• Journal of Digital Convergence
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• v.12 no.8
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• pp.229-234
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• 2014

Vehicular electronic communication system has continued to develop in favor of high performance and user convenience with the evolution of auto industry. Yet, due to the nature of communication system, concerns over intruder attacks in transmission sections have been raised with a need for safe and secure communication being valued. Any successful intruder attacks on vehicular operation and control systems as well as on visual equipment could result in serious safety and privacy problems. Thus, research has focused on hardware-based security and secure communication protocols. This paper proposed a safe and secure vehicular communication protocol, used the formal verification tool, Casper/FDR to test the security of the proposed protocol against different types of intruder attacks, and verified that the proposed protocol was secure and ended without problems.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1659-1663
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• 1991

A new control design methodology is presented here which is based on a nonlinear time-series reference model. It is indicated by highly nonlinear simulations that such designs successfully stabilize troublesome aircraft maneuvers undergoing large changes in angle of attack as well as large electric power transients due to line faults. In both applications, the nonlinear controller was significantly better than the corresponding linear adaptive controller. For the electric power network, a flexible a.c. transmission system (FACTS) with series capacitor power feedback control is studied. A bilinear auto-regressive moving average (BARMA) reference model is identified from system data and the feedback control manipulated according to a desired reference state. The control is optimized according to a predictive one-step quadratic performance index (J). A similar algorithm is derived for control of rapid changes in aircraft angle of attack over a normally unstable flight regime. In the latter case, however, a generalization of a bilinear time-series model reference includes quadratic and cubic terms in angle of attack. These applications are typical of the numerous plants for which nonlinear adaptive control has the potential to provide significant performance improvements. For aircraft control, significant maneuverability gains can provide safer transportation under large windshear disturbances as well as tactical advantages. For FACTS, there is the potential for significant increase in admissible electric power transmission over available transmission lines along with energy conservation. Electric power systems are inherently nonlinear for significant transient variations from synchronism such as may result for large fault disturbances. In such cases, traditional linear controllers may not stabilize the swing (in rotor angle) without inefficient energy wasting strategies to shed loads, etc. Fortunately, the advent of power electronics (e.g., high-speed thyristors) admits the possibility of adaptive control by means of FACTS. Line admittance manipulation seems to be an effective means to achieve stabilization and high efficiency for such FACTS. This results in parametric (or multiplicative) control of a highly nonlinear plant.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.205-210
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• 2015

The auto industry has significantly evolved to the extent that much attention is paid to M2M (Machine-to-Machine) communication. In M2M communication which was first used in meteorology, environment, logistics, national defense, agriculture and stockbreeding, devices automatically communicate and operate in accordance with varying situations. M2M system is applied to vehicles, specifically to device-to-device communication inside cars, vehicle-to-vehicle communication, communication between vehicles and traffic facilities and that between vehicles and surroundings. However, communication systems are characterized by potential intruders' attacks in transmission sections, which may cause serious safety problems if vehicles' operating system, control system and engine control parts are attacked. Thus, device-to-device secure communication has been actively researched. With a view to secure communication between vehicular devices, the present study drew on hash functions and complex mathematical formulae to design a protocol, which was then tested with Casper/FDR, a tool for formal verification of protocols. In brief, the proposed protocol proved to operate safely against a range of attacks and be effective in practical application.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.77-82
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• 2007

In this paper, we proposed the image processing techniques for extracting the cracks in a concrete surface crack image and the enhanced Max-Min neural network for recognizing the directions of the extracted cracks. The image processing techniques used are the closing operation or morphological techniques, the Sobel masking for extracting for edges of the cracks, and the iterated binarization for acquiring the binarized image from the crack image. The cracks are extracted from the concrete surface image after applying two times of noise reduction to the binarized image. We proposed the method for automatically recognizing the directions of the cracks with the enhanced Max-Min neural network. Also, we propose an enhanced Max-Min neural network by auto-tuning of learning rate using delta-bar-delta algorithm. The experiments using real concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• Progress in Superconductivity
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• v.4 no.1
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• pp.42-47
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• 2002

Measuring magnetic fields with a SQUID sensor always requires preliminary adjustments such as optimum bas current determination and flux-locking point search. A conventional magnetoencephalography (MEG) system consists of several dozens of sensors and we should condition each sensor one by one for an experiment. This timeconsuming job is not only cumbersome but also impractical for the common use in hospital. We had developed a serial port communication protocol between SQUID sensor controllers and a personal computer in order to control the sensors. However, theserial-bus-based control is too slow for adjusting all the sensors with a sufficient accuracy in a reasonable time. In this work, we introduce programmatic control sequence that saves the number of the control pulse arrays. The sequence separates into two stages. The first stage is a function for searching flux-locking points of the sensors and the other stage is for determining the optimum bias current that operates a sensor in a minimum noise level Generally, the optimum bias current for a SQUID sensor depends on the manufactured structure, so that it will not easily change about. Therefore, we can reduce the time for the optimum bias current determination by using the saved values that have been measured once by the second stage sequence. Applying the first stage sequence to a practical use, it has taken about 2-3 minutes to perform the flux-locking for our 37-channel SQUID magnetometer system.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.153-176
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• 1993

Structual ambiguity is one of those problem that arise in the analysis of natural language sentences.It has been considered very difficult to solve the problem.Structural ambiguity,however,should be resolved no matter how difficult it may be.Otherwise natural language processing could be virtually impossible.A statistical approach to structural disambiguation is proposed in this dissertation.The information-theoretic concept of mutual information has been empolyed in resolving structural ambiguity Mutual information can be acquired in an automatic way.from text corpora. If a structural disambiguation subsystem had the capability of self-evaluating whether the results of structural disambiguation are correct or not.it would be possible to develop a more intelligent natural language proessing system.In this paper,the concept of confidence measure is also proposed to endow the disambiguation subsystem with such intelligence.Confidence measure is a numeric value calculated after structural disambiguation. Some experiments were performed in order to show the validity of the approach.Mutual information was auto matically acquired from a corpus of 1.6milion words that were collected from scientific abstracts.The accuracy of structural disambiguation was 80%when performed over 1,639 test sentences.Notice that there was no manual tuning in advance for the experiments.The task of detecting and correcting errors in structural disambiguation will be performed very effectively if the concept of confidence measure is employed in the process.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.52-64
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• 1995

Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.