Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,th...Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.展开更多
Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected ...Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds.展开更多
Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)method...Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).展开更多
Combined Heat and Power Economic Dispatch(CHPED)is an important problem in the energy field,and it is beneficial for improving the utilization efficiency of power and heat energies.This paper proposes a Modified Genet...Combined Heat and Power Economic Dispatch(CHPED)is an important problem in the energy field,and it is beneficial for improving the utilization efficiency of power and heat energies.This paper proposes a Modified Genetic Algorithm(MGA)to determine the power and heat outputs of three kinds of units for CHPED.First,MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions,and its convergence can be enhanced.Second,MGA modi-fies the mutation operator by introducing a disturbance coefficient based on guassian distribution,which can decrease the risk of being trapped into local optima.Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED.In comparison with the other algorithms,MGA has reduced generation costs by at least 562.73$,1068.7$,522.68$and 1016.24$,respectively,for instances 3,4,7 and 8,and it has reduced generation costs by at most 848.22$,3642.85$,897.63$and 3812.65$,respectively,for instances 3,4,7 and 8.Therefore,MGA has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.展开更多
An improved parallel weighted bit-flipping（PWBF） algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the ...An improved parallel weighted bit-flipping（PWBF） algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the original algorithm are parallelized. The simulation experiments demonstrate that the improved PWBF algorithm provides about 0. 1 to 0. 3 dB coding gain over the original PWBF algorithm. And the improved algorithm achieves a higher convergence rate. The choice of the threshold is also discussed, which is used to determine whether a bit should be flipped during each iteration. The appropriate threshold can ensure that most error bits be flipped, and keep the right ones untouched at the same time. The improvement is particularly effective for decoding quasi-cyclic low-density paritycheck（QC-LDPC） codes.展开更多
As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions an...As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.展开更多
The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind powe...The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.展开更多
Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident...Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident x-rays,fewer measurements with sufficient signal-to-noise ratio(SNR)are always anticipated.Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously.In this paper,a method based on a modified compressive sensing algorithm with conjugate gradient descent method(CGDGI)is developed to solve the problems encountered in available XGI methods.Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI.The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.展开更多
All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide...All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.展开更多
A new blind equalization algorithm based on the modified constant modulus algorithm （MCMA） and dithered signederror constant modulus algorithm （DSE-CMA） is proposed. This dithered signed-error MCMA （DSE-MCMA） ca...A new blind equalization algorithm based on the modified constant modulus algorithm （MCMA） and dithered signederror constant modulus algorithm （DSE-CMA） is proposed. This dithered signed-error MCMA （DSE-MCMA） can not only reduce the computational complexity, but also recover the phase rotation in the complex channel. Simulation results have verified the analysis and indicated the good property of DSE-MCMA.展开更多
To develop the pressure control algorithm for active braking of adaptive cruise control（ACC） system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuato...To develop the pressure control algorithm for active braking of adaptive cruise control（ACC） system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuators,it is demonstrated that different duty of pulse-width modulation（PWM） signals could control the pressure changing rate of the wheel cylinder.To obtain that signal,a modified proportional-integral-differential（PID） control algorithm is developed using the variable parameter method,the control value reset method,the dead zone method and the integral saturation method.Experimental results show that the delay and overshoot of the pressure response could be reduced considerably using the modified PID algorithm compared with the conventional one.The proposed pressure control algorithm could be used for the further development of the ACC＇s controller.展开更多
The dynamics of complex gene regulation systems can be simulated by the Gillespie algorithm. The classic Gillespie algorithm is appropriate to simulate a stochastic
This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key...This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.展开更多
Pipe-routing for ship is formulated as searching for the near-optimal pipe paths while meeting certain objectives in an environment scattered with obstacles. Due to the complex construction in layout space, the great ...Pipe-routing for ship is formulated as searching for the near-optimal pipe paths while meeting certain objectives in an environment scattered with obstacles. Due to the complex construction in layout space, the great number of pipelines, numerous and diverse design constraints and large amount of obstacles, finding the optimum route of ship pipes is a complicated and time-consuming process. A modified NSGA-II algorithm based approach is proposed to find the near-optimal solution to solve the problem. By simplified equipment models, the layout space is firstly divided into three dimensional （3D） grids to build its mathematical model. In the modified NSGA-II algorithm, the concept of auxiliary point is introduced to improve the search range of maze algorithm （MA） as well as to guarantee the diversity of chromosomes in initial population. Then the fix-length coding mechanism is proposed, Fuzzy set theory is also adopted to select the optimal solution in Pareto solutions. Finally, the effectiveness and efficiency of the proposed approach is demonstrated by the contrast test and simulation. The merit of the proposed algorithm lies in that it can provide more appropriate solutions for the designers while subject certain constrains.展开更多
E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking d...E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.展开更多
Computational efficiency has become a key issue in genomic prediction(GP) owing to the massive historical datasets accumulated. We developed hereby a new super-fast GP approach(SHEAPY) combining randomized Haseman-Els...Computational efficiency has become a key issue in genomic prediction(GP) owing to the massive historical datasets accumulated. We developed hereby a new super-fast GP approach(SHEAPY) combining randomized Haseman-Elston regression(RHE-reg) with a modified Algorithm for Proven and Young(APY) in an additive-effect model, using the former to estimate heritability and then the latter to invert a large genomic relationship matrix for best linear prediction. In simulation results with varied sizes of training population, GBLUP, HEAPY|A and SHEAPY showed similar predictive performance when the size of a core population was half that of a large training population and the heritability was a fixed value, and the computational speed of SHEAPY was faster than that of GBLUP and HEAPY|A. In simulation results with varied heritability, SHEAPY showed better predictive ability than GBLUP in all cases and than HEAPY|A in most cases when the size of a core population was 4/5 that of a small training population and the training population size was a fixed value. As a proof of concept, SHEAPY was applied to the analysis of two real datasets. In an Arabidopsis thaliana F2 population, the predictive performance of SHEAPY was similar to or better than that of GBLUP and HEAPY|A in most cases when the size of a core population(2 0 0) was 2/3 of that of a small training population(3 0 0). In a sorghum multiparental population,SHEAPY showed higher predictive accuracy than HEAPY|A for all of three traits, and than GBLUP for two traits. SHEAPY may become the GP method of choice for large-scale genomic data.展开更多
To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellit...To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90%when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.展开更多
In order to find roots of maximal monotone operators, this paper introduces and studies the modified approximate proximal point algorithm with an error sequence {e k} such that || ek || \leqslant hk || xk - [(x)\tilde...In order to find roots of maximal monotone operators, this paper introduces and studies the modified approximate proximal point algorithm with an error sequence {e k} such that || ek || \leqslant hk || xk - [(x)\tilde]k ||\left\| { e^k } \right\| \leqslant \eta _k \left\| { x^k - \tilde x^k } \right\| with ?k = 0￥ ( hk - 1 ) < + ￥\sum\limits_{k = 0}^\infty {\left( {\eta _k - 1} \right)} and infk \geqslant 0 hk = m\geqslant 1\mathop {\inf }\limits_{k \geqslant 0} \eta _k = \mu \geqslant 1 . Here, the restrictions on {η k} are very different from the ones on {η k}, given by He et al (Science in China Ser. A, 2002, 32 (11): 1026–1032.) that supk \geqslant 0 hk = v < 1\mathop {\sup }\limits_{k \geqslant 0} \eta _k = v . Moreover, the characteristic conditions of the convergence of the modified approximate proximal point algorithm are presented by virtue of the new technique very different from the ones given by He et al.展开更多
A modified sequential linear programming algorithm is presented, whose subproblem is always solvable, for the extended linear complementarity problem (XLCP), the global convergence of the algorithm under assumption of...A modified sequential linear programming algorithm is presented, whose subproblem is always solvable, for the extended linear complementarity problem (XLCP), the global convergence of the algorithm under assumption of X-row sufficiency or X-colunm monotonicity is proved. As a result, a sufficient condition for existence and boundedness of solution to the XLCP are obtained.展开更多
Contemporarily,the development of distributed generations(DGs)technologies is fetching more,and their deployment in power systems is becom-ing broad and diverse.Consequently,several glitches are found in the recent st...Contemporarily,the development of distributed generations(DGs)technologies is fetching more,and their deployment in power systems is becom-ing broad and diverse.Consequently,several glitches are found in the recent studies due to the inappropriate/inadequate penetrations.This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system.The assessment procedure is carried out using MATLAB software and Mod-ified Salp Swarm Algorithm(MSSA)that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations.This algorithm modifies two control parameters of the actual SSA algorithm and offers a perfect balance between the exploration and exploitation.Further,the effectiveness of the proposed schemes is assessed using various reliability indices.Also,the available capacity of the extended system is computed for the best configuration of the considered system.The results confirm the level of reli-able operation of the extended DGs along with the standard RTS system.Speci-fically,the overall reliability of the system displays superior performance when the tie lines 1 and 2 of the DG connected with buses 9 and 10,respectively.The reliability indices of this case namely SAIFI,SAIDI,CAIDI,ASAI,AUSI,EUE,and AEUE shows enhancement about 12.5%,4.32%,7.28%,1.09%,4.53%,12.00%,and 0.19%,respectively.Also,a probability of available capacity at the low voltage bus side is accomplished a good scale about 212.07 times/year.展开更多
基金received funding from the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633)2023 University Student Innovation and Entrepreneurship Training Program(202311463009Z)+1 种基金Changzhou Science and Technology Support Project(CE20235045)Open Project of Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology(2021JSSPD12).
文摘Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.
基金supported by the National Natural Science Foundation of China(Grant No.42407232)the Sichuan Science and Technology Program(Grant No.2024NSFSC0826).
文摘Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds.
文摘Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).
基金supported by the National Natural Science Foundation of China(NSFC)under Grant 61873272,62073327in part by the Natural Science Foundation of Jiangsu Province under Grant BK20200086,BK20200631.
文摘Combined Heat and Power Economic Dispatch(CHPED)is an important problem in the energy field,and it is beneficial for improving the utilization efficiency of power and heat energies.This paper proposes a Modified Genetic Algorithm(MGA)to determine the power and heat outputs of three kinds of units for CHPED.First,MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions,and its convergence can be enhanced.Second,MGA modi-fies the mutation operator by introducing a disturbance coefficient based on guassian distribution,which can decrease the risk of being trapped into local optima.Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED.In comparison with the other algorithms,MGA has reduced generation costs by at least 562.73$,1068.7$,522.68$and 1016.24$,respectively,for instances 3,4,7 and 8,and it has reduced generation costs by at most 848.22$,3642.85$,897.63$and 3812.65$,respectively,for instances 3,4,7 and 8.Therefore,MGA has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.
基金The National High Technology Research and Development Program of China (863Program) ( No2009AA01Z235,2006AA01Z263)the Research Fund of the National Mobile Communications Research Laboratory of Southeast University(No2008A10)
文摘An improved parallel weighted bit-flipping（PWBF） algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the original algorithm are parallelized. The simulation experiments demonstrate that the improved PWBF algorithm provides about 0. 1 to 0. 3 dB coding gain over the original PWBF algorithm. And the improved algorithm achieves a higher convergence rate. The choice of the threshold is also discussed, which is used to determine whether a bit should be flipped during each iteration. The appropriate threshold can ensure that most error bits be flipped, and keep the right ones untouched at the same time. The improvement is particularly effective for decoding quasi-cyclic low-density paritycheck（QC-LDPC） codes.
基金supported by the National Natural Science Foundation of China under Grant 62473328by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.
基金supported by the China Datang Corporation project“Study on the performance improvement scheme of in-service wind farms”,the Fundamental Research Funds for the Central Universities(2020MS021)the Foundation of State Key Laboratory“Real-time prediction of offshore wind power and load reduction control method”(LAPS2020-07).
文摘The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFA0206004,2017YFA0206002,2018YFC0206002,and 2017YFA0403801)National Natural Science Foundation of China(Grant No.81430087)。
文摘Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident x-rays,fewer measurements with sufficient signal-to-noise ratio(SNR)are always anticipated.Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously.In this paper,a method based on a modified compressive sensing algorithm with conjugate gradient descent method(CGDGI)is developed to solve the problems encountered in available XGI methods.Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI.The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.
基金We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number(TURSP-2020/313),Taif University,Taif,Saudi Arabia.
文摘All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.
基金Supported by the National Natural Science Foundation of China (60372057)
文摘A new blind equalization algorithm based on the modified constant modulus algorithm （MCMA） and dithered signederror constant modulus algorithm （DSE-CMA） is proposed. This dithered signed-error MCMA （DSE-MCMA） can not only reduce the computational complexity, but also recover the phase rotation in the complex channel. Simulation results have verified the analysis and indicated the good property of DSE-MCMA.
基金Supported by the Ministerial Level Advanced Research Foundation(40401040302)
文摘To develop the pressure control algorithm for active braking of adaptive cruise control（ACC） system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuators,it is demonstrated that different duty of pulse-width modulation（PWM） signals could control the pressure changing rate of the wheel cylinder.To obtain that signal,a modified proportional-integral-differential（PID） control algorithm is developed using the variable parameter method,the control value reset method,the dead zone method and the integral saturation method.Experimental results show that the delay and overshoot of the pressure response could be reduced considerably using the modified PID algorithm compared with the conventional one.The proposed pressure control algorithm could be used for the further development of the ACC＇s controller.
文摘The dynamics of complex gene regulation systems can be simulated by the Gillespie algorithm. The classic Gillespie algorithm is appropriate to simulate a stochastic
文摘This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.
基金Supported by National Nature Science Foundation of China(Grant No:51275340)
文摘Pipe-routing for ship is formulated as searching for the near-optimal pipe paths while meeting certain objectives in an environment scattered with obstacles. Due to the complex construction in layout space, the great number of pipelines, numerous and diverse design constraints and large amount of obstacles, finding the optimum route of ship pipes is a complicated and time-consuming process. A modified NSGA-II algorithm based approach is proposed to find the near-optimal solution to solve the problem. By simplified equipment models, the layout space is firstly divided into three dimensional （3D） grids to build its mathematical model. In the modified NSGA-II algorithm, the concept of auxiliary point is introduced to improve the search range of maze algorithm （MA） as well as to guarantee the diversity of chromosomes in initial population. Then the fix-length coding mechanism is proposed, Fuzzy set theory is also adopted to select the optimal solution in Pareto solutions. Finally, the effectiveness and efficiency of the proposed approach is demonstrated by the contrast test and simulation. The merit of the proposed algorithm lies in that it can provide more appropriate solutions for the designers while subject certain constrains.
文摘E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.
基金supported by the National Natural Science Foundation of China to Guo-Bo Chen(31771392)Zhejiang Provincial People’s Hospital Research Startup to Guo-Bo Chen(ZRY2018A004)。
文摘Computational efficiency has become a key issue in genomic prediction(GP) owing to the massive historical datasets accumulated. We developed hereby a new super-fast GP approach(SHEAPY) combining randomized Haseman-Elston regression(RHE-reg) with a modified Algorithm for Proven and Young(APY) in an additive-effect model, using the former to estimate heritability and then the latter to invert a large genomic relationship matrix for best linear prediction. In simulation results with varied sizes of training population, GBLUP, HEAPY|A and SHEAPY showed similar predictive performance when the size of a core population was half that of a large training population and the heritability was a fixed value, and the computational speed of SHEAPY was faster than that of GBLUP and HEAPY|A. In simulation results with varied heritability, SHEAPY showed better predictive ability than GBLUP in all cases and than HEAPY|A in most cases when the size of a core population was 4/5 that of a small training population and the training population size was a fixed value. As a proof of concept, SHEAPY was applied to the analysis of two real datasets. In an Arabidopsis thaliana F2 population, the predictive performance of SHEAPY was similar to or better than that of GBLUP and HEAPY|A in most cases when the size of a core population(2 0 0) was 2/3 of that of a small training population(3 0 0). In a sorghum multiparental population,SHEAPY showed higher predictive accuracy than HEAPY|A for all of three traits, and than GBLUP for two traits. SHEAPY may become the GP method of choice for large-scale genomic data.
基金The National Key Research and Development Plan of China(No.2018YFB0505103)the National Natural Science Foundation of China(No.61873064)。
文摘To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90%when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.
基金Supported both by the Teaching and Research Award Fund for Outstanding Young Teachers inHigher Educational Institutions of MOEChinaand by the Dawn Program Fund in Shanghai
文摘In order to find roots of maximal monotone operators, this paper introduces and studies the modified approximate proximal point algorithm with an error sequence {e k} such that || ek || \leqslant hk || xk - [(x)\tilde]k ||\left\| { e^k } \right\| \leqslant \eta _k \left\| { x^k - \tilde x^k } \right\| with ?k = 0￥ ( hk - 1 ) < + ￥\sum\limits_{k = 0}^\infty {\left( {\eta _k - 1} \right)} and infk \geqslant 0 hk = m\geqslant 1\mathop {\inf }\limits_{k \geqslant 0} \eta _k = \mu \geqslant 1 . Here, the restrictions on {η k} are very different from the ones on {η k}, given by He et al (Science in China Ser. A, 2002, 32 (11): 1026–1032.) that supk \geqslant 0 hk = v < 1\mathop {\sup }\limits_{k \geqslant 0} \eta _k = v . Moreover, the characteristic conditions of the convergence of the modified approximate proximal point algorithm are presented by virtue of the new technique very different from the ones given by He et al.
文摘A modified sequential linear programming algorithm is presented, whose subproblem is always solvable, for the extended linear complementarity problem (XLCP), the global convergence of the algorithm under assumption of X-row sufficiency or X-colunm monotonicity is proved. As a result, a sufficient condition for existence and boundedness of solution to the XLCP are obtained.
文摘Contemporarily,the development of distributed generations(DGs)technologies is fetching more,and their deployment in power systems is becom-ing broad and diverse.Consequently,several glitches are found in the recent studies due to the inappropriate/inadequate penetrations.This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system.The assessment procedure is carried out using MATLAB software and Mod-ified Salp Swarm Algorithm(MSSA)that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations.This algorithm modifies two control parameters of the actual SSA algorithm and offers a perfect balance between the exploration and exploitation.Further,the effectiveness of the proposed schemes is assessed using various reliability indices.Also,the available capacity of the extended system is computed for the best configuration of the considered system.The results confirm the level of reli-able operation of the extended DGs along with the standard RTS system.Speci-fically,the overall reliability of the system displays superior performance when the tie lines 1 and 2 of the DG connected with buses 9 and 10,respectively.The reliability indices of this case namely SAIFI,SAIDI,CAIDI,ASAI,AUSI,EUE,and AEUE shows enhancement about 12.5%,4.32%,7.28%,1.09%,4.53%,12.00%,and 0.19%,respectively.Also,a probability of available capacity at the low voltage bus side is accomplished a good scale about 212.07 times/year.
