scholarly journals Genetic Algorithms vs. Knowledge-Based Control of PHB Production

2019 ◽  
Vol 19 (2) ◽  
pp. 104-116
Author(s):  
Marius Olteanu ◽  
Nicolae Paraschiv ◽  
Petia Koprinkova-Hristova
Keyword(s):  
Fuzzy Logic Controller ◽  
Control Variable ◽  
Nonlinear Process ◽  
Good Choice ◽  
Optimal Time ◽  
Control Task ◽  
Time Profiles ◽  
Knowledge Based ◽  
Highly Nonlinear ◽  
Phb Production

Abstract The paper proposes an approach using Genetic Algorithm (GA) for development of optimal time profiles of key control variable of Poly-HydroxyButyrate (PHB) production process. Previous work on modeling and simulation of PHB process showed that it is a highly nonlinear process that needs special controllers based on human experience, as such fuzzy logic controller proved to be a good choice. Fuzzy controllers are not totally replaced, due to the specific process knowledge that they contain. The achieved results are compared with previously proposed knowledge-based approach to the same optimal control task.

scholarly journals Design and Implementation of Fuzzy Approximation PI Controller for Automatic Cruise Control System

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Pallab Maji ◽  
Sarat Kumar Patra ◽  
Kamalakanta Mahapatra
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Controller ◽  
Large Change ◽  
Nonlinear Process ◽  
Cruise Control ◽  
Fuzzy Logic Systems ◽  
Heuristic Knowledge ◽  
Cruise Control System ◽  
Highly Nonlinear

Fuzzy logic systems have been widely used for controlling nonlinear and complex dynamic systems by programming heuristic knowledge. But these systems are computationally complex and resource intensive. This paper presents a technique of development and porting of a fuzzy logic approximation PID controller (FLAC) in an automatic cruise control (ACC) system. ACC is a highly nonlinear process and its control is trivial due to the large change in parameters. Therefore, a suitable controller based on heuristic knowledge will be easy to develop and provide an effective solution. But the major problem with employing fuzzy logic controller (FLC) is its complexity. Moreover, the designing of Rulebase requires efficient heuristic knowledge about the system which is rarely found. Therefore, in this paper, a novel rule extraction process is used to derive a FLAC. This controller is then ported on a C6748 DSP hardware with timing and memory optimization. Later, it is seamlessly connected to a network to support remote reconfigurability. A performance analysis is drawn based on processor-in loop test with Simulink model of a cruise control system for vehicle.

Analog Circuit Failure Analysis Using Time-Resolved Emission

2005 ◽  
Author(s):  
B.J. Cain ◽  
G.L. Woods ◽  
A. Syed ◽  
R. Herlein ◽  
Toshihiro Nomura
Keyword(s):  
Analog Circuits ◽  
Integrated Circuit ◽  
Analog Circuit ◽  
Photon Emission ◽  
Nonlinear Process ◽  
Time Resolved ◽  
Non Invasive ◽  
Highly Nonlinear ◽  
Excellent Spatial Resolution ◽  
5 Ghz

Abstract Time-Resolved Emission (TRE) is a popular technique for non-invasive acquisition of time-domain waveforms from active nodes through the backside of an integrated circuit. [1] State-of-the art TRE systems offer high bandwidths (> 5 GHz), excellent spatial resolution (0.25um), and complete visibility of all nodes on the chip. TRE waveforms are typically used for detecting incorrect signal levels, race conditions, and/or timing faults with resolution of a few ps. However, extracting the exact voltage behavior from a TRE waveform is usually difficult because dynamic photon emission is a highly nonlinear process. This has limited the perceived utility of TRE in diagnosing analog circuits. In this paper, we demonstrate extraction of voltage waveforms in passing and failing conditions from a small-swing, differential logic circuit. The voltage waveforms obtained were crucial in corroborating a theory for some failures inside an 0.18um ASIC.

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

Subpixel determination of wormhole tip position in 4D tomography of dissolving limestone cores

2021 ◽  
Author(s):  
Rishabh Prakash Sharma ◽  
Max P. Cooper ◽  
Anthony J.C. Ladd ◽  
Piotr Szymczak
Keyword(s):  
Flow Field ◽  
Pore Space ◽  
Nonlinear Process ◽  
Linear Interpolation ◽  
Flow Fields ◽  
Porous Rocks ◽  
Dissolution Experiment ◽  
Novel Approach ◽  
Highly Nonlinear ◽  
Computed Microtomography

<p>Dissolution of porous rocks by reactive fluids is a highly nonlinear process resulting in a variety of dissolution patterns, the character of which depends on physical conditions such as flow rate and reactivity of the fluid. Long, finger-like dissolution channels, “wormholes”, are the main subject of interest in the literature, however, the underlying dynamics of their growth remains unclear. </p><p>While analyzing the tomography data on wormhole growth.  one open question is to define the exact position of the tip of the wormhole. Near the tip the wormhole gradually thins out and the proper resolution of its features is hindered by the finite spatial resolution of the tomographs. In particular, we often observe in the near-tip region several disconnected regions of porosity growth, which - as we hypothesized - are connected by the dissolution channels at subpixel scale. In this study, we show how these features can be better resolved by using numerically calculated flow fields in the reconstructed pore-space. </p><p>We used 70 micrometers, 16-bit grayscale X-ray computed microtomography (XCMT) time series scans of limestone cores, 14mm in diameter and 25mm in length. Scans were performed during the entire dissolution experiment with an interval of 8 minutes. These scans were further processed using a 3-phase segmentation proposed by Luquot et al.[1], in which grayscale voxels are converted to macro-porosity, micro-porosity and grain phases from their grayscale values. The macro-porous phase is assigned a porosity of 1, while the grain phase is assigned 0. Micro-porous regions are assigned an intermediate value determined by linear interpolation between pore and grain threshold using grayscale values. An OpenFOAM based, Darcy-Brinkman solver, porousFoam, is then used to calculate the flow field in this extracted porosity field. </p><p>Porosity contours reconstructed from the tomographs show some disconnected porosity growth near the tip region which later become part of the wormhole in subsequent scans. We have used a novel approach by including the micro-porosity phase in pore-space to calculate the flow-fields in the near-tip region. The calculated flow fields clearly show an extended region of focused flow in front of the wormhole tip, which is a manifestation of the presence of a wormhole at the subpixel scale. These results show that micro-porosity plays an important role in dissolution and 3-phase segmentation combined with the flow field calculations is able to capture the sub-resolved dissolution channels. </p><p> </p><p> [1] Luquot, L., Rodriguez, O., and Gouze, P.: Experimental characterization of porosity structure and transport property changes in limestone undergoing different dissolution regimes, Transport Porous Med., 101, 507–532, 2014</p>

Dissection of the neuron network in the catfish inner retina. III. Interpretation of spike kernels

1989 ◽  
Vol 61 (6) ◽  
pp. 1110-1120 ◽  
Author(s):  
M. J. Korenberg ◽  
H. M. Sakai ◽  
K. Naka
Keyword(s):  
Ganglion Cell ◽  
Nonlinear Dynamic ◽  
Ganglion Cells ◽  
Light Stimulus ◽  
Nonlinear Process ◽  
Neuron Network ◽  
Postsynaptic Potential ◽  
Highly Nonlinear ◽  
Neural Information ◽  
Cascade Structure

1. Three independent sets of evidence have been obtained to show that similar first- and second-order light-evoked kernels are computed for the ganglion cell when the system output is taken to be either the spike train (discrete signal) or the postsynaptic potential (analog signal). In this paper we show that the similarity of postsynaptic potential (PSP) kernels and spike kernels is readily explained by assuming an underlying cascade structure for the neural information processing. The cascade structure enables spike kernels to be mathematically related very simply to the process of generating the postsynaptic potentials of ganglion cells. 2. Mathematical analysis of the cascade structure also suggests why spike kernels appear to differ slightly from PSP kernels. The relation between the two sets of kernels predicted from our analysis is substantiated here by experiment and reveals an interconnection between several of the signals measured. 3. Our experimental results, in particular, suggest that the neuronal circuitry leading from the light stimulus to the generation of ganglion cell spike discharges can be represented as follows: either a Wiener (LN) or a dynamic linear-static nonlinear-dynamic linear (LNL) structure is followed by a highly nonlinear process [static or brief-memory Hammerstein static-nonlinear dynamic-linear (NL) structure] of spike generation. Cross-correlation between the analog input and spike output enables identification of these structures.

scholarly journals Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Seng-Chi Chen ◽  
Van-Sum Nguyen ◽  
Dinh-Kha Le ◽  
Nguyen Thi Hoai Nam
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Logic Controller ◽  
Magnetic Bearing ◽  
Operating Conditions ◽  
Active Magnetic Bearing ◽  
Highly Nonlinear ◽  
Radial Basis ◽  
Amb System

Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

Prediction of Extreme Wave Slamming Loads on a Fixed Platform

2018 ◽  
Author(s):  
Grzegorz P. Filip ◽  
Wenzhe Xu ◽  
Kevin J. Maki
Keyword(s):  
Wave Structure ◽  
Nonlinear Process ◽  
Extreme Value ◽  
Wave Loads ◽  
Oil Platforms ◽  
Offshore Oil Platforms ◽  
Highly Nonlinear ◽  
Extreme Load ◽  
Wave Slamming ◽  
Extreme Responses

Design of offshore oil platforms requires accurate prediction of the maximum wave loads due to slamming on horizontal decks. The physical processes that influence the load are the propagation of irregular short-crested wind-driven storm seas, wave breaking, and wave-structure interaction. Furthermore, the ocean is a stochastic environment, so the load and its maximum can be considered as random variables. Ideally, the designer would like to know not only the most probable extreme load, but also the extreme load distribution. In this paper we will use a novel technique to prescribe wave environments that lead to extreme responses so that high-fidelity simulations of the highly-nonlinear process can be investigated in detail. Specifically, the dynamics of the relative motion of the sea surface and the platform will be assumed via the selection of a sea spectrum, and the extreme-value probability distribution function (PDF) will be calculated for a given exposure window. The novel aspect of the work is in the way that a set of deterministic sea environments will be generated that are amenable for simulation with a state-of-the-art computational-fluid dynamics (CFD) software. The resulting wave environments will be simulated to estimate the extreme-value PDF.

H-EQPE Model and L-Checkpoint Algorithm

2015 ◽  
Vol 7 (4) ◽  
pp. 20-35 ◽  
Author(s):  
Chun-Kit Ngan ◽  
Lin Li
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Parametric Estimation ◽  
Optimal Decision ◽  
Optimal Time ◽  
Diabetes Research ◽  
Knowledge Based ◽  
Optimal Monitoring ◽  
Optimal Time Point ◽  
Time Point

The authors propose a Hypoglycemic Expert Query Parametric Estimation (H-EQPE) model and a Linear Checkpoint (L-Checkpoint) algorithm to detect hypoglycemia of diabetes patients. The proposed approach combines the strengths of both domain-knowledge-based and machine-learning-based approaches to learn the optimal decision parameter over time series for monitoring the symptoms, in which the objective function (i.e., the maximal number of detections of hypoglycemia) is dependent on the optimal time point from which the parameter is learned. To evaluate the approach, the authors conducted an experiment on a dataset from the Diabetes Research in Children Network group. The L-Checkpoint algorithm learned the optimal monitoring decision parameter, 99 mg/dL, and achieved the maximal number of detections of hypoglycemic symptoms. The experiment shows that the proposed approach produces the results that are superior to those of the domain-knowledge-based and the machine-learning-based approaches, resulting in a 99.2% accuracy, 100% sensitivity, and 98.8% specificity.

scholarly journals Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model

2017 ◽  
Vol 12 (4) ◽  
Author(s):  
Wan Sieng Yeo ◽  
Agus Saptoro ◽  
Perumal Kumar
Keyword(s):  
Predictive Performance ◽  
Nonlinear Process ◽  
Partial Least Square ◽  
Least Square ◽  
Soft Sensors ◽  
Highly Nonlinear ◽  
Nonlinear Features ◽  
Weighted Kernel ◽  
Input Variables ◽  
Using Data

AbstractLocally weighted partial least square (LW-PLS) model has been commonly used to develop adaptive soft sensors and process monitoring for numerous industries which include pharmaceutical, petrochemical, semiconductor, wastewater treatment system and biochemical. The advantages of LW-PLS model are its ability to deal with a large number of input variables, collinearity among the variables and outliers. Nevertheless, since most industrial processes are highly nonlinear, a traditional LW-PLS which is based on a linear model becomes incapable of handling nonlinear processes. Hence, an improved LW-PLS model is required to enhance the adaptive soft sensors in dealing with data nonlinearity. In this work, Kernel function which has nonlinear features was incorporated into LW-PLS model and this proposed model is named locally weighted kernel partial least square (LW-KPLS). Comparisons between LW-PLS and LW-KPLS models in terms of predictive performance and their computational loads were carried out by evaluating both models using data generated from a simulated plant. From the results, it is apparent that in terms of predictive performance LW-KPLS is superior compared to LW-PLS. However, it is found that computational load of LW-KPLS is higher than LW-PLS. After adapting ensemble method with LW-KPLS, computational loads of both models were found to be comparable. These indicate that LW-KPLS performs better than LW-PLS in nonlinear process applications. In addition, evaluation on localization parameter in both LW-PLS and LW-KPLS is also carried out.

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