• Journal of IKEEE
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• v.18 no.2
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• pp.234-239
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• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.585-591
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• 2015

In transcription for spoken language research, labeling is a work linking text-represented utterance to recorded speech. Most existing labeling tools have been working manually. Semi-automatic labeling we are proposing consists of automation module and manual adjustment module. Automation module extracts voice boundaries utilizing G.Saha's algorithm, and predicts utterance boundaries using the number and length of utterance which established utterance text. For maintaining existing manual tool's accuracy, we provide manual adjustment user interface revising the auto-labeling utterance boundaries. The implemented tool of our semi-automatic algorithm speed up to 27% than existing manual labeling tools.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.43-52
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• 2012

Semantic role labeling is the research area analyzing the semantic relationship between elements in a sentence and it is considered as one of the most important semantic analysis research areas in natural language processing, such as word sense disambiguation. However, due to the lack of the relative linguistic resources, Korean semantic role labeling research has not been sufficiently developed. We, in this paper, propose an automatic predicate-argument analyzer to begin constructing the Korean PropBank which has been widely utilized in the semantic role labeling. The analyzer has mainly two components: the semantic lexical dictionary and the automatic predicate-argument extractor. The dictionary has the case frame information of verbs and the extractor is a module to decide the semantic class of the argument for a specific predicate existing in the syntactically annotated corpus. The analyzer developed in this research will help the construction of Korean PropBank and will finally play a big role in Korean semantic role labeling.

• MALSORI
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• no.35_36
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• pp.175-188
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• 1998

Database segmented and labeled up to phoneme level plays an important role in phonetic research and speech engineering. However, it usually requires manual segmentation and labeling, which is time-consuming and may also lead to inconsistent consequences. Automatic segmentation and labeling can be introduced to solve these problems. In this paper, we investigate a method to improve the performance of automatic segmentation and labeling system, where Spectral Variation Function(SVF), modification of silence model, and use of energy variations in postprocessing stage are considered. In this paper, SVF is applied in three ways: (1) addition to feature parameters, (2) postprocessing of phoneme boundaries, (3) restricting the Viterbi path so that the resulting phoneme boundaries may be located in frames around SVF peaks. In the postprocessing stage, positions with greatest energy variation during transitional period between silence and other phonemes were used to modify boundaries. In order to evaluate the performance of the system, we used 452 phonetically balanced word(PBW) database for training phoneme models and phonetically balanced sentence(PBS) database for testing. According to our experiments, 83.1% (6.2% improved) and 95.8% (0.9% improved) of phoneme boundaries were within 20ms and 40ms of the manually segmented boundaries, respectively.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.357-362
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• 2002

Most of battery industries are growing explosively as a core strategy industry for the development of the semi-conductor, the LCD, and the mobile communication device. In this thesis, dynamic characteristics of the steel can labeling machine on the automatic cell assembly line are studied. Dynamic characteristic analysis consists of dynamic behavior analysis and finite element analysis and is necessary for effective design of machines. In the dynamic behavior analysis, the displacement, velocity, applied force and angular velocity of each components are simulated according to each part. In the FEA, stress analysis, mode analysis, and frequency analysis are performed for each part. The results of these simulations are used for the design specification investigation and compensation for optimal design of cell manufacturing line. Therefore, Virtual Engineering of the steel can labeling machine on the automatic cell assembly line systems are modeled and simulated. 3D motion behavior is visualized under real-operating condition on the computer window. Virtual Prototype make it possible to save time by identifying design problems early in development, cut cost by reducing making hardware prototype, and improve quality by quickly optimizing full-system performance. As the first step of CAE which integrates design, dynamic modeling using ADAMS and FEM analysis using NASTRAN are developed.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.701-715
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• 2021

The drastic advance of recent deep learning technologies is heavily dependent on training datasets which are essential to train models by themselves with less human efforts. In comparison with the work to design deep learning models, preparing datasets is a long haul; at the moment, in the domain of vision intelligent, datasets are still being made by handwork requiring a lot of time and efforts, where workers need to directly make labels on each image usually with GUI-based labeling tools. In this paper, we overview the current status of vision datasets focusing on what datasets are being shared and how they are prepared with various labeling tools. Particularly, in order to relieve the repetitive and tiring labeling work, we present an interactive smart image annotating system with which the annotation work can be transformed from the direct human-only manual labeling to a correction-after-checking by means of a support of automatic labeling. In an experiment, we show that automatic labeling can greatly improve the productivity of datasets especially reducing time and efforts to specify regions of objects found in images. Finally, we discuss critical issues that we faced in the experiment to our annotation system and describe future work to raise the productivity of image datasets creation for accelerating AI technology.

• Korea Trade Review
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• v.44 no.2
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• pp.143-157
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• 2019

The purpose of this study is to review domestic and foreign origin labeling system in order to implement origin labeling system in the perspective of protecting the interests of consumers, and to suggest governmental improvements by analyzing problems embedded in current labeling system. The results analysis show complexity of related legal system, lack of expertise at the stage of labeling, and inefficiency of crackdown authority. The improvement could be suggested in two ways: supporting plans for the ones who have duty of labeling and improvement plans in origin management system. As supporting plans, we suggest the need for an automatic origin determination system, appropriate education on origin stakeholders, and introduction of origin certification system. For improvement plans, there are unification of country of origin labeling related laws, utilization of FTA product specific rules, and QR code, expert confirmation system. Since the origin labeling issue has become important, proactive and quick responses must follow with thorough examination the effect of the origin labeling on consumer welfare.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.217-233
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• 2011

In this paper, an automatic word spacing system is proposed, which assumes sentences with no spaces between the words and segments them into proper words. Segmentation is regarded as a labeling problem in that segmentation can be done by attaching appropriate labels to each syllables of the given sentences. The system is based on Conditional Random Fields, which were reported to show excellent performance in labeling problems. The system is trained with a corpus of 1.12 million syllables, and evaluated with 2,114 sentences, 93 thousand syllables. The best results obtained are 98.84% of syllable-based accuracy and 95.99% of word-based accuracy.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.435-436
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• 2007

Although previous speech analysis programs usually provide speech analysis and phoneme labeling functionalities, they require much time in manual labeling and support only English alphabets. To solve these problems, we develop a new Windows-based program with an improved phoneme and tone labeling method as well as the conventional speech analysis functionalities. The developed program has the unique feature in semi-automatic phoneme and tone labeling based on hidden Markov models.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.50-59
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• 1997

In this paper, we implement an automatic speech segmentation and labeling system which marks phone boundaries automatically for constructing the Korean speech database. We specify and implement the system based on conventional speech segmentation and labeling techniques, and also develop the graphic user interface(GUI) on Hangul $Motif^{TM}$ environment for the users to examine the automatic alignment boundaries and to refine them easily. The developed system is applied to 16kHz sampled speech, and the labeling unit is composed of 46 phoneme-like units(PLUs) and silence. The system uses both of the phonetic and orthographic transcription as input methods of linguistic information. For pattern-matching method, hidden Markov models(HMM) is employed. Each phoneme model is trained using the manually segmented 445 phonetically balanced word (PBW) database. In order to evaluate the performance of the system, we test it using another database consisting of sentence-type speech. According to our experiment, 74.7% of phoneme boundaries are within 20ms of the true boundary and 92.8% are within 40ms.