• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.318-326
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• 2004

The ebXML (Electronic Business using eXtensible Markup Language) Specification Schema is to provide nominal set of specification elements necessary to specify a collaboration between business partners based on XML. As a part of ebXML Specification Schema, BPSS (Business Process Specification Schema) has been provided to support the direct specification of the set of elements required to configure a runtime system in order to execute a set of ebXML business transactions. The BPSS is available in two stand-alone representations, a UML version and an XML version. Due to the limitations of UML notations and XML syntax, however, current ebXML BPSS specification is insufficient to specify formal semantic constraints of modeling elements completely. In this study, we propose a classification schema for the BPSS semantic constraints and describe how to represent those semantic constraints formally using OCL (Object Constraint Language). As a way to verify a Business Process Specification (BPS) with the formal semantic constraint modeling, we suggest a rule-based approach to represent the formal constraints and to use the rule-based constraints specification to verify BPSs in a CLIPS prototype implementation.

• Asia pacific journal of information systems
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• v.14 no.1
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• pp.79-100
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• 2004

As a part of ebXML(Electronic Business using eXtensible Markup Language) framework, BPSS(Business Process Specification Schema) has been provided to support the direct specification of the set of elements required to configure a runtime system in order to execute a set of ebXML business transactions. The BPS,' is available in two stand-alone representations, a UML version and an XML version. Due to the limitations of UML notations and XML syntax, however, current ebXML BPSS specification fails to specify formal semantic constraints completely. In this study, we propose a constraint classification scheme for the BPSS specification and describe how to formally represent those semantic constraints using OCL(Object Constraint Language). As a way to validate p Business Process Specification(BPS) with the formal semantic constraints, we suggest a rule-based approach to represent the formal constraints and demonstrate its detailed mechanism for applying the rule-based constraints to the BPS with a prototype implementation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.376-379
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• 2003

In this paper we describe DCClass, a tool for fuzzy information granulation with transparency constraints. The tool is particularly suited to solve fuzzy classification problems, since it is able to automatically extract information granules with class labels. For transparency pursuits, the resulting information granules are represented in form of fuzzy Cartesian product of one-dimensional fuzzy sets. As a key feature, the proposed tool is capable to self-determining the optimal granularity level of each one-dimensional fuzzy set by exploiting class information. The resulting fun information granules can be directly translated in human-comprehensible fuzzy rules to be used for class inference. The paper reports preliminary experimental results on a medical diagnosis problem that shows the utility of the proposed tool.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.252-255
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• 2008

The purpose of construction project management is to achieve planned quality by adequate cost and schedule of the project, thus the effective time management is a critical factor to actualize the object. But the traditional time management by using milestone has several limitations which are not sufficiently considered mutual relation and variated from the task. On this wise, applying the concept and principles of lean construction to the project will be the best way not only reduce waste and variation, but also improve the productivity and ability to overcome limitation as mentioned above. Thus the study, Last Planner System, specially focuses on constraint analysis which is used in lookahead planning system. The results of this research will provide the constraints classification that is able to improve work reliable and percent plan complete when time planning by controlling constraints of project.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.70-78
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• 1992

In this paper, an approach for 3-D object segmentation and classification, which is based on eigen-values of polynomial function as their surface features, using neural network is proposed. The range images of 3-D objects are classified into surface primitives which are homogeneous in their intrinsic eigenvalue properties. The misclassified regions due to noise effect are merged into correct regions satisfying homogeneous constraints of Hopfield neural network. The proposed method has advantage of processing both segmentation and classification simultaneously.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.162-176
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• 2007

Machine Learning (ML) has become very popular within Data Mining (KDD) and Artificial Intelligence (AI) research and their applications. In the ML and KDD contexts, two main approaches can be used for inducing a Bayesian Network (BN) from data, namely, Conditional Independence (CI) and the Heuristic Search (HS). When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at increasing the computational efficiency. In this paper a new CI based approach to induce BCs from data is proposed and two algorithms are presented. Such approach is based on the Markov Blanket concept in order to impose some constraints and optimize the traditional PC learning algorithm. Experiments performed with the ALARM, as well as other six UCI and three artificial domains revealed that the proposed approach tends to execute fewer comparison tests than the traditional PC. The experiments also show that the proposed algorithms produce competitive classification rates when compared with both, PC and Naive Bayes.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.149-159
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• 2001

There have been lots of researches for providing a personalized service to a customer using one-to-one marketing and collaborative filtering techniques in E-Commerce. However, there are technical difficulties for providing the recommendation of products far users, which often involve high complexity of computation. In this paper, we have presented an integrated method of classification problem solving method and constraint based configuration techniques. This method can reduce a complexity of computation by classifying a solution domain space that has a higher complexity of composition. Thereafter, we have modeled customers constraints and the components of products to configure a complete system by passing it to constraint processing module in Constraint Satisfaction Problems. Constraint-based configuration uses the constraint propagation using the constraints of buyers and the constraints among PC components to configure a proper product for a customer. We have transformed and applied vector space modeling method in the field of information retrieval to consider a customer satisfaction in addition to the CSP. Finally, we have applied our system to test data fur evaluating a customers satisfaction and performance of the proposed system.

• Journal of Apiculture
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• v.34 no.2
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• pp.169-180
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• 2019

Beekeeping has been practiced for centuries in Ethiopia. Currently, there are three broad classification of honey production systems in Ethiopia; these are traditional (forest and backyard), transitional(intermediate) and modern(frame beehive) systems. Ethiopian honey production is characterized by the widespread use of traditional technology resulting in relatively low honey yield and poor honey quality. Despite the challenges and constraints, Ethiopia has the largest bee population in Africa with over 10 million bee colonies, of which 5 to 7.5million are hived while the remaining exists in the wild. Consequently, these figures, indeed, has put Ethiopia as the leading honey and beeswax producer in Africa. In fact, Ethiopia has even bigger potential than the current honey production due to the availability of plenty apicultural resources such as natural forests with adequate apiculture flora, water resources and a high number of existing bee colonies. However, lack of well-trained man powers, lack of standardization, problems associated with honey bee pests and diseases, high price and limited availability of modern beekeeping equipment's for beekeepers and absconding and migration of bee colonies are some of the major constraints reported for beekeeping in Ethiopia. In this review, an attempt was made to present all beekeeping practices in Ethiopia. The opportunities and major constraints of the sector were also discussed.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2004.04a
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• pp.219-229
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• 2004

In this article a comprehensive review of the Rock Mass Rating and Q-rockmass classification systems is made with reference to their scope with in the constraints of underground mining operations. The modifications suggested by KIGAM for both the RMR and Q for the calculation of a safe unsupported span were tested for Daesung and Pyunghae underground limestone mines. Even though the suggested modifications were site specific, the additional parameters considered in the above classification systems are very significant for a design of stable underground openings, considering any general mining conditions.