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Anovel approach of noise statistics estimate using H∞ filter in target tracking

Xie WANG,Mei-qin LIU,Zhen FAN,Sen-lin ZHANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 5,   Pages 449-457 doi: 10.1631/FITEE.1500262

Abstract: First, we apply the H∞ filter to obtain the system state estimates without the common assumptions aboutThen by applying state estimates obtained from the H∞ filter, better estimates of the noise mean and

Keywords： Noise estimate     H∞     filter     Target tracking    

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An Estimate Method of Parametric in Reliability Engineering

Han Ming

Strategic Study of CAE 2003, Volume 5, Issue 3,   Pages 51-56

Abstract:

In this paper, the Bayesian method, an estimate method for parameter in reliability engineering isThe author gives definition of the new Bayesian estimate for failure probability and failure rate, andshows the estimate of the failure probability and the failure rate by new Bayesian method.

Keywords： reliability engineering     parameter estimate     new Bayesian estimate     failure probability    

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Development of an analytical model to estimate the churning losses in high-speed axial piston pumps

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-021-0671-1

Abstract: design stage of high-speed axial piston pumps, but accurate analytical models are not available to estimate

Keywords： axial piston pump     rotating parts     high rotational speed     churning losses     drag torque    

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A novel multiple-outlier-robust Kalman filter Research Articles

Yulong HUANG, Mingming BAI, Yonggang ZHANG,heuedu@163.com,mingming.bai@hrbeu.edu.cn,zhangyg@hrbeu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 422-437 doi: 10.1631/FITEE.2000642

Abstract: This paper presents a novel multiple-outlier-robust Kalman filter (MORKF) for linear stochastic discrete-time systems. A new is first proposed to evaluate the similarity between two random vectors from dimension to dimension. Then, the proposed MORKF is derived via maximizing a based cost function. The MORKF guarantees the convergence of iterations in mild conditions, and the boundedness of the approximation errors is analyzed theoretically. The selection strategy for the similarity function and comparisons with existing robust methods are presented. Simulation results show the advantages of the proposed filter.

Keywords： Kalman filtering     Multiple statistical similarity measure     Multiple outliers     Fixed-point iteration     Stateestimate    

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Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston

Qun CHAO, Jianfeng TAO, Junbo LEI, Xiaoliang WEI, Chengliang LIU, Yuanhang WANG, Linghui MENG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 176-185 doi: 10.1007/s11465-020-0616-0

Abstract: This paper presents a scaling law derived from an analytical cavitation model to estimate the speed limitations

Keywords： axial piston pump     cavitation     speed limitation     scaling law    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 990-1002 doi: 10.1007/s11709-022-0864-7

Abstract: This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.

Keywords： artificial neural network     grey wolf optimize algorithm     compressive strength     self-compacting concrete    

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Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German

Muhammad WASEEM, Frauke KACHHOLZ, Jens TRÄNCKNER

Frontiers of Agricultural Science and Engineering 2018, Volume 5, Issue 4,   Pages 420-431 doi: 10.15302/J-FASE-2018243

Abstract:

Various process-based models are extensively being used to analyze and forecast catchment hydrology and water quality. However, it is always important to select the appropriate hydrological and water quality modeling tools to predict and analyze the watershed and also consider their strengths and weaknesses. Different factors such as data availability, hydrological, hydraulic, and water quality processes and their desired level of complexity are crucial for selecting a plausible modeling tool. This review is focused on suitable model selection with a focus on desired hydrological, hydraulic and water quality processes (nitrogen fate and transport in surface, subsurface and groundwater bodies) by keeping in view the typical lowland catchments with intensive agricultural land use, higher groundwater tables, and decreased retention times due to the provision of artificial drainage. In this study, four different physically based, partially and fully distributed integrated water modeling tools, SWAT (soil and water assessment tool), SWIM (soil and water integrated model), HSPF (hydrological simulation program– FORTRAN) and a combination of tools from DHI (MIKE SHE coupled with MIKE 11 and ECO Lab), have been reviewed particularly for the Tollense River catchment located in North-eastern Germany. DHI combined tools and SWAT were more suitable for simulating the desired hydrological processes, but in the case of river hydraulics and water quality, the DHI family of tools has an edge due to their integrated coupling between MIKE SHE, MIKE 11 and ECO Lab. In case of SWAT, it needs to be coupled with another tool to model the hydraulics in the Tollense River as SWAT does not include backwater effects and provision of control structures. However, both SWAT and DHI tools are more data demanding in comparison to SWIM and HSPF. For studying nitrogen fate and transport in unsaturated, saturated, and river zone, HSPF was a better model to simulate the desired nitrogen transformation and transport processes. However, for nitrogen dynamics and transformations in shallow streams, ECO Lab had an edge due its flexibility for inclusion of user-desired water quality parameters and processes. In the case of SWIM, most of the input data and governing equations are similar to SWAT but it does not include water bodies (ponds and lakes), wetlands and drainage systems. In this review, only the processes that were needed to simulate the Tollense River catchment were considered, however the resulted model selection criteria can be generalized to other lowland catchments in Australia, North-western Europe and North America with similar complexity.

Keywords： diffuse pollution     ECO Lab     HSPF     lowland catchment     MIKE 11     MIKE SHE     modeling tools     SWAT     SWIM     Tollense River     water quality    

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Advances in tissue state recognition in spinal surgery: a review

Hao Qu, Yu Zhao

Frontiers of Medicine 2021, Volume 15, Issue 4,   Pages 575-584 doi: 10.1007/s11684-020-0816-3

Abstract: In the past, tactile sensation was the primary method used by surgeons to understand the state of theTherefore, it has become the focus of spinal surgery research so as to strengthen the objectivity of tissue stateThis article reviews the progress of different tissue state recognition methods in spinal surgery and

Keywords： spinal surgery     tissue state recognition     image     force sensing     bioelectrical impedance    

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Effectiveness of state incentives for promoting wind energy: A panel data examination

Deepak SANGROYA,Jogendra NAYAK

Frontiers in Energy 2015, Volume 9, Issue 3,   Pages 247-258 doi: 10.1007/s11708-015-0364-8

Abstract: The Indian government, as well as different state governments, are adopting policy instruments such asThis paper evaluates the effectiveness of state level incentives for the development of wind energy in

Keywords： India     wind energy development     state incentives     econometric analysis     panel data    

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Maximum independent set in planning freight railway transportation

Gainanov Damir N., Mladenovic NENAD, Rasskazova V. A.

Frontiers of Engineering Management 2018, Volume 5, Issue 4,   Pages 499-506 doi: 10.15302/J-FEM-2018031

Abstract:

This work is devoted to the problem of planning of freight railway transportation. We define a special conflict graph on the basis of a set of acceptable train routes. The investigation aims to solve the classical combinatorial optimization problem in relation to the maximum independent set of vertices in undirected graphs. The level representation of the graph and its tree are introduced. With use of these constructions, the lower and upper bounds for the number of vertices in the maximum independent set are obtained.

Keywords： independent set     algorithm     planning of transportation     two-sided estimate    

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learningand classification blocks into an intelligent and compact learning system and detect the structural stateof a steel frame; the input was a series of vibration signals, and the output was a structural stateIt is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals

Keywords： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Biological pretreatment of corn stover by solid state fermentation of

Jian ZHANG, Xin REN, Wenqun CHEN, Jie BAO

Frontiers of Chemical Science and Engineering 2012, Volume 6, Issue 2,   Pages 146-151 doi: 10.1007/s11705-012-1220-6

Abstract: Solid state fermentation of corn stover with the white-rot fungus was carried out and the efficiencywas biologically pretreated for nine days, and the hydrolysis yield decreased sharply if the solid state

Keywords： biological pretreatment     Phanerochaete chrysosporium     solid state fermentation     biorecalcitrance    

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Development of a constitutive model for rockfills and similar granular materials based on the disturbed state

Mehdi VEISKARAMI, Ali GHORBANI, Mohammadreza ALAVIPOUR

Frontiers of Structural and Civil Engineering 2012, Volume 6, Issue 4,   Pages 365-378 doi: 10.1007/s11709-012-0178-2

Abstract: further, the stress-strain curve shows an elasto-plastic behavior which suggests using the disturbed state

Keywords： constitutive model     granular material     rockfill     plasticity     disturbed state concept     stress level    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy doi: 10.1007/s11708-023-0891-7

Abstract: analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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Title Author Date Type Operation

Anovel approach of noise statistics estimate using H∞ filter in target tracking

Xie WANG,Mei-qin LIU,Zhen FAN,Sen-lin ZHANG

Journal Article

An Estimate Method of Parametric in Reliability Engineering

Han Ming

Journal Article

Development of an analytical model to estimate the churning losses in high-speed axial piston pumps

Journal Article

A novel multiple-outlier-robust Kalman filter

Yulong HUANG, Mingming BAI, Yonggang ZHANG,heuedu@163.com,mingming.bai@hrbeu.edu.cn,zhangyg@hrbeu.edu.cn

Journal Article

Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston

Qun CHAO, Jianfeng TAO, Junbo LEI, Xiaoliang WEI, Chengliang LIU, Yuanhang WANG, Linghui MENG

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Journal Article

A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Journal Article

Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German

Muhammad WASEEM, Frauke KACHHOLZ, Jens TRÄNCKNER

Journal Article

Advances in tissue state recognition in spinal surgery: a review

Hao Qu, Yu Zhao

Journal Article

Effectiveness of state incentives for promoting wind energy: A panel data examination

Deepak SANGROYA,Jogendra NAYAK

Journal Article

Maximum independent set in planning freight railway transportation

Gainanov Damir N., Mladenovic NENAD, Rasskazova V. A.

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article

Biological pretreatment of corn stover by solid state fermentation of

Jian ZHANG, Xin REN, Wenqun CHEN, Jie BAO

Journal Article

Development of a constitutive model for rockfills and similar granular materials based on the disturbed state

Mehdi VEISKARAMI, Ali GHORBANI, Mohammadreza ALAVIPOUR

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article