• Title/Summary/Keyword: deep machine learning

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Korean Machine Reading Comprehension for Patent Consultation Using BERT (BERT를 이용한 한국어 특허상담 기계독해)

  • Min, Jae-Ok;Park, Jin-Woo;Jo, Yu-Jeong;Lee, Bong-Gun
    • KIPS Transactions on Software and Data Engineering
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    • v.9 no.4
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    • pp.145-152
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    • 2020
  • MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.

Filter-mBART Based Neural Machine Translation Using Parallel Corpus Filtering (병렬 말뭉치 필터링을 적용한 Filter-mBART기반 기계번역 연구)

  • Moon, Hyeonseok;Park, Chanjun;Eo, Sugyeong;Park, JeongBae;Lim, Heuiseok
    • Journal of the Korea Convergence Society
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    • v.12 no.5
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    • pp.1-7
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    • 2021
  • In the latest trend of machine translation research, the model is pretrained through a large mono lingual corpus and then finetuned with a parallel corpus. Although many studies tend to increase the amount of data used in the pretraining stage, it is hard to say that the amount of data must be increased to improve machine translation performance. In this study, through an experiment based on the mBART model using parallel corpus filtering, we propose that high quality data can yield better machine translation performance, even utilizing smaller amount of data. We propose that it is important to consider the quality of data rather than the amount of data, and it can be used as a guideline for building a training corpus.

Efficient Implementation of Convolutional Neural Network Using CUDA (CUDA를 이용한 Convolutional Neural Network의 효율적인 구현)

  • Ki, Cheol-Min;Cho, Tai-Hoon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.6
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    • pp.1143-1148
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    • 2017
  • Currently, Artificial Intelligence and Deep Learning are rising as hot social issues, and these technologies are applied to various fields. A good method among the various algorithms in Artificial Intelligence is Convolutional Neural Networks. Convolutional Neural Network is a form that adds Convolution Layers to Multi Layer Neural Network. If you use Convolutional Neural Networks for small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning should take long time when the size of the learning data is large and the structure of layers is complicated. In these cases, GPU-based parallel processing is frequently needed. In this paper, we developed Convolutional Neural Networks using CUDA, and show that its learning is faster and more efficient than learning using some other frameworks or programs.

Smart Home Service System Considering Indoor and Outdoor Environment and User Behavior (실내외 환경과 사용자의 행동을 고려한 스마트 홈 서비스 시스템)

  • Kim, Jae-Jung;Kim, Chang-Bok
    • Journal of Advanced Navigation Technology
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    • v.23 no.5
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    • pp.473-480
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    • 2019
  • The smart home is a technology that can monitor and control by connecting everything to a communication network in various fields such as home appliances, energy consumers, and security devices. The Smart home is developing not only automatic control but also learning situation and user's taste and providing the result accordingly. This paper proposes a model that can provide a comfortable indoor environment control service for the user's characteristics by detecting the user's behavior as well as the automatic remote control service. The whole system consists of ESP 8266 with sensor and Wi-Fi, Firebase as a real-time database, and a smartphone application. This model is divided into functions such as learning mode when the home appliance is operated, learning control through learning results, and automatic ventilation using indoor and outdoor sensor values. The study used moving averages for temperature and humidity in the control of home appliances such as air conditioners, humidifiers and air purifiers. This system can provide higher quality service by analyzing and predicting user's characteristics through various machine learning and deep learning.

Video smoke detection with block DNCNN and visual change image

  • Liu, Tong;Cheng, Jianghua;Yuan, Zhimin;Hua, Honghu;Zhao, Kangcheng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.9
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    • pp.3712-3729
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    • 2020
  • Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

Evaluation of soil-concrete interface shear strength based on LS-SVM

  • Zhang, Chunshun;Ji, Jian;Gui, Yilin;Kodikara, Jayantha;Yang, Sheng-Qi;He, Lei
    • Geomechanics and Engineering
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    • v.11 no.3
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    • pp.361-372
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    • 2016
  • The soil-concrete interface shear strength, although has been extensively studied, is still difficult to predict as a result of the dependence on many factors such as normal stresses, surface roughness, particle sizes, moisture contents, dilation angles of soils, etc. In this study, a well-known rigorous statistical learning approach, namely the least squares support vector machine (LS-SVM) realized in a ubiquitous spreadsheet platform is firstly used in estimating the soil-structure interface shear strength. Instead of studying the complicated mechanism, LS-SVM enables to explore the possible link between the fundamental factors and the interface shear strengths, via a sophisticated statistic approach. As a preliminary investigation, the authors study the expansive soils that are found extensively in most countries. To reduce the complexity, three major influential factors, e.g., initial moisture contents, initial dry densities and normal stresses of soils are taken into account in developing the LS-SVM models for the soil-concrete interface shear strengths. The predicted results by LS-SVM show reasonably good agreement with experimental data from direct shear tests.

AI Processor Technology Trends (인공지능 프로세서 기술 동향)

  • Kwon, Youngsu
    • Electronics and Telecommunications Trends
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    • v.33 no.5
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    • pp.121-134
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    • 2018
  • The Von Neumann based architecture of the modern computer has dominated the computing industry for the past 50 years, sparking the digital revolution and propelling us into today's information age. Recent research focus and market trends have shown significant effort toward the advancement and application of artificial intelligence technologies. Although artificial intelligence has been studied for decades since the Turing machine was first introduced, the field has recently emerged into the spotlight thanks to remarkable milestones such as AlexNet-CNN and Alpha-Go, whose neural-network based deep learning methods have achieved a ground-breaking performance superior to existing recognition, classification, and decision algorithms. Unprecedented results in a wide variety of applications (drones, autonomous driving, robots, stock markets, computer vision, voice, and so on) have signaled the beginning of a golden age for artificial intelligence after 40 years of relative dormancy. Algorithmic research continues to progress at a breath-taking pace as evidenced by the rate of new neural networks being announced. However, traditional Von Neumann based architectures have proven to be inadequate in terms of computation power, and inherently inefficient in their processing of vastly parallel computations, which is a characteristic of deep neural networks. Consequently, global conglomerates such as Intel, Huawei, and Google, as well as large domestic corporations and fabless companies are developing dedicated semiconductor chips customized for artificial intelligence computations. The AI Processor Research Laboratory at ETRI is focusing on the research and development of super low-power AI processor chips. In this article, we present the current trends in computation platform, parallel processing, AI processor, and super-threaded AI processor research being conducted at ETRI.

Few-Shot Image Synthesis using Noise-Based Deep Conditional Generative Adversarial Nets

  • Msiska, Finlyson Mwadambo;Hassan, Ammar Ul;Choi, Jaeyoung;Yoo, Jaewon
    • Smart Media Journal
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    • v.10 no.1
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    • pp.79-87
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    • 2021
  • In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

A Comparative Study between the Parameter-Optimized Pacejka Model and Artificial Neural Network Model for Tire Force Estimation (타이어 힘 추정을 위한 파라미터 최적화 파제카 모델과 인공 신경망 모델 간의 비교 연구)

  • Cha, Hyunsoo;Kim, Jayu;Yi, Kyongsu;Park, Jaeyong
    • Journal of Auto-vehicle Safety Association
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    • v.13 no.4
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    • pp.33-38
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    • 2021
  • This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

A Preliminary Study of the Development of DNN-Based Prediction Model for Quality Management (DNN을 활용한 건설현장 품질관리 시스템 개발을 위한 기초연구)

  • Suk, Janghwan;Kwon, Woobin;Lee, Hak-Ju;Lee, Chanwoo;Cho, Hunhee
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2022.11a
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    • pp.223-224
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    • 2022
  • The occurrence of defect, one of the major risk elements, gives rise to construction delays and additional costs. Although construction companies generally prefer to use a method of identifying and classifying the causes of defects, a system for predicting the rise of defects becomes important matter to reduce this harmful issue. However, the currently used methods are kinds of reactive systems that are focused on the defects which occurred already, and there are few studies on the occurrence of defects with prediction systems. This paper is about preliminary study on the development of judgemental algorithm that informs us whether additional works related to defect issue are needed or not. Among machine learning techniques, deep neural network was utilized as prediction model which is a major component of algorithm. It is the most suitable model to be applied to the algorithm when there are 8 hidden layers and the average number of nodes in each hidden layer is 70. Ultimately, the algorithm can identify and defects that may arise in later and contribute to minimize defect frequency.

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