This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast ...This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast iron(CI)filler particles.Hybrid G-E composite with added cast iron(CI)filler particles enhances stiffness,strength,and vibration damping,offering enhanced performance for vibration-sensitive engineering applications.Unlike conventional approaches,this work simultaneously employs Artificial Neural Networks(ANN)for highaccuracy property prediction and Response Surface Methodology(RSM)for in-depth analysis of factor interactions and optimization.A total of 24 experimental test data sets of varying input factors(granite weight%,epoxy weight%,and CI filler weight%)were utilized to train and test the prediction models using an ANN approach and further analyze the interaction effects using RSM.Mechanical properties,including tensile,compressive,and flexural strength,elastic modulus,density and damping properties measured under various testing conditions,were set as output parameters for prediction.This study analyzed and optimized the performance of the ANN model using Bayesian Regularization and Levenberg-Marquardt algorithms to identify the best performing number of neurons in the hidden layer for achieving the highest prediction accuracy.The proposed ANN framework achieved an exceptional average determination coefficient(R2)exceeding 99%,with Bayesian Regularization demonstrating remarkable stability in the 22-neuron range and minimal variation across all properties.RSM and ANN form a powerful framework for predicting and optimizing hybrid G-E composite properties,enabling efficient design for vibration-critical applications with reduced experimental effort and performance optimization.展开更多
The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementat...The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.展开更多
The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary gro...The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.展开更多
Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall...Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.展开更多
This study evaluates the Fuzzy Analytical Hierarchy Process(FAHP)as a multi-criteria decision(MCD)support tool for selecting appropriate additive manufacturing(AM)techniques that align with cleaner production and envi...This study evaluates the Fuzzy Analytical Hierarchy Process(FAHP)as a multi-criteria decision(MCD)support tool for selecting appropriate additive manufacturing(AM)techniques that align with cleaner production and environmental sustainability.The FAHP model was validated using an example of the production of aircraft components(specifically fuselage)employing AM technologies such as Wire Arc Additive Manufacturing(WAAM),laser powder bed fusion(L-PBF),Binder Jetting(BJ),Selective Laser Sintering(SLS),and Laser Metal Deposition(LMD).The selection criteria prioritized eco-friendly manufacturing considerations,including the quality and properties of the final product(e.g.,surface finish,high strength,and corrosion resistance),service and functional requirements,weight reduction for improved energy efficiency(lightweight structures),and environmental responsibility.Sustainability metrics,such as cost-effectiveness,material efficiency,waste minimization,and environmental impact,are central to the evaluation process.A computer-aided modeling approach was also used to simulate the performance of aluminum(AA7075 T6),steel(304),and titanium alloy(Ti6Al4V)for fuselage development.The results demonstrate that MCD approaches such as FAHP can effectively guide the selection of AM technologies that meet functional and technical requirements while minimizing environmental degradation footprints.Furthermore,the aluminumalloy outperformed the other materials investigated in the simulation with the lowest stress concentration and least deformation.This study contributes to advancing cleaner production practices by providing a decision-making framework for sustainable and eco-friendly manufacturing,enabling manufacturers to adopt AM technologies that promote environmental responsibility and sustainable development,while maintaining product quality and performance.展开更多
Background:The Vietnamese swine represents a promising animal model due to its anatomical,physiological,and pathophysiological similarities to humans.Notably,the arrangement of lobes and ducts in the mammary glands is...Background:The Vietnamese swine represents a promising animal model due to its anatomical,physiological,and pathophysiological similarities to humans.Notably,the arrangement of lobes and ducts in the mammary glands is highly comparable to that of humans and is histologically indistinguishable.Leveraging these advantages through the chemical induction of carcinogenesis in this model offers a robust approach to mimic human exposure to carcinogenic compounds.Methods:This study elaborates on a protocol for developing a representative model of MNU-induced invasive breast carcinoma in three Vietnamese swine,validated histologically and immunologically.It evaluates not only the tissue similarity with humans,but also the development of chemically induced mammary tumors in an immunologically competent animal.Moreover,this study addresses the existing gap in histological knowledge regarding mammary tissue in the porcine model.Results:Our findings suggest that this model encompasses the full spectrum of cancer.It incorporates the key elements of a tumor microenvironment that enable tumor growth and propagation,such as immune cells,blood vessels,fibroblasts,extracellular matrix,fatty acids,and signaling molecules.Conclusions:This model offers significant potential to advance the understanding of cancer pathogenesis and facilitate the development of innovative therapeutic strategies by closely replicating human tumor biology.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path plannin...This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.展开更多
Thermal deformation of aluminum alloy casting materials for manufacturing the tire mold was numerically investigated.The AC7A and AC4C casting material was selected as casting material and the metal casting device was...Thermal deformation of aluminum alloy casting materials for manufacturing the tire mold was numerically investigated.The AC7A and AC4C casting material was selected as casting material and the metal casting device was used in order to manufacture the mold product of automobile tire in the actual industrial field.The temperature distribution and the cooling time of casting materials were numerically calculated by finite element analysis (FEA).Also,the thermal deformation such as displacement and stress distribution was calculated from the temperature results.The thermal deformation was closely related to the temperature difference between the surface and inside of the casting.The numerical analysis results reveal that the thermal deformation of AC7A casting material is higher than that of AC4C casting material.Also,the thermal deformation results at the central part are larger than that on the side of casting because of the shrinkage caused by the cooling speed difference.展开更多
Horizontal axis wind turbines are some of the most widely used clean energy generators in the world.Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultan...Horizontal axis wind turbines are some of the most widely used clean energy generators in the world.Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultaneously minimize the cost of energy.This work presents the optimization of new MEXICO blades for a horizontal axis wind turbine at the wind speed of 10 m/s.The optimization problem is posed to maximize the power coefficient while the design variables are twist angles on the blade radius and rotating axis positions on a chord length of the airfoils.Computational fluid dynamics was used for the aerodynamic simulation.Surrogate-assisted optimization was applied to reduce computational time.A surrogate model called a Kriging model,using a Gaussian correlation function along with various regression models,was applied while a genetic algorithm was used as an optimizer.The results obtained in this study are discussed and compared with those obtained from the original model.It was found that the Kriging model with linear regression gives better results than the Kriging model with second-order polynomial regression.The optimum blade obtained in this study showed better performance than the original blade at a low wind speed of 10 m/s.展开更多
This paper surveys the recent advances on the modeling and control of hysteresis of piezoelectric actuators(PTAs)in the context of high precision applications of atomic force microscopes(AFMs).The current states,findi...This paper surveys the recent advances on the modeling and control of hysteresis of piezoelectric actuators(PTAs)in the context of high precision applications of atomic force microscopes(AFMs).The current states,findings,and outcomes on hysteresis modeling and control in terms of achievable bandwidth and accuracy are discussed in detailed.Future challenges and the scope of possible research are presented to pave the way to video rate atomic force microscopy.展开更多
Friction stir welding（FSW） is a solid state joining technique developed to join high strength aluminum alloys and various ceramic reinforced metal matrix composites（MMCs）.FSW produces sound welds in MMCs without a...Friction stir welding（FSW） is a solid state joining technique developed to join high strength aluminum alloys and various ceramic reinforced metal matrix composites（MMCs）.FSW produces sound welds in MMCs without any deleterious reaction between reinforcement and matrix.The present work focused on the effect of FSW parameters on the tensile strength of Al-B4C composite joints.The central composite design of four factors and five levels was used to control the number of experiments.A mathematical model was developed to analyze the influence of FSW parameters.The results indicated that the joint fabricated using rotational speed of 1000 r/min,welding speed of 1.3 mm/s,axial force of 10 kN and the reinforcement of 12% showed larger tensile strength compared with the other joints.The developed model was optimized to maximize the tensile strength using generalized reduced gradient method.The metallographic analysis of the joints showed the presence of various zones such as weld nugget（WN） zone,thermo mechanically affected zone（TMAZ） and heat affected zone（HAZ）.The substantial grain refinement of aluminum matrix as well as significant size reduction of B4C particles was observed in the weld nugget.TMAZ was plastically deformed,thermally affected and exhibited elongated aluminum grains.展开更多
In order to provide data for joints control of our recently designed crucian hke biomlmetlc robot fish, an A-ray photograph technology was adopted to determine the number and length of vertebral joints. A frame-by-fra...In order to provide data for joints control of our recently designed crucian hke biomlmetlc robot fish, an A-ray photograph technology was adopted to determine the number and length of vertebral joints. A frame-by-frame analysis of high-speed videotapes was conducted to quantify the kinematics of crucian at four speeds （12.651 cm·s^-1, 18.201 cm·s^-1, 21.901 cm·s^-1, 24.368cm·s^-1） during cruising. In addition to a brief introduction to experimental conditions and methods, we analyzed the influence of individual diversity on the absolute length as well as the non-dimensional length of vertebral joints. We also presented the maximal angular velocity and acceleration of vertebral joints under four swimming speeds, and provided the change of relative rotation angle, angular difference, angular velocity and angular acceleration of the rear vertebral joints with time at a certain swimming speed of 12.651 cm·s^-1. At last, we presented the maximal lateral displacement of each mark at that speed. The study found that the influence of individual diversity on the non-dimensional length of vertebral joints is not significant; the maximal angular velocity and acceleration of vertebral joints increase with swimming speed; angular difference, angular velocity and angular acceleration exhibit two maximal values over one period at a certain swimming speed.展开更多
This paper reports the effect of friction stir welding （FSW） process parameters on tensile strength of cast LM6 aluminium alloy. Joints were made by using different combinations of tool rotation speed, welding speed...This paper reports the effect of friction stir welding （FSW） process parameters on tensile strength of cast LM6 aluminium alloy. Joints were made by using different combinations of tool rotation speed, welding speed and axial force each at four levels. The quality of weld zone was investigated using macrostructure and microstructure analysis. Tensile strength of the joints were evaluated and correlated with the weld zone hardness and microstructure. The joint fabricated using a rotational speed of 900 r/min, a welding speed of 75 mm/min and an axial force of 3 kN showed superior tensile strength compared with other joints. The tensile strength and microhardness of the welded joints for the optimum conditions were 166 MPa and 64.8 Hv respectively.展开更多
Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,trajectory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these...Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,trajectory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques.Due to high nonlinearities,strong couplings and under-actuation,the control design process of a quadcopter is a rather challenging task.Therefore,the main objective of this work is demonstrated through two main aspects.The first is the design of an adaptive neuro-fuzzy inference system(ANFIS)controller to develop the attitude and altitude of a quadcopter.The second is to create a systematic framework for implementing flight controllers in embedded systems.A suitable model of the quadcopter is also developed by taking into account aerodynamics effects.To show the effectiveness of the ANFIS approach,the performance of a well-trained ANFIS controller is compared to a classical proportional-derivative(PD)controller and a properly tuned fuzzy logic controller.The controllers are compared and tested under several different flight conditions including the capability to reject external disturbances.In the first stage,performance evaluation takes place in a nonlinear simulation environment.Then,the ANFIS-based controllers alongside attitude and position estimators,and precision landing algorithms are implemented for executions in a real-time autopilot.In precision landing systems,an IR-camera is used to detect an IR-beacon on the ground for precise positioning.Several flight tests of a quadcopter are conducted for results validation.Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.展开更多
Cavitation bubble collapse has a great influence on the temperature of hydraulic oil. Herein, cone-type throttle valve experiments are carried out to study the thermodynamic processes of cavitation. First, the process...Cavitation bubble collapse has a great influence on the temperature of hydraulic oil. Herein, cone-type throttle valve experiments are carried out to study the thermodynamic processes of cavitation. First, the processes of growth and collapse are analysed, and the relationships between the hydraulic oil temperature and bubble growth and collapse are deduced. The effect of temperature is then considered on the hydraulic oil viscosity and saturated vapour pressure. Additionally, an improved form of the Rayleigh–Plesset equation is developed. The effect of cavitation on the hydraulic oil temperature is experimentally studied and the effects of cavitation bubble collapse in the hydraulic system are summarised. Using the cone-type throttle valve as an example, a method to suppress cavitation is proposed.展开更多
Inspired by the simple yet amazing morphology of the Octopus, we propose the design, fabrication, and characterization of multi-material bio-inspired soft Octopus robot (Octobot). 3D printed molds for tentacles and he...Inspired by the simple yet amazing morphology of the Octopus, we propose the design, fabrication, and characterization of multi-material bio-inspired soft Octopus robot (Octobot). 3D printed molds for tentacles and head were used. The tentacles of the Octobot were casted using Ecoflex-0030 while head was fabricated using relatively flexible material, i.e., OOMOO-25. The head is attached to the functionally responsive tentacles (each tentacle is of 79.12 mm length and 7 void space diameter), whereas Shape Memory Alloy (SMA) muscle wires of 0.5 mm thickness are used in Octobot tentacles for dual thrust generation and actuation of Octobot. The tentacles were separated in two groups and were synchronously actuated. Each tentacle of the developed Octobot contains a pair of SMA muscles (SMA-α and SMA-β). SMA-α muscles being the main actuator, was powered by 9 V, 350 mA power supply, whereas SMA-β was used to provide back thrust and thus helps to increase the actuation frequency. Simulation work of the proposed model was performed in the SolidWorks environment to verify the vertical velocity using the octopus tentacle actuation. The design morphology of Octobot was optimized using simulation and TRACKER software by analyzing the experimental data of angle, displacement, and velocity of real octopus. The as-developed Octobot can swim at variable frequencies (0.5–2 Hz) with the average speed of 25 mm/s (0.5 BLS). Therefore, the proposed soft Octopus robot showed an excellent capability of mimicking the gait pattern of its natural counterpart.展开更多
A pulsed,picosecond Nd:YAG laser with a wavelength of 532 nm is used to texture the surface of grade 5 titanium alloy(Ti–6Al–4V)for minimizing its wear rate.The wear properties of the base samples and laser surface ...A pulsed,picosecond Nd:YAG laser with a wavelength of 532 nm is used to texture the surface of grade 5 titanium alloy(Ti–6Al–4V)for minimizing its wear rate.The wear properties of the base samples and laser surface textured samples are analyzed by conducting wear tests under a sliding condition using pin-on-disk equipment.The wear tests are conducted based on the Box–Benhken design,and the interaction of the process parameters is analyzed using response surface methodology.The wear analysis is conducted by varying the load,rotating speed of the disc,and track diameter at room temperature with a sliding distance of 1500 m.The results demonstrate that the laser textured surfaces exhibited a lower coefficient of friction and good anti-wear properties as compared with the non-textured surfaces.A regression model is developed for the wear analysis of titanium alloy using the analysis of variance technique.It is also observed from the analysis that the applied load and sliding distance are the parameters that have the greatest effect on the wear behavior followed by the wear track diameter.The optimum operating conditions have been suggested based on the results obtained from the numerical optimization approach.展开更多
文摘This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast iron(CI)filler particles.Hybrid G-E composite with added cast iron(CI)filler particles enhances stiffness,strength,and vibration damping,offering enhanced performance for vibration-sensitive engineering applications.Unlike conventional approaches,this work simultaneously employs Artificial Neural Networks(ANN)for highaccuracy property prediction and Response Surface Methodology(RSM)for in-depth analysis of factor interactions and optimization.A total of 24 experimental test data sets of varying input factors(granite weight%,epoxy weight%,and CI filler weight%)were utilized to train and test the prediction models using an ANN approach and further analyze the interaction effects using RSM.Mechanical properties,including tensile,compressive,and flexural strength,elastic modulus,density and damping properties measured under various testing conditions,were set as output parameters for prediction.This study analyzed and optimized the performance of the ANN model using Bayesian Regularization and Levenberg-Marquardt algorithms to identify the best performing number of neurons in the hidden layer for achieving the highest prediction accuracy.The proposed ANN framework achieved an exceptional average determination coefficient(R2)exceeding 99%,with Bayesian Regularization demonstrating remarkable stability in the 22-neuron range and minimal variation across all properties.RSM and ANN form a powerful framework for predicting and optimizing hybrid G-E composite properties,enabling efficient design for vibration-critical applications with reduced experimental effort and performance optimization.
基金funded by the National Council of Science,Technology,and Technological Innovation(CONCYTEC)the National Program of Scientific Research and Advanced Studies(PROCIENCIA)under the E041-2022-“Applied Research Projects”competition.Contract number:PE501078609-2022-PROCIENCIA.
文摘The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.
文摘The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.
基金This work was supported by the Deanship of Scientific Research,King Khalid University,Kingdom of Saudi Arabia under research Grant Number(R.G.P.2/100/41).
文摘Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.
文摘This study evaluates the Fuzzy Analytical Hierarchy Process(FAHP)as a multi-criteria decision(MCD)support tool for selecting appropriate additive manufacturing(AM)techniques that align with cleaner production and environmental sustainability.The FAHP model was validated using an example of the production of aircraft components(specifically fuselage)employing AM technologies such as Wire Arc Additive Manufacturing(WAAM),laser powder bed fusion(L-PBF),Binder Jetting(BJ),Selective Laser Sintering(SLS),and Laser Metal Deposition(LMD).The selection criteria prioritized eco-friendly manufacturing considerations,including the quality and properties of the final product(e.g.,surface finish,high strength,and corrosion resistance),service and functional requirements,weight reduction for improved energy efficiency(lightweight structures),and environmental responsibility.Sustainability metrics,such as cost-effectiveness,material efficiency,waste minimization,and environmental impact,are central to the evaluation process.A computer-aided modeling approach was also used to simulate the performance of aluminum(AA7075 T6),steel(304),and titanium alloy(Ti6Al4V)for fuselage development.The results demonstrate that MCD approaches such as FAHP can effectively guide the selection of AM technologies that meet functional and technical requirements while minimizing environmental degradation footprints.Furthermore,the aluminumalloy outperformed the other materials investigated in the simulation with the lowest stress concentration and least deformation.This study contributes to advancing cleaner production practices by providing a decision-making framework for sustainable and eco-friendly manufacturing,enabling manufacturers to adopt AM technologies that promote environmental responsibility and sustainable development,while maintaining product quality and performance.
基金C.E.Vera-Tizatl(CVU:708156)thank the National Council for Science and Technology(CONACYT,Mexico)for the scholarship granted。
文摘Background:The Vietnamese swine represents a promising animal model due to its anatomical,physiological,and pathophysiological similarities to humans.Notably,the arrangement of lobes and ducts in the mammary glands is highly comparable to that of humans and is histologically indistinguishable.Leveraging these advantages through the chemical induction of carcinogenesis in this model offers a robust approach to mimic human exposure to carcinogenic compounds.Methods:This study elaborates on a protocol for developing a representative model of MNU-induced invasive breast carcinoma in three Vietnamese swine,validated histologically and immunologically.It evaluates not only the tissue similarity with humans,but also the development of chemically induced mammary tumors in an immunologically competent animal.Moreover,this study addresses the existing gap in histological knowledge regarding mammary tissue in the porcine model.Results:Our findings suggest that this model encompasses the full spectrum of cancer.It incorporates the key elements of a tumor microenvironment that enable tumor growth and propagation,such as immune cells,blood vessels,fibroblasts,extracellular matrix,fatty acids,and signaling molecules.Conclusions:This model offers significant potential to advance the understanding of cancer pathogenesis and facilitate the development of innovative therapeutic strategies by closely replicating human tumor biology.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
文摘This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.
基金Project supported by Research Funds from Chosun University(2009),Korea
文摘Thermal deformation of aluminum alloy casting materials for manufacturing the tire mold was numerically investigated.The AC7A and AC4C casting material was selected as casting material and the metal casting device was used in order to manufacture the mold product of automobile tire in the actual industrial field.The temperature distribution and the cooling time of casting materials were numerically calculated by finite element analysis (FEA).Also,the thermal deformation such as displacement and stress distribution was calculated from the temperature results.The thermal deformation was closely related to the temperature difference between the surface and inside of the casting.The numerical analysis results reveal that the thermal deformation of AC7A casting material is higher than that of AC4C casting material.Also,the thermal deformation results at the central part are larger than that on the side of casting because of the shrinkage caused by the cooling speed difference.
基金funded by the Thailand Research Fund(RTA6180010).
文摘Horizontal axis wind turbines are some of the most widely used clean energy generators in the world.Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultaneously minimize the cost of energy.This work presents the optimization of new MEXICO blades for a horizontal axis wind turbine at the wind speed of 10 m/s.The optimization problem is posed to maximize the power coefficient while the design variables are twist angles on the blade radius and rotating axis positions on a chord length of the airfoils.Computational fluid dynamics was used for the aerodynamic simulation.Surrogate-assisted optimization was applied to reduce computational time.A surrogate model called a Kriging model,using a Gaussian correlation function along with various regression models,was applied while a genetic algorithm was used as an optimizer.The results obtained in this study are discussed and compared with those obtained from the original model.It was found that the Kriging model with linear regression gives better results than the Kriging model with second-order polynomial regression.The optimum blade obtained in this study showed better performance than the original blade at a low wind speed of 10 m/s.
文摘This paper surveys the recent advances on the modeling and control of hysteresis of piezoelectric actuators(PTAs)in the context of high precision applications of atomic force microscopes(AFMs).The current states,findings,and outcomes on hysteresis modeling and control in terms of achievable bandwidth and accuracy are discussed in detailed.Future challenges and the scope of possible research are presented to pave the way to video rate atomic force microscopy.
基金Naval Research Board, DRDO, Govt. of INDIA, vide funded projectRef. no. DNRD/05/4003/NRB/85 dt 30.10.2006 for sponsoring FSW machine
文摘Friction stir welding（FSW） is a solid state joining technique developed to join high strength aluminum alloys and various ceramic reinforced metal matrix composites（MMCs）.FSW produces sound welds in MMCs without any deleterious reaction between reinforcement and matrix.The present work focused on the effect of FSW parameters on the tensile strength of Al-B4C composite joints.The central composite design of four factors and five levels was used to control the number of experiments.A mathematical model was developed to analyze the influence of FSW parameters.The results indicated that the joint fabricated using rotational speed of 1000 r/min,welding speed of 1.3 mm/s,axial force of 10 kN and the reinforcement of 12% showed larger tensile strength compared with the other joints.The developed model was optimized to maximize the tensile strength using generalized reduced gradient method.The metallographic analysis of the joints showed the presence of various zones such as weld nugget（WN） zone,thermo mechanically affected zone（TMAZ） and heat affected zone（HAZ）.The substantial grain refinement of aluminum matrix as well as significant size reduction of B4C particles was observed in the weld nugget.TMAZ was plastically deformed,thermally affected and exhibited elongated aluminum grains.
文摘In order to provide data for joints control of our recently designed crucian hke biomlmetlc robot fish, an A-ray photograph technology was adopted to determine the number and length of vertebral joints. A frame-by-frame analysis of high-speed videotapes was conducted to quantify the kinematics of crucian at four speeds （12.651 cm·s^-1, 18.201 cm·s^-1, 21.901 cm·s^-1, 24.368cm·s^-1） during cruising. In addition to a brief introduction to experimental conditions and methods, we analyzed the influence of individual diversity on the absolute length as well as the non-dimensional length of vertebral joints. We also presented the maximal angular velocity and acceleration of vertebral joints under four swimming speeds, and provided the change of relative rotation angle, angular difference, angular velocity and angular acceleration of the rear vertebral joints with time at a certain swimming speed of 12.651 cm·s^-1. At last, we presented the maximal lateral displacement of each mark at that speed. The study found that the influence of individual diversity on the non-dimensional length of vertebral joints is not significant; the maximal angular velocity and acceleration of vertebral joints increase with swimming speed; angular difference, angular velocity and angular acceleration exhibit two maximal values over one period at a certain swimming speed.
文摘This paper reports the effect of friction stir welding （FSW） process parameters on tensile strength of cast LM6 aluminium alloy. Joints were made by using different combinations of tool rotation speed, welding speed and axial force each at four levels. The quality of weld zone was investigated using macrostructure and microstructure analysis. Tensile strength of the joints were evaluated and correlated with the weld zone hardness and microstructure. The joint fabricated using a rotational speed of 900 r/min, a welding speed of 75 mm/min and an axial force of 3 kN showed superior tensile strength compared with other joints. The tensile strength and microhardness of the welded joints for the optimum conditions were 166 MPa and 64.8 Hv respectively.
文摘Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,trajectory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques.Due to high nonlinearities,strong couplings and under-actuation,the control design process of a quadcopter is a rather challenging task.Therefore,the main objective of this work is demonstrated through two main aspects.The first is the design of an adaptive neuro-fuzzy inference system(ANFIS)controller to develop the attitude and altitude of a quadcopter.The second is to create a systematic framework for implementing flight controllers in embedded systems.A suitable model of the quadcopter is also developed by taking into account aerodynamics effects.To show the effectiveness of the ANFIS approach,the performance of a well-trained ANFIS controller is compared to a classical proportional-derivative(PD)controller and a properly tuned fuzzy logic controller.The controllers are compared and tested under several different flight conditions including the capability to reject external disturbances.In the first stage,performance evaluation takes place in a nonlinear simulation environment.Then,the ANFIS-based controllers alongside attitude and position estimators,and precision landing algorithms are implemented for executions in a real-time autopilot.In precision landing systems,an IR-camera is used to detect an IR-beacon on the ground for precise positioning.Several flight tests of a quadcopter are conducted for results validation.Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.
基金Projects(51505289,51275123)supported by the National Natural Science Foundation of China
文摘Cavitation bubble collapse has a great influence on the temperature of hydraulic oil. Herein, cone-type throttle valve experiments are carried out to study the thermodynamic processes of cavitation. First, the processes of growth and collapse are analysed, and the relationships between the hydraulic oil temperature and bubble growth and collapse are deduced. The effect of temperature is then considered on the hydraulic oil viscosity and saturated vapour pressure. Additionally, an improved form of the Rayleigh–Plesset equation is developed. The effect of cavitation on the hydraulic oil temperature is experimentally studied and the effects of cavitation bubble collapse in the hydraulic system are summarised. Using the cone-type throttle valve as an example, a method to suppress cavitation is proposed.
基金This work was supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(NRF-2022R1A2C2004771)Internal Research Grant by ORIC,SukkurIBA University 2022.
文摘Inspired by the simple yet amazing morphology of the Octopus, we propose the design, fabrication, and characterization of multi-material bio-inspired soft Octopus robot (Octobot). 3D printed molds for tentacles and head were used. The tentacles of the Octobot were casted using Ecoflex-0030 while head was fabricated using relatively flexible material, i.e., OOMOO-25. The head is attached to the functionally responsive tentacles (each tentacle is of 79.12 mm length and 7 void space diameter), whereas Shape Memory Alloy (SMA) muscle wires of 0.5 mm thickness are used in Octobot tentacles for dual thrust generation and actuation of Octobot. The tentacles were separated in two groups and were synchronously actuated. Each tentacle of the developed Octobot contains a pair of SMA muscles (SMA-α and SMA-β). SMA-α muscles being the main actuator, was powered by 9 V, 350 mA power supply, whereas SMA-β was used to provide back thrust and thus helps to increase the actuation frequency. Simulation work of the proposed model was performed in the SolidWorks environment to verify the vertical velocity using the octopus tentacle actuation. The design morphology of Octobot was optimized using simulation and TRACKER software by analyzing the experimental data of angle, displacement, and velocity of real octopus. The as-developed Octobot can swim at variable frequencies (0.5–2 Hz) with the average speed of 25 mm/s (0.5 BLS). Therefore, the proposed soft Octopus robot showed an excellent capability of mimicking the gait pattern of its natural counterpart.
基金SASTRA University for the valuable help and support provided
文摘A pulsed,picosecond Nd:YAG laser with a wavelength of 532 nm is used to texture the surface of grade 5 titanium alloy(Ti–6Al–4V)for minimizing its wear rate.The wear properties of the base samples and laser surface textured samples are analyzed by conducting wear tests under a sliding condition using pin-on-disk equipment.The wear tests are conducted based on the Box–Benhken design,and the interaction of the process parameters is analyzed using response surface methodology.The wear analysis is conducted by varying the load,rotating speed of the disc,and track diameter at room temperature with a sliding distance of 1500 m.The results demonstrate that the laser textured surfaces exhibited a lower coefficient of friction and good anti-wear properties as compared with the non-textured surfaces.A regression model is developed for the wear analysis of titanium alloy using the analysis of variance technique.It is also observed from the analysis that the applied load and sliding distance are the parameters that have the greatest effect on the wear behavior followed by the wear track diameter.The optimum operating conditions have been suggested based on the results obtained from the numerical optimization approach.
