In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in so...The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in southwestern China as the engineering prototype,large-scale three-dimensional(3D)physical model tests were conducted on a 3D-printed complex geological model containing two faults.Based on the selfdeveloped 3D loading system and excavation device,the macroscopic failure of fault-slip rockbursts was simulated indoors.The stress,strain,and fracturing characteristics of the surrounding rock near the two faults were systematically evaluated during excavation and multistage loading.The test results effectively revealed the evolution and triggering mechanism of fault-slip rockbursts.After the excavation of a highstress tunnel,stress readjustment occurred.Owing to the presence of these two faults,stress continued to accumulate in the rock mass between them,leading to the accumulation of fractures.When the shear stress on a fault surface exceeded its shear strength,sudden fault slip and dislocation occurred,thus triggering rockbursts.Rockbursts occurred twice in the vault between the two faults,showing obvious intermittent characteristics.The rockburst pit was controlled by two faults.When the faults remained stable,tensile failure predominated in the surrounding rock.However,when the fault slip was triggered,shear failure in the surrounding rock increased.These findings provide valuable insights for enhancing the comprehension of fault-slip rockbursts.展开更多
Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to tr...Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.展开更多
Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual conne...Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.展开更多
A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional an...A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.展开更多
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force...A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.展开更多
The streamflow over the Yellow River basin is simulated using the PRECIS （Providing REgional Climates for Impacts Studies） regional climate model driven by 15-year （1979-1993） ECMWF reanalysis data as the initial ...The streamflow over the Yellow River basin is simulated using the PRECIS （Providing REgional Climates for Impacts Studies） regional climate model driven by 15-year （1979-1993） ECMWF reanalysis data as the initial and lateral boundary conditions and an off-line large-scale routing model （LRM）. The LRM uses physical catchment and river channel information and allows streamflow to be predicted for large continental rivers with a 1°×1° spatial resolution. The results show that the PRECIS model can reproduce the general southeast to northwest gradient distribution of the precipitation over the Yellow River basin, The PRECIS- LRM model combination has the capability to simulate the seasonal and annual streamflow over the Yellow River basin. The simulated streamflow is generally coincident with the naturalized streamflow both in timing and in magnitude.展开更多
A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are runn...A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are running in parallel.The neural network algorithm is used to modify the adaptive noise filtering algorithm based on the mean value and variance of the＂current＂statistical model for maneuvering targets, and then the multiple model tracking algorithm of the multiple processing switch is used to improve the precision of tracking maneuvering targets.The modified algorithm is proved to be effective by simulation.展开更多
In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of faul...In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.展开更多
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A...Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.展开更多
Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining wal...Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.展开更多
Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qu...Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.展开更多
Many biodynamic models have been derived using trial and error curve-fitting technique, such that the error between the computed and measured biodynamic response functions is minimum. This study developed a biomechani...Many biodynamic models have been derived using trial and error curve-fitting technique, such that the error between the computed and measured biodynamic response functions is minimum. This study developed a biomechanical model of the human body in a sitting posture without backrest for evaluating the vibration transmissibility and dynamic response to vertical vibration direction. In describing the human body motion, a three biomechanical models are discussed (two models are 4-DOF and one model 7-DOF). Optimization software based on stochastic techniques search methods, Genetic Algorithms (GAs), is employed to determine the human model parameters imposing some limit constraints on the model parameters. In addition, an objective function is formulated comprising the sum of errors between the computed and actual values (experimental data). The studied functions are the driving-point mechanical impedance, apparent mass and seat- to-head transmissibility functions. The optimization process increased the average goodness of fit and the results of studied functions became much closer to the target values (Experimental data). From the optimized model, the resonant frequencies of the driver parts computed on the basis of biodynamic response functions are found to be within close bounds to that expected for the human body.展开更多
Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three dif...Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three different pile-soil models were used to study a large 10 MW monopile wind turbine.By modeling the three models in the SACS software,this paper analyzed the motion response of the overall structure under the conditions of wind and waves.According to the given working conditions,this paper concludes that under the condition of independent wind,the average value of the tower top x-displacement of the rigid connection method is the smalle st,and the standard deviation is the smallest under the condition of independent wave.The results obtained by the p-y curve method are the most conservative.展开更多
An intuitive 2D model of circular electrical impedance tomography （EIT） sensor with small size electrodes is established based on the theory of analytic functions. The validation of the model is proved using the res...An intuitive 2D model of circular electrical impedance tomography （EIT） sensor with small size electrodes is established based on the theory of analytic functions. The validation of the model is proved using the result from the solution of Laplace equation. Suggestions on to electrode optimization and explanation to the ill-condition property of the sensitivity matrix are provided based on the model, which takes electrode distance into account and can be generalized to the sensor with any simple connected region through a conformal transformation. Image reconstruction algorithms based on the model are implemented to show feasibility of the model using experimental data collected from the EIT system developed in Tianjin University. In the simulation with a human chestlike configuration, electrical conductivity distributions are reconstructed using equi-potential backprojection （EBP） and Tikhonov regularization （TR） based on a conformal transformation of the model. The algorithms based on the model are suitable for online image reconstruction and the reconstructed results are aood both in size and position.展开更多
Aiming at the problem of gate allocation of transit flights,a flight first service model is established.Under the constraints of maximizing the utilization rate of gates and minimizing the transit time,the idea of“fi...Aiming at the problem of gate allocation of transit flights,a flight first service model is established.Under the constraints of maximizing the utilization rate of gates and minimizing the transit time,the idea of“first flight serving first”is used to allocate the first time,and then the hybrid algorithm of artificial fish swarm and simulated annealing is used to find the optimal solution.That means the fish swarm algorithm with the swallowing behavior is employed to find the optimal solution quickly,and the simulated annealing algorithm is used to obtain a global optimal allocation scheme for the optimal local region.The experimental data show that the maximum utilization of the gate is 27.81%higher than that of the“first come first serve”method when the apron is not limited,and the hybrid algorithm has fewer iterations than the simulated annealing algorithm alone,with the overall passenger transfer tension reducing by 1.615;the hybrid algorithm has faster convergence and better performance than the artificial fish swarm algorithm alone.The experimental results indicate that the hybrid algorithm of fish swarm and simulated annealing can achieve higher utilization rate of gates and lower passenger transfer tension under the idea of“first flight serving first”.展开更多
This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly m...This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.展开更多
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
基金funding support from the National Natural Science Foundation of China(Grant Nos.42177136 and 52309126).
文摘The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in southwestern China as the engineering prototype,large-scale three-dimensional(3D)physical model tests were conducted on a 3D-printed complex geological model containing two faults.Based on the selfdeveloped 3D loading system and excavation device,the macroscopic failure of fault-slip rockbursts was simulated indoors.The stress,strain,and fracturing characteristics of the surrounding rock near the two faults were systematically evaluated during excavation and multistage loading.The test results effectively revealed the evolution and triggering mechanism of fault-slip rockbursts.After the excavation of a highstress tunnel,stress readjustment occurred.Owing to the presence of these two faults,stress continued to accumulate in the rock mass between them,leading to the accumulation of fractures.When the shear stress on a fault surface exceeded its shear strength,sudden fault slip and dislocation occurred,thus triggering rockbursts.Rockbursts occurred twice in the vault between the two faults,showing obvious intermittent characteristics.The rockburst pit was controlled by two faults.When the faults remained stable,tensile failure predominated in the surrounding rock.However,when the fault slip was triggered,shear failure in the surrounding rock increased.These findings provide valuable insights for enhancing the comprehension of fault-slip rockbursts.
基金support by the Open Project of Xiangjiang Laboratory(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28,ZK21-07)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(CX20230074)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJZ03)the Science and Technology Innovation Program of Humnan Province(2023RC1002).
文摘Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.
基金sponsored by the General Program of the National Natural Science Foundation of China(Grant Nos.52079129 and 52209148)the Hubei Provincial General Fund,China(Grant No.2023AFB567)。
文摘Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.
基金The National Key Technology R&D Program of China during the 11th Five Year Plan Period(No.2008BAJ11B01)
文摘A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.
基金supported by the Ministry of Trade,Industry & Energy(MOTIE,Korea) under Industrial Technology Innovation Program (No.10063424,'development of distant speech recognition and multi-task dialog processing technologies for in-door conversational robots')
文摘A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.
文摘The streamflow over the Yellow River basin is simulated using the PRECIS （Providing REgional Climates for Impacts Studies） regional climate model driven by 15-year （1979-1993） ECMWF reanalysis data as the initial and lateral boundary conditions and an off-line large-scale routing model （LRM）. The LRM uses physical catchment and river channel information and allows streamflow to be predicted for large continental rivers with a 1°×1° spatial resolution. The results show that the PRECIS model can reproduce the general southeast to northwest gradient distribution of the precipitation over the Yellow River basin, The PRECIS- LRM model combination has the capability to simulate the seasonal and annual streamflow over the Yellow River basin. The simulated streamflow is generally coincident with the naturalized streamflow both in timing and in magnitude.
文摘A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are running in parallel.The neural network algorithm is used to modify the adaptive noise filtering algorithm based on the mean value and variance of the＂current＂statistical model for maneuvering targets, and then the multiple model tracking algorithm of the multiple processing switch is used to improve the precision of tracking maneuvering targets.The modified algorithm is proved to be effective by simulation.
基金the National Natural Science Foundation of China (No. 50677062)the New Century Excellent Talents in Uni-versity of China (No. NCET-07-0745)the Natural Science Foundation of Zhejiang Province, China (No. R107062)
文摘In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.
基金supported by the Center for Mining,Electro-Mechanical research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnamfinancially supported by the Hunan Provincial Department of Education General Project(19C1744)+1 种基金Hunan Province Science Foundation for Youth Scholars of China fund(2018JJ3510)the Innovation-Driven Project of Central South University(2020CX040)。
文摘Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.
基金supported by the Fujian Science Foundation for Outstanding Youth(Grant No.2023J06039)the National Natural Science Foundation of China(Grant No.41977259 and No.U2005205)Fujian Province natural resources science and technology innovation project(Grant No.KY-090000-04-2022-019)。
文摘Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.
基金supported by the National Natural Science Foundation of China(61573017 61703425)+2 种基金the Aeronautical Science Fund(20175796014)the Shaanxi Province Natural Science Foundation Research Project(2016JQ6062 2017JM6062)
文摘Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.
文摘Many biodynamic models have been derived using trial and error curve-fitting technique, such that the error between the computed and measured biodynamic response functions is minimum. This study developed a biomechanical model of the human body in a sitting posture without backrest for evaluating the vibration transmissibility and dynamic response to vertical vibration direction. In describing the human body motion, a three biomechanical models are discussed (two models are 4-DOF and one model 7-DOF). Optimization software based on stochastic techniques search methods, Genetic Algorithms (GAs), is employed to determine the human model parameters imposing some limit constraints on the model parameters. In addition, an objective function is formulated comprising the sum of errors between the computed and actual values (experimental data). The studied functions are the driving-point mechanical impedance, apparent mass and seat- to-head transmissibility functions. The optimization process increased the average goodness of fit and the results of studied functions became much closer to the target values (Experimental data). From the optimized model, the resonant frequencies of the driver parts computed on the basis of biodynamic response functions are found to be within close bounds to that expected for the human body.
基金financially supported by the Open Research Fund of Hunan Provincial Key Laboratory of Key Technology on Hydropower Development (Grant No.PKLHD202003)the National Natural Science Foundation of China (Grant Nos.52071058 and 51939002)+1 种基金the National Natural Science Foundation of Liaoning Province (Grant No.2022-KF-18-01)Fundamental Research Funds for the Central University (Grant No.DUT20ZD219)。
文摘Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three different pile-soil models were used to study a large 10 MW monopile wind turbine.By modeling the three models in the SACS software,this paper analyzed the motion response of the overall structure under the conditions of wind and waves.According to the given working conditions,this paper concludes that under the condition of independent wind,the average value of the tower top x-displacement of the rigid connection method is the smalle st,and the standard deviation is the smallest under the condition of independent wave.The results obtained by the p-y curve method are the most conservative.
基金Supported by National Natural Science Foundation of China (No.60532020,60301008,60472077,50337020), the High Tech-nique Research and Development Program of China (No.2001AA413210).
文摘An intuitive 2D model of circular electrical impedance tomography （EIT） sensor with small size electrodes is established based on the theory of analytic functions. The validation of the model is proved using the result from the solution of Laplace equation. Suggestions on to electrode optimization and explanation to the ill-condition property of the sensitivity matrix are provided based on the model, which takes electrode distance into account and can be generalized to the sensor with any simple connected region through a conformal transformation. Image reconstruction algorithms based on the model are implemented to show feasibility of the model using experimental data collected from the EIT system developed in Tianjin University. In the simulation with a human chestlike configuration, electrical conductivity distributions are reconstructed using equi-potential backprojection （EBP） and Tikhonov regularization （TR） based on a conformal transformation of the model. The algorithms based on the model are suitable for online image reconstruction and the reconstructed results are aood both in size and position.
基金This paper is supported by The National Nature Science Foundation of China(No.61703426).
文摘Aiming at the problem of gate allocation of transit flights,a flight first service model is established.Under the constraints of maximizing the utilization rate of gates and minimizing the transit time,the idea of“first flight serving first”is used to allocate the first time,and then the hybrid algorithm of artificial fish swarm and simulated annealing is used to find the optimal solution.That means the fish swarm algorithm with the swallowing behavior is employed to find the optimal solution quickly,and the simulated annealing algorithm is used to obtain a global optimal allocation scheme for the optimal local region.The experimental data show that the maximum utilization of the gate is 27.81%higher than that of the“first come first serve”method when the apron is not limited,and the hybrid algorithm has fewer iterations than the simulated annealing algorithm alone,with the overall passenger transfer tension reducing by 1.615;the hybrid algorithm has faster convergence and better performance than the artificial fish swarm algorithm alone.The experimental results indicate that the hybrid algorithm of fish swarm and simulated annealing can achieve higher utilization rate of gates and lower passenger transfer tension under the idea of“first flight serving first”.
基金supported by the National Key R&D Program of China with Grant number 2019YFB1803400the National Natural Science Foundation of China under Grant number 62071114the Fundamental Research Funds for the Central Universities of China under grant numbers 3204002004A2 and 2242022k30005。
文摘This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.
