In this paper, a modified neural network approach called the Guided Neural Network is proposed for the longitudinal dynamics identification of a vehicle using the well-known gradient descent algorithm. The main contribution of this paper is to take account of the known information about the system in identification and to enhance the convergence of the identification errors. In this approach, the identification is performed in two stages. First, the Guiding Network is utilized to obtain an approximate dynamic characteristics from the known information such as nonlinear models or expert's experiences. Then the errors between the plant and Guiding Network are compensated using the Compensating Network with the gradient descent algorithm. With this approach, the convergence speed of the identification error can be enhanced and more accurate dynamic model can be obtained. The proposed approach is applied to the longitudinal dynamics identification of a vehicle and the resultant performance enhancement is given.

This paper presents new encoding methods for the binary genetic algorithm (BGA) and new converting methods for the real-coded genetic algorithm (RCGA). These methods are developed for the specific case in which some parameters have to be searched in wide ranges since their actual values are not known. The oversampling effect which occurs at large values in the wide range search are reduced by adjustment of resolutions in mantissa and exponent of real numbers mapped by BGA. Owing to an intrinsic similarity in chromosomal operations, the proposed encoding methods are also applied to RCGA with remapping (converting as named above) from real numbers generated in RCGA. A simple probabilistic analysis and benchmark with two ill-scaled test functions are carried out. System identification of a simple electrical circuit is also undertaken to testify effectiveness of the proposed methods to real world problems. All the optimization results show that the proposed encoding/converting methods are more suitable for problems with ill-scaled parameters or wide parameter ranges for searching.

This paper proposes a new computational optimization method modified from the dynamic encoding algorithm for searches (DEAS). Despite the successful optimization performance of DEAS for both benchmark functions and parameter identification, the problem of exponential computation time becomes serious as problem dimension increases. The proposed optimization method named univariate DEAS (uDEAS) is especially implemented to reduce the computation time using a univariate local search scheme. To verify the algorithmic feasibility for global optimization, several test functions are optimized as benchmark. Despite the simpler structure and shorter code length, function optimization performance show that uDEAS is capable of fast and reliable global search for even high dimensional problems.

Recently, a data-selective method has been proposed to achieve low misalignment in affine projection algorithm (APA) by keeping the condition number of an input data matrix small. We present an improved method, and a complexity reduction algorithm for the APA with the data-selective method. Experimental results show that the proposed algorithm has lower misalignment and a lower condition number for an input data matrix than both the conventional APA and the APA with the previous data-selective method.

The collision of ID signals from a large number of co-located passive RFID tags is a serious problem; to realize a practical RFID systems we need an effective anti-collision algorithm. This letter presents an adaptive algorithm to minimize the total time slots and the number of rounds required for identifying the tags within the RFID reader's interrogation zone. The proposed algorithm is based on the framed ALOHA protocol, and the frame size is adaptively updated each round. Simulation results show that our proposed algorithm is more efficient than the conventional algorithms based on the framed ALOHA.

It is very difficult to obtain a linearizing feedback and a coordinate transformation map, even though the system is feedback linearizable. It is known that finding a desired transformation map and feedback is equivalent to finding an integrating factor for an annihilating one-form. In this paper we develop a numerical algorithm for an integrating factor involving a set of partial differential equations and corresponding zero-form using the C.I.R method. We employ a tensor product splines as an interpolation method to data which are resulted from the numerical algorithm in order to obtain an approximate integrating factor and a zero-form in closed forms. Next, we obtain a coordinate transformation map using the approximate integrating factor and zero-form. Finally, we construct a stabilizing controller based on a linearized system with the approximate coordinate transformation.

Gains of a roll force AGC (Automatic Gain Controller) have been calculated at the first locked-on-time by FSU (Finishing-mill Set-Up model) in hot strip mills and usually these values are not adjusted during the operating time. Consequently, this conventional scheme cannot cope with the continuous variation of system parameters and circumstance, though the gains can be changed manually with the aid of experts to prevent a serious situation such as inferior mass production. Hence, partially uncontrolled variation still remains on delivery thickness. This paper discusses an effective online algorithm which can adjust the gains of the existing control system by considering the effect of time varying variables. This algorithm improves the performance of the system without additional cost and guarantees the stability of the conventional system. Specifically, this paper reveals the major factors that cause the variation of strip and explores the relationship between AGC gains and the effects of those factors through the analysis of thickness signal which occupy different frequency bands. The proposed tuning algorithm is based on the above relationship and realized through ANFIS (Adaptive-Neuro-based Fuzzy Interface System) which is a very useful method because its fuzzy logics reflect the experiences of professionals about the uncertainty and the nonlinearity of the system. The effectiveness of the algorithm is shown by several simulations which are carried out by using the field data of POSCO corporation (South Korea).

This letter presents a new mathematical expression for the excess mean-square error (EMSE) of the affine projection (AP) algorithm. The proposed expression explicitly shows the proportional relationship between the EMSE and the condition number of the input signals.

The dynamic encoding algorithm for searches (DEAS) is a recently developed algorithm that comprises a series of global optimization methods based on variable-length binary strings that represent real variables. It has been successfully applied to various optimization problems, exhibiting outstanding search efficiency and accuracy. Because DEAS manages binary strings or matrices, the decoding rules applied to the binary strings and the algorithm's structure determine the aspects of local search. The decoding rules used thus far in DEAS have some drawbacks in terms of efficiency and mathematical analysis. This paper proposes a new decoding rule and applies it to univariate DEAS (uDEAS), validating its performance against several benchmark functions. The overall optimization results of the modified uDEAS indicate that it outperforms other metaheuristic methods and obviously improves upon older versions of DEAS series.

In this paper, we provide a bound of the continuous ARE solution in terms of a matrix associated with Lyapunov solutions. Based on the new matrix-type bound, we also consider various scalar bounds and compare them with existing bounds. The major advantage of our results over existing results is that the new bounds can be always obtained if the stabilizing solution exists, whereas all existing bounds might not be computed because they require other conditions additional to the existence condition.

The main objective of vehicle suspensions is to improve ride comfort and road holding ability. Though passive suspensions consist of spring and damper, active suspensions adopt an actuator in addition to passive suspensions. In this paper, a quarter car model with an asymmetric hydraulic actuator is used. Moreover, the damping coefficient of the damper, which is changed according to the actuator velocity, is considered. The LPV (Linear Parameter Varying) model is obtained by applying feedback linearization technique. Next, a gain-scheduled controller, based on LQ regulator with different weighting factor, is designed according to the actuator velocity and the stability of the proposed controller is also proved. The effectiveness of the proposed controller is shown by numerical simulations.

In this paper, we present a recently developed pattern search method called Genetic Pattern Search algorithm (GPSA) for the global optimization of cost function subject to simple bounds. GPSA is a combined global optimization method using genetic algorithm (GA) and Digital Pattern Search (DPS) method, which has the digital structure represented by binary strings and guarantees convergence to stationary points from arbitrary starting points. The performance of GPSA is validated through extensive numerical experiments on a number of well known functions and on robot walking application. The optimization results confirm that GPSA is a robust and efficient global optimization method.

Obtaining a linearizing feedback and a coordinate transformation map is very difficult, even though the system is feedback linearizable. It is known that finding a desired transformation map and feedback is equivalent to finding an integrating factor for an annihilating one-form for single input nonlinear systems. It is also known that such an integrating factor can be approximated using the simple C.I.R method and tensor product splines. In this paper, it is shown that m integrating factors can always be approximated whenever a nonlinear system with m inputs is feedback linearizable. Next, m zero-forms can be constructed by utilizing these m integrating factors and the same methodology in the single input case. Hence, the coordinate transformation map is obtained.

Oxide material can make transparent devices with transparent electrodes. We developed a transparent oscillator and rectifier circuits with oxide TFTs. The source/drain and gate electrodes were made by indium thin oxide (ITO), and active layer made by transparent material of IGZO (Indium Gallium Zinc Oxide) on a glass substrate. The RC oscillator was composed of bootstrapped inverters, and 813 kHz oscillation frequency was accomplished at VDD = 15 V. For DC voltage generation from RF, transparent rectifier was fabricated and evaluated. This DC voltage from rectifier powered to the oscillator which operated successfully to create RF. For data transmission, RF transmission was evaluated with RF from the transparent oscillator. An antenna was connected to the oscillator and RF transmission to a receiving antenna was verified. Through this transmission antenna, RF was transmitted to a receiving antenna successfully. For transparent system of RFID, transparent antenna was developed and verified sending and receiving of data.

This paper provides a normalized Iterative Feedback Tuning (IFT) method that assures the boundedness of the gradient vector estimate (ρ) and the Hessian matrix estimate without the assumption that the internal signals are bounded. The proposed method uses the unbiased Gauss-Newton direction by the addition of the 4-th experiment. We also present blended control criteria and a PID-like controller as new design choices. In examples, the normalized IFT method results in a good convergence although the internal signal or the measurement noise variance is large.

Sliding mode control (SMC) is known to be robust with respect to matched uncertainties. However, it does not guarantee stability of systems with mismatched uncertainties. In this paper, we propose a new method to design a sliding surface for linear systems with mismatched uncertainties. The proposed sliding surface provides a new stability criterion of the reduced-order system origin with respect to mismatched uncertainties. A numerical example is given to illustrate the effectiveness of the proposed method.