• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.388-395
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• 2014

Nowadays unmanned ground systems are used in supporting of surveillance and explosive ordnance disposal. Also, we expect that will be used to remarkably enhance combat capability through network-based cooperative operations with other combat systems. In order to effectively develop those unmanned systems, we needs a systematic method to analyze combat effectiveness and validate required operation capabilities. In this paper, we propose a practical approach to simulate remote-operated unmanned ground systems by using OneSAF, an US-Army simulation framework. First of all, we design a simulation model of unmanned system by integrating with core components for wireless communications and remote control of mobility and fire. Next, we extend OneSAF functionality to create communication links that connects a remote controller with an unmanned vehicle and define a simulated behavior to operate unmanned vehicles via the communication links. Finally, we demonstrate the feasibility of the proposed model within OneSAF and summarize system effectiveness analysis results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.730-737
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• 2016

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load's specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.15-22
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• 2019

Awireless sensor network utilizes small sensors with a low cost and low power being deployed over a wide area. They monitor the surrounding environment and gather the associated information to transmit it to a base station via multi-hop transmission. Most of the research has mainly focused on static sensors that are located in a fixed position. Unlike a wireless sensor network based on static sensors, we can exploit drone-based technologies for more efficient wireless networks in terms of coverage and energy. In this paper, we introduce a transmission power model and a video encoding power model to design the network environment. We also explain a priority mapping scheme, and deploy drones oriented for network coverage and energy consumption. Through our simulations, this research shows coverage and energy improvements in adrone-based wireless sensor network with fewer sensors, compared to astatic sensor-based wireless sensor network. Concretely, coverage increases by 30% for thedrone-based wireless sensor network with the same number of sensors. Moreover, we save an average of 25% with respect to the total energy consumption of the network while maintaining the coverage required.

• The Journal of Bigdata
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• v.4 no.2
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• pp.159-173
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• 2019

Last-Mile delivery optimization plays a key role in the urban supply chain operation, which is the most expensive and time-consuming and most complicated part of the whole delivery process. The urban consolidation center (UCC) is regarded as a significant asset for supporting customer demand in the last-mile delivery service. It is the key benefit of UCC to improve the load balance of vehicles and to reduce the total traveling distance by finding the better route with the well-organized multi-leg vehicle journey in the urban area. This paper presents the model using multiple scenario analysis integrated with mathematical optimization techniques using Geographic Information System (GIS). The model aims to find the best solution for the distribution network consisted of DC and UCC, which is applied to the case of Ulaanbaatar Mongolia. The proposed methodology integrates two sub-models, location-allocation model and vehicle routing problem. The multiple scenarios devised by selecting locations of UCC are compared considering the general performance and delivery patterns together. It has been adopted to make better decisions the quantitative metrics such as the economic value of capital cost, operating cost, and balance of using available resources. The result of this research may help the manager or public authorities who should design the distribution network for the last mile delivery service optimization using UCC within the urban area.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.5
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• pp.813-827
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• 2017

Surveillance cameras installed in tunnels capture the various video frames effected by dynamic and variable factors. In addition, localizing and managing the cameras in tunnel is not affordable, and quality of capturing frame is effected by time. In this paper, we introduce a new method to detect and track the vehicles in tunnel by using surveillance cameras installed in a tunnel. It is difficult to detect the video frames directly from surveillance cameras due to the motion blur effect and blurring effect on lens by dirt. In order to overcome this difficulties, two new methods such as Differential Frame/Non-Maxima Suppression (DFNMS) and Haar Cascade Detector to track cars are proposed and investigated for their feasibilities. In the study, it was shown that high precision and recall values could be achieved by the two methods, which then be capable of providing practical data and key information to an automatic accident detection system in tunnels.

• IMMUNE NETWORK
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• v.2 no.2
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• pp.109-114
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• 2002

Background: To investigate the role of sympathetic nervous system (SNS) in moxibustion-induced immunomodulation, the effects of chemical sympathectomy on moxibustion-induced changes in splenic NK cell cytotoxicity, T and B cell proliferation were studied in Sprague-Dawley male rats. Methods: Chemical sympathectomy was achieved with intraperitoneal injection of 6-hydroxydopamine 50 mg/kg/day for 3 successive days. Direct moxibustion (6-minute interval, 9 moxa ball, each of which weighing 0.007 g and burning for 40 seconds) was applied on unilateral anterior tibial muscle region where Zusanli (ST36) acupoint is located, once a day for 7 successive days. NK cell cytotoxicity was measured by $4hr-^{51}Cr$ release assay. Mitogen-induced lymphocyte proliferation was analyzed by [$^3H$]-thymidine incorporation assay. Results: NK cell cytotoxicity was suppressed by moxibustion, more in sympathectomized rats than in vehicle-treated rats. T cell proliferation induced by concanavalin A was not affected by moxibustion. B cell proliferation induced by lipopolysaccharide showed no significant change in vehicle-treated rats, but an increase in sympathectomized rats by moxibustion. Sympathectomy alone induced augmentation of NK cell cytotoxicity and suppression of T cell proliferation. Conclusion: These results suggest that SNS has no direct relation with moxibution-induced immunomodulation but has an important role in the mechanism to keep the homeostasis of immune system by tonically inhibiting excessive changes of various immune components.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.2
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• pp.66-72
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• 2010

Recently a large number of electronic control devices are abruptly increasing in vehicles as the electronic industry advances. Also the number of wiring harnesses and complexity of wiring is proportionally increased to the devices. A newly developed connector named bridge connector is introduced in this study for the purpose of the wiring structure changes. A wiring structure among distributed control systems by using serial communication is proposed with the bridge connector in this paper. The bridge connector contains a auto-recovery fuse made by PTC thermistor material for the protection of the over-currents on local control devices. The auto-recovery function of the fuse is needed for the maintenance free system in the distributed controls. The PTC fuse characteristics organized in this study is tested and the results are showed in detail for the real application.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

• Transactions of the KSME C: Technology and Education
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• v.3 no.3
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• pp.165-173
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• 2015

This paper presents numerical results of static structural stability analysis in development of a vertical transfer device of a PRT(Personal Rapid Transit) vehicle. The vertical transfer of a fully occupied vehicle operating on a road network is the first attempt, which is expected to contribute to overcome the limitations of conventional 2-dimensional operation mode. In particular, the vertical transfer apparatus designed based on vertical circulating conveyors is capable of continuous transfer without time delay so that it enables to accommodate a high traffic density. This system has been frequently used in a logistics field; however, it is essential to assess a structural integrity because an external force by a vehicle weight is exerted on the conveyors in the form of a concentrated load unlike a conventional logistic transport. In this study, prior to the production process, the structural performance of the pilot design in an early stage is numerically evaluated using the commercial finite element method (FEM) solver (i.e., $Ansys^{(R)}$).