• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.25-39
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• 2008

The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1433-1435
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• 2003

Very decisive progress was made in advancing fundamental POL-IN-SAR theory and algorithm development during the past decade. This was accomplished with the aid of airborne & shuttle platforms supporting single -to-multi-band multi-modal POL-SAR and also some POL-IN-SAR sensor systems, which will be compared and assessed with the aim of establishing the hitherto not completed but required missions such as tomographic and holographic imaging. Because the operation of airborne test-beds is extremely expensive, aircraft platforms are not suited for routine monitoring missions which is better accomplished with the use drones or UAVs. Such unmanned aerial vehicles were developed for defense applications, however lacking the sophistic ation of implementing advanced forefront POL-IN-SAR technology. This shortcoming will be thoroughly scrutinized resulting in the finding that we do now need to develop most rapidly POL-IN-SAR drone-platform technology especially for environmental stress-change monitoring with a great variance of applications beginning with flood, bush/forest-fire to tectonic-stress (earth-quake to volcanic eruptions) for real-short-time hazard mitigation. However, for routine global monitoring purposes of the terrestrial covers neither airborne sensor implementation - aircraft and/or drones - are sufficient; and there -fore multi-modal and multi-band space-borne POL-IN-SAR space-shuttle and satellite sensor technology needs to be further advanced at a much more rapid phase. The existing ENVISAT with the forthcoming ALOSPALSAR, RADARSAT-2, and the TERRASAT will be compared, demonstrating that at this phase of development the fully polarimetric and polarimetric-interferometric modes of operation must be viewed and treated as preliminary algorithm verification support modes and at this phase of development are still not to be viewed as routine modes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1353-1358
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• 2021

IoT (Internet of Things) emerges from various technologies such as communications, micro processors and embedded system and so on. The IoT has also been used to UAV (Unmanned Aerial Vehicle) system. In manned aircraft, a pilot and co-pilot should control FCS (Flight Control System) with FBW(Fly By Wire) system for flight operation. In contrast, the flight operation in UAV system is remotely and fully managed by GCS (Ground Control System) almost in real time. To make it possible the communication channel should be necessary between the UAV and the GCS. There are many protocols between two systems. Amongst them, MAVLink (Macro Air Vehicle Link) protocol is representatively used due to its open architecture. MAVLink does not define any securities itself, which results in high vulnerability from external attacks. This paper proposes the method to enhance data security in GCS network by applying cryptographic methods to the MAVLink messages in order to support safe UAV operations.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.365-371
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• 2021

In this study, The Virtual Power Plant (VPP) solution platform considered in this study minimizes the cost and investment risk associated with the construction of power generation and transmission facilities. In addition, it includes a Demand Response (DR) program operation function to meet consumers' electricity demand. With the introduction of VPP, it is possible to provide more eco-friendly and efficient power by responding to changes in consumer load in real time through existing generators and DR programs without large-scale facility investment in power generation and transmission/distribution sectors. In order to link the communication device to the solar power and ESS linkage device, it is necessary to transmit data in the control/state between the device device and the edge system and develop an IoT device and interworking platform (OneM2M).

• Journal of the Society of Naval Architects of Korea
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• v.58 no.6
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• pp.366-374
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• 2021

This paper presents the analysis of the speed-power performance in the real sea using a large container vessel data provided as a test bed from a shipping company. To perform a speed trial of the vessel during a commercial voyage, the on-board measuring device and various operation data acquisition systems were mounted on the vessel for long-term performance monitoring and the voyage operated under the container loading condition close to the design draft was adopted. The content of this paper consists of Part I and Part II. Part I, such as this paper, contains the speed trial method and analysis results of the operating vessel. Part II contains the analysis of the speed-power characteristics change over time and before and after hull cleaning using operation data measured from the voyage operated under a condition similar to the speed trial.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.11-16
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• 2012

Simulation is the imitation of the operation of a real-world process or system over time. It concerns the study of the operating characteristics of real systems. Typically, a simulation project consists of several steps such as data collection, coding, model verification, model validation, experimental design, output data analysis, and implementation. Among these steps of a simulation study this paper focus on statistical analysis methods of simulation output data. Specially, we explain how to develop confidence interval estimators for mean ${\mu}$ in terminating and non-terminating simulation cases. We, then, explore the estimation techniques for $f({\mu})$, where the function $f({\bullet})$ is a nonlinear that is continuously differentiable in a neighborhood of ${\mu}$ with $f'({\mu}){\neq}0$.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.1-14
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• 2000

The berth and crane scheduling problem in a container terminal encompasses the whole process of assigning berth to each ship, determining the duration of berthing, assigning container cranes to each ship, and determining the specific start and end time of each crane service, for all the ships scheduled to be arriving at the terminal during a certain scheduling horizon. This problem is basically a constraint satisfaction problem in which cranes and berths should be assigned in such a way that all the spatial and temporal constraints are satisfied without any interference. However, it is also an optimization problem because the requested arrival and departure time should be met for as many of the scheduled ships as possible, while the operation cost of the terminal should be minimized. In this paper, we present an effective and efficient approach to solving this type of problem, which combines constrain satisfaction search and heuristic repair. We first employ a constraint satisfaction search to find a feasib1e solution. Then, the feasible solution is modified to a more optimal one by iteratively applying our heuristic repair operations within the framework of constraint satisfaction search. Experimental results with a real data from Pusan East Container Terminal showed that our approach can derive a schedule of satisfactory quality in a very short time.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.109-117
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• 2012

Recently, large-scale multi-hazards have been occurred in the various areas of the world. A variety of Earth observation sensors such as satellite EO, aerial and terrestrial LiDAR have been utilized for global natural disaster monitoring. Especially, commercial satellites which observe the Earth regularly and repeatedly, and acquire images with cm-level high spatial resolution enable its applications to extend in the fields of disaster management from advanced disaster monitoring to timely recovery. However, due to existing satellite operation systems with some limitations in almost real-time and wide regional disaster response, close international collaborations between satellite operating organizations like NASA, JAXA, KARI etc. have been required for collecting satellite images in time through a satellite platform with multi-sensors or satellite constellation. For responding domestic natural disaster such as heavy snowfall and extreme rainfall in 2011, this paper proposes a disaster management system for timely decision-making; rapid acquisition of satellite imagery, data processing, GIS analysis, and digital mapping through cooperation with NDMI in Korea and International Charter-Space and Major disasters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.88-102
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• 2016

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.141-147
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• 2021

In this paper, in order to improve the utilization of hyperspectral large-capacity data feature information by reducing complex computations by dimension reduction of neural network inputs in embedded systems, the band selection algorithm is applied in each subset. Among feature extraction and feature selection techniques, the feature selection aim to improve the optimal number of bands suitable for datasets, regardless of wavelength range, and the time and performance, more than others algorithms. Through this experiment, although the time required was reduced by 1/3 to 1/9 times compared to the others band selection technique, meaningful results were improved by more than 4% in terms of performance through the K-neighbor classifier. Although it is difficult to utilize real-time hyperspectral data analysis now, it has confirmed the possibility of improvement.