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

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2002.05a
• /
• pp.306-311
• /
• 2002

This paper propose a personalized defection detection and prevention procedure based on the observation that potential defectors have tendency to take a couple of months or weeks. For this purpose, possible states of customer behavior are determined from past behavior data using SOM (Self-Organizing Map). For the evaluation of the proposed procedure, a case study has been conducted for a Korean online game site. The result demonstestes that the proposed procedure can assist defection prevention effectively and detect potential defectors without deterioration of prediction accuracy comparison to prediction by MLP. Our procedure can be applied to various service industries that can capture fluent customer behavior data such as telecommunications, internet access services, and content services, too.

• The KIPS Transactions:PartB
• /
• v.11B no.1
• /
• pp.53-62
• /
• 2004

The processing of emotional information is very important in Human-Computer Interaction (HCI). In particular, it is very important in video information processing to deal with a user's affection. To handle emotional information, it is necessary to represent meaningful features and detect them efficiently. Even though it is not an easy task to detect emotional events from low level features such as colour and motion, it is possible to detect them if we use statistical analysis like Linear Discriminant Analysis (LDA). In this paper, we propose a representation scheme for emotion-related features and a defection method. We experiment with extracted features from video to detect emotional events and obtain desirable results.

• Proceedings of the KIEE Conference
• /
• 1997.07c
• /
• pp.924-926
• /
• 1997

A high impedance fault(HIF) in a power system could be due to a downed conductor, and is a dangerous situation because the current may be too small to be detected by conventional means. In this paper, HIF(High impedance fault) and LIF(Low impedance fault) detection methods were reviewed. No single defection method can detect all electrical conditions resulting from downed conductor faults, because high impedance fault have arc phenomena, asymmetry and randomness. Neural network are well-suited for solving difficult signal processing and pattern recognition problem. This paper presents the application of artificial neural network(ANN) to detect the HIF and LIF. Test results show that the neural network was able to identify the high impedance fault by real-time operation. Furthermore, neural network was able to discriminate the HIF from the LIF.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.1858-1861
• /
• 2005

Automatic nut sorting machine used to calculate the size of inserted nut and detect the black spot defection is introduced in this paper. Because most of farm products are imported from the underdeveloped countries, domestic farm products have no place to be sold in market. To overcome this critical situation, lowering the productivity cost is strongly demanded to compete with foreign corps. Imaged processed nut sorting algorithm is developed to the automatic nut sorting machine to remove the sorting time which takes lots of man power. This system is composed of mainly two parts, mechanical parts and vision system. The purpose of mechanical part is supplying the nuts automatically to make computer system capture the images of objects. Simplified mechanical system was assembled followed by 3D simulation by Pro/E design for the adaptive cost effects. Several image processing algorithms are designed to detect the spot defects and calculate the size of nuts. Test algorithm shows good results to the designed automatic nut sorting system.

• International Journal of Safety
• /
• v.1 no.1
• /
• pp.28-35
• /
• 2002

Kullback Discrimination Information (KDI) is one of the pattern recognition methods. KDI defined as a measure of the mutual dissimilarity computed between two time series was studied for detection and classification of bearing flaking on outer-race and inner-races. To model the damages, the bearings in normal condition, outer-race flaking condition and inner-races flaking condition were provided. The vibration sensor was attached by the bearing housing. This produced the total 25 pieces of data each condition, and we chose the standard data and measure of distance between standard and tested data. It is difficult to detect the flaking because similar pulses come out when balls pass the defection point. The detection and classification method for inner and outer races are defected by KDI and nearest neighbor classification rule is proposed and its high performance is also shown.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.9
• /
• pp.92-99
• /
• 2003

Quality inspection of textile products is an important problem for fabric manufacturers. This paper presents an automatic vision-based system for quality control of web textile fabrics. Typical web material is 1-3m wide and is driven with speeds ranging from 20m/min to 200m/min. At the present, the quality assessment procedures are performed manually by expert. But worker can not detect more than 60％ of the present defect and inspect the fabric if moving faster than 30m/min. To increase the overall quality and homogeneity of textile, an automated visual inspection system is needed fur the productivity. However, the existing inspection system are too expensive to purchase for small companies. In this paper, the proposed PC based real-time inspection algorithm gives low cost textile inspection system, high detection rate with good accuracy and low rate of false alarms. The method shows good results in the detection of several types of fabric defects.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11b
• /
• pp.200-203
• /
• 2005

Ball bearings not only sustain the system, but permit the rotational component to rotate. Excessive radial or axial load and many other reasons can cause faults to be created and grown rapidly in each component. The grown faults make noise and vibration, which can make the system unstable. Therefore, it is important to detect faults as early as possible. For this reason, there have been many researches on fault detection method of early faults in a ball bearing. The fault defection methods can be categorized to several groups by signal processing methods. Not all the methods are efficient for finding early faults. We select representative methods known as efficient for detecting early faults and compare the results for inspecting which method is effective.

• Journal of Welding and Joining
• /
• v.11 no.1
• /
• pp.42-51
• /
• 1993

In this study, investigations were conducted in calculating parameters of elastic-plastic fracture mechanics using single specimen. The validity of these testing methods was judged by the confirmation of multiple specimen method of stop block test. The results were as follows: In order to measure a fracture toughness using the instrumented impact test, two general requirement must be considered; One, setting up proper impact velocity considered the effect of loading and the other, the necessity of low blow test for obtaining true energy by the compliance correction. It was possible to detect a crack initiation point by calculating the compliance changing rate from a load-defection curve. Criterion of a stable crack growth, $T_{mat}$ could be estimated by using key-curve method for a base metal. and combining Kaiser's rebound compliance with Paris-Hutchison's $T_{appl}$ equation for the brittled zone of welding heat affected.at affected.d.

• Proceedings of the KIEE Conference
• /
• 2005.10c
• /
• pp.56-59
• /
• 2005

This paper describes a novel method of detecting excitation position in Switched Reluctance Motor(SRM) drive. Some strategics for position sensorless control methods of the SRM include the measurement of phase current. The principle of the rotor position estimation is based on the defection of phase current according to rotor position. This sensorless method is very simple to detect excitation position estimation and gives efficient control of drive system. The suggested method is verified by some simulations and experimental tests