Research on fast detection method of infrared small targets under resourceconstrained conditions

Infrared small target detection is a common task in infrared image processing.Under limited computa⁃tional resources.Traditional methods for infrared small target detection face a trade-off between the detection rate and the accuracy.A fast infrared small target detection method tailored for resource-constrained conditions is pro⁃posed for the YOLOv5s model.This method introduces an additional small target detection head and replaces the original Intersection over Union(IoU)metric with Normalized Wasserstein Distance(NWD),while considering both the detection accuracy and the detection speed of infrared small targets.Experimental results demonstrate that the proposed algorithm achieves a maximum effective detection speed of 95 FPS on a 15 W TPU,while reach⁃ing a maximum effective detection accuracy of 91.9 AP@0.5,effectively improving the efficiency of infrared small target detection under resource-constrained conditions.

线性规划内点法

现代科学技术和现代管理是推动经济发展的两个车轮.随着我国经济的发展,现代管理科学也有了高速发展并成为当前年青人择业(学)的一个热门.在现代管理科学中有一门主课,称为“运筹学”.随着计算机的发展,解决了运筹学中大量、复杂的数学计算问题,因而又促进了运筹学在理论和应用上的发展.线性规划内点法是当前国际运筹学界的热点课题,为便于广大管理干部和各专业技术人员了解现代管理科学中的这一新进展,我们发表了武汉化工学院胡清淮教授承编写讲义的第九章.因篇幅较长,分期载出.

线性规划内点法(续)

1≤j≤nx1=x°+ap△y=（2.999,0.433,1.000,2.567,0.001.15.866）Ty1=y°+aD△z=（1.015,1.098,-1.293,0.082）Tz1=z°+aD△x=（0.001,2.209,1.721,0.373,17.374,0.082）Td=（x1）Tzl=4.940,继续迭代.迭代2:由于以下的计算都是重复送代1的计算过程,故下面只列出每次迭代计算的有关参数和最后结果.

线性规划内点法(续)

现代科学技术和现代管理是推动经济发展的两个车轮.随着我国经济的发展,现代管理科学也有了高速发展并成为当前年前人择业(学)的一个热门.在现代管理科学中有一门主课,称为“运筹学”.随着计算机的发展,解决了运筹学中大量、复杂的数学计算问题.因而又促进了运筹学在理论和应用上的发展.线性规划内点法是当前国际运筹学界的热点课题,为便于广大管理干部和各专业技术人员了解现代管理科学中的这一新进展,我们发表了武汉化工学院胡清淮教授承编讲义的第九章.因篇幅较长.分期载出.

A non-monotone Phase-1 method in linear programming

To gain superior computational efficiency, it might be necessary to change the underlying philosophy of the simplex method. In this paper, we propose a Phase-1 method along this line. We relax not only the conventional condition that some function value increases monotonically, but also the condition that all feasible variables remain feasible after basis change in Phase-1. That is, taking a purely combinatorial approach to achieving feasibility. This enables us to get rid of ratio test in pivoting, reducing computational cost per iteration to a large extent. Numerical results on a group of problems are encouraging.

An Evolutionary Programming Based on Hidden Neuron Modifiable Radial Basis Function Networks

In this paper, an improved radial basis function networks named hidden neuron modifiable radial basis function (HNMRBF) networks is proposed for target classification, and evolutionary programming (EP) is used as a learning algorithm to determine and modify the hidden neuron of HNMRBF nets. The result of passive sonar target classification shows that HNMRBF nets can effectively solve the problem of traditional neural networks, i. e. learning new target patterns on line will cause forgetting of the old patterns.

Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm

To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.

Discontinuous flying particle swarm optimization algorithm and its application to slope stability analysis

A new version of particle swarm optimization(PSO) called discontinuous flying particle swarm optimization(DFPSO) was proposed,where not all of the particles refreshed their positions and velocities during each iteration step and the probability of each particle in refreshing its position and velocity was dependent on its objective function value.The effect of population size on the results was investigated.The results obtained by DFPSO have an average difference of 6% compared with those by PSO,whereas DFPSO consumes much less evaluations of objective function than PSO does.

Performance analysis and improvement of geothermal binary cycle power plant in oilfield

In order to improve the efficiency of a geothermal power plant, oil wells in the high water cut stage were used as geothermal wells, thereby improving the recovery ratio and economic benefit. A new function that reflects both the technical and economic performances was put forward and used as the objective function. An organic Rankine cycle (ORC) was analyzed through the energetic and exergetic analyses, and the reasons for low efficiency were pinpointed. Results indicate that geothermal water directly transferring heat to the working fluid reduces energy dissipation and increases cycle efficiencies. The net power output with an internal heat exchanger (IHE) is averagely 5.3% higher than that without an IHE. R601a and R601 can be used to replace R123 for geothermal water below 110℃. Moreover, the modified ORC dramatically outperforms the actual one.

Back analysis for soil slope based on measuring inclination data

Based on the analysis of several objective functions,a new method was proposed.Firstly,the feature of the inclination curve was analyzed.On this basis,the soil could be divided into several blocks with different displacements and deformations.Then,the method of the soil division was presented,and the characteristic of single soil block was studied.The displacement of the block had two components:sliding and deformation.Moreover,a new objective function was constructed according to the deformation of the soil block.Finally,the sensitivities of the objective functions by traditional method and the new method were calculated,respectively.The result shows that the new objective function is more sensitive to mechanical parameters and the inversion result is close to that obtained by the large direct shear apparatus.So,this method can be used in slope back analysis and its effectiveness is proved.

A compound objective reconfiguration of distribution networks using hierarchical encoded particle swarm optimization

With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.

A multi-objective gravitational search algorithm based approach of power system stability enhancement with UPFC

On the basis of the theoretical analysis of a single-machine infinite-bus （SMIB）, using the modified linearized Phil- lips-Heffron model installed with unified power flow controller （UPFC）, the potential of the UPFC supplementary controller to enhance the dynamic stability of a power system is evaluated by measuring the electromechanical controllability through singular value decomposition （SVD） analysis. This controller is tuned to simultaneously shift the undamped electromeehanical modes to a prescribed zone in the s-plane. The problem of robust UPFC based damping controller is formulated as an optimization problem according to the eigenvalue-based multi-objective function comprising the damping factor, and the damping ratio of the undamped electromechanical modes to be solved using gravitational search algorithm （GSA） that has a strong ability to find the most optimistic results. The different loading conditions are simulated on a SMIB system and the rotor speed deviation, internal voltage deviation, DC voltage deviation and electrical power deviation responses are studied with the effect of this flexible AC transmission systems （FACTS） controller. The results reveal that the tuned GSA based UPFC controller using the proposed multi-objective function has an excellent capability in damping power system with low frequency oscillations and greatly enhances the dynamic stability of the power systems.

Reflection full-waveform inversion using a modified phase misfit function

Reflection full-waveform inversion （RFWI） updates the low- and high- wavenumber components, and yields more accurate initial models compared with conventional full-waveform inversion （FWI）. However, there is strong nonlinearity in conventional RFWI because of the lack of low-frequency data and the complexity of the amplitude. The separation of phase and amplitude information makes RFWI more linear. Traditional phase-calculation methods face severe phase wrapping. To solve this problem, we propose a modified phase-calculation method that uses the phase-envelope data to obtain the pseudo phase information. Then, we establish a pseudophase-information-based objective function for RFWI, with the corresponding source and gradient terms. Numerical tests verify that the proposed calculation method using the phase-envelope data guarantees the stability and accuracy of the phase information and the convergence of the objective function. The application on a portion of the Sigsbee2A model and comparison with inversion results of the improved RFWI and conventional FWI methods verify that the pseudophase-based RFWI produces a highly accurate and efficient velocity model. Moreover, the proposed method is robust to noise and high frequency.

Design and optimization in multiphase homing trajectory of parafoil system

In order to realize safe and accurate homing of parafoil system,a multiphase homing trajectory planning scheme is proposed according to the maneuverability and basic flight characteristics of the vehicle.In this scenario,on the basis of geometric relationship of each phase trajectory,the problem of trajectory planning is transformed to parameter optimizing,and then auxiliary population-based quantum differential evolution algorithm(AP-QDEA)is applied as a tool to optimize the objective function,and the design parameters of the whole homing trajectory are obtained.The proposed AP-QDEA combines the strengths of differential evolution algorithm(DEA)and quantum evolution algorithm(QEA),and the notion of auxiliary population is introduced into the proposed algorithm to improve the searching precision and speed.The simulation results show that the proposed AP-QDEA is proven its superior in both effectiveness and efficiency by solving a set of benchmark problems,and the multiphase homing scheme can fulfill the requirement of fixed-points and upwind landing in the process of homing which is simple in control and facile in practice as well.

A new algorithm for estimating gillnet selectivity

The estimation of gear selectivity is a critical issue in fishery stock assessment and management.Several methods have been developed for estimating gillnet selectivity,but they all have their limitations,such as inappropriate objective function in data fitting,lack of unique estimates due to the difficulty in finding global minima in minimization,biased estimates due to outliers,and estimations of selectivity being influenced by the predetermined selectivity functions.In this study,we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet selectivity.The proposed algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers.According to the estimated gillnet selectivity curve,the selectivity function can also be determined.This study suggests that the proposed algorithm is not sensitive to outliers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool.We suggest the proposed approach be used in estimating gillnet selectivity.

EXPLORATION ON METHOD OF AUTO-CLASSIFICATION FOR MAIN GROUND OBJECTS OF THREE GORGES RESERVOIR AREA

Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting the main types of ground objects in the Three Gorges Reservoir area under relatively high accuracy, after finishing such preprocessing tasks as correcting the topographical spectrum and synthesizing the data. Taking the specialized image analysis software-eCognition as the platform, the research achieved the goal of classifying through choosing samples, picking out the best wave bands, and producing the identifying functions. At the same time the extraction process partly dispelled the influence of such phenomena as the same thing with different spectrums, different things with the same spectrum, border transitions, etc. The research did certain exploration in the aspect of technological route and method of using automatic extraction of the remote sensing image to obtain the information of land cover for the regions whose ground objects have complicated spectrums.

Reactor Network Synthesis Based on Instantaneous Objective Function Characteristic Curves

It is believed that whether the instantaneous objective function curves of plug-flow-reactor (PFR) and continuous-stirred-tank-reactor (CSTR) overlap or not, they have a consistent changing trend for complex reactions (steady state, isothermal and constant volume). As a result of the relation of the objective functions (selectivity or yield) to the instantaneous objective functions (instantaneous selectivity or instantaneous reaction rate), the optimal reactor network configuration can be determined according to the changing trend of the instantaneous objective function curves. Further, a recent partition strategy for the reactor network synthesis based on the instantaneous objective function characteristic curves is proposed by extending the attainable region partition strategy from the concentration space to the instantaneous objective function-unreacted fraction of key reactant space. In this paper, the instantaneous objective function is closed to be the instantaneous selectivity and several samples are examined to illustrate the proposed method. The comparison with the previous work indicates it is a very convenient and practical systematic tool of the reactor network synthesis and seems also promising for overcoming the dimension limit of the attainable region partition strategy in the concentration space.

Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm

In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated,based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress-strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.

NSGA Ⅱ based multi-objective homing trajectory planning of parafoil system

Homing trajectory planning is a core task of autonomous homing of parafoil system.This work analyzes and establishes a simplified kinematic mathematical model,and regards the homing trajectory planning problem as a kind of multi-objective optimization problem.Being different from traditional ways of transforming the multi-objective optimization into a single objective optimization by weighting factors,this work applies an improved non-dominated sorting genetic algorithm Ⅱ(NSGA Ⅱ) to solve it directly by means of optimizing multi-objective functions simultaneously.In the improved NSGA Ⅱ,the chaos initialization and a crowding distance based population trimming method were introduced to overcome the prematurity of population,the penalty function was used in handling constraints,and the optimal solution was selected according to the method of fuzzy set theory.Simulation results of three different schemes designed according to various practical engineering requirements show that the improved NSGA Ⅱ can effectively obtain the Pareto optimal solution set under different weighting with outstanding convergence and stability,and provide a new train of thoughts to design homing trajectory of parafoil system.

Optimization method of fi rst-arrival waveform inversion based on the L-BFGS algorithm

The fi rst arrival waveform inversion(FAWI)has a strong nonlinearity due to the objective function using L2 parametrization.When the initial velocity is not accurate,the inversion can easily fall into local minima.In the full waveform inversion method,adding a cross-correlation function to the objective function can eff ectively reduce the nonlinearity of the inversion process.In this paper,the nonlinearity of this process is reduced by introducing the correlation objective function into the FAWI and by deriving the corresponding gradient formula.We then combine the first-arrival wave travel-time tomography with the FAWI to form a set of inversion processes.This paper uses the limited memory Broyden-Fletcher-Goldfarb-Shanno(L-BFGS)algorithm to improve the computational effi ciency of inversion and solve the problem of the low effi ciency of the FAWI method.The overthrust model and fi eld data test show that the method used in this paper can eff ectively reduce the nonlinearity of inversion and improve the inversion calculation effi ciency at the same time.