Optimizing road safety: integrated analysis of motorized vehicle using lattice ordered complex linear diophantine fuzzy soft set.

In this manuscript, we delve into the realm of lattice ordered complex linear diophantine fuzzy soft set, which constitutes an invaluable extension to the existing Fuzzy set theories. Within this exploration, we investigate basic operations such as ⊕ and ⊗ , together with their properties and theorems. This manuscript is more amenable in two ways, i.e., it enables real-life problems involving parametrization tool and applications with an existing order between the components of the parameter set based on the preference in the complex frame of reference. Adaptive cruise control (ACC) is a system designed for maintaining distance between two vehicles and to sustain a manually provided input speed. The purpose of cars with ACC is to avoid a collision that frequently happens nowadays, thereby improving road safety regulations amidst rising collision rates. The fundamental aim of this manuscript is to prefer an applicable car with ACC together with its latest model by defining a peculiar postulation of lattice ordered complex linear diophantine fuzzy soft set ( L O C L D F S S ^ ) . Emphasizing real-life applicability, we illustrate the effectiveness and validity of our suggested methodology in tackling current automotive safety concerns, providing useful guidance on reducing challenges related to contemporary driving conditions.

[Structural Design and Analysis of Portable Intelligent Wheelchair for Knee Rehabilitation].

Objective: In order to address the issues of inconvenience, high medical costs, and lack of universality associated with traditional knee rehabilitation equipment, a portable intelligent wheelchair for knee rehabilitation was designed in this study. Methods: Based on the analysis of the knee joint's structure and rehabilitation mechanisms, an electric pushrod-driven rehabilitation institution was developed. A multi-functional module was designed with a modular approach, and the control of the wheelchair body and each functional module was implemented using an STM32 single-chip microcomputer. A three-dimensional model was established using SolidWorks software. In conjunction with Adams and Ansys simulation software, kinematic and static analyses were conducted on the knee joint rehabilitation institution and its core components. A prototype was constructed to verify the equipment's actual performance. Results: According to the prototype testing, the actual range of motion for the knee joint swing rod is 15.1°~88.9°, the angular speed of the swing rod ranges from -7.9 to 8.1°/s, the angular acceleration of the swing rod varies from -4.2 to 1.6°/s², the thrust range of the electric pushrod is -82.6 to 153.1 N, and the maximum displacement of the load pedal is approximately 1.7 mm, with the leg support exhibiting a maximum deformation of about 1.5 mm. Conclusion: The intelligent knee joint rehabilitation wheelchair meets the designed functions and its actual performance aligns with the design criteria, thus validating the rationality and feasibility of the structural design.

Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling.

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable 'grey-box' AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale 'world model' by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.

Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions.

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.

Verification and Validation of Advanced Control Systems for a Spinal Joint Wear Simulator.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

Enhancement of fine particle removal through flue gas cooling in a spray tower with packing materials.

The efficient removal of fine particles from coal-fired flue gas poses challenges for conventional electrostatic precipitators and bag filters. Recently, a novel approach incorporating deep cooling of the flue gas has been proposed to enhance the removal of gaseous pollutants and particles. However, the achievable efficiency and underlying mechanisms of particle capture within the gas cooling system remain poorly understood. This study aims to elucidate the effectiveness of gas cooling in enhancing the removal of particles through a laboratory-scale spray tower equipped with packing materials. The results demonstrate a significant increase in particle removal efficiency, from 63.4 % to over 98 %, as the temperature of the spray liquid decreases from 20℃ to -20℃. Notably, this enhancement is particularly pronounced for particles sized 0.1-1 µm, with efficiency rising from approximately 40 % to 95 %, effectively eliminating the penetration window. Moreover, we find that the spray flow rate positively influences particle removal capability, while the height of the packing section exhibits an optimal value. Beyond this optimal height, particle removal performance may decline due to an inadequate liquid-to-packing ratio. To provide insight into the capture process, we introduce a single-droplet model demonstrating that particle capture is primarily enhanced through the augmented thermophoretic force.

Cylinder-piston application for surge prevention and energy efficiency in an industrial compression unit.

Compressions are prevalent in industrial applications and are notable for their substantial energy consumption. Therefore, the simulation and analysis of the compression process are essential for maintenance and energy conservation efforts. These systems are prone to potentially unstable surge conditions, necessitating the use of traditional anti-surge valves that result in considerable energy losses. Ensuring the near-optimal operation of these systems is critical to minimizing energy consumption. In this article, a conceptual framework for a cylinder-piston mechanism is delineated, intended for design and operation as an active surge control system. Additionally, a modular quasi-one-dimensional model is articulated for the transient simulation of an industrial compression system, which integrates models for both the anti-surge and active control systems. The manuscript presents a novel design, featured by a cylinder-piston system integrated with a robust controller, posited as a potential alternative to traditional anti-surge systems. The effectiveness of this design in expanding the operational envelope of the compression system and surge prevention is rigorously examined. Moreover, a thermodynamic model, grounded in the fundamental laws of mass, momentum, and energy conservation, is applied to each component of the system. Furthermore, the manuscript explores the benefits of the innovative design in achieving a marked decrease in energy wastage. Simulation results from a test scenario reveals that the implementation of the cylinder-piston design, as opposed to the conventional anti-surge system, can diminish energy losses and associated pollutant emissions by approximately 33 percent.

Intelligent Inspection Method and System of Plastic Gear Surface Defects Based on Adaptive Sample Weighting Deep Learning Model.

After injection molding, plastic gears often exhibit surface defects, including those on end faces and tooth surfaces. These defects encompass a wide range of types and possess complex characteristics, which pose challenges for inspection. Current visual inspection systems for plastic gears suffer from limitations such as single-category defect inspection and low accuracy. There is an urgent industry need for a comprehensive and accurate method and system for inspecting defects on plastic gears, with improved inspection capability and higher accuracy. This paper presents an intelligent inspection algorithm network for plastic gear defects (PGD-net), which effectively captures subtle defect features at arbitrary locations on the surface compared to other models. An adaptive sample weighting method is proposed and integrated into an improved Focal-IoU loss function to address the issue of low inspection accuracy caused by imbalanced defect dataset distributions, thus enhancing the regression accuracy for difficult defect categories. CoordConv layers are incorporated into each inspection head to improve the model's generalization capability. Furthermore, a dataset of plastic gear surface defects comprising 16 types of defects is constructed, and our algorithm is trained and tested on this dataset. The PGD-net achieves a comprehensive mean average precision (mAP) value of 95.6% for the 16 defect types. Additionally, an online inspection system is developed based on the PGD-net algorithm, which can be integrated with plastic gear production lines to achieve online full inspection and automatic sorting of plastic gear defects. The entire system has been successfully applied in plastic gear production lines, conducting daily inspections of over 60,000 gears.

Insights into Flexible Bioinspired Fins for Unmanned Underwater Vehicle Systems through Deep Learning.

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Recent work has explored the use of time-series neural network surrogate models to predict thrust and power from vehicle design and fin kinematics. We expand upon this work, creating new forward neural network models that encapsulate the effects of the material stiffness of the fin on its kinematic performance, thrust, and power, and are able to interpolate to the full spectrum of kinematic gaits for each material. Notably, we demonstrate through testing of holdout data that our developed forward models capture the thrust and power associated with each set of parameters with high resolution, enabling highly accurate predictions of previously unseen gaits and thrust and FOM gains through proper materials and kinematics selection. As propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations, a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, is used to evaluate different fin designs and kinematics and allow for comparison with other bio-inspired platforms. We use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. The forward model demonstrates the ability to capture the highest thrust and FOM with good precision, which enables us to improve thrust generation by 83.89% and efficiency by 137.58% with proper fin stiffness and kinematics selection, allowing us to improve material selection for bio-inspired fin design.

Neural network predictive controller based on the improved TPA-LSTM model for ultra-supercritical units.

To mitigate the impact of large-scale renewable energy power on the national grid in China, it is imperative to enhance the flexible peaking capability of coal-fired thermal power units. The coordinated control system, central to the load control of coal-fired units, faces challenges such as multivariable coupling, sluggish response, and uncertain coal quality parameters. This paper introduces a neural network predictive controller based on the improved TPA-LSTM model, aimed at addressing these issues. Initially, a data-driven control model is established to break through the limitations of traditional linear predictive control and effectively handle disturbance uncertainties. Then, a multivariable coordinated control strategy based on the neural network controller is designed, achieving effective decoupling of multiple parameters and ensuring high adaptability across all load conditions. Additionally, by integrating an automatic model updating mechanism, the system can recalibrate in real-time when model mismatches occur due to equipment aging, maintenance, or changes in coal quality, thereby enhancing overall control performance. Simulation results demonstrate that this strategy has excellent control effectiveness, meeting the flexible peaking demands of 1000 MW ultra-supercritical units. The calibration feature of the data-driven model significantly improves control performance following model mismatches.

Research on Gate Opening Control Based on Improved Beetle Antennae Search.

To address the issues of sluggish response and inadequate precision in traditional gate opening control systems, this study presents a novel approach for direct current (DC) motor control utilizing an enhanced beetle antennae search (BAS) algorithm to fine-tune the parameters of a fuzzy proportional integral derivative (PID) controller. Initially, the mathematical model of the DC motor drive system is formulated. Subsequently, employing a search algorithm, the three parameters of the PID controller are optimized in accordance with the control requirements. Next, software simulation is employed to analyze the system's response time and overshoot. Furthermore, a comparative analysis is conducted between fuzzy PID control based on the improved beetle antennae search algorithm, and conventional approaches such as the traditional beetle antennae search algorithm, the traditional particle swarm algorithm, and the enhanced particle swarm algorithm. The findings indicate the superior performance of the proposed method, characterized by reduced oscillations and accelerated convergence compared to the alternative methods.

Efficient Cyberattack Detection Methods in Industrial Control Systems.

The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional-Integral-Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched. The first method verifies whether the value of the control signal sent to the control plant in the previous step is the actual value generated by the controller. The second method relies on detecting sudden, unusual changes in output variables, taking into account the inertial nature of dynamic plants. In the third method, a copy of the controller parameters is used to detect an attack on the controller's parameters implemented in the PLC. The fourth method uses the golden run in attack detection.

Zearalenone modulates the function of goat endometrial cells via the mitochondrial quality control system.

Zearalenone (ZEN) is a mycotoxin known for its estrogen-like effects, which can disrupt the normal physiological function of endometrial cells and potentially lead to abortion in female animals. However, the precise mechanism by which ZEN regulates endometrial function remains unclear. In this study, we found that the binding receptor estrogen receptors for ZEN is extensively expressed across various segments of the uterus and within endometrial cells, and a certain concentration of ZEN treatment reduced the proliferation capacity of goat endometrial epithelial cells (EECs) and endometrial stromal cells (ESCs). Meanwhile, cell cycle analysis revealed that ZEN treatment leaded to cell cycle arrest in goat EECs and ESCs. To explore the underlying mechanism, we investigated the mitochondrial quality control systems and observed that ZEN triggered excessive mitochondrial fission and disturbed the balance of mitochondrial fusion-fission dynamics, impaired mitochondrial biogenesis, increased mitochondrial unfolded protein response and mitophagy in goat EECs and ESCs. Additionally, ZEN treatment reduced the activities of mitochondrial respiratory chain complexes, heightened the production of hydrogen peroxide and reactive oxygen species, and caused cellular oxidative stress and mitochondrial dysfunction. These results suggest that ZEN has adverse effects on goat endometrium cells by disrupting the mitochondrial quality control system and affecting cell cycle and proliferation. Understanding the underlying molecular pathways involved in ZEN-induced mitochondrial dysfunction and its consequences on cell function will provide critical insights into the reproductive toxicity of ZEN and contribute to safeguarding the health and wellbeing of animals and humans exposed to this mycotoxin.

Design and experimental evaluation of a variable pesticide application control system for the air-assisted rubber tree powder sprayer.

BACKGROUND: In order to address the issues of uneven pesticide deposition and low pesticide utilization in rubber gardens caused by the traditional diffuse plant protection spraying method, this study focuses on the air-assisted powder sprayer and proposes a variable pesticide application control system. A variable pesticide application decision-making model integrating the leaf area index (LAI) was designed based on powdery mildew control standards and individual rubber tree information. According to the target powder spraying accuracy requirements, a control model of the air velocity adjustment device was established and a fuzzy proportional-integral-differential (PID) air velocity control system was developed. RESULTS: The simulation results indicate that the wind speed control system exhibits a maximum overshoot of 2.18% and an average response time of 1.48 s. The field experiment conducted in a rubber plantation revealed that when the air-assisted powder sprayer operates in the variable powder spraying mode, the average response time of the control system is 2.5 s. The control accuracy of each executive mechanism exceeded 95.9%. The deposition coefficient of variation (CV) at different canopy heights was relatively consistent, with values of 35.38%, 36.26% and 36.90%. In comparison to the quantitative mode, the variable mode showed a significant 20.03% increase in the effective utilization rate of sulfur powder. CONCLUSION: These research findings provide valuable technical support for the advancement of mechanized variable powder spraying equipment in rubber tree cultivation. © 2024 Society of Chemical Industry.

On the design of deep learning-based control algorithms for visually guided UAVs engaged in power tower inspection tasks.

This paper focuses on the design of Convolution Neural Networks to visually guide an autonomous Unmanned Aerial Vehicle required to inspect power towers. The network is required to precisely segment images taken by a camera mounted on a UAV in order to allow a motion module to generate collision-free and inspection-relevant manoeuvres of the UAV along different types of towers. The images segmentation process is particularly challenging not only because of the different structures of the towers but also because of the enormous variability of the background, which can vary from the uniform blue of the sky to the multi-colour complexity of a rural, forest, or urban area. To be able to train networks that are robust enough to deal with the task variability, without incurring into a labour-intensive and costly annotation process of physical-world images, we have carried out a comparative study in which we evaluate the performances of networks trained either with synthetic images (i.e., the synthetic dataset), physical-world images (i.e., the physical-world dataset), or a combination of these two types of images (i.e., the hybrid dataset). The network used is an attention-based U-NET. The synthetic images are created using photogrammetry, to accurately model power towers, and simulated environments modelling a UAV during inspection of different power towers in different settings. Our findings reveal that the network trained on the hybrid dataset outperforms the networks trained with the synthetic and the physical-world image datasets. Most notably, the networks trained with the hybrid dataset demonstrates a superior performance on multiples evaluation metrics related to the image-segmentation task. This suggests that, the combination of synthetic and physical-world images represents the best trade-off to minimise the costs related to capturing and annotating physical-world images, and to maximise the task performances. Moreover, the results of our study demonstrate the potential of photogrammetry in creating effective training datasets to design networks to automate the precise movement of visually-guided UAVs.

Age-related differences in functional connectivity associated with pain modulation.

Growing evidence suggests that aging is associated with impaired endogenous pain modulation, and that this likely underlies the increased transition from acute to chronic pain in older individuals. Resting-state functional connectivity (rsFC) offers a valuable tool to examine the neural mechanisms behind these age-related changes in pain modulation. RsFC studies generally observe decreased within-network connectivity due to aging, but its relevance for pain modulation remains unknown. We compared rsFC within a set of brain regions involved in pain modulation between young and older adults and explored the relationship with the efficacy of distraction from pain. This revealed several age-related increases and decreases in connectivity strength. Importantly, we found a significant association between lower pain relief and decreased strength of three connections in older adults, namely between the periaqueductal gray and right insula, between the anterior cingulate cortex (ACC) and right insula, and between the ACC and left amygdala. These findings suggest that the functional integrity of the pain control system is critical for effective pain modulation, and that its function is compromised by aging.

Research on the parameter design and calibration and control of EV in-vehicle active sound generation system.

In order to improve the interior sound quality of electric vehicles (EVs) under acceleration and uniform speed conditions, to balance the comfort and dynamics of the interior sound, and to improve the accuracy and performance of the active sound generation system (ASGS), this article carries out the research related to the parameter design, sound calibration, evaluation methodology, and control system of the EV ASGS. Propose an in-vehicle sound design method focusing on three dimensions, including engine order composition, spectral energy distribution, and sound amplitude enhancement in the typical speed range, and determine the in-vehicle sound design scheme and the total sound value target. Focus on the sound parameter design, calibration and evaluation methods of EV ASGS considering the frequency response characteristics of the loudspeaker, sound amplitude control accuracy, sound quality, and psychoacoustic parameters, clarify the active sound parameter settings of EVs, complete the analysis of sound extraction methods, complete the engine order sound fitting, and design the ASGS of the EV interior by combining the subjective and objective evaluations. Develop the control software and hardware of the ASGS, complete the construction and accuracy verification of the ASGS based on the in-vehicle sound system, and realize the sound calibration of the ASGS under the static conditions of the real vehicle and the verification of the target achievement. The real-vehicle test shows that the ASGS reduces the sharpness of 1.0 acum and 0.52 acum under acceleration and constant speed conditions, respectively, and improves the comfort and dynamics of in-vehicle sound. The objective and subjective evaluation results show that the parameter design, selection and accuracy of the sound calibration and evaluation methods of the ASGS in the EV determines the accuracy and effect of the ASGS.

Enhancing Fused Deposition Modeling Precision with Serial Communication-Driven Closed-Loop Control and Image Analysis for Fault Diagnosis-Correction.

Additive manufacturing (AM) also commonly known as 3D printing is an advanced technique for manufacturing complex three-dimensional (3D) parts by depositing raw material layer by layer. Various sub-categories of additive manufacturing exist including directed energy deposition (DED), powder bed fusion (PBF), and fused deposition modeling (FDM). FDM has gained widespread adoption as a popular method for manufacturing 3D parts, even for heavy-duty industrial applications. However, challenges remain, particularly regarding part quality. Print parameters such as print speed, nozzle temperature, and flow rate can significantly impact the final product's quality. To address this, implementing a closed-loop quality control system is essential. This system consistently monitors part surface quality during printing and adjusts print parameters upon defect detection. In this study, we propose a simple yet effective image analysis-based closed-loop control system, utilizing serial communication and Python v3.12, a widely accessible software platform. The system's accuracy and robustness are evaluated, demonstrating its effectiveness in ensuring FDM-printed part quality. Notably, this control system offers superior speed in restoring part quality to normal upon defect detection and is easily implementable on commercially available FDM 3D printers, fostering decentralized quality manufacturing.

Out-of-Mold Sensor-Based Process Parameter Optimization and Adaptive Process Quality Control for Hot Runner Thin-Walled Injection-Molded Parts.

Injection molding is a highly nonlinear procedure that is easily influenced by various external factors, thereby affecting the stability of the product's quality. High-speed injection molding is required for production due to the rapid cooling characteristics of thin-walled parts, leading to increased manufacturing complexity. Consequently, establishing appropriate process parameters for maintaining quality stability in long-term production is challenging. This study selected a hot runner mold with a thin wall fitted with two external sensors, a nozzle pressure sensor and a tie-bar strain gauge, to collect data regarding the nozzle peak pressure, the timing of peak pressure, the viscosity index, and the clamping force difference value. The product weight was defined as the quality indicator, and a standardized parameter optimization process was constructed, including injection speed, V/P switchover point, packing, and clamping force. Finally, the optimized process parameters were applied to the adaptive process control experiments using the developed control system operated within the micro-controller unit (MCU). The results revealed that the control system effectively stabilized the product weight variation and standard deviation of 0.677% and 0.0178 g, respectively.

Design and Implementation of a Linear Active Disturbance Rejection Control-Based Position Servo Control System of an Electromotive Valve for Exhaust Gas Recirculation.

An exhaust gas recirculation (EGR) valve is used to quickly and dynamically adjust the amount of recirculated exhaust gas, which is critical for improving engine fuel economy and reducing emissions. To address problems relating to the precise positioning of an electromotive (EM) valve under slowly varying plant dynamics and uncertain disturbances, we propose a servo control system design based on linear active disturbance rejection control (LADRC) for the EGR EM valve driven by a limited angle torque motor (LATM). By analyzing the structure of the LATM and the transmission, the dynamic model of the system is derived. In addition, to solve the problems caused by slowly varying plant dynamics and uncertain disturbances, we combine the effects of uncertain model parameters and external disturbances as the total disturbance, which is estimated in real time by an extended state observer (ESO) and then compensated. In addition, accurate angular information is obtained using a non-contact magnetic angle measurement method, and a high-speed digital communication channel is established to help implement a closed-loop position control system with improved responsiveness and accuracy. Simulation and experimental results show that the proposed servo system design can effectively ensure the precision and real-time performance of the EM valve under slowly changing plant dynamics and uncertain disturbances. The proposed servo system design achieves a full-stroke valve control accuracy of better than 0.05 mm and a full-stroke response time of less than 100 ms. The controlled valve also has good robustness under shock-type external disturbances and excellent airflow control capability. The repeatability of the airflow control is generally within 5%, and the standard deviation is less than 0.2 m3/h.