Recently, the rapid advancement of Industrial Internet of Things (IIoT) technology has led to the utilization of various communication protocols. Among these, the Message Queue Telemetry Transport (MQTT) protocol has ...
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Recently, the rapid advancement of Industrial Internet of Things (IIoT) technology has led to the utilization of various communication protocols. Among these, the Message Queue Telemetry Transport (MQTT) protocol has become prevalent. Operating on a publish/subscribe model, MQTT's popularity in IIoT because of simplicity, lightweight nature, and ease of implementation. However, traditional MQTT protocol standards have neglected critical security concerns during the communication process, such as ineffective identity authentication, weak data confidentiality, and a lack of access control functions. To address these challenges, this paper introduces a security -enhanced scheme for the MQTT protocol based on domestic cryptographic algorithms. The proposed method enforces mutual identity authentication between the MQTT Client and the MQTT Broker using digital certificates. It also implements access control through an Access control List (ACL) and ensures end -to -end data security via the symmetric encryption algorithm SM4. Through comprehensive security analysis and experimental validation, this paper demonstrates that the enhanced scheme effectively rectifies the MQTT protocol's security deficiencies, achieving these improvements with small overhead.
In the dynamic landscape of advanced manufacturing, the confluence of laser powder bed fusion (LPBF) and machine learning (ML) has recently garnered significant attention in many applications. This review investigates...
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In the dynamic landscape of advanced manufacturing, the confluence of laser powder bed fusion (LPBF) and machine learning (ML) has recently garnered significant attention in many applications. This review investigates the confluence of LPBF and ML, specifically within the specific domain of stainless steel. Firstly, it delves into LPBF principles, including an overview of critical process parameters and associated defects. Secondly, the paper meticulously addresses the distinct challenges posed by steel in additive manufacturing (AM), highlighting factors such as chemical composition, anisotropic microstructure, and oxide film formation, all of which require specialized considerations. Thirdly, the spotlight shifts to the pivotal role of ML, covering predictive modeling for process parameters, real-time defect detection, and quality control. This paper highlights recent advances, revealing how data-driven approaches can accelerate process understanding and part qualification. Eventually, this review offers insights into the future integration of ML in LPBF for steel, providing valuable perspectives on potential advancements in the field of AM.
process analytical technology (PAT) tools are an important part of process monitoring and control in pharmaceutical continuous manufacturing (CM) that help ensure product quality. However, there is hesitancy to adopt ...
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process analytical technology (PAT) tools are an important part of process monitoring and control in pharmaceutical continuous manufacturing (CM) that help ensure product quality. However, there is hesitancy to adopt PAT due, in part, to the high start-up costs. A portion of the cost is the calibration burden associated with developing an appropriate multivariate dataanalysis (MVDA) method to extract the desired information from the spectral outputs of spectroscopic PAT tools. This has generated research interest in reduced calibration burden MVDA methods, such as iterative optimization technology (IOT) algorithms, as alternatives to conventional modeling approaches like partial least squares (PLS) regression. The goal of the presented research is to compare the calibration burden of three different MVDA methods (direct IOT, indirect IOT, PLS regression) at two drug loading levels (low and high) of pharmaceutical powder blends in a CM line. The blends were binary mixtures consisting of an active pharmaceutical ingredient and a coprocessed excipient blend. The coprocessed excipient blend was leveraged to reduce formulation complexity and streamline process development, benefiting the application of IOT algorithm. Calibration burden was assessed in terms of time, material, and financial costs. Utilizing a near-infrared spectroscopic PAT tool, it was found that MVDA methods that utilized IOT algorithms demonstrated a notably reduced calibration burden compared to the PLS models, while predicting blend potency with similar accuracy.
In order to improve the accuracy of low voltage ride-through (LVRT) control parameter identification for direct-drive wind turbines, a identification method based on the GRU-ISSA algorithm is proposed. Firstly, the ke...
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作者：
Rakov, Dmitry
Department of technological processes and systems control Moscow Russia
Clustering of the morphological set of engineering solutions in the synthesis of systems is the process of grouping similar solutions in order to facilitate their analysis and selection of the best option. This method...
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The increasing demand for spontaneity, comfort, and efficiency leads to the increasing complexity of dual-clutch transmissions and their shift controls. Friction modeling of piston seals plays an essential role in ach...
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The increasing demand for spontaneity, comfort, and efficiency leads to the increasing complexity of dual-clutch transmissions and their shift controls. Friction modeling of piston seals plays an essential role in achieving a better understanding of the determinant factors of energy losses and, consequently, the realization of more efficient transmission hydraulic actuators. This paper studies the performance of a dual-clutch transmission during the gear-shifting process of a vehicle power train model. The Modified Generalized Maxwell-Slip friction model with Genetic Algorithm (MGMS-GA) parameters identification is used to include the effect of seal types. The gear shift comfort analysis and evaluation, with four different seal types, has been performed based on objectification. Simulations with O-Ring, D-Ring, Bonded, and Total control System - Polytetrafluoroethylene piston seals were performed to show the validity of the model in practical scenarios. The study shows the superiority of the MGMS-GA in representing the experimental data and enhancing gearshift control and comfort. Moreover, it showed that the Total control System-Polytetrafluoroethylene piston seal presented the highest performance among all seal types.
This paper explores the implementation of a high-fidelity model for robomoulding, an advanced polymer processing technique derived from rotational moulding. Robomoulding produces large, hollow, stress-free polymer pro...
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The expansion of urban centers necessitates enhanced efficiency and sustainability in their transportation infrastructure and mobility systems. The big data obtainable from various transportation modes potentially off...
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Currently, the discussion on the modeling method characterized by the small electrodynamic shakers, especially the mathematic analytical method, is relatively sparse. In this paper, taking BK4809 as an example, a mode...
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The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions am...
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The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions among electrospinning variables. This research explores the application of Long Short-Term Memory (LSTM) networks and multiple regression models to forecast the diameters of Titanium Dioxide (TiO2) and Polyvinyl pyrrolidone (PVP) nanofibers, facilitating improved process regulation and enhancement. TiO2 + PVP nanofibers were fabricated under diverse conditions, including changes in applied voltage, solution concentration, and distance between tip to collector. The acquired data underwent analysis using LSTM and regression models to assess their predictive capabilities. The outcomes revealed that both approaches effectively estimated nanofiber diameters;however, the regression model surpassed LSTM with a lower error rate of 0.077% compared to 0.305%. This indicates that while LSTM captures nonlinear relationships, conventional regression models yield more precise predictions in this scenario. These findings underscore the potential of machine learning in advancing electrospinning technology by minimizing trial-and-error experiments and boosting nanofiber production efficiency. The incorporation of artificial intelligence-driven modeling into the electrospinning process sets the stage for more accurate control over fiber morphology, resulting in enhanced material properties and expanded applications in biomedical, environmental, and energy sectors.
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