A Modal Identification Algorithm Combining Blind Source Separation and State Space Realization

A modal identification algorithm is developed, combining techniques from Second Order Blind Source Separation (SOBSS) and State Space Realization (SSR) theory. In this hybrid algorithm, a set of correlation matrices is generated using time-shifted, analytic data and assembled into several Hankel matrices. Dissimilar left and right matrices are found, which diagonalize the set of nonhermetian Hankel matrices. The complex-valued modal matrix is obtained from this decomposition. The modal responses, modal auto-correlation functions and discrete-time plant matrix (in state space modal form) are subsequently identified. System eigenvalues are computed from the plant matrix to obtain the natural frequencies and modal fractions of critical damping. Joint Approximate Diagonalization (JAD) of the Hankel matrices enables the under determined (more modes than sensors) problem to be effectively treated without restrictions on the number of sensors required. Because the analytic signal is used, the redundant complex conjugate pairs are eliminated, reducing the system order (number of modes) to be identified half. This enables smaller Hankel matrix sizes and reduced computational effort. The modal auto-correlation functions provide an expedient means of screening out spurious computational modes or modes corresponding to noise sources, eliminating the need for a consistency diagram. In addition, the reduction in the number of modes enables the modal responses to be identified when there are at least as many sensors as independent (not including conjugate pairs) modes. A further benefit of the algorithm is that identification of dissimilar left and right diagonalizers preclude the need for windowing of the analytic data. The effectiveness of the new modal identification method is demonstrated using vibration data from a 6 DOF simulation, 4-story building simulation and the Heritage court tower building.

Combined iterative cross-correlation demodulation scheme for mixing space borne automatic identification system signals

Aiming at the potential presence of mixing automatic identification system(AIS) signals,a new demodulation scheme was proposed for separating other interfering signals in satellite systems.The combined iterative cross-correlation demodulation scheme,referred to as CICCD,yielded a set of single short signals based on the prior information of AIS,after the frequency,code rate and modulation index were estimated.It demodulates the corresponding short codes according to the maximum peak of cross-correlation,which is simple and easy to implement.Numerical simulations show that the bit error rate of proposed algorithm improves by about 40% compared with existing ones,and about 3 dB beyond the standard AIS receiver.In addition,the proposed demodulation scheme shows the satisfying performance and engineering value in mixing AIS environment and can also perform well in low signal-to-noise conditions.

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.

A SYSTEM IDENTIFICATION METHOD USING VECTOR SPACE BASE FUNCTION

This paper presents a new system identification approach using vector space base functions, and proposes two network structures based on Gamma sequence and Laguerre sequence. After analyzing and comparing these structures in detail, some simulation results to demonstrate the conclusions are given.

Encapsulating the Role of Solution Response Space Roughness on Global Optimal Solution: Application in Identification of Unknown Groundwater Pollution Sources

A major challenge of any optimization problem is to find the global optimum solution. In a multi-dimensional solution space which is highly non-linear, often the optimization algorithm gets trapped around some local optima. Optimal Identification of unknown groundwater pollution sources poses similar challenges. Optimization based methodology is often applied to identify the unknown source characteristics such as location and flux release history over time, in a polluted aquifer. Optimization based models for identification of these characteristics of unknown ground-water pollution sources rely on comparing the simulated effects of candidate solutions to the observed effects in terms of pollutant concentration at specified sparse spatiotemporal locations. The optimization model minimizes the difference between the observed pollutant concentration measurements and simulated pollutant concentration measurements. This essentially constitutes the objective function of the optimization model. However, the mathematical formulation of the objective function can significantly affect the accuracy of the results by altering the response contour of the solution space. In this study, two separate mathematical formulations of the objective function are compared for accuracy, by incorporating different scenarios of unknown groundwater pollution source identification problem. Simulated Annealing (SA) is used as the solution algorithm for the optimization model. Different mathematical formulations of the objective function for minimizing the difference between the observed and simulated pollutant concentration measurements show different levels of accuracy in source identification results. These evaluation results demonstrate the impact of objective function formulation on the optimal identification, and provide a basis for choosing an appropriate mathematical formulation for unknown pollution source identification in contaminated aquifers.

A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm

This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module, system records the data sequences of flighttests as inputs (control signals for servos) and outputs (aircraft’s attitude and velocity information).After data preprocessing, thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model, the small unmanned aerial rotorcraft can perform hovering,turning, and straight flight tasks in real flight tests.

Modal identification of multi-degree-of-freedom structures based on intrinsic chirp component decomposition method

Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.

Variable Stiffness Identification and Configuration Optimization of Industrial Robots for Machining Tasks

Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence.However,the low structural stiffness of a robot significantly affects its positional accuracy and the machining quality of its operation equipment.Studying robot stiffness characteristics and optimization methods is an effective method of improving the stiffness performance of a robot.Accordingly,aiming at the poor accuracy of stiffness modeling caused by approximating the stiffness of each joint as a constant,a variable stiffness identification method is proposed based on space gridding.Subsequently,a task-oriented axial stiffness evaluation index is proposed to quantitatively assess the stiffness performance in the machining direction.In addition,by analyzing the redundant kinematic characteristics of the robot machining system,a configuration optimization method is further developed to maximize the index.For numerous points or trajectory-processing tasks,a configuration smoothing strategy is proposed to rapidly acquire optimized configurations.Finally,experiments on a KR500 robot were conducted to verify the feasibility and validity of the proposed stiffness identification and configuration optimization methods.

Modeling and Identification of Multirate Systems

Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.

Reproducing wavelet kernel method in nonlinear system identification

By combining the wavelet decomposition with kernel method, a practical approach of universal multiscale wavelet kernels constructed in reproducing kernel Hilbert space （RKHS） is discussed, and an identification scheme using wavelet support vector machines （WSVM） estimator is proposed for nordinear dynamic systems. The good approximating properties of wavelet kernel function enhance the generalization ability of the proposed method, and the comparison of some numerical experimental results between the novel approach and some existing methods is encouraging.

An Approximated Voxel Approach for the Identification and Modelling of Ligand-Binding Sites

Most protein-ligand interactions take place on surfaces and include but not limited to factors such as chemical composition, hydrophobicity, electronegavitiy and shape complementarity. Past studies showed that protein-protein interactions occur on comparatively fiat regions whereas protein-ligand bindings involve crevices. In the search for such sites various approaches have been designed and developed each of which is algorithmically unique. The use of grid units or voxels has been demonstrated in early studies with relatively good results obtained. We present here an approximated approach comprising of the use of voxels and computer vision methods in the search for ligand-binding areas. Each test protein is modelled and analysed in 2D with all corresponding residues graphically presented for successfully identified sites. The study was carried out on 2 sets of proteins： FK506-bound proteins and heme-bound proteins with promising results obtained for all test cases.

广义确定性标识网络

随着智能制造、智能交通等重大国家战略实施,确定性成为信息网络尤其是行业专网的新焦点.现有确定性网络技术始终关注网络传输要素(带宽、时隙等)来保障数据流的确定性传输.然而,仅靠保障传输要素无法支撑新兴行业应用的多样化需求.例如,在算网融合场景,智算任务要求同时保障传输与计算要素的确定性来实现高性能通信;在绿色通信场景,需要考虑节点能量要素的确定性以维持网络稳定运行.针对上述需求,本文基于前期提出的标识网络技术,研究面向传输、计算、存储、能量等多要素的广义确定性网络.首先提出广义确定性标识网络架构,包括差异化服务层、异构融合网络层和智慧化适配层.差异化服务层和异构融合网络层,分别实现差异化确定性应用需求和异构化确定性网络要素的统一标识和描述,并通过标识解析映射实现确定性信息向智慧化适配层的统一封装和传递;智慧化适配层完成差异化确定性应用需求和异构化确定性网络要素的适配.现有确定性资源适配方法,即使仅考虑单一网络内的基本确定性要素,仍面临计算时间长、求解复杂性高、灵活度低等问题,为了支持更加复杂的多确定性要素、多种异构网络的协同适配,设计了基于深度强化学习的端到端的确定性调度(End-to-end Deterministic resource scheduling,E2eDet)算法,该算法可统一化、端到端地为混合数据流协同分配多种确定性网络资源,满足不同应用的差异化确定性需求.实验表明,E2eDet比DeepCQF和Random算法分别提升了28.4%和6.38倍数据流调度数量,同时E2eDet可以较好地权衡计算时间和调度能力.

轨道车辆垂向轮轨力时域识别对比及其机器学习修正

为了尽可能减小轨道车辆垂向轮轨力时域识别中存在的误差,以时域法为基础,开展了基于机器学习修正的轨道车辆垂向轮轨力识别研究.首先建立了车辆动力学仿真模型,获取了车辆在随机轨道激励下以250km/h速度行驶时的轴箱加速度响应和垂向轮轨力.其次,建立了Green函数法、状态空间法这2种时域法对应的动载荷识别模型,对状态空间法的初值误差进行了分析,并引入多项式拟合法修正其趋势项误差,进而对比分析了2种方法的计算精度和计算效率.然后,针对时域法存在的识别误差,提出采用NARX(nonlinear autoregressive models with exogenous inputs)模型对识别误差进行训练和预测,用于消减模型中存在的如响应观测不全与观测噪声等因素造成的影响,进而对时域法识别结果进行修正,提高识别精度.最后,通过一个10自由度轨道车辆垂向动力学模型,对方法的正确性进行了验证.研究结果表明:2种方法对轨道车辆垂向轮轨力均具有较高的识别精度,对于各轮对的识别精度各有优劣;在计算效率方面,状态空间法比Green函数法更优;经NARX模型修正的2种时域法对轨道车辆垂向轮轨力均具有很好的识别效果,识别值与正演值的Pearson相关系数大于0.99,为极强相关.基于NARX模型的机器学习误差修正方法可有效提高时域识别精度,可以为后续轨道车辆轮轨力预测提供参考,具有较强的工程运用价值.

用途管制导向下历史空间边界识别方法研究

当前,作为重要资源的历史空间受制于传统边界认知模式,在管理中存在范围模糊、对象不明等问题。以用途管制为导向,基于价值关联的时间逻辑、空间逻辑分析历史空间边界的形成机制,以历史空间价值为统领,构建历史空间三维模型,分析价值在国土空间层面传导关联的内在作用,整合历史空间边界识别指标,形成识别方法。在此基础上,以安徽省固镇县中心区及其县域和垓下村进行实证分析,提出历史空间用途管制范围的分区分层、融入体系的管控研究方法,为建立健全历史空间用途管制制度提供研究基础。

深部地下空间储能安全与应急保障技术现状与发展趋势

伴随“三深引领”的科技攻关目标,我国油气资源勘探正由浅表地层向深部地层进发,千米级深部地下工程建设已成常态。随着深地工程的探索,枯竭油气藏、盐穴、采空区等可利用的深部地下空间涌现,深部地下空间在大规模能源储存利用上已被证明具有储量大、分布广、安全性高、经济成本低和环境友好等优势,深部地下空间工业化储能应用是优化我国能源储存结构、保障国家能源储存战略需求的有力手段。然而,我国对深部地下空间储能安全与应急保障技术研究起步较晚,目前常用的地面油气储存手段受限于地理环境、工程建设以及运输方式等条件,同时地面油气储备库事故存在致因因素复杂、关联性强、受灾波及范围广等安全难题,深部地下空间能源安全储存迫在眉睫。当下我国对深部地下空间储能的利用聚焦于枯竭油气藏的再开发,现有的深地安全保障技术落后于深地储能基础设施安全保障的现实需求。因此,本文系统性总结了深地储气、储氢、碳封存以及压缩空气蓄能等深地储能技术研究现状,以能源行业绿色转型兼备战略能源安全储存为目标,强调能源储存、碳封存以及资源深度开采的三位一体,提出了深地空间储能利用双碳循环架构。同时系统性辨识了深地储能设备设施面临的风险,涵盖深地储能设计期、施工期、运行期和废弃期的全生命周期,揭示我国深地储能安全与应急保障技术发展需求、发展难点,构建了深地储能全生命周期安全与应急保障技术框架,提出我国2024—2050年期间深部地下空间储能安全与应急保障技术发展建议,为完善我国深部地下空间储能安全与应急保障技术体系提供参考。

基于激光雷达与遥感数据的地理空间要素识别方法

通过地理空间要素识别实现地理测绘和环境有效监测,提出基于激光雷达与遥感数据的地理空间要素识别方法。采用激光雷达的遥感监测方法实现对地理空间特征的对象集变化检测,根据地理空间测绘对象的纹理、形状、空间关系,采用遥感数据融合和空间信息表达方法实现对地理空间要素特征的专家知识融合,构建地理空间要素识别的遥感专家数据库。仿真结果表明,采用该方法进行地理空间要素识别的决策融合性较好。该方法也实现了对土地利用自然特征变化的检测,使得识别准确率的最大值可达到0.925,且识别耗时始终处于2.5 s以下。

国外城市非正式绿色空间研究进展与思考

【目的】存量发展背景下,城市公园等正式绿色空间增量困难、服务功能日益局限,而数量较多的非正式绿色空间尚未引起足够重视,未充分发挥价值。探究非正式绿色空间的独特价值与利用方式,可为用地约束下人居环境提升与绿化功能创新提供线索。【方法】对Web of Science核心数据库登载的47篇典型文献进行了分析,从识别技术、游憩价值、生态价值和更新模式四方面,系统探知国外非正式绿色空间研究进展。【结果】非正式绿色空间具有触发多样游憩感知、满足多元使用需求、提升绿地获取公平性等游憩价值,以及丰富城市生物多样性、提升生态系统调节能力等生态价值。同时,国外实践表明,运用公共政策、政府支持、社会推动的综合策略可助力实现非正式绿色空间更新改造。【结论】结合国情并借鉴国外理论与实践,建议运用主动响应居民需求、创建地理信息数据库、创新规划管理范式三大策略优化国内城市非正式绿色空间建设,以期公平、生态、节约地推进城市绿化建设。

基于改进经验小波变换的海洋平台结构模态参数自动识别方法

针对经验小波变换(empirical wavelet transform,EWT)方法在处理低信噪比信号中频谱分割边界容易产生误判的问题,提出了一种改进经验小波变换(improved empirical wavelet transform,IEWT)的结构模态参数自动识别方法。首先计算信号的互功率谱矩阵,采用奇异值分解(SVD)及尺度空间(SSPP)方法确定频谱的分割边界,将信号分解为若干固有模态函数(IMF)分量,再结合随机减量技术(RDT)和希尔伯特变换(HT)实现模态参数的自动识别。使用IEWT方法对自由振动响应信号及ASCE Benchmark模型信号进行模态参数识别,并分别与EWT方法、基于自回归功率谱的经验小波变换(AR-EWT)方法及小波变换(WT)方法进行对比,结果表明IEWT方法能够自适应确定频谱分割边界,对结构的频率及阻尼比等模态参数具有较高的识别精度;进一步将该方法应用到实验室海洋平台模型的模态参数识别中,证明该方法可用于复杂噪声环境下的低频结构的模态参数识别。

考虑暂态过电压特性的架空输电线路雷击故障时空预警方法

针对运行环境复杂多变,故障率持续居高不下,雷击故障占比较大,并具有随机性与无法预知性的问题,提出考虑暂态过电压特性的架空输电线路雷击故障时空预警方法。深入解析雷击机理与雷电流,确定雷击(感应雷与直击雷)输电线路暂态过电压计算方式,通过小波多分辨率分解算法处理输电线路暂态信号,评估暂态过电压在信号频段中的能量比,识别累计故障,判断雷击点与故障点是否一致,利用单端检测确定雷击点的位置,完成架空输电线路雷击故障的时空预警。实验数据显示,在不同实验组别背景下,当阈值pΔ取值为9.3时,雷击故障识别精度达到最大值92%,雷击点定位精度高达80%。可以获得精准的雷击故障识别结果与雷击点定位结果,充分证实了提出方法具备较好的雷击故障时空预警效果。

基于类对比簇分配异构迁移学习的空间滚动轴承寿命阶段识别

针对变工况条件下因样本数据分布差异大、可训练用样本较少以及不同寿命阶段样本数量不均等造成的空间滚动轴承寿命阶段识别准确率较低的问题,提出一种无监督迁移学习方法--类对比簇分配异构迁移学习(CAHTL)。在CAHTL中,通过异构迁移学习将历史工况下少量有类标签样本和当前工况的无类标签样本(即待测样本)迁移到公共特征空间内,使得不同工况样本之间的分布差异最小化;利用源域聚类簇点构建目标域样本特征的正负样本实现两域样本的数量再分配,再对两域正负样本进行对比学习以使待测样本分类性更好;通过计算待测样本与聚类簇点的相似度完成待测样本分类,且该分类过程无需参数学习,因此可避免样本不均等情况下对于不同寿命阶段样本识别准确率差距过大和在少有类标签训练样本情况下网络出现过拟合的问题;利用随机梯度下降和动量更新对CAHTL参数进行不同步更新,以保持样本特征的一致性并提高CAHTL的收敛速度。CAHTL可利用空间滚动轴承历史工况下的少量、非均等的已知寿命阶段的训练样本对当前工况的待测样本进行较高精度的寿命阶段识别。空间滚动轴承寿命阶段识别实例验证了该方法的有效性。