• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.141-148
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• 2003

Customer retention is a common concern for many industries and a critical issue for the survival in today's greatly compressed marketplace. Current customer retention models only focus on detection of potential defectors based on the likelihood of defection by using demographic and customer profile information. In this paper, we propose a dynamic procedure for defection detection and prevention using past and current customer behavior by utilizing SOM and Markov chain. The basic idea originates from the observation that a customer has a tendency to change his behavior (i.e. trim-out his usage volumes) before his eventual withdrawal. This gradual pulling out process offers the company the opportunity to detect the defection signals. With this approach, we have two significant benefits compared with existing defection detection studies. First, our procedure can predict when the potential defectors could withdraw and this feature helps to give marketing managers ample lead-time for preparing defection prevention plans. The second benefit is that our approach can provide a procedure for not only defection detection but also defection prevention, which could suggest the desirable behavior state for the next period so as to lower the likelihood of defection. We applied our dynamic procedure for defection detection and prevention to the online gaming industry. Our suggested procedure could predict potential defectors without deterioration of prediction accuracy compared to that of the MLP neural network and DT.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.78-88
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• 2017

With the growing use of BIM in the AEC industry, various new applications are being developed to meet these specific needs. Such developments have increased the importance of Industry Foundation Classes, which is the international standard for sharing BIM data and thus ensuring interoperability. However, mapping individual BIM objects to IFC entities is still a manual task, and is a main cause for errors or omissions during data transfers. This research focused on addressing this issue by applying novelty detection, which is a technique for detecting anomalies in data. By training the algorithm to learn the geometry of IFC entities, misclassifications (i.e., outliers) can be detected automatically. Two IFC classes (ifcWall, ifcDoor) were trained using objects from three BIM models. The results showed that the algorithm was able to correctly identify 141 of 160 outliers. Novelty detection is thus suggested as a competent solution to resolve the mapping issue, mainly due to its ability to create multiple inlier boundaries and ex ante training of element geometry.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.559-572
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• 2017

Cyberbullying has been an emerging issue in recent years where research has revealed that users generally spend an increasing amount of time in social networks and forums to keep connected with each other. However, issue arises when cyberbullies are able to reach their victims through these social media platforms. There are different types of cyberbullying and like traditional bullying; it causes victims to feel overly selfconscious, increases their tendency to self-harm and generally affects their mental state negatively. Such situations occur due to security issues such as user anonymity and the lack of content restrictions in some social networks or web forums. In this paper, we highlight the existing solutions, which are Intrusion Prevention System and Intrusion Detection System from a number of researchers. However, even with such solutions, cyberbullying acts still occurs at an alarming rate. As such, we proposed an alternative solution that aims to prevent cyberbullying activities at a younger age, e.g., young children. The application would provide an alternative method to preventing cyberbullying activities among the younger generations in the future.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1171-1177
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• 2017

This special issue introduces recent researches on KOMPSAT(KOrea Multi-Purpose SATellite) image processing and applications. In this paper, the status of KOMPSAT development and national satellite image application policy are introduced and the implications of the papers presented in the special issue are discussed. Satellite image resources and application policy that can be utilized through continuous satellite development are considered to be systematically prepared. Therefore, if data processing and application technology development for various fields such as forest and urban change detection, image correction technology introduced in this paper are continuously carried out, it is expected that the competitiveness of national satellite image will be further strengthened.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.31-36
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• 2014

Social network services(SNS) including Twitter and Facebook are good resources to extract various issues like public interest, trend and topic. This paper proposes a method to extract query-related issues by calculating relatedness between terms in Twitter. As a term that frequently appears near query terms should be semantically related to a query, we calculate term relatedness in retrieved documents by summing proximity that is proportional to term frequency and inversely proportional to distance between words. Then terms, relatedness of which is bigger than threshold, are extracted as query-related issues, and our system shows those issues with a connected network. By analyzing single transitions in a connected network, compound words are easily obtained.

• BMB Reports
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• v.48 no.6
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• pp.313-318
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• 2015

Matrix metalloproteinases (MMPs) are zinc-dependent endopeptidases that degrade the extracellular matrix (ECM) and regulate the extracellular microenvironment. Despite the significant role that MMP activity plays in cell-cell and cell-ECM interactions, migration, and differentiation, analyses of MMPs in vitro and in vivo have relied upon their abundance using conventional immunoassays, rather than their enzymatic activities. To resolve this issue, diverse nanoprobes have emerged and proven useful as effective activity-based detection tools. Here, we review the recent advances in luminescent nanoprobes and their applications in in vitro diagnosis and in vivo imaging of MMP activity. Nanoprobes with the purpose of sensing MMP activity consist of recognition and detection units, which include MMP-specific substrates and luminescent (fluorescent or bioluminescent) nanoparticles, respectively. With further research into improvement of the optical performance, it is anticipated that luminescent nanoprobes will have great potential for the study of the functional roles of proteases in cancer biology and nanomedicine. [BMB Reports 2015; 48(6): 313-318]

• The Journal of the Korean dental association
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• v.49 no.8
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• pp.443-450
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• 2011

The leading paradigm of dentistry had been focused on the rehabilitation treatment that identifies active caries, manages them surgically, and restores their original functions. However, changes in the external environment including the current disease prevalence require dentistry to have a paradigm shift. The new paradigm suggests the detection of caries in their earlier stages over the visual diagnosis of cavities, and the reversal of the incipient caries by non-surgical approach. For this to be achieved, a high-technology detection device recognizing changes in the earlier stages which can not he visually observed is needed. Development of early caries detection device has recently become a major issue in preventive dentistry on account of this need, and QLF(Quantitified Light induced Fluorescence) conspicuously stands out among the newly released devices. In this study, the fundamental concept of QLF(Quantitified Light induced Fluorescence) and the possible clinical applications of the earlier intraoral camera model as well as the recently designed digital camera model will be discussed.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.330-339
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• 2015

Power quality (PQ) problems are becoming a big issue, since delicate complex electronic devices are widely used. We present a new denoising technique using discrete wavelet transform (DWT), where a modified correlation thresholding is used in order to reliably detect the PQ disturbances. We consider various PQ disturbances on the basis of IEEE-1159 standard over noisy environments, including voltage swell, voltage sag, transient, harmonics, interrupt, and their combinations. These event signals are decomposed using DWT for the detection of disturbances. We then evaluate the PQ disturbance detection ratio of the proposed denoising scheme over Gaussian noise channels. Simulation results also show that the proposed scheme has an improved signal-to-noise ratio (SNR) over existing scheme.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.416-424
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• 2016

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.