• Title/Summary/Keyword: K2-learning algorithm

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Using Skeleton Vector Information and RNN Learning Behavior Recognition Algorithm (스켈레톤 벡터 정보와 RNN 학습을 이용한 행동인식 알고리즘)

  • Kim, Mi-Kyung;Cha, Eui-Young
    • Journal of Broadcast Engineering
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    • v.23 no.5
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    • pp.598-605
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    • 2018
  • Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

A Study on Reliability Analysis According to the Number of Training Data and the Number of Training (훈련 데이터 개수와 훈련 횟수에 따른 과도학습과 신뢰도 분석에 대한 연구)

  • Kim, Sung Hyeock;Oh, Sang Jin;Yoon, Geun Young;Kim, Wan
    • Korean Journal of Artificial Intelligence
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    • v.5 no.1
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    • pp.29-37
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    • 2017
  • The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

Learning method of a Neural Network using Genetic Algorithm for 3 Bit Parity Discrimination (패리티 판별을 위한 유전자 알고리즘을 사용한 신경회로망의 학습법)

  • Choi, Jae-Seung;Kim, Chung-Hwa
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.44 no.2 s.314
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    • pp.11-18
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    • 2007
  • Back propagation algorithm based on a gradient-decent method has been widely used to the training of a neural network. However, this algorithm have some problems such as dropping the minimum value in a local area according to an initial value and setting the number of units in a hidden layer when training the neural network. Accordingly, to solve the above-mentioned problems, this paper proposes a genetic algorithm using the training method of the neural network. Thus, the improved genetic algorithm using a new crossover and mutation method is proposed to discriminate 3 bit parity. Experiments confirm that the proposed system is effective for training speed after demonstrating for generation gap, the number of units in the hidden layer, and the number of individuals.

Unsupervised Learning Model for Fault Prediction Using Representative Clustering Algorithms (대표적인 클러스터링 알고리즘을 사용한 비감독형 결함 예측 모델)

  • Hong, Euyseok;Park, Mikyeong
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.2
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    • pp.57-64
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    • 2014
  • Most previous studies of software fault prediction model which determines the fault-proneness of input modules have focused on supervised learning model using training data set. However, Unsupervised learning model is needed in case supervised learning model cannot be applied: either past training data set is not present or even though there exists data set, current project type is changed. Building an unsupervised learning model is extremely difficult that is why only a few studies exist. In this paper, we build unsupervised models using representative clustering algorithms, EM and DBSCAN, that have not been used in prior studies and compare these models with the previous model using K-means algorithm. The results of our study show that the EM model performs slightly better than the K-means model in terms of error rate and these two models significantly outperform the DBSCAN model.

Estimating Farmland Prices Using Distance Metrics and an Ensemble Technique (거리척도와 앙상블 기법을 활용한 지가 추정)

  • Lee, Chang-Ro;Park, Key-Ho
    • Journal of Cadastre & Land InformatiX
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    • v.46 no.2
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    • pp.43-55
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    • 2016
  • This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

Prediction of watermelon sweetness using a reflected sound (반향 소리를 이용한 기계 학습 기반 수박의 당도 예측)

  • Kim, Ki-Hoon;Woo, Ji-Hwan
    • Journal of the Korea Convergence Society
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    • v.11 no.8
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    • pp.1-6
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    • 2020
  • There are various approaches to evaluate a watermelon sweetness. However, there are some limitations to evaluating cost, watermelon damage, and subjective issue. In this study, we developed a novel approach to predict a watermelon sweetness using reflected sound and the machine learning algorithm. It was observed that higher brix watermelon produced higher spectral power is reflected sound. Based on the spectral-temporal features of reflected sound, the machine learning algorithms could accurately predict the sweetness group at a rate of 83.2 and 59.6 % in 2-groups and 3-groups classification, respectively.

Vehicle Classification by Road Lane Detection and Model Fitting Using a Surveillance Camera

  • Shin, Wook-Sun;Song, Doo-Heon;Lee, Chang-Hun
    • Journal of Information Processing Systems
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    • v.2 no.1
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    • pp.52-57
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    • 2006
  • One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

Performance Analysis of Deep Learning-based Image Super Resolution Methods (딥 러닝 기반의 초해상도 이미지 복원 기법 성능 분석)

  • Lee, Hyunjae;Shin, Hyunkwang;Choi, Gyu Sang;Jin, Seong-Il
    • IEMEK Journal of Embedded Systems and Applications
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    • v.15 no.2
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    • pp.61-70
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    • 2020
  • Convolutional Neural Networks (CNN) have been used extensively in recent times to solve image classification and segmentation problems. However, the use of CNNs in image super-resolution problems remains largely unexploited. Filter interpolation and prediction model methods are the most commonly used algorithms in super-resolution algorithm implementations. The major limitation in the above named methods is that images become totally blurred and a lot of the edge information are lost. In this paper, we analyze super resolution based on CNN and the wavelet transform super resolution method. We compare and analyze the performance according to the number of layers and the training data of the CNN.

Spatio-Temporal Analysis of Trajectory for Pedestrian Activity Recognition

  • Kim, Young-Nam;Park, Jin-Hee;Kim, Moon-Hyun
    • Journal of Electrical Engineering and Technology
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    • v.13 no.2
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    • pp.961-968
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    • 2018
  • Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

AI Education Programs for Deep-Learning Concepts (딥러닝 개념을 위한 인공지능 교육 프로그램)

  • Ryu, Miyoung;Han, SeonKwan
    • Journal of The Korean Association of Information Education
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    • v.23 no.6
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    • pp.583-590
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    • 2019
  • The purpose of this study is to develop an educational program for learning deep learning concepts for elementary school students. The model of education program was developed the deep-learning teaching method based on CT element-oriented teaching and learning model. The subject of the developed program is the artificial intelligence image recognition CNN algorithm, and we have developed 9 educational programs. We applied the program over two weeks to sixth graders. Expert validity analysis showed that the minimum CVR value was more than .56. The fitness level of learner level and the level of teacher guidance were less than .80, and the fitness of learning environment and media above .96 was high. The students' satisfaction analysis showed that students gave a positive evaluation of the average of 4.0 or higher on the understanding, benefit, interest, and learning materials of artificial intelligence learning.