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.

/pdf/metaheuristics-from-design-to-implementation-1vqdfv8nk9.pdf

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

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2008/data/papers/1124.pdf

Wavelet Based OE Model Identification with Random Missing Data

Controller design for high-order descriptor linear systems based on requirements on tracking performance and disturbance rejection

Pose tracking is a fundamental technology needs to be solved in on-orbit service. Visual pose tracking provides an inexpensive solution while it remains a large obstacle to track satellites under space illumination changing condition. This paper combines point feature with edge feature to track a model-known satellite. It makes use of the distinctiveness of point feature and robustness of edge feature, and treats them differently through a weight-self-tune procedure according to their residuals respectively. Simulation results show that the proposed combination approach improves the tracking accuracy and holds the robustness simultaneously.

Combining point and edge for satellite pose tracking under illumination varying

This paper applies the wavelet reconstruction technique to the identification of attitude model of small satellites when there exists missing phenomenon in the input or/and output experiment data. Two kinds of missing data existing in the satellite attitude control systems, namely, the multirate case and the random missing type are both considered. By employing the wavelet analysis, two corresponding identification algorithms are respectively proposed under the condition of these two kinds of missing measurements. Finally, the simulations and comparisons are given to verify the effectiveness of proposed methods.

Attitude model identification of small satellites with missing data

In this paper, the existed problems about optimal torque control of rigid spacecraft attitude maneuver with constraints are discussed. The pseudospectral method is adopted to solve the problem which has torque constraint or state constraints, and the simulation analysis on the problem in the case of unconstrained or the torque constraint or state constraints is made. The simulation results show that this problem can be effectively solved by the pseudospectral method.

Jingyan Song

Papers

Wavelet Based OE Model Identification with Random Missing Data

Controller design for high-order descriptor linear systems based on requirements on tracking performance and disturbance rejection

Combining point and edge for satellite pose tracking under illumination varying

Attitude model identification of small satellites with missing data

Optimal torque control constraint rigid spacecraft attitude maneuver with constraints