改进粒子群优化在稳定平台多空间分析模型的应用

常规伺服系统根据电机轴系转动进行模型分析,以轴系所在的基座空间作为参照系。稳定平台的被控量以惯性空间作为参照系,因此不适合用常规伺服系统模型来建模。针对稳定平台的多参照系问题,文章采用以惯性空间作为电机轴系转动参照系的多空间分析模型,并将改进粒子群算法应用于该模型。粒子群算法作为一种群智能算法,广泛应用于参数优化。文中通过惯性权重改进和越界改进,利用改进后的粒子群算法进行稳定平台PID参数的优化和整定。通过仿真和硬件实验平台验证,结果表明:在稳定平台多空间分析模型基础之上,采用改进粒子群算法优化后的PID控制器可以使稳定平台有更高的稳定精度、更好的鲁棒性,有效地隔离了外部的震动和干扰。

韵律相关的汉语语音识别系统研究

首先,给出结合韵律信息的系统框架。然后,针对汉语的特点,解决了韵律相关的语音识别系统中建模单元选择、模型训练等问题,并在多空间概率分布隐马尔可夫模型(multiple-space distribution hidden Markov mod-el,MSD-HMM)框架下构建了韵律相关的语音识别系统。最后,通过语音识别的实验验证了方法的有效性。在"863"测试集上,该方法能够达到76.18%的带调音节识别正确率。

煤气机

[篇名 ] 1 kW residential gas engine cogeneration system, [篇名] A class of engine-uity, [篇名] A numerical study on combustion and exhaust gas emissions characteristics of a dual-fuel natural gas engine using a multi-dimensional model combined with detailed kinetics, [ 篇名 ] Acquisitions, Product Development Fuel Arrow Engine's Growth, [ 篇名 ] An emission and performance comparison of the natural gas Cummins Westport Inc. C-Gas Plus versus diesel in heavy- duty trucks, [ 篇名 ] Analysis and visualization of compressed natural gas spray under high temperature and pressure conditions, [篇名] Augmentation of cooling output by silica gel-water adsorption cycle utilizing the waste heat of GHP - optimization of heat and mass transfer characteristics for adsorber model.

Prediction of Soil Depth from Digital Terrain Data by Integrating Statistical and Visual Approaches

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models （DEMs） with geographic information systems （GIS） and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the A1-Muwaqqar Watershed （70 km2） in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.

A high-level synthesis based dual-module redundancy with multi-residue detection(DMR-MRD)fault-tolerant method for on-board processing satellite communication systems

On board processing(OBP) satellite systems have obtained more and more attentions in recent years because of their high efficiency and performance.However,the OBP transponders are very sensitive to the high energy particles in the space radiation environments.Single event upset(SEU)is one of the major radiation effects,which influences the satellite reliability greatly.Triple modular redundancy(TMR) is a classic and efficient method to mask SEUs.However,TMR uses three identical modules and a comparison logic,the circuit size becomes unacceptable,especially in the resource limited environments such as OBP systems.Considering that,a new SEU-tolerant method based on residue code and high-level synthesis(HLS) is proposed,and the new method is applied to FIR filters,which are typical structures in the OBP systems.The simulation results show that,for an applicable HLS scheduling scheme,area reduction can be reduced by 48.26%compared to TMR,while fault missing rate is 0.15%.

Spatial Dynamics of Cropland and Cropping Pattern Change Analysis Using Landsat TM and IRS P6 LISS III Satellite Images with GIS

The study of the spatial patterns and temporal changes of cropland is important to understand the underlying factors and the functional effects of the agricultural landscape. On the other hand, crop dynamics mapping is essential to know the overall agro-spatial diversity of the area. Therefore, this paper addressed a spatio-temporal analysis of cropland and cropping pattern change in the Bogra district of Bangladesh over the last 16 years （between 1988/89 and 2004/05）. In this paper, crop mapping from multi-temporal and multi-sensor satellite images was described. Landsat TM and IRS P6 LlSS Ⅲ satellite images were used with GIS for spatial dynamics of cropland and cropping pattern change analysis. First, seasonal cropland maps were derived from object-based classification of satellite images, then two-date classified image differencing with GIS overlay technique and decision rules were applied. Cropping pattern change was analyzed in a spatial and quantitative way for the 16 years and for this, Integrated Land and Water Information System （ILWIS） and Land Change Modular （LCM） of IDRISl Andes were used. The results showed that in the area, mono crop cultivation was found in summer, but in winter, areas under different crop cultivation had changed dramatically. Change analysis showed that the changes mainly occurred in the north northwest and southwest of the areas, and during the time the highest change area was found under the rice-potato pattern.

Combining spatial modelling and radar to identify and protect avian migratory hot-spots

Migrating landbirds are known to follow coast lines and concentrate on peninsulas prior to crossing water bodies, es- pecially during daylight but also at night, creating enhanced potential collision hazards with man-made objects. Knowing where these avian migration ＂hot-spots＂ occur in time and space is vital to improve flight safety and inform the spatial planning process （e.g. environmental assessments for offshore windfarms）. We developed a simple spatial model to identify avian migration hot- spots in coastal areas based on prevailing migration orientation and coastline features known, from visual and radar observations, to concentrate migrating landbirds around land masses. Regional scale model validation was achieved by combining nocturnal passerine movement data gathered from two tier radar coverage （long-range dual-polarization Doppler weather radar and short- range marine surveillance radar） and standardised bird ringing. Applied on a national scale, the model correctly identified the ten most important Danish coastal hot-spots for spring migrants and predicted the relative numbers of birds that concentrated at each site. These bird numbers corresponded well with historical observational data. Here, we provide a potential framework for the es- tablishment of the first three-dimensional avian airspace sanctuaries, which could contribute to more effective conservation of long-distance migratory birds [Current Zoology 60 （5）： 680-691, 2014].

A reduced-order-model-based multiple-in multiple-out gust alleviation control law design method in transonic flow

Gust alleviation is very important to a large flexible aircraft.A nonlinear low-order aerodynamic state space model is required to model the nonlinear aeroelastic responses due to gust.Based on the proper orthogonal decomposition method,a reduced order modeling of gust loads was proposed.And then the open-loop and closed-loop reduced order state space model for the transonic aeroelastic system was developed.The static output feed back control scheme was used to design a simple multiple-in multiple-out(MIMO)gust alleviation control law.The control law was demonstrated with the Goland+wing model with four control surfaces.The simulation results of different discrete gusts show the capability and good performance of the designed MIMO controller in transonic gust alleviation.

Planning for space station long-duration orbital mission under multi-constraints

An integrated nonlinear planning(NLP) model is built for space station long-duration orbital missions considering both the vehicle visiting schedules and the interaction effects between target phasing,vehicle return adjusting and Earth observation aiming.A two-level optimization approach is proposed to solve this complicated problem.The up-level problem employs the launch times of visiting vehicles as design variables,considers the constraints of crew rotations,resource resupplies and rendezvous launch windows,and is solved by a genetic algorithm.The low-level problems employ the maneuver impulses and burn times within each orbital mission as design variables,and a high-efficient shooting iteration method is proposed based on an analytical equation for the phase angle correction considering the J 2 perturbation.The results indicate that the integrated NLP model for space station long-duration orbital missions is effective,and the proposed optimization approach can obtain the optimal solutions that satisfy the multiple constraints and reduce the total propellant consumption.

Numerical Simulation for Thermal Distribution of Air-conditioner Outdoor Units

Air-conditioners used in high-rise residential or office buildings often have outdoor units installed on the side-walls or roofs in the confined space of the buildings.The heat released from the outdoor units causes the tem-perature rise in the confined installation space and leads to uncomfortable thermal environment and deteriorative performance of air-conditioners.The factors affecting the performance of air-conditioners are the heat released from the outdoor units,the solar radiation and the ventilation of the confined installation space,which are inves-tigated in this study and simulated by FLUENT using the porous model and discrete ordinates (DO) radiation model.It is proved that the porous model is more reliable than the solid model.The optimal installation distance from the supporting wall is obtained.The average temperature of the exit without wind is 1.18% higher than that with wind.The results show that the heat released from the outdoor units and the ventilation of the confined in-stallation space are the main factors affecting the thermal environment in the confined installation space.The in-fluence of the solar radiation is negligible.

A two-stage parametric subspace model for efficient contrast-preserving decolorization

The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete searching solver has a great potential in its c6nversion ability. In this paper, we present a two-step strategy to efficiently extend the parameter searching solver to a two-order multivariance polynomial model, as a sum of three subspaces. We show that the first subspace in the two-order model is the most important and the second one can be seen as a refinement. In the first stage of our model, the gradient correlation similarity （Gcs） measure is used on the first subspace to obtain an immediate grayed image. Then, Gcs is applied again to select the optimal result from the immettiate grayed image plus the second subspace-induced candidate images. Experimental results show the advantages of the proposed approach in terms of quantitative evaluation, qualitative evaluation, and algorithm complexity.

A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field

The texture analysis is often discussed in image processing domain, but most methods are limited within gray-level image or color image, and the present conception of texture is defined mainly based on gray-level image of single band. One of the essential characters of remote sensing image is multidimensional or even high-dimensional, and the traditional texture conception cannot contain enough information for these. Therefore, it is necessary to pursuit a proper texture definition based on remote sensing images, which is the first discussion in this paper. This paper describes the mapping model of spectral vector in two-dimensional image space using Markov random field (MRF), establishes a texture model of multiband remote sensing image based on MRF, and analyzes the calculations of Gibbs potential energy and Gibbs parameters. Further, this paper also analyzes the limitations of the traditional Gibbs model, prefers a new Gibbs model avoiding estimation of parameters, and presents a new texture segmentation algorithm for hy-perspectral remote sensing image later.

A simple construction of optimal estimation in multivariate marginal Cox regression

The multivariate extension of the Cox model proposed by Wei,Lin and Weissfeld in 1989 has been widely used for analyzing multivariate survival data.Under the model assumption,failure times from an individual are assumed to marginally follow their respective proportional hazards regression relation,leaving the joint distribution completely unspecified.This paper presents a simple approach to efficiency improvement through segmentation of stochastic integrals in the marginal estimating equations and incorporation of the limiting covariance structure.It is shown that when partition of the time interval is done at a suitable rate,the resulting estimator is consistent and asymptotically normal.Through the reproducing kernel Hilbert space arising from the covariance function of the limiting Gaussian process,it is also shown that the proposed estimator is asymptotically optimal within a reasonable class of estimators under marginal specification.Simulations are conducted to assess the finite-sample performance of the proposed method.

Bi-dimension decomposed hidden Markov models for multi-person activity recognition

We present a novel model for recognizing long-term complex activities involving multiple persons. The proposed model, named ‘decomposed hidden Markov model’ (DHMM), combines spatial decomposition and hierarchical abstraction to capture multi-modal, long-term dependent and multi-scale characteristics of activities. Decomposition in space and time offers conceptual advantages of compaction and clarity, and greatly reduces the size of state space as well as the number of parameters. DHMMs are efficient even when the number of persons is variable. We also introduce an efficient approximation algorithm for inference and parameter estimation. Experiments on multi-person activities and multi-modal individual activities demonstrate that DHMMs are more efficient and reliable than familiar models, such as coupled HMMs, hierarchical HMMs, and multi-observation HMMs.

A multigroup model for cholera dynamics and control

We propose a general multigroup model for cholera dynamics that involves both direct and indirect transmission pathways and that incorporates spatial heterogeneity. Under biologically feasible conditions, we show that the basic reproduction number R0 remains a sharp threshold for cholera dynamics in multigroup settings. We verify the analysis by numerical simulation results. We also perform an optimal control study to explore optimal vaccination strategy for cholera outbreaks.