• Journal of Korea Multimedia Society
• /
• v.13 no.8
• /
• pp.1115-1127
• /
• 2010

In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity, and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.

• Journal of Aerospace System Engineering
• /
• v.12 no.5
• /
• pp.76-88
• /
• 2018

Space agencies such as NASA, ESA, and the US military provide guidelines and standards for PA(product assurance) requirements and plans. In recent years, major satellite manufacturers around the world have been mitigating PA requirements and processes by tailoring. PA tailoring has been implemented to improve the cost and schedule efficiency. PA tailoring can be accomplished based on various factors such as mission, classification of mission risk, complexity, development cost, life cycle, etc. In this study, PA tasks according to the mission risk classification proposed by NASA are investigated, and the tailoring method is suggested for the optimization of the development cost and schedule. In particular, the classification of mission risk for the satellites under development or operation in Korea is performed, and PA characteristics in accordance with mission risk are analyzed.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.221-224
• /
• 2005

Hyperspectral image data have great potential to depict more detailed information on biophysical characteristics of surface materials, which are not usually available with multispectral data. This study aims to test the potential of hyperspectral data for classifying five soil classes defined by the vector product interim terrain data (VITD). In this study, we try to classify surface materials of bare soil over the study area in Korea using both hyperspectral and multispectral image data. Training and test samples for classification are selected with using VITD vector map. The spectral angle mapper (SAM) method is applied to the EO-I Hyperion data and Landsat ETM+ data, that has been radiometrically corrected and geo-rectified. Higher classification accuracy is obtained with the hyperspectral data for classifying five soil classes of gravel, evaporites, inorganic silt and sand.

• Smart Media Journal
• /
• v.13 no.6
• /
• pp.44-53
• /
• 2024

Companies in modern society are increasingly recognizing sentiment classification as a crucial task, emphasizing the importance of accurately understanding consumer opinions opinions across various platforms such as social media, product reviews, and customer feedback for competitive success. Extensive research is being conducted on sentiment classification as it helps improve products or services by identifying the diverse opinions and emotions of consumers. In sentiment classification, fine-tuning with large-scale datasets and pre-trained language models is essential for enhancing performance. Recent advancements in artificial intelligence have led to high-performing sentiment classification models, with the ELECTRA model standing out due to its efficient learning methods and minimal computing resource requirements. Therefore, this paper proposes a method to enhance sentiment classification performance through efficient fine-tuning of various datasets using the KoELECTRA model, specifically trained for Korean.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2004.04a
• /
• pp.302-306
• /
• 2004

When we include environment side safety, environmental material's reliability technology and study for the application method, the evidence supporting the investment of R&D person and financial. Clearly, the most important task in electrical and electronics company's product soldering process the probability of heavy metals exclude is to identify the mechanisms by which they may take place. Therefore, this study emphasis on the application environmental material classification and reliability technology.

• Journal of Cadastre & Land InformatiX
• /
• v.46 no.2
• /
• pp.239-251
• /
• 2016

Hyperspectral image is widely used for land cover classification because it has a number of narrow bands and allow each pixel to include much more information in comparison with previous multi-spectral image. However, Higher spectral resolution of hyperspectral image results in an increase in data volumes and a decrease in noise efficiency. SAM(Spectral Angle Mapping), a method based on vector inner product to compare spectrum distribution, is a highly valuable and popular way to analyze continuous spectrum of hyperspectral image. SAM is shown to be less accurate when it is used to analyze hyperspectral image for land cover classification using spectral library. this inaccuracy is due to the effects of atmosphere. We suggest a decision tree based method to compensate the defect and show that the method improved accuracy of land cover classification.

• Asia pacific journal of information systems
• /
• v.29 no.4
• /
• pp.838-855
• /
• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Korean Journal of Computational Design and Engineering
• /
• v.13 no.3
• /
• pp.163-174
• /
• 2008

In this paper, for integrating of data on car parts we model information of parts that PDM system manages. Ontology of car parts applies existing ontology mapping research to integrate into car ontology. We propose a method for semantic integration of PLM object of MEMPHIS based on the integrated ontology. Through our method, we introduce C# ontology model to apply existing C# applications with ontology. We also classify ontology integration into three through examples and explain them. While semantically integrating PLM objects based on the integrated ontology, we explain the need for change of PLM object type and describe the process of change for PLM object type by examples.

• Natural Product Sciences
• /
• v.20 no.4
• /
• pp.243-250
• /
• 2014

A simple GC method with a FID detector was developed in order to determine two main compounds (${\beta}$-sitosterol and lupenone) for Adenophorae Radix. ${\beta}$-Sitosterol and lupenone were analyzed by the gradient thermal ramping method. Nitrogen was used as the carrier gas at 108 kPa. The flow rate of gas was 2.0 mL/min; $2{\mu}L$ of filtered sample was injected at a split ratio of 1 : 80. This method was fully validated with respect to linearity, precision, accuracy and robustness. Further, this GC-FID method was applied successfully in order to quantify two compounds in an Adenophorae Radix extract. The GC analytical method for classification analysis was performed by repeated analysis of 59 reference samples in order to differentiate between Adenophora triphylla var. japonica Hara and 14 Codonopsis lanceolata. The results indicate that the GC-FID method is suitable and reliable for the quality evaluation of Adenophorae Radix.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.12
• /
• pp.1172-1180
• /
• 2004

Recently, the gene expression data, product of high-throughput technology, appeared in earnest and the studies related with it (so-called bioinformatics) occupied an important position in the field of biological and medical research. The microarray is a revolutionary technology which enables us to monitor several thousands of genes simultaneously and thus to gain an insight into the phenomena in the human body (e.g. the mechanism of cancer progression) at the molecular level. To obtain useful information from such gene expression measurements, it is essential to analyze the data with appropriate techniques. However the high-dimensionality of the data can bring about some problems such as curse of dimensionality and singularity problem of matrix computation, and hence makes it difficult to apply conventional data analysis methods. Therefore, the development of method which can effectively treat the data becomes a challenging issue in the field of computational biology. This research focuses on the gene selection and classification for cancer subtype discrimination based on gene expression (microarray) data.