• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.497-497
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• 2000

This paper is concerned with the design of LMI based H$_{\infty}$ controller for a line of sight(LOS) stabilization system. This system which is even linearized to analyse nonlinear characteristic has also a lot of uncertainties. In addition, the angular velocity disturbance from the vehicle's driving deteriorates the stabilized LOS, main purpose of this system. In case of fast driving, particularly, all components which are ignored and skipped to make mathematical modelling act as the uncertainties against this system. The robustness against these uncertainties has been also continuously demanded including the well tracking performance for the target. Therefore, this paper employed H$_{\infty}$ control theory to satisfy these problems and LMI method to make suitable controller with few constraints for this system. Although this system matrix doesn't have full rank, this method make it possible to design H$_{\infty}$ controller and deal with R and S matrices for reducing its order. Consequently, this paper shows that the re-analyses on the real disturbances are achieved and the proposed robust controller for them has better disturbance attenuation and tracking performance. This paper contributes the applicability of reduced order H$_{\infty}$ controller to real system by handling LMI..

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.92-95
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• 2001

This study presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM fellows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.730-737
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• 2001

In this paper, an stabilization control method using adaptive neuro-fuzzy controller(ANFC) is proposed for modeling of nonlinear complex systems. The proposed adaptive neuro-fuzzy controller implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks from input and output data of processes. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.57-66
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• 2022

The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.1
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• pp.84-88
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• 2010

Recently, it is essential to have the system which can track down and identity the random object in the space in which security is a high priority. Due to the fact that we mentioned above, in this paper. We suggest the intelligent video surveillance system effective image-process-education in this paper. The experiment was conducted to check and track down the entering vehicle. And, Pan-Tilt-Zoom camera was used to obtain the enlarged image of the object while a vehicle was making stop in target area. As a result, the experiment has shown the data as following. When the object is in motion, success rate is 97.4%, while success rate is 91% when the object is motionless. By using the suggested system, effective image-process-education is should be achieved because the students who participate in the class can have simultaneous access to the system for real time image data and camera control.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1423-1431
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• 2018

IoT(Internet of Things) is applied to technologies such as agriculture and dairy farming, making it possible to cultivate crops easily and easily in cities.In particular, IoT technology that intelligently judge and control the growth environment of cultivated crops in the agricultural field is being developed. In this paper, we propose a method of predicting the growth environment of plants by learning the moisture supply cycle of plants using the intelligent object internet. The proposed system finds the moisture level of the soil moisture by mapping learning and finds the rules that require moisture supply based on the measured moisture level. Based on these rules, we predicted the moisture supply cycle and output it using media, so that it is convenient for users to use. In addition, in order to reduce the error of the value measured by the sensor, the information of each plant is exchanged with each other, so that the accuracy of the prediction is improved while compensating the value when there is an error. In order to evaluate the performance of the growth environment prediction system, the experiment was conducted in summer and winter and it was verified that the accuracy was high.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2113-2119
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• 2006

In this paper, we design the r-Sensor protocol for reliable data transmission in the Intelligent Home Service based on the wireless sensor network environment. The r-Sensor protocol improve the reliability of data transmission and node fairness using simple routing algorithm, congestion control, and loss recovery method that minimize the load of relay node. Reposed routing algorithm find out upstream and downstream nodes using the Network Management packet. Meanwhile, loss recovery algorithm uses the Aggregated-Nack. To apply supposed algorithm, the IHS-AMR(Intelligent Home Service - Automatic Meter Reader) and sensor node are designed and implemented in hardware. The IHS-AMR provides remote metering service and also offers home safety service by internetworking with sensor network, mobile phone network and internet.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.11-17
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• 2020

In this paper, we propose a method to build a knowledge base based on natural language processing for intelligent battlefield awareness service. The current command and control system manages and utilizes the collected battlefield information and tactical data at a basic level such as registration, storage, and sharing, and information fusion and situation analysis by an analyst is performed. This is an analyst's temporal constraints and cognitive limitations, and generally only one interpretation is drawn, and biased thinking can be reflected. Therefore, it is essential to aware the battlefield situation of the command and control system and to establish the intellignet decision support system. To do this, it is necessary to build a knowledge base specialized in the command and control system and develop intelligent battlefield awareness services based on it. In this paper, among the entity names suggested in the exobrain corpus, which is the private data, the top 250 types of meaningful names were applied and the weapon system entity type was additionally identified to properly represent battlefield information. Based on this, we proposed a way to build a battlefield-aware knowledge base through mention extraction, cross-reference resolution, and relationship extraction.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.113-123
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• 2011

Traffic signal control is one of the most cost-effective means of improving urban mobility. With the recent progress of ITS (Intelligent Transportation System) and the installation of the real time traffic control systems, traffic signal control is conducted in online and real time. Normally, time-of-day (TOD) signal control is used with the system, but no definite methodology has yet been available for efficient TOD signal planing designing. Such method and process are in need to optimize the traffic signal timing plan. This paper proposes the optimization of TOD signal timings on arterials. The effects of the signal timings from the proposed method were assessed in the field. The proposed includes the methods determining the separation of the TOD break points and the TOD intervals. Those were tested on an arterial consisting of ten coordinated signalized intersections. It was found from the test results that the proposed TOD signal timing plans outperformed the previous signal timings.