PtRu/C and PtNi/C catalysts were prepared by microwave-assistant polyol process, and their microstructure and morphology were characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The results showed that the average diameters of PtRu and PtNi alloy nanoparticles in the catalysts are 2.7 nm and 3.0 nm respectively, and the alloy nanoparticles are homogeneous in size and highly dispersed on the carbon support. Compared with the Pt/C catalyst, the synthesized PtRu/C and PtNi/C catalysts exhibit lower onset oxidation potential and more stable polarization current for methanol electrooxidation. The facts indicate that the PtRu/C and PtNi/C catalysts have more durable electrocatalytic performance for methanol oxidation and better resistance to CO-poisoning than Pt/C catalyst. Because the Ru and Ni metals in the alloys can form oxygen-containing species with their surface adsorbed water under low potential, the adsorbed intermediates such as CO on Pt metal surface can be oxidized to CO2 and the CO-poisoning of the catalysts is avoided.

Impact prediction was incorporated with the dynamic expression of network in order to realize the impact prediction of design change in network flow system, and a dynamic model based on network flow Petri net (NFPN) was proposed to predict these impacts. Petri net was utilized to describe network flow and its design changes, and the NFPN dynamic model was established. The construction of full reachable graph of Petri net was associated with the impact prediction of change based on the analysis of hierarchical network flow. The analysis of the integral network was recursively accomplished through the simplification of reachable graph by multi-level abstraction. Application to the reconstruction and design of distribution network verified the feasibility and validity of the method. The method greatly reduces the complexity of state space, and is applicable to large scale and complicated network．

Effect of different reaction conditions on properties of DBBF-modified porcine hemoglobin products

A new scheme of the power system was proposed in order to meet the microminiaturization need of ZDPS-1A. To improve the power level of the satellite and to obtain a high efficiency power system, high efficient and high-tech devices such as GaInP2/GaAs/Ge solar cells, Li-ion batteries were adopted, and industrial power management ICs were widely used in the design of the power conversion and distribution circuit. Besides, the electric system was optimized and the operation mode of the satellite was reasonably planned to improve the reliability of the power system. Furthermore, a simulation model of the power system based on Matlab/Simulink was established to study the energy balance performance of the satellite. Analysis of the simulation, ground test and onorbit data for nearly one year of the power system, show that the solar cells can output 3.7 W in the case of simulation and 4.5 W under actual operating conditions, that the performance of the battery has deteriorated along time, and that the power dissipation of some loads has increased along time while the energy balance of the satellite is sustained.

The confidentiality of information in e-commerce activities leads to negotiation participants are unable to get the opponent’s utility function, thereby affecting the negotiation performance. To solve this, a bilateral and multi-issue negotiation model based on transductive support vector machine (TSVM-NM) was proposed. In this model, the proposals generated in the procedure of negotiation are stored in negotiation history database. The model constructs labeled data and unlabeled data by making full use of the implicit information in negotiation history and analyzing that whether those proposals fall in opponent’s acceptable utility zone. Those data become the training samples of TSVM. Then the estimation of opponent’s utility function was obtained by learning the training samples. With the combination of self’s utility function and the estimation of opponent’s utility function, a constrained optimization problem is formed, which is to be resolved by particle swarm optimization (PSO). The optimal solution is the self’s counter-offer. Experimental results show that this model can shorten the negotiation time and increase both the success rate of negotiation and the joint utility, in the environments where information is private and the prior knowledge is not available.

A method of measuring and evaluating color motion artifacts which combined a flash spectroradiometer with simulation method was presented. Alternative patterns step response curves which show the difference between different colors were obtained with flash spectrum SPR-3000. Perceived response curve was obtained after doing Fourier transformation, weighting contrast sensitivity function and counter-Fourier transformation. Extended blur edge width (EBEW) and perceived blur edge width (PBEW) were introduced here. And PBEW was used to evaluate color motion artifacts. In the experiment, monochromatic patterns with different wavelengths were tested. And the result proved that under the same moving conditions, motion artifacts will chang in different degrees while the testing colors are changing．

Shadow pixels in images can lead to the uncertainty of image content, which is harmful to computer vision tasks. Therefore, shadow detection is often used as a preprocessing step of computer vision algorithm. A shadow detection network was proposed by combining semantic information contained in input images and correlation between pixels. Pre-trained deep network ResNeXt101 was used as feature extraction front-end module to extract semantic information of the image. The baseline structure of the network was built to up-sample feature layers, encouraged by the design idea of U-Net. Non-local operations were added before the output layer to provide global information for each pixel and establish the relationship between pixels. At the same time, an attention generation module and an attention fusion module were developed to further improve shadow detection accuracy. Two common shadow detection datasets named SBU and UCF were utilized for verification. Experiment results showed that the proposed network outperformed previous methods in both visual effect and objective indicator. The proposed network showed 14.4% reduction on SBU and 14.9% reduction on UCF for the balance error rate, compared with the state-of-the-art framework.

A reasonable method was proposed to calculate active earth pressure on retaining wall with multiple layer of cohesive soil. Based on sliding plane hypothesis, a formula was derived for the calculation of active earth pressure on retaining wall with multi-layered cohesive soil considering the cohesion force on sliding plane and the adhesive force on interface of backfill and retaining wall. The formula can also calculate the active earth pressure on retaining wall with homogeneous backfill. The calculation results of the present study were compared with that of improved layer method and measured value, and the results accorded well with the measuring results. The active earth pressure acting on retaining wall with granular soil can be calculated by the method of layer analysis, whereas, it is suggested to adopt the proposed method to calculate the active force with cohesive soil.

The force feedback technology was introduced in the virtual painting process;a novel hairy brush modeling method was proposed. During Chinese calligraphy and painting process, the brush deformation, ink transfer process between hairy brush and paper were simulated in real time. Firstly, the geometry model of the hairy brush was represented with the two-layer structure of skeleton and surface. Then,a spring-mass model was adopted to simulate hairy brush skeleton and surface deformation when force was exerted on hairy brush, and the dynamic control of force to hairy brush deformation was realized in real time. A real time algorithm of the ink quantity of hairy brush was put forward to simulate ink transfer process between hairy brush and paper. The virtual painting system with force feedback technology was established based on the hardware components HP xw 8600 workstation and PHANTOM Desktop haptic device. In this system, users realize real time painting with the Phantom Desktop haptic device, which can effectively enhance the reality of virtual painting process to users.

A thorough analysis and cross-comparison of recent relevant works was provided, outlining a closed-loop process for EEG data analysis based on deep learning. EEG data were introduced, and the application of deep learning in three key stages: preprocessing, feature extraction, and model generalization was unfolded. The research ideas and solutions provided by deep learning algorithms in the respective stages were delineated, including the challenges and issues encountered at each stage. The main contributions and limitations of different algorithms were comprehensively summarized. The challenges faced and future directions of deep learning technology in handling EEG data at each stage were discussed.

A model based on improved YOLOv5m was proposed for wolfberry pest detection in a complex environment. The next generation vision transformer (Next-ViT) was used as the backbone network to improve the feature extraction ability of the model, and the key target features were given more attention by the model. An adaptive fusion context enhancement module was added to the neck to enhance the model’s ability to understand and process contextual information, and the precision of the model for the small object (aphids) detection was improved. The C3 module in the neck network was replaced by using the C3_Faster module to reduce the model footprint and further improve the model precision. Experimental results showed that the proposed model achieved a precision of 97.0% and a recall of 92.1%. The mean average precision (mAP50) was 94.7%, which was 1.9 percentage points higher than that of the YOLOv5m, and the average precision of aphid detection was improved by 9.4 percentage points. The mAP50 of different models were compared and the proposed was 1.6, 1.6, 2.8, 3.5, and 1.0 percentage points higher than the mainstream models YOLOv7, YOLOX, DETR, EfficientDet-D1, and Cascade R-CNN, respectively. The proposed model improves the detection performance while maintaining a reasonable model footprint.

According to characteristics of moving dynamic loads on bridges and tracking the time domain method (TDM), the moving dynamic axle loads were identified from bridge bending responses, acceleration responses or their combination based on a preconditioned conjugate gradient method (PCGM). It aimed at obtaining the most accurate preconditioned matrix by comparing effect of preconditioned matrixes on identification accuracy. Simulation results showed that the PCGM could accurately identify the dynamic axle loads on bridges in most cases. However, there was different identification accuracy and immunity to measured noise and to the ill-posed problems of the system equation if different preconditioned matrixes were used. Meanwhile, choosing proper preconditioned matrix could effectively improve both of identification accuracy and efficiency of the PCGM.
Key words: preconditioned matrix|moving force

An interactive visualization generation method for time series data based on transfer learning was proposed in order to address the inconsistency in data distribution across time-series data and facilitate the application of pattern analysis to other data. Transfer component analysis was applied to transfer features extracted from each time series data. The user’s analysis on one of the time series data served as labels. The classifier was trained on the source domain and applied to multiple target domains in order to achieve pattern recommendations. Two case studies and expert interviews with real-world weather data and bearing signal data were conducted to verify the effectiveness and practicality of the method by improving the efficiency of temporal data exploration and reducing the impact of inconsistent data distribution.

The theory analysis and simulations were conducted to explain how the ultrasonic wave aberration affects second harmonic imaging in medical ultrasonic diagnosis. Simulation results indicate that in the case of aberration, the energy of second harmonic is mainly generated in the near field and the profile of second harmonic is nearly the same as the fundamental. Though second harmonic is generated deeply, it experiences serious aberration. When the back scattering acoustic beam goes through body wall, it makes pulse to generate worse aberration. The ability of nonlinear amplitude fluctuation is weakened and the aberration’s amplitude is larger with the increase of frequency.