USING GENETIC ALGORITHM TO OPTIMIZE THE DESIGN OF CHARGE PUMP CIRCUITS

In this article, a novel high-power-factor electrolytic-capacitorless light-emitting diode (LED) driver based on the use of a ripple port is presented. The ripple port consists of adding an extra power processing circuit at the output of the converter that injects energy to compensate for the output current and voltage ripples. In this work, the main converter is built by using a flyback converter operating in the discontinuous conduction mode (DCM). The ripple port is implemented by adding an extra winding to the flyback coupled inductors, together with an extra switch, a capacitor, and an inductor. The additional switch is used to inject energy to the output in the time intervals, in which the flyback output current is low, thus reducing the output current ripple. The proposed converter is analyzed in detail, and a design methodology is proposed. A design example for a 27-V/1-A LED lamp is presented. Simulation and laboratory experiments have also been carried out. It is demonstrated that the proposed solution can achieve a reduction on the output current ripple of around 65% compared to the solution without ripple port for the same total capacitance and maintain a high power factor of 0.994.

The internal dynamics governing the modular multilevel converter can be described by considering the total capacitor's voltages of each arm and the circulating currents. In this study, dynamics of the total capacitor voltage of the modular multilevel converter (MMC) is analyzed. Depending on the internal control approach adopted which ensure a symmetrical energy distribution in the arms of the converter, the capacitor's voltage ripple is evaluated, highlighting effects of both the fundamental and the double frequency. Interestingly, it shows a reduction of the overall requirement of the installed energy. Simulation results are presented to verify the accuracy of the analysis.

The Weinberg converter is a suitable choice for the battery discharge regulator of a geostationary satellite due to its high efficiency, continuous input and output current, and soft switching of switches and diodes. Nevertheless, this converter has high output current ripple during the commutation period of its output diodes. Therefore, the output RMS current and output voltage ripple are high, which may directly affect the lifetime and total capacitance of bus capacitors and hence, the lifetime and volume of the geostationary satellite. Thus, reducing the output current ripple is important to the design of the geostationary satellite. An improved Weinberg converter with an additional inductor and capacitor is proposed in this paper to reduce the output current and voltage ripple. It features soft switching of all switches, no reverse recovery problems of the diodes, and reduced magnetizing offset current in the coupled inductor. The proposed converter is verified with experimental results from a 750W, 100kHz, 36-50Vdc prototype.

Dual Randomized Pulse Width Modulation (DRPWM) is renowned for its better effectiveness than Simple Randomized Pulse Width Modulation (SRPWM) in reducing conducted Electro-Magnetic Interferences (EMI) in power converters. However, the introduction of low-frequency ripples into the output voltage by dual randomization has not yet been addressed; this effect is investigated in this paper for a buck converter operating in both the continuous conduction mode (CCM) and the discontinuous conduction mode (DCM). First, the modulating principle is presented. Then, a general analytical expression for power spectral density (PSD) of the input current is derived and validated for the proposed DRPWM scheme for both the CCM and DCM. A comparison of the PSDs of the input current for all RPWM schemes in both the CCM and DCM shows the PSD spreading effectiveness of the dual scheme as compared to simple schemes. Finally, the low-frequency output ripple is analysed using the PSD of output voltage. The results reveal that the output voltage ripple is affected by all the randomized schemes in both the CCM and the DCM. Also, the dual scheme (RCFM-RPPM) introduces the highest low-frequency voltage ripple, especially in the CCM and for low duty cycles. In DCM, the RPPM scheme gives the lowest voltage ripple, while the RCFM scheme gives the lowest voltage ripple in the CCM. The results are confirmed by both theory and simulations.

A new performance and area optimization algorithm for complex VLSI systems is presented. It is widely believed within the VLSI CAD community that the relationship between delay and silicon area of a VLSI chip is convex. This conclusion is based on a simplified linear RC model to predict gate delays. In the proposed optimization algorithm, a nonlinear, non‐RC based transistor delay model was used which resulted in a non‐convex relationship between the delay and the silicon area of a VLSI chip. Genetic algorithms are better suited for discrete, non‐convex, non‐linear optimization problems than traditional calculus‐based algorithms. By using the genetic algorithms in the performance and area optimization, we are able to find the optimal values for both delay and silicon area for the ISCAS benchmark circuits.

3-D technology promises higher integration density and lower interconnection complexity and delay. At present, however, not much work on circuit applications has been done due to lack of insight into 3-D circuit architecture and performance. One of the purposes of realizing 3-D integration is to reduce the Interconnect complexity and delay of 2-D, which is widely avowed as the barrier to the continued performance gains in the future technology generations. Therefore, in this paper, we present a stochastic 3-D interconnect model, study the impact of 3-D integration on circuit performance and power consumption. We show that 3-D structures effectively reduce the number of long delay nets, significantly reduce the number of repeaters, and dramatically improve the circuit performance. With 3-D integration, a circuit can be clocked at frequencies much higher (double, even triple) than with 2-D. However, we also show that the impacts of vertical wires on chip area and interconnect delay can be limiting factors on the vertical integration of device layers; and that 3-D integration offers limited relief of power consumption.

With rapid development of technology, interconnect will occupy much chip area and gradually dominate circuit performance, interconnect optimization in high level design become more and more important. In this paper, we formulate an interconnect reuse based high level synthesis algorithm with GA (Genetic Algorithm) approach. The main contribution is that we put forward a novel coding method and design corresponding genetic operator for avoidance of generation of infeasible solutions. Experiments will show the efficiency of the algorithm.

The solid-state transformers with input-series output-parallel structure are considered for various applications where MVAC to LVDC conversion is needed. However, due to the inherent second harmonic pulsating power in the single-phase AC system, large DC link capacitors are required to smooth the voltage ripple of all floating cells, which reduces achievable power density. The active power decoupling method can shift away the pulsating power from the DC link capacitors, leading to a significant reduction to the DC link capacitance and resulting voltage ripple. So far, the application of active power decoupling is limited to single-level converters, and little research on the impact in modular converters has been done. This paper investigates the feasibility of applying active power decoupling to solid-state transformers. The active power filter with dc-split-capacitor circuit is selected to minimize the DC link capacitance. Simulation results show that the active power filter can significantly reduce the second harmonic voltage ripple and the total DC link capacitance of the SST. A prototype of the active power filter has been built and tested to verify the design.

As the progress with all kinds of mixed-mode signal circuits, the requirements of power management become increasingly stringent. Therefore it takes all kinds of high-performance linear regulator to produce a very clean and stable voltage. Here cascading technique is used to increase the output impedance in this architecture. The output voltage is less susceptible to variation of input voltage, resulting in a clean and stable voltage which is used the operating voltage of internal circuits in a mixed-mode signal integrated circuit chip. This paper using the TSMC 0.35μm CMOS 2P4M process to implement the design of high PSRR LDO regulator, having chip area, 1.34 mW consumption power. The chip supply voltage can from 2.9V to 3.3V with -106dB and -65dB PSRR at 1KHz and 100KHz, and its output voltage can stable at 1.2V and less than 2.4mV ripple voltage at maximum loading current 20 mA.

This paper presents the modeling and control of a DC-DC buck converter with reduced output ripple which is achieved without additional switching devices. The converter results from replacing the LC output network in a conventional buck converter for an LCL network which is proposed to be implemented by using coupled inductors. Low output ripple is required when dealing with sensitive loads as in LED lighting applications and high precision test equipment. The functionality of the buck converter is preserved and the output current and voltage ripples are highly reduced. Active damping must be provided in order to mitigate resonant behaviors. A model-based controller is proposed which achieves active damping and output voltage regulation. A main feature of this controller is that it has an estimation scheme that deals with power change conditions under a constant output voltage. Numerical simulations are presented to evaluate the closed-loop performance.

This work presents a 1GHz ΔΣ fractional-N PLL based on the noise filtering by FIR-embedded phase interpolator (PI). The proposed PI scheme greatly improves phase linearity by a dual-referenced interpolation and realizes FIR filtering without using multiple CPs, PFDs, and dividers. The designed fractional-N PLL shows a comparable phase-noise performance to that of an integer-N PLL. The PLL is implemented in 0.13 μm CMOS technology.

Performance metrics are measures of success or performance that can be used to evaluate how well a model makes accurate predictions or classifications. However, there is no single measure since each performance metric emphasizes a different classification aspect. Model selection procedures based on information criteria offer a quantitative measure that balances model complexity with goodness of fit, providing a better alternative to classical approaches. In this paper, we introduce and develop a novel Information Complexity-Receiver Operating Characteristic, abbreviated as ICOMP-ROC, criterion approach to fit and study the performance of ROC curve models. We construct and derive the Universal ROC (UROC) for a combination of sixteen Bi-distributional ROC models to choose the best Bi-distributional ROC by minimizing the ICOMP-ROC criterion. We conduct large-scale Monte Carlo simulations using the sixteen Bi-distributional ROC models with the Normal-Normal and Weibull-Gamma pairs as the pseudo-true ROC models. We report the frequency of hits of the ICOMP-ROC criterion, showing its remarkable recovery rate. In addition to Bi-distributional fitting, we consider a high-dimensional real Magnetic Resonance Imaging (MRI) of the Brain dataset and Wisconsin Breast Cancer (WBC) dataset to study the performance of the common performance metrics and the ICOMP-ROC criterion using several machine learning (ML) classification algorithms. We use the genetic algorithm (GA) to reduce the dimensions of these two datasets to choose the best subset of the features to study and compare the performance of the newly proposed ICOMP-ROC criterion along with the traditional performance metrics. The choice of a suitable metric is not just contingent upon the ML model used, but it also depends upon the complexity and high dimensionality of the input datasets, since the traditional performance metrics give different results and have inherent limitations. Our numerical results show the consistency and reliability of the ICOMP-ROC criterion over the traditional performance metrics as a clever model selection criterion to choose the best fitting Bi-distributional ROC model and the best classification algorithm among the ones considered. This shows the utility and the versatility of our newly proposed approach in ROC curve modeling that integrates and robustifies currently used procedures.

Composite materials offer a wide range of advantages in terms of strength, weight, and versatility, making them a popular choice for structural applications. The performance of composite structures depends not only on the material constituents but also on the topology and arrangement of these components. However, designing an optimal configuration that maximizes the desired performance metrics can be a challenging task due to the large design space and complex interactions between the components. It presents a novel approach for optimizing the multi-component topology and material orientation design of composite structures using artificial intelligence (AI) techniques. The objective is to develop an automated and efficient methodology that can identify the most optimal configuration for a given set of performance requirements. The proposed methodology combines AI algorithms, such as genetic algorithms and machine learning, with advanced computational modeling techniques. The genetic algorithms are employed to explore the design space and search for the best combination of topology and material orientations. Concurrently, machine learning techniques are utilized to model the complex relationships between the design variables and performance metrics, enabling the identification of design patterns and accelerating the optimization process.
 To achieve this, a comprehensive framework is established, encompassing the generation of an initial population of candidate designs, evaluation of their performance using numerical simulations or experimental testing, application of genetic algorithms to iteratively evolve the population by selecting the fittest designs and introducing variations, and utilization of machine learning models to predict the performance of new designs and guide the optimization process. The effectiveness of the proposed methodology is demonstrated through a case study involving the design of a composite aerospace structure. The results reveal that the AI-based optimization approach significantly outperforms traditional trial-and-error methods, leading to improved performance metrics such as strength, stiffness, and weight.
 The findings have significant implications for the design and manufacturing of composite structures in various industries, including aerospace, automotive, and civil engineering. The automated optimization process enabled by AI techniques can efficiently explore the design space, identify innovative solutions, and ultimately enhance the overall performance of composite structures.

The Electronic Design Automation (EDA) tools have achieved high degree of maturity and reliability over the years for digital design. The design of analog circuits is a challenge attributed to the existence of multi-dimensional tradeoffs among multiple analog performance metrics like gain, bandwidth, power dissipation, supply voltage, input/output impedances, linearity, voltage swings and noise. The analog design proves to be a significant bottleneck in a System on Chip (SoC) implementation due to lack of automation techniques. To address this issue, the algorithms of the functioning of human brain or soft computing techniques can be gainfully deployed. This work proposes an integrated MaxFit Genetic Algorithm (GA) and GA-SPICE framework to achieve multi-objective optimization of analog design automation. The design of two-stage op-amp is demonstrated in this framework to optimize the objectives of open-loop DC gain, phase margin, unity gain-bandwidth, slew rate, power dissipation and area. The design is performed by proposed MaxFit GA programming of op-amp design equations in MATLAB environment and the design is transmitted seamlessly to LTspice to perform SPICE simulations for design verification. The dynamic fitness evaluation on SPICE generated performance metrics at each iteration of GA programming by transmitting them to MATLAB environment enhances the robustness of analog design significantly.

The success of anaerobic digestion (AD) process for biogas production is contingent upon complex mix of operating factors, process conditions, and feedstock types, which could be affected by inadequate understanding of microbial, kinetic, and physicochemical processes. To address these limitations, efforts have been directed toward developing mathematical and intelligent models. Although mathematical models provide near-optimal solutions, they are time consuming, highly expensive, and demanding. Intelligent standalone models are also limited by their low predictive capability and inability to guarantee global optimal solution for the prediction of cumulative biogas yield for FFV waste. However, hyperparameter optimization of such models is essential to improve the prediction performance for cumulative biogas yield for FFV waste. Therefore, this study applies a genetic algorithm (GA) to optimize an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of cumulative biogas production. Seven (7) input variables, organic loading rate (OLR), volatile solids (VS), pH, hydraulic retention time (HRT), temperature, retention time, and reaction volume, were considered with cumulative biogas production as the output. The effect of varying clustering techniques was evaluated. The three (3) clustering techniques evaluated are fuzzy c-means and subtractive clustering and grid partitioning. The hybrid model was evaluated based on some verified statistical performance metrics. Optimal root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and standard deviation error (error STD) of 0.0529, 0.0326,7.6742, and 0.0474, respectively, were reported at the model testing phase for the subtractive clustering technique being the best-performing model. The results confirm the capacity of hybrid evolutionary (genetic) algorithm based on subtractive clustering technique to predict the biogas yield from FFV and serve as an effective tool for the upscaling of anaerobic digestion units as well as in techno-economic studies toward more efficient energy utilization.Graphical abstract