• Publications of The Korean Astronomical Society
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• v.38 no.2
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• pp.91-98
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• 2023

We present a novel method that can enhance the detection success rate of interstellar objects. Interstellar objects are objects that are not gravitationally bound to our solar system and thus are believed to have originated from other planetary systems. Since the finding of two interstellar objects, 1l/'Oumuamua in 2017 and 2l/Borisov in 2019, much attention has been paid to finding new interstellar objects. In this paper, we propose the use of Heliospheric Imagers (HIs) for the survey of interstellar objects. In particular, we show HI data taken from Solar TErrestrial RElation Observatory/Sun Earth Connection Coronal and Heliospheric Investigation and demonstrate their ability to detect 'Oumuamua-like interstellar objects. HIs are designed to monitor and study space weather by observing the solar wind traveling through interplanetary space. HIs provide the day-side observations and thus it can dramatically enlarge the observable sky range when combined with the traditional night-side observations. In this paper, we first review previous methods for detecting interstellar objects and demonstrate that HIs can be used for the survey of interstellar objects.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.63-76
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• 2023

Identity and access management in cloud computing is one of the leading significant issues that require various security countermeasures to preserve user privacy. An authentication mechanism is a leading solution to authenticate and verify the identities of cloud users while accessing cloud applications. Building a secured and flexible authentication mechanism in a cloud computing platform is challenging. Authentication techniques can be combined with other security techniques such as intrusion detection systems to maintain a verifiable layer of security. In this paper, we provide an interactive, flexible, and reliable multi-factor authentication mechanisms that are primarily based on a proposed Authentication Method Selector (AMS) technique. The basic idea of AMS is to rely on the user's previous authentication information and user behavior which can be embedded with additional authentication methods according to the organization's requirements. In AMS, the administrator has the ability to add the appropriate authentication method based on the requirements of the organization. Based on these requirements, the administrator will activate and initialize the authentication method that has been added to the authentication pool. An intrusion detection component has been added to apply the users' location and users' default web browser feature. The AMS and intrusion detection components provide a security enhancement to increase the accuracy and efficiency of cloud user identity verification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2222-2240
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• 2023

Brand fandom refers to a collection of consumers with strong emotions toward a brand. Studying the dynamics of brand fandom can help brands understand which services or strategies influence their consumers to become a part of brand fandom. However, existing literature on fandom in the last three decades has mainly used qualitative methods, and there is still a lack of research on fandom using quantitative methods. Specifically, previous studies lack a framework for locating fandoms from online textual data and analyzing their dynamics. This study proposes a framework for exploring brand fandom dynamics based on online textual data. This framework consists of four phases based on the design thinking model: Preparing Data, Defining Fandom Categories, Generating Fandom Dynamics, and Analyzing Fandom Dynamics. This framework uses techniques such as social network analysis and process mining, combined with brand personality theory. We demonstrate the applicability of this framework using case studies of two Korean home appliance brands. The dataset contains 14,593 posts by consumers in 374 online communities. The results show that the proposed framework can analyze brand fandom dynamics using textual customer data. Our study contributes to the interdisciplinary research at the intersection of data-driven service design and consumer culture quantification.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.303-309
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• 1997

This study is carried out to investigate performance differences between two alarm presentation methods from the viewpoint of human factors and to provide items to be improved. One of the alarm display methods considered in this study displays alarm lists on VDT combined with hardwired alarm panels. The other method displays alarms on plant mimic diagrams of VDT. This alarm display method has other features for operator aid with which operator can get detailed information on the activated alarm in the mimic diagrams, and the capability for alarm processing such as alarm reduction and prioritization. To compare the twodisplay methods, a human factor experiment was performed with a plant simulator in the ITF(Integrated Test Fcility) that plant operators run for 4 event scenarios. During the experiment, physiological measurements, system and operator action log, and audio/video recordings were collected. Operators' subjective opinion was collected as well after the experiment. Time, error rate and situation awareness were major human factor criteria used for the comparison during the analysis stage of the experiment. No statistical significance was found in the results of our statistical comparison analysis. Several findings were identified, however, through the analysis of subjective opinions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.840-860
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• 2023

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed. By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion. A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms. The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator.

• ETRI Journal
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• v.45 no.2
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• pp.213-225
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• 2023

Physical layer security (PLS) can improve the security of both terrestrial and nonterrestrial wireless communication networks. This study proposes a simplified framework for nonterrestrial cyclic prefixed orthogonal variable spreading factor (OVSF)-encoded multiple-input and multiple-output nonorthogonal multiple access (NOMA) systems to ensure complete network security. Various useful methods are implemented, where both improved sine map and multiple parameter-weighted-type fractional Fourier transform encryption schemes are combined to investigate the effects of hybrid PLS. In addition, OVSF coding with power domain NOMA for multi-user interference reduction and peak-toaverage power ratio (PAPR) reduction is introduced. The performance of $\frac{1}{2}$-rated convolutional, turbo, and repeat and accumulate channel coding with regularized zero-forcing signal detection for forward error correction and improved bit error rate (BER) are also investigated. Simulation results ratify the pertinence of the proposed system in terms of PLS and BER performance improvement with reasonable PAPR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.85-94
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• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.

• Membrane and Water Treatment
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• v.13 no.1
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• pp.1-6
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• 2022

The regulations of the International Maritime Organization (IMO) have been steadily strengthened in ship emissions. Accordingly, there is a growing need for development of related technologies for the removal of contaminants that may occur during the treatment of SOx and NOx using a wet scrubber. However, this system also leads to wastewater production when the exhaust gas is scrubbed. In this research, we evaluated the performance of an ion selective resin process in accordance with scrubber wastewater discharge regulations, specifically nitrate discharge, by the IMO. Accelerated real and synthetic wastewater of wet scrubbers, contained high amounts of TDS with high nitrate, is used as feed water in lab scale systems. Furthermore, a pilot scale dissolved air flotation (DAF) using microbubble generator with ion exchange resin process was combined and developed in order to apply for the treatment of wet scrubber wastewater. The results of the present study revealed that operating conditions, such as resin property, bed volume (BV), and inlet wastewater flow rate, significantly affect the removal performance. Finally, through a pilot test, DAF with ion exchange resin process showed a noticeable improvement of the nitrate removal rate compared to the single DAF process.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.49-58
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• 2023

Renewable energy is not a new terminology. One of the fastest growing renewable energies is solar energy. The implementation of solar energy provides several advantages including the reduction of some of the environmental risks of fossil fuel consumption. This research elaborated the importance of the adaption of solar energy by developing a spatial decision support system (SDSS), while the Residential Solar Energy Adoption (RSEA) is an instantiation artifact in the form of an SDSS. As a GIS web-based application, RSEA allows stakeholders (e.g., utility companies, policymakers, service providers homeowners, and researchers) to navigate through locations on a map interactively. The maps highlight locations with high and low solar energy adoption potential that enables decision-makers (e.g., policymakers, solar firms, utility companies, and nonprofit organizations) to make decisions. A combined qualitative and quantitative methodological approach was used to evaluate the application's usability and user experience, and results affirmed the ability of the factors of utility, usefulness, and a positive user experience of the residential solar energy adoption of spatial decision support system (RSEA-SDSS). RSEA-SDSS in improving the decision-making process for potential various stakeholders, in utility, solar installations, policy making, and non-profit renewable energy domains.