• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.2 no.4
• /
• pp.42-51
• /
• 2003

In this paper, we are going to analyze on relation of an output spectrum along phase distortion of power amplifier in wireless LAN system, and then considered an ACPR characteristic of power amplifier and consideration of an OFDM method for this. Also, we did implementation for OFDM modulation and transmission section of an IEEE 802.11a standard to have transmission speed of the maximum 54Mbps in order to know an OFDM modulation method and relation of non-linear characteristic of power amplifier. The non-linear characteristic of power amplifier did modeling with AM-to-AM and AM-to-PM, and we analyzed an output spectrum characteristic along phase distortion composed input signal supply for power amplifier. When output spectrum analysis results phase distortion increased, and an AM-to-PM characteristic of power amplifier in 5 degrees, the output spectrum was satisfied with a demand spectrum in P1 dB, but 10-20 degrees were able to confirm what cannot be satisfied with a demand spectrum in phase distortion. Also, an output spectrum of power amplifier by frequency re-growth generated by a non-linear characteristic of power amplifier did not satisfied in P1dE. therefore, a back-off value was requested according to an AM-to-PM distortion degree, and smaller back-off value were able to know what demand became in case of modulation section that used OFDM.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.3 no.3
• /
• pp.63-74
• /
• 1999

A sliding mode fuzzy control (SMFC) algorithm is presented for vibration of large structures. Rule-base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the nonlinear control algorithms. Fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation. Non-linearity of the control rule makes the controller more effective than linear controllers. Design procedure based on the present fuzzy control is more convenient than those of the conventional algorithms based on complex mathematical analysis, such as linear quadratic regulator and sliding mode control(SMC). Robustness of presented controller is illustrated by examining the loop transfer function. For verification of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator-structure interaction, modeling error, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as $H_{mixed 2/{\infty}}$ optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is an efficient and attractive control method, since the vibration responses of the structure can be reduced very effectively and the design procedure is simple and convenient.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.35C no.6
• /
• pp.39-47
• /
• 1998

A fourth-order $\Sigma$-$\Delta$ modulator is designed and implemented in 0.6 $\mu\textrm{m}$ CMOS technology. The modulator is verified by introducing nonlinear factors such as DC gain and slew rate in system model that determines the transfer function in S-domain and in time-domain. Dynamic range is more than 110 dB and the peak SM is 102.6 dB at a clock rate of 2.8224 MHz for voiceband signal. The structure of a ∑-$\Delta$ modulator is a modified fourth-order ∑-$\Delta$ modulator using direct feedback loop method, which improves performance and consumes less power. The transmission zero for noise is located in the first-second integrator loop, which reduces entire size of capacitors, reduces the active area of the chip, improves the performance, and reduces power dissipation. The system is stable because the output variation with respect to unit time is small compared with that of the third integrator. It is easy to implement because the size of the capacitor in the first integrator, and the size of the third integrator is small because we use the noise reduction technique. This paper represents a new design method by modeling that conceptually decides transfer function in S-domain and in Z-domain, determines the cutoff frequency of signal, maximizes signal power in each integrator, and decides optimal transmission-zero frequency for noise. The active area of the prototype chip is 5.25$\textrm{mm}^2$, and it dissipates 10 mW of power from a 5V supply.

• Journal of Korean Society of Environmental Engineers
• /
• v.22 no.10
• /
• pp.1905-1919
• /
• 2000

The main motivation of this research is to develop an intelligent control strategy for Activated Sludge Process (ASP). ASP is a complex and nonlinear dynamic system because of the characteristic of wastewater, the change in influent flow rate, weather conditions, and etc. The mathematical model of ASP also includes uncertainties which are ignored or not considered by process engineer or controller designer. The ASP is generally controlled by a PID controller that consists of fixed proportional, integral, and derivative gain values. The PID gains are adjusted by the expert who has much experience in the ASP. The ASP model based on $Matlab^{(R)}5.3/Simulink^{(R)}3.0$ is developed in this paper. The performance of the model is tested by IWA(International Water Association) and COST(European Cooperation in the field of Scientific and Technical Research) data that include steady-state results during 14 days. The advantage of the developed model is that the user can easily modify or change the controller by the help of the graphical user interface. The ASP model as a typical nonlinear system can be used to simulate and test the proposed controller for an educational purpose. Various control methods are applied to the ASP model and the control results are compared to apply the proposed intelligent control strategy to a real ASP. Three control methods are designed and tested: conventional PID controller, fuzzy logic control approach to modify setpoints, and fuzzy-PID control method. The proposed setpoints changer based on the fuzzy logic shows a better performance and robustness under disturbances. The objective function can be defined and included in the proposed control strategy to improve the effluent water quality and to reduce the operating cost in a real ASP.

• Korean Journal of Poultry Science
• /
• v.43 no.1
• /
• pp.1-14
• /
• 2016

Prediction of growth patterns of commercial chicken strains is important. It can provide visual assessment of growth as function of time and prediction body weight (BW) at a specific age. The aim of current study is to compare the three nonlinear functions (i.e., Logistic, Gompertz, and von Betalanffy) for modeling the growth of twenty five commercial Korean native chicken (KNC) strains reared under a battery cage system until 32 weeks of age and to evaluate the three models with regard to their ability to describe the relationship between BW and age. A clear difference in growth pattern among 25 strains were observed and classified in to the groups according to their growth patterns. The highest and lowest estimated values for asymptotic body weight (C) for 3H and 5W were given by von Bertalanffy and Logistic model 4629.7 g for 2197.8 g respectively. The highest estimated parameter for maturating rate (b) was given by Logistic model 0.249 corresponds to the 2F and lowest in von Bertalanffy model 0.094 for 4Y. According to the coefficient of determination ($R^2$) and mean square of error (MSE), Gompertz and von Bertalanffy models were suitable to describe the growth of Korean native chicken. Moreover, von Bertalannfy model was well described the most of KNC growth with biologically meaningful parameter compared to Gompertz model.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.487-496
• /
• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.46 no.5
• /
• pp.26-35
• /
• 2009

OFDM (Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OFDM signal consists of a number of independently modulated subcarriers and the superposition of these subcarriers causes a problem that can give a large PAPR(Peak-to-Average Power Ratio). Phase rotation method can reduce the PAPR without nonlinear distortion by multiplying phase weighting factors. But computational complexity of searching phase weighting factors is increased exponentially with the number of subblocks and considered phase factor. Therefore, a new method, which can reduce computational complexity and detect phase weighting factors efficiently, should be developed. In this paper, a modeling process is introduced, which apply metaheuristic algerian in phase rotation method and optimize in PTS (Particle Swarm Optimization) scheme. Proposed algorithm can solve the computational complexity and guarantee to reduce PAPR We analyzed the efficiency of the PAPR reduction through a simulation when we applied the proposed method to telecommunication systems.

• Journal of Wetlands Research
• /
• v.18 no.3
• /
• pp.313-323
• /
• 2016

Water temperature affects physical and biological processes in ecologies on river system and is important conditions for growth rate and spawning of fish species. The objective of this study is to compare models for water temperature during the summer season for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada). For this, three different models, which are CEQUEAU, Auto-regressive Moving Average with eXogenous input and Nonlinear Autoregressive with eXogenous input, were applied and compared. Also, future water temperature in the Fourchue river were simulated and analyzed its result based on the CMIP5 climate models, RCP 2.6, 4.5, 8.5 climate change scenarios. As the result of the study, the water temperature in the Fourchue river are actually changed and median water temperature will increase $0.2{\sim}0.7^{\circ}C$ in June and could decrease by $0.2{\sim}1.1^{\circ}C$ in September. Also, the UILT ($24.9^{\circ}C$) for brook trout are also likely to occurred for several days.

• Wind and Structures
• /
• v.30 no.4
• /
• pp.433-450
• /
• 2020

The strong turbulence characteristic of typhoon not only will significantly change flow field characteristics surrounding the large-scale wind turbine and aerodynamic force distribution on surface, but also may cause morphological evolution of coast dune and thereby form sand storms. A 5MW horizontal-axis wind turbine in a wind power plant of southeastern coastal areas in China was chosen to investigate the distribution law of additional loads caused by wind-sand coupling movement of coast dune at landing of strong typhoons. Firstly, a mesoscale Weather Research and Forecasting (WRF) mode was introduced in for high spatial resolution simulation of typhoon "Megi". Wind speed profile on the boundary layer of typhoon was gained through fitting based on nonlinear least squares and then it was integrated into the user-defined function (UDF) as an entry condition of small-scaled CFD numerical simulation. On this basis, a synchronous iterative modeling of wind field and sand particle combination was carried out by using a continuous phase and discrete phase. Influencing laws of typhoon and normal wind on moving characteristics of sand particles, equivalent pressure distribution mode of structural surface and characteristics of lift resistance coefficient were compared. Results demonstrated that: Compared with normal wind, mesoscale typhoon intensifies the 3D aerodynamic distribution mode on structural surface of wind turbine significantly. Different from wind loads, sand loads mainly impact on 30° ranges at two sides of the lower windward region on the tower. The ratio between sand loads and wind load reaches 3.937% and the maximum sand pressure coefficient is 0.09. The coupling impact effect of strong typhoon and large sand particles is more significant, in which the resistance coefficient of tower is increased by 9.80% to the maximum extent. The maximum resistance coefficient in typhoon field is 13.79% higher than that in the normal wind field.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.1
• /
• pp.9-14
• /
• 2004

Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.