• Clinical and Experimental Pediatrics
• /
• v.61 no.4
• /
• pp.101-107
• /
• 2018

Recent advances in genetics have determined that a number of epilepsy syndromes that occur in the first year of life are associated with genetic etiologies. These syndromes range from benign familial epilepsy syndromes to early-onset epileptic encephalopathies that lead to poor prognoses and severe psychomotor retardation. An early genetic diagnosis can save time and overall cost by reducing the amount of time and resources expended to reach a diagnosis. Furthermore, a genetic diagnosis can provide accurate prognostic information and, in certain cases, enable targeted therapy. Here, several early infantile epilepsy syndromes with strong genetic associations are briefly reviewed, and their genotype-phenotype correlations are summarized. Because the clinical presentations of these disorders frequently overlap and have heterogeneous genetic causes, next-generation sequencing (NGS)-based gene panel testing represents a more powerful diagnostic tool than single gene testing. As genetic information accumulates, genetic testing will likely play an increasingly important role in diagnosing pediatric epilepsy. However, the efforts of clinicians to classify phenotypes in nondiagnosed patients and improve their ability to interpret genetic variants remain important in the NGS era.

• Journal of Korea Multimedia Society
• /
• v.10 no.6
• /
• pp.745-753
• /
• 2007

The wireless medium is known to be time-varying which could affect and result to a poor network's performance. As a solution, an opportunistic scheduling and power control algorithm based on IEEE 802.11 MAC protocol is proposed in this paper. The algorithm opportunistically exploits the channel condition for better network performance. Convex optimization problems were also formulated i.e. the overall transmission power of the system is minimized and the "net-utility" of he system is maximized. We have proven that an optimal transmission power vector may exist, satisfying the maximum power and SINR constraints at all receivers, thereby minimizing overall transmission power and maximizing net-utility of the system.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.8
• /
• pp.932-940
• /
• 1999

In this paper, a new neural networks and neural network based sliding mode controller are proposed. The new neural networks are an mor self-recurrent neural networks which use a recursive least squares method for the fast on-line leammg. The error self-recurrent neural networks converge considerably last than the back-prollagation algorithm and have advantage oi bemg less affected by the poor initial weights and learning rate. The controller for suspension system is designed according to sliding mode technique based on new proposed neural networks. In order to adapt shding mode control mnethod, each frame dstance hetween ground and vehcle body is estimated md controller is designed according to estimated neural model. The neural networks based sliding mode controller approves good peiformance throllgh computer sirnulations.

• Journal of Information Technology Applications and Management
• /
• v.9 no.1
• /
• pp.97-110
• /
• 2002

Many companies are using EDI (Electronic Data Interchange) for the electronic transmission of documents and information to and from other companies. The appearance of Internet can enhance existing EDI systems. Existing EDI systems have several problems such as poor system interoperability and high expense of VAN. This paper prognoses a new EDI system utilizing Internet to provide open communication environment by using XML (extensible Markup language) and this applies it to the EDI service for Hospital Demand. XML is a mark-up language extending HTML which is a standard language for the expression of WWW (World-Wide Web) pages. XML is more structural than HTML, thus it is more suitable for the repetitive tasks of EDI and for the maintenance of databases. XML can transmit EDI documents in the open communication environment of Internet and users can easily access the documents with web browsers. Therefore we can provide EDI services within more open environment and we can build an EDI system with lower expense.

• Mycobiology
• /
• v.44 no.2
• /
• pp.67-78
• /
• 2016

Rice contaminated with fungal species during storage is not only of poor quality and low economic value, but may also have harmful effects on human and animal health. The predominant fungal species isolated from rice grains during storage belong to the genera Aspergillus and Penicillium. Some of these fungal species produce mycotoxins; they are responsible for adverse health effects in humans and animals, particularly Aspergillus flavus, which produces the extremely carcinogenic aflatoxins. Not surprisingly, there have been numerous attempts to devise safety procedure for the control of such harmful fungi and production of mycotoxins, including aflatoxins. This review provides information about fungal and mycotoxin contamination of stored rice grains, and microbe-based (biological) strategies to control grain fungi and mycotoxins. The latter will include information regarding attempts undertaken for mycotoxin (especially aflatoxin) bio-detoxification and microbial interference with the aflatoxin-biosynthetic pathway in the toxin-producing fungi.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.298-299
• /
• 2019

In this paper, we propose a stable mutual authentication protocol between unmanned combat systems in tactical wireless networks where long distance communications are not always guaranteed due to a poor channel condition. The proposed protocol generates an authentication code using hash collision of arbitrarily selected random data. The authentication requester encrypts and transmits it to the authenticator. They performs authentication by sharing the valid authentication code. We analyze the safety of the proposed method for various attack scenarios.

• Journal of information and communication convergence engineering
• /
• v.21 no.3
• /
• pp.198-207
• /
• 2023

Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.

• International Journal of Computer Science & Network Security
• /
• v.23 no.6
• /
• pp.68-76
• /
• 2023

Nowadays, the power of data analytics in general and visual data analytics, in particular, have been proven to be an important area that would help development in any domain. Many well-known IT services best practices have touched on the importance of data analytics and visualization and what it can offer to information technology service management. Yet, little research exists that summarises what is already there and what can be done to utilise further the power of data analytics and visualization in this domain. This paper is divided into two main parts. First, a number of IT service management tools have been summarised with a focus on the data analytics and visualization features in each of them. Second, interviews with five senior IT managers have been conducted to further understand the usage of these features in their organisations and the barriers to fully benefit from them. It was found that the main barriers include a lack of good understanding of some visualization design principles, poor data quality, and limited application of the technology and shortage in data analytics and visualization expertise.

• Proceedings of the IEEK Conference
• /
• 2000.11d
• /
• pp.101-104
• /
• 2000

An efficient bit rate distribution technique that distributes available bits for multiple objects based on motion vector magnitude, size of object shape, and coding distortion is presented. This coding concept using the three parameters was exploited in MPEG-4 multiple object coding. But the scheme is likely to produce poor results such as allocating more bits to less important objects and degrading picture quality, due to the lack of analysis and research in view of human visual aspect. In this paper importance of each object is represented by the three parameters and visually analyzed. Target bits are distributed according to coding distortion using the pre-assigned shape and motion information.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.317-320
• /
• 2003

In this paper, we present real-time, accurate face region detection and tracking technique for an intelligent surveillance system. It is very important to obtain the high-resolution images, which enables accurate identification of an object-of-interest. Conventional surveillance or security systems, however, usually provide poor image quality because they use one or more fixed cameras and keep recording scenes without any clue. We implemented a real-time surveillance system that tracks a moving person using pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction. Color information in the ROI is updated to extract features for optimal tracking and zooming. The experiment with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.