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基于ARIMA和Kalman滤波的道路交通状态实时预测 Article

东伟徐,永东王,利民贾,勇秦,宏辉董

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 287-302 doi: 10.1631/FITEE.1500381

Abstract: 道路交通流预测不仅可以为出行者提供实时有效的信息，而且可以帮助他们选择最佳路径，减少出行时间，实现道路交通路径诱导，缓解交通拥堵。本文提出了一种基于ARIMA模型和Kalman滤波算法的道路交通流预测方法。首先，基于道路交通历史数据建立时间序列的ARIMA模型。其次，结合ARIMA模型和Kalman滤波法构建道路交通预测算法，获取Kalman滤波的测量方程和更新方程。然后，基于历史道路交通数据进行算法的参数设定。实验结果表明，基于ARIMA模型和Kalman滤波的实时道路交通状态预测方法是可行的，并且可以获得很高的精度。

Keywords： ARIMA模型；Kalman滤波；建模；训练；预测

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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

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

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Convergence analysis of distributed Kalman filtering for relative sensing networks Research

Che LIN, Rong-hao ZHENG, Gang-feng YAN, Shi-yuan LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9, Pages 1063-1075 doi: 10.1631/FITEE.1700066

Abstract:

We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysisinformation and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman

Keywords： Relative sensing network Distributed Kalman filter Schur stable Linear matrix inequality

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Pre-Trained Language Models and Their Applications Review

Haifeng Wang, Jiwei Li, Hua Wu, Eduard Hovy, Yu Sun

Engineering 2023, Volume 25, Issue 6, Pages 51-65 doi: 10.1016/j.eng.2022.04.024

Abstract:

Pre-trained language models have achieved striking success in natural language processing (NLP), leading to a paradigm shift from supervised learning to pre-training followed by fine-tuning. The NLP community has witnessed a surge of research interest in improving pre-trained models. This article presents a comprehensive review of representative work and recent progress in the NLP field and introduces the taxonomy of pre-trained models. We first give a brief introduction of pre-trained models, followed by characteristic methods and frameworks. We then introduce and analyze the impact and challenges of pre-trained models and their downstream applications. Finally, we briefly conclude and address future research directions in this field.

Keywords： Pre-trained models Natural language processing

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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: Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large

Keywords： Kalman filter Gaussian filter Time series estimation Bayesian filtering Nonlinear filtering Constrained

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Performance analysis of two EM-based measurement bias estimation processes for tracking systems None

Zhi-hua LU, Meng-yao ZHU, Qing-wei YE, Yu ZHOU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9, Pages 1151-1165 doi: 10.1631/FITEE.1800214

Abstract: With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes

Keywords： Non-linear state-space model Measurement bias Extended Kalman filter Extended Kalman smoothing Expectation-maximization

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Progress in Neural NLP: Modeling, Learning, and Reasoning Review

Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum

Engineering 2020, Volume 6, Issue 3, Pages 275-290 doi: 10.1016/j.eng.2019.12.014

Abstract:

Natural language processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling computers to understand and process human languages. In the last five years, we have witnessed the rapid development of NLP in tasks such as machine translation, question-answering, and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data. In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, sentence embedding, and sequence-to-sequence modeling, which are widely used in modern NLP engines. In the learning section, we will introduce widely used learning methods for NLP models, including supervised, semi-supervised, and unsupervised learning; multitask learning; transfer learning; and active learning. We view reasoning as a new and exciting direction for neural NLP, but it has yet to be well addressed. In the reasoning section, we will review reasoning mechanisms, including the knowledge, existing non-neural inference methods, and new neural inference methods. We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks. At the end of this paper, we will briefly outline our thoughts on the future directions of neural NLP.

Keywords： Natural language processing Deep learning Modeling learning and Reasoning

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Studies on Precise Spacecraft Navigation and Positioning Using GPS

Xiang Kaiheng,Qu Guangji

Strategic Study of CAE 2004, Volume 6, Issue 1, Pages 86-91

Abstract: paper, GPS measurement technology, Encke method to solve satellite orbit perturbation and generalized Kalman

Keywords： spacecraft navigation GPS carrier phase Kalman filtering

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Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm Research Articles

Rui Wang, Yahui Li, Hui Sun, Youmin Zhang,h-sun@cauc.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 1, Pages 1-140 doi: 10.1631/FITEE.2000206

Abstract: This paper presents the design of a new event-triggered Kalman consensus filter (ET-KCF) algorithm forThe proposed algorithm integrates the traditional , information freshness calculation method, and Kalman

Keywords： Distributed Kalman consensus filter (KCF) Event-triggered mechanism Age of information (AoI) Stability

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Technically feasible approach to earthquake prediction

Liu Defu,Kang Chunli

Strategic Study of CAE 2009, Volume 11, Issue 6, Pages 159-165

Abstract:

Earthquake prediction is an undertaking of public welfare.But earthquakes cannot be successfully predicted at present due to technological reasons.Earthquake prediction should be studied earnestly to adapt the demand of society for earthquake prediction at present.In order to study the possiblity of predicting the Wenchuan M8.0 Earthquake occurred on May 12,2008, based on the earthquake information itsself, this paper has suggesed a kind of numerical modeling method for predicting the earthquake magnitudes,and a method for predicting seismogenic areas by means of the Outgoing-Long-Wave-Radiation (OLR) information of satellite remote sensing. The results show that it is a technically feasible approach.

Keywords： earthquake OLR numerical modeling predicting

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Filtering and tracking with trinion-valued adaptive algorithms Article

Xiao-ming GOU,Zhi-wen LIU,Wei LIU,You-gen XU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8, Pages 834-840 doi: 10.1631/FITEE.1601164

Abstract: Moreover, the trinion model can effectively represent the general relationship of state evolution in Kalman

Keywords： Three-dimensional processes Trinion Least mean squares Kalman filter

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Forecast of fire accidents based on Grey-Markov model

Mao Zhanli,ZhuYi,Yang Bozhong,ZhuLei

Strategic Study of CAE 2010, Volume 12, Issue 1, Pages 98-101

Abstract:

The occurrence of fire accidents is influenced by many complex factors, and it has the characteristics of random and fluctuation, so grey model and Markov model are combined together to establish a new Grey-Markov model in this paper .The paper adopts grey model and Markov model to show grey feature and random separately, at last the model is used to predict fire accidents in countryside. The result shows the forecast precision of Grey-Markov model is higher than the forecast precision of grey model, the model can satisfy the demand in forecast precision, and it can be used for fire accidents forecast.

Keywords： Grey model Markov model fire accidents forecast

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Analysis of GM（1，1）Model and Its Application in Fire Risk Prediction

Chen Zijin,Wang Fuliang,Lu Shouxiang

Strategic Study of CAE 2007, Volume 9, Issue 5, Pages 91-94

Abstract:

Theoretical analysis of grey prediction model GM(1, 1) is present in this paper. Monotonicity of predicted value and its variation tendency predicted by GM(1, 1)model is proved. Based on the monotonicity of predicted value and its variation tendency, applicability criterion of GM(1, 1) is brought forward. Example applications of the criterion in fire risk grey prediction are discussed.

Keywords： fire forecast GM(1 1) rate of the fire injured

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Prediction method of foundation vibration responses induced by impact loading using modified anderson model

Fang Bo

Strategic Study of CAE 2014, Volume 16, Issue 11, Pages 96-102

Abstract:

A synthetic method, which combines theoretical model and field measurement data was put forward to predict vibration effects induced by impact loading. A series of targeted field measurements were proceeded by hammer impact tests. The Anderson model was modified and verified by the data measured in field hammer impact tests. Then the impact induced vibration was predicted using the modified Anderson model. Finally, the prediction results were compared with the measured results. The results indicates that the prediction results approximately approach to the measured results.

Keywords： prediction method impact loading vibration effects anderson model

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Exploiting a depth contextmodel in visual tracking with correlation filter Article

Zhao-yun CHEN, Lei LUO, Da-fei HUANG, Mei WEN, Chun-yuan ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5, Pages 667-679 doi: 10.1631/FITEE.1500389

Abstract: Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.

Keywords： Visual tracking Depth context model Correlation filter Region growing