• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.47-52
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• 2020

The general flatbed printer system is composed of a PC and a dedicated S/W, which is inconvenient to use. In the end, there is a need for a technology that can easily and conveniently use various types of printing through simplification, smartization, etc. of a flatbed printer system configuration. That is, there is an increasing demand for multi-dimensional printer capable of printing on various types of materials with one printer and capable of printing various types of products. Therefore, in this paper, we developed a flatbed printer system capable of multi-dimensional printing using Head Encoder/Trigger control. To this end, we developed a flatbed printer that connects the internal module of the flatbed printer with an input type detection sensor and controls all operating states by the head encoder and head trigger signals of the printer through separate main controllers. Through this, the development and diffusion of IoT technology will expand the printer control of the smart environment to the developed form throughout the industry. It is expected to contribute to the development of the 3D printing industry in the future.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.985-990
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• 1995

Recently, the development of high speed and high precision NC-lathe for piston head machining is needed for the complexity and diversity of the piston head shape used in automobile reciprocating engine. THe piston head has many complex shapes in the aspect of fuel economy, such as ovality, profile, double ovality and recess. Among them, for the maching of the over shape of 0.1~1mm the cutting tool should move periodically symchronized with the rotation of piston workpiece. The cutting tool feeed system must have high positioning accuracy for the precise machining, high speed for the fast maching and high dynamic stiffness for the cutting force. The linear brushless DC motor is used for satisfying these coditions. The ballbush guide and supporting guide using turcite is used for the guidance of the feed drive system. Linear encoder, digital servo ampllifer and controller are used for driving the motor. THis paper presents the design and simulation of the new tool feed system for noncircular machining.

• ETRI Journal
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• v.43 no.6
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• pp.1038-1048
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• 2021

We propose an end-to-end neural coreference resolution for the Korean language that uses an attention mechanism to point to the same entity. Because Korean is a head-final language, we focused on a method that uses a pointer network based on the head. The key idea is to consider all nouns in the document as candidates based on the head-final characteristics of the Korean language and learn distributions over the referenced entity positions for each noun. Given the recent success of applications using bidirectional encoder representation from transformer (BERT) in natural language-processing tasks, we employed BERT in the proposed model to create word representations based on contextual information. The experimental results indicated that the proposed model achieved state-of-the-art performance in Korean language coreference resolution.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.540-543
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• 2021

본 논문에서는 Multi-Head Attention 대신 Spatial Gating Unit을 사용하는 GMLP[1]에 작은 Attention 신경망을 추가한 모델을 구성하여 뉴스와 위키피디아 데이터로 사전학습을 실시하고 한국어 다운스트림 테스크(감성분석, 개체명 인식)에 적용해 본다. 그 결과, 감성분석에서 Multilingual BERT보다 0.27%높은 Accuracy인 87.70%를 보였으며, 개체명 인식에서는 1.6%높은 85.82%의 F1 Score를 나타내었다. 따라서 GMLP가 기존 Transformer Encoder의 Multi-head Attention[2]없이 SGU와 작은 Attention만으로도 BERT[3]와 견줄만한 성능을 보일 수 있음을 확인할 수 있었다. 또한 BERT와 추론 속도를 비교 실험했을 때 배치사이즈가 20보다 작을 때 BERT보다 1에서 6배 정도 빠르다는 것을 확인할 수 있었다.

• Journal of KIISE
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• v.44 no.8
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• pp.774-781
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• 2017

Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1038-1043
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• 2015

When the study on the relationship between the Head-of-Bed (HOB) angle and ventilator-associated pneumonia is performed, the fact that the HOB angle can only be measured intermittently imposes a significant limitation on the study. Therefore, there has been demand for the development of a device that can measure the HOB angle continuously. In this paper, we propose the rapid prototyping of an HOB measurement system using open-source hardware and software. The proposed system helps to maintain the HOB angle at a particular angle by displaying the angle and helps the medical study of pneumonia patients by enabling continuous data acquisition. Firstly, we eliminate the process of making an MCU board by utilizing an open-source hardware mbed LPC1768. Secondly, we reduce the software development time by using libraries and hence enabling the easy use of peripherals. Thirdly, for rapid prototyping, we build the enclosure of the proposed system using a 3D printer. The proposed system can be attached and detached to and from a bed. Therefore, we can attach it to the bed of a patient for whom measurement of the HOB angle is necessary. Finally, we check the measurement performance and the validity of the proposed system through an experiment utilizing an incremental encoder.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.277-280
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• 1998

The paper provides an overview of those requirements. A detailed description is given of the construction of the new channel code, called 24/25 code, that compiles with the given constraints and involves only a minor drawback in terms of the overhead needs. The servo position information is recorded as low frequency componets, pilot tracking tones, which are embedded in the recorded stream of binary digits. Pilot tracking Tones are used to derive head position reference information in camcorders and DVCRs. A simple pilot tone encoder has been designed by using a new approach, "2 path precoder". Owing to this method, the hardware size can be significantly reduced. the correctness of the method has been verified by theoretical analysis and by extensive simulation.imulation.

• ETRI Journal
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• v.39 no.5
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• pp.652-661
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• 2017

A mention has a noun or noun phrase as its head and constructs a chunk that defines any meaning, including a modifier. Mention detection refers to the extraction of mentions from a document. In mentions, coreference resolution refers to determining any mentions that have the same meaning. Pointer networks, which are models based on a recurrent neural network encoder-decoder, outputs a list of elements corresponding to an input sequence. In this paper, we propose mention detection using pointer networks. This approach can solve the problem of overlapped mention detection, which cannot be solved by a sequence labeling approach. The experimental results show that the performance of the proposed mention detection approach is F1 of 80.75%, which is 8% higher than rule-based mention detection, and the performance of the coreference resolution has a CoNLL F1 of 56.67% (mention boundary), which is 7.68% higher than coreference resolution using rule-based mention detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.285-292
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• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.