Generalized Semi-Infinite Optimization and its Application in Robotics' Path Planning Problem

title: Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy
abstract: Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality. Recently, outlier detection for high-dimensional stream data became a new emerging research problem. A key observation that motivates this research is that outliers in high-dimensional data are projected outliers, i.e., they are embedded in lower-dimensional subspaces. Detecting projected outliers from high-dimensional stream data is a very challenging task for several reasons. First, detecting projected outliers is difficult even for high-dimensional static data. The exhaustive search for the outlying subspaces where projected outliers are embedded is a NP problem. Second, the algorithms for handling data streams are constrained to take only one pass to process the streaming data with the conditions of space limitation and time criticality. The currently existing methods for outlier detection are found to be ineffective for detecting projected outliers in high-dimensional data streams. 
In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensional data streams. SPOT employs an innovative window-based time model in capturing dynamic statistics from stream data, and a novel data structure containing a set of top sparse subspaces to detect projected outliers effectively. SPOT also employs a multi-objective genetic algorithm as an effective search method for finding the outlying subspaces where most projected outliers are embedded. The experimental results demonstrate that SPOT is efficient and effective in detecting projected outliers for high-dimensional data streams. The main contribution of this thesis is that it provides a backbone in tackling the challenging problem of outlier detection for high-dimensional data streams. SPOT can facilitate the discovery of useful abnormal patterns and can be potentially applied to a variety of high demand applications, such as for sensor network data monitoring, online transaction protection, etc.

title: D-GridMST: Clustering Large Distributed Spatial Databases
abstract: In this paper, we will propose a distributable clustering algorithm, called Distributed-GridMST (D-GridMST), which deals with large distributed spatial databases. D-GridMST employs the notions of multi-dimensional cube to partition the data space involved and uses density criteria to extract representative points from spatial databases, based on which a global MST of representatives is constructed. Such a MST is partitioned according to users’ clustering specification and used to label data points in the respective distributed spatial database thereafter. Since only the compact information of the distributed spatial databases is transferred via network, D-GridMST is characterized by small network transferring overhead. Experimental results show that D-GridMST is effective since it is able to produce exactly the same clustering result as that produced in centralized paradigm, making D-GridMST a promising tool for clustering large distributed spatial databases.

title: A Framework for Efficient Association Rule Mining in XML Data
abstract: In this article, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document first are preprocessed to transform either to an Indexed XML Tree (IX-tree) or to Multirelational Databases (Multi-DB), depending on the size of the XML document and the memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized metapatterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent undergeneralization or overgeneralization. Resulting generalized metapatterns are used to generate large ARs that meet the support and confidence levels. A greedy algorithm is also presented in order to integrate data selection and large itemset generation to enhance the efficiency of the AR mining process. The experiments conducted show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

title: A Color Indexing Scheme Using Two-level Clustering Processing for Effective and Efficient Image Retrieval
abstract: In this paper, we present a clustering-based color indexing scheme for effective amd efficient image retrieval, which is essentially an exploration on the application of clustering technique to image retrieval. In our approach, the color features are clustered automatically using a color clustering algorithm twice (called two-level clustering processing) and two color feature summarizations are obtained, e.g. Local Color Centriods (LCCs) and Global Color Centroids (GCCs). Based upon LCCs and GCCs, a three-level R-tree is bulit for indexing database images and performing effecitve and efficient image retrieval. The experiments show that this indexing scheme is effective and efficient in performing image retrieval.

title: Detecting Projected Outliers in High-Dimensional Data Streams
abstract: In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective Genetic Algorithm (MOGA) is employed as an effective search method for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.

title: Coordination Research on Key Parameters in Generation Competition Based on Dynamic Simulation
abstract: Power generation adequacy is critical for the reliability of electricity supply service, and therefore its investment draws much attention. This paper builds a dynamic simulation model to solve the setting of two key parameters, the energy price cap and the capacity price in the generation market, and applies this model to various cases to analyze the generation capacity development. Based on the simulation results, this paper analyzes the sensitivity of generation capacity to the key parameters. Furthermore, this paper proposes an evaluation index system for the simulation model, and discusses the correlation between the two key parameters, from the views of reliability and economy. The research shows that there might be potential unmarked misunderstandings in the setting of key parameters, and suggests that under a certain electricity bill for customers, parameter combination of low energy price cap and high capacity price should be made, in order to curb the fluctuation of generation capacity and maintain the system reliability.

title: Gait Planning Of Quadruped Robot Based On Third-Order Spline Interpolation
abstract: This paper presented a brief description for the gait planning of quadruped robot named Aibo ERS-7 which is a standard platform in the RoboCup 4-legged league. We approach a spline shaped locus to reduce the dimension of the parameter optimizing space and solve the problem of the significant bias between the planned locus and the real one. The result shows that the spline shaped locus is effective in finding the optimized locus shape in a short time. Finally, the robot achieves a gait faster than any previously known learned gait for Aibo

title: Evolutionary Algorithm Based on Overlapped Gene Expression
abstract: Inspired by the overlap gene expression in biological study, this paper proposes a novel evolutionary algorithm-EAOGE i.e. Evolutionary Algorithm based on Overlapped Gene Expression. Different from existing works, EAOGE suggests a new expression structure of genes with probabilities of overlapped expression for some segments. The main contributions are: (1) Proposing a novel model and an algorithm of gene expression while borrowing some ideas from artificial immunity algorithm; (2) Analyzing the expressing space and encode characteristic of the new model; (3) The extensive experiments in function finding shows that new model is 2.8~9.7 times faster than usual GEP method, and in higher-degree polynomial function finding, the success rate of EAOGE is over 10 times than usual GEP.

title: Explicit Routing for Traffic Engineering in Labeled Optical Burst-Switched WDM Networks
abstract: Optical burst switching (OBS) is a promising technique for supporting high-capacity bursty data traffic over optical wavelength-division-multiplexed (WDM) networks. A label-switched path can be established to forward a burst control packet (BCP) if each OBS node is augmented with an IP/MPLS controller. Such a network is called a labeled OBS (LOBS) network, and it can exploit the explicit routing and constraint-based routing properties supported in the MPLS framework to perform traffic and resource engineering. In this paper, we consider the traffic-engineering problem in a LOBS network, using explicit routing to balance network load and to reduce the burst-loss probability. We model the traffic-engineering problem in a LOBS network as an optimization problem and propose two new objective functions to minimize network congestion. Our illustrative numerical examples show that the burst-loss probability can be significantly reduced using optimization technique, when compared with shortest-path routing.

title: The LLAMA Middleware Support for Accountable Service-Oriented Architecture
abstract: Enterprises are turning to SOA for the flexible deployment of business processes. While current monitoring tools can detect service execution problems in an enterprise's servers and report such problems to human decision makers, they may not closely monitor the external services they use, diagnose the root cause of process problems, and automatically reconfigure the process to replace faulty services. This paper presents the LLAMA middleware support for service process monitoring, run-time management, and configuration. Instances of accountability agents are deployed to selectively monitor some services' performance. These agents in turn allow LLAMA's Accountability Authority (AA) to diagnose process problems and apply any necessary reconfiguration. The project also builds tools to simplify the setup and deployment of LLAMA components. Our experimental results indicate that using LLAMA contributes only a modest amount of system overhead, and that the diagnosis process is swift and sufficiently accurate.