Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

Feature mapping space and sample determination for person re-identification

Person re-identification(Re-ID) is integral to intelligent monitoring systems.However,due to the variability in viewing angles and illumination,it is easy to cause visual ambiguities,affecting the accuracy of person re-identification.An approach for person re-identification based on feature mapping space and sample determination is proposed.At first,a weight fusion model,including mean and maximum value of the horizontal occurrence in local features,is introduced into the mapping space to optimize local features.Then,the Gaussian distribution model with hierarchical mean and covariance of pixel features is introduced to enhance feature expression.Finally,considering the influence of the size of samples on metric learning performance,the appropriate metric learning is selected by sample determination method to further improve the performance of person re-identification.Experimental results on the VIPeR,PRID450 S and CUHK01 datasets demonstrate that the proposed method is better than the traditional methods.

Sequential Approximation of Functions in Sobolev Spaces Using Random Samples

We present an iterative algorithm for approximating an unknown function sequentially using random samples of the function values and gradients. This is an extension of the recently developed sequential approximation (SA) method, which approximates a target function using samples of function values only. The current paper extends the development of the SA methods to the Sobolev space and allows the use of gradient information naturally. The algorithm is easy to implement, as it requires only vector operations and does not involve any matrices. We present tight error bound of the algorithm, and derive an optimal sampling probability measure that results in fastest error convergence. Numerical examples are provided to verify the theoretical error analysis and the effectiveness of the proposed SA algorithm.

Dynamic batch selective sampling based on version space analysis

A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classification boundary; meanwhile, within a batch, examples labeled previously fail to provide instructive information for the selection of the rest. As a result, using the examples selected in batch mode for model refinement will jeopardize the classification performance. Based on the duality between feature space and parameter space under the SVM active learning fi：amework, dynamic batch selective sampling is proposed to address the problem. We select a batch of examples dynamically, using the examples labeled previously as guidance for further selection. In this way, the selection of feedback examples is determined by both the existing classification model and the examples labeled previously. Encouraging experimental results demonstrate the effectiveness of the proposed algorithm.

Effect of sample diameter on primary dendrite spacing of directionally solidified Al-4%Cu alloy

The relationship between primary dendrite arm spacing and sample diameter was studied during directional solidification for Al-4%Cu (mass fraction) alloy. It is shown that primary dendrite spacing is decreased with the decrease of the sample diameter at given growth rate. By regressing the relationship between primary dendrite arm spacing and the growth rate, the primary dendrite arm spacing complies with 461.76v-0.53, 417.92v-0.28 and 415.83v-0.25 for the sample diameter of 1.8, 3.5 and 7.2 mm, respectively. The primary dendrite spacing, growth rate and thermal gradient for different sample diameters comply with 28.77v-0.35G-0.70, 23.17v-0.35G-0.70 and 23.84v-0.35G-0.70, respectively. They are all consistent with the theoretical model λ1 =k b v-a1G-b1, and b1/a1=2. By analyzing the experimental results with classical models, it is shown that KURZ-FISHER model fits for the primary dendrite spacing in smaller sample diameters with weaker thermosolute convection. Whereas TRIVEDI model is suitable for describing primary dendrite arm spacing with a larger diameter (d>2 mm) where convection should be considered.

Reconstruction of sampling in shift-invariant space using generalized inverse

A method that attempts to recover signal using generalized inverse theory is presented to obtain a good approximation of the signal in reconstruction space from its generalized samples. The proposed approaches differ with the assumptions on reconstruction space. If the reconstruction space satisfies one-to-one relationship between the samples and the reconstruction model, then we propose a method, which achieves consistent signal reconstruction. At the same time, when the number of samples is more than the number of reconstruction functions, the minimal-norm reconstruction signal can be obtained. Finally, it is demonstrated that the minimal-norm reconstruction can outperform consistent signal reconstruction in both theory and simulations for the problem.

Compressive sampling and reconstruction in shift-invariant spaces associated with the fractional Gabor transform

In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling and accurate reconstruction for chirp-like signals containing time-varying characteristics.Under the proposed scheme,we introduce the fractional Gabor transform to make a stable expansion for signals in the joint time-fractional-frequency domain.Then the compressive sampling and reconstruction system is constructed under the compressive sensing and shift-invariant space theory.We establish the reconstruction model and propose a block multiple response extension of sparse Bayesian learning algorithm to improve the reconstruction effect.The reconstruction error for the proposed system is analyzed.We show that,with considerations of noises and mismatches,the total error is bounded.The effectiveness of the proposed system is verified by numerical experiments.It is shown that our proposed system outperforms the other systems state-of-the-art.

Adaptive Sampling for Near Space Hypersonic Gliding Target Tracking

For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.

Method to generate training samples for neural network used in target recognition

Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.

Regular and Irregular Sampling Linear Transforms in Series of Shift-Invariant Spaces

In this article we show that there exists an analogue of the Fourier duality technique in the setting a series of shift-invariant spaces.Really,every a series shift-invariant spaceΣ^𝑛𝑖=1𝑉𝜙𝑖𝑖with a stable generator^𝑛𝑖=1𝜙𝑖is the range space of a bounded one-to-one linear operator𝑇𝑇between𝐿𝐿2(0,1)and𝐿𝐿2(R).We show regular and irregular sampling formulas inΣ𝑛𝑛𝑖𝑖=1𝑉𝑉𝜙𝜙𝑖𝑖are obtained by transforming.

Asymmetric Multi-channel Sampling in a Series of Shift Invariant Spaces

We show asymmetric multi-channel sampling on a series of a shift invariant spaces ∑a^m=1v（φ（ta）） with a series of Riesz generators ∑a^m=1φ（ta） in L2（R）, where each channeled signal is assigned a uniform but distinct sampling rate. We use Fourier duality between ∑a^m=1v（φ（ta））and L2[0, 2π] to find conditions under which there is a stable asymmetric multi-channel sampling formula on ∑a^m=1v（φ（ta））.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Adaptive Space Expansion for Fast Motion Planning

The sampling process is very inefficient for sam-pling-based motion planning algorithms that excess random sam-ples are generated in the planning space.In this paper,we pro-pose an adaptive space expansion(ASE)approach which belongs to the informed sampling category to improve the sampling effi-ciency for quickly finding a feasible path.The ASE method enlarges the search space gradually and restrains the sampling process in a sequence of small hyper-ellipsoid ring subsets to avoid exploring the unnecessary space.Specifically,for a con-structed small hyper-ellipsoid ring subset,if the algorithm cannot find a feasible path in it,then the subset is expanded.Thus,the ASE method successively does space exploring and space expan-sion until the final path has been found.Besides,we present a particular construction method of the hyper-ellipsoid ring that uniform random samples can be directly generated in it.At last,we present a feasible motion planner BiASE and an asymptoti-cally optimal motion planner BiASE*using the bidirectional exploring method and the ASE strategy.Simulations demon-strate that the computation speed is much faster than that of the state-of-the-art algorithms.The source codes are available at https://github.com/shshlei/ompl.

Space-time Dynamics of Dendroctonus valens Population in China and Spatial Sampling Technique based on Its Spatial Distribution Pattern

Red turpentine beetle （RTB）, Dendroctongs valens LeConte, is a destructive forest invasive species in China, it mainly attacks Pings tabuliformis and P. bungeana. So far it has spread rapidly to the provinces of Shanxi, Hebei, Henan, Shanxi and Beijing since its first outbreak in Shanxi Province in 1998, and has caused extensive tree mortality. Space-time dynamics of D. valens population and spatial sampling technique based on its spatial distribution pattern were ana- lyzed using geostatistical methods in the pure P. tabuliforis forests and mixedwood stands which were at different damage levels. According to the spatial distribu- tion of D. valeas population, the specific spatial sampling technique was also studied, and then was compared with traditional sampling technique. The spatial sam- piing technique combined with sampling theory and the biological characteristics of D. valens population, which not only could calcnlate the error of the sampling, but also could discuss the optimal sampling number and the optimum size of plot according to different damage levels and different stand types. This helps to explain population expansion and colonization mechanism of D. valens, and to provide a good reference for adopting snitable control measures.

Balloon-based exposed payload designed for astrobiological research in Earth’s near space

Earth’s near space,located in the region between 20 and 100 km above sea level,is characterized by extreme conditions,such as low temperature,low atmospheric pressure,harsh radiation,and extreme dryness.These conditions are analogous to those found on the surface of Mars and in the atmosphere of Venus,making Earth’s near space a unique natural laboratory for astrobiological research.To address essential astrobiological questions,teams from the Chinese Academy of Sciences(CAS)have developed a scientific balloon platform,the CAS Balloon-Borne Astrobiology Platform(CAS-BAP),to study the effects of near space environmental conditions on the biology and survival strategies of representative organisms in this terrestrial analog.Here,we describe the versatile Biological Samples Exposure Payload(BIOSEP)loaded on the CAS-BAP with respect to its structure and function.The primary function of BIOSEP is to expose appropriate biological specimens to the harsh conditions of near space and subsequently return the exposed samples to laboratories for further analysis.Four successful flight missions in near space from 2019 to 2021 have demonstrated the high reliability and efficiency of the payload in communicating between hardware and software units,recording environmental data,exposing sample containers,protecting samples from external contamination,and recovering samples.Understanding the effects of Earth’s near space conditions on biological specimens will provide valuable insights into the survival strategies of organisms in extreme environments and the search for life beyond Earth.The development of BIOSEP and associated biological exposure experiments will enhance our understanding of the potential for life on Mars and the habitability of the atmospheric regions of other planets in the solar system and beyond.

Overview of multirate sampled-data control system

The basic analysis and synthesis approaches for multirate sampled-data control system are reviewed. After giving the definition and some properties of multirate system are given, its origination, development and design methods are discussed in detail. Finally, some remarks, expectations and conclusions on the present research status and the research directions are given.

高分辨率与低分辨率3D-SPACE序列在磁共振胰岛管水成像中的应用对比

目的对比高分辨率与低分辨率三维快速自转回波成像技术(3D-Sampling Perfection with Application-Optimized Contrasts by using Different Flip Angle Evolutions,3D-SPACE)序列在磁共振胰胆管水成像(Magnetic Resonance Cholangiopancreatography,MRCP)中的应用效果。方法使用Siemens Avanto 1.5T磁共振扫描仪对44名受检者行胰胆管水成像,同时采用3D-SPACE的高分辨率与低分辨率序列扫描,两名高年资医师对成像效果进行评价和评分,评分的结果采用秩和校验进行统计学分析。结果高分辨率3D-SPACE原始图像对微小病变显示更清晰;低分辨率3D-SPACE成像时间更快;最大密度投影(Maximum Intensity Projection,MIP)重建图像质量无统计学差异(胆总管显示评分差异:P=0.899,左、右肝内胆管、主胰管显示评分差异:P=0.623)。结论对于配合程度较差的受检者应用低分辨率3D-SPACE更易获得较理想图像。

Stability analysis of networked control systems with time-varying sampling periods

In this paper, we present an interval model of networked control systems with time-varying sampling periods and time-varying network-induced delays and discuss the problem of stability of networked control systems using Lyapunov stability theory. A sufficient stability condition is obtained by solving a set of linear matrix inequalities. In the end, the illustrative example demonstrates the correctness and effectiveness of the proposed approach.

Guaranteed cost control of networked control systems under limited communication capacity and variable sampling

The problem of guaranteed cost control for the networked control systems(NCSs) with time-varying delays, time-varying sampling intervals and signals quantization was investigated, wherein the physical plant was continuous-time one, and the control input was discrete-time one. By using an input delay approach and a sector bound method, the network induced delays, quantization parameter and sampling intervals were presented in one framework in the case of the state and the control input by quantized in a logarithmic form. A novel Lyapunov function with discontinuity, which took full advantages of the NCS characteristic information, was exploited. In addition, it was shown that Lyapunov function decreased at the jump instants. Furthermore, the Leibniz-Newton formula and free-weighting matrix methods were used to obtain the guaranteed cost controller design conditions which were dependent on the NCS characteristic information. A numerical example was used to illustrate the effectiveness of the proposed methods.