• The Journal of Industrial Distribution & Business
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• v.9 no.7
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• pp.19-32
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• 2018

Purpose - This paper aims at exploring the effects of failure types such as failure in promotion orientation and failure in prevention orientation on consumers' consumption behavior, and the moderation role of lay theories in the effects. Research design, data, and methodology - This study employed 2 between-subject designs(failure in promotion orientation vs. failure in prevention orientation) and also 2 (failure in promotion orientation vs. failure in prevention orientation) with implicit self as the within-subject. Chinese consumers participated in the empirical study, and to verify the hypotheses ANOVA, T-test and regression analysis were used. Results - Consumers were more likely to choose adaptive consumption behavior rather than compensatory consumption behavior when they were encountered with failure in promotion orientation versus failure in prevention orientation. Lay theories did play the moderation role in the effect of failure types on consumption behavior. The incremental theorists who think that effort is an important way to accomplish their learning goals showed more willingness to conduct compensatory consumption behavior rather than adaptive consumption behavior. Conclusions - Marketers should put more attention on the ways by which their products can help consumers self-improve when consumers encounter with failure. They should also be aware of the importance of consumers' mindsets when designing and developing advertising messages.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.357-381
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• 2009

Although it is known that there are personality characteristic variances in the sensitivity to environmental feedback, the trait individual difference has scarcely been explored in the context of recognition memory decision. The present study investigated this issue by examining the relationship between the feedback-based adaptive flexibility of recognition criterion positioning and personality differences in general sensitivity to non-laboratory outcomes. Experiment 1 demonstrated that veridical feedback itself had little effect on the recognition decision criterion whereas Experiment 2 demonstrated that biased feedback manipulations selectively restricted to high confidence errors, induced shifts even in the overall Old/New category criterion. Critically, individual differences in stable personality characteristic linked to reward seeking(Behavioral Activation System-BAS) and anxiety avoidance (Behavioral Inhibition System-BIS) has been shown to predict the sensitivity of subjects to this form of feedback-induced criterion learning. This data further support the idea that incremental reinforcement-based learning mechanism not often considered important during explicit recognition decisions may play a key role in criterion setting.

• Journal of KIISE
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• v.43 no.6
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• pp.718-723
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• 2016

In streaming data analysis, detecting concept drift accurately is important to maintain the performance of classification model. Error rates are usually used for concept drift detection. However, by describing prediction results with only binary values of 0 or 1, useful information about a behavior pattern of a classifier can be lost. In this paper, we propose an effective concept drift detection method which describes performance pattern of a classifier by utilizing probability estimates for class prediction and detects a significant change in a classifier behavior. Experimental results on synthetic and real streaming data show the efficiency of the proposed method for detecting the occurrence of concept drift.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.37-44
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• 2008

Data stream has the tendency to change in Patterns over time. Also known as concept drift, such problem can reduce the predictive performance of a classification model CVFDT and IOLIN tried to solve the problem of a concept drift through incremental classification model updates. The local changes in patterns. however was revealed to be unable to resolve the problems of local concept drift that occurs by influencing on total classification results. In this paper, we propose adapted IOLIN system that improves system's predictive performance by detecting the local concept drift. The experimental result shows that adaptive IOLIN, the Proposed method, is about 2.8% in accuracy better than IOLIN and about 11.2% in accuracy better than CVFDT.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.