两参数自型过程的刻划

刻划了两参数自型过程的性质。

多次电离介质的烧蚀波自型解

研究了介质在高温边界作用下的烧蚀热波现象.特别设置了满足普遍热力学关系的多次电离介质状态方程,在烧蚀热波头上设置了强激波条件,应用量纲分析方法,得到了烧蚀邻域的渐近自型解.其显示了烧蚀波结构和各种定标关系.

耗散稀薄气体的动力学模型的L^1渐近稳定性

本文讨论了一种具有非弹性碰撞的麦克斯韦分子模型,首先利用傅立叶变换的方法分析该模型的自型解在特殊范数下的渐近稳定性,然后通过引入的Sobolev空间,证明该自型解在L1范数下的渐近稳定性.

Output Voltage Model and Mechanical-Magnetic Design of Magnetostrictive Vibration Energy Harvester with a Rotating Up-Frequency Structure1

A vibration energy harvester can harvest vibration energy in the environment and convert it into electrical energy to power the sensors in the Internet of Things.Human walking contains high-quality vibration energy,which serves as the energy source for vibration energy harvesters due to its abundant availability,high energy conversion efficiency,and environmental friendliness.It is difficult to harvest human walking vibration due to its low frequency.Converting the low-frequency vibration of human walking into high-frequency vibration has attracted attention.In previous studies,vibration energy harvesters typically increase frequency by raising excitation frequency or inducing free vibration.When walking frequency changes,the up-frequency method of raising the excitation frequency changes the voltage frequency,resulting in the best load resistance change and reducing the output power.The up-frequency method of inducing free vibration does not increase the external excitation frequency,which has relatively low output power.This paper designs a magnetostrictive vibration energy harvester with a rotating up-frequency structure.It consists of a rotating up-frequency structure,a magnetostrictive structure,coils,and bias magnets.The main body of the rotating up-frequency structure comprises a torsion bar and a flywheel with a dumbbell-shaped hole.The magnetostrictive structure includes four magnetostrictive metal sheets spliced by Galfenol and steel sheets.The torsion bar and flywheel interact to convert low-frequency linear vibration into rotating high-frequency excitation vibration of the flywheel.The flywheel plucks the magnetostrictive metal sheet with a high excitation frequency to generate free vibration.The vibration energy harvester increases the excitation frequency while inducing free vibration,which can effectively improve the output power.To characterize the excitation vibration and free vibration,based on the theory of Euler-Bernoulli beam theory,the vibration equation of the magnetostrictive metal sheet after being excited is given.According to the classical machine-magnetic coupling model and the Jiles-Atherton physical model,the relationship between stress and magnetization strength is derived.Combined with Faraday's law of electromagnetic induction,the distributed dynamic output voltage model is established.This model can predict the output voltage at different excitation frequencies.Based on this model,the mechanical-magnetic structural parameter optimization design is carried out.The parameters of the magnetostrictive metal sheet,the bias magnet,and the rotating up-frequency structure are determined.A comprehensive experimental system is established to test the device.The peak-to-peak voltage and output voltage signal by the proposed model are compared.The average relative deviation of the peak-to-peak voltage and the output voltage signal is 4.9%and 8.2%,respectively.The experimental results show that the output power is proportional to the excitation frequency.The optimum load resistance is always 800Ωas the excitation frequency changes,simplifying the impedance-matching process.The maximum peak-to-peak voltage of the device is 58.60 V,the maximum root mean square(RMS)voltage is 9.53 V,and the maximum RMS power is 56.20 mW.The magnetostrictive vibration energy harvester with a rotating up-frequency structure solves the problem of impedance matching,which improves the output power.The proposed distributed dynamic output voltage model can effectively predict the output characteristics.This study can provide structural and theoretical guidance for up-frequency structure vibration energy harvesters for human walking vibration.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

Bearing mechanism of roof and rib support structure in automatically formed roadway and its support design method

Non-pillar mining technology with automatically formed roadway is a new mining method without coal pillar reservation and roadway excavation.The stability control of automatically formed roadway is the key to the successful application of the new method.In order to realize the stability control of the roadway surrounding rock,the mechanical model of the roof and rib support structure is established,and the influence mechanism of the automatically formed roadway parameters on the compound force is revealed.On this basis,the roof and rib support structure technology of confined lightweight concrete is proposed,and its mechanical tests under different eccentricity are carried out.The results show that the bearing capacity of confined lightweight concrete specimens is basically the same as that of ordinary confined concrete specimens.The bearing capacity of confined lightweight concrete specimens under different eccentricities is 1.95 times higher than those of U-shaped steel specimens.By comparing the test results with the theoretical calculated results of the confined concrete,the calculation method of the bearing capacity for the confined lightweight concrete structure is selected.The design method of confined lightweight concrete support structure is established,and is successfully applied in the extra-large mine,Ningtiaota Coal Mine,China.

Vertical human-induced vibration of pedestrian bridge

The double degrees-of-freedom（DOFs）parallel model is adopted to analyze static vertical human-induced vibration with the finite element analysis（FEA）method.In the first-order symmetric vibration mode,the periods of the spring-mass model gradually decrease with the increase in K1 and K2,but they are always greater than the period of the add-on mass model.Meanwhile,the periods of the spring-mass model decrease with the decrease in m1 and m2,but they are always greater than the period of the hollow bridge model.Since the human＇s two degrees-of-freedom vibrate in the same direction as that of the bridge mid-span,the existence of human＇s rigidity leads to the reduction in the rigidity of the spring-mass model.In the second-order symmetric vibration mode,the changes of rigidity K2 and mass m2 result in the disappearance or occurrence of some vibration modes.It can be concluded that compared with the spring-mass model,the results of the add-on mass model lean to lack of safety to the structure;besides,the DOF with a smaller ratio of mass to rigidity plays the chief role in the vibration of the structure.

Applicability of Some Stomatal Models to Natural Conditions

Under natural conditions, the use of vapor pressure deficit between mesophyll cell surface and ambient air ( VPD s ) instead of atmospheric humidity factors in some stomatal models may markedly promote the applicability of stomatal models. It has been pointed out from theoretical analysis that the expression of the responses of stomatal conductance to VPD s is equivalent to the expression of responses of stomatal conductance to water loss of transpiration in stomatal models.

Applicahon of a Simplified Model Reference Adaptive Algorithm

The application of a simplifed model reference adaptive control(SMRAC) on a typical Pump controlled motor electrohydraulic servo system is studied here. The algorithm of first-order scalar SMRAC ac second-order vector SMRAC are derived. Computer simulations of the algorithms are presented. Experimental results prove that the method of control adopted here perform satisfactorily over a wide range of operating conditions.

A comparative study of the denoising methods of Thematic Mapper images for forest areas

The noises of remote sensing images, caused by imaging system and ground environment, negatively affect the accuracy and efficiency in extracting forest information from remote sensing images. The denoising is critical for image classifications for forest areas. The objective of this research is to assess the effectiveness of currently used spatial filtering methods for extracting with forest information related from Landsat 5 TM images. Five spatial filtering methods including low-pass filter, median filter, mean filter, sigma filter and enhanced self-adaptive filter were examined. A set of evaluation indices was designed to assess the ability of each denoising method for flatness, edge/boundary retention and enhancement. Based on the designed evaluation indices and visual assessment, it was found that sigma filter （D=1） and enhanced self-adaptive filter were the most effective denoising methods in classifying TM images for forest areas.

Adaptive topology learning of camera network across non-overlapping views

An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.

River channel flood forecasting method of coupling wavelet neural network with autoregressive model

Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.

MODEL REFERENCE ADAPTIVE CONTROL BASED ON NONLINEAR COMPENSATION FOR TURBOFAN ENGINE

The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.

Analysis and Modeling of the Central Air-Conditioning System in Intelligent Buildings

The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed and a type of periodically autoregressive model (PAR) based on the improved genetic algorithms (IGA) were used to perform the optimum energy saving scheduling. The example of the Liangmahe Plaza was taken to show the effectiveness of the methods.

DIRECT SELF-REPAIRING CONTROL FOR HELICOPTER VIA QUANTUM CONTROL AND ADAPTIVE COMPENSATOR

A direct self-repairing control approach is proposed for helicopter via quantum control techniques and adaptive compensator when some complex faults occur. For a linear varying-parameter helicopter control system, the model reference adaptive control law is designed and an adaptive compensator is used for improving its self-re- pairing capability. To enhance anti-interference capability of helicopter, quantum control feedforward is added be- tween fault and disturbance. Simulation results illustrate the effectiveness and feasibility of the approach.

Principle, Modeling and Control of DC-DC Convertors for EV

DC DC convertors can convert the EV's high voltage DC power supply into the low voltage DC power supply. In order to design an excellent convertor one must be guided by theory of automatic control. The principle and the method of design, modeling and control for DC DC convertors of EV are introduced. The method of the system response to a unit step function input and the frequency response method are applied to researching the convertor's mathematics model and control characteristic. Experiments show that the designed DC DC convertor's output voltage precision is high, the antijamming ability is strong and the adjustable performance is fast and smooth.

A Recursive Algorithm for Offspring's Genotype Frequency of Selfing Population on Multiple Alleles with Limited Loci

This paper studies the offspring＇s genotype frequency of the selfing population on multiple alleles with limited loci.A recursive algorithm is given for it.It is discovered that the genotype frequency of homozygous gene of limited loci increases by generations.Relative increment reduces by generations and the genotype frequency tends to a definite value finally.The genotype frequency of limited loci with hybrid gene tends to 0 finally.But it is possibility that the genotype frequency increases in previous generations then reduces later.It is found that the number of the hybrid gene are more,the speeds tending to 0 are quicker.

Analysis and Optimum Design of Differential Inductors Using Distributed Capacitance Model

A distributed capacitance model for monolithic inductors is developed to predict the equivalently parasitical capacitances of the inductor.The ratio of the self-resonant frequency (f SR) of the differential-driven symmetric inductor to the f SR of the single-ended driven inductor is firstly predicted and explained.Compared with a single-ended configuration,experimental data demonstrate that the differential inductor offers a 127% greater maximum quality factor and a broader range of operating frequencies.Two differential inductors with low parasitical capacitance are developed and validated.

高原练兵——火炮齐射 剑指苍穹

祁连之南,横断以西平均海拔达4000米以上,这里是'飞起玉龙三百万'的世界屋脊,也是'鸟飞绝、人踪灭'的生命禁区。就在这里从初夏到深秋、到寒冬,我军多型坦克、步战车、自型火炮、火箭炮,上演着一幕幕实战实训的壮阔图景。