To address the issues of current harmonics generated during the operation of Permanent Magnet Synchronous Motor (PMSM), which lead to reduced efficiency, lifespan, and stability of the motor, an improved harmonic current suppression method is proposed. Firstly, building on the current mean value method for harmonic current extraction, a combination of a low-pass filter and the improved current mean value method (LPF-CAM) is employed. This results in a new current harmonic extraction method based on LPF-CAM, which enhances the accuracy of harmonic DC component extraction. Subsequently, a harmonic compensation voltage acquisition method based on a PI controller is used to generate compensation voltage for harmonic current suppression. Compared to traditional methods, this strategy simplifies control system design and reduces computational complexity, offering high practical value and potential for wide application. Simulation and experimental results show that this strategy effectively suppresses harmonic current and improves the stability of the Permanent Magnet Synchronous Motor.

A well-designed energy management control strategy can enhance the operational life span of fuel cell vehicles, reduce their energy consumption costs during operation, and boost overall energy utilization efficiency of the vehicle system, thereby fundamentally improving the economic and safety aspects of fuel cell vehicles. This paper proposes an adaptive optimization algorithm to optimize the equivalent factor, resulting in reduced equivalent hydrogen consumption and achieving improved economy. Concurrently, it ensures that the lithium-ion battery SOC in fuel cell vehicles remains at a stable level, thereby extending battery life. A simulation model is built and experiments are conducted in MATLAB/Simulink. The results show that compared to the traditional PID method, the adaptive optimization algorithm exhibits lower equivalent hydrogen consumption and a smaller change in lithium-ion battery SOC before and after operation, substantiating the superiority of the proposed control algorithm.

With the development of the times and continuous improvement in modern motor control technology, permanent magnet synchronous motors (PMSMs) are widely used across various industries due to their simple structure and ease of operation. However, in practical operation, PMSMs tend to produce current harmonics, which increase losses, generate heat, and reduce the efficiency and lifespan of the motor. This paper focuses on suppressing current harmonics, with harmonic injection PMSM as the research subject. It analyzes the traditional compensation voltage acquisition method based on Deadbeat Predictive Current Control (referred to as “compensation voltage acquisition method based on DPCC”) for suppressing current harmonics. Building upon this analysis, it proposes an improved compensation voltage acquisition method based on Modified Deadbeat Predictive Current Control (referred to as “compensation voltage acquisition method based on TSD-DPCC”). Finally, the effectiveness of the proposed method is verified through simulation results using the Matlab/Simulink platform.

Blockchain sharding has emerged as a promising solution to the scalability challenges in traditional blockchain systems by partitioning the network into smaller, manageable subsets called shards. Despite its potential, existing sharding solutions face significant limitations in handling dynamic workloads, ensuring secure cross-shard transactions, and maintaining system integrity. To address these gaps, we propose DynaShard, a dynamic and secure cross-shard transaction processing mechanism designed to enhance blockchain sharding efficiency and security. DynaShard combines adaptive shard management, a hybrid consensus approach, plus an efficient state synchronization and dispute resolution protocol. Our performance evaluation, conducted using a robust experimental setup with real-world network conditions and transaction workloads, demonstrates DynaShard's superior throughput, reduced latency, and improved shard utilization compared to the fast transaction scheduling in blockchain sharding (FTSBS) method. Specifically, DynaShard achieves up to a 42.6% reduction in latency and a 78.77% improvement in shard utilization under high transaction volumes and varying cross-shard transaction ratios. These results highlight DynaShard's ability to outperform state-of-the-art sharding methods, ensuring scalable and resilient blockchain systems. We believe that DynaShard's innovative approach will significantly impact future developments in blockchain technology, paving the way for more efficient and secure distributed systems.

Similarity search on encrypted data can identify similar data and handle misspelled keywords in a privacy-preserving manner and thus has received a lot of attention. However, existing schemes suffer from imprecise or predefined distance thresholds, which means that they do not always return the expected search results. Moreover, these schemes either do not consider document addition or lack forward security in this dynamic setting. In this article, we present a Similar Keyword Matching (SKM) framework that accurately calculates the Hamming distance between keywords through a new keyword representation called uni-pos-gram. Based on our framework, we propose a basic scheme for similarity search over encrypted data called SimSE that offers adjustable Hamming distance thresholds and an enhanced scheme called SimSE-F that provides forward security. Security analysis demonstrates that our schemes effectively safeguard the privacy of documents, indexes, and searches. Empirical experiments using real-world datasets demonstrate the efficiency and practical applicability of our schemes.

With more and more attention to the security of cloud storage, people are increasingly inclined to remotely store their encrypted data. In what follows, many searchable encryption (SE) schemes have been proposed. Unfortunately, the existing SE schemes under multi-user and multi-owner model are usually inefficient in owner-level and attribute-level access controls. Therefore, this paper aims to further improve the efficiency of the encrypted search with two-level access control. In our design, the owner-level permission is inspired by BLS signature and the attribute-level permission is based on CP-ABE, both of which are driven by simplifying the related keys as much as possible, allowing users to efficiently search data from multiple owners by using a single trapdoor. In addition, the proposed scheme can also efficiently support permission revocation, thanks to our simplified key for a user which can be independently updated without affecting other users. For the sake of security, we perform the random masking technique on encrypted index and searching trapdoor for hiding the keyword embedded in them, while keeping their matching relationship for keyword search. The proposed scheme is strictly proven to have the security properties of keyword secrecy, keyword irreplaceability, two-level controlled search and forward secrecy. Finally, we give plenty of theoretical analysis and experimental results to validate the superiority of the proposed scheme.

The main research content of this paper is as follows: Firstly, the observer-based MTPA vector control strategy is adopted, and the observer is used to improve the anti-interference ability of the system. On the Matlab simulation platform, the simulation model of the drive control system of hydrogen fuel cell vehicle is constructed, and the test scheme of the drive control system of hydrogen fuel cell vehicle is designed. The simulation results show that the design of the hydrogen fuel cell vehicle drive control system in this paper can meet the requirements of hydrogen fuel cell vehicle drive response time, speed control precision and other technical indicators at start-up, acceleration and deceleration, and constant speed operating conditions. And in the working condition, the motor can quickly follow the given value, and has a strong anti-interference ability; Relative to the stable value of stator current, the speed and torque of the motor can maintain high stability, the speed of the motor, torque and stator current fast follow and strong anti-interference ability, to ensure the stable operation of the system.

The vector control system of permanent magnet synchronous motor is taken as the research object. The traditional permanent magnet synchronous motor system adopts current hysteresis tracking PWM to speed regulation, which has some shortcomings in anti-interference ability and dynamic performance. In order to improve the anti-interference ability and dynamic performance of the motor, through the analysis of the principle of vector synthesis, a speed regulation system based on coordinate transformation and decoupling technology is designed, and voltage feedforward compensation and space vector pulse width technology (SVPWM) are adopted to improve the dynamic response of the system. Finally, Matlab/Simulink simulation platform is used to verify and analyze the anti-interference ability and tracking performance of the system. After experimental setting, the experimental results show that the designed motor control strategy can effectively adjust the expected speed of the motor system, and has good anti-interference ability and can effectively optimize the dynamic performance.

Aiming at the problems that DC/DC converters require low voltage ripple and low current ripple in high-power applications, a fuzzy sliding mode control method for DC/DC converters was proposed to apply in hydrogen fuel vehicles in this work. And a four-phase interleaved boost converter topology was built in this control system by using Matlab/Simulink. The results of simulation evaluation turned out that utilizing the present proposed control algorithm could effectively accelerate the system response speed and reduce the output current ripple and voltage ripple compared with the traditional double closed-loop PID algorithm. Moreover, before and after changing the load, the system showed good dynamics and strong robustness, which could better meet the requirements of automotive DC/DC converter.

Fringe projection is widely used in scientific research and industrial measurement, due to its cost-effectiveness and precision. Nonetheless, acquiring massive fringe projection image datasets for real-world objects poses a challenge due to the limitation of photography. Furthermore, conventional method of developing deep learning models in Python often takes considerable effort in programming ancillary functions like image input, training progress control and so on. This paper introduces a practical methodology for researchers and engineers to effortlessly prepare datasets and design models for fringe projection using MATLAB. By applying algorithm to simulate fringe projection procedure, abundant image datasets can be generated and utilized for model pretraining. Besides, a phase unwrapping model was designed with MATLAB's deep network designer tool, which could achieve its expected function without complicated programming.

Smart contracts, self-executing agreements directly encoded in code, are fundamental to blockchain technology, especially in decentralized finance (DeFi) and Web3. However, the rise of Ponzi schemes in smart contracts poses significant risks, leading to substantial financial losses and eroding trust in blockchain systems. Existing detection methods, such as PonziGuard, depend on large amounts of labeled data and struggle to identify unseen Ponzi schemes, limiting their reliability and generalizability. In contrast, we introduce PonziSleuth, the first LLM-driven approach for detecting Ponzi smart contracts, which requires no labeled training data. PonziSleuth utilizes advanced language understanding capabilities of LLMs to analyze smart contract source code through a novel two-step zero-shot chain-of-thought prompting technique. Our extensive evaluation on benchmark datasets and real-world contracts demonstrates that PonziSleuth delivers comparable, and often superior, performance without the extensive data requirements, achieving a balanced detection accuracy of 96.06% with GPT-3.5-turbo, 93.91% with LLAMA3, and 94.27% with Mistral. In real-world detection, PonziSleuth successfully identified 15 new Ponzi schemes from 4,597 contracts verified by Etherscan in March 2024, with a false negative rate of 0% and a false positive rate of 0.29%. These results highlight PonziSleuth's capability to detect diverse and novel Ponzi schemes, marking a significant advancement in leveraging LLMs for enhancing blockchain security and mitigating financial scams.

Sealed-bid auction is a common mechanism for selling and buying commodities. However, existing auction schemes to protect bids require at least squared computation and communication complexity for the bidders or rely on trusted auctioneers or third parties. To address the above problems, we propose a secure and efficient sealed-bid auction framework, called FACT. We design a lightweight threshold fully homomorphic encryption scheme as the building block. Our framework does not rely on any trusted auctioneer and fulfills a stronger security guarantee, called full privacy, i.e., only the seller and the winning bidder can determine the auction result. While our framework applies to first-price sealed-bid, it can easily be extended to support secondprice sealed-bid (i.e., Vickrey auction) with the same security guaranteed. Our framework also supports the dynamic joining and exiting of sellers and bidders. Meanwhile, our framework reduces the bidders' overhead and the number of interactions to a constant level. We formally prove the security of our framework in the semi-honest adversary model. We implement FACT and run experiments comparing its performance against existing schemes. We find that our framework not only achieves a stronger security guarantee but also shows significant performance improvement compared to existing schemes.

Aiming at the problem of controlling the excess ratio of oxygen in the air supply subsystem of proton exchange membrane fuel cells.Firstly,a control-oriented fourth-order nonlinear dynamic model of the proton exchange membrane fuel cells system is established,and a fitting curve equation between the stack load current and the optimal oxygen excess ratio is constructed.Subsequently,a sliding mode controller using a new compound reaching law is designed,and the parameters in the sliding mode control are optimized and tuned using the sand cat swarm optimization algorithm.Finally,the improved sliding mode controller is simulated and verified,and compared with PID and other three sliding mode controllers.The simulation results show that when the load current of the stack changes,the improved sliding mode controller can adjust the driving voltage of the air compressor according to the cathode flow deviation parameter.At this time,the real-time oxygen excess ratio of the system will quickly approach the optimal oxygen excess ratio.The deviation between the two can be controlled within 0.1%,and the required average adjustment time and error performance indicators are better than those of the comparison group.

To make the bidirectional DC/DC converter have better performance and stability, this paper presents a RBF neural network-based variable structure control approach. Mathematically describe the working principle of bi-directional DC/DC converter and construct a variable structure controller using RBF neural network according to the design requirements. The variable structure control is optimized by the RBF neural network algorithm and the chattering phenomenon in the variable structure control is reduced. The simulation model is built and simulated in MATLAB/Simulink, and results of the simulation show that the dynamic response performance and robustness of the control system are improved after using the RBF neural variable structure control optimized voltage controller.

In order to solve the problems of large torque speed pulsation and waveform distortion in the traditional PI control method for weak magnetic control of PMSM of electric vehicles, a new weak magnetic control method for PMSM was proposed based on sliding mode control in this work. Firstly, the mathematical model of the motor is established, then the controller is designed according to the weak magnetic control theory and the principle of sliding mode control, and finally the simulation analysis is established by MATLAB/Simulink. The results of simulation evaluation turned out that compared with the traditional PI weak magnet control, the response speed, anti-interference ability and robustness of control system were improved when the proposed control method was used.

To satisfy the asymmetric uplink (UL) and downlink (DL) requirements for various services in 5G and future 6G systems, the third-generation partnership project (3GPP) employs dynamic time division duplexing (TDD) technology. Although the introduction of dynamic TDD technology can improve the overall performance of the network, it also remains cross-link interference (CLI). In addition, for a specific service, the actual throughput ratio between UL and DL (UL/DL throughput ratio) provided by the cell with the configured TDD is not consistent with that of service required. Then, the actual throughput of the service will be determined by the TDD UL/DL and link rates of the cell under the limitation of the service demand of UL/DL throughput ratio, which is analyzed and named the UL/DL bottleneck in this paper. Therefore, we consider the dynamic TDD configuration under the UL/DL bottleneck and the CLI problem. We propose an actual throughput model of the service considering UL/DL bottleneck for UAV-assisted cellular network scenarios, and design an intelligent TDD UL/DL configuration algorithm based on genetic algorithm (GA) to solve the optimal ratio. Simulation results show that compared with traditional algorithms, the proposed algorithm can obviously improve the actual throughput of services in the network.

Watermarking has been widely adopted for protecting the intellectual property (IP) of Deep Neural Networks (DNN) to defend the unauthorized distribution. Unfortunately, studies have shown that the popular data-poisoning DNN watermarking scheme via tedious model fine-tuning on a poisoned dataset (carefully-crafted sample-label pairs) is not efficient in tackling the tasks on challenging datasets and production-level DNN model protection. To address the aforementioned limitation, in this paper, we propose a plug-and-play watermarking scheme for DNN models by injecting an independent proprietary model into the target model to serve the watermark embedding and ownership verification. In contrast to the prior studies, our proposed method by incorporating a proprietary model is free of target model fine-tuning without involving any parameters update of the target model, thus the fidelity is well preserved and scalable to challenging real tasks. Experimental results on real-world challenging datasets (e.g., ImageNet) and production-level DNN models demonstrated its effectiveness, fidelity w.r.t. the functionality preservation of the target model, robustness against popular watermark removal attacks, and the plug-and-play deployment. The source code and models are available at https://github.com/AntigoneRandy/PTYNet.

Inference outsourcing enables model owners to deploy their machine learning models on cloud servers to serve users. In this paradigm, the privacy of model owners and users should be considered. Existing solutions focus on Convolutional Neural Networks (CNNs) but their efficiency is much lower than GALA, which is a solution that only protects user privacy. Furthermore, these solutions adopt approximations that reduce the model accuracy and thus require model owners to retrain the models. In this paper, we present an efficient CNN inference outsourcing solution that protects the privacy of both model owners and users. Specifically, we design secure two-party computation protocols based on two non-colluding cloud servers, which calculate with additive secret shares of the model and the user’s input. Our protocols avoid the expensive permutation operations in linear calculations and approximations in non-linear calculations. We implement our solution on realistic CNNs and experimental results show that our solution is even 2–4 times faster than GALA.

With the development of information networks, the entities from different network domains interact with each other more and more frequently. Therefore, identity management and authentication are essential in cross-domain setting. The traditional Public Key Infrastructure (PKI) architecture has some problems, including single point of failure, inefficient certificate revocation status management and also lack of privacy protection, which cannot meet the demand of cross-domain identity authentication. Blockchain is suitable for multi-participant collaboration in multi-trust domain scenarios. In this paper, a cross-domain certificate management scheme CD-BCM based on the consortium blockchain is proposed. For the issue of Certificate Authority’s single point of failure, we design a multi-signature algorithm. In addition, we propose a unified structure for batch certificates verification and conversion, which improve the efficiency of erroneous certificate identification. Finally, by comparing with current related schemes, our scheme achieves good functionality and scalability in the scenario of cross-domain certificate management.

基于大数据视角,对体育教学评价中的大数据采集、治理和应用进行了探讨.研究认为,大数据技术改变了原有的以经验评判或实证研究体育教学特征的理论假设方法,通过各类技术方式采集数据并进行分析处理,能够实时地将教师的"教"与学生的"学"的过程数字化,为提高体育教学质量,实现基于数据分析的评估和决策提供了依据.同时,可让教师实时了解学生对运动基础知识和技能的掌握情况,对学生的自主锻炼和个体化发展进行探索,对体育教学评价理论进行初步探索,为多方参与教育评价,实现发展性学生评估提供了良好的支持.