• Title, Summary, Keyword: machine learning

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Semi-Supervised Learning for Fault Detection and Classification of Plasma Etch Equipment (준지도학습 기반 반도체 공정 이상 상태 감지 및 분류)

  • Lee, Yong Ho;Choi, Jeong Eun;Hong, Sang Jeen
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.4
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    • pp.121-125
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    • 2020
  • With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

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A Method for Spam Message Filtering Based on Lifelong Machine Learning (Lifelong Machine Learning 기반 스팸 메시지 필터링 방법)

  • Ahn, Yeon-Sun;Jeong, Ok-Ran
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1393-1399
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    • 2019
  • With the rapid growth of the Internet, millions of indiscriminate advertising SMS are sent every day because of the convenience of sending and receiving data. Although we still use methods to block spam words manually, we have been actively researching how to filter spam in a various ways as machine learning emerged. However, spam words and patterns are constantly changing to avoid being filtered, so existing machine learning mechanisms cannot detect or adapt to new words and patterns. Recently, the concept of Lifelong Learning emerged to overcome these limitations, using existing knowledge to keep learning new knowledge continuously. In this paper, we propose a method of spam filtering system using ensemble techniques of naive bayesian which is most commonly used in document classification and LLML(Lifelong Machine Learning). We validate the performance of lifelong learning by applying the model ELLA and the Naive Bayes most commonly used in existing spam filters.

Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method

  • Park, Min-Jun;Kim, Hyeon-June
    • Journal of Sensor Science and Technology
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    • v.30 no.2
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    • pp.76-81
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    • 2021
  • In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

Sensor Data Collection & Refining System for Machine Learning-Based Cloud (기계학습 기반의 클라우드를 위한 센서 데이터 수집 및 정제 시스템)

  • Hwang, Chi-Gon;Yoon, Chang-Pyo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.2
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    • pp.165-170
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    • 2021
  • Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.

Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications

  • Yang, Haesang;Byun, Sung-Hoon;Lee, Keunhwa;Choo, Youngmin;Kim, Kookhyun
    • Journal of Ocean Engineering and Technology
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    • v.34 no.4
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    • pp.277-284
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    • 2020
  • Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

Adaptive Recommendation System for Health Screening based on Machine Learning

  • Kim, Namyun;Kim, Sung-Dong
    • International journal of advanced smart convergence
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    • v.9 no.2
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    • pp.1-7
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    • 2020
  • As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

2-class Maxtreme Learning Machine(MLM) for Mobile Touchstroke using Sequential Fusion (모바일 터치스트로크 데이터를 이용한 2-class Maxtreme Learning Machine(MLM))

  • Choi, Seok-Min;Teoh, Andrew Beng-Jin
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.362-364
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    • 2018
  • 핸드폰 사용자가 늘어나면서 이와 관련하여 개인 정보 보안에 대한 중요성이 대두되고 있다. 이에 따라 제안된 알고리즘은 Extreme learning machine 으로부터 착안하여 변형하여 고안한 Maxtreme Learning Machine(MLM) 으로, 사용자들의 터치 스트로크 특성 벡터를 제안 알고리즘으로 학습하여 사용자들을 검증한다. 또한 특성 벡터의 순차적 융합 기법을 이용하여 더 많은 정보를 바탕으로 사용자를 높은 정확도로 검증 할 수 있다.

Machine Learning by Decision Tree Algorithm (Decision Tree 를 이용한 Machine Learning)

  • Jung, W.C.;Choi, K.S.;Kim, J.H.
    • Electronics and Telecommunications Trends
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    • v.8 no.4
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    • pp.205-211
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    • 1993
  • 필요한 자료의 제공만으로 컴퓨터 스스로 논리 체계를 세워 나가는 Machine Learning은 인공 지능의 한 분야로서 여러 방면에서 활발한 연구가 진행되고 있다. 본 고에서는 Machine Learning 의 기본적인 여러가지 방식 중의 하나인 Decision Tree 방법을 소개하고 문제점 및 연구 방향을 서술한다.

Artificial Neural Network: Understanding the Basic Concepts without Mathematics

  • Han, Su-Hyun;Kim, Ko Woon;Kim, SangYun;Youn, Young Chul
    • Dementia and Neurocognitive Disorders
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    • v.17 no.3
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    • pp.83-89
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    • 2018
  • Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron. The purpose of this review is to explain the fundamental concepts of artificial neural networks.

Advanced Technologies in Blockchain, Machine Learning, and Big Data

  • Park, Ji Su;Park, Jong Hyuk
    • Journal of Information Processing Systems
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    • v.16 no.2
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    • pp.239-245
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    • 2020
  • Blockchain, machine learning, and big data are among the key components of the future IT track. These technologies are used in various fields; hence their increasing application. This paper discusses the technologies developed in various research fields, such as data representation, Blockchain application, 3D shape recognition and classification, query method, classification method, and search algorithm, to provide insights into the future paradigm. In this paper, we present a summary of 18 high-quality accepted articles following a rigorous review process in the fields of Blockchain, machine learning, and big data.