• Current Optics and Photonics
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• v.6 no.3
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• pp.260-269
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• 2022

Segmented planar imaging detector for electro-optical reconnaissance (SPIDER) is an emerging technology for optical imaging. However, this novel detection approach is faced with degraded imaging quality. In this study, a 6 × 6 planar waveguide is used after each lenslet to expand the field of view. The imaging principles of field-plane waveguide structures are described in detail. The local multiple-sampling simulation mode is adopted to process the simulation of the improved imaging system. A novel image-reconstruction algorithm based on deep learning is proposed, which can effectively address the defects in imaging quality that arise during image reconstruction. The proposed algorithm is compared to a conventional algorithm to verify its better reconstruction results. The comparison of different scenarios confirms the suitability of the algorithm to the system in this paper.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.561-589
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• 2022

When multiple classifications and regression trees are combined, tree-based ensemble models, such as random forest (RF) and Bayesian additive regression trees (BART), are produced. We compare the model structures and performances of various ensemble models for regression settings in this study. RF learns bootstrapped samples and selects a splitting variable from predictors gathered at each node. The BART model is specified as the sum of trees and is calculated using the Bayesian backfitting algorithm. Throughout the extensive simulation studies, the strengths and drawbacks of the two methods in the presence of missing data, high-dimensional data, or highly correlated data are investigated. In the presence of missing data, BART performs well in general, whereas RF provides adequate coverage. The BART outperforms in high dimensional, highly correlated data. However, in all of the scenarios considered, the RF has a shorter computation time. The performance of the two methods is also compared using two real data sets that represent the aforementioned situations, and the same conclusion is reached.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.90-95
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• 2023

With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.32.2-32.2
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• 2021

Radio relics, Mpc-size elongated diffuse radio emissions found at galaxy cluster outskirts, are known as the result of shock acceleration during the cluster merger. Theories have claimed that low Mach number shocks are too inefficient to create the observed properties of radio relics. Alternative scenarios such as fossil cosmic ray electrons (CRes) from AGNs are required to explain the observations. However, how exactly the fossil CRes from AGNs can supply the Mpc-size radio relic is still an open question. In this study, we present our recent uGMRT radio observation results of the merging galaxy cluster Abell 514. We found three remarkable AGN jet tails that may have undergone multiple reorientations and extend nearly 800 kpc. Using multi-frequency data, we have performed spectral analysis along the AGN tails and track how the tails lose or gain energy as they propagate in the intracluster medium. We will discuss whether these AGN jets can provide sufficient seed CRes to radio relics.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1091-1094
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• 2008

유비쿼터스 컴퓨팅 환경에서는 동적으로 발생하는 수많은 사용자나 환경에 대한 정보를 수집하여 적합한 서비스를 사용자에게 제공해야 한다. 이러한 유비쿼터스 환경을 워크플로우 형태로 기술할 수 있는 uWDL 을 이용하여 동적으로 변화하는 환경의 각각의 사용자에게 적합한 서비스를 제공하기에 한계가 있다. 이를 해결하기 위한 방법으로 본 논문에서는 다중 시나리오를 지원하기 위한 방법인 멀티플로우와 서브플로우를 시나리오에 적용하여 해결 방안을 제시한다.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.295-306
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• 2023

This paper presents a multi-frequency and multi-constellation Global Navigation Satellite System (GNSS) signal generator that simulates intermediate frequency level digital signal samples for testing GNSS receivers. GNSS signal generators are ideally suited for testing the performance of GNSS receivers and algorithms under development in the laboratory for specific user locations and environments. The proposed GNSS signal generator features a fully-reconfigurable structure with the ability to adjust signal parameters, which is beneficial to generate desired signal characteristics for multiple scenarios including multi-constellation and frequencies. Successful signal acquisition, tracking, and navigation are demonstrated on a verified Software Defined Radio (SDR) in this study. This work has implications for future studies and advances the research and development of new GNSS signals.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.123-130
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• 2024

This paper proposes an EMPC (Explicit Model Predictive Controller) for temperature tracking control based on heat load prediction by an ESO (Extended State Observer) for a variable cooling circulation system with multiple indoor units connected to one outdoor unit. In this system, heat transfer and heat loss relative to the input temperature are modeled using system dynamics. Using this model, we design an EMPC based on an ESO that is robust to temperature changes and depends on airflow. To determine the stability of both the controller and the observer, asymptotic stability is verified through Lyapunov stability analysis. Finally, to validate the performance of the proposed controller, simulations are conducted under three scenarios with varying airflow, set temperature, and heat load.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2051-2066
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• 2017

This paper deals with reconfigurable secured mobile systems where the reconfigurability has the potential of providing a required adaptability to change the system requirements. The reconfiguration scenario is presented as a run-time automatic operation which allows security mechanisms and the addition-removal-update of software tasks. In particular, there is a definite requirement for filtering and intrusion detection mechanisms that will use fewer resources and also that will improve the security on the secured mobile devices. Filtering methods are used to control incoming traffic and messages, whereas, detection methods are used to detect malware events. Nevertheless, when different reconfiguration scenarios are applied at run-time, new security threats will be emerged against those systems which need to support multiple security objectives: Confidentiality, integrity and availability. We propose in this paper a new approach that efficiently detects threats after reconfigurable scenarios and which is based on filtering and intrusion detection methods. The paper's contribution is applied to Android where the evaluation results demonstrate the effectiveness of the proposed middleware in order to detect the malicious events on reconfigurable secured mobile systems and the feasibility of running and executing such a system with the proposed solutions.