• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.25-30
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• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.43-49
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• 2023

Knowledge of minimum horizontal stress (S_hmin) is a significant step in determining full stress tensor. It provides crucial information for the production of sand, hydraulic fracturing, determination of safe mud weight window, reservoir production behavior, and wellbore stability. Calculating the S_hmin using indirect methods has been proved to be awkward because a lot of data are required in all of these models. Also, direct techniques such as hydraulic fracturing are costly and time-consuming. To figure these problems out, this work aims to apply the long-short-term memory (LSTM) algorithm to S_hmin time-series prediction. 13956 datasets obtained from an oil well logging operation were applied in the models. 80% of the data were used for training, and 20% of the data were used for testing. In order to achieve the maximum accuracy of the LSTM model, its hyper-parameters were optimized significantly. Through different statistical indices, the LSTM model's performance was compared with with other machine learning methods. Finally, the optimized LSTM model was recommended for S_hmin prediction in the well logging operation.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.145-145
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• 2021

Water distribution networks (WDNs) are designed to satisfy water requirement of an urban community. One of the central issues in human history is providing sufficient quality and quantity of water through WDNs. A WDN consists of a great number of pipelines with different ages, lengths, materials, and sizes in varying degrees of deterioration. The available annual budget for rehabilitation of these infrastructures only covers part of the network; thus it is important to manage the limited budget in the most cost-effective manner. In this study, a novel pipe replacement scheduling approach is proposed in order to smooth the annual investment time series based on a life cycle cost assessment. The proposed approach is applied to a real WDN currently operating in South Korea. The proposed scheduling plan considers both the annual budget limitation and the optimum investment on pipes' useful life. A non-dominated sorting genetic algorithm is used to solve a multi-objective optimization problem. Three decision-making objectives, including the minimum imposed LCC of the network, the minimum standard deviation of annual cost, and the minimum average age of the network, are considered to find optimal pipe replacement planning over long-term time period. The results indicate that the proposed scheduling structure provides efficient and cost-effective rehabilitation management of water network with consistent annual budget.

• Journal of the military operations research society of Korea
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• v.17 no.1
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• pp.105-129
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• 1991

Dynamic Time Warping(in short DTW) is a kind of sequence comparison method. It is widely used in human speech recognition. The timing difference between two speech patterns to be compared is removed by warping the time axes of the speech pattern by minimising the time-normalised distance between them. In the process of finding the minimum time-normalised distance. the efficient method is dynamic programming problem. This paper describes the concept of dynamic time warping method, mathematical formulation and an application.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.99-99
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• 2011

A statistical study of coronal hole merging and splitting has been performed through Solar Cycle 23. The NOAA/SESC solar synoptic maps are examined to identify inarguably clear events of coronal hole merging and splitting. The numbers of merging events and splitting events are more or less comparable regardless of the phase in the solar cycle. The number of both events, however, definitely shows the phase dependence in the solar cycle. It apparently has a minimum at the solar minimum whereas its maximum is located in the declining phase of the sunspot activity, about a year after the second peak in Solar Cycle 23. There are more events of merging and splitting in the descending phase than in the ascending phase. Interestingly, no event is found at the local minimum between the two peaks of the sunspot activity. This trend can be compared with the variation of the average magnetic field strength and the radial field component in the solar wind through the solar cycle. In Ulysses observations, both of these quantities have a minimum at the solar minimum while their maximum is located in the descending phase, a while after the second peak of the sunspot activity. At the local minimum between the two peaks in the solar cycle, the field strength and the radial component both have a shallow local minimum or an inflection point. At the moment, the physical reason for these resembling tendencies is difficult to understand with existing theories. Seeing that merging and splitting of coronal holes are possible by passage of opposite polarity magnetic structures, we may suggest that the energizing activities in the solar surface such as motions of flux tubes are not exactly in phase with sunspot generation, but are more active some time after the sunspot maximum.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.629-632
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• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.5
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• pp.193-199
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• 1984

This paper presents a direct adaptive pole placement control scheme which is applicable to discretetime non-minimum phase systems. It is proved that by this scheme the poles can be placed at the desired locations and the overall state vector of the system is uniformly bounded if the reference input is sufficiently rich, and also proved that in case of insufficiently rich reference input the overall system can still be stabilized though the poles may not be placed exactly at the desired locations. The effectiveness of this scheme is verified by digital computer simulations.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.215-218
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• 1998

This paper proposes a blind multi-user detector using Code-Constrained Minimum Variance (CCMV) method which directly detects the DS-CDMA signals in a multipath fading channel without estimating the channels. This algorithm reduces the complexity of computation by making a small size data matrix with the order of the channel length. Advantageously it requires to know the spreading code and the time delay of only the desired user.

• Journal of Korean Society for Quality Management
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• v.17 no.2
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• pp.81-92
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• 1989

A class of standard optimization techniques to estimate the stationary transition probabilities among states is discussed. With the use of aggregate time series data on employed labor in industrial sectors, the alternative restricted estimates including minimum absolute deviation, unweighted, weighted, generalized inverse, minimum chi-square and maximum likelihood are evaluated and compared. Analytic and numerical results are shown favorably with the viewpoint of the validity and predictive potentiality of model.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.434-439
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• 1993

This paper present a sensor based obstacle avoidance method which is based on a VFH(Vector Field Histogram) method. The basic idea of obstacle avoidance is to find a minimum obstacle direction and distance. From the minimum sonar index and the target direction high level system determine steering angle of mobile robot. The sonar sensor system consists of 12 ultra sonic sensor, and each sensor have its direction and safety value. This method has advantage on calculation speed and small memory. This method is implemented on indoor autonomous vehicle'ALiVE-2'.