• Title/Summary/Keyword: data learning process

Search Result 2,087, Processing Time 0.035 seconds

An Intelligent Fire Leaning and Detection System (지능형 화재 학습 및 탐지 시스템)

  • Cheoi, Kyungjoo
    • Journal of Korea Multimedia Society
    • /
    • v.18 no.3
    • /
    • pp.359-367
    • /
    • 2015
  • In this paper, we propose intelligent fire learning and detection system using hybrid visual attention mechanism of human. Proposed fire learning system generates leaned data by learning process of fire and smoke images. The features used as learning feature are selected among many features which are extracted based on bottom-up visual attention mechanism of human, and these features are modified as learned data by calculating average and standard variation of them. Proposed fire detection system uses learned data which is generated in fire learning system and features of input image to detect fire.

The Relationship between Learning Strategies and Congnitive Learning Abilities (학습전략과 인지적 학습능력과의 관계 분석 연구)

  • 김종순
    • Journal of Gifted/Talented Education
    • /
    • v.6 no.1
    • /
    • pp.93-109
    • /
    • 1996
  • The purpose of this study was to investigate the relationship between learning strategies and cognitive learning abilities with achievement scores of elementary school children. To achieve this purpose, 109 sixth grade children were sampled in Seoul-City, and the 'Questionnaire on the Learning Strategies and Learning Abilities Test' were administered to them. The collected data were analyzed by Pearson's Product Moment Correlation and Multiple Regression Analysis. The major findings of this study were as follows: Firstly, there appeared to be statistically significant correlations between learning strategies and achievement scores. The process of thinking variable of learning strategies were most significantly correlated with achievement scores(r=.251- .458, p<.01). The calculated R2 indicated that the combined effects of process of thinhng and affective domain on the achievement scores were about 21.5%. Secondly, there appeared to be statistically significant correlations between cognitive learning abilities and achievement scores. The verbal reasoning and verbal comprehension variable of cognitive learning abilities were most significantly correlated with achievement scores(r=.215-,493, p<.01). The calculated R2 indicated that the verbal reasoning and verbal comprehension variable of cognitive learning abilities explained about 27.6% of the variance of achievement scores. Thirdly, there appeared to be no statistically significant correlations between learning strategies and cognitive learning abilities. The results of this study shows that the development of learning strategies and cognitive learning abilities could improve the achievement scores in school learning.

  • PDF

Improvement of SOM using Stratification

  • Jun, Sung-Hae
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.9 no.1
    • /
    • pp.36-41
    • /
    • 2009
  • Self organizing map(SOM) is one of the unsupervised methods based on the competitive learning. Many clustering works have been performed using SOM. It has offered the data visualization according to its result. The visualized result has been used for decision process of descriptive data mining as exploratory data analysis. In this paper we propose improvement of SOM using stratified sampling of statistics. The stratification leads to improve the performance of SOM. To verify improvement of our study, we make comparative experiments using the data sets form UCI machine learning repository and simulation data.

A Deep Learning Application for Automated Feature Extraction in Transaction-based Machine Learning (트랜잭션 기반 머신러닝에서 특성 추출 자동화를 위한 딥러닝 응용)

  • Woo, Deock-Chae;Moon, Hyun Sil;Kwon, Suhnbeom;Cho, Yoonho
    • Journal of Information Technology Services
    • /
    • v.18 no.2
    • /
    • pp.143-159
    • /
    • 2019
  • Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

Wavelet-like convolutional neural network structure for time-series data classification

  • Park, Seungtae;Jeong, Haedong;Min, Hyungcheol;Lee, Hojin;Lee, Seungchul
    • Smart Structures and Systems
    • /
    • v.22 no.2
    • /
    • pp.175-183
    • /
    • 2018
  • Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

Failure Prognostics of Start Motor Based on Machine Learning (머신러닝을 이용한 스타트 모터의 고장예지)

  • Ko, Do-Hyun;Choi, Wook-Hyun;Choi, Seong-Dae;Hur, Jang-Wook
    • Journal of the Korean Society of Manufacturing Process Engineers
    • /
    • v.20 no.12
    • /
    • pp.85-91
    • /
    • 2021
  • In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

The Effects of the Online Learning Using Virtual Reality (VR) Geological Data: Focused on the Geo-Big Data Open Platform (가상현실(VR) 지질자료 개발을 통한 원격수업의 효과 분석: 지오빅데이터 오픈플랫폼 활용을 중심으로)

  • Yoon, Han Do;Kim, Hyoungbum;Kim, Heoungtae
    • Journal of the Korean Society of Earth Science Education
    • /
    • v.15 no.1
    • /
    • pp.47-61
    • /
    • 2022
  • In this study, We developed VR (Virtual Reality) geological resources based on the Geo Big Data of the Big Data platform that provided by the Korea Institute of Geoscience and Mineral Material (KIGAM). So students selected the theme of lessons by using these resources and we operated Remote classes using the materials that developed as to Virtual Reality. Therefore, the geological theme maps provided by the Geo Big Data Open Platform were reconstructed and produced materials were created for Study about Real Korean geological outcrops grounded in Virtual Reality. And Topographic information data was used to produce class materials for Remote classes. Twenty students were selected by Random sampling, and data were collected by conducting a survey including interviews to confirm the change in students' perception of remote classes in virtual reality geological data development and the effect of the classes, so data were analyzed through inductive categorization. The results of this study are as follows. First, students showed positive responses in terms of interest, utilization, and knowledge utilization as taking remote classes for developing geological data in virtual reality geological data. This is the result of showing the adaptability of diverse and flexible learning getting away from a fixed framework by motivating and encouraging students and inducing cooperation for communication. Second, students recognized distance education in the development of Virtual Reality geological data as 'Realistic hands-on learning process', 'Immersive learning process by motivation', and 'Learning process of acquiring knowledge in the field of earth science'.

An Efficient Guitar Chords Classification System Using Transfer Learning (전이학습을 이용한 효율적인 기타코드 분류 시스템)

  • Park, Sun Bae;Lee, Ho-Kyoung;Yoo, Do Sik
    • Journal of Korea Multimedia Society
    • /
    • v.21 no.10
    • /
    • pp.1195-1202
    • /
    • 2018
  • Artificial neural network is widely used for its excellent performance and implementability. However, traditional neural network needs to learn the system from scratch, with the addition of new input data, the variation of the observation environment, or the change in the form of input/output data. To resolve such a problem, the technique of transfer learning has been proposed. Transfer learning constructs a newly developed target system partially updating existing system and hence provides much more efficient learning process. Until now, transfer learning is mainly studied in the field of image processing and is not yet widely employed in acoustic data processing. In this paper, focusing on the scalability of transfer learning, we apply the concept of transfer learning to the problem of guitar chord classification and evaluate its performance. For this purpose, we build a target system of convolutional neutral network (CNN) based 48 guitar chords classification system by applying the concept of transfer learning to a source system of CNN based 24 guitar chords classification system. We show that the system with transfer learning has performance similar to that of conventional system, but it requires only half the learning time.

Design of Falling Recognition Application System using Deep Learning

  • Kwon, TaeWoo;Lee, Jong-Yong;Jung, Kye-Dong
    • International Journal of Internet, Broadcasting and Communication
    • /
    • v.12 no.2
    • /
    • pp.120-126
    • /
    • 2020
  • Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

Deep Learning based Image Recognition Models for Beef Sirloin Classification (딥러닝 이미지 인식 기술을 활용한 소고기 등심 세부 부위 분류)

  • Han, Jun-Hee;Jung, Sung-Hun;Park, Kyungsu;Yu, Tae-Sun
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.44 no.3
    • /
    • pp.1-9
    • /
    • 2021
  • This research examines deep learning based image recognition models for beef sirloin classification. The sirloin of beef can be classified as the upper sirloin, the lower sirloin, and the ribeye, whereas during the distribution process they are often simply unified into the sirloin region. In this work, for detailed classification of beef sirloin regions we develop a model that can learn image information in a reasonable computation time using the MobileNet algorithm. In addition, to increase the accuracy of the model we introduce data augmentation methods as well, which amplifies the image data collected during the distribution process. This data augmentation enables to consider a larger size of training data set by which the accuracy of the model can be significantly improved. The data generated during the data proliferation process was tested using the MobileNet algorithm, where the test data set was obtained from the distribution processes in the real-world practice. Through the computational experiences we confirm that the accuracy of the suggested model is up to 83%. We expect that the classification model of this study can contribute to providing a more accurate and detailed information exchange between suppliers and consumers during the distribution process of beef sirloin.