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The bifurcation phenomena occurring in a current mode controlled buck converter when it shifts from continuous conduction mode (CCM) to discontinuous conduction mode (DCM) have been reported. A sampled data model has been developed considering CCM as well as DCM. The bifurcation phenomena observed in such converters have been verified experimentally.

A digital controller for LED driver is presented. The controller is used in the flyback converter which works in discontinuous current mode (DCM). A comparator is adopted to shape the auxiliary winding voltage based on traditional primary side regulation (PSR). A five-states finite state machine (FSM) is designed to deal with the shaped auxiliary winding signal. The FSM extracts the system time information from the shaped signal and controls all the sequences in the whole system. To achieve high accuracy of the output current, an adaptive delay compensation scheme is adopted. A LED prototype whose typical output power is 7 W is used in both simulation and experiment to demonstrate the effectiveness of this novel digital controller. The measurement results show that the line regulation when the input ac voltage varies from 85–265 V is 3.4% and the load regulation when the load varies from 3–10 LEDs is 2.5%.

A three-level buck (TLB) converter has the characteristics of higher voltage conversion efficiency, lower inductor current ripples, output voltage ripples and voltage stresses on switches when compared with the buck converters in continuous conduction mode (CCM). With a TLB converter integrated on a chip, we cannot avoid its discontinuous conduction mode (DCM) operation due to a smaller inductance and load variation. In this paper, we’ll present and discuss the analysis, design and control of a TLB converter under DCM operation, implemented in a 65nm CMOS process. Transistor level simulation results show that when the TLB converter operates at 100MHz with a 5nH on-chip inductor, a 10nF output capacitor and a 10nF flying capacitor, it can achieve an output conversion range of 0.7–1.2V from a 2.4V input supply, with a peak efficiency of 81.5%@120mW. The output load transient response is 100mV with 101ns for undershoot, and 86mV with 110ns for overshoot when $I_{\mathrm{\text{OUT}}}=10$–100mA. The maximum output voltage ripple is less than 19mV.

Achieving higher voltage at load side is essential in many applications, especially in small-scale solar PV systems. Traditional boost converter fails to lift the voltage level beyond five times due to saturation in the passive elements. In order to attain high voltage gain ratio, a novel switched-inductor and switched-capacitor networks-based high-gain step-up converter is proposed in this paper. It can attain higher voltage gain at lower duty ratios, thereby reducing the losses associated with the converter and enhancing the efficiency. The detailed analysis of the proposed converter has been made under the CCM and DCM conditions. Moreover, investigation on device stresses is carried out and the results are compared with recently derived converters. Also, the performance comparison of the proposed converter with other published topologies is presented in this paper. In order to validate the operation details, the measured results are presented and the same have been confirmed with the theoretical approach. Furthermore, experimental works have been carried out and the results are presented to confirm the feasibility of the operation.

In this paper, we have performed the optimized structures of the red emitting material, 4-(dicyanomethylene)-2-methyl-6-[p-(dimethyl amino) styryl]-4H-pyran (DCM), with different polarity solvent environments by using the density functional theory (DFT) method, B3LYP/6-31G*. The time-dependent density functional theory (TD-DFT) and the polarizable continuum model (PCM) have been used to obtain the optical properties in the solvent environment. It has been observed that when the solvent polarity increases, the DCM molecule exhibits the red shift in the maximum absorption wavelength and enhances the oscillator strength (f). The solvent polarity also enhances the electron transfer ability from the electron-donating dimethylamine group (-N(CH₃)₂) to the electron-withdrawing =C(CN)₂ group. The S₀ → S₁ transition of DCM is found to be π–π*. The maximum absorption wavelengths of different solvent environments are found to be consistent with the reported experimental results.

This paper contributes toward the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body quadratic programming (QP) control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372m/s, which is the highest walking velocity achieved by the iCub robot.

Discrete choice modeling (DCM) is widely used in economics, social studies, and marketing research for estimating utilities and preference probabilities of multiple alternatives. Data for the model is elicited from the respondents who are presented with several sets of items characterized by various attributes, and each respondent chooses the best alternative in each set. Estimation of utilities is usually performed in a multinomial-logit (MNL) modeling and software for Hierarchical Bayesian (HB) technique is usually applied to find individual utilities by iterative estimations. This paper describes an easy and convenient empirical Bayesian way to construct priors and combine them with the likelihood on individual level data. This allows the modeler to obtain posterior estimation of MNL utilities in noniterative evaluations. Logistic modeling for the posterior frequencies is performed using the linear link of their logarithm of odds that clarifies the results of DCM modeling. The problem of overfitting is considered and the optimum balance between signal and noise in the precision of individual prediction and the smoothing of overall data is suggested. Actual market research data are used and the results are discussed.