Ideal Input Research Articles

Velocity mapping of fast flows using a linearly ramped gradient waveform

We report a new pure phase encoding measurement for velocity mapping. Velocity-sensitization is achieved using a repeating, linearly ramped gradient waveform instead of rectangular bipolar pulsed field gradients. This approach reduces eddy current effects and results in the sample experiencing a gradient waveform that more closely matches the ideal input. Errors in k-space mapping and calculated velocity values are reduced when contrasted with the previous measurement method. Velocity maps were acquired of high-speed (c. 6 m/s) water flow through a pipe constriction. The application of linearly ramped gradient waveforms to non-velocity-encoded imaging measurements is discussed.

Matched-field geoacoustic inversion using propagation invariant in a range-dependent waveguide.

Although matched-field geoacoustic inversion (MFI) has been successfully applied for seabed sediment parameters, its estimation capabilities are limited due to the range dependence of ocean sound-speed profiles, bathymetry, and bottom sediment. This paper proposes a method called propagation invariant (PI). As a purely data-driven method, PI eliminates the effect of intervening range-dependent variations on acoustic propagation and depends only on the "local" environmental characteristics, thus providing ideal input data for MFI. In this paper, the PI concept is applied for MFI in a range-dependent environment by simulations. Results show good MFI performance even for a highly range-dependent waveguide.

Comparative analysis of the R&D investment performance of Korean local governments

This study examines Korea's research and development (R&D) investment performance at the local government level using slack-based model data envelopment analysis (SBM-DEA). The SBM methodology, which has replaced the traditional DEA model, is expected to provide more reliable empirical results for R&D investment performance. We confirm the statistical reliability of our results by conducting bootstrapping. The average score of Korea's R&D investment performance is 67.7%, implying that there is a 32.3% potential for efficiency improvement. Among the 16 local governments examined, Seoul, Gwangju, Daegu, and Gangwon show better performance with an average value higher than 0.8. We decomposed R&D investment efficiency into pure R&D investment technical efficiency and scale efficiency and derived implications regarding the input scales. We also reported benchmark information from trend-setting local governments that indicate ideal input mixes for fast-following local governments. Since no local government was found to be in the CRS group, we suggest that all local governments should transform their R&D investment input mix toward upscaling or downsizing.

Adoption of Power Electronic Converters Multi-Module Cascade System in Power Energy Circuit Control

In order to solve the problem of system instability caused by power electronic converter in multi-module cascade operation in fields of smart grids and new energy, in this study, the impedance matching stability analysis of multi-module cascade system is presented. First, based on equivalent transfer function between the ideal input impedance and output impedance of submodule, the impedance matching stability analysis method of multi-module cascade system is put forward. Then, taking Buck circuit as an example, the two-terminal impedance model of Buck circuit is constructed, and the stability criterion of cascade system is given. Finally, the stability of single module and multi - module - cascade are analyzed. The stability analysis method proposed in this study shows that the single module system in Buck circuit has good input and output. The stability analysis of multi-module cascade system in Buck circuit shows that the impedance matching stability analysis method is feasible and correct. This study provides a good theoretical basis for the stability of the multi - module converter cascade system in power energy field.

Short-term wind power prediction based on EMD-LSTM combined model

In recent years, China is actively developing wind power generation. Wind energy is a natural factor with volatility, variability and uncontrollability, which will cause fluctuations in wind farm output. The accurate prediction of wind power is conducive to grid dispatchers deploying scheduling plans or doing ahead of schedule Adjustments to reduce losses to a certain extent are also conducive to improving wind power grid-connected capacity. In this paper, using the advantages of empirical mode decomposition EMD algorithm in nonlinear and non-stationary data processing, a wind farm power prediction model based on EMD-LSTM is established. First, the relevant data is preprocessed to obtain the ideal input sequence, and the LSTM network model is used. The NWP wind speed is corrected first, and the revised forecasted NWP wind speed sequence is closer to the actual wind speed. Then use EMD to decompose the wind power data sequence into data components of different scales, and then use the LSTM long and short-term memory network to model the decomposed IMF components and residual RES respectively, and sum the prediction results of each component and residual As the final prediction result. The research results show that the method described in this paper effectively improves the prediction.

Effect of rigid connection to an asymmetric building on the random seismic response

Connection of adjacent buildings with stiff links is an efficient approach for seismic pounding mitigation. However, use of highly rigid links might alter the torsional response in asymmetric plans and although this was mentioned in the literature, no quantitative study has been done before to investigate the condition numerically. In this paper, the effect of rigid coupling on the elastic lateral-torsional response of two adjacent one-story column-type buildings has been studied by comparison to uncoupled structures. Three cases are considered, including two similar asymmetric structures, two adjacent asymmetric structures with different dynamic properties and a symmetric system adjacent to an adjacent asymmetric one. After an acceptable validation against the actual earthquake, the traditional random vibration method has been utilized for dynamic analysis under Ideal white noise input. Results demonstrate that rigid coupling may increase or decrease the rotational response, depending on eccentricities, torsional-to-lateral stiffness ratios and relative uncoupled lateral stiffness of adjacent buildings. Results are also discussed for the case of using identical cross section for all columns supporting each plan. In contrast to symmetric systems, base shear increase in the stiffer building may be avoided when the buildings lateral stiffness ratio is less than 2. However, the eccentricity increases the rotation of the plans for high rotational stiffness of the buildings.

Monitoring of corrosive environment aggressiveness and development of a sensor for monitoring of the water film in exposure chamber

Abstract Exposure of copper in corrosive environment is possible way, how to obtain artificial patina. Various solutions based on chloride, ammonia or polysulfide are commonly use in this purpose. Furthermore, it appears that the patina is also formed in an environment with an increased concentration of SO2 in the atmosphere. This procedure was tested in a small (30 l) exposure chamber, where the aggressiveness of the environment was monitored and where the effect of alternating the condensation and drying phases was shown to be positive. Based on this experiment, a 2 m3 pilot chamber was designed for which a water film sensor was developed and tested to ensure drying of the object surface. Monitoring of the aggressiveness of the environment showed that the pH and SO2 concentrations in the atmosphere are stable after approximately 5 hours and the ideal input SO2 concentration is 17.7 g m-3 at which the pH stabilizes at 2.7-3. By recording the voltage variation on the sensor, it was possible to monitor the formation and drying of the water film during the cycling of the condensation and drying phases.

Learning vector quantization neural network for surface water extraction from Landsat OLI images

There is a growing concern over surface water dynamics due to an increased understanding of water availability and management with current climate trends. Remote sensing has now become an effective means of water extraction due to the availability of an enormous amount of data with diverse spatial, spectral, and temporal resolutions. However, water extraction from optical remote sensing data is associated with several major difficulties, such as the applicability of the extraction method over large areas and complex environments; shadow contamination from clouds, buildings, and mountains; and disclosure of shadowed water and exclusion of floating and submerged plants. To address these difficulties, a learning vector quantization (LVQ) neural network-based method was proposed and implemented to extract water using Landsat 8 imageries. This method is capable of separating water from clouds, build-up areas, shadows, and shadowed water by the ideal input of bands 1 to 7 and normalized difference vegetation index. This model learns water across Sri Lanka. Eight OLI scenes were tested, and the performance was compared with five widely used machine learning algorithms: support vector machine, K-nearest neighbor, discriminant analysis, combination of modified normalized difference water index and modified fuzzy clustering method, and K-means clustering methods. This method performed the best, achieving overall accuracies and the kappa coefficients between 97.8% and 99.7% and between 0.96 and 0.99, respectively. Results have demonstrated robustness, consistency, and preciseness in various dark surfaces, noisiest water environments, and highly water scarce scenes. LVQ revealed a good generalizing ability to detect all types of water with less amount of training samples. This method can be easily adaptable for other sensors and global water to support water resource studies.

A non-radial approach to improve the interval cost efficiency score in DEA

This paper suggests an approach for improving the cost efficiency score of inefficient decision making units (DMUs) with interval data. Corresponding to each inefficient DMU, we consider an interval ideal input vector and then develop two non-radial models to determine a point in the production possibility set that can produce the given interval output vector with the minimum possible cost and is closest point to the corresponding ideal point. By replacing the observed interval input vector with the interval input vector of the obtained point, one can improve the lower bound of the interval cost efficiency score as much as possible while the upper bound can achieve one. A numerical example also will be designed to demonstrate the applicability of the proposed approach.

Modelling and optimization of a pilot-scale entrained-flow gasifier using artificial neural networks

This paper explores the construction and validation of an artificial neural network (ANN) in order to accurately and efficiently predict the performance of a pilot-scale gasifier unit. This ANN model consists of multiple sub-networks that individually predict each of the desired gasifier outputs as a function of key system parameters. The ANN was trained using data generated for a large set of randomly-generated input conditions from a pilot-scale gasifier reduced order model (ROM) developed previously. The fully-trained ANN was validated by comparing its performance to the aforementioned ROM model. The validated ANN model was subsequently implemented into two optimization studies in order to determine the operating conditions necessary to maximize the carbon conversion under different limitations for the peak temperature of the gasifier and to determine the ideal input conditions of maximizing both the carbon conversion and production of hydrogen gas which are two conflicting objectives. This case study further showcases the benefit of the ANN, which was able to obtain accurate predictions for the gasifier results similar to the results generated by the ROM model at a much lower computational cost.

Advanced X-ray CT scanning can boost tree ring research for earth system sciences.

Tree rings, as archives of the past and biosensors of the present, offer unique opportunities to study influences of the fluctuating environment over decades to centuries. As such, tree-ring-based wood traits are capital input for global vegetation models. To contribute to earth system sciences, however, sufficient spatial coverage is required of detailed individual-based measurements, necessitating large amounts of data. X-ray computed tomography (CT) scanning is one of the few techniques that can deliver such data sets. Increment cores of four different temperate tree species were scanned with a state-of-the-art X-ray CT system at resolutions ranging from 60 μm down to 4.5 μm, with an additional scan at a resolution of 0.8 μm of a splinter-sized sample using a second X-ray CT system to highlight the potential of cell-level scanning. Calibration-free densitometry, based on full scanner simulation of a third X-ray CT system, is illustrated on increment cores of a tropical tree species. We show how multiscale scanning offers unprecedented potential for mapping tree rings and wood traits without sample manipulation and with limited operator intervention. Custom-designed sample holders enable simultaneous scanning of multiple increment cores at resolutions sufficient for tree ring analysis and densitometry as well as single core scanning enabling quantitative wood anatomy, thereby approaching the conventional thin section approach. Standardized X-ray CT volumes are, furthermore, ideal input imagery for automated pipelines with neural-based learning for tree ring detection and measurements of wood traits. Advanced X-ray CT scanning for high-throughput processing of increment cores is within reach, generating pith-to-bark ring width series, density profiles and wood trait data. This would allow contribution to large-scale monitoring and modelling efforts with sufficient global coverage.

Adaptive Neural Event-Triggered Control for Discrete-Time Strict-Feedback Nonlinear Systems.

This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which relies on system states only and is unrelated to virtual control laws. In this case, the high-order neural networks (NNs) are used to approximate the ideal control input (but not the virtual control laws), and then the corresponding adaptive neural controller is developed under the ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, is designed to guarantee the uniformly ultimate boundedness (UUB) of NN weight estimates for all sampling times. In virtue of the bounded NN weight estimates and a dead-zone operator, the ET condition together with an adaptive ET threshold coefficient is constructed to guarantee the UUB of the closed-loop networked control system through the Lyapunov stability theory, thereby largely easing the network communication load. The proposed ET condition is easy to implement because of the avoidance of: 1) the use of the intermediate ET conditions in the backstepping procedure; 2) the computation of virtual control laws; and 3) the redundant triggering of events when the system states converge to a desired region. The validity of the presented scheme is demonstrated by simulation results.

Abstract 2299: Development of a novel next-generation sequencing (NGS)-based assay for measurable residual disease (MRD) in FLT3-ITD acute myeloid leukemia (AML) and its potential clinical application in patients

Abstract Introduction The presence of MRD in patients with AML who are in morphologic remission has been shown to be a powerful predictor of eventual relapse. FMS-like tyrosine kinase 3 (FLT3) internal tandem duplications (ITD) confer a negative prognostic impact by increasing risk of relapse. The ability to detect FLT3-ITD mutations in remission bone marrow specimens is hampered by the limited sensitivity at 1% of PCR-based assays. To address such limitations, we developed a novel NGS-based MRD assay for the detection of FLT3-ITD mutations. Method Genomic DNA was isolated from bone marrow (BM) aspirates or peripheral blood (PB) samples. PCR was performed to amplify exons 13 to 15 of the FLT3 gene. Highly diverse NGS libraries were then generated and sequenced using Illumina’s sequencer. Using a custom bioinformatics approach, unique FLT3-ITD mutations of varying lengths were identified and mutant allelic frequency calculated. The assay was validated using clinical samples and contrived samples. For accuracy assessment, 30 samples (including PB or BM remnant patient DNA and cell line DNA, above DNA diluted in normal DNA) were included. Data were compared with a Fragment Size Analysis by Capillary Electrophoresis assay. To assess precision, the assay was validated at multiple levels evaluating intra-assay, inter-assay, inter-operator, inter-instrument and inter-reagent lot precision. DNA samples from selected mutant cell lines representing different FLT3-ITD lengths were spiked into normal DNA to evaluate assay sensitivity and linearity. Summary The ideal DNA input range was established as 300 ng to 500 ng. In all FLT3-ITD-positive cell line samples covering diverse FLT3-ITD lengths (6 bp to 156 bp), the FLT3-ITD MRD NGS assay showed 100% concordance with the reference assay. The assay is also capable of detecting ITDs as expected after the samples were diluted in normal DNA to mimic samples from patients in remission. All acceptance criteria for the different precision parameters were met. The lower limit of detection of 0.013% regardless of ITD length was established but the assay was capable of detecting FLT3-ITD mutations at a level as low as 0.003% without false-positive results. The assay was linear (R2 = 0.958) down to FLT3-ITD allele frequency levels of 0.035% or the lower limit of quantitation. Conclusion Analytical validation results established the role of this NGS-based MRD assay for the clinical management of FLT3-ITD AML. The FLT3-ITD MRD NGS assay demonstrated high sensitivity and high specificity by detecting the unique length of each patient’s mutation with no false-positive results in expected negative samples. This assay might be helpful in defining the depth of remission, identifying persistent disease, and helping to guide decision making in the use of FLT3 inhibitors as continuation therapy. Citation Format: Wenge Shi, Christian Laing, Wei Ding, Marc Mycoco, Jelveh Lameh, Reinhold Pollner. Development of a novel next-generation sequencing (NGS)-based assay for measurable residual disease (MRD) in FLT3-ITD acute myeloid leukemia (AML) and its potential clinical application in patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2299.

Achieving yield confidence in a flow shop with single rework station via capacity expansion and process improvement

ABSTRACTThis paper provides an improvement strategy for a flow shop with single rework station by modeling the flow shop as a capacitated manufacturing network (CMN) to achieve the required yield confidence utilizing a confidence-based analysis. We define the yield confidence as a performance indicator to evaluate the probability that a specified level of demand will be satisfied. Specifically, this paper considers capacity expansion and process improvement issues. First, a model is proposed to represent the CMN and to characterize the probability that a unit of material is successfully transformed into a final product. This model further identifies the required amount of (ideal) input to satisfy the customer demand and to achieve the desired yield confidence. In particular, this ideal input can be seen as the least wasteful input for the CMN. Capacity expansion and process improvement are considered when the current capacity of the CMN is insufficient to process the ideal input. The approach identifies the target output ratios of stations needed to achieve a process improvement goal. Two benchmark examples are provided to demonstrate the model building, capacity expansion, and process improvement steps.

Voltage-Driven Near Field Beam Shifting in an InP Photonic Integrated Circuit

A voltage-driven, continuously-tuned beam shifting device is proposed, simulated, and demonstrated in an InP photonic integrated circuit. Electrodes are applied on the left and right side of a mode-expanded waveguide to electronically control the refractive index profile and shift the optical beam by means of reverse bias voltage. The feasibility of a beam shifting of 4.9 μm with a power consumption of the order of 4.5 pW and voltage of 10 V is numerically predicted for a beam shifting element with ideal electrical isolation and fundamental input mode excitation. The reduction in beam-shifting efficiency is subsequently quantified for imperfect electrical isolation and in the presence of higher order mode excitation at the input to the beam shifting element. An experimental proof of concept is implemented by realizing the device on a generic photonic integration platform and co-integrating an on-chip tunable laser. High-resolution near-field measurements show near-field beam shifting of 1 μm with an applied voltage range from -4 to 0 V.

Experimental study of a high-tolerance piston-cylinder pair in the alpha Ross-yoke Stirling refrigerator

Optimal design of the sealing mechanism in the piston-cylinder pair of Stirling refrigerators is beneficial in terms of system’s cost, reliability, efficiency and cooling capacity; yet this is often overlooked in the literature. We report on our experimental study of a high-tolerance lubricated piston-cylinder pair with cast iron-steel materials as an integral part of the Ross-yoke alpha-type Stirling refrigerator. We measured: the gas leakage rate through the sealing gap; the friction force in the piston-cylinder pair; and the refrigerator temperature during operation. Our results show that owing to careful design, the loss of the ideal cooling capacity due to gas leakages amounted to 2.2%, and that the friction loss to the ideal cycle power input was 8.1%. We also discuss which type of lubricants demonstrates a higher resistance to operational loading and what engineering problems must be solved for reliable operation of the sealing mechanism.

Complementary Integrated Circuits Based on n-Type and p-Type Oxide Semiconductors for Applications Beyond Flat-Panel Displays

Oxide semiconductors are highly attractive for fabrication of large-area thin-film electronics because of their high electrical performance, low process temperature, high uniformity, and ease of industrial manufacturing. n-type oxide semiconductors, such as InGaZnO, are highly developed and have already been commercialized for backplane drivers of flat-panel displays. To date, developing CMOS technology is still an urgent issue in order to build low-power electronic circuits based on oxide semiconductors. In this paper, various CMOS circuits, including inverters, NAND, NOR, XOR, d-latches, full adders, and 7-, 11-, 21-, and 51-stage ring oscillators (ROs), are fabricated based on sputtered p-type tin monoxide and n-type InGaZnO. The inverters show rail-to-rail output voltage behavior, low average static power consumption of 8.84 nW, high noise margin level up to ~40% supply voltage, high yield of 98%, and high uniformity with negligible standard deviation. The NAND, NOR, XOR, d-latches, and full adders show desirably ideal input–output characteristics. The performances of ROs indicate small stage delay of $\sim 1~\mu \text{s}$ , extremely high uniformity and high yieldwhich are essential for large-area thin-film electronics. This paper may inspire constructions of low power, large area, large scale, and high-performance transparent/flexible CMOS circuits fully based on oxide semiconductors for applications beyond flat-panel displays.

Exploring PID tuning strategies considering noise impact in the IPDT plant control

This paper deals with one of the aspects influencing exploitation of the new opportunities provided by the development of the automated control technology base and resulting from the ability to work with relatively short sampling periods. It explores features of two integrated tuning procedures (ITP) for design of filtered series PID controllers of integral-plus-dead-time (IPDT) plants. Under ITP it means a joint design of both the PID controller and the nth order binomial low-pass filter parameters. Since the multiple real dominant pole method is able to yield closed loop performance scalable in a broad range, for an effective tuning of two filer parameters it is necessary to propose some rules helping in exploiting created options. For this purpose, shape related requirements put on the ideal input and output step responses are used. These performance measures are based on concept of piecewise monotonic signals. They represent additional constraints respected in designing as fast as possible transients. Deviations from the ideal shapes due to the noise and plant-model mismatch are quantified in terms of a modified total variation and used in different ways in the proposed ITPs. Possible tuning scenarios and superiority of the proposed approaches in comparison to the traditional PID controller design are demonstrated by simulation. Importance of the presented design for applications in different embedded controllers and programmable devices is stressed.

Improved Reference Generation of Active and Reactive Power for Matrix Converter With Model Predictive Control Under Input Disturbances

The existing model predictive control schemes for the matrix converter perform well under ideal input conditions, but are not optimal when source voltages are unbalanced and distorted. The analysis shows that, under input disturbances, the input filter capacitors cause active power fluctuation and further output power quality degradation, which has been ignored in literature. Therefore, a method is proposed to extract the fluctuation with a digital filter and compensate it in the reference generation of source active power, so as to eliminate the output low-frequency harmonics. In the case of input voltage unbalance, a method is proposed to generate the reference source reactive power which could achieve sinusoidal source currents, without the need of separating sequence components. Moreover, effects of source voltage distortions, which degrade the input power quality significantly and are ignored in literature, are suppressed with the proposed generation method of reference reactive power. The extensive experimental results in nine cases have verified the satisfactory steady-state and dynamic performance of the proposed methods.

Evaluation of the effect of the thermal cycle on the characteristics of welded joints through the variation of the heat input of the austhenitic AISI 316L steels by the GMAW process

Welding processes are generally used in almost every industrial segment, but there are some applications that demand special attention to the product obtained, due to the security it must offer against possible failures.In the present study, the influence of the heat input variation on the weld bead geometry, the austenitic grain size in the HAZ, the microhardness in the HAZ (heat affect zone), and the δ ferrite volumetric fraction using the gas metal arc welding (GMAW) performed with pulsed chain spray transfer, on AISI316L steel plates. The samples had their microstructure characterized by optical microscopy, in their different regions, performing a comparative study of these regions for the different welding conditions. From the variations in the thermal contributions it was demonstrated that the choice of the ideal heat input for a given process has an important effect on the phenomena that occur during welding. The increase of the contribution caused a variation of almost 300% in the austenitic grain size, approximately 100% in the δ ferrite volumetric fraction, and about 240% in the area of the fusion zone (FZ). The temperature distributions of the plates were recorded using a thermal camera in order to monitor the maximum temperatures in each region of the welded joint, the microstructures resulting from the process were later compared with the results of the thermal cycles obtained through numerical simulations. The results showed good agreement and also contributed to the validation of the proposed numerical method, which allowed a better understanding of the thermal history during welding and, consequently, of their effects on the microstructural transformations developed in the AISI 316L steel.