• Journal of Information Processing Systems
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• 제17권1호
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• pp.107-123
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• 2021

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.121-136
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• 2023

Wireless Sensor Networks (WSNs) have many potential applications and unique challenges. Some problems of WSNs are: severe resources' constraints, low reliability and fault tolerant, low throughput, low scalability, low Quality of Service (QoS) and insecure operational environments. One significant solution against mentioned problems is hierarchical and clustering-based multipath routing. But, existent algorithms have many weaknesses such as: high overhead, security vulnerabilities, address-centric, low-scalability, permanent usage of optimal paths and severe resources' consumption. As a result, this paper is proposed an energy-aware, congestion-aware, location-based, data-centric, scalable, hierarchical and clustering-based multipath routing algorithm based on Numerical Taxonomy technique for homogenous WSNs. Finally, performance of the proposed algorithm has been compared with performance of LEACH routing algorithm; results of simulations and statistical-mathematical analysis are showing the proposed algorithm has been improved in terms of parameters like balanced resources' consumption such as energy and bandwidth, throughput, reliability and fault tolerant, accuracy, QoS such as average rate of packet delivery and WSNs' lifetime.

• Journal of the Korean Society of Safety
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• 제31권5호
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• pp.49-53
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• 2016

The goal of this study is to analyze accidents occurred at experimental laboratory and to suggest hierarchical taxonomy applicable to prepare countermeasures reducing the experimental laboratory accidents. Recent 5 years accidents were analyzed and classified according to their primary cause, facility or human. Then in case of facility, the accidents were further classified whether they can be fixed by organization or by individual. In case of human factor, they were classified into physical, chemical, or biological to prepare precise measures. Depending on the adequacy of appropriate practice, several measures were suggested such as; whether to improve training of laboratory workers, or to improve training the system, or to improve or prepare practice substantially. A new taxonomy for laboratory accident was suggested complying other governmental agencies' classification such as KOSHA and KGS. Additionally, two kinds of possibilities were suggested such as possibility of major accident and possibility of disaster which can be defined as laboratory accident causing large scale of harmful consequence to residential area or environment by fire, explosion and/or toxic release of hazardous chemicals and/or microbiology.

• Asia pacific journal of information systems
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• 제9권1호
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• pp.17-38
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• 1999

While the value of information technology has long been a hot issue, few solid results have been found as of yet. It is partly due to methodological factors and model underspecifcation. This study empirically develops a ITS(information technology structure) taxonomy and investigates the relationships between ITS taxonomy and business performance in the Korean firms. Among factors that impact business performance, organization structure and control system are selected and they are hypothesized to moderate-the relationships between ITS taxonomy and business performance. By surveying 91 manufacturing firms and applying hierarchical cluster analysis, four ITS are identified : centralized, decentralized, centralized cooperative, decentralized cooperative. ANOVA, correlation analysis and crosstable analysis say the presence of moderating effect of organization structure and control system. Cooperative ITS is best in business performance. Centralized ITS is related to functional organizational form. Decentralized ITS is related to product organizational form with decentralized decision making, Centralized cooperative ITS is related to matrix organizational form. Decentralized cooperative ITS is related to matrix organizational form with high integration. These findings have implications for the opportunities and challenges to match information technology with organization structure and control system.

• Journal of Digital Convergence
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• 제10권6호
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• pp.275-281
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• 2012

This research proposes the method to develop the recommendation system of automobile parts using task ontology technology. The proposed intelligent recommendation system is designed to learn the assembly process of automobile parts and the automobile parts are composed by ontology method for the recommendation of the parts. Using hierarchical taxonomy based on is-a relationship, the relationship between each part that makes up automotive engine was set. Each part has each different weighted value according to the knowledge of automobile experts. The weighted value is created by the number of selection that the users of the automobile recommendation system select while using the system and the final value calculated by the multiplication of the weighted value, which is recorded within the system. As a result, the users can easily identify which factor in which part is important by the output in the order of the priority. The intelligent recommendation system for automobile parts is a system to inform of the assembly, the usage and the importance of automobile parts without any specialized knowledge by expressing the parts that are closely related with the applicable parts when selecting any part on the basis of the generated data for the automobile parts that are difficult to access by users.

• Journal of the Institute of Electronics Engineers of Korea CI
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• 제45권5호
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• pp.148-154
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• 2008

In a ubiquitous computing environment, a ubiquitous smart space is required to help devices provide intelligent services. The smart space embedded with mobile devices should have the capabilities of collecting data and refining the data to contact. Unfortunately, the context information in a ubiquitous smart space has many ambiguous characteristics. Therefore, it is necessary to adapt a standard taxonomy for contact information in the smart space and to implement an inference technique of the context information based on taxonomy. Rule-based inference engine, such as CLIPS, Jess, was employed for providing situation-aware services. However, it is difficult for these engines to be used in resource limited mobile devices. In this paper, we propose a light-weight inference engine providing autonomous situation aware services in mobile environment. It can be utilized for personal mobile devices tuck as mobile phone, PMP and navigation. It can also support both generalized rules and specialized rules as using hierarchical taxonomy information.

• The Journal of the Acoustical Society of Korea
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• 제27권3호
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• pp.111-118
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• 2008

In this paper, we propose a new automatic taxonomy generation algorithm for the audio genre classification. The proposed algorithm automatically generates hierarchical taxonomy based on the estimated classification accuracy at all possible nodes. The estimation of classification accuracy in the proposed algorithm is conducted by applying the training data to classifier using k-fold cross validation. Subsequent classification accuracy is then to be tested at every node which consists of two clusters by applying one-versus-one support vector machine. In order to assess the performance of the proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigated classification performance using the proposed algorithm and previous flat classifiers. The classification accuracy reaches to 89 percent with proposed scheme, which is 5 to 25 percent higher than the previous flat classification methods. Using low-dimensional feature vectors, in particular, it is 10 to 25 percent higher than previous algorithms for classification experiments.

• Journal of KIISE:Computing Practices and Letters
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• 제15권6호
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• pp.414-423
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• 2009

Folksonomy, which is collaborative classification created by freely selected keywords, is one of the driving factors of the web 2.0. Folksonomy has advantage of being built at low cost while its weakness is lack of hierarchical or systematic structure in comparison with taxonomy. If we can build classifier that is able to classify web resources from collective intelligence in taxonomy, we can build taxonomy at low cost. In this paper, targeting folksonomy in Slashdot.org, we define a general model and show that collective intelligence, which can build classifier, really exists in folksonomy using a stability value. We suggest method that builds SVM classifier using stability that is result from this collective intelligence. The experiment shows that our proposed method managed to build taxonomy from folksonomy with high accuracy.

• Journal of the Korea Society for Simulation
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• 제4권1호
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• pp.1-11
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• 1995

The model of a FMS (Flexible Manufacturing System) admits to a natural hierarchical decomposition of highly decoupled units with similar structure and control. The FMS fractal architecture model represents a hierarchical structure built from elements of a single basic design. A SES (System Entity Structure) is a structural knowledge representation scheme that contains knowledge of decomposition, taxonomy, and coupling relationships of a system necessary to direct model synthesis. A substructure of a SES is extracted for use as the skeleton for a model. This substructure is called pruned SES and the extraction operation of a pruned SES from a SES is called pruning (or pruning operation). This paper presents a pruning operation called recursive pruning. It is applied to SES for generating a model structure whose sub-structure contains copies if itself as in FMS fractal architecture. Another pruning operation called delay pruning is also presented. Combined with recursive pruning the delay pruningis a useful tool for representing and constructing complex systems.

• Research in Mathematical Education
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• 제14권2호
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• pp.143-172
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• 2010

In this paper, we present an analysis of how students understand some statistical terms, mainly from inferential statistics, which are taught at the high school level. We focus our analysis on those terms that present more difficulties and are persistent in spite of having been studied until the college level. This analysis leads us to a hierarchical classification of responses at different levels of understanding using the SOLO theoretical framework.