Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution...Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.展开更多
Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedic...The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.展开更多
Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-p...Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
In this work,an oscillating-body wave energy converter(OBWEC)with a hydraulic power take-off(PTO)system named“Dolphin 1”is designed,in which the hydraulic PTO system is equivalent to a transfer station and plays a c...In this work,an oscillating-body wave energy converter(OBWEC)with a hydraulic power take-off(PTO)system named“Dolphin 1”is designed,in which the hydraulic PTO system is equivalent to a transfer station and plays a crucial role in ensuring the stability of the electrical energy output and the efficiency of the overall system.A corresponding mathematical model for the hydraulic PTO system has been established,the factors that influence its performance have been studied,and an algorithm for solving the optimal working pressure has been derived in this paper.Moreover,a PID control method to enable the hydraulic PTO system to automatically achieve optimal performance under different wave conditions has been designed.The results indicate that,compared with single-chamber hydraulic cylinders,double-chamber hydraulic cylinders have a wider application range and greater performance;the accumulator can stabilize the output power of the hydraulic PTO system and slightly increase it;excessively large or small hydraulic motor displacement hinders system performance;and each wave condition corresponds to a unique optimal working pressure for the hydraulic PTO system.In addition,the relationship between the optimal working pressure P_(m)and the pressure P_(h)of the wave force acting on the piston satisfies P_(m)^(2)=∫_(t_(1))^(t_(2))P_(h)^(2)dt/(t_(2)-t_(1)).Furthermore,adjusting the hydraulic motor displacement automatically via a PID controller ensures that the actual working pressure of the hydraulic PTO system consistently reaches or approaches its theoretically optimal value under various wave conditions,which is a very effective control method for enhancing the performance of the hydraulic PTO system.展开更多
To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in t...To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in the operator data center.Fibonacci tree optimization algorithm(FTO)is embedded into the analysis prediction and the online scheduling stages,the FTO traffic scheduling strategy is proposed.By taking the global optimal and the multi-modal optimization advantage of FTO,the traffic scheduling optimal solution and many suboptimal solutions can be obtained.The experiment results show that the FTO traffic scheduling strategy can schedule traffic in data center networks reasonably,and improve the load balancing in the operator data center network effectively.展开更多
Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Op...Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.展开更多
In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based bou...In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.展开更多
Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while...Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.展开更多
Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex syst...Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.展开更多
Tumor initiation and progression are highly intricate biolog-ical processes,and mutation-driven tumorigenesis is a pri-mary underlying cause.Personalized cancer vaccines have been developed to exploit these specific m...Tumor initiation and progression are highly intricate biolog-ical processes,and mutation-driven tumorigenesis is a pri-mary underlying cause.Personalized cancer vaccines have been developed to exploit these specific mutations,particu-larly in the form of tumor neoantigens,to induce immune responses,particularly the activation of CD8+T cells,which can attack malignant cells.Since tumor mutations result in protein sequence alterations distinct from those in normal tissues,therapies that precisely target these alterations could,in principle,confer effective tumor control while minimizing off-target effects.展开更多
This study investigates the collaborative optimization of upgrading,retrofitting,and maintaining large offshore oil extraction equipment.It examines the key challenges and interdependencies inherent in these processes...This study investigates the collaborative optimization of upgrading,retrofitting,and maintaining large offshore oil extraction equipment.It examines the key challenges and interdependencies inherent in these processes and proposes integrated solutions,including sensor-network-based monitoring and digital twin-driven management platforms.The findings demonstrate notable improvements in operational efficiency and reductions in equipment downtime,underscoring both the economic and safety benefits of the proposed approach and providing a reference framework for future optimization strategies in offshore engineering.展开更多
The mental health issues of college students have become an increasingly prominent social problem,exerting severe impacts on their academic performance and overall well-being.Early identification of Interpersonal Sens...The mental health issues of college students have become an increasingly prominent social problem,exerting severe impacts on their academic performance and overall well-being.Early identification of Interpersonal Sensitivity(IS)in students serves as an effective approach to detect psychological problems and provide timely intervention.In this study,958 freshmen from higher education institutions in Zhejiang Province were selected as participants.We proposed a Multi-Strategy Artemisinin Optimization(MSAO)algorithm by enhancing the Artemisinin Optimization(AO)framework through the integration of a group-guided elimination strategy and a two-stage consolidation strategy.Subsequently,the MSAO was combined with the Fuzzy K-Nearest Neighbor(FKNN)classifier to develop the bMSAO-FKNN predictive model for assessing college students’IS.The proposed algorithm’s efficacy was validated through the CEC 2017 benchmark test suite,while the model’s performance was evaluated on the IS dataset,achieving an accuracy rate of 97.81%.These findings demonstrate that the bMSAO-FKNN model not only ensures high predictive accuracy but also offers interpretability for IS prediction,making it a valuable tool for mental health monitoring in academic settings.展开更多
Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Insp...Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.展开更多
Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles,surface radiation balance,weather and climate changes,and vegetation photosynthesis.Accurate solar radiation pred...Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles,surface radiation balance,weather and climate changes,and vegetation photosynthesis.Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry.This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period.Solar energy is the cheapest form of clean energy,and due to the intermittent nature of the energy,accurate forecasting across multiple timeframes is necessary for efficient generation and demand management.Solar radiation prediction using deep learning(DL)includes the applications of neural network methods,namely Convolutional Neural Network(CNN)or Long Short-Term Memory(LSTM)models,to forecast and model solar irradiance patterns.By leveraging meteorological variables and historical solar radiation data,DL algorithms can capture complex spatial and temporal dependencies,resulting in accurate predictions.This article presents a novel Solar Radiation Prediction model utilizing a Boosted Coyote Optimization Algorithm with Deep Learning(SRP-BCOADL).The SRP-BCOADL model initially normalizes the input data using a min-max normalization approach to improve the robust nature under different scales.Besides,the SRP-BCOADL technique uses a Deep Long Short-Term Memory Autoencoder(DLSTM-AE)system for precisely forecasting solar radiation levels.The model’s accuracy is further improved through hyperparameter optimization using the BCOA.The performance analysis of the SRP-BCOADL technique is tested using solar radiation data.Extensive experimental outcomes prove that the SRP-BCOADL method obtains better results over other techniques.The Mean Squared Error(MSE)is just 0.13 kWh/m^(2),is much lower when compared to other models.The Root Mean Squared Error(RMSE)is also reduced to 0.36 kWh/m^(2),and the Mean Absolute Error(MAE)reaches a minimal level of 0.276 kWh/m^(2).展开更多
Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challen...Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.展开更多
The machining precision of blades is critical to the service performance of aero engines.The Leading Edge(LE) of high-pressure compressor blades poses a challenge for precision machining due to its thin size, high deg...The machining precision of blades is critical to the service performance of aero engines.The Leading Edge(LE) of high-pressure compressor blades poses a challenge for precision machining due to its thin size, high degree of bending, and significant change of curvature. Aimed at optimizing the machining error, this paper presents a framework that integrates toolpath planning and process parameter regulation. Firstly, an Iterative Subdivision Algorithm(ISA) for clamped Bspline curve is proposed, based on which toolpath planning method towards the LE is developed.Secondly, the removal effect of Cutter Contact(CC) point on the sampling points is investigated in the calculation of grinding dwell time by traversing in u-v space. A global material removal model is constructed for the solution. Thirdly, the previous two steps are interconnected based on the Improved Whale Optimization Algorithm(IWOA), and the optimal parameter combination is searched using the Root Mean Square Error(RMSE) of the machining error as the objective function. Based on this, the off-line programming and robotic grinding experiments are carried out. The experimental results show that the proposed method with error optimization can achieve 0.0143 mm mean value and 0.0160 mm standard deviations of LE surface error, which is an improvement of32.5% and 33.9%, respectively, compared with previous method.展开更多
Electron beam fluorescence technology is an advanced non-contact measurement in rarefied flow fields,and the fluorescence signal intensity is positively correlated with the electron beam current.The ion bombardment se...Electron beam fluorescence technology is an advanced non-contact measurement in rarefied flow fields,and the fluorescence signal intensity is positively correlated with the electron beam current.The ion bombardment secondary emission electron gun is suitable for the technology.To enhance the beam current,COMSOL simulations and analyses were conducted to examine plasma density distribution in the discharge chamber under the effects of various conditions and the electric field distribution between the cathode and the spacer gap.The anode shape and discharge pressure conditions were optimized to increase plasma density.Additionally,an improved spacer structure was designed with the dual purpose of enhancing the electric field distribution between the cathode-spacer gaps and improving vacuum differential effects.This design modification aims to increase the pass rate of secondary electrons.Both simulation and experimental results demonstrated that the performance of the optimized electron gun was effectively enhanced.When the electrode voltage remains constant and the discharge gas pressure is adjusted to around 8 Pa,the maximum beam current was increased from 0.9 mA to 1.6 mA.展开更多
Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation o...Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation of new holes.However,most pertinent investigations in the field rely predominantly on fixed background mesh,which is never remeshed.Consequently,the mesh element partitioned by material interface during the optimization process necessitates approximation by using artificial interpolation models to obtain its element stiffness or other properties.This paper introduces a novel approach to topology op-timization by integrating the PLSM with body-fitted adaptive mesh and Helmholtz-type filter.Primarily,combining the PLSM with body-fitted adaptive mesh enables the regeneration of mesh based on the zero level-set interface.This not only precludes the direct traversal of the material interface through the mesh element during the topology optimization process,but also improves the accuracy of calculation.Additionally,the incorporation of a Helmholtz-type partial differential equation filter,relying solely on mesh information essential for finite element discretization,serves to regulate the topological complexity and the minimum feature size of the optimized structure.Leveraging these advantages,the topology optimization program demonstrates its versa-tility by successfully addressing various design problems,encompassing the minimum mean compliance problem and minimum energy dissipation problem.Ultimately,the result of numerical example indicates that the optimized structure exhibits a dis-tinct and smooth boundary,affirming the effective control over both topological complexity and the minimum feature size of the optimized structure.展开更多
基金financial supports from National Natural Science Foundation of China(Grant Nos.U23A20368 and 62175006)Academic Excellence Foundation of BUAA for PhD Students.
文摘Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
基金The funding for this publication was provided by Johannes Kepler University(JKU),Linz.Special thanks to Prof.Zongmin DENG from Beihang University for his invaluable guidance,insightful feedback,and constructive criticism,which greatly enhanced the quality of this manuscript.We extend our heartfelt gratitude to the PARSIFAL team for providing the supporting materials,which inspired this study.
文摘The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057 and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028.
文摘Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52071094 and 51979065).
文摘In this work,an oscillating-body wave energy converter(OBWEC)with a hydraulic power take-off(PTO)system named“Dolphin 1”is designed,in which the hydraulic PTO system is equivalent to a transfer station and plays a crucial role in ensuring the stability of the electrical energy output and the efficiency of the overall system.A corresponding mathematical model for the hydraulic PTO system has been established,the factors that influence its performance have been studied,and an algorithm for solving the optimal working pressure has been derived in this paper.Moreover,a PID control method to enable the hydraulic PTO system to automatically achieve optimal performance under different wave conditions has been designed.The results indicate that,compared with single-chamber hydraulic cylinders,double-chamber hydraulic cylinders have a wider application range and greater performance;the accumulator can stabilize the output power of the hydraulic PTO system and slightly increase it;excessively large or small hydraulic motor displacement hinders system performance;and each wave condition corresponds to a unique optimal working pressure for the hydraulic PTO system.In addition,the relationship between the optimal working pressure P_(m)and the pressure P_(h)of the wave force acting on the piston satisfies P_(m)^(2)=∫_(t_(1))^(t_(2))P_(h)^(2)dt/(t_(2)-t_(1)).Furthermore,adjusting the hydraulic motor displacement automatically via a PID controller ensures that the actual working pressure of the hydraulic PTO system consistently reaches or approaches its theoretically optimal value under various wave conditions,which is a very effective control method for enhancing the performance of the hydraulic PTO system.
基金supported by National Natural Science Foundation of China(No.62163036).
文摘To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in the operator data center.Fibonacci tree optimization algorithm(FTO)is embedded into the analysis prediction and the online scheduling stages,the FTO traffic scheduling strategy is proposed.By taking the global optimal and the multi-modal optimization advantage of FTO,the traffic scheduling optimal solution and many suboptimal solutions can be obtained.The experiment results show that the FTO traffic scheduling strategy can schedule traffic in data center networks reasonably,and improve the load balancing in the operator data center network effectively.
基金funded by Researchers Supporting Programnumber(RSPD2024R809),King Saud University,Riyadh,Saudi Arabia.
文摘Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.
文摘In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20242194)the National Natural Science Foundation of China(Grant Nos.52175251 and 52205268)+1 种基金the Industry Key Technology Research Fund Project of Northwestern Polytechnical University(Grant No.HYGJXM202318)the National Basic Scientific Research Program(Grant No.JCKY2021206B005).
文摘Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.
文摘Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.
基金supported by the National Natural Science Foundation of China(82341042 and 32270993)the PhD program of the Interdisciplinary Research Center,Sun Yat-sen University.
文摘Tumor initiation and progression are highly intricate biolog-ical processes,and mutation-driven tumorigenesis is a pri-mary underlying cause.Personalized cancer vaccines have been developed to exploit these specific mutations,particu-larly in the form of tumor neoantigens,to induce immune responses,particularly the activation of CD8+T cells,which can attack malignant cells.Since tumor mutations result in protein sequence alterations distinct from those in normal tissues,therapies that precisely target these alterations could,in principle,confer effective tumor control while minimizing off-target effects.
文摘This study investigates the collaborative optimization of upgrading,retrofitting,and maintaining large offshore oil extraction equipment.It examines the key challenges and interdependencies inherent in these processes and proposes integrated solutions,including sensor-network-based monitoring and digital twin-driven management platforms.The findings demonstrate notable improvements in operational efficiency and reductions in equipment downtime,underscoring both the economic and safety benefits of the proposed approach and providing a reference framework for future optimization strategies in offshore engineering.
文摘The mental health issues of college students have become an increasingly prominent social problem,exerting severe impacts on their academic performance and overall well-being.Early identification of Interpersonal Sensitivity(IS)in students serves as an effective approach to detect psychological problems and provide timely intervention.In this study,958 freshmen from higher education institutions in Zhejiang Province were selected as participants.We proposed a Multi-Strategy Artemisinin Optimization(MSAO)algorithm by enhancing the Artemisinin Optimization(AO)framework through the integration of a group-guided elimination strategy and a two-stage consolidation strategy.Subsequently,the MSAO was combined with the Fuzzy K-Nearest Neighbor(FKNN)classifier to develop the bMSAO-FKNN predictive model for assessing college students’IS.The proposed algorithm’s efficacy was validated through the CEC 2017 benchmark test suite,while the model’s performance was evaluated on the IS dataset,achieving an accuracy rate of 97.81%.These findings demonstrate that the bMSAO-FKNN model not only ensures high predictive accuracy but also offers interpretability for IS prediction,making it a valuable tool for mental health monitoring in academic settings.
基金supported by the National Natural Science Foundation of China(No.12104396)the National Key R&D Program of China(Nos.2021YFF1200404 and 2021YFA1201200)+2 种基金the National Independent Innovation Demonstration Zone Shanghai Zhangjiang Major Projects(No.ZJZX2020014)the Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University(No.SN-ZJU-SIAS-003)the Shanghai Artificial Intelligence Lab(No.P22KN00272),China.
文摘Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah Bint Abdulrahman University,through the Program of Research Project Funding after Publication,grant No.(RPFAP-79-1445).
文摘Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles,surface radiation balance,weather and climate changes,and vegetation photosynthesis.Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry.This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period.Solar energy is the cheapest form of clean energy,and due to the intermittent nature of the energy,accurate forecasting across multiple timeframes is necessary for efficient generation and demand management.Solar radiation prediction using deep learning(DL)includes the applications of neural network methods,namely Convolutional Neural Network(CNN)or Long Short-Term Memory(LSTM)models,to forecast and model solar irradiance patterns.By leveraging meteorological variables and historical solar radiation data,DL algorithms can capture complex spatial and temporal dependencies,resulting in accurate predictions.This article presents a novel Solar Radiation Prediction model utilizing a Boosted Coyote Optimization Algorithm with Deep Learning(SRP-BCOADL).The SRP-BCOADL model initially normalizes the input data using a min-max normalization approach to improve the robust nature under different scales.Besides,the SRP-BCOADL technique uses a Deep Long Short-Term Memory Autoencoder(DLSTM-AE)system for precisely forecasting solar radiation levels.The model’s accuracy is further improved through hyperparameter optimization using the BCOA.The performance analysis of the SRP-BCOADL technique is tested using solar radiation data.Extensive experimental outcomes prove that the SRP-BCOADL method obtains better results over other techniques.The Mean Squared Error(MSE)is just 0.13 kWh/m^(2),is much lower when compared to other models.The Root Mean Squared Error(RMSE)is also reduced to 0.36 kWh/m^(2),and the Mean Absolute Error(MAE)reaches a minimal level of 0.276 kWh/m^(2).
基金funded by National Natural Science Foundation of China(grant No.52405255)Special Program of Huzhou(grant No.2023GZ05)+1 种基金Projects of Huzhou Science and Technology Correspondent(grant No.2023KT76)Guangdong Basic and Applied Basic Research Foundation(grant No.2025A1515010487)。
文摘Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.
基金supported by the National Natural Science Foundation of China (No. 52075059)Graduate Scientific Research and Innovation Foundation of Chongqing (No. CYB23021)the Innovation Fund of Aero Engine Corporation of China (No. ZZCX-2022-019)。
文摘The machining precision of blades is critical to the service performance of aero engines.The Leading Edge(LE) of high-pressure compressor blades poses a challenge for precision machining due to its thin size, high degree of bending, and significant change of curvature. Aimed at optimizing the machining error, this paper presents a framework that integrates toolpath planning and process parameter regulation. Firstly, an Iterative Subdivision Algorithm(ISA) for clamped Bspline curve is proposed, based on which toolpath planning method towards the LE is developed.Secondly, the removal effect of Cutter Contact(CC) point on the sampling points is investigated in the calculation of grinding dwell time by traversing in u-v space. A global material removal model is constructed for the solution. Thirdly, the previous two steps are interconnected based on the Improved Whale Optimization Algorithm(IWOA), and the optimal parameter combination is searched using the Root Mean Square Error(RMSE) of the machining error as the objective function. Based on this, the off-line programming and robotic grinding experiments are carried out. The experimental results show that the proposed method with error optimization can achieve 0.0143 mm mean value and 0.0160 mm standard deviations of LE surface error, which is an improvement of32.5% and 33.9%, respectively, compared with previous method.
文摘Electron beam fluorescence technology is an advanced non-contact measurement in rarefied flow fields,and the fluorescence signal intensity is positively correlated with the electron beam current.The ion bombardment secondary emission electron gun is suitable for the technology.To enhance the beam current,COMSOL simulations and analyses were conducted to examine plasma density distribution in the discharge chamber under the effects of various conditions and the electric field distribution between the cathode and the spacer gap.The anode shape and discharge pressure conditions were optimized to increase plasma density.Additionally,an improved spacer structure was designed with the dual purpose of enhancing the electric field distribution between the cathode-spacer gaps and improving vacuum differential effects.This design modification aims to increase the pass rate of secondary electrons.Both simulation and experimental results demonstrated that the performance of the optimized electron gun was effectively enhanced.When the electrode voltage remains constant and the discharge gas pressure is adjusted to around 8 Pa,the maximum beam current was increased from 0.9 mA to 1.6 mA.
基金supported by the National Natural Science Foundation of China(Grant Nos.12372200 and 12072242).
文摘Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation of new holes.However,most pertinent investigations in the field rely predominantly on fixed background mesh,which is never remeshed.Consequently,the mesh element partitioned by material interface during the optimization process necessitates approximation by using artificial interpolation models to obtain its element stiffness or other properties.This paper introduces a novel approach to topology op-timization by integrating the PLSM with body-fitted adaptive mesh and Helmholtz-type filter.Primarily,combining the PLSM with body-fitted adaptive mesh enables the regeneration of mesh based on the zero level-set interface.This not only precludes the direct traversal of the material interface through the mesh element during the topology optimization process,but also improves the accuracy of calculation.Additionally,the incorporation of a Helmholtz-type partial differential equation filter,relying solely on mesh information essential for finite element discretization,serves to regulate the topological complexity and the minimum feature size of the optimized structure.Leveraging these advantages,the topology optimization program demonstrates its versa-tility by successfully addressing various design problems,encompassing the minimum mean compliance problem and minimum energy dissipation problem.Ultimately,the result of numerical example indicates that the optimized structure exhibits a dis-tinct and smooth boundary,affirming the effective control over both topological complexity and the minimum feature size of the optimized structure.
