查看更多>>摘要：This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker's efficiency,then a mathematical model is established to minimize the completion time.The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered.Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency.In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth.The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality.Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.
查看更多>>摘要：Graphene(Gr)has unique properties including high electrical conductivity;Thus,graphene/copper(Gr/Cu)composites have attracted increasing attention to replace traditional Cu for electrical applications.However,the problem of how to control graphene to form desired Gr/Cu composite is not well solved.This paper aims at exploring the best parameters for preparing graphene with different layers on Cu foil by chemical vapor deposition(CVD)method and studying the effects of different layers graphene on Gr/Cu composite's electrical conductivity.Graphene grown on single-sided and double-sided copper was prepared for Gr/Cu and Gr/Cu/Gr composites.The resultant electrical conductivity of Gr/Cu composites increased with decreasing graphene layers and increasing graphene volume fraction.The Gr/Cu/Gr composite with monolayer graphene owns volume fraction of less than 0.002％,producing the best electrical conductivity up to 59.8 × 106 S/m,equivalent to 104.5％IACS and 105.3％pure Cu foil.
查看更多>>摘要：For years,foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients'health-related quality of life(HRQoL).Diabetes foot ulcers impact 15％of all diabetic patients at some point in their lives.The facilities and resources used for DFU detection and treatment are only available at hospitals and clinics,which results in the unavailability of feasible and timely detection at an early stage.This necessitates the development of an at-home DFU detection system that enables timely predictions and seamless communication with users,thereby preventing amputations due to neglect and severity.This paper proposes a feasible system consisting of three major modules:an IoT device that works to sense foot nodes to send vibrations onto a foot sole,a machine learning model based on supervised learning which predicts the level of severity of the DFU using four different classification techniques including XGBoost,K-SVM,Random Forest,and Decision tree,and a mobile application that acts as an interface between the sensors and the patient.Based on the severity levels,necessary steps for prevention,treatment,and medications are recommended via the application.
查看更多>>摘要：Birds are a huge hazard to agriculture all around the world,causing harm to profitable field crops.Growers use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents.This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways.The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology.This study's goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.
查看更多>>摘要：A rudimentary aspect of human life is the health of an individual,and most commonly the wellbeing is impacted in a colossal manner through the consumption of food.The intake of calories therefore is a crucial aspect that must be meticulously monitored.Various health gremlins can be largely circumvented when there is a substantial balance in the number of calories ingested versus the quantity of calories expended.The food calorie estimation is a popular domain of research in recent times and is meticulously analyzed through various image processing and machine learning techniques.However,the need to scrutinize and evaluate the calorie estimation through various platforms and algorithmic approaches aids in providing a deeper insight on the bottlenecks involved,and in improvising the bariatric health of an individual.This paper pivots on comprehending a juxtaposed approach of food calorie estimation through the use of employing Convolution Neural Network(CNN)incorporated in Internet of Things(IoT),and using the Django framework in Python,along with query rule-based training to analyze the subsequent actions to be followed post the consumption of food calories in the constructed webpage.The comparative analysis of the food calorie estimate implemented in both platforms is analyzed for the swiftness of identification,error rate and classification accuracy to appropriately determine the optimal method of use.The simulation results for IoT are carried out using the Raspberry Pi 4B model,while the Anaconda prompt is used to run the server holding the web page.
查看更多>>摘要：A system for fully automatic selection of welding specifications in resistance welding equipment has been developed to address the problem of workers frequently choosing the wrong specifications during manual welding of multiple parts on a single machine in automobile factories.The system incorporates an automatic recognition system for different workpiece materials using the added machine fixture,visual detection system for nuts and bolts,and secondary graphical confirmation to ensure the correctness of specification calling.This system achieves reliable,fully automatic selection of welding specifications in resistance welding equipment and has shown significant effects in improving welding quality for mass-produced workpieces,while solving the problem of specification calling errors that can occur with traditional methods involving process charts and code adjustments.This system is particularly suitable for promoting applications in manual welding of multiple parts on a single machine in automobile factories,ensuring correct specification calling and welding quality.
查看更多>>摘要：In the current study,the calcium copper titanate(CCTO)/epoxy,barium titanate(BT)/epoxy and CCTO-BT/epoxy composite samples with variable volume fractions of CCTO and BT are fabricated using hand lay-up and compression moulding process.The composite samples are characterized for the frequency dependence on dielectric properties,conductivity,impedance spectroscopy and electrical modulus.X-ray diffraction(XRD)representation of CCTO-BT/epoxy composite samples confirmed the presence of both CCTO and BT ceramic samples separately.The dielectric characteristics of hybrid CCTO-BT/epoxy composite samples with CCTO∶BT ratio of 40∶60,60∶40,and 50∶50 was found relatively better than those of single ceramic filler reinforced epoxy composites.AC conductivity analysis shows improvement in the results of hybrid filler-filled CCTO-BT/epoxy composites in comparison with single filler-filled epoxy composite.50∶50 CCTO-BT/epoxy composite shows the best AC conductivity value of～2.2 × 10-5 ohm-1·m-1 at a higher frequency of 1 MHz.The impedance analysis confirms the higher insulating properties for hybrid 40∶60 and 60∶40 CCTO-BT/epoxy composites with respect to the single and other hybrid ceramic epoxy composites.The analysis suggests the hybrid CCTO-BT/epoxy composites to be adopted as a potential dielectric material for energy storage devices and other electronic applications.
查看更多>>摘要：The awareness amongst the researchers to develop an environment friendly sustainable material leads to explore new class of plant-based fiber for making composites.Hybridization of such plant-based fiber with inclusion of engineered fiber is one of the promising methods to not only enhanced the mechanical performance but also suppressed the drawbacks that associate with such plant-based fiber to some extent.A usual hand lay-up method was taken-up in this work to fabricate four layered of hybrid kenaf(K)/glass(G)polyester laminates with different stacking order such as KKKK,KGKG,KGGK,GKKG and GGGG.The erosive character of the laminates was examined under three distinct particle velocities(48 m/s,70 m/s,82 m/s)and four different impact angles(30°,45°,60°,90°).All fabricated laminates exhibited a semi-ductile character at lower velocities(48 m/s and 70 m/s)as peak wear rate was observed at 45° impact angle.However,they showed a semi-brittle character at high velocity(82 m/s)as maximum rate of erosion was noticed at 60° impact angle.Again,the influence of stacking order of piles on erosion wear was also clearly noticed.Moreover,the semi-brittle/semi-ductile characterization was also evidenced in accordance to the range of erosion efficiencies.The micro-structures of worn surfaces were inspected thoroughly from the images of scanning electron microscope(SEM)to evident the mechanism of erosion.
查看更多>>摘要：Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems.Existing methods primarily focus on data management,rather than emphasizing efficiency.Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demand-supply balancing.Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs.Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns.The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization.We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels.Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters.The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE).The proposed model demonstrates superior performance,achieving a maximum accuracy of 65.917％and a minimum MSE of 0.096.These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.
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