Chin Kuan Ho

Applying Area Extension PSO in Robotic Swarm

Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves better performance by balancing its essential intelligent behaviours more intelligently. AEPSO incorporates knowledge with the aim of choosing proper behaviors in each situation. This study provides a comparison between the variations of Basic PSO and AEPSO aiming to address dynamic and time dependent constraint problems in simulated robotic search. The problem is set up in a multi-robot learning scenario. The scenario is based on the use of a team of simulated robots (hereafter referred to as agents) who participate in survivor rescuing missions. The experiments are classified into three simulations. At first, agents employ variations of basic PSO as their decision maker and movement controllers. The first simulation investigates the impacts of swarm size, parameter adjustment, and population density on agents’ performance. Later, AEPSO is employed to improve the performance of the swarm in the same simulations. The final simulation investigates the feasibility of AEPSO in time-dependent, dynamic and uncertain environments. As shown by the results, AEPSO achieves an appreciable level of performance in dynamic, time-dependence and uncertain simulated environments and outperforms the variations of basic PSO, Linear Search and Random Search used in the simulations.

Modeling Variability in Software Product Line Using First Order Logic

Software Product Line (SPL) is a new methodology that develops software products by configuring a software product from artifacts repository. Variability is one of the important issues in designing SPL. It reflects the diversity and commonality of the artifacts in a product line. The success of SPL is basically dependent on model’s variability. SPL contains three main issues: variability modeling, configuration of new software and the analysis of SPL. Validation is a vital operation among all these issues. In the literature, many methods of modeling variability are proposed but none of them focuses on the validation. In this paper, an intelligent method for validating SPL is introduced. The proposed method is based on two layers. The higher layer is a graphical representation (satisfying visualization condition), and the lower layer is logical representation of the variability using first order logic (FOL). The way that the proposed method can be used to model variability, and support the analysis and validation of SPL is described. Later a new operation in SPL validation issue is presented. Finally, the implementation of the two basic operations in the analysis of SPL is illustrated

Investigating Inconsistency Detection as a Validation Operation in Software Product Line

Software product Line (SPL) is an emerging methodology for developing software products. A successful software product is highly dependent on the validity of a SPL. Therefore, validation is a significant process within SPL. In this paper, inconsistency detection is investigated as operation for validating SPL. Intelligent rules are formulated detecting inconsistency based on deducing the results from predefined cases. First, variability is modeled using First Order Logic (FOL) predicates as a prerequisite for inconsistency detection. Later, inconsistency is categorized in three groups. For each group a general form is formulated that can coffer all possible cases. Finally, an intelligent rule (based on FOL) is illustrated for implementing each possibility. As results, all cases of inconsistency in the domain-engineering process are defined.

Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)

Particle Swarm Optimization (PSO) method is an Evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animals group work and social behaviors. Particle Swarm Optimization with Area Extension (AEPSO) was introduced to solve the weaknesses of Basic PSO in static, dynamic optimization tasks (i.e. a group of robots disarm a set of time bomb placed at random in environment). This paper, investigated the effectiveness of AEPSO in a Real-Time problem with a noisy environment. We also explored the effectiveness of different communication ranges and help request on AEPSO.Particle swarm optimization (PSO) method is an evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animals group work and social behaviors. Particle swarm optimization with area extension (AEPSO) was introduced to solve the weaknesses of basic PSO in static, dynamic optimization tasks (i.e. a group of robots disarm a set of time bomb placed at random in environment). This paper, investigated the effectiveness of AEPSO in a real-time problem with a noisy environment. We also explored the effectiveness of different communication ranges and help request on AEPSO.

A hybrid distance measure for clustering expressed sequence tags originating from the same gene family.

Background Clustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact that these clustering methods work satisfactorily on a majority of the EST datasets, they have a common weakness. They are prone to deliver unsatisfactory clustering results when dealing with ESTs from the genes derived from the same family. The root cause is the distance measures applied on them are not sensitive enough to separate these closely related genes. Methodology/Principal Findings We propose a hybrid distance measure that combines the global and local features extracted from ESTs, with the aim to address the clustering problem faced by ESTs derived from the same gene family. The clustering process is implemented using the DBSCAN algorithm. We test the hybrid distance measure on the ten EST datasets, and the clustering results are compared with the two alignment-free EST clustering tools, i.e. wcd and PEACE. The clustering results indicate that the proposed hybrid distance measure performs relatively better (in terms of clustering accuracy) than both EST clustering tools. Conclusions/Significance The clustering results provide support for the effectiveness of the proposed hybrid distance measure in solving the clustering problem for ESTs that originate from the same gene family. The improvement of clustering accuracies on the experimental datasets has supported the claim that the sensitivity of the hybrid distance measure is sufficient to solve the clustering problem.

Defining Variability in DSS: An Intelligent Method for Knowledge Representation and Validation

Managing knowledge is both a challenging and complex task. There is a number techniques for making decisions in todays knowledge-based economies. Decision support systems (DSS) have been developed to aid the decision-making process to find solutions for multiple problems. One aspect that hinders successful decision making is the issue of variability definition. The aim of this paper is to define variability by proposing an intelligent method. Knowledge representation is based in two layer:1) the upper layer i.e. graphical representation and 2) the lower layer i.e. a mathematical algorithm. We present a method that defines and provides auto-support for five operations in knowledge validation particularly dependency constraint rules, propagation, delete-cascade, logical inconsistency and dead choice detection.

An Interactive Method for Validating Stage Configuration

Software product Line (SPL) is an emerging methodology for developing software products. Stage-configuration is one the important processes applying to the SPL. In stage-configuration, different groups and different people make configuration choices in different stages. Therefore, a successful software product is highly dependent on the validity of stage-configuration process. In this paper, a rule-based method is proposed for validating stage-configuration in SPL. A logical representation of variability using First Order Logic (FOL) is provided. Five operations: validation rules, explanation and corrective explanation, propagation and delete-cascade, filtering and cardinality test are studied as proposed operations for validating stage-configuration. The relevant contributions of this paper are: implementing automated consistency checking among constraints during stage-configuration process based on three levels (Variant- to-variant, variant-to-variation point, and variation point-to-variation point), define interactive explanation and corrective explanation, define a filtering operation to guide the user within stage-configuration, and define (explicitly) delete-cascade validation.

Knowledge Representation and Validation in a Decision Support System: Introducing a Variability Modelling Technique

Knowledge has become the main value driver for modern organizations and has been described as a critical competitive asset for organizations. An important feature in the development and application of knowledge-based systems is the knowledge representation techniques used. A successful knowledge representation technique provides a means for expressing knowledge as well as facilitating the inference processes in both human and machines [19]. The limitation of symbolic knowledge representation has led to the study of more effective models for knowledge representation [17]. Malhotra [14] defines the challenges of the information-sharing culture of the future knowledge management systems as the integration of decision-making and actions across inter-enterprise boundaries. This means a decision making process will undergo different constraints. Therefore, existence of a method to validate a Decision Support System (DSS) system is highly recommended. In the third generation of knowledge management, the knowledge representation acts as boundary objects around which knowledge processes can be organized [26]. Knowledge is viewed in a constructionist and pragmatic perspective and a good knowledge is something that allows flexible and effective thinking and construction of knowledge-based artifacts [26]. This paper answers the two questions of [26] and [14] in the context of a DSS: 1) how to define and represent knowledge objects and 2) how to validate a DSS. For any decision, there are many choices that the decision maker can select from [7]. The process of selection takes place at a decision point and the selected decision is a choice. For example, if someone wants to pay for something, and the payment mode is either by cash or by credit card, the payment mode is the decision point; cash and credit card are choices. Now, we can conclude that the choices and decision points represent the knowledge objects in DSS. Choices, decision points and the constraint dependency rules between these two are collectively named as variability. Task variability is defined in [5] as the number of exceptions encountered in the characteristics of the work. The study in [5] tested the importance of variability in the system satisfaction. Although there are many existing approaches for representing knowledge DSS, the design and implementation of a good and useful method that considers variability in DSS is much desired.

Cooperative learning of homogeneous and heterogeneous particles in Area Extension PSO

Particle Swarm Optimization with Area Extension (AEPSO) is a modified PSO that performs better than basic PSO in static, dynamic, noisy, and real-time environments. This paper investigates the effectiveness of cooperative learning AEPSO in a simulated environment. The environment is a 2D landscape planted with various types of bombs with arbitrary explosion times and locations. The simulated-robotspsila task (i.e., swarm particles) is to disarm these bombs. Different bombs must be disarmed with appropriate robots (i.e., disarming skills and bomb types must correspond) and the robots (hereafter, referred to as agents) do not have full observations of the environment due to uncertainties in their perceptions. In this study, each agent has the ability to disarm different type of bombs in heterogeneous scenario while each agent has the ability to disarm all types of bombs in homogeneous scenario. We found that AEPSO shows reliable performance in both heterogeneous and homogeneous scenarios as compared to the basic PSO. We also found that the proposed cooperative learning is robust in environment where agentspsila perception are distorted with noise.

Clustering of expressed sequence tags with distance measure based on Burrows-Wheeler transform

Expressed sequence tag (ESTs) are a technology used for gene discovery and transcriptome analysis. They are single-read short fragments of expressed gene produced from mRNA extracted from a living cell. Clustering is a vital computational step in the processing of ESTs, its main goal is to ensure that all ESTs originated from the same mRNA are grouped together. Basically, the clustering algorithms of EST can be classified into two approaches, i.e. alignment-based and alignment-free. The latter approach is preferred in recent years, due to its faster speed and satisfactory outcome. In this paper, we proposed and implemented an EST clustering algorithm based on the alignment-free approach, where we introduced a measurement of distance between ESTs using the combination of Burrows-Wheeler transform, window length and word-tuple. We assessed the proposed method with a dataset downloaded from the Unigene. The preliminary result shows high clustering quality with this method, where the accuracy of clustering (evaluated using F-measure) can achieve up to 0.9671.