Statistical Analysis with Missing Data

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.

Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Computational Intelligence in Wireless Sensor Networks: A Survey

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data

A bayesian network approach to traffic flow forecasting

Shape is one of the fundamental visual features in pattern recognition and target tracking. And shape normalization is a very important pre-processing step in image understanding. In general, there are four basic forms of planar shape distortions caused by changes in viewer's location: translation, rotation, scaling and skewing. A good shape descriptor should be invariant to these distortions. In this paper, we study and compare three shape normalization algorithms: J.G. Leu's shape compacting algorithm and its two modified versions: Wang's image ellipse algorithm and Liang J.J.'s principal axis algorithm. Experiments on a set of images show that all of these algorithms have some drawbacks and we give some advices for modification.

A comparative study of three shape normalization algorithms

The bottleneck of the GIS-T data gathering is the heterogeneity between the information sources. The main purpose of this paper is to make clear the idea of semantic matching and to present a prototype implementation framework. After a short presentation of GIS-T characteristics and approaches of information interoperability, we focus an the establishment of the semantic matching, one of the most critical issue in the fusion. The matching problem is addressed in detail at three levels: the classes, the roles and the entities. Then we will go on to describe a proof-of-concept framework system that we are developing.

Towards GIS-T information fusion

Video surveillance technology has been widely used in intelligent transportation system (ITS) to measure traffic flow parameters and detect accidents. Occlusion of moving objects by stationary or other moving foreground objects always causes tracking errors. This paper presents a novel module for vehicle tracking under the condition of occlusion, which can be conveniently added to the existing video surveillance systems as a complementary part. The appearance information of the object (shape and texture) is combined with the spatial-temporal Markov random field (MRF) based tracking model through a kind of special structuring elements in mathematical morphology (MM) theory. As a result, the sites in MRF are reduced, which saves computational cost greatly, and the assumption on the vehicle shape model in prior is not needed. Experiments on various kinds of image sequences show that the proposed module can effectively track vehicles with severe occlusion.

A novel module of tracking vehicles with occlusion

The trajectory tracking problem for the 6- degree-of-freedom (DOF) parallel robot is analyzed in this paper, which is a typical multi-input multi-output (MIMO) systems control issue. A novel high-efficiency model-based control scheme is proposed based on the off-line multi-body dynamics of the parallel robot, which extensively enhances the on-line computation efficiency. Moreover, the velocity signals are not required in the proposed feedback control scheme by introducing the extended state observer (ESO), which solves the performance degradation caused by the measurement noise of velocity signals in practice. Finally, an electrical 6-DOF parallel robot is employed to verify the advantages of the proposed controller in comparison with the enhanced PID controller.

High-efficiency model-based control of 6-DOF parallel robot using position measurement only

In this paper, the problem of H2 filtering for polytopic discrete-time linear parameter-varying systems is studied. A gain scheduled filter, which not only can guarantee the filtering error system to be quadratically stable but also has a prescribed H2 performance, is determined. The filter design is implemented by means of a convex optimization procedure and the time-varying parameters are supposed to affect all system matrices. Sufficient conditions are given for the existence of the desired filters from the solution of a linear matrix inequality problem formulated only in terms of the vertices of the polytope. A numerical example shows that the proposed filter design method is more effective and less conservative than some existing results.

Jingyan Song

Papers

A comparative study of three shape normalization algorithms

Towards GIS-T information fusion

A novel module of tracking vehicles with occlusion

High-efficiency model-based control of 6-DOF parallel robot using position measurement only

H2 Gain Scheduled Filtering for Polytopic Discrete-time Linear Parameter-Varying Systems