Characteristics and influencing mechanisms of production-living-ecological space dynamics in the Three Gorges Reservoir Area(TGRA),Chongqing,China

The Three Gorges Reservoir Area(TGRA)is an important ecological barrier in the Yangtze River Basin,China.Therefore,it is of great importance to understand the spatio-temporal variation and the driving factors of production-living-ecological spaces for sustainable and high-quality development in the TGRA.This study investigated the dynamic variation of production-living-ecological spaces in the TGRA by employing land use data in 2000,2005,2010,2015,and 2018,and detected the influencing factors by using the Geographic detector(GeoDetector).Results implied that the structure and dynamic trajectories of production-living-ecological spaces in the TGRA varied in both horizontal and vertical directions,and the study area was dominated by ecological space.A spatial orientation towards the northeast was detected in the evolution of production-living-ecological spaces during 2000-2018.In terms of quantity,the transition from ecological space(grassland and woodland)to agriculture land accounted for the largest proportion from 2000 to 2018.However,the reverse transition from agriculture land to ecological space has increased since 2000 with the efforts of“Grain for Green”.In terms of temporal scale,there was a fluctuating trend in production space with the continuous expansion of living space,while ecological space showed an inverted U-shaped trend during 2000-2018.The dynamic pattern of production-living-ecological spaces in the TGRA was influenced by both physical and socio-economic variables as basic determinants and dominant driving factors,respectively.Finally,the harmonization and protection of production-living-ecological spaces still require policy-makers’efforts.This work may have potential in advancing our understanding about land use conflicts,and provide a reference for rational layout of spatial functions and the realization of sustainable development in the TGRA.

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.

An Evaluation System for Agriculture and Tourism Coupling Degree of Rural Complex Based on Production-living-ecological Space

Rural complex is a bright spot for China to realize the development of new industries for rural revitalization.Starting from the concept,development history and current situation of rural complex,based on the perspective of production-living-ecological space,according to the different characteristics of agriculture and tourism,3 secondary indices and 18 tertiary indices were selected,and each of them was weighted using the Delphi expert consultation method and analytic hierarchy process(AHP)to construct an evaluation system for the coupling degree of agriculture and tourism in rural complex.The results show gross domestic product and forest coverage have the most significant impact on agriculture,economic benefits and the ecological water quality of tourist attractions have the most significant impact on the tourism industry,and tourism economic benefits and agricultural ecological benefits have the most significant impact on rural complex.

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.

Evaluation of the production-living-ecology space function suitability of Pingshan County in the Taihang mountainous area,China

A rational utilization of land is a matter of importance in sustainable development of mountainous area.The land function in mountainous areas has a close connection with space structure of ecology,production and living.To promote a harmonious development of the relationship between people and nature in mountainous areas,it is necessary to coordinate their relationships of space functions.Suitability evaluation of basic unit function associated with multi-scale space analysis is a prerequisite to a reasonable optimization of land function structure.In this study,an optimized evaluation index system of combination functions was introduced into the assessment of ecological spatial functional suitability in ecological fragile regions by adding three indicators,namely,soil erosion sensitivity,landscape ecological risk and ecological sensitivity.The principle of"taking high"(referred to a function with high suitability to be regarded as the main function of an evaluation unit)and ecological priority(referred to the case,supposing the suitability of a unit’s three functions is consistent,the main function is determined to be the ecological function)were used to determine the main function of an evaluation unit.Pingshan County,China,located at the eastern foot of the Taihang Mountain,was targeted in this case study.The production-livingecology space(PLES)function in Pingshan was identified by applying our improved valuation indexes.Further,the functional suitability distribution of the combination of elements was obtained by using overlapping comprehensive analysis method,considering the tradeoff of the functional suitability of combination elements.The regions suitable for production/living were distributed in relatively flat piedmont plains,whereas the regions suitable for ecology were distributed in the mountain areas of middle and low altitudes.Therefore,to maintain a sustainable development in mountainous areas,an improved scheme of development for Pingshan should be to delineate ecologically fragile areas,to build ecological industrial parks near existing scenic spots,to protect basic agricultural production areas,and to increase investment in science and technology,including reasonable ecological compensation.This study can provide reference for the planning of sustainable development in the Taihang Mountain area and similar regions.

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.

Ecological function transitions of land use in the Beibu Gulf Economic Zone from the perspective of production-living-ecological space

The ecological function of land use is the basis for developing an ecological civilization and realizing sustainable development.This paper may help guide the coordination of economic development and ecological development in China’s coastal border areas.Based on theoretical analysis,this paper studies the spatiotemporal evolution of the functional spaces and the ecological function transitions of land use in the Beibu Gulf Economic Zone(BGEZ)by analyzing patterns,processes,and factors by applying eco-environmental quality index,grid subdivision,kriging interpolation,barycenter model,and Geodetector.This paper constructs a theoretical framework of ecological function transitions of land use based on the research idea of“system-pattern-process-factor”,and carries out empirical research.Some conclusions can be drawn as follows:(1)The ecological space in the BGEZ has continuously decreased,converting mainly into agricultural production space and industrial-mining production space.The production space has expanded slowly.The area of living space in the BGEZ has increased rapidly.(2)The ecological function of land use in the BGEZ has continued to weaken,especially in the southern part of the BGEZ.The“high-sub-high”quality zones of ecological function are retreating to the north.(3)There were more deterioration transition areas than optimization transition areas of ecological function in the BGEZ.The former were mostly located in the central and southern urbanized areas of the BGEZ,whereas the latter were mostly located in the edge zones of district and county units.(4)As for the driving factors of the deterioration transitions of land use ecological function in the BGEZ,the significant trend of“de-ecologization”of the land cover/land use structure was the dominant driving factor;the interactions among the“natural-socioeconomic-managerial”systems were the main recessive factor.The natural system played a fundamental role,and the driving force of natural factors was the strongest.The industrialization,urbanization,and GDP increment in the socioeconomic system and the policy positioning of development intensity in the managerial system played a significant role.The interactions among natural factors,road construction,and industrial non-agricultural transformation had a“fuze”effect on stimulating driving forces.

基于CiteSpace的中医体质辨识研究可视化计量分析

目的以"中医体质辨识"为主题开展系统的可视化分析,探索中医体质辨识领域的发展现状、研究热点、发展趋势,为后续研究提供借鉴。方法以中国知网(CNKI)数据库为数据源检索相关文献,并将所得数据文件导入CiteSpace软件,对作者、机构和关键词进行共现分析并得到相关知识图谱。结果共纳入1099篇文献,通过CiteSpace共现分析找到中医体质辨识领域研究的高产机构、具有代表性的研究者以及高频关键词。研究发现各个研究者及机构缺乏沟通合作,研究热点主题有体质研究、治未病、健康管理、与现代生物医学相结合。结论中医体质辨识领域的研究逐年增加,知识图谱揭示了中医体质辨识在健康管理、与现代生物医学相结合将是未来的研究趋势。

Separation of closely spaced modes by combining complex envelope displacement analysis with method of generating intrinsic mode functions through filtering algorithm based on wavelet packet decomposition

One of the important issues in the system identification and the spectrum analysis is the frequency resolution, i.e., the capability of distinguishing between two or more closely spaced frequency components. In the modal identification by the empirical mode decomposition （EMD） method, because of the separating capability of the method, it is still a challenge to consistently and reliably identify the parameters of structures of which modes are not well separated. A new method is introduced to generate the intrin- sic mode functions （IMFs） through the filtering algorithm based on the wavelet packet decomposition （GIFWPD）. In this paper, it is demonstrated that the CIFWPD method alone has a good capability of separating close modes, even under the severe condition beyond the critical frequency ratio limit which makes it impossible to separate two closely spaced harmonics by the EMD method. However, the GIFWPD-only based method is impelled to use a very fine sampling frequency with consequent prohibitive computational costs. Therefore, in order to decrease the computational load by reducing the amount of samples and improve the effectiveness of separation by increasing the frequency ratio, the present paper uses a combination of the complex envelope displacement analysis （CEDA） and the GIFWPD method. For the validation, two examples from the previous works are taken to show the results obtained by the GIFWPD-only based method and by combining the CEDA with the GIFWPD method.

Eigen-Space Decomposition(ESD)Method for the Design of Internal Model Controller(IMC)from Noisy Input and Output Plant Data

A novel approach to design Internal Model Controller(IMC)is proposed in this paper directly from measuredinput and output plant data,which are assumed to becontaminated by measurement noise.In order to avoidthe complicated structure-identification problem inmost cases,two Finite Impulse Response(FIR)modelsare taken to represent the plant model and the internalmodel controller respectively.Taking account of mea-surement noise both in the plant input and its output,anESD based Total Least Squares(TLS)solution is appliedfor the unbiased identification of the plant model and itsinverse model,the latter constitutes the internal modelcontroller according to the principle that the internalmodel controller approximates the inverse dynamics ofthe plant model.Simulations are given for a testifica-tion.

Jamming Monitoring Technology of the Space Situational Awareness Facilities:A Comprehensive Survey

Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding diagnosis of the jamming signals are essential to normal opera-tions and the innovations in anti-jamming equipment.This paper demonstrates a comprehensive survey on jamming monitoring algorithms and applications.The methods in dealing with jamming signals are summarized primarily.Subsequently,the jamming detection,identification,and direc-tion finding techniques are addressed separately.Based on the established studies,we also provide some potential trends of the demonstrated jamming monitoring issues.

State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.