ADC Model Research Articles

Procedure for the synthesis of models of electrotechnical complexes

A procedure for developing visual mathematical models of multichannel electrotechnical complexes which reduces time of synthesis of mathematical models and error likelihood was developed. The procedure includes two stages: representation of an electrotechnical complex in a form of a structure of the energy path and its transformation into a visual-block model. Representation of the system in a form of a structure of energy paths is based on the principle of system decomposition proposed by the authors which involves definition of six types of structural elements in the power conversion structure: source and receiver, distributor and consolidator, converter and energy storage. The principle of decomposition allows one to create a library of models of subblock, components of the visual-block model and introduce unification of the subblock designation. To illustrate the proposed procedure, an example of building a visual model of a DC drive for a rolling mill roll and its implementation on a personal computer were considered. A fragment of the library of components of the visual-block model with a mathematical description of the components included in the considered example was given. The introduced unification creates conditions for effective work of developers in elaboration of this procedure of model synthesis in terms of formation of a library of subblocs. In addition, unification of the form of representation of the library of components creates conditions for effective communication of researchers and developers within the frames of complex integrated projects. The model at the stage of developing structure of energy paths is a convenient tool for visualizing system operation and contributes to understanding of its functioning. The form of the obtained mathematical model is convenient for its further transformation into a model in variables of state which, in turn, is the starting point for synthesis of control systems

Deciphering moral intuition: How agents, deeds, and consequences influence moral judgment.

Moral evaluations occur quickly following heuristic-like intuitive processes without effortful deliberation. There are several competing explanations for this. The ADC-model predicts that moral judgment consists in concurrent evaluations of three different intuitive components: the character of a person (Agent-component, A); their actions (Deed-component, D); and the consequences brought about in the situation (Consequences-component, C). Thereby, it explains the intuitive appeal of precepts from three dominant moral theories (virtue ethics, deontology, and consequentialism), and flexible yet stable nature of moral judgment. Insistence on single-component explanations has led to many centuries of debate as to which moral precepts and theories best describe (or should guide) moral evaluation. This study consists of two large-scale experiments and provides a first empirical investigation of predictions yielded by the ADC model. We use vignettes describing different moral situations in which all components of the model are varied simultaneously. Experiment 1 (within-subject design) shows that positive descriptions of the A-, D-, and C-components of moral intuition lead to more positive moral judgments in a situation with low-stakes. Also, interaction effects between the components were discovered. Experiment 2 further investigates these results in a between-subject design. We found that the effects of the A-, D-, and C-components vary in strength in a high-stakes situation. Moreover, sex, age, education, and social status had no effects. However, preferences for precepts in certain moral theories (PPIMT) partially moderated the effects of the A- and C-component. Future research on moral intuitions should consider the simultaneous three-component constitution of moral judgment.

Modeling of a transferred arc inside a crucible with gas injection through a hollow cathode

A transferred arc system consisting of a crucible type anode and hollow cathode with gas injection is being considered for melting and evaporation of materials to improve the heat transfer efficiency. Knowledge of arc behavior inside the crucible and the effect of gas injection through the cathode on the characteristics of the arc are required to optimize the process parameters for better process efficiency. The available literature on this is very limited. A 2D steady-state axi-symmetrical mathematical model of a DC transferred arc is developed and the plasma arc created between a hollow cathode with gas injection and a crucible anode is simulated. The effects of cathode geometry and gas flow through the cathode on the arc characteristics are studied for different electrode gaps and arc currents. The effect of gas flow through the cathode on the arc voltage is clarified for various electrode gaps and arc currents. The gas flow through the cathode is strong enough to push the arc root attachment from the center of the anode and the plasma covers the entire surface of the crucible bottom at higher gas flow rates. Irrespective of the gas flow rate, arc current, and arc length, the higher arc heating efficiency is achieved when the arc root attachment starts to move away from the center of the anode/arc voltage is minimal. The characteristics of the arc inside the crucible and open arc are compared for different flow rates of the gas injected through the cathode. The present model is validated by comparing the predicted results with previously published results.

Relationship between executive function, attachment style, and psychotic like experiences in typically developing youth

Psychotic like experiences (PLE's) are common in the general population, particularly during adolescence, which has generated interest in how PLE's emerge, and the extent to which they reflect either risk for, or resilience to, psychosis. The “attachment-developmental-cognitive” (ADC) model is one effort to model the effect of risk factors on PLEs. The ADC model proposes attachment insecurity as an early environmental insult that can contribute to altered neurodevelopment, increasing the likelihood of PLE's and psychosis. In particular, early-life attachment disruptions may negatively impact numerous aspects of executive function (EF), including behavioral inhibition and emotion regulation. Yet despite the relationship of disrupted attachment to EF impairments, no studies have examined how these factors may combine to contribute to PLE's in adolescents. Here, we examined the relative contributions of daily-life EF and attachment difficulties (avoidance and anxiety) to PLEs in typically developing youth (N=52; ages 10–21). We found that EF deficits and high attachment insecurity both accounted for a significant proportion of the variance in PLE's, and interacted to predict PLE manifestation. Specifically, positive PLEs were predicted by greater trouble monitoring behavioral impact, less difficulty completing tasks, greater difficulty regulating emotional reactions, greater difficulty controlling impulses and higher attachment anxiety. Negative PLEs were predicted by greater difficulty in alternating attention, transitioning across situations, and regulating emotional reactions as well as higher attachment anxiety. These results are consistent with the ADC model, providing evidence that early-life attachment disruptions may impact behavioral regulation and emotional control, which together may contribute to PLEs.

Analysis Method of Compound ADC Modeling

A new type of ADC with a precision of 10-bit and a sampling rate of 250M is implemented by modeling. The new composite ADC proposed in this paper achieves high power consumption and reduced area with high accuracy and medium speed by combining the advantages and disadvantages of pipeline ADC and SAR ADC. In this paper, the system modeling and simulation verification are carried out for the new composite structure ADC. In the premise of considering many non-ideal factors, the input end access 36.6MHz input signal, sampling clock frequency using 250MHz, the system can achieve SNR = 58.5dB, SFDR = 68.8dB, ENOB = 9.3 and other design indicators. The design of this system model provides the direction for the determination of important module design parameters in the actual circuit-level design system. At the same time, it provides the basis for analyzing the influence of various non-ideal factors on static and dynamic characteristics and the ADC energy in ADC system design.

Design of Digital Acquisition Circuit for Inertial Sensor Based on FPGA

In order to realize high-speed analog sampling and noise analysis, the paper put forward a design scheme of the high-speed data acquisition and analysis system based on FPGA, the system uses Cyclone series FPGA with high-speed A/D converter, and using fast Fourier analysis core of the Altera Company. The ADC model is AD9643 chip. The test results show that the prototype can simultaneously collect three output signals, and real-time processing to the host computer display. The measured gyro resonance frequency is stable at 324.88 kHz, fluctuation range is 9Hz, and the mean square deviation of the peak value is 0.008 V on the output voltage of reference point. The detection system is stable and has high precision. The digital detection circuit can be applied to the signal processing of MEMS gyroscope, which can improve the stability and anti-jamming of MEMS gyroscope.

Accelerating the Diffusion-Weighted Imaging Biomarker in the clinical practice: comparative study

Diffusion Weighted Imaging (DWI) methods (ADC and IVIM models) extract meaningful information about the microscopic motions of water of human tissues from MRIs. This is a non invasive method which plays an important role in the diagnosis of ischemic strokes, high grade gliomas or tumors.In the La Fe Polytechnic and University Hospital, the DWI methods aforementioned are used in clinical practice and Matlab is used as a development tool for his out of box performance and fast prototyping. However, each image takes hours to compute due to Matlab’s environment and interpreted functions. Because of this, its use in clinical practice is limited.In this paper we present three compiled versions on which different parallel paradigms based on multicore (OpenMP) and GPU (CUDA) are applied. These implementations have managed to reduce the computation time to less than one minute, therefore, it allows easing their use in daily clinical practice at a cheap acquisition cost.

A Scalable Bandwidth Mismatch Calibration Technique for Time-Interleaved ADCs

This paper presents a foreground calibration method for both a sampler and a track-and-hold (T/H) buffer bandwidth mismatch in highly time-interleaved analog-to-digital converters (TI-ADCs). The T/H buffer bandwidth mismatch stems from the length difference of interconnect lines between the buffer and the channel ADC, while the sampler bandwidth mismatch arises from the mismatch in a switch and a sampling capacitor. To address both mismatches along with other mismatches residing in TI-ADCs, this papers utilizes least-squares (LS) minimization technique in order to extract mismatch parameters while injecting a sinewave at two distinct frequencies. Programmable capacitor arrays (PCAs) are used to tune the bandwidth of sampler, and correcting buffer bandwidth mismatch is performed in digital-domain. The method presented here is scalable to arbitrary number of interleaved paths, and can easily be combined with existing calibration methods for gain, offset, and timing-skew mismatches. Numerical experiment via Monte-Carlo simulations demonstrates significant performance improvement in the spurious-free dynamic range (SFDR) from 38 dB to 75 dB for a 32-channel time-interleaved ADC model that includes all major mismatches.

Optimal switching Lyapunov‐based control of power electronic converters

SummaryThis paper presents new ideas and insights towards a novel optimal control approach for power electronic converters. The so‐called stabilizing or Lyapunov‐based control paradigm is adopted, which is well known in the area of energy‐based control of power electronic converters, in which the control law takes a nonlinear state‐feedback form parameterized by a positive scalar λ. The first contribution is the extension to an optimal Lyapunov‐based control paradigm involving the specification of the optimal value for the parameter λ in a typical optimal control setting. The second contribution is the extension to more flexible optimal switching‐gain control laws, where the optimal switching surfaces are parameterized by a number of positive scalars λj. Systematic derivation of gradient information to apply gradient‐descent algorithms is provided. The proposed techniques are numerically evaluated using the exact switched model of a DC–DC boost converter. Copyright © 2016 John Wiley & Sons, Ltd.

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1).

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.

A Novel Approach to Design SAR-ADC: Design Partitioning Method

This paper presents a successive approximation register analog-to-digital converter (SAR-ADC) design optimization platform. A novel ADC modeling approach, design partitioning method (DPM), is used in the proposed platform. It uses lookup tables along with conventional behavioral modeling technique, which captures the design parameters and process nonidealities. An individualistic as well as collective study of the impact of these parameters and nonidealities on the performance of SAR-ADC has been carried out. The proposed platform aims to achieve a perfect balance between architecture evaluation time (3.2% with respect to SPICE netlist) and accuracy in performance estimation (98% with respect to SPICE netlist). A comparative study between SPICE model, structural modeling, and the proposed modeling scheme (DPM) is reported here. As a case study, a 14-bit, 4.2MSPS SAR-ADC architecture is evaluated, designed, and verified.

Dynamic Model of a DC Motor-Gear-Alternator (MGA) System

Dynamic Model of a DC Motor-Gear-Alternator (MGA) System

Adaptation and Enhancement of Generalized Polynomial Chaos for Industrial Applications

AbstractThis contribution presents ideas, how to adapt stochastic elements to industrial applications and reduce the number of simulations in comparison to Monte Carlo. Especially when the mathematical model equations are not known and just an input/output interface is given. For this so called black box model which can be generated by many simulation tools the generalized polynomial chaos based on the idea of Wiener [1], and extended by Xiu and Karniadakis [2] is a suitable technique. One main advantage of this concept can be seen in the ability to produce functional representations of stochastic variability. This yields in a reduction of the simulation number compared to classical methods and can intensified by using a sparse grids approach [3]. Looking at industrial applications the concept can be applied to a huge amount of systems. For example, components including electronic drives, fuel injection valves, and mechanical parts. As an example of use, a simplified model of a DC drive with six uncertain parameters is taken into account. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Introduction to the Special Issue on the 20th IMEKO TC4 International Symposium and the 18th TC4 Workshop on ADC and DAC Modelling and Testing

Introduction to the Special Issue on the 20th IMEKO TC4 International Symposium and the 18th TC4 Workshop on ADC and DAC Modelling and Testing

Modelling and Simulation of Boost Converter with Maximum Power Point Tracking (MPPT) for Photovoltaic Application

This paper presents the proposed model and simulation of a DC to DC converter with maximum power point tracking (MPPT) using fuzzy logic controller (FLC) for a standalone Photovoltaic (PV) System. This research will focus on the developing high performance DC to DC converter with fuzzy logic controller based to extract the maximum power that generated by the PV panel. The system composed of the PV array and DC-DC boost converter with MPPT system. The maximum power point tracking control is based on adaptive fuzzy logic to control ON/OFF time of IGBT switch of DC-DC boost converter. The proposed DC to DC converter is designed by using the Multisim software while the controller programme will be carried out by using the Matlab Simulink software. Pulse width modulation will be generated by the controller to trigger the IGBT gate. The performance of the proposed model is evaluated by the simulation and the result show that our proposed converter can convert more power from generated voltage. By using the fuzzy logic method to track the maximum power of the PV array, it is faster and the voltage is stable.

Mathematical models for the diffusion magnetic resonance signal abnormality in patients with prion diseases.

In clinical practice signal hyperintensity in the cortex and/or in the striatum on magnetic resonance (MR) diffusion-weighted images (DWIs) is a marker of sporadic Creutzfeldt–Jakob Disease (sCJD). MR diagnostic accuracy is greater than 90%, but the biophysical mechanisms underpinning the signal abnormality are unknown. The aim of this prospective study is to combine an advanced DWI protocol with new mathematical models of the microstructural changes occurring in prion disease patients to investigate the cause of MR signal alterations. This underpins the later development of more sensitive and specific image-based biomarkers. DWI data with a wide a range of echo times and diffusion weightings were acquired in 15 patients with suspected diagnosis of prion disease and in 4 healthy age-matched subjects. Clinical diagnosis of sCJD was made in nine patients, genetic CJD in one, rapidly progressive encephalopathy in three, and Gerstmann–Sträussler–Scheinker syndrome in two. Data were analysed with two bi-compartment models that represent different hypotheses about the histopathological alterations responsible for the DWI signal hyperintensity. A ROI-based analysis was performed in 13 grey matter areas located in affected and apparently unaffected regions from patients and healthy subjects. We provide for the first time non-invasive estimate of the restricted compartment radius, designed to reflect vacuole size, which is a key discriminator of sCJD subtypes. The estimated vacuole size in DWI hyperintense cortex was in the range between 3 and 10 µm that is compatible with neuropathology measurements. In DWI hyperintense grey matter of sCJD patients the two bi-compartment models outperform the classic mono-exponential ADC model. Both new models show that T2 relaxation times significantly increase, fast and slow diffusivities reduce, and the fraction of the compartment with slow/restricted diffusion increases compared to unaffected grey matter of patients and healthy subjects. Analysis of the raw DWI signal allows us to suggest the following acquisition parameters for optimized detection of CJD lesions: b = 3000 s/mm2 and TE = 103 ms. In conclusion, these results provide the first in vivo estimate of mean vacuole size, new insight on the mechanisms of DWI signal changes in prionopathies and open the way to designing an optimized acquisition protocol to improve early clinical diagnosis and subtyping of sCJD.

A Priori Prediction of Tumor Payload Concentrations: Preclinical Case Study with an Auristatin-Based Anti-5T4 Antibody-Drug Conjugate

The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.

Enhancement of magnetic flux distribution in a DC superconducting electric motor

Most motor designs require an air gap between the rotor and stator to enable the armature to rotate freely. The interaction of magnetic flux from rotor and stator within the air gap will provide the thrust for rotational motion. Thus, the understanding of magnetic flux in the vicinity of the air gap is very important to mathematically calculate the magnetic flux generated in the area. In this work, a finite element analysis was employed to study the behavior of the magnetic flux in view of designing a synchronous DC superconducting electric motor. The analysis provides an ideal magnetic flux distribution within the components of the motor. From the flux plot analysis, it indicates that flux losses are mainly in the forms of leakage and fringe effect. The analysis also shows that the flux density is high at the area around the air gap and the rotor. The high flux density will provide a high force area that enables the rotor to rotate. In contrast, the other parts of the motor body do not show high flux density indicating low distribution of flux. Consequently, a bench top model of a DC superconducting motor was developed where by motor with a 2-pole type winding was chosen. Each field coil was designed with a racetrack-shaped double pancake wound using DI-BSCCO Bi-2223 superconducting tapes. The performance and energy efficiency of the superconducting motor was superior when compared to the conventional motor with similar capacity.

Research on ADC Model Based on Improved Analytic Hierarchy Process

 Abstract—ADC model has been widely used in effectiveness evaluation, and the capability matrix C plays a vital role when building ADC model. Based on the analysis of ADC model, construct the matrix C by the improved Analytic Hierarchy Process (AHP) is proposed. The problems of traditional AHP are analyzed, which exist in the structure of capability matrix C. Then, the approximate degree used to solve the weight of judgment matrix is simulated and analyzed. Based on the analysis and the simulation, the improved AHP is proposed combined with entropy method. The results show that the improved AHP is not only to solve the weight of judgment matrix, but also to solve the difficulty when structuring capability matrix C.

Guest Editorial Special Section on IMEKO 2011 International Workshop on ADC Modeling, Testing and Data Converter Analysis and Design and the IEEE 2011 ADC Forum

The six papers in this special section were presented at the 2011 International Workshop on ADC Modeling, Testing, and Data Converter Analysis and Design (IMEKO) and the IEEE 2011 ADC Forum.