• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.57-74
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• 2017

The Electronic Shelf Label (ESL) is an alternative to the paper price label attached to merchandise shelves and is attracting attention as a retail IoT infrastructure that will lead the innovation of offline retail outlets. In general, when introducing a substitute product, the company tends to consider the financial factors such as the efficiency of the investment cost compared to the existing product or the reduction of the operating cost. However, considering only financial factors in the decision-making process, it may not properly reflect the various values associated with corporate strategy and the requirements of stakeholders. In this study, 8 evaluation items (Investment Cost, Operating Cost, Quality Level, Customer Management, Job Efficiency, Maintenance, Functional Expandability, and Store Image) based on BSC's 4 perspectives (Financial, Customer, Internal Business Process, Learning & Growth), and using AHP (Analytic Hierarchy Process) to measure the priorities of evaluation items for domestic small supermarket employees. As a result of the research, priority was given in order of Customer, Learning & Growth, Internal Business Process, and Financial aspects among the evaluation items for adopting the price label, and the electronic price label was supported with higher importance than the paper price label. In contrast to the priorities of the financial aspects of most prior studies, the items of Learning & growth and customer perspectives have relatively high priorities. In particular, respondents classified by job group, The priorities of the 8 evaluation items were different among the groups. These results are expected to provide implications for both companies (retail outlets) and ESL providers (manufacturers and service providers) who are considering the introduction of ESL.

• Journal of the Korean earth science society
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• v.33 no.1
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• pp.94-109
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• 2012

The purpose of this study is to analyse the descriptive characteristics of the label texts related to Earth Science at a science museum and a natural history museum in Korea. The data were collected from Korean National Science Museum and Seodaemun Natural History Museum. The analysis framework was modified according to the Systemic Functional Linguistics. As a result, characteristics of the labels are 1) mostly declarative sentences, 2) appropriate amount of scientific information, and 3) mainly 'facts'. Moreover, all of the text genre are 4) 'logical expositions'. Particularly in Korean National Science Museum, the labels present 5) more scientific words among the entire terminologies and 6) more than half subjects omitted or long nominalized. Those results may imply that the labels can lead one-way communication regarding the culture of science rather than two-way. This study presents the descriptive characteristics of the label texts to make educationally meaningful communication possible by building an open structure between visitors' own culture in everyday life and the culture of science.

• The Journal of Bigdata
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• v.8 no.2
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• pp.177-187
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• 2023

Semi-supervised learning (SSL) is an effective approach to training models using a small amount of labeled data and a larger amount of unlabeled data. However, many papers in the field use a fixed threshold when applying pseudo-labels without considering the feature-wise differences among images of different classes. In this paper, we propose a SSL method for synthetic aperture radar (SAR) image classification that applies different thresholds for each class instead of using a single fixed threshold for all classes. We propose a threshold learning module into the model, considering the differences in feature distributions among classes, to dynamically learn thresholds for each class. We compare the application of a SSL SAR image classification method using different thresholds and examined the advantages of employing class-specific thresholds.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.472-484
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• 2019

This paper proposes a time-domain sound event detection algorithm using DNN (Deep Neural Network). In this system, time domain sound waveform data which is not converted into the frequency domain is used as input to the DNN. The overall structure uses CRNN structure, and GLU, ResNet, and Squeeze-and-excitation blocks are applied. And proposed structure uses structure that considers features extracted from several layers together. In addition, under the assumption that it is practically difficult to obtain training data with strong labels, this study conducted training using a small number of weakly labeled training data and a large number of unlabeled training data. To efficiently use a small number of training data, the training data applied data augmentation methods such as time stretching, pitch change, DRC (dynamic range compression), and block mixing. Unlabeled data was supplemented with insufficient training data by attaching a pseudo-label. In the case of using the neural network and the data augmentation method proposed in this paper, the sound event detection performance is improved by about 6 %(based on the f-score), compared with the case where the neural network of the CRNN structure is used by training in the conventional method.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.445-456
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• 2017

In this paper, we propose a method to detect spam tweets containing unhealthy information by using an n-gram dictionary under limited labeling. Spam tweets that contain unhealthy information have a tendency to use similar words and sentences. Based on this characteristic, we show that spam tweets can be effectively detected by applying a Naive Bayesian classifier using n-gram dictionaries which are constructed from spam tweets and normal tweets. On the other hand, constructing an initial training set requires very high cost because a large amount of data flows in real time in a twitter. Therefore, there is a need for a spam detection method that can be applied in an environment where the initial training set is very small or non exist. To solve the problem, we propose a method to generate pseudo-labels by utilizing twitter's retweet function and use them for the configuration of the initial training set and the n-gram dictionary update. The results from various experiments using 1.3 million korean tweets collected from December 1, 2016 to December 7, 2016 prove that the proposed method has superior performance than the compared spam detection methods.

• Journal of KIISE
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• v.44 no.9
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• pp.887-892
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• 2017

Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.712-715
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• 2023

딥러닝 기술의 발전은 고품질의 대규모 데이터에 크게 의존한다. 그러나, 데이터의 품질과 일관성을 유지하는 것은 상당한 비용과 시간이 소요된다. 이러한 문제를 해결하기 위해 최근 연구에서 최소한의 비용으로 최대의 성능을 추구하는 액티브 러닝(active learning) 기법이 주목받고 있는데, 액티브 러닝은 모델 관점에서 불확실성(uncertainty)이 높은 데이터들을 샘플링 하는데 중점을 둔다. 하지만, 레이블 생성에 있어서 여전히 많은 시간적, 자원적 비용이 불가피한 점을 고려할 때 보완이 불가피 하다. 본 논문에서는 의사-라벨링(pseudo labeling)을 활용한 준지도학습(semi-supervised learning) 방식과 학습 손실을 동시에 사용하여 모델의 불확실성(uncertainty)을 측정하는 방법론을 제안한다. 제안 방식은 레이블의 신뢰도(confidence)와 학습 손실의 최적화를 통해 비용 효율적인 데이터 레이블 생성 방식을 제안한다. 특히, 레이블 데이터의 품질(quality) 및 일관성(consistency) 측면에서 딥러닝 모델의 정확도 성능을 높임과 동시에 적은 데이터만으로도 효과적인 학습이 가능할 수 있는 메커니즘을 제안한다.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.20 no.3
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• pp.113-120
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• 2014

Packaging has played important function on marketing as a silent salesman. As an interface of consumer and products, packaging fulfills several functions such as protection, sales promotion, communication and convenience during the distribution process. For the appearance of self-service sales method, packaging could be regarded as important salesman influencing consumers' preference on shelf in the shop. In this study, Vitamin water was selected as the proper target product for lower impact of prices and brands. With previous studies, Vitamin water packaging elements were classified as 'packaging material', 'packaging shape', 'label color', 'logo layout', then each packaging element was consist of details. To measure the influence of each packaging element on consumers' preference quantitatively and to minimize respondent's subjective judgment, Analytic Hierarchy Process (AHP) was used as a tool. Through AHP result, packaging element of the most influence on consumers' preference is 'label color (0.370)', and 'container shape (0.246)', 'container material (0.230)', 'logo layout (0.154)' was in order. Among the detail packaging element, 'plastic (0.405)' has the greatest influence in 'container material' and 'cylinder (0.423)' in 'container shape', 'magenta (0.329)' in 'label color', 'vertical layout (0.572)' in 'logo layout'.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.70-75
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• 2001

This paper proposes a syllabic segmentation method for the korean continuous speech. This method are formed three major steps as follows. (1) labeling the vowel, consonants, silence units and forming the Token the sequence of speech data using the segmental parameter in the time domain, pitch, energy, ZCR and PVR. (2) scanning the Token in the structure of korean syllable using the parser designed by the finite state automata, and (3) re-segmenting the syllable parts witch have two or more syllables using the pseudo-syllable nucleus information. Experimental results for the capability evaluation toward the proposed method regarding to the continuous words and sentence units are 73.5％, 85.9％, respectively.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.266-275
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• 2006

Mostly mechanical products are connected by several components instead of single accessory in product process. Although majority of assembly process is automated, the fault analysis is not automated because it needs expert knowledge in various fields to support inclusive decision-marking. This paper proposes an assembly fault analysis support system that uses image regions which can be easily accessed and understood by experts of various fields. An assembly fault analysis support system helps effective fault analysis from assembly by integrating image regions, product design information, and fault detection information. The proposed method enables fault information access from multimedia information by segmenting product images. After product images are segmented by labeling, design information and fault information are integrated in extended Attributed Relational Graph.