Doo Kwon Baik

Model for accurate speed measurement using double-loop detectors

Vehicle speed is an important parameter in measurements of road traffic. At present, double-loop detectors are generally used for vehicular speed measurement. However, these detectors incur errors caused by scanning time, spacing between double loops, irregular vehicle trajectories, and the presence of multiple vehicles in the detection zone. This paper suggests a new model that uses an error-filtering algorithm to improve the accuracy of speed measurements. In the field tests, all percent errors of the vehicular speeds measured by the proposed model were within the error tolerance limit (plusmn5%). Furthermore, the variance of percent errors was reduced. Therefore, it can be concluded that the proposed model significantly improves vehicle-speed-measuring accuracy

Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks

Vehicle class is an important parameter in the process of road-traffic measurement. Currently, inductive-loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve the accuracy, the authors suggest a new algorithm for ILD using back-propagation neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and frequency waveform. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.5%. The results verified that the proposed algorithm improves the vehicle-classification accuracy compared to the conventional method based on ILD

SPARQL graph pattern rewriting for OWL-DL inference queries

This paper focuses on the issue of OWL-DL ontology queries implemented in SPARQL. Currently, ontology repositories construct inference ontology models, and match SPARQL queries to the models, to derive inference results. Because an inference model uses much more storage space than the original model, and cannot be reused as inference requirements vary, this method is not suitable for large-scale deployment. To solve this problem, this paper proposes a novel method that passes rewritten SPARQL queries to the original ontology model, to retrieve inference results. We define OWL-DL inference rules and apply them to rewriting Graph Patterns in queries. The paper classifies the inference rules and discusses how these rules affect query rewriting. To illustrate the advantages of our proposal, we present a prototype system based on Jena, and address query optimization, to eliminate the disadvantages of augmented query sentences. We perform a set of query tests and compare the results with related works. The results show that the proposed method results in significantly improved query efficiency, without compromising completeness or soundness.

SemQL: a semantic query language for multidatabase systems

An essential prerequisite to achieving interoperability in multidatabase systems is to be able to identify semantically equivalent or related data items in component databases. Another problem in multidatabase systems is allowing users to handle information from different databases that refer to the same realworld entity. In this paper, we provide semantic networks so that multidatabase systems can detect and resolve semantic heterogeneities among component databases. And we provide a semantic query language, SemQL, to capture the concepts about what users want. It enables users to issue queries to a large number of autonomous databases without prior knowledge of their schemas.

A Traffic Accident Detection Model using Metadata Registry

In this research, we suggested a traffic accident detection model and installed a system for automatically detecting, recording, and reporting traffic accidents at intersections. A system with these properties would be beneficial in determining the cause of accidents and the features of the intersection that impact safety. Additionally, we suggested and designed the metadata registry for the system to improve the interoperability. In a field test, the suggested model achieved a false alarm rate (FAR) of 0.34 times 10-6 percent. Considering that a California #7a algorithm (expressway incident detection algorithm) showed a FAR of 0.08 ~ 0.34 percent, our result is a remarkable achievement

Symmetric RBAC model that takes the separation of duty and role hierarchies into consideration

RBAC is a family of reference models in which permissions are assigned to roles, and users are also assigned to appropriate roles. Studies on the permission-role part of RBAC model are relatively insufficient compared with those on the user-role part, and researches on symmetric RBAC models to overcome this is also in an incipient stage. Therefore there is difficulty in assigning permissions suitable for roles. This paper proposes a symmetric RBAC model that supplements the constraints on permission assignment set forth by previous studies. The proposed symmetric RBAC model reflects the conflicts of interests between roles and the sharing and integration of permissions on the assignment of permissions by presenting the constraints on permission assignment that take the separation of duties and role hierarchies into consideration. In addition, by expressing constraints prescribing prerequisite relations between permissions through AND/OR graphs, it is possible to effectively limit the complicated prerequisite relations of permissions. The constraints on permission assignment for the proposed symmetric RBAC model reduce errors in permission assignment by properly detailing rules to observe at the time of permission assignment.

A database synchronization algorithm for mobile devices

This paper proposes an SAMD (Synchronization Algorithms based on Message Digest) algorithm based on message digest in order to facilitate data synchronization between a server-side database and a mobile database. The SAMD algorithm makes the images at the server-side database and the mobile database uses message digest tables to compare two images in order to select the rows needed for synchronization. If the two images are different, the synchronization progresses according to synchronization policy. The SAMD algorithm does not use techniques that are dependent on specific database venders; neither does it use triggers, stored procedures or timestamps. The SAMD uses only the standard SQL functions for the synchronization. Therefore the SAMD algorithm can be used in any combinations of server-side database and mobile database because of its independence of database vender. This feature is important in order to build efficient mobile business systems because the upcoming mobile business environment has heterogeneous characteristics in which diverse mobile devices, mobile databases, and RDBMS exist.

Intelligent semantic concept mapping for semantic query rewriting/optimization in ontology-based information integration system

Recently, ontology has been recognized as one of the most suitable global conceptual models for information integration architecture due to its easy taxonomical classification of data model and plentiful support of integrity constraint. However, the concept of mapping from global ontology to local information systems depends mostly on the simple metadata structures that allow for the mapping elements to be extracted with an If-Then-Else comparison statement. This kind of mapping is not suitable for ontology based data model in which the concepts are in the multiple subsumption relations. That is, there needs to be a semantic concept mapping in the case of a global concept that is to be mapped to the most specialized/generalized local concept in multiple Is-A structure, which cannot be mapped with simple direct one to one mapping. This kind of mapping needs inference mechanism to map one concept to substantially many target concepts in the concept inclusion hierarchy for an effective semantic query rewriting/optimization. In this paper, we provide an innovative method for semantic ontology concept mapping using Metadata-Based Logic (MBL) approach, which is equipped with knowledge inference mechanism so that the mapping elements can be reasoned automatically. We present semantic mapping patterns to accommodate subsumption problem and detect incoherence for a given global query. The experimental results gave viable results on the semantic query rewriting/optimization.

A Method of Service Identification for Product Line

Service-oriented information technology is a new emerging paradigm. It can drive the development with rapid, low-cost and easy composition of distributed applications in software product. However, it has several problems such as how to identify service and determine service granularity. This paper proposes a solution that identifies service by using ontology in product line. We analyze service identification methodologies and approaches, and then define a service of feature model from various extensions of feature-oriented domain analysis. To compensate for ambiguity and expression, ontology is used to precisely define relation and type among features. A service-oriented method for service identification using ontology can identify a service easily and quickly with proper granularity. This method also provides a way of grouping features and evaluating service after refining service candidate. This service-oriented method can make service with suitable granularity, and improve reusability of a service unit for identified service in software product line development.

An Approach to Analyzing Commonality and Variability of Features using Ontology in a Software Product Line Engineering

In a product line engineering, several studies have been made on analysis of feature which determines commonality and variability of product. Fundamentally, because the studies are based on developers intuition and domain experts experience, stakeholders lack common understanding of feature and a feature analysis is informal and subjective. Moreover, the reusability of software products, which were developed, is insufficient This paper proposes an approach to analyzing commonality and variability of features using semantic-based analysis criteria which is able to change feature model of specific domain to feature-ontology. For the purpose, first feature attributes were made, create a feature model following the meta model, transform it into feature-ontology, and save it to meta feature-ontology repository. Henceforth, when we construct a feature model of the same product line, commonality and variability of the features can be extracted, comparing it with meta feature ontology through a semantic similarity analysis method, which is proposed. Furthermore, a tool for a semantic similarity-comparing algorithm was implemented and an experiment with an electronic approval system domain in order to show the efficiency of the approach was conducted. A meta feature model can definitely be created through this approach, to construct a high-quality feature model based on common understanding of a feature. The main contributions are a formulating a method of extracting commonality and variability from features using ontology based on semantic similarity mapping and a enhancement of reusability of feature model.