• Journal of Information Technology Services
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• v.13 no.1
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• pp.197-207
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• 2014

With the recent advance in smartphones, users are allowed to use mobile applications anytime anywhere, and change their way to interact with smart environment and people. As a result, the need for developing context-aware applications on smartphones has a great attention from users and developers. This paper proposes a context-aware framework for supporting UI/UX of smartphones. The proposed framework collects a wide range of sensory data from smartphones and allows developers to analyze and model context models for their desired apps. In addition, it also supports real-time inference within the apps to make them to adapt to context. In order to show effectiveness of the proposed framework, we introduce two smartphone apps: context-aware home screen and automatic detection of smartphone problem use. Therefore, we expect that the proposed framework will help developers easily implement their apps with respect to context-awareness.

• Current Photovoltaic Research
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• v.7 no.4
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• pp.85-96
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• 2019

Renewable energy is emerging as a reliable alternative source of energy, it is much safer, cleaner than conventional sources and has contributed significantly in this sector. However, there are still some challenges that needed to address this evolving technology. Artificial Intelligence (A. I.) can assess the past, optimize the present, and forecast the future. Therefore, A. I. will resolve most of these problems. Artificial intelligence is complex in nature, but it reduces error and aims to reach a greater degree of precision which make renewables smarter. This paper provides an overview of frequently used A. I. methods in solar energy applications. A sample algorithm is also provided for literature purposes and knowledge transfer.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.159-165
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• 2015

The need for the recognition of music emotion has become apparent in many music information retrieval applications. In addition to the large pool of techniques that have already been developed in machine learning and data mining, various emerging applications have led to a wealth of newly proposed techniques. In the music information retrieval community, many studies and applications have concentrated on tag-based music recommendation. The limitation of music emotion tags is the ambiguity caused by a single music tag covering too many subcategories. To overcome this, multiple tags can be used simultaneously to specify music clips more precisely. In this paper, we propose a novel technique to rank the proper tag combinations based on the acoustic similarity of music clips.

• IE interfaces
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• v.22 no.3
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• pp.252-262
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• 2009

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.197-206
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• 2011

The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

• Journal of Life Science
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• v.17 no.5 s.85
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• pp.714-718
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• 2007

The high-throughput sequencing of a lot of genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable attention in recent years since exact promoter prediction can give a clue to the elucidation of overall genetic networks. In this study, applications of support vector machine(SVM) to promoter prediction are explored to show a right approaches to discriminate between promoter and non-promoter regions by means of SVM. The results of various experiments show that encoding method, encoding region and learning data constitution can play an important role in the performance of SVM.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4317-4335
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• 2018

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

• Proceedings of the Optical Society of Korea Conference
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• 1990.02a
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• pp.215-218
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• 1990

A method for optical representation and modulation of synaptic interconnections between neurons using photoanisotropic amorphous As2S3 thin film is discussed. Experimental results show that the proposed method can be used for the representation of not only excitatory synaptic connections but also inhibitory synaptic connections. Applications of the method to the implementation of optical learning machine is also discussed.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.9-12
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• 2016

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.