• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.329-337
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• 2000

This study describes a pressure tracking control of hydroforming process which is used for precision forming of sheet metals. The hydroforming operation is performed in the high-pressure chamber strictly controlled by pressure control valve and by the upward motion of a punch moving at a constant speed, The pressure tracking control is very difficult to design and often does not guarantee satisfactory performances be-cause of the punch motion and the nonlinearities and uncertainties of the hydraulic components. To account for these nonlinearities and uncertainties of the process and iterative learning controller is proposed using Cerebellar Model Arithmetic Computer (CMAC). The experimental results show that the proposed learning control is superior to any fixed gain controller in the sense that it enables the system to do the same work more effectively as the number of operation increases. In addition reardless of the uncertainties and nonlinearities of the form-ing process dynamics it can be effectively applied with little a priori knowledge abuot the process.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.635-642
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• 2003

Reinforcement learning got successful results in a lot of applications such as control and scheduling. Various function approximation methods have been studied in order to improve the learning speed and to solve the shortage of storage in the standard reinforcement learning algorithm of Q-Learning. Most function approximation methods remove some special quality of reinforcement learning and need prior knowledge and preprocessing. Fuzzy Q-Learning needs preprocessing to define fuzzy variables and Local Weighted Regression uses training examples. In this paper, we propose a function approximation method, Fuzzy Q-Map that is based on on-line fuzzy clustering. Fuzzy Q-Map classifies a query state and predicts a suitable action according to the membership degree. We applied the Fuzzy Q-Map, CMAC and LWR to the mountain car problem. Fuzzy Q-Map reached the optimal prediction rate faster than CMAC and the lower prediction rate was seen than LWR that uses training example.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2980-2991
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• 2012

Since wireless sensor networks are broadly used in various areas, there have been a number of protocols developed to satisfy specific constraints of each application. The most important and common requirements regardless of application types are to provide a long network lifetime and small end-to-end delay. In this paper, we propose Early Preamble MAC (EP-MAC) with improved energy conservation and low latency. It is based on CMAC but adopts a new preamble type called 'early preamble'. In EP-MAC, a transmitting node can find quickly when a next receiving node wakes up, so EP-MAC enables direct data forwarding in the next phase. From numerical analysis, we show that EP-MAC improves energy consumption and latency greatly compared to CMAC. We also implemented EP-MAC with NS-2, and through extensive simulation, we confirmed that EP-MAC outperforms CMAC.

• Computational Structural Engineering : An International Journal
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• v.3 no.1
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• pp.39-47
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• 2003

Generalized CMAC (GCMAC) is a type of neural network known to be fast in learning. The network may be useful in structural engineering applications such as the identification and the control of structures. The derivatives of a trained GCMAC is relatively poor in accuracy. Therefore to improve the accuracy, a new algorithm is proposed. If GCMAC is directly differentiated, the accuracy of the derivative is not satisfactory. This is due to the quantization of input space and the shape of basis function used. Using the periodicity of the predicted output by GCMAC, the derivative can be improved to the extent of having almost no error. Numerical examples are considered to show the accuracy of the proposed algorithm.

• Journal of Communications and Networks
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• v.13 no.5
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• pp.463-471
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• 2011

Cooperative communications using relays in wireless networks have similar effects of multiple-input and multiple-output without the need of multiple antennas at each node. To implement cooperation into a system, efficient protocols are desired. In IEEE 802.11 families such as a/b/g, mobile stations can automatically adjust transmission rates according to channel conditions. However throughput performance degradation is observed by low-rate stations in multi-rate circumstances resulting in so-called performance anomaly. In this paper, we propose active relay-based cooperative medium access control (AR-CMAC) protocol, in which active relays desiring to transmit their own data for cooperation participate in relaying, and it is designed to increase throughput as a solution to performance anomaly. We have analyzed the performance of the simplified AR-CMAC using an embedded Markov chain model to demonstrate the gain of AR-CMAC and to verify it with our simulations. Simulations in an infrastructure network with an IEEE 802.11b/g access point show noticeable improvement than the legacy schemes.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.57-66
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• 2018

It is known that a single-key and a related-key attacks on AES-128 are possible for at most 7 and 8 rounds, respectively. The security of CMAC, a typical block-cipher-based MAC algorithm, has very high possibility of inheriting the security of the underlying block cipher. Since the attacks on the underlying block cipher can be applied directly to the first block of CMAC, the current security margin is not sufficient compared to what the designers of AES claimed. In this paper, we consider HMAC-DM-AES-128 as an alternative to CMAC-AES-128 and analyze its security for reduced rounds of AES-128. For 2-round AES-128, HMAC-DM-AES-128 requires the precomputation phase time complexity of $2^{97}$ AES, the online phase time complexity of $2^{98.68}$ AES and the data complexity of $2^{98}$ blocks. Our work is meaningful in the point that it is the first security analysis of MAC based on hash modes of AES.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.7-12
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• 1990

In this paper, we propose a new manipulator control scheme based on the CMAG neural network. The proposed control consists of two components. The feedforward component is an output of trained CMAC neural network and the feedback component is a modified sliding mode control. The CMAC accepts the position, velocity and acceleration of manipulator as input and outputs two values for the controller : One is the nominal torque used for feedforward compensation(M1 network) and the other is the inertia matrix related information used for the feedback component(M2 network). Since the used control algorithm guarantees the robust trajectory tracking in spite of modeling errors, the CMAC mapping errors due to the memory limitation are little worth consideration.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.999-1005
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• 1988

An iterative learning control scheme is presented with the aid of CMAC module. By enforcing the role of linear controller with the introduction of velocity feedback, it becomes possible to make the trajectory error equation stable. One advantage of this control scheme is that it does not require acceleration feedback. Computer simulation results shows a good performance of the scheme even in the case where the gravity is not compensated.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.290-297
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• 2004

The control laws for cart-pole system are studied to see the schemes on which the control laws are made. Also, the odd symmetry of the relation between the output of the control laws and the system state vector is observed. Utilizing the symmetry in quantizing the system state variables and implementing the control laws into look-up table is discussed. Then, a CMAC is trained for a nonlinear control law for a cart-pole system such that the symmetry is conserved and its learning performance is evaluated. It is found that utilizing the symmetry is to reduce the memory requirement as well as the training period while improving the learning quality in terms of preserving the symmetry.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2433-2435
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• 2002

The purpose of this paper is to introduce a case study of developing a miniature biped robot. The biped robot has a total of twenty-one degrees of freedom(DOF) ; There are two legs which have six DOF each, two arms which have three DOF each and a waist which has three DOF. RC servo-motors were used as actuators. We have developed motor controller, sensor controller and ISA-interface card. Motor controller, PWM generator, can control eight motors Sensor controller is connected to eight FSR(Force Sensing Resistors). For high level controller communicate with low level controller, ISA-interface card has developed. For the stable walking, CMAC(Cerebellar Model Articulation Controller) neural network algorithm is applied to our system CMAC is robust at noise.