Resource Type

Journal Article 177

Year

2024 1

2023 11

2022 11

2021 13

2020 6

2019 12

2018 11

2017 16

2016 6

2015 4

2014 6

2012 8

2011 6

2010 10

2009 5

2008 4

2007 7

2006 5

2005 5

2004 6

open ︾

Keywords

Process intensification 4

Artificial intelligence 3

Gaussian process 3

Adsorption 2

Autonomous driving 2

Blast furnace 2

Colloids 2

DSM (design structure matrix) 2

Data-driven 2

Machine learning 2

Process systems engineering 2

Reinforcement learning 2

Smart manufacturing 2

Soft sensors 2

mechanism 2

1 D stress-waves 1

2 1

4-dinitroanisole 1

Abrasive waterjet 1

open ︾

Search scope:

排序： Display mode:

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking Research Article

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1647-1656 doi: 10.1631/FITEE.2300348

Abstract: The performance of existing maneuvering methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both and model-based algorithms. The time-varying constant velocity model is integrated into the (GP) of to improve the performance of GP prediction. This integration is further combined with a generalized algorithm to realize multi-. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the GP motion tracker.

Keywords： Target tracking     Gaussian process     Data-driven     Online learning     Model-driven     Probabilistic data association    

HTML PDF Collect

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search Research Article

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1557-1573 doi: 10.1631/FITEE.2200515

Abstract: s (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the and (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on imageclassification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.

Keywords： Convolutional neural network     Gaussian process     Hybrid model     Hyperparameter optimization     Mixed-variable     Particle swarm optimization    

HTML PDF Collect

Novel 3D point set registration method based on regionalized Gaussian process map reconstruction Research

Bo Li, Yu Zhang, Wen-jie Zhao, Ping Li,jameslb20@hotmail.com,zhangyu80@zju.edu.cn,zhaowenjie8@zju.edu.cn,pli@iipc.zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 5,   Pages 649-808 doi: 10.1631/FITEE.1900457

Abstract: has been a topic of significant research interest in the field of mobile intelligent unmanned systems. In this paper, we present a novel approach for a three-dimensional scan-to-map . Using (GP) regression, we propose a new type of map representation, based on a regionalized GP map reconstruction algorithm. We combine the predictions and the test locations derived from the GP as the predictive points. In our approach, the correspondence relationships between predictive point pairs are set up naturally, and a rigid transformation is calculated iteratively. The proposed method is implemented and tested on three standard point set datasets. Experimental results show that our method achieves stable performance with regard to accuracy and efficiency, on a par with two standard methods, the iterative closest point algorithm and the normal distribution transform. Our mapping method also provides a compact point-cloud-like map and exhibits low memory consumption.

Keywords： 点集配准；高斯过程；智能无人系统    

HTML PDF Collect

Identification of important factors influencing nonlinear counting systems Research Article

Xinmin ZHANG, Jingbo WANG, Chihang WEI, Zhihuan SONG,xinminzhang@zju.edu.cn,wangjingbobo@zju.edu.cn,chhwei@zju.edu.cn,songzhihuan@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 1,   Pages 123-133 doi: 10.1631/FITEE.2000324

Abstract: Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and engineering. In this work, a of the (SA-GGPR) model is proposed to identify of the . In SA-GGPR, the GGPR model with Poisson likelihood is adopted to describe the . The GGPR model with Poisson likelihood inherits the merits of nonparametric kernel learning and Poisson distribution, and can handle complex s. Nevertheless, understanding the relationships between model inputs and output in the GGPR model with Poisson likelihood is not readily accessible due to its nonparametric and kernel structure. SA-GGPR addresses this issue by providing a quantitative assessment of how different inputs affect the system output. The application results on a simulated and a real have demonstrated that the proposed SA-GGPR method outperforms several state-of-the-art methods in identification accuracy.

Keywords： Important factors     Nonlinear counting system     Generalized Gaussian process regression     Sensitivity analysis     Steel casting-rolling process    

HTML PDF Collect

A saliency and Gaussian net model for retinal vessel segmentation Research Articles

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1075-1086 doi: 10.1631/FITEE.1700404

Abstract: Retinal vessel segmentation is a significant problem in the analysis of fundus images. A novel deep learning structure called the Gaussian net (GNET) model combined with a saliency model is proposed for retinal vessel segmentation. A saliency image is used as the input of the GNET model replacing the original image. The GNET model adopts a bilaterally symmetrical structure. In the left structure, the first layer is upsampling and the other layers are max-pooling. In the right structure, the final layer is max-pooling and the other layers are upsampling. The proposed approach is evaluated using the DRIVE database. Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models. The proposed algorithm performs well in extracting vessel networks, and is more accurate than other deep learning methods. Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.

Keywords： Retinal vessel segmentation     Saliency model     Gaussian net (GNET)     Feature learning    

HTML PDF Collect

ApproximateGaussian conjugacy: parametric recursive filtering under nonlinearity,multimodality, uncertainty, and constraint, and beyond Review

Tian-cheng LI, Jin-ya SU, Wei LIU, Juan M. CORCHADO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 1913-1939 doi: 10.1631/FITEE.1700379

Abstract: Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.

Keywords： Kalman filter     Gaussian filter     Time series estimation     Bayesian filtering     Nonlinear filtering     Constrained filtering     Gaussian mixture     Maneuver     Unknown inputs    

HTML PDF Collect

A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving Article

Jinxin Liu, Yugong Luo, Zhihua Zhong, Keqiang Li, Heye Huang, Hui Xiong

Engineering 2022, Volume 19, Issue 12,   Pages 228-239 doi: 10.1016/j.eng.2021.12.020

Abstract:

In mixed and dynamic traffic environments, accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles (AVs) to accomplish reasonable behavioral decisions and guarantee driving safety. In this paper, we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction, which consists of a driving inference model (DIM) and a trajectory prediction model (TPM). The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network. The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information. To further improve the prediction accuracy and realize uncertainty estimation, we develop a Gaussian process (GP)-based TPM, considering both the short-term prediction results of the vehicle model and the driving motion characteristics. Afterward, the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios. The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.

Keywords： Autonomous driving     Dynamic Bayesian network     Driving intention recognition     Gaussian process     Vehicle trajectory prediction    

HTML PDF Collect

The Improving Characteristics of the Gradual Gaussian Multidimensional Pre-filter for Optical Flow Estimation

Fu Jun,Xu Weipu

Strategic Study of CAE 2004, Volume 6, Issue 12,   Pages 56-61

Abstract:

Based on the unified estimation-theoretic framework, an effective method of using the gradual Gaussian multidimensional pre-filter to improve the optical flow estimation is presented. The pre-filtering and smoothing effect, which attenuate the temporal aliasing and the interesting signal structure of the optical flow field, are altered with adjusting the spatiotemporal standard deviation parameters. The first 50 frames of the standard Flower Garden and Football video sequence are tested as the reference image sequences, and the LK algorithm as the reference optical flow computing method. Experimental results in objective evaluation show that the optimum temporal standard deviation parameter is 0.4, the optimum spatial standard deviation parameter is in a range of 1.6～2.0 under the condition that the pre-filtering window size is 5 × 5 pixels. After pre-filtering the image sequence by the Gaussian multidimensional filter, the average PSNR of the reconstructed frames enhance 2.572 dB, higher than that using the standard optical flow computing method by nearly 13.6 % .

Keywords： optical flow computing     Gaussian multidimensional filter     PSNR     motion estimation    

HTML PDF Collect

High-precision Numerical Computation of High-degree Gauss quadrature Nodes

Zhang Qingli,Wang Xiaomei,Yin Shaotang,Jiang Haihe

Strategic Study of CAE 2008, Volume 10, Issue 2,   Pages 35-40

Abstract:

Gauss quadrature is used widely in many fields such as the engineering numerical computation, X-ray diffraction profile analysis, spectroscopy,and so on. The nodes and weight factors of Gauss-quadrature are essential data to the numerical integration. A method to compute the zeroes of the high-degree Legendre, Laguerre and Hermite polynomials, which are the nodes of Gauss-Legendre, Gauss-Laguerre and Gauss-Hermite Quadrature, respectively, is studied, and a very efficient algorithm scan-iteration method(SIM) is given. According to the properties of Legendre, Laguerre and Hermite polynomials, their definitions are modified a little, and the stable recursive relations to compute their value are obtained. To extract these polynomials, their root intervals are searched with a certain step within a certain range. After the intervals of all roots are obtained, the roots with the desired precision can be gotten by the general iteration methods such as secant or bisection method. Numerical experiments indicate that the method is very efficient and the high-precise roots of Legendre, Laguerre and Hermite polynomials can be extracted.

Keywords： Gauss quadrature     Legendre polynomial     Laguerre polynomial     Hermite polynomial     extract roots    

HTML PDF Collect

Membrane Crystallization for Process Intensification and Control: A Review Review

Xiaobin Jiang, Yushan Shao, Lei Sheng, Peiyu Li, Gaohong He

Engineering 2021, Volume 7, Issue 1,   Pages 50-62 doi: 10.1016/j.eng.2020.06.024

Abstract:

Crystallization is a fundamental separation technology used for the production of particulate solids. Accurate nucleation and growth process control are vitally important but difficult. A novel controlling technology that can simultaneously intensify the overall crystallization process remains a significant challenge. Membrane crystallization (MCr), which has progressed significantly in recent years, is a hybrid technology platform with great potential to address this goal. This review illustrates the basic concepts of MCr and its promising applications for crystallization control and process intensification, including a state-of-the-art review of key MCr-utilized membrane materials, process control mechanisms, and optimization strategies based on diverse hybrid membranes and crystallization processes. Finally, efforts to promote MCr technology to industrial use, unexplored issues, and open questions to be addressed are outlined.

Keywords： Membrane crystallization     Nucleation     Process control     Process intensification    

HTML PDF Collect

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Engineering 2021, Volume 7, Issue 9,   Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028

Abstract:

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the
collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.

Keywords： Process monitoring     Multimode process     Dictionary learning     Transfer learning    

HTML PDF Collect

Research of Collaborative Design Process Management Based on Activity Method

Hao Yongping,Zhang Jianfu,Shi Chunjing,Shao Weiping

Strategic Study of CAE 2005, Volume 7, Issue 12,   Pages 69-73

Abstract:

By analyzing the components of an activity and the relationship of the process modeling, a topological structure of the collaborative design process management system was presented. According to the situation and characteristic of the product development process, a number of the important issues about process modeling, AU design environment, process monitoring and the data exchange between systems were discussed. At last, a user interface of the collaborative design environment and the display of process monitoring are also given.

Keywords： activity theory     activity unit     process modeling     design process management    

HTML PDF Collect

The demonstration,decision process and practice of Three Gorges Project

Pan Jiazheng

Strategic Study of CAE 2011, Volume 13, Issue 7,   Pages 4-8

Abstract:

The world-famous Three Gorges Project (TGP) is the largest hydropower station in the world and also the largest water resources and hydropower project constructed in China. The impoundment of Three Gorges Reservoir reached the design water level of 175 m for the first time on October 26, 2010, which marked that the various functions such as flood control, power generation and navigation of TGP can meet the design requirements. It took nearly 100 years from conception, demonstration, design, construction and operation to final completion of TGP. How was the conception of TGP proposed? What a role it should be? What are different opinions existed? How was the project demonstrated? What was the conclusion of demonstration? Those are the issues that many people care about but do not quite understand. A compendious introduction is made in memory of the achievement of the century dream.

Keywords： Three Gorges Project     demonstration and decision process    

HTML PDF Collect

Integrated Membrane Separation Processes

Gao Congjie,Yu Sanchuan,Jin keyong

Strategic Study of CAE 2000, Volume 2, Issue 7,   Pages 43-46

Abstract:

The application of integrated membrane separation processes in water purification, wastewater treatment, cleaning manufacture, etc. , was reviewed in this paper. Processes such as preparation of ultrapure water, drinking water purification, sewage treatment and reuse, organic wastewater treatment, multipurpose use of whey, sea water desalination were discussed in detail. The advantages and prospect of integrated membrane separation process were also analyzed in the review.

Keywords： membrane separation     integrated membrane separation process     water purification     desalination     cleaning manufacture    

HTML PDF Collect

Research on the Nearshore Process of the Typical Coast of the Bohai Bay

Yun Caixing

Strategic Study of CAE 2001, Volume 3, Issue 3,   Pages 42-51

Abstract:

Since the coast of the Bohai Bay is a typical plain silty one, the problems about engineering sediments, such as fine sediment transportation and how to reduce silting amount for the port and waterway, have come across one after another in the construction of the Tianjin Port and the Huanghua Port of Hebei Province, the two largest artificial ports in China. On the basis of the data observed in situ in the coastal area of the Dakou River in the southwestern Bohai Bay, this paper analyzes comprehensively hydrodynamics, sediment trans-portation, sedimentation of the coastal beach and alluvial and silting evolution, which provide a theoretical basis for the strategic decision of “the shallow water used as the deep water” in the plain silty coast.

Keywords： the Bohai Bay     nearshore process     sediment transportation    

HTML PDF Collect

Title Author Date Type Operation

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Journal Article

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Journal Article

Novel 3D point set registration method based on regionalized Gaussian process map reconstruction

Bo Li, Yu Zhang, Wen-jie Zhao, Ping Li,jameslb20@hotmail.com,zhangyu80@zju.edu.cn,zhaowenjie8@zju.edu.cn,pli@iipc.zju.edu.cn

Journal Article

Identification of important factors influencing nonlinear counting systems

Xinmin ZHANG, Jingbo WANG, Chihang WEI, Zhihuan SONG,xinminzhang@zju.edu.cn,wangjingbobo@zju.edu.cn,chhwei@zju.edu.cn,songzhihuan@zju.edu.cn

Journal Article

A saliency and Gaussian net model for retinal vessel segmentation

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Journal Article

ApproximateGaussian conjugacy: parametric recursive filtering under nonlinearity,multimodality, uncertainty, and constraint, and beyond

Tian-cheng LI, Jin-ya SU, Wei LIU, Juan M. CORCHADO

Journal Article

A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving

Jinxin Liu, Yugong Luo, Zhihua Zhong, Keqiang Li, Heye Huang, Hui Xiong

Journal Article

The Improving Characteristics of the Gradual Gaussian Multidimensional Pre-filter for Optical Flow Estimation

Fu Jun,Xu Weipu

Journal Article

High-precision Numerical Computation of High-degree Gauss quadrature Nodes

Zhang Qingli,Wang Xiaomei,Yin Shaotang,Jiang Haihe

Journal Article

Membrane Crystallization for Process Intensification and Control: A Review

Xiaobin Jiang, Yushan Shao, Lei Sheng, Peiyu Li, Gaohong He

Journal Article

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Journal Article

Research of Collaborative Design Process Management Based on Activity Method

Hao Yongping,Zhang Jianfu,Shi Chunjing,Shao Weiping

Journal Article

The demonstration,decision process and practice of Three Gorges Project

Pan Jiazheng

Journal Article

Integrated Membrane Separation Processes

Gao Congjie,Yu Sanchuan,Jin keyong

Journal Article

Research on the Nearshore Process of the Typical Coast of the Bohai Bay

Yun Caixing

Journal Article