• BMB Reports
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• v.46 no.1
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• pp.41-46
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• 2013

Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1124-1128
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• 2005

In this paper, a plan-coordination architecture is proposed for multi-agent control in the fractal manufacturing system (FrMS). A fractal in FrMS is a set of distributed agents whose goal can be achieved through cooperation, coordination, and negotiation with other agents. Since each agent in the FrMS generates, achieves, and modifies its own plan fragments autonomously during the coordination process with other agents, it is necessary to develop a systematic methodology for the achievement of global plan in the manufacturing system. The heterarchical structure of the FrMS provides a compromised plan-coordination approach, it compromise a centralized plan-generation/execution (which mainly focuses on the maximization of throughput) with a distributed one (which focuses on the autonomy of each module and flexibility of the whole system). Plan-coordinators in lower level fractal independently generate plan fragments according to the global plan of higher level fractal, and plan-coordinators in higher level fractal mediate/coordinate the plan fragments to enhance the global performance of the system. This paper assumes that generation method of the plan fragments and the negotiation policy of the fractal is achieved by a simple process, and we mainly focuses on the information exchanging and distributed decision making process to coordinate the combinations of plan fragments within a limited exchange of information.

• Journal of the Society of Disaster Information
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• v.5 no.2
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• pp.108-122
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• 2009

Purpose of this research about reduction the scholastic systematic triangular position of the security guard martial art which repeats a development is insufficient with demand of the while society to recognize and for the philosophic value research of security guard martial art composition principle puts out with the one method and from the reporter to search the aesthetics which appears does. In order to attain the goal of the research which sees the literature which relates with an security guard martial art widly, was an investigation and observed the aesthetics from concept and martial art of aesthetics and this the technical free use ability from actual site of the technical find which leads the practice voluntary repetition practice of security guard martial art with character and the body guard aesthetic integral part experience possibly did, there being will be able to acquire an aesthetic inspiration, confirmed. So the security guard martial art follows the composition principle of maximization central attitude and shock point breath control and mental intensive etc. of reinforcement of direction shock of relativity redundancy mental moral culture body agreement characteristic force and relaxation force and is completed and will be able to embody an aesthetic value with aesthetic elements of technical polishing process inside goes about reduction.

• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.209-215
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• 2018

Recent research in animal behavior has shown that gradient information plays an important role in finding food and home. It is also important in optimization of performance because it indicates how the inputs should be adjusted for maximization/minimization of a performance index. We introduce perturbation as an additional input to obtain gradient information. Unlike the typical approach of calculating the gradient from the derivative, the proposed processing is very robust to noise since it is performed as a summation. Experimental results prove the validity of the process of spatial gradient acquisition. Quantitative indices for measuring the effect of the amplitude and the frequency are developed based on linear regression analysis. Drones are very useful for environmental monitoring and an autonomous path planning is required for unstructured environment. Guiding the drone for finding the origin of the interested physical property is done by estimating the gradient of the sensed value and generating the drone trajectories in the direction which maximizes the sensed value. Simulation results show that the proposed method can be successfully applied to identify the source of the physical quantity of interest by utilizing it for path planning of an autonomous drone in 3D environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.905-911
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• 2005

We propose an efficient adaptive subcarrier selection scheme, in which the active subcarriers and their modulation and coding schemes (MCSs) are selected at the receiver, and subsequently conveyed to the transmitter using limited feedback We theoretically show that capacity maximization can be achieved by selecting subcarriers with highest signal-to-noise ratios (SNRs) and adapting the number of active subcarriers according to channel environments. Furthermore, an ordering based adaptive subcarrier selection algorithm is proposed to select the optimal active subcarriers with low complexity. Numerical results show that the proposed adaptive subcarrier selection scheme provides higher capacity than that obtained by water-filling approaches, even with limited feedback.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.179-187
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• 2011

Recently, cognitive radio attracts lots of interest to effectively utilize the limited spectral resource. In the previous researches, we proposed a method to enhance the system capacity of the overall network by using Selfish Symbiotic architecture and Non-selfish Symbiotic architecture. In this research, we further enhance the previous works to OFDM-based CR networks by using efficient subchannel allocation. The system performance is evaluated through intensive simulations with multiple primary users as well as a single primary user with different numbers of CR users.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5B
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• pp.327-337
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• 2012

There have been lots of researches on the optimal relay selection in relay-based systems. However, most researches have been focused on the maximization of transmission capacity with a constraint of sum power at both transmitter and relays. In Ad-hoc networks where relays have batteries of limited power, it is imperative to minimize the energy consumption while maintaining the required quality-of-service (QoS). In this paper, we propose an optimal relay selection strategy to minimize the relay power consumption while satisfying the required signal-to-noise ratio (SNR). Through intensive simulations, we show the proposed method is more effective in terms of energy consumption and guarantee lower transmission failure probability in multi-hop Ad-hoc environments.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.461-470
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• 2005

A dynamic supply chain that is composed of many different companies with different rent roles and interests allows free joining and secession. Buyers place orders simultaneously and manufacturers should compete each other for the orders. The purpose of our paper is how to find the optimal formation of supply chain ill a global viewpoint while allowing each member company to pursue his local goal The dynamic nature of supply chain formation causes the variation of cost depending on how many orders a manufacturer would accept. We propose a multi-agent based negotiation protocol that efficiently leads to the formation of optimal supply chain without giving up maximization of the individual profit in multi-agent environment of the make-to-order industry. The goal of the negotiation is to form a supply chain to minimize the overall sum of manufacturers' manufacturing cost, and earliness cost and tardiness cost based on SET model. We compare the negotiation protocol with Branch & Bound method. Finally, the validity and performance of buyer's negotiation has been tested experimentally.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.149-154
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• 2014

The cross-correntropy concept can be expressed with inner products of two different probability density functions constructed by Gaussian-kernel density estimation methods. Blind algorithms based on the maximization of the cross-correntropy (MCC) and a symbol set of randomly generated N samples yield superior learning performance, but have a huge computational complexity in the update process at the aim of weight adjustment based on the MCC. In this paper, a method of reducing the computational complexity of the MCC algorithm that calculates recursively the gradient of the cross-correntropy is proposed. The proposed method has only O(N) operations per iteration while the conventional MCC algorithms that calculate its gradients by a block processing method has $O(N^2)$. In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.