• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.523-535
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• 2017

Local intelligent services aim at controlling local services such as cooling or lightening services in a certain local area, using Internet-of-Things (IoT) sensor data in the area. As the IoT paradigm has evolved, local intelligent services have gained increasing attention. However, most of the local intelligent service mechanism proposed so far do not directly take the users' presence and service preference information into account for controlling local services. This study proposes an individual presence-and-preference-based local service system (IPP-LISS). We present a intelligent service control algorithm and implement a prototype system of IPP-LISS. Typically, the intelligence part of IPP-LISS including the prediction models, is generated on remote server in the cloud because of their compute-intense aspect. However, this can cause huge data traffic between IoT devices and servers in the cloud. The emerging mobile edge computing technology will be a promising solution of this challenge of IPP-LISS. In this paper, we implement IPP-LISS in the cloud, and then, based on the implementation result, we discuss applying the mobile edge computing technology to the IPP-LISS application.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Journal of the Korean Magnetics Society
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• v.22 no.3
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• pp.109-115
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• 2012

NeFeB anisotropy bonded magnet has proposed a new paradigm of weight reduction of small motors by replacing the conventional ferrite permanent magnets with its high magnetic property of 25 MGOe during last five years. It has also advanced by leaps and bounds in the field of motor industry for automobiles and electric power tools. And it has led a new innovation of fifty percent weight lightening compared to its current motors by correctly focusing on fuel performance improvement through weight lightening that automobile industry chased. There was, however, another price skyrocketing in 2011 after China had announced its export regulation in rare earth materials in July, 2010. And this price change has an extensive impact on the industries that consume rare earth magnets. This environmental change has caused technical challenge to improve the performance by using least amount of rare earth elements in NdFeB anisotropy bonded magnets, and led to make a new technical approach to a new applied field. In this article, we will show how each nation deals with this industrial issue, and introduce development trend and application of anisotropic NdFeB bonded magnets, so-called MAGFINE made by Aichi Steel Corp.

• 대한공업교육학회지
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• v.44 no.1
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• pp.209-231
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• 2019

The purpose of this study was to extract the factors of core competency required for technology teachers and to analyze the educational needs for extracted core competency factors and to search for the application of core competencies in the in-service technology teachers education. This study was conducted by literature review, expert validation, and needs assessment method. The survey was conducted by 92 low-carrier technology teachers who participated in in-service technology teachers education for upgrading to first grade teacher certificate. Data were analyzed the factor analysis, needs assessment, and IPA analysis using SPSS 24. The core competencies with high education needs were selected by the score of the Borich formula and the IPA analysis. As a result of the study, 29 factors of core competencies were chosen as the priority: challenge, planning ability, decision making ability, future orientation, intellectual property utilization ability, communication ability, and creative thinking etc. Based on the conclusions of this study, I would suggest the following. It is to create a new in-service education program reflected on core competencies that have high educational needs of low-carrier technology teachers. In addition, a strategy that reflects core competencies methodically in existing in-service teachers education program is needed. Future research should be followed by research on curriculum design to enhance high needed core competencies of low-carrier technology teachers.

• Membrane Journal
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• v.34 no.3
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• pp.192-203
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• 2024

The advancement of commercially viable gas separation membranes plays a pivotal role in improving CO₂ separation efficiency. High-molecular-weight poly(ethylene oxide) (high-Mw PEO) emerges as a promising option due to its high CO₂ solubility, affordability, and robust mechanical attributes. However, the crystalline nature of high-Mw PEO hinders its application in gas separation membranes. This study proposes a straightforward blending approach by incorporating various polymeric additives into high-Mw PEO to address this challenge. Four commercially available, water-soluble polymers, i.e. poly(ethylene glycol) (PEG), poly(propylene glycol) (PPG), poly(acrylic acid) (PAA), and poly(vinyl pyrrolidone) (PVP) are examined as additives to enhance membrane performance by improving miscibility and reducing PEO crystallinity. Contrary to expectations, PEG and PPG fail to inhibit the crystalline structure of PEO and result in membrane flaws. Conversely, PAA and PVP demonstrate greater success in altering the crystal structure of PEO, yielding defect-free membranes. A thorough investigation delves into the correlation between changes in the crystalline structure of high-Mw PEO blend membranes and their gas separation performance. Drawing from our findings and previously documented outcomes, we offer insights into designing and selecting additive polymers for high-Mw PEO, aiming at the creation of cost-effective, commercially viable CO₂ separation membranes.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.61-68
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• 2019

Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Journal of Plant Biotechnology
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• v.50
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• pp.142-154
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• 2023

Plant abscission is a natural process in which plant organs or tissues undergo detachment, a strategy selected by nature for the disposal of nonessential organs and widespread dissemination of seeds and fruits. However, from an agricultural perspective, the abscission of seeds or fruits represents a major factor that reduces crop productivity and product quality. Therefore, during the crop domestication process in traditional agriculture, mutants exhibiting suppressed abscission were selected and crossbred, thereby enabling the production of modern crop varieties such as rice, tomatoes, canola, and soybeans. These crops possess a unique trait of retaining ripe fruits or seeds in contrast to disposal via abscission. During the previous century, research on quantitative trait loci along with genetic and molecular biological studies on Arabidopsis thaliana have elucidated various cell biological mechanisms, signaling pathways, and transcription regulators involved in abscission. Additionally, it has been revealed that various hormone signals, which are involved in plant growth, play crucial roles in modulating abscission activity. Researchers have developed several chemical treatments that target these hormones and signal transduction pathways to enhance crop yields. This review aimed to introduce the previously identified signal transduction pathways and pivotal regulators implicated in abscission activity. Moreover, this review will discuss the future direction of research required to investigate crop abscission mechanisms for their potential application in smart farming and other areas of agriculture, as well as areas within model systems that require extensive research.

• Journal of KIISE:Information Networking
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• v.33 no.4
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• pp.333-342
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• 2006

The cdma2000 lxEV - DO mobile communication system provides broadcast and multicast services (BCMCS) to meet an increasing demand from multimedia data services. The servicing of video streams over a BCMCS network must, however, face a challenge from the unreliable and error-prone nature of the radio channel. The BCMCS network uses Reed-Solomon coding integrated with the MAC protocol for error recovery. We analyze this coding technique and show that it is not effective in the case of slowly moving mobiles. To improve the playback quality of an MPEG-4 FGS video stream, we propose the Hybrid error recovery scheme, which combines Reed-Solomon with ARQ, using slots which are saved by reducing the Reed-Solomon coding overhead. The target packets to be retransmitted are prioritized by a utility function to reduce the packet error rate in the application layer within a fixed retransmission budget. This is achieved by considering of the map of the error control block at each mobile node. The proposed Hybrid error recovery scheme also uses the characteristics of MPEG-4 FGS (fine granularity scalability) to improve the video quality even when conditions are adverse: slow-moving nodes and a high error rate in the physical channel.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.57-64
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• 2023

The ROK military faces a significant challenge in its vigilance mission due to demographic problems, particularly the current aging population and population cliff. This study demonstrates the crucial role of the 4th industrial revolution and its core artificial intelligence algorithm in maximizing work efficiency within the Command&Control room by mechanizing simple tasks. To achieve a fully developed military surveillance system, we have chosen multi-object tracking (MOT) technology as an essential artificial intelligence component, aligning with our goal of an intelligent and automated surveillance system. Additionally, we have prioritized data visualization and user interface to ensure system accessibility and efficiency. These complementary elements come together to form a cohesive software application. The CCTV video data for this study was collected from the CCTV cameras installed at the 1st and 2nd main gates of the 00 unit, with the cooperation by Command&Control room. Experimental results indicate that an intelligent and automated surveillance system enables the delivery of more information to the operators in the room. However, it is important to acknowledge the limitations of the developed software system in this study. By highlighting these limitations, we can present the future direction for the development of military surveillance systems.