (Science)
Journal Prestige (SJR): 0.143
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1995-8188 - ISSN (Online) 1007-1172
Published by Springer-Verlag Homepage  [2469 journals]
  • Rigid-Membrane Method for Determining Stress Distribution of Membrane
           Structure Based on Laser Scanner System

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      Abstract: Abstract The determination of stress distribution is important for the safe use of membrane structures in practical engineering, which is difficult to be obtained by existing measurement methods and analysis methods. This paper proposes a rigid-membrane method to determine the stress distribution of the membrane, which expands the stiffness of the membrane, applies the load of the membrane in equilibrium to the membrane shape of the equilibrium state, and performs nonlinear finite element analysis. The rigid-membrane method inversely acquires the stress distribution of the membrane based only on the shape and load distribution in equilibrium obtained from the numerical simulation of a membrane structure under water loads, and determines the modulus magnitude and mesh size required to rigidize the membrane. The accuracy of the rigid-membrane method is verified by the small differences between the stress distributions obtained from the proposed method and numerical simulations. The equilibrium membrane shape in the actual project can be scanned and reconstructed by the laser scanner system without any pre-processing, and the load is determined by the water level, internal pressure, etc. Based on the actual membrane shape and water load distribution, the rigid-membrane method determines the real stress distribution of the membrane in the test of flat membrane subjected to ponding water, which verifies that the rigid-membrane method is a practical method to determine the stress distribution only by the membrane shape and external load distribution.
      PubDate: 2022-07-22
       
  • Numerical Investigation on Dynamic Response Characteristics of
           Fluid-Structure Interaction of Gas-Liquid Two-Phase Flow in Horizontal
           Pipe

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      Abstract: Abstract Fluid-structure interaction (FSI) of gas-liquid two-phase flow in the horizontal pipe is investigated numerically in the present study. The volume of fluid model and standard k-ε turbulence model are integrated to simulate the typical gas-liquid two-phase flow patterns. First, validation of the numerical model is conducted and the typical flow patterns are consistent with the Baker chart. Then, the FSI framework is established to investigate the dynamic responses of the interaction between the horizontal pipe and gas-liquid two-phase flow. The results show that the dynamic response under stratified flow condition is relatively flat and the maximum pipe deformation and equivalent stress are 1.8 mm and 7.5 MPa respectively. Meanwhile, the dynamic responses induced by slug flow, wave flow and annular flow show obvious periodic fluctuations. Furthermore, the dynamic response characteristics under slug flow condition are maximum; the maximum pipe deformation and equivalent stress can reach 4 mm and 17.5 MPa, respectively. The principal direction of total deformation is different under various flow patterns. Therefore, the periodic equivalent stress will form the cyclic impact on the pipe wall and affect the fatigue life of the horizontal pipe. The present study may serve as a reference for FSI simulation under gas-liquid two-phase transport conditions.
      PubDate: 2022-07-22
       
  • Multi-GNSS Fusion Real-Time Kinematic Algorithm Based on Extended Kalman
           Filter Correction Model for Medium-Long Baselines

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      Abstract: Abstract In the case of a medium-long baseline, for real-time kinematic (RTK) positioning, the fixed rate of integer ambiguity is low due to the distance between the base station and the observation station. Moreover, the atmospheric delay after differential processing cannot be ignored. For correcting the residual atmospheric errors, we proposed that a GPS/BDS/Galileo/GLONASS four-system fusion RTK positioning algorithm, which is based on the extended Kalman filter (EKF) algorithm. After realizing the spatio-temporal unification of multiple global navigation satellite systems (GNSSs), we introduced a parameter estimation of atmospheric errors based on the EKF model, using the least-squares integer ambiguity decorrelation adjustment (LAMBDA) to calculate the integer ambiguity. After conducting experiments for different baselines, the proposed RTK positioning algorithm can achieve centimeter-level positioning accuracy in the case of medium-long baselines. In addition, the time required to solve the fixed solution is shorter than that of the traditional RTK positioning algorithm.
      PubDate: 2022-07-22
       
  • High Curvature Stripe Profile Extraction Algorithm of Line Structured
           Light Measuring System

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      Abstract: Abstract In the line structured light measuring system, the accuracy of the process of laser stripe directly affects the measurement results. Therefore, the extraction algorithm for the laser stripe, especially the surface with high reflection and high curvature, is very important. The imaging principle of line structured light, the light intensity distribution law of laser stripe and the extraction algorithm have been studied, and a stripe profile extraction method based on real light intensity distribution has been proposed. In this algorithm, fast region of interest extraction, stripe width estimation, and adaptive filtering on the striped image are performed. Then the energy center of the stripe at the sub-pixel level is extracted. Finally, the low-quality center points are eliminated, and the context information is used to recover the missing central points. Simulated images generated based on the imaging principle of line structured light and real experimental images were used to evaluate the accuracy and repeatability of the proposed method. The results show that the method behaves excellently at the edges of high-curvature stripes; the maximum error is only 1.6 pixels, which is 1/10 of the classic Steger algorithm; the experiment repeatability is only 8.8 µm, which is 2.7 times that of the Steger method. Therefore, the proposed method improves the accuracy of object contour extraction, and is especially suitable for contour detection of objects with high curvature.
      PubDate: 2022-07-22
       
  • Spectrum-Sensing Method for Arc Fault Detection in Direct Current System
           with Lithium Batteries

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      Abstract: Abstract We mainly study the detection of arc faults in the direct current (DC) system of lithium battery energy storage power station. Lithium battery DC systems are widely used, but traditional DC protection devices are unable to achieve adequate protection of equipment and circuits. We build an experimental platform based on an energy storage power station with lithium batteries. Then, the data collection of normal current and arc-fault current is completed under multiple conditions. And the waveforms of obvious and weak signals as the arc occurs are presented. We analyze the principles and application range of several common spectrum-sensing methods and study the feasibility of applying them to the arc detection field. Finally, the covariance absolute value detection algorithm is selected, and the average value of the current is filtered out to make the algorithm adapt to the arc detection field. The result shows that the detection probability in 500 sets of experimental data has reached 98%.
      PubDate: 2022-07-22
       
  • Retinal Vessel Segmentation via Adversarial Learning and Iterative
           Refinement

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      Abstract: Abstract Retinal vessel segmentation is a challenging medical task owing to small size of dataset, micro blood vessels and low image contrast. To address these issues, we introduce a novel convolutional neural network in this paper, which takes the advantage of both adversarial learning and recurrent neural network. An iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image gradually. Recurrent unit preserves high-level semantic information for feature reuse, so as to output a sufficiently refined segmentation map instead of a coarse mask. Moreover, an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions, thus greatly reducing topology errors of segmentation. The experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17% and 80.64%, respectively. Our method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods.
      PubDate: 2022-07-22
       
  • Statistical Characteristics Analysis Based on F/A-XX Fighter Using
           Adapative Kernel Density Estimation Algorithm

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      Abstract: Abstract The sixth-generation fighter has superior stealth performance, but for the traditional kernel density estimation (KDE), precision requirements are difficult to satisfy when dealing with the fluctuation characteristics of complex radar cross section (RCS). To solve this problem, this paper studies the KDE algorithm for F/A-XX stealth fighter. By considering the accuracy lack of existing fixed bandwidth algorithms, a novel adaptive kernel density estimation (AKDE) algorithm equipped with least square cross validation and integrated squared error criterion is proposed to optimize the bandwidth. Meanwhile, an adaptive RCS density estimation can be obtained according to the optimized bandwidth. Finally, simulations verify that the estimation accuracy of the adaptive bandwidth RCS density estimation algorithm is more than 50% higher than that of the traditional algorithm. Based on the proposed algorithm (i.e., AKDE), statistical characteristics of the considered fighter are more accurately acquired, and then the significant advantages of the AKDE algorithm in solving cumulative distribution function estimation of RCS less than 1 m2 are analyzed.
      PubDate: 2022-07-22
       
  • System Life and Reliability Modeling of a Multiple Power Takeoffs
           Accessory Gearbox Transmission

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      Abstract: Abstract A mathematical model for system life and reliability of a multiple power takeoffs aeroengine accessory gearbox transmission is presented. The geometry model of gear train is distributed into several subsystems by different transmitted powers. The lives of each component are combined to determine the units, subsystems and entire system lives sequentially according to a strict series probability model. The unit and subsystem interface models are defined to dispose the loads of common components. The algorithm verification is presented and a numerical example is given to illustrate the use of this program. The initial design could not fulfill the life requirement. A design modification shows that the gear train has a more balanced life distribution by strengthening the weak parts, and the overall life of entire system is increased above the design requirement. This program can help the designer to approach an optimal accessory gearbox transmission design efficiently.
      PubDate: 2022-07-22
       
  • Characteristics of High-Pressure Spray of a Gasoline Direct Injection
           Injector Under Non-Flash Boiling and Flash Boiling Conditions

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      Abstract: Abstract The increasingly stringent emission regulations and fuel consumption requirements have elevated the demands of internal combustion engines with higher fuel efficiency and lower emissions. It has been widely demonstrated that flash boiling spray can generate shorter and wider spray with improved atomization and evaporation to promote a better air-fuel mixing process. In this study, macroscopic (far-field) spray morphologies and primary breakup (near-field) characteristics of a two-hole gasoline direct injection injector are investigated under non-flash boiling and flash boiling conditions. High speed macroscopic and microscopic imaging was used to capture the overall spray structure and near-field characteristics, respectively. N-Hexane is used as the test fuel with the injection pressure ranging from 10 MPa up to 40 MPa. For sub-cooled liquid fuel sprays, increasing fuel pressure contributes to enhanced fuel atomization and evaporation. Evident collapses occurred under flare flash boiling conditions, and higher injection pressure weakened this phenomenon since the spray cone angle decreased due to a higher injection velocity.
      PubDate: 2022-07-22
       
  • Multiple Detection Model Fusion Framework for Printed Circuit Board Defect
           Detection

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      Abstract: Abstract The printed circuit board (PCB) is an indispensable component of electronic products, which determines the quality of these products. With the development and advancement of manufacturing technology, the layout and structure of PCB are getting complicated. However, there are few effective and accurate PCB defect detection methods. There are high requirements for the accuracy of PCB defect detection in the actual production environment, so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method (DDMV) and the defect detection by multi-model learning method (DDML). With the purpose of reducing wrong and missing detection, the DDMV and DDML integrate multiple defect detection networks with different fusion strategies. The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets. The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score, and the area under curve value of DDML is also higher than that of any other individual detection model. Furthermore, compared with DDMV, the DDML with an automatic machine learning method achieves the best performance in PCB defect detection, and the F1-score on the two datasets can reach 99.7% and 95.6% respectively.
      PubDate: 2022-07-22
       
  • Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform,
           Recurrent Neural Network and Error Correction

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      Abstract: Abstract Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy. However, owing to the stochastic and intermittent of wind speed, predicting wind speed accurately is difficult. A new hybrid deep learning model based on empirical wavelet transform, recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper. The empirical wavelet transformation is applied to decompose the original wind speed series. The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy. The error correction strategy based on deep long short term memory network is developed to modify the prediction errors. Four actual wind speed series are utilized to verify the effectiveness of the proposed model. The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.
      PubDate: 2022-07-22
       
  • Effect of Moving Endwall on Hub Leakage Flow of Cantilevered Stator in a
           Linear Compressor Cascade

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      Abstract: Abstract The cantilevered stator has the advantages of reducing mass and axial length of highly loaded compressor. The details of the hub leakage flow resulting from the clearance between the high-speed moving hub and the cantilevered stator hub are unclear. In this paper, the effect of a moving endwall on the hub leakage flow of a cantilevered stator in a linear compressor cascade was studied. After the simulation method was verified with the experimental results, the time-averaged results of unsteady Reynolds averaged Navier-Stokes (URANS) were selected to study a case with a hub clearance of 2 mm. The results show that the effect of the moving endwall of the cantilevered cascade on the general characteristics with below 30% span increases the leakage mass flow rate and reduces the static pressure coefficient at three conditions of 0°, 6°, and −7° incidences, and the change is most significant at −7° incidence. The effect of the moving endwall on the total pressure loss coefficient varies with different operating conditions, which decreases by 15.94% at 0° incidence, and increases by 4.77% and 18.51% at 6° incidence and −7° incidence, respectively. The influence of the moving endwall is below 14% span at −7° incidence, below 23% span at 0° incidence, and below 30% span at 6° incidence. These effects correspond to the static pressure coefficient and the difference of static pressure coefficient representing the blade loading. When designing the cantilevered stator and matching between the stages of a multistage compressor, the quantitative research results of this paper have certain guiding significance.
      PubDate: 2022-07-22
       
  • Information Entropy of Angular Spectrum for Quantitatively Evaluating Eddy
           Current Distribution Varying in Time Domain

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      Abstract: Abstract Eddy current (EC) distribution induced by EC sensors determines the interaction between the defect in the testing specimen and the EC, so quantitatively evaluating EC distribution is crucial to the design of EC sensors. In this study, two indices based on the information entropy are proposed to evaluate the EC energy allocated in different directions. The EC vectors induced by a rotational field EC sensor varying in the time domain are evaluated by the proposed methods. Then, the evaluating results are analyzed by the principle of EC testing. It can be concluded that the two indices can effectively quantitatively evaluate the EC distributions varying in the time domain and are used to optimize the parameters of the rotational EC sensors.
      PubDate: 2022-07-22
       
  • Semantic Entity Recognition and Relation Construction Method for Assembly
           Process Document

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      Abstract: Abstract Assembly process documents record the designers’ intention or knowledge. However, common knowledge extraction methods are not well suitable for assembly process documents, because of its tabular form and unstructured natural language texts. In this paper, an assembly semantic entity recognition and relation construction method oriented to assembly process documents is proposed. First, the assembly process sentences are extracted from the table through concerned region recognition and cell division, and they will be stored as a key-value object file. Then, the semantic entities in the sentence are identified through the sequence tagging model based on the specific attention mechanism for assembly operation type. The syntactic rules are designed for realizing automatic construction of relation between entities. Finally, by using the self-constructed corpus, it is proved that the sequence tagging model in the proposed method performs better than the mainstream named entity recognition model when handling assembly process design language. The effectiveness of the proposed method is also analyzed through the simulation experiment in the small-scale real scene, compared with manual method. The results show that the proposed method can help designers accumulate knowledge automatically and efficiently.
      PubDate: 2022-07-22
       
  • Real-Time Calculation Method for Temperature Distribution of
           Temperature-Controlled Radiofrequency Ablation

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      Abstract: Abstract Precise temperature control and temperature distribution prediction are of great significance for radiofrequency ablation. This research proposes a real-time calculation method for the temperature distribution of radiofrequency ablation combined with proportional-integral temperature control. The thermo-electrical coupling was simplified into a linear relationship based on the study of the influence of temperature-dependent electrical conductivity and thermal conductivity on the PI-controlled radiofrequency ablation temperature distribution, which increases the computational efficiency by 150 times. The average calculation time for radiofrequency ablation of 10 min is about 23 s, and the difference between the calculation results of this method and that from COMSOL Multiphysics is no more than 1 °C. This method is not only used for single-probe, but also for double-probes radiofrequency ablation in this paper.
      PubDate: 2022-07-22
       
  • Influence of Forming Pressure on Properties of Yttrium Iron Garnet Ferrite

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      Abstract: Abstract The Ca-Sn co-substituted yttrium iron garnet (YIG) ferrite materials were prepared by the traditional oxide solid-state reaction method, and the influence of forming pressure on the density, morphology and magnetic properties of YIG ferrite was systematically studied. The results show that the density of YIG ferrite green body increases with the increase of the forming pressure, while the density of its sintered body shows a trend of first increasing and then decreasing. At the same time, the ferromagnetic resonance (FMR) linewidth of YIG sample first decreases and then increases. Meanwhile, the effects of forming pressure on the saturation magnetization, remanence and coercivity of the sample can be ignored. This study proves that the density and FMR linewidth of YIG materials can be controlled by regulating the forming pressure and the best performance is obtained for the sample prepared under a forming pressure of 5 MPa.
      PubDate: 2022-07-22
       
  • Target Detection Algorithm Based On Human Judge Mechanism

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      Abstract: Abstract A D-G-YOLOV3 algorithm was proposed to identify and judge recyclables, which introduced a dense feature network to replace the feature pyramid network. The network closely connects and fits the feature maps and simulates human judgment mechanism. A three-stage judgment is made for judgment objects with lower confidence. Based on the judgment of the original image, the second-stage judgment is carried out after the channel contrast is increased. Finally, sampling is performed on the region of interest where the second-stage confidence score wins for the third stage of judgment, and then judgment result is sent to the gated recurrent unit network for final inference. The result shows that through experiments on the same recyclables data set, the algorithm reduces the missed detection rate by 15.54%, and the false detection rate by 0.97%, while improves the accuracy rate by 16.51%.
      PubDate: 2022-06-27
       
  • Comfort Kneeling Seat for School-Age Children

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      Abstract: Abstract Kneeling seat is an ergonomic chair that can help the human body’s spine in a sitting posture to be closer to the natural state. In this study, we used non-contact camera method to measure visual distance, using surface electromyography (sEMG) combined with subjective evaluation. We studied the obvious effects of seat angle and leg support angle in kneeling sitting posture on the ride comfort of healthy female school-age children without myopia. Using three experiment seat angles (10°,20° and 30°), we found that as the sitting angle increased, the absolute value of the slope of the erector spinae linearity curve, MPF-t, gradually decreased. At 30°, the slope of MPF-t was −0.26, the descent speed was the slowest, the activity of erector spinae was relatively lowest, and the comfort of children’s waist was also improved, while the comfort of calf gastrocnemius decreased, just the opposite. At the same time, leg support angles of 20°, 30° and 40° were used. And in the study we found that the elevation of the leg support angle had no significant effect on the erector spinae muscle, but had a significant effect on the gastrocnemius muscle. When the leg support angle was 30°, the slope of MPF-t was −0.42, and the gastrocnemius comfort reached its peak.
      PubDate: 2022-06-25
       
  • Tree Detection Algorithm Based on Embedded YOLO Lightweight Network

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      Abstract: Abstract To avoid colliding with trees during its operation, a lawn mower robot must detect the trees. Existing tree detection methods suffer from low detection accuracy (missed detection) and the lack of a lightweight model. In this study, a dataset of trees was constructed on the basis of a real lawn environment. According to the theory of channel incremental depthwise convolution and residual suppression, the Embedded-A module is proposed, which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model. According to residual fusion theory, the Embedded-B module is proposed, which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion. The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions. Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17% and 69.91% average precision values respectively for trunk and spherical tree, and 77.04% mean average precision value. The number of convolution parameters is 1.78 × 106, and the calculation amount is 3.85 billion float operations per second. The size of weight file is 7.11 MB, and the detection speed can reach 179 frame/s. This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots.
      PubDate: 2022-06-25
       
  • Tail-Bound Cost Analysis over Nondeterministic Probabilistic Programs

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      Abstract: Abstract For probabilistic programs, there is some work for qualitative and quantitative analysis about expectation or mean, such as expected termination time, and expected cost analysis. However, another non-trivial issue is about tail bounds (i.e., upper bounds of tail probabilities), which can provide high-probability guarantees to extreme events. In this work, we focus on the problem of tail-bound cost analysis over nondeterministic probabilistic programs, which aims to automatically obtain the tail bound of resource usages over such programs. To achieve this goal, we present a novel approach, combined with a suitable concentration inequality, to derive the tail bound of accumulated cost until program termination. Our approach can handle both positive and negative costs. Moreover, our approach enables an automated template-based synthesis of supermartingales and leads to an efficient polynomial-time algorithm. To show the effectiveness of our approach, we present experimental results on various programs and make a comparison with state-of-the-art tools.
      PubDate: 2022-06-25
       
 
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