To solve the problems of severe centralization and vulnerability to data tampering in traditional seafood traceability systems, the blockchain technology was employed to design the seafood traceability system based on hazard analysis and critical control points (HACCP). The multiple linear regression was used to process the sample data indicators and confirm the critical control points in the farming process. Based on the determination of key information, the smart contracts were designed to verify the uploaded data at each stage of seafood traceability. From the perspective of system feasibility, the detailed designs were conducted for system functionality, consensus topology and traceability coding. The seafood traceability system was developed and subjected to function and performance testing. The results show that the system transaction throughput is consistently around 320 transactions per second. The average transaction latency is approximate 0.5 s, and the transaction success rate is about 99.8%. The performance is fully capable of meeting the needs of production practices.

The three-dimensional dislocation cultivation system of factory hydroponic lettuce can be developed on the basis of increasing the number of cultivated plants per unit area without affecting the light of lettuce.To improve the mechanization rate of the system and reduce labor intensity, according to the characteristics of the three-dimensional dislocation cultivation, the planting cup was proposed to raise to a certain height before pushing the planting tray, and the colonization cup could cross the cultivation trough plug. The colonization tray was pushed up the cultivation trough, and a set of planting tray handling equipment was developed. Taking the lettuce colonization tray as test object, the Box-Behnken experimental design method was used to explore the factors affecting the success rate of the handling equipment on the plate, and the optimal parameter combination was discussed. The test results show that when the horizontal installation error of the cultivation tank is 0-5 mm with the guide plate angle of 60° and the advancing speed of 0.22 m/s, the largest success rate of the upper plate of 99.33% can be achieved， which can meet the three-dimensional dislocation production requirements of factory hydroponic lettuce.

The dynamic graph structure was proposed to address the non-Euclidean property of vehicle interactions for realizing the exchange of vehicle information among highway vehicles. The multi-modal driving behavior trajectory prediction model was designed with incorporating graph convolutional neural network (GCN), convolutional social pooling (CS) and long short-term memory network (LSTM). The LSTM encoder-decoder was used as foundation framework, and the vehicle interaction relationships were effectively extracted by convolution and graph convolution. The maximum pooling and average pooling techniques were introduced to achieve feature extraction and background information retention. The results show that by the proposed model, the root mean square error (RMSE) is 4.03 m in long-term horizon (5 s) and is significantly improved by 10.8% compared to the baseline model. Compared to other deep learning models, the proposed model exhibits higher accuracy. The prediction performance of the model is 8% to 11% better than that of the baseline model in different traffic scenarios. The ablation experiments can confirm the effectiveness of each module in the model. The proposed model can predict the probability distribution and corresponding trajectories of vehicles in various modalities over long-term horizon in the future.

 Kitchen waste is composed of complex and diverse ingredients with a variety of organic matter and inorganic salts, which is perishable and foul-smelling under high temperature conditions in summer. To reduce the deterioration rate of kitchen waste in the collection and storage process and cool down the kitchen waste in community residences for short time before treatment，the intelligent temperature-controlled dustbin was designed based on the fan-pad cooling device. Several temperature sensors were distributed inside the dustbin and controlled by Siemens PLC.The execution conditions of the fan-pad cooling device were programmed, and the PID algorithm was used for software optimization, while the data was visualized by touch screen. The intelligent control of the fan-pad cooling system was realized to maintain the internal temperature of the dustbin in the set range，and the system debugging and test verification were conducted. The results show that the detection error of the intelligent temperature control system is less than 0.5 ℃, which meets the accuracy requirements. When the outdoor temperature is as high as 35 ℃, the average temperature in the dustbin can be maintained at about 25 ℃, which meets the constant temperature storage requirements of kitchen waste.

The performance of Beidou satellite-based augmentation system (BDSBAS) single frequency augmentation service was evaluated and analyzed according to the shortcomings of the research on the corresponding operational performance support of the BDSBAS with the actual data. The calculation process of BDSBAS accuracy enhancement and integrity enhancement was comprehensively sorted out based on the relevant international standards. The process of accuracy enhancement and integrity enhancement was achieved through the publicly broadcast BDSBAS-B1C messages, and the enhancement results were used to evaluate the service performance of BDSBAS in China in the second quarter of 2022 in terms of precision, integrity and availability. The results show that during the test period, the BDSBAS single frequency augmentation service can support the accuracy and availability required for approach procedure with vertical guidance (APV)-I operation, and the horizontal and vertical accuracies respectively range from 1.5 to 5.5 m and from 2.5 to 10.3 m with the availability of APV-I operation of 99.871% (better than 99.000%). Although the transient integrity does not support APV-I, the positioning error is completely enveloped by the protection level without misleading information and dangerous misleading information events.

To solve the frost failure problem on super-hydrophobic surfaces in low temperature and high humidity environment, the slippery surface was fabricated by coating with lubricant oil on the rough super-hydrophobic surface. The wettability and microstructures of slippery surfaces were determined. The experimental platforms of frost prevention system and vertical defrosting test system were designed and constructed to investigate the dynamic behavior of frosting and defrosting on slippery surface, super-hydrophobic surface and pure copper surface. The results show that on the slippery surface, the dynamic lubricant film can be generated in the porous and rough structures of the substrate, which can effectively reduce the frost crystal nucleation and prevent the formation of "ice bridge". The frost mass and propagation speed wave on slippery surface are significantly lower than those on super-hydrophobic surface and pure copper surface, which shows better frost inhibition performance. During the process of vertical defrosting and drainage, the defrosting efficiency on each surface is proportional to the frosting mass. The mass and surface coverage of residual water upon defrosting on slippery surfaces are slightly higher than that on super-hydrophobic surface and lower than that on pure copper surface, which exhibits good defrosting and drainage performance.

To investigate the effects of volume replacement rate of rubber coarse and fine aggregates on the stress-strain curve, the concept of volume replacement rate of rubber coarse and fine aggregates was defined. By collecting 110 existing experimental stress-strain curves from published literatures, the effects of rubber coarse and fine aggregate volume replacement rate on the key parameters of stress-strain relationship of fiber reinforced polymer(FRP) confined rubber concrete with different sections were analyzed. Based on the analysis results, the stress-strain relationship model and compressive strength model of FRP confined rubber concrete were established. The factor effects of the volume replacement rate of rubber coarse and fine aggregates and the cross-sectional shape of rubber concrete were considered in the proposed models. The model evaluation results show that the compressive strength ratio of FRP confined rubber concrete is increased with the increasing of the volume replacement rate of rubber coarse and fine aggregates, the FRP confinement stress ratio and the cross-sectional corner radius ratio. The proposed compressive strength model can accurately predict the compressive strength of FRP confined rubber concrete with rubber particles replacing part of fine aggregate or coarse and fine aggregate, and the average error value is 1.03. The predicted curves of the proposed stress-strain model are consistent with the experimental stress-strain curves of FRP confined rubber concrete.

To eliminate the influence of unknown disturbance and uncertainty on vehicle longitudinal control, the layered vehicle longitudinal control system based on disturbance observer and terminal sliding mode control was designed. The disturbance observer was used to observe the system disturbance in real time and realize the feedback compensation of system for reducing the switching gain of sliding mode control and suppressing the chattering problem of sliding mode control. The stability of the control system was proved by Lyapunov function, and the joint simulation experiments were conducted by Carsim/Simulink. The results show that under the two simulation conditions, the controller has good tracking effect on the reference speed, and the maximum speed error is only 0.22 m/s. The disturbance observer can accurately and quickly estimate the system disturbance with the estimation error of only 0.003 14, and the switching logic actuator performs well. Compared with the traditional sliding mode control, the designed controller has better stability with decreased chattering problem of sliding mode control, and the stable tracking of vehicle longitudinal speed is realized.

To solve the problems of the existing construction quality inspection with time-consuming information retrieval and data integrity concerns, according to the requirement analysis of engineering quality acceptance management, the traceability and immutability of blockchain and the efficiency and responsiveness of information processing of human-machine interaction (HMI), the construction quality inspection system based on blockchain and HMI was proposed. The proposed system was verified by the real-world case, and the performance evaluation was conducted by read/write latency and data storage. The results show that under ideal network conditions, the read and write latencies for construction quality data are below 1.5 seconds and 3.5 seconds, respectively.The storage capacity for accumulating construction quality acceptance transaction data is within the manageable range. The results align with the established performance requirements for blockchain systems as outlined in existing research.

To improve the ride comfort of commercial vehicle, the parameters of the suspension system of commercial vehicle were optimized. Based on the actual test data of the air spring, the stiffness characteristic curve was fitted, and the multi-body dynamic model of the front and rear suspension and the entire vehicle was established. The smoothness of the vehicle under random input and pulse input conditions was simulated. The parametric multi-platform co-simulation model of the suspension system was built, and the degree of influence of the design parameters of the suspension system on ride comfort was obtained through the global sensitivity analysis. The multi-objective response surface approximation model for vehicle ride comfort was established, and the multi-objective genetic optimization algorithm was used to optimize the parameters in the feasible region. The results show that under the random input conditions, the root mean square(RMS) values of the weighted acceleration at the driver and passenger positions are respectively reduced by 12.50% and 29.71% when the vehicle speed is 100 km/h. Under pulse input conditions, the maximum vertical accelerations at the driver and passenger positions are respectively reduced by 14.69% and 31.28% when the vehicle speeds are respective 20 km/h and 30 km/h, and the optimization effect is obvious.

The hierarchical control strategy was proposed to solve the problem that distributed electric vehicle was easy to lose stability under lane change or extreme conditions with high speed and low adhesion coefficient. The stability of the vehicle was analyzed, and the opening conditions of the controller were judged. In the upper controller, the fuzzy sliding mode control was adopted. The additional yaw moment was obtained by integral sliding mode control. In order to reduce the chattering of integral sliding mode controller, the fuzzy controller was used to optimize. In the lower controller, the longitudinal force distribution types were adopted based on tire load rate and axial load ratio, and the two types of torque distribution were compared. Through Carsim and MATLAB/Simulink co-simulation, the verification tests were carried out on high speed pavement with high adhesion and low speed pavement with low adhesion, respectively. The results show that the proposed stability control strategy can effectively improve the vehicle stability, and both of the two torque distributors have good control effects, while the effect of the longitudinal force distribution based on axle load ratio is better.

The liquid is discharged into the surrounding atmospheric environment through the nozzle or orifice with small inner diameter. When the outlet pressure or flow is increased, the flow mode of the liquid at the end of the capillary can change from periodic dripping (PD) to dripping faucet (DF) with latter jetting (J). Through changing the flow rate, three typical atomization modes of PD，DF and J for deionized water and ethanol were recorded by high-speed digital camera, and the critical Weber number of the transition of atomization mode was obtained. The results show that in the process of PD, the shape of droplets is gradually changed from spherical to pear-shape with the increasing of outer diameter of the orifice. As the flowrate is increased, two liquids are gradually reached the jetting regime from the dripping mode through the dripping faucet. Compared with deionized water, the transition of ethanol exists in small range and only in the inner diameter of 0.33-0.60 mm, and it is very easy to transition directly from periodic dripping to jetting regime. The difference of atomization mode transition between deionized water and absolute ethanol is caused by the different surface tension. The critical Weber number corresponding to the transition from dripping to jetting mode can realize the prediction of the atomization mode transition process.

To solve the problem of low segmentation accuracy for unknown novel classes in existing few shot semantic segmentation models, the few shot semantic segmentation algorithm based on meta-learning was proposed. The depth-separable convolutions were utilized to improve the traditional backbone network, and the encoder pre-training on the ImageNet dataset was performed. The pre-trained backbone network was used to map the support and query images into deep feature space. Using the ground truth masks of the support images, the support features were separated into object foreground and background, and the adaptive meta-learning classifier was constructed using vision transformer. The extensive experiments on the PASCAL-5i dataset were completed. The results show that the proposed model achieves mIoU (mean Intersection over Union) (1 shot) of 47.1%, 58.3% and 60.4% on VGG-16, ResNet-50 and ResNet-101 backbone networks, respectively, and it achieves mIoU of 49.6%, 60.2% and 62.1% under the 5 shot setting. On the COCO-20i dataset, mIoU (1 shot) values of 23.6%, 30.3% and 30.7% are achieved with mIoU values of 30.1%, 34.7% and 35.2% under the 5 shot setting.