In order to solve the problem of high effective inductor current and peak value in the quadrilateral inductor current control strategy of four-switch Buck-Boost (FSBB) converter, a boundary conduction mode (BCM) control strategy was proposed, which shortened the freewheeling phase without power transmission to zero in the existing quadrilateral inductor current control strategy, so as to reduce the RMS and peak value of inductor current. Firstly, the current waveforms of the FSBB converter in different modes of working modes and inductor currents were analyzed. Secondly, the constraints of the FSBB converter to achieve soft switching under all working conditions were analyzed, and the value rules of the inductor current are obtained. Then, the variation of inductor current in different modes was analyzed, and the control method in critical continuous mode was given, when the input and output voltage difference was small, increase the output power by increasing the duty cycle of the first or third stage, and when the input and output voltage difference was large, the FSBB converter works in the critical continuous state of inductor current, which effectively reduces the effective value and peak value of inductor current. Finally, a simulation model was built. The results show that the proposed BCM control strategy can achieve zero-voltage turn-on and has good dynamic response ability.

The digital twin technology of the distribution network is an important product resulting from the integration and development of the power system and information technology. The technology simulates the physical behavior and operational status of the distribution network in a digital space by constructing a virtual model of the physical distribution network, enabling comprehensive simulation and analysis. Due to the diverse systems and complex states involved, the existing digital twin simulation platform technology for distribution networks still requires improvement. A wavelet-LSTM fusion model for power state and weather factors was constructed based on the existing wavelet transform and long short-term memory (LSTM) neural network. The high-dimensional input data were converted into detail and contour coefficients using discrete wavelet transform. Subsequently, LSTM neural networks were constructed to process the data and fuse the results, thereby forming accurate prediction outcomes. This method was validated on real datasets, showing that the wavelet-LSTM fusion model significantly improves the mean absolute percentage error (MAPE) compared to the existing LSTM network. Additionally, the method was tested on datasets from different industries. Compared to wavelet-Lasso, LSTM, and STL-LSTM, it exhibits better performance in terms of MAPE, demonstrating that the wavelet LSTM prediction method can be applied to state data from various sectors, thereby providing robust support for future state prediction of digital twins.

Neurosurgical procedures like cerebral vascular bypass, brain tissue dissection, and neurorrhaphy often lack microsurgical instruments with delicate force perception. To enhance manual precision and tactile force perception during surgery, a novel multi DOFs rope-driven micromanipulation wrist gripper designed was introduced for surgical robots. This wrist gripper was powered by a screw drive controlling six ropes, facilitating dexterous movements at the distal end of miniature instruments. The high gear reduction ratio of the screw drive enhances driving precision, thereby achieving high operational accuracy and stability of the distal wrist gripper. Moreover, a force sensor was integrated between the ropes’ rear end and the screw drive to monitor the tension in the ropes in real time. Based on the tension in the ropes, a computational model for estimating the contact force at the distal end of the wrist gripper was proposed, enabling the perception of external contact forces. Experimental results show that under open-loop control, the average motion tracking error of the wrist gripper is less than 1°, and the maximum mean value of force estimation error is 53.85 mN.

Process model discovery algorithms are capable of extracting process models from event logs, but different algorithms have varying capabilities in handling event logs. Currently, most research on evaluating these algorithms involves indirect evaluation methods, which have limitations. To address this issue, a method was proposed to directly evaluate the reliability of process model discovery algorithms, using reliability as an important evaluation metric. The original event log was preprocessed to obtain an incremental sub-log collection, the process model discovery algorithm was applied to the incremental sub-logs and the original event log to obtain process models, and the reliability of the business process model discovery algorithm was evaluated through quality assessment. Based on nine public simulation event logs and four real event logs, multiple model discovery algorithms were experimented on from the aspects of weak reliability, noise interference reliability, and strong reliability. The experimental results showed that the reliability values of Heuristic Miner, Inductive Miner-infrequent, Inductive Miner, and Alpha Miner were 4, 3.2, 2.4, and 1.6, respectively. Higher reliability values indicated stronger reliability of the algorithms. Thus, the proposed method can effectively evaluate the reliability of the algorithms.

To enhance the classification accuracy of lower limb movements, this paper was introduced a hybrid recognition model based on surface electromyography (sEMG) that combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). Initially, sEMG data were collected from 20 subjects performing four types of gait movements: ascending stairs, descending stairs, walking, and squatting. Subsequently, the collected sEMG data underwent preprocessing, and both time domain and frequency domain features were extracted to serve as inputs for the machine learning recognition model. The CNN-LSTM model was then constructed for lower limb action recognition and compared against the performances of CNN, LSTM, and SVM (support vector machine,)models. The results demonstrate that the CNN-LSTM model outperforms the CNN, LSTM, and SVM models by 2.16%, 8.34%, and 11.16% in accuracy, respectively, thereby proving its superior classification performance. This model provides an effective solution for enhancing lower limb motor functions, offering significant benefits for rehabilitation medical equipment and power assist devices.

With the deepening research on federated learning, it has been observed that the privacy protection strategies employed within federated learning fall short of fully guaranteeing the security and confidentiality of user data. Moreover, the training process in federated learning encounters challenges regarding model convergence. In response to these aforementioned issues, an innovative solution termed adaptive differential privacy (DP-AdaMod) was proposed. Primarily, the model training process was fine-tuned by incorporating an adaptive learning rate algorithm to mitigate model fluctuations and the adverse effects of overfitting. Consequently, this enhancement led to improved training efficiency and optimal performance. Secondly, the application of differential privacy techniques ensured the privacy security in federated learning through the deliberate introduction of noise into the model gradients. Additionally, accurate quantification of privacy loss was achieved by implementing the moment accountant mechanism, facilitating a balanced trade-off between privacy preservation and analytical accuracy. This meticulous approach served to fortify system security. Lastly, the efficacy of the proposed solution was ascertained through comprehensive simulation experiments. The results substantiate the superior performance of the proposed method, evident by its exceptional accuracy, efficient utilization of privacy budget, and other notable facets.

With the continuous development of container cloud technology, it is of great significance to predict and analyze the overall trend and peak of cloud resource requests for efficient utilization and reasonable allocation of container cloud resources. Deep learning technology for load prediction has become a key technology to solve the unbalanced utilization of container cloud resources. Aiming at the problems of low prediction accuracy and insufficient capture sequence features existing in the current single model and combination model of load prediction, a cloud resource combination prediction model based on temporal convolutional network-long short-term memory(TCN-LSTM)was proposed. The hollow convolution in the combination model increased the sensitivity field without reducing the feature size to obtain longer time series features. The residual network could transfer information across layers to accelerate the convergence of the network, and the obtained time series features could effectively improve the prediction accuracy of LSTM. Useing Alibaba’s publicly available dataset to make predictions, the experiment shows that the proposed model is compared with the single prediction model and other combined models, and the error index-mean absolute error(MAE) is reduced by 8%~13.7% and root mean squared error (RMSE) by 9.8%~13.1%, which proves the effectiveness of the proposed model.

Due to the increasing scarcity of precious woods and the severe environmental issues caused by overexploitation, it is necessary to mimic the appearance of precious woods by dyeing ordinary wood. Computer-assisted dyeing technology was utilized to achieve high-precision dyeing of ordinary wood, thus creating substitutes that resemble precious woods and reducing dependence on them. Initially, based on the concept of gene expression programming (GEP), a multi-expression programming (MEP) algorithm was proposed to predict dye ratios. Considering the complex interactions among various dyes, multi-gene expression was employed. The MEP algorithm can handle these complex interactions between multiple dyes, resulting in more intuitive functional expressions. To enhance the function mining accuracy of MEP, the probabilities of mutation and recombination operators ware adaptively adjusted, and parallel programming was employed to boost function mining efficiency. Compared to gene expression programming results, MEP delves deeper into functional relationships and achieves a relative deviation of 0.113 in color prediction.

At present, the research on bamboo mainly focuses on large-diameter bamboos such as moso bamboo, and the research and application of square bamboo and small-diameter bamboos are rare. Based on the test method of physical and mechanical properties of bamboo, the mechanical tests were carried out on 111 Zhaotong square bamboo flake samples born in 3~5 years, and the tensile and compressive strength, elastic modulus and other parameters were determined. Based on the experimental data, the intensity probability distribution was determined and its distribution characteristics were verified. Combined with the research results of timber structure and the reliability limit state design method, the strength standard value and design value of square bamboo were obtained, and its feasibility was verified by comparison with moso bamboo, larch and other materials. The reliability index was corrected for the design value of the compressive strength of the grain that did not meet the requirements of the code. The result enriches the database of mechanical properties of small-diameter bamboo, and also opens up a new direction for the further research and application of bamboo.

China’s offshore wind power sector is rapidly progressing with large-scale and efficient projects. To support large-capacity wind turbines, there is an increasing need for advanced tower structures that offer superior structural performance and economic efficiency. One such innovative solution is the hybrid fiber-reinforced composite materials (FRP)-concrete-steel prestressed double-skin wind turbine towers (PDSWTs) proposed by scholars at The Hong Kong Polytechnic University. PDSWTs boast excellent durability, high load-bearing capacity, and exceptional stability, resulting in the potential to reduce production and maintenance costs throughout the service life. A study on the analysis and design procedures of PDSWTs was presented using a tower that supports 12 MW offshore wind turbine as an example, based on the provisions in current design standards and finite element modelling. The results demonstrate that: the tower successfully meets the frequency requirements of wind turbine; the tower possesses high ultimate resistance under compression-bending, shear and torsional loadings of ultimate limit states; the strain, stress, crack width and tower deformation under serviceability limit states meet the requirements of current design standards; furthermore, a simplified method is proposed to check the fatigue resistance of steel and concrete in the tower sections. The result is expected to provide references for the design and application of PDSWTs.