• Asian Journal of Innovation and Policy
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• v.8 no.1
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• pp.73-96
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• 2019

This study suggests a new approach for identifying core robot tech-nologies based on technological cross-impact. Specifically, the approach applies data mining techniques and multi-criteria decision-making methods to the co-classification information of registered patents on the robots. First, a cross-impact matrix is constructed with the confidence values by applying association rule mining (ARM) to the co-classification information of patents. Analytic network process (ANP) is applied to the co-classification frequency matrix for deriving weights of each robot technology. Then, a technique for order performance by similarity to ideal solution (TOPSIS) is employed to the derived cross-impact matrix and weights for identifying core robot technologies from the overall cross-impact perspective. It is expected that the proposed approach could help robot technology managers to formulate strategy and policy for technology planning of robot area.

• Journal of Korea Technology Innovation Society
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• v.10 no.3
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• pp.531-554
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• 2007

This paper suggests a new model connecting various knowledge activities through classification systems such as classifications of research, technology, industry, major and job. Although research activities are linked to technology and industry areas or to education and job areas, there is no effort to link these kinds of activities. There are a few studies to link research and technology or research and education respectively. But, there have been no studies to connect technology-industry linkage and education-job linkage. This paper suggests that research area can be a basis of link between technology-industry linkage and education-job linkage. The methods building the links are not simple, but easy; 1) setting up new science/research classification system having two dimensions of research and application, 2) building electronic systems and databases allowing fields for several classification systems, and 3) making rules using multi-dimensional classification systems following the purpose of the programs. The model is designed to meet the needs of nationwide R&D and human resources policies, and for the preparation of knowledge society to grasp the relationship between sequential activities using knowledge. If we know the interactive relationships between various areas, we can trace related phenomena in different activities with restricted information.

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.421-426
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• 2001

The pneumatic classification and acidic leaching behaviors of phosphor sludge have been examined to establish the recycling system of rare earth components contained in waste fluorescent lamp. At first, separation characteristic of rare earth components and calcium phosphate in phosphor sludge was investigated by pneumatic classification. After pneumatic classification of phosphor sludge, rare earth components were leached in various acidic solutions and sodium hydroxide solution. For recovery of soluble component in leaching solution, rare earth components were separated as hydroxide and oxalate precipitations. The experimental results obtained are summarized as follows: (1) In classification process, rare earth components in phosphor sludge were concentrated to 29.3% from 13.3%, and its yield was 32.9%. (2) In leaching process, sulfuric acid solution was more effective one as a leaching solvent of rare earth component than other solutions. Y and Eu components in phosphor sludge were dissolved in sulfuric acid solution of 1.5 k㏖/㎥, and other rare earth components were rarely dissolved in leaching solution. Leaching degrees of Y and Eu were respectively 92% and 98% in the following optimum leaching conditions; sulfuric acid concentration is 1.5 k㏖/㎥ , leaching temperature 343 K, leaching time 3.6 ks and pulp concentration 30 kg/㎥. (3) Y and Eu components of phosphor sludge contained in waste fluorescent lamp were, effectively recovered by three processes of pneumatic classification, sulfuric acid leaching and oxalate precipitation methods. Their recovery was finally about 65 %, and its purity was 98.2%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• Journal of Radiation Industry
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• v.9 no.1
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• pp.29-35
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• 2015

Radiation technology is the one for developing new products or processes by applying radiation or for creating new functions in industry, research and medical fields, and its application is increasing consistently. For securing an advanced technology competitiveness, it is required to create a new added value by information consumer through providing an efficient system for supporting information, which is the infrastructure for research and development, contributed to its collection, analysis and use with a rapidity and structure in addition to some direct research and development. Provision of the management structure for information resources is especially crucial for efficient operating the system for supporting information in radiation technology, and then a standard classification structure of information must be first developed as the system for supporting information will be constructed. The standard classification structure has been analyzed by reviewing the definition of information resources in radiation technology, and those classification structures in similar systems operated by institute in radiation and other scientific fields. And, a draft version of the standard classification structure has been then provided as 7 large, 25 medium and 71 small classifications, respectively. The standard classification structure in radiation technology will be developed in 2015 through reviewing this draft version and experts' opinion. Finally, developed classification structure will be applied to the system for supporting information by considering the plan for constructing this system and database, and requirements for designing the system. Furthermore, this structure will be designed in the system for searching information by working to the individual need of information consumers.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.147-155
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• 2010

Landcover information of access-denied area was extracted from low-medium and high resolution satellite image. Training for supervised classification was performed to refer visually by landcover map which is made and distributed from The Ministry of Environment. The classification result was compared by relating data of FACC land classification system. As we rasterize digital military map with same pixel size of satellite classification, the accuracy test was performed by image to image method. In vegetation case, ancillary data such as NDVI and image for seasons are going to improve accuracy. FACC code of FDB need to recognize the properties which can be automated.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.64-69
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• 2021

In this paper, a method for improving the defect classification performance in low contrast, ununiformity and featureless steel plate surfaces has been studied based on deep convolution neural network and transfer-learning neural network. The steel plate surface images have low contrast, ununiformity, and featureless, so that the contrast between defect and defect-free regions are not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. A classifier based on a deep convolution neural network is constructed to extract features automatically for effective classification of images with these characteristics. As results of the experiment, AlexNet-based transfer-learning classifier showed excellent classification performance of 99.43% with less than 160 seconds of training time. The proposed classification system showed excellent classification performance for low contrast, ununiformity, and featureless surface images.

• Journal of Biosystems Engineering
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• v.39 no.2
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• pp.96-100
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• 2014

Purpose: The objective of this study was to develop a classification index and the grade levels for a five-grade energy efficiency classification of agricultural dryers in Korea. Methods: The classification index and the grade levels were determined by using the performance test data published by the FACT over the last eight years to reflect a state of the art technology for agricultural dryers in Korea. The five grades were designed to have the classified dryers distributed normally over the grades with 15% for the $1^{st}$ grade, 20% for the $2^{nd}$ grade, 30% for the $3^{rd}$ grade, 20% for the $4^{th}$ grade and 15% for the $5^{th}$ grade. Results: The classification index was defined as the total amount of fuel and electrical energy consumed per 1% of the wet basis moisture content evaporated from a unit mass of grain or agricultural crops during the drying process: 1 MT of paddy rice for grain dryers and 1 kg of red pepper for agricultural crop dryers as the standard mass. Conclusions: The grade levels for the five-grade energy efficiency classification of grain dryers, kerosene dryers, and electric dryers were proposed in terms of the classification index value.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.277-284
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• 2010

As technology is developed, the quantity of new vocabularies is increasing more rapidly. So many vocabularies of technology have various meanings for each part and are used diversely according to circumstances. Therefore, the necessity of reasonable methods of standardization and purification is increasing and it is necessary to establish a classification system of terminology for the first phase of the standardization. Firstly, based on classification systems of power and IT standard dictionaries, scientific and technological standard, SPARK, power IT fields of IEC and organization units of corporations, we propose a new classification system for the standardization of power IT terminologies. The classification system consists of a hierarchical structure with general classification, application fields and specific technologies while keeping the conventional matrix-type classification system. And interpretation methods of power IT terminologies, which are classified according to the new classification system for the standardization of power IT terminologies, is proposed. The interpretation works of the power IT terminologies confirm that the classification system is systematic and the interpretation process is efficient.