Application of a remote image surveillance system in a robotic weapon

As one of the major steps toward a fully intelligent autonomous robotic weapon, we have made progress in three major areas, (1) design of the surveillance system by an AVR microcontroller, (2) implementation of the design mechanism, and (3) performance of the human- machine interface surveillance system via the LabVIEW graphical programming environment, so that the supervisor can control the vehicle with a keyboard or a specially adapted mouse. In order to accomplish all these achievements, there have been major additions and overhauls in both system software codes and system circuit board developments. All these developments, including a new algorithm and hardware implementation, are described in this article. The experimental results have shown the practicality of the AVR microcontroller, the LabVIEW graphical programming environment, and ZigBee wireless technology applied to a robotic weapon.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.     

Hybrid feature selection by combining filters and wrappers

Feature selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid feature selection method which combines two feature selection methods – the filters and the wrappers. Candidate features are first selected from the original feature set via computationally-efficient filters. The candidate feature set is further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by two bioinformatics problems, namely, protein disordered region prediction and gene selection in microarray cancer data. Experimental results show that equal or better prediction accuracy can be achieved with a smaller feature set. These feature subsets can be obtained in a reasonable time period.     

A kernel‐based core growing clustering method

In this paper, a novel clustering method in the kernel space is proposed. It effectively integrates several existing algorithms to become an iterative clustering scheme, which can handle clusters with arbitrary shapes. In our proposed approach, a reasonable initial core for each of the cluster is estimated. This allows us to adopt a cluster growing technique, and the growing cores offer partial hints on the cluster association. Consequently, the methods used for classification, such as support vector machines (SVMs), can be useful in our approach. To obtain initial clusters effectively, the notion of the incomplete Cholesky decomposition is adopted so that the fuzzy c‐means (FCM) can be used to partition the data in a kernel defined‐like space. Then a one‐class and a multiclass soft margin SVMs are adopted to detect the data within the main distributions (the cores) of the clusters and to repartition the data into new clusters iteratively. The structure of the data set is explored by pruning the data in the low‐density region of the clusters. Then data are gradually added back to the main distributions to assure exact cluster boundaries. Unlike the ordinary SVM algorithm, whose performance relies heavily on the kernel parameters given by the user, the parameters are estimated from the data set naturally in our approach. The experimental evaluations on two synthetic data sets and four University of California Irvine real data benchmarks indicate that the proposed algorithms outperform several popular clustering algorithms, such as FCM, support vector clustering (SVC), hierarchical clustering (HC), self‐organizing maps (SOM), and non‐Euclidean norm fuzzy c‐means (NEFCM). © 2009 Wiley Periodicals, Inc.4     

Optimal fault-tolerant Hamiltonicity of star graphs with conditional edge faults

The star graph is viewed as an attractive alternative to the hypercube. In this paper, we investigate the Hamiltonicity of an n-dimensional star graph. We show that for any n-dimensional star graph (n≥4) with at most 3n−10 faulty edges in which each node is incident with at least two fault-free edges, there exists a fault-free Hamiltonian cycle. Our result improves on the previously best known result for the case where the number of tolerable faulty edges is bounded by 2n−7. We also demonstrate that our result is optimal with respect to the worst case scenario, where every other node of a cycle of length 6 is incident with exactly n−3 faulty noncycle edges.     

Developing cooperation mechanism for multi-agent systems with Petri nets

Although the contract net protocol answers some of the questions in cooperative distributed problem solving (CDPS), it raises many others that CDPS researchers are still trying to answer. In contract net protocol, an agent may play the role of a manager or a bidder. Without a coordination mechanism, a manager may acquire excessive resources from the bidders in forming a collaborative network to execute the assigned task and thus hinder the progress of the tasks assigned to other managers due to resource contention. As a result, application of contract net protocol may not always lead to feasible solutions to accomplish tasks effectively. As a general framework for exchanging messages, the original contract net protocol does not prescribe how agents should cooperate. How to develop a collaborative mechanism to effectively perform the tasks is an important issue. This paper aims to improve the insufficiency of the contract net by developing a mechanism to facilitate cooperation of agents to accomplish their tasks while avoiding undesirable states and enhance the overall system performance in manufacturing systems. To achieve these objectives, detail process models about how agents accomplish their tasks are required. Due to the advantages in modeling concurrent, synchronous and/or asynchronous activities, Petri nets are adopted in this paper. Based on Petri net models, we study the information needed for agents to make cooperative decisions, mechanism to make agents cooperate, and how to enhance the performance in the system level by taking advantage of the agents’ cooperation capabilities. To characterize the condition for cooperation, we represent the collaborative networks formed based on the contract net protocol with Petri nets and then find the condition for a collaborative network to be feasible. The feasible condition also serves as a condition for the development of cooperation mechanism for managers. We propose a cooperation mechanism based on the idea of resource donation, including unilateral resource donation and reciprocal resource donation. Implementation architecture has also been proposed to realize our methodology.     

A collaborative desktop tagging system for group knowledge management based on concept space

The advent of internet has led to a significant growth in the amount of information available, resulting in information overload, i.e. individuals have too much information to make a decision. To resolve this problem, collaborative tagging systems form a categorization called folksonomy in order to organize web resources. A folksonomy aggregates the results of personal free tagging of information and objects to form a categorization structure that applies utilizes the collective intelligence of crowds. Folksonomy is more appropriate for organizing huge amounts of information on the Web than traditional taxonomies established by expert cataloguers. However, the attributes of collaborative tagging systems and their folksonomy make them impractical for organizing resources in personal environments.This work designs a desktop collaborative tagging (DCT) system that enables collaborative workers to tag their documents. This work proposes an application in patent analysis based on the DCT system. Folksonomy in DCT is built by aggregating personal tagging results, and is represented by a concept space. Concept spaces provide synonym control, tag recommendation and relevant search. Additionally, to protect privacy of authors and to decrease the transmission cost, relations between tagged and untagged documents are constructed by extracting document’s features rather than adopting the full text.Experimental results reveal that the adoption rate of recommended tags for new documents increases by 10% after users have tagged five or six documents. Furthermore, DCT can recommend tags with higher adoption rates when given new documents with similar topics to previously tagged ones. The relevant search in DCT is observed to be superior to keyword search when adopting frequently used tags as queries. The average precision, recall, and F-measure of DCT are 12.12%, 23.08%, and 26.92% higher than those of keyword searching.DCT allows a multi-faceted categorization of resources for collaborative workers and recommends tags for categorizing resources to simplify categorization easier. Additionally, DCT system provides relevance searching, which is more effective than traditional keyword searching for searching personal resources.     

A self-adaptation scheme for workflow management in multi-agent systems

Business processes, operational environment, variability of resources and user needs may change from time to time. An effective workflow management software system must be able to accommodate these changes. The ability to dynamically adapt to changes is a key success factor for workflow management systems. Holonic multi-agent systems (HMS) provide a flexible and reconfigurable architecture to accommodate changes based on dynamic organization and collaboration of autonomous agents. Although HMS provides a potential architecture to accommodate changes, the dynamic organization formed in HMS poses a challenge in the development of a new software development methodology to dynamically compose the services and adapt to changes as needed. This motivates us to study and propose a methodology to design self-adaptive software systems based on the HMS architecture. In this paper, we formulate a workflow adaptation problem (WAP) and propose an interaction mechanism based on contract net protocol (CNP) to find a solution to WAP to compose the services based on HMS. The interaction mechanism relies on a service publication and discovery scheme to find a set of task agents and a set of actor agents to compose the required services in HMS. We propose a viable self-adaptation scheme to reconfigure the agents and the composed services based on cooperation of agents in HMS to accommodate the changes in workflow and capabilities of actors. We propose architecture for our design methodology and present an application scenario to illustrate our idea.     

Alpha-cut implemented fuzzy clustering algorithms and switching regressions.

In the fuzzy c-means (FCM) clustering algorithm, almost none of the data points have a membership value of 1. Moreover, noise and outliers may cause difficulties in obtaining appropriate clustering results from the FCM algorithm. The embedding of FCM into switching regressions, called the fuzzy c-regressions (FCRs), still has the same drawbacks as FCM. In this paper, we propose the alpha-cut implemented fuzzy clustering algorithms, referred to as FCMalpha, which allow the data points being able to completely belong to one cluster. The proposed FCMalpha algorithms can form a cluster core for each cluster, where data points inside a cluster core will have a membership value of 1 so that it can resolve the drawbacks of FCM. On the other hand, the fuzziness index m plays different roles for FCM and FCMalpha. We find that the clustering results obtained by FCMalpha are more robust to noise and outliers than FCM when a larger m is used. Moreover, the cluster cores generated by FCMalpha are workable for various data shape clusters, so that FCMalpha is very suitable for embedding into switching regressions. The embedding of FCMalpha into switching regressions is called FCRalpha. The proposed FCRalpha provides better results than FCR for environments with noise or outliers. Numerical examples show the robustness and the superiority of our proposed methods.     

Overexpression and elevated plasma level of tumor-associated antigen 90K/Mac-2 binding protein in colorectal carcinoma

The cancer cell secretome may contain potentially useful biomarkers. Previously, we have analyzed the colorectal carcinoma (CRC) cell secretome. In this study, tumor‐associated antigen 90K (TAA90K)/Mac‐2 binding protein (Mac‐2BP), one of the CRC cell secreted proteins, was chosen for evaluation as a potential CRC biomarker because its mRNA level was also found to be significantly elevated in CRC tissues and in a more metastatic CRC cell line from the analysis of two public domain array‐based datasets. Immunohistochemical analysis of 241 CRC specimens showed that TAA90K/Mac‐2BP was positively detected in 52.7% of the tumors, but weakly or not detected in over 95% of the adjacent nontumor epithelial cells. The plasma TAA90K/Mac‐2BP levels were significantly higher in CRC patients (N = 280) versus healthy controls (N = 147) (7.77 ± 3.49 vs. 5.72 ± 2.67 μg/mL, p<0.001). Moreover, combination of TAA90K/Mac‐2BP and carcinoembryonic antigen (CEA) could outperform CEA alone in discriminating CRC patients from healthy persons in this case‐control study. Our results collectively indicate that analysis of cancer cell secretome is a feasible strategy for identifying cancer biomarker candidates, and the TAA90K/Mac‐2BP may be a potential CRC biomarker.