• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.6-13
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• 2006

In this paper, we studied vehicle to vehicle (VTV) communication system using DSRC of 5.8 GHz bands. Nowadays, in the road traffic system is going intelligent and advancing, communication between driving vehicle is very important technology for ITS. We can contrive smoothness and safety traffic flowing by exchanging information about velocity, location, braking and driving condition of nearby vehicles. Therefore, we developed and verified the system which required for the communication among vehicles using DSRC technology of 5.8 GHz band hasa 1 Mbps data rate in the high mobility condition. For this, we developed DSRC modem, data link layer and logic link layer to make it possible that communication between vehicles of perfectly operation, and developed application service program for VTV communication. We performed to communication test in the general road and ascent road. In case of the general mad, obtained VTV communication results are more than number of 17 with in 300 m LOS coverage, and total communication time are $2.34{\sim]18.7$ msec that considered maximum 8-transaction. We blow that obtained results can be used VTV communication or the in areas form the feasibility road test as a function or various conditions. In the future, this system is very useful of advanced safety vehicle (ASV) and super smart vehicle system (SSVS) and so on.

• Journal of IKEEE
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• v.24 no.2
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• pp.638-648
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• 2020

Currently, the method of user-follwoing in intelligent cooperative robots usually based in vision system and using Lidar is common and have excellent performance. But in the closed space of Corona 19, which spread worldwide in 2020, robots for cooperation with medical staff were insignificant. This is because Medical staff are all wearing protective clothing to prevent virus infection, which is not easy to apply with existing research techniques. Therefore, in order to solve these problems in this paper, the ultrasonic sensor is separated from the transmitting and receiving parts, and based on this, this paper propose that estimating the user's position and can actively follow and cooperate with people. However, the ultrasonic sensors were partially applied by improving the Median filter in order to reduce the error caused by the short circuit in communication between hard reflection and the number of light reflections, and the operation technology was improved by applying the curvature trajectory for smooth operation in a small area. Median filter reduced the error of degree and distance by 70%, vehicle running stability was verified through the training course such as 'S' and '8' in the result.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.91-103
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• 2008

In order to simulate the behavior of automated manufacturing systems, the performance of material handling systems should be measured dynamically. Multi-Agent technology could be well adapted for the development of simulator for distributed and intelligent manufacture systems. A multi-agent system is composed of one coordination agent and multiple application agents. Issues in AGVS simulator can be classified by the set-up and operating problems. Decisions on the number of vehicles, bi- or uni-directional guide-path, etc. are fallen into the set-up problem category, while deadlock tree algorithm and conflict resolution are in operating problem. In this paper, a multi-agent based deadlock-free simulator for automated guided vehicle system(AGVS) are proposed through the use of multi-agent technologies and the development of deadlock-free algorithm. In this AGVS simulator proposed, well-known Floyd algorithm is used to create AGVS Guide path, through which AGVS move. Also, AGVs avoid vehicle conflict and deadlock using check path algorithm. And Moving vehicle agents are operated in real-time control by coordination agent. AGV position is dynamically calculated based on the concept of rolling time horizon. Simulator receives and presents operating information of vehicle in AGVS Gaunt chart. The performance of the proposed algorithm and developed simulator based on multi-agent are validated through set of experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.87-104
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• 2015

Currently the IoT (Internet of Things) environments and related technologies are being developed rapidly through the networks for connecting many intelligent objects. The IoT is providing artificial intelligent services combined with context recognition based knowledge and communication methods between human and objects and objects to objects. With the help of IoT technology, many research works are being developed using the C-ITS (Cooperative Intelligent Transport System) which uses road infrastructure and traveling vehicles as traffic control infrastructures and resources for improving and increasing driver's convenience and safety through two way communication such as bus-only lane and license plate recognition and road accidents, works ahead reports, which are eventually for advancing traffic effectiveness. In this paper, a system for deciding whether the traveling vehicle is possible or not to drive on bus-only lane in highway is researched using the lane and number plate recognition on the road in C-ITS traffic infrastructure environments. The number plates of vehicles on the straight ahead and sides are identified after the location of bus-only lane is discovered through the lane recognition method. Research results and experimental outcomes are presented which are supposed to be used by traffic management infrastructure and controlling system in future.

• 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.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.59-73
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• 2023

In the era of the 4th Industrial Revolution, manufacturing is the implementation of smart factories through digital transformation, and refers to consumer-centered intelligent factories that combine next-generation digital new technologies and manufacturing technologies beyond the existing factory automation level. In order to successfully settle such a smart factory, it is necessary to train professionals. However, education for smart factories is difficult to have actual field mechanical facilities or overall production processes. Therefore, there is a need for a system that can visualize and control the flow and process of logistics at the actual production site. In this paper, the logistics flow of the actual site was implemented as a small FMS, a physical system, and the production process was implemented as a digital system. In real-time synchronization of the physical system and the digital system, the location of AGV and materials, and the process state can be monitored to see the flow of logistics and process processes at the actual manufacturing site. The developed digital twin system can be used as an effective educational system for training manpower in smart factories.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.