In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same i...In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.展开更多
The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies m...The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.展开更多
A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of ...A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of shielding gases with varying He-Ar ratios,and the coupling between arc plasma and laser-induced metal plume.The accuracy of the model is validated using a high-speed camera.The effects of varying He contents in the shielding gas on both the temperature and flow velocity of hybrid plasma,as well as the distribu-tion of laser-induced metal vapor mass,were investigated separately.The maximum temperature and size of arc plasma decrease as the He volume ratio increases,the arc distribution becomes more concentrated,and its flow velocity initially decreases and then sharply increases.At high helium content,both the flow velocity of hybrid plasma and metal vapor are high,the metal vapor is con-centrated on the right side of keyhole,and its flow appears chaotic.The flow state of arc plasma is most stable when the shielding gas consists of 50%He+50%Ar.展开更多
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this chal...The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.展开更多
To integrate the satellite communications with the LTE/5G services, the concept of Hybrid Satellite Terrestrial Relay Networks(HSTRNs) has been proposed. In this paper, we investigate the secure transmission in a HSTR...To integrate the satellite communications with the LTE/5G services, the concept of Hybrid Satellite Terrestrial Relay Networks(HSTRNs) has been proposed. In this paper, we investigate the secure transmission in a HSTRN where the eavesdropper can wiretap the transmitted messages from both the satellite and the intermediate relays. To effectively protect the message from wiretapping in these two phases, we consider cooperative jamming by the relays, where the jamming signals are optimized to maximize the secrecy rate under the total power constraint of relays. In the first phase, the Maximal Ratio Transmission(MRT) scheme is used to maximize the secrecy rate, while in the second phase, by interpolating between the sub-optimal MRT scheme and the null-space projection scheme, the optimal scheme can be obtained via an efficient one-dimensional searching method. Simulation results show that when the number of cooperative relays is small, the performance of the optimal scheme significantly outperforms that of MRT and null-space projection scheme. When the number of relays increases, the performance of the null-space projection approaches that of the optimal one.展开更多
Hibridization is one of breeding strategy to increase productivity of crop including physic nut (Jatropha curcas Linn.). This study aimed to obtain information productivity per hectare and seed oil content of 11 numbe...Hibridization is one of breeding strategy to increase productivity of crop including physic nut (Jatropha curcas Linn.). This study aimed to obtain information productivity per hectare and seed oil content of 11 numbers of physic nut hybrids and their parental in four dry lands. The research was conducted in four dry land: Kalipare-Malang, Oro-oro Pule-Kejayan Pasuruan, Kedung Pengaron-Pasuruan and Jorongan-Leces Probolinggo. The materials used in this research are the eleven result numbers of physic nut hybrids, they are SP38XHS49, SP8XHS49, SP8XSP16, SP8XSP38, SP33XHS49, SM35XHS49, SM35XSP38, IP1AXHS49, IP1AXSP38, IP1PXHS 49, IP1PXSP38, and their parental, they are HS49, SP16, SP38, SP8, SP33, SM35, IP1A, IP1P, IP3P. Observation was done during the plants’ generative phase, on the second harvest. The results showed that SP38XHS49 hybrid on Kedung Pengaron, produces the highest seeds dry weight per hectare (1170 kg/ha) with 62.33 gram of dry weight of 100 seeds and the oil content is 32.56%. The highest average of dry seed productions from all planting sites achieved on the crossing between SP38XHS49 (658.75 kg/hectare) and followed by SP8XHS49 (607.5 kg/hectare). If the comparison of the four locations, the highest average productivity of physic nut achieved on location Jorongan, Leces, Probolinggo. In general, the data proves that the hybrid result from the crossing shows the higher production level compare to their parental. The dry weight of 100 seeds produced ranged from 54.03 grams to 68.29 grams. Of all four planting sites, it shows that the highest 100 seeds dry weight achieved by the crossing between IP1P-XHS49 which is 64.63 grams. The seed oil content ranged from 27.04 to 35.24 percent. The highest average of seed oil content achieved by the crossing between SM35XSP38 (32.035%).展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
The vanadium redox flow battery(VRFB)is an emerging energy storage technology featuring long cycle life.During its operation,VRFB requires periodic maintenance to restore its capacity.To thoroughly understand and anal...The vanadium redox flow battery(VRFB)is an emerging energy storage technology featuring long cycle life.During its operation,VRFB requires periodic maintenance to restore its capacity.To thoroughly understand and analyse its aging characteristics,accurate modelling of VRFB is crucial.In this paper,a hybrid physics-based and data-driven modelling framework is proposed for VRFB.First,a reduced-order electrochemical model for VRFB is established considering two main aging mechanisms:electrolyte volume transfer and ion crossover.Then,two key empirical parameters related to the aging dynamic are fully analysed.Finally,a Kolmogorov-Arnold network(KAN)is constructed with prior information from the electrochemical model to produce high-precision voltage prediction.A real-world test platform is built to validate the proposed method.It achieves the maximum prediction error of less than 1%in short,middle,and long-term aging experiments.展开更多
In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of ...In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis shift.The proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network.Then,we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds.Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller.展开更多
To improve physical education in vocational colleges,a hybrid teaching model should be developed,taking into account local conditions,gradual progress,and deep integration.The process includes resetting teaching goals...To improve physical education in vocational colleges,a hybrid teaching model should be developed,taking into account local conditions,gradual progress,and deep integration.The process includes resetting teaching goals,optimizing teaching content,adjusting teaching segments,and improving teaching evaluation.Teachers can use video resources to interact with students before class,set up different student display projects during the course,encourage group cooperation and inter-group assessment,conduct in-class tests and knowledge competitions to reinforce students’sports skills,and suggest appropriate after-class activities.An online and offline self-study model can also motivate students to participate in sports.展开更多
Physical therapy students can experience elevated levels of stress due to the pressure to be successful, changes in the environment, personal concerns, the lack of spare time, increased work, or financial burdens. The...Physical therapy students can experience elevated levels of stress due to the pressure to be successful, changes in the environment, personal concerns, the lack of spare time, increased work, or financial burdens. The purpose of this study was to examine the perceived stress and coping strategies of Doctor of Physical Therapy (DPT) students enrolled in a hybrid-learning curriculum during the COVID-19 pademic. A total of 73 students enrolled in the DPT hybrid-learning curriculum responded to a survey which consisted of socio-demographics, the 10-item Perceived Stress Scale (PSS), and the 28-item Brief COPE. A general question regarding stress relating to COVID-19 was presented as a sliding percentage. Data analysis included a Spearman correlation, a Kruskal-Wallis test, and a linear regression to evaluate coping mechanisms against PSS scores. The mean (± SD) score on the PSS was 22.65 (± 10.21) and the Brief COPE was 59.18 (± 10.61). A non-significant negative correlation was found between the PSS and Brief COPE (r = -0.024). A third of the variation in the perceived stress score could be accounted for by students utilizing coping mechanisms regardless of other factors (R<sup>2</sup> = 0.35). No significant differences were found when comparing PSS and Brief Cope to age, hours worked per week and term. Perceived stress was higher in females compared to males, but the results were not significant. Stress related to COVID-19 mean percentage reported by DPT students was 49.03%. During a global pandemic, DPT students enrolled in a hybrid-learning curriculum reported elevated levels of stress but reported higher adaptive versus maladaptive coping strategies. It can be beneficial that universities evaluate the stress and coping methods of students to potentially avoid the negative impacts of stress.展开更多
Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in ...Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.展开更多
Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of elec...Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.展开更多
基金the Science and Technology Major Project of China(No.2019ZX04020001-004,2017ZX04007001)。
文摘In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.
基金supported by National Natural Science Foundation of China(Grant No.52375380)National Key R&D Program of China(Grant No.2022YFB3402200)the Key Project of National Natural Science Foundation of China(Grant No.12032018).
文摘The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.
基金supported by the National Natural Science Foundation of China(Grant No.52375340,51975263,52405366).
文摘A three-dimensional numerical model of laser-arc hybrid plasma for aluminum alloy fillet joints is developed in this study.This mod-el accounts for the geometric complexity of fillet joints,the physical properties of shielding gases with varying He-Ar ratios,and the coupling between arc plasma and laser-induced metal plume.The accuracy of the model is validated using a high-speed camera.The effects of varying He contents in the shielding gas on both the temperature and flow velocity of hybrid plasma,as well as the distribu-tion of laser-induced metal vapor mass,were investigated separately.The maximum temperature and size of arc plasma decrease as the He volume ratio increases,the arc distribution becomes more concentrated,and its flow velocity initially decreases and then sharply increases.At high helium content,both the flow velocity of hybrid plasma and metal vapor are high,the metal vapor is con-centrated on the right side of keyhole,and its flow appears chaotic.The flow state of arc plasma is most stable when the shielding gas consists of 50%He+50%Ar.
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
文摘The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.
基金supported in part by the National Natural Science Foundation of China under Grant No.61871032in part by Chinese Ministry of Education-China Mobile Communication Corporation Research Fund under Grant MCM20170101in part by the Open Research Fund of Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education (Guilin University of Electronic Technology) under Grant CRKL190204
文摘To integrate the satellite communications with the LTE/5G services, the concept of Hybrid Satellite Terrestrial Relay Networks(HSTRNs) has been proposed. In this paper, we investigate the secure transmission in a HSTRN where the eavesdropper can wiretap the transmitted messages from both the satellite and the intermediate relays. To effectively protect the message from wiretapping in these two phases, we consider cooperative jamming by the relays, where the jamming signals are optimized to maximize the secrecy rate under the total power constraint of relays. In the first phase, the Maximal Ratio Transmission(MRT) scheme is used to maximize the secrecy rate, while in the second phase, by interpolating between the sub-optimal MRT scheme and the null-space projection scheme, the optimal scheme can be obtained via an efficient one-dimensional searching method. Simulation results show that when the number of cooperative relays is small, the performance of the optimal scheme significantly outperforms that of MRT and null-space projection scheme. When the number of relays increases, the performance of the null-space projection approaches that of the optimal one.
文摘Hibridization is one of breeding strategy to increase productivity of crop including physic nut (Jatropha curcas Linn.). This study aimed to obtain information productivity per hectare and seed oil content of 11 numbers of physic nut hybrids and their parental in four dry lands. The research was conducted in four dry land: Kalipare-Malang, Oro-oro Pule-Kejayan Pasuruan, Kedung Pengaron-Pasuruan and Jorongan-Leces Probolinggo. The materials used in this research are the eleven result numbers of physic nut hybrids, they are SP38XHS49, SP8XHS49, SP8XSP16, SP8XSP38, SP33XHS49, SM35XHS49, SM35XSP38, IP1AXHS49, IP1AXSP38, IP1PXHS 49, IP1PXSP38, and their parental, they are HS49, SP16, SP38, SP8, SP33, SM35, IP1A, IP1P, IP3P. Observation was done during the plants’ generative phase, on the second harvest. The results showed that SP38XHS49 hybrid on Kedung Pengaron, produces the highest seeds dry weight per hectare (1170 kg/ha) with 62.33 gram of dry weight of 100 seeds and the oil content is 32.56%. The highest average of dry seed productions from all planting sites achieved on the crossing between SP38XHS49 (658.75 kg/hectare) and followed by SP8XHS49 (607.5 kg/hectare). If the comparison of the four locations, the highest average productivity of physic nut achieved on location Jorongan, Leces, Probolinggo. In general, the data proves that the hybrid result from the crossing shows the higher production level compare to their parental. The dry weight of 100 seeds produced ranged from 54.03 grams to 68.29 grams. Of all four planting sites, it shows that the highest 100 seeds dry weight achieved by the crossing between IP1P-XHS49 which is 64.63 grams. The seed oil content ranged from 27.04 to 35.24 percent. The highest average of seed oil content achieved by the crossing between SM35XSP38 (32.035%).
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by the National Natural Science Foundation of China[grant number 62325306][grant number62273237].
文摘The vanadium redox flow battery(VRFB)is an emerging energy storage technology featuring long cycle life.During its operation,VRFB requires periodic maintenance to restore its capacity.To thoroughly understand and analyse its aging characteristics,accurate modelling of VRFB is crucial.In this paper,a hybrid physics-based and data-driven modelling framework is proposed for VRFB.First,a reduced-order electrochemical model for VRFB is established considering two main aging mechanisms:electrolyte volume transfer and ion crossover.Then,two key empirical parameters related to the aging dynamic are fully analysed.Finally,a Kolmogorov-Arnold network(KAN)is constructed with prior information from the electrochemical model to produce high-precision voltage prediction.A real-world test platform is built to validate the proposed method.It achieves the maximum prediction error of less than 1%in short,middle,and long-term aging experiments.
文摘In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis shift.The proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network.Then,we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds.Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller.
文摘To improve physical education in vocational colleges,a hybrid teaching model should be developed,taking into account local conditions,gradual progress,and deep integration.The process includes resetting teaching goals,optimizing teaching content,adjusting teaching segments,and improving teaching evaluation.Teachers can use video resources to interact with students before class,set up different student display projects during the course,encourage group cooperation and inter-group assessment,conduct in-class tests and knowledge competitions to reinforce students’sports skills,and suggest appropriate after-class activities.An online and offline self-study model can also motivate students to participate in sports.
文摘Physical therapy students can experience elevated levels of stress due to the pressure to be successful, changes in the environment, personal concerns, the lack of spare time, increased work, or financial burdens. The purpose of this study was to examine the perceived stress and coping strategies of Doctor of Physical Therapy (DPT) students enrolled in a hybrid-learning curriculum during the COVID-19 pademic. A total of 73 students enrolled in the DPT hybrid-learning curriculum responded to a survey which consisted of socio-demographics, the 10-item Perceived Stress Scale (PSS), and the 28-item Brief COPE. A general question regarding stress relating to COVID-19 was presented as a sliding percentage. Data analysis included a Spearman correlation, a Kruskal-Wallis test, and a linear regression to evaluate coping mechanisms against PSS scores. The mean (± SD) score on the PSS was 22.65 (± 10.21) and the Brief COPE was 59.18 (± 10.61). A non-significant negative correlation was found between the PSS and Brief COPE (r = -0.024). A third of the variation in the perceived stress score could be accounted for by students utilizing coping mechanisms regardless of other factors (R<sup>2</sup> = 0.35). No significant differences were found when comparing PSS and Brief Cope to age, hours worked per week and term. Perceived stress was higher in females compared to males, but the results were not significant. Stress related to COVID-19 mean percentage reported by DPT students was 49.03%. During a global pandemic, DPT students enrolled in a hybrid-learning curriculum reported elevated levels of stress but reported higher adaptive versus maladaptive coping strategies. It can be beneficial that universities evaluate the stress and coping methods of students to potentially avoid the negative impacts of stress.
基金Supported by Zhejiang Provincial Key Research and Development Program(Grant No.2021C04015)。
文摘Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.
基金supported by the National Natural Science Foundation of China(No.U23A20651)the Central Government Guides Local Science and Technology Development Foundation(No.2023ZYDF022)+1 种基金the Sichuan Science and Technology Program(2024ZDZX0031)the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(No.SKLMRDPC23KF19).
文摘Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.
