• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.521-543
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• 2023

Reynolds-averaged Navier-Stokes (RANS) models are extensively employed in industrial settings for the purpose of simulating intricate fluid flows. However, these models are subject to certain limitations. Notably, disparities persist in the Reynolds stresses when comparing the RANS model with high-fidelity data obtained from Direct Numerical Simulation (DNS) or experimental measurements. In this work we propose an approach to mitigate these discrepancies while retaining the favorable attributes of the Menter Shear Stress Transport (SST) model, such as its significantly lower computational expense compared to DNS simulations. This strategy entails incorporating an explicit algebraic model and employing a neural network to correct the turbulent characteristic time. The imposition of realizability constraints is investigated through the introduction of penalization terms. The assimilated Reynolds stress model demonstrates good predictive performance in both in-sample and out-of-sample flow configurations. This suggests that the model can effectively capture the turbulent characteristics of the flow and produce physically realistic predictions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2A
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• pp.144-153
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• 2006

In this paper, we propose an efficient video data transmission protocol using the cross-layer approach in ad hoc networks. Due to node movement, the MANET is frequently changing path and each path has different transmission rate so that it has low performance when transmitters select a constant transmission late at the encoding time. Because MANET is running limited energy, efficient energy management is important because it increases network life time and network throughput. Therefore we need an effective video transmission method that considers physical layer channelstatistics, node's energy status, and network topology changes at the same time unlike the OSI recommendation protocol in that each layer isindependent and hard to transmit adaptively video data according to the network conditions. Therefore, in this paper we propose a cross-layer effective video transmission protocol and mechanism that can select an optimal path using multilayer information such as node's residual energy, channel condition and hop counts and can determine the adequate coding rate adaptively.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.28-36
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• 2010

Some context is characterized by a single event in computing environment, but many other contexts are determined by a lot of things which occur with a space and a time. The Realtime Sensor Network context-awareness service that interacts with the physical space can have property such as time. A methodology that is specified the relationship between the contexts and the service needs to be developed to Realtime context-awareness deal with spatio-temporal. In this paper, we propose an approach which should include spatio-temporal property in the context model, and verify its effectiveness using enhanced Petri-Net. The context-awareness service modeling of Realtime Sensor Network is discussed the properties of model such as basic Petri-Net, patterned Petri-Net, or Spatio-temporal Petri-Net. The proposed methodology demonstrated using an example that is SAEMANGUEM warming watching system. The use of Spatio-temporal Petri-Net will contribute not only to develop the application but also to model the spatio-temporal context awareness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.117-124
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• 2021

This study is a study to recognize hand movements using a neural network for wrist rehabilitation. The rehabilitation of the hand aims to restore the function of the injured hand to the maximum and enable daily life, occupation, and hobby. It is common for a physical therapist, an occupational therapist, and a security tool maker to form a team and approach a doctor for a hand rehabilitation. However, it is very inefficient economically and temporally to find a place for treatment. In order to solve this problem, in this study, patients directly use smart devices to perform rehabilitation treatment. Using this will be very helpful in terms of cost and time. In this study, a wrist rehabilitation dataset was created by collecting data on 4 types of rehabilitation exercises from 10 persons. Hand gesture recognition was constructed using a neural network. As a result, the accuracy of 93% was obtained, and the usefulness of this system was verified.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1141-1149
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• 2020

Most of the industrial control network is an independent closed network, which is operated for a long time after installation, and thus the OS is not updated, so security threats increase and security vulnerabilities exist. The zero-day attack defense must be applied with the latest patch, but in a large-scale industrial network, it requires a higher level of real-time and non-disruptive operation due to the direct handling of physical devices, so a step-by-step approach is required to apply it to a live system. In order to solve this problem, utility-specific patch impact assessment is required for reliable patch application. In this paper, we propose a method to test and safely install the patch using the regression analysis technique and show the proven results. As a patch impact evaluation methodology, the maximum allowance for determining the safety of a patch was derived by classifying test types based on system-specific functions, performance, and behavior before and after applying the patch. Finally, we report the results of case studies applied directly to industrial control networks, the OS patch has been updated while ensuring 99.99% availability.

• Journal of the Korea Society for Simulation
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• v.24 no.3
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• pp.61-68
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• 2015

Mobile ad hoc networks (MANETs) are self-organized dynamic networks populated by mobile nodes. This paper presents the improved cross-layer approach to complement the recent works for video transmission services on MANET. We use a statistical design of experiment and analysis in order to investigate interactions between major factors of each layer effectively with minimizing ns-3 simulation run time. The proposed cross-layer approach considers MANET protocol layers (i.e., physical, network and transmission layers) and an application layer (i.e., a video encoder) as factors simultaneously. In addition, the approach defines an objective video quality metric as a response variable. The result of this paper can be applicable as a preliminary research to design an optimized video transmission application which has ability to adjust controllable factors to dynamic uncontrollable factors.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.95-102
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• 2007

Recently, a lot of researchers focus on the sensor middleware that hide the complexity of application developments and provide the abstraction of functions to upper application layer. Although there we several factors to design sensor middleware, the attribute-based naming is considered to be an essential factor among them. However, most existing researches were not designed to reflect the characteristics of sensor networks and have the limitation of attribute-based query extension. This study adopts the concept of Virtual Counterpart to suggest the structure there attribute-based naming is supported by virtual sensor nodes of the middleware on the sink node. Unlike traditional data-centric middleware in which individual sensor nodes process attribute-based query, virtual sensor nodes mapped to physical sensor nodes are running on the middleware of the sink node and process attribute-based query as a proxy of the physical sensor. This approach enables attribute-based naming independent of physical infrastructure and easy extensibility.

• Journal of Ecology and Environment
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• v.31 no.3
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• pp.233-241
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• 2008

Functional feeding groups (FFGs) of benthic macroinvertebrates are guilds of invertebrate taxa that obtain food in similar ways, regardless of their taxonomic affinities. They can represent a heterogeneous assemblage of benthic fauna and may indicate disturbances of their habitats. The proportion of different groups can change in response to disturbances that affect the food base of the system, thereby offering a means of assessing disruption of ecosystem functioning. In this study, we used benthic macroinvertebrate communities collected at 650 sites of 23 different water types in the province of Overijssel, The Netherlands. Physical and chemical environmental factors were measured at each sampling site. Each taxon was assigned to its corresponding FFG based on its food resources. A multilayer perceptron (MLP) using a backpropagation algorithm, a supervised artificial neural network, was applied to evaluate the influence of environmental variables to the FFGs of benthic macroinvertebrates through a sensitivity analysis. In the evaluation of input variables, the sensitivity analysis with partial derivatives demonstrates the relative importance of influential environmental variables on the FFG, showing that different variables influence the FFG in various ways. Collector-filterers and shredders were mainly influenced by $Ca^{2+}$ and width of the streams, and scrapers were influenced mostly with $Ca^{2+}$ and depth, and predators were by depth and pH. $Ca^{2+}$ and depth displayed relatively high influence on all four FFGs, while some variables such as pH, %gravel, %silt, and %bank affected specific groups. This approach can help to characterize community structure and to ecologically assess target ecosystems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.141-150
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• 2011

In this paper, we propose a Cross-Layer Cooperative Routing(CLCR) architecture to support transmission efficiency in mobile wireless sensor networks. The main features and contributions of the proposed architecture and method are as follows. First, the clustering which uses the location information of nodes is utilized as infrastructure. Second, a cross-layer strategy which uses the technologies of network layer, MAC layer, physical layer together to support transmission efficiency and channel efficiency for cooperative-aided routing and transmission. Third, we consider realistic approach in the view points of the mobile ad-hoc wireless sensor networks while conventional methods just consider fixed sensor network environments. The performance evaluation of the proposed method is performed via simulation using OPNET and theoretical analysis. The results of performance evaluation of the proposed CLCR show improvement of transmission efficiency by the proposed CLCR.

• Journal of Ginseng Research
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• v.46 no.4
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• pp.609-619
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• 2022

Obesity is a primary factor provoking various chronic disorders, including cardiovascular disease, diabetes, and cancer, and causes the death of 2.8 million individuals each year. Diet, physical activity, medications, and surgery are the main therapies for overweightness and obesity. During weight loss therapy, a decrease in energy stores activates appetite signaling pathways under the regulation of neuropeptides, including anorexigenic [corticotropin-releasing hormone, proopiomelanocortin (POMC), cholecystokinin (CCK), and cocaine- and amphetamine-regulated transcript] and orexigenic [agoutirelated protein (AgRP), neuropeptide Y (NPY), and melanin-concentrating hormone] neuropeptides, which increase food intake and lead to failure in attaining weight loss goals. Ginseng and ginsenosides reverse these signaling pathways by suppressing orexigenic neuropeptides (NPY and AgRP) and provoking anorexigenic neuropeptides (CCK and POMC), which prevent the increase in food intake. Moreover, the results of network pharmacology analysis have revealed that constituents of ginseng radix, including campesterol, beta-elemene, ginsenoside Rb1, biotin, and pantothenic acid, are highly correlated with neuropeptide genes that regulate energy balance and food intake, including ADIPOQ, NAMPT, UBL5, NUCB2, LEP, CCK, GAST, IGF1, RLN1, PENK, PDYN, and POMC. Based on previous studies and network pharmacology analysis data, ginseng and its compounds may be a potent source for obesity treatment by regulating neuropeptides associated with appetite.