• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.68.1-68.1
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• 2012

We present a comprehensive study of the stellar populations in two faint M31 dwarf satellites, And XI and And XIII. Using deep archival images from the Wide Field Planetary Camera 2 (WFPC2) onboard the Hubble Space Telepscope (HST), we characterize the horizontal branch (HB) morphologies and the RR Lyrae (RRL) populations of these two faint dwarf satellites. Our new template light curve fitting routine (RRFIT) detected RRL populations from both galaxies. The mean periods of $RR_{ab}$ (RR0) stars in And XI and And XIII are < $P_{ab}$ > = $0.621{\pm}0.040$, and < $P_{ab}$ > = $0.648{\pm}0.038$ respectively. Even though the RRL populations show a lack of $RR_{ab}$ stars with high amplitudes (Amp(V) > 1.0 mag) and relatively short periods ($P_{ab}$ ~ 0.5 days), their period - V band amplitude (P-Amp(V)) relations track the lower part of the general P-Amp(V) trend in the M31 outer halo RRL populations. The metallicities of $RR_{ab}$ stars were calculated via the [Fe/H]-log $P_{ab}$-Amp(V) relationship of Alcock et al. The metallicities thus obtained ($[Fe/H]_{And}$ $_{XI}=-1.75%$; $[Fe/H]_{And}$ $_{XIII}=-1.74$) are consistent with the values calculated from the RGB slope indicating that our measurements are not significantly affected by the evolutionary effects of RRL stars. We discuss the origins of And XI and And XIII based on a comparative analysis of the luminosity-metallicity (L-M) relation of Local Group dwarf galaxies.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

• Journal of Distribution Science
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• v.8 no.2
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• pp.5-12
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• 2010

The allotment rate for railway transportation keeps an yearly 6% in Korea. However, the railway logistics will cause the opposite result according to the continuous investment and logistics rationalization. The study on railway logistics as well as inter-Korean railway that might highly contribute to the development of railway logistics is not enough at all. The purpose of this paper is to study the revitalization strategy for inter-Korean railway by forecasting the demand and the scale of railway logistics depot. The revitalization strategies for inter-Korean railway through railway logistics depot are as followings. First, it is necessary to strengthen the partnership with coal user in the logistics depot. Second, it is encouraged to provide the financial assistance that are needed in the maintenance of the decrepit North Korea's track as well as the establishment of Donghae northern line that is from Gangneung to Jejin. Third, the railway cost on long/short transportation and large sized shipper is needed to apply in a flexible way. Fourth, it is necessary to obtain the railway traffic right by involving the foreign mining development. Fifth, it is encouraged to constantly find the small sized shipper like cement company.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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• pp.8-17
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• 2006

Presented in this paper was to evaluate and improve the usability of a web-based logistics information system. The system was developed for the domestic company to track and monitor its own transportation vehicles and for the customers to check the current location of their packages by using Global Positioning System (GPS) and Short Massage Service (SMS). Since the initial system was developed under a tight schedule set by the company, the system designers and programmers did not focus on the usability of the system but on the functionality. Consequently, some usability problems of the system were discovered during the heuristic usability evaluation. This study was required to solve these usability issues. Usability problems of the initial system were identified and analyzed, and the user's requirements for the system were re-evaluated to meet the company's expectation. Several alternative designs were developed by fitted guidelines and then a updated system was developed. The updated system had an empirical usability test to find how much the initial system was improved from the heuristic evaluation. Two kinds of data were gathered during the tests: objective (completion time and number of errors) and subjects' preference. Data showed the updated system is better than the initial system in terms of usability. Presented in this paper includes introduction of the Usability evaluation, usability engineering process applied in this research, alternative design of GUI, usability test and results.

• Atmosphere
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• v.31 no.3
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• pp.311-326
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• 2021

To improve the predictability of high-impact weather phenomena around Seoul, where a larger number of people are densely populated, KMA conducted the intensive observation from 22 June to 20 September in 2020 over the Seoul area. During the intensive observation period (IOP), the dropsonde from NIMS Atmospheric Research Aircraft (NARA) and the radiosonde from KMA research vessel Gisang1 were observed in the Yellow Sea, while, in the land, the radiosonde observation data were collected from Icheon and Incheon. Therefore, in this study, the effects of radiosonde and dropsonde data during the IOP were investigated by Observing System Experiment (OSE) based on Korean Integrated Model (KIM). We conducted two experiments: CTL assimilated the operational fifteen kinds of observations, and EXP assimilated not only operational observation data but also intensive observation data. Verifications over the Korean Peninsula area of two experiments were performed against analysis and observation data. The results showed that the predictability of short-range forecast (1~2 day) was improved for geopotential height at middle level and temperature at lower level. In three precipitation cases, EXP improved the distribution of precipitation against CTL. In typhoon cases, the predictability of EXP for typhoon track was better than CTL, although both experiments simulated weaker intensity as compared with the observed data.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Platform Technology
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• v.11 no.1
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• pp.38-51
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• 2023

We propose an intelligent interface algorithm using hand gesture recognition information based on artificial intelligence. This method is functionally an interface that recognizes various motions quickly and intelligently by using MediaPipe and artificial intelligence techniques such as KNN, LSTM, and CNN to track and recognize user hand gestures. To evaluate the performance of the proposed algorithm, it is applied to a self-made 2D top-view racing game and robot control. As a result of applying the algorithm, it was possible to control various movements of the virtual object in the game in detail and robustly. And the result of applying the algorithm to the robot control in the real world, it was possible to control movement, stop, left turn, and right turn. In addition, by controlling the main character of the game and the robot in the real world at the same time, the optimized motion was implemented as an intelligent interface for controlling the coexistence space of virtual and real world. The proposed algorithm enables sophisticated control according to natural and intuitive characteristics using the body and fine movement recognition of fingers, and has the advantage of being skilled in a short period of time, so it can be used as basic data for developing intelligent user interfaces.

• Journal of English Language & Literature
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• v.58 no.6
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• pp.1143-1165
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• 2012

Tinted with ontological concern, Ted Hughes passes through an existential climate, eventually confirms death( or nothingness) as the new foundation of his poetry, and explores the various paradoxical effects of nothingness. Nihilism, fraught with rather negative and traumatic themes such as death, melancholy, and despair can, however, generate being (even in multiple modes), animalistic vitality, and insubstantial specters. Among these new functions of nothingness animality and spectrality are the most notable in Hughes's poetry. A considerable number of animals and bioorganisms that Hughes introduces exhibit the enormous energy derived from the dignity of death, from subversive challenges against the established hierarchy, and from new and dynamic multifaceted sources of nothingness. In other words, Hughes's animals, yield surplus power beyond themselves, as if they are demi-gods; in short, they feature the sublime as unidentified terrifying effects of nothingness. In a sense, animality means allowing some level of violence without legal sanction. Hughes inaugurates this kind of all bigotry-eradicating violence and attempts to subvert higher beings such as humans and gods, and existing doctrines: thrushes rise up against the animal and human worlds; a rush of ghostly crabs at night press through the human world. Hughes also resists the highest being, God, employing the technique of rewriting God's theology. Dirty, anomalous crows attack, subvert, and dismember the delicate, indurate, and thorough system of logos. Hughes, of course, does not place the animals merely in lofty regard, aware of the ulterior deprivation of the sublime animality, the trace of existential negativity. Thus, a seemingly omnipotent crow can become a mere beggar guzzling ice cream from the garbage bin on the beach. In addition, the violent and dignified aspects of nothingness can be transformed to reveal the thin and trivial traits as unreliable specters. Dark, heavy, and terrible nullity lessens its own volume and mass, and exposes the airy waves of shadows or specters. However, owing to nullity's untraceable track, the scarcity and unfamiliarity of the phantoms inversely display their foreign gigantic effects such as fantasy and violence.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.155-164
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• 2010

This paper proposes a robust detection method for ship wake search using two wake sensors. A long trailing wake in the rear of a surface ship is generated along the track of surface ships. In this paper, we assume that the nearer the surface ship, the stronger wake strength is and a two-sensor based wake homing torpedo can sense for the wake strength. On this assumption we propose a simple wake detection and search method using information of wake strength. Experimental results using monte-carlo simulation demonstrate that the proposed method yields better performance in search time than previous method, which uses a single sensor. Our method is shown faster by about 45 seconds than previous method to achieve the same performance. Also, it can improve the detection performance of torpedo in the case of short wake length.