• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.65-72
• /
• 2019

Data augmentation is a method that increases the amount of data through various algorithms based on a small amount of sample data. When machine learning and deep learning techniques are used to solve real-world problems, there is often a lack of data sets. The lack of data is at greater risk of underfitting and overfitting, in addition to the poor reflection of the characteristics of the set of data when learning a model. Thus, in this paper, through the layer-wise data augmenting method at each layer of deep neural network, the proposed method produces augmented data that is substantially meaningful and shows that the method presented by the paper through experimentation is effective in the learning of the model by measuring whether the method presented by the paper improves classification accuracy.

• Journal of Biomedical Engineering Research
• /
• v.24 no.3
• /
• pp.151-158
• /
• 2003

Numerous studies of short-term, beat-to-beat variability in cardiovascular signals have used linear analysis techniques. However, no study has been done about the appropriateness of linear techniques or the comparison between linearities and nonlinearities in short-term, beat-to-beat variability. This paper aims to verify the appropriateness of linear techniques by investigating nonlinearities in short-term, beat-to-beat variability. We compared linear autoregressive moving average(ARMA) with nonlinear neural network(NN) models for predicting current instantaneous heart rate(HR) and mean arterial blood pressure(BP) from past HRs and BPs. To evaluate these models. we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that 10 technique provides adequate characterization of the system dynamics responsible for generating short-term, beat-to-beat variability.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.2
• /
• pp.69-76
• /
• 2018

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a new deep neural network model for referring expression comprehension. The proposed model finds out the region of the referred object in the given image by making use of the rich information about the referred object itself, the context object, and the relationship with the context object mentioned in the referring expression. In the proposed model, the object matching score and the relationship matching score are combined to compute the fitness score of each candidate region according to the structure of the referring expression sentence. Therefore, the proposed model consists of four different sub-networks: Language Representation Network(LRN), Object Matching Network (OMN), Relationship Matching Network(RMN), and Weighted Composition Network(WCN). We demonstrate that our model achieves state-of-the-art results for comprehension on three referring expression datasets.

• Journal of the Economic Geographical Society of Korea
• /
• v.1 no.1
• /
• pp.137-149
• /
• 1998

It is likely that the spatial structure of the intraurban accessibility as well as the accessibility value of each of the nodes in the subway network is affected by the addition of new linkages. The changes in the accessibility at individual nodes also affect the accessibility in the surrounding areas at some distances away from the nodes. Graph-theoretic algorithms have been developed as a proper measurement scheme for the nodal accessibility in tracked transport networks such as subway networks. However, the graph-theoretic measurements have limitations to estimate the spatial diffusion effect on the surrounding areas. This study proposes a new model for the spatial diffusion effect estimation of nodal accessibility increment in the subway network toward the surrounding areas. Since the distance decay trend of subway station use reflect the spatial diffusion effect of the accessibility of subway station toward the surrounding area. The model is deduced from the subway station use density function which is formulated by the questionnaire survey data.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.9
• /
• pp.369-374
• /
• 2014

Path generation methods are required for safe and efficient driving in unmanned autonomous vehicles. There are two kinds of paths: global and local. A global path consists of all the way points including the source and the destination. A local path is the trajectory that a vehicle needs to follow from a way point to the next in the global path. In this paper, we propose a novel method for local path generation through machine learning, with an effective curve function used for initializing the trajectory. First, reinforcement learning is applied to a set of candidate paths to produce the best trajectory with maximal reward. Then the optimal steering angle with respect to the trajectory is determined by training an artificial neural network. Our method outperformed existing approaches and successfully found quality paths in various experimental settings, including the cases with obstacles.

• Convergence Security Journal
• /
• v.11 no.1
• /
• pp.11-18
• /
• 2011

Wireless mesh network technology with transmission speeds similar to wired and wireless technology means to build, compared with wired networks, building a more efficient network to provide convenience and flexibility. The wireless mesh network router nodes in the energy impact of the mobility is less constrained and has fewer features entail. However, the characteristics of various kinds due to network configuration settings and the choice of multiple paths that can occur when the system overhead and there are many details that must be considered. Therefore, according to the characteristics of these network routing technology that is reflected in the design and optimization of the network is worth noting. In this paper, a multi-path setting can be raised in order to respond effectively to the problem of the router node data loss and bandwidth according to traffic conditions and links to elements of the hop count evaluation by using a genetic algorithm as a workaround for dynamic routing the routing metric for wireless mesh network scheme is proposed.

• Journal of the Korea Convergence Society
• /
• v.8 no.9
• /
• pp.237-247
• /
• 2017

The Effects of Social Capital on Occupational Aspiration in University Students This study aims to understand the impact of social capital (parent-child relationship, human network, and SNS use) on the occupational aspiration level in university students based on Coleman's Social Capital Concept. To achieve this, statistical significance test was conducted through correlation analysis and hierarchical multiple regression analysis. The major study findings are as follows. As a result of correlation analysis, it was found that Other variables, except for parent's occupational of social capital had a positive effect on vocational aspiration. Also. as a result of hierarchical multiple regression analysis, it was found that parent-child relationship, human network obtained from social activities, Social Networking Service use and usefulness of information - social capital factors - had a statistically significant impact on occupational aspiration. Such findings suggest that the process of possessing and acquiring social capital among university students served as an important achievement-oriented value in terms of social mobility.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.4
• /
• pp.283-292
• /
• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.2
• /
• pp.423-428
• /
• 2017

These days, various and tons of education information are rapidly increasing and spreading due to Internet and smart devices usage. Recently, as e-Learning usage increasing, many instructors and students (learners) need to set a goal to maximize learners' result of education and education system efficiency based on big data analytics via online recorded education historical data. In this paper, the author applied Word2Vec algorithm (neural network algorithm) to find similarity among education words and classification by clustering algorithm in order to objectively recognize and analyze online recorded education historical data. When the author applied the Word2Vec algorithm to education words, related-meaning words can be found, classified and get a similar vector values via learning repetition. In addition, through experimental results, the author proved the part of speech (noun, verb, adjective and adverb) have same shortest distance from the centroid by using clustering algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.477-480
• /
• 2010

This paper develops a sensor gateway for using Internet for traffic flow control and remote monitoring, it suggest the required protocol with authentication and encryption. The traffic Surveillance and Control System is an important service to the ITS(Intelligent Transportation System). The traffic surveillance and control system's TCP / IP and the Internet network using is may cause damage means accessing from unauthorized users, Subsequent authentication and encryption of data is essential.