Academic literature on the topic 'Complex non-linear least-square (CNLS)'
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Journal articles on the topic "Complex non-linear least-square (CNLS)"
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Tessier, Tanner M., Katelyn M. MacNeil, and Joe S. Mymryk. "Piggybacking on Classical Import and Other Non-Classical Mechanisms of Nuclear Import Appear Highly Prevalent within the Human Proteome." Biology 9, no. 8 (July 23, 2020): 188. http://dx.doi.org/10.3390/biology9080188.
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One of the most conserved cellular pathways among eukaryotes is the extensively studied classical protein nuclear import pathway mediated by importin-α. Classical nuclear localization signals (cNLSs) are recognized by importin-α and are highly predictable due to their abundance of basic amino acids. However, various studies in model organisms have repeatedly demonstrated that only a fraction of nuclear proteins contain identifiable cNLSs, including those that directly interact with importin-α. Using data from the Human Protein Atlas and the Human Reference Interactome, and proteomic data from BioID/protein-proximity labeling studies using multiple human importin-α proteins, we determine that nearly 50% of the human nuclear proteome does not have a predictable cNLS. Surprisingly, between 25% and 50% of previously identified human importin-α cargoes do not have predictable cNLS. Analysis of importin-α cargo without a cNLS identified an alternative basic rich motif that does not resemble a cNLS. Furthermore, several previously suspected piggybacking proteins were identified, such as those belonging to the RNA polymerase II and transcription factor II D complexes. Additionally, many components of the mediator complex interact with at least one importin-α, yet do not have a predictable cNLS, suggesting that many of the subunits may enter the nucleus through an importin-α-dependent piggybacking mechanism.
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Journal, Baghdad Science. "Theoretical Studies of Sum Optical Properties for InAs (001) by Surface Differential Reflectivity." Baghdad Science Journal 4, no. 2 (June 3, 2007): 255–59. http://dx.doi.org/10.21123/bsj.4.2.255-259.
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The real and imaginary part of complex dielectric constant for InAs(001) by adsorption of oxsagen atoms has been calculated, using numerical analysis method (non-linear least square fitting). As a result a mathematical model built-up and the final result show a fairly good agreement with other genuine published works.
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Sun, Zhenzhou, Hongchao Lu, Jiefeng Chen, and Jialong Jiao. "An Efficient Noise Elimination Method for Non-stationary and Non-linear Signals by Averaging Decomposed Components." Shock and Vibration 2022 (May 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/2068218.
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In this paper, a moving-average method of smoothing noise based on complex exponential decomposition is applied to eliminate noise of a non-stationary signal and a non-linear signal produced by Bouc–Wen model, which are added to white Gaussian noise to simulate the noise in measured signal. The method uses a sliding window cutting the entire non-stationary and/or non-linear signal into small segments and considers that the small segments are stable and linear. The segments are decomposed into a series of components via complex exponential decomposition, and the high-energy components are reserved to reconstruct de-noised signal. Then, due to the overlap of the reconstructed segments, the average value at the same time point of reconstruction signal is regarded as the de-noised data. A non-stationary signal and a non-linear signal are selected to investigate the performance of the proposed method, the results show that the proposed method has better de-noising efficiency compared with the wavelet shrinkage method and the Savitzky–Golay filter method based on EMD (EMD-SG) for dealing with the signals with SNR of 10 dB, 15 dB, and 20 dB, and de-noised signal using the proposed method has the highest signal-to-noise ratio (SNR) and the least root mean square error (RMSE).
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Wang, Bo, Muhammad Shahzad, Xianglin Zhu, Khalil Ur Rehman, and Saad Uddin. "A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation." Sensors 20, no. 11 (June 11, 2020): 3335. http://dx.doi.org/10.3390/s20113335.
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l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used as a process model in the non-linear model predictive control to predict product concentration. GWO is also used to solve the non-convex optimization problem in non-linear model predictive control (GWO-NMPC) for calculating optimal future inputs. The proposed GWO-based prediction model (GWO-LSSVM) and non-linear model predictive control (GWO-NMPC) are compared with the Particle Swarm Optimization (PSO)-based prediction model (PSO-LSSVM) and non-linear model predictive control (PSO-NMPC) to validate their effectiveness. The comparative results show that the prediction accuracy, adaptability, real-time tracking ability, overall error and control precision of GWO-based predictive control is better compared to PSO-based predictive control.
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Vieira, Daniela, Jérôme Allard, Kathleen Taylor, Edward J. Harvey, and Geraldine Merle. "Zincon-Modified CNTs Electrochemical Tool for Salivary and Urinary Zinc Detection." Nanomaterials 12, no. 24 (December 13, 2022): 4431. http://dx.doi.org/10.3390/nano12244431.
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Recently, the abnormal level of zinc emerged as a powerful indicator or risk factor for metabolic, endocrine, neurodegenerative and cardiovascular diseases, including cancer. Electrochemical detection has been explored to quantify zinc in a precise, rapid, and non-expensive way; however, most of the current electrochemical systems lack in specificity. In this work we studied a highly selective and sensitive electrochemical method to detect quickly and reliably free zinc ions (Zn2+). The surface of the working electrode was modified with zincon electropolymerized on carbon nanotube (CNT) to enable the binding of zinc in complex body fluids. After being physicochemically characterized, the performances of the zincon-CNT complex was electrochemically assessed. Square Wave Voltammetry (SWV) was used to determine the calibration curve and the linear range of zinc quantification in artificial saliva and urine. This zincon- CNT system could specifically quantify mobile Zn2+ in salivary and urinary matrices with a sensitivity of ~100 ng·mL−1 and a limit of detection (LOD) of ~20 ng·mL−1. Zincon-modified CNT presented as a desirable candidate for the detection and quantification of free zinc in easily body fluids that potentially can become a diagnostic non-invasive testing platform.
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Haseeb, Abdul, Umar Waleed, Muhammad Mansoor Ashraf, Faisal Siddiq, Muhammad Rafiq, and Muhammad Shafique. "Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads." Energies 16, no. 2 (January 4, 2023): 609. http://dx.doi.org/10.3390/en16020609.
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The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique’s results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time.
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Y, Rama Krishna, Subbaiah P.V, and Prabhakara Rao B. "A Novel Approach for Hybrid of Adaptive Amplitude Non-Linear Gradient Decent (AANGD) and Complex Least Mean Square (CLMS) Algorithms for Smart Antennas." International Journal of Wireless & Mobile Networks 5, no. 1 (February 28, 2013): 119–26. http://dx.doi.org/10.5121/ijwmn.2013.5110.
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Ronquillo-Lomeli, Guillermo, Gilberto Herrera-Ruiz, José Ríos-Moreno, Irving Ramirez-Maya, and Mario Trejo-Perea. "Total Suspended Particle Emissions Modelling in an Industrial Boiler." Energies 11, no. 11 (November 9, 2018): 3097. http://dx.doi.org/10.3390/en11113097.
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Particulate matter emission into the atmosphere is a massive-scale problem. Fossil fuel combustion is an important source of this kind of pollution. The knowledge of total suspended particle (TSP) emissions is the first step for TSP control. The formation of TSP emissions is poorly understood; therefore new approaches for TSP emissions source modelling are required. TSP modelling is a multi-variable non-linear problem that would only require basic information on boiler operation. This work reports the development of a non-linear model for TSP emissions estimation from an industrial boiler based on a one-layer neural network. Expansion polynomial basic functions combined with an orthogonal least-square and model structure selection approach were used for modelling. The model required five independent boiler variables for TSP emissions estimation. Data from the data acquisition system of a 350 MW industrial boiler were used for model development and validation. The results show that polynomial expansion basic functions are an excellent approach to solve modelling problems related to complex non-linear systems in the industry.
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Kumar, Prashant, Sarvesh Sonkar, Ajoy Kanti Ghosh, and Deepu Philip. "Sensor Based System Identification in Real Time for Noise Covariance Deficient Models." Defence Science Journal 72, no. 5 (November 1, 2022): 665–78. http://dx.doi.org/10.14429/dsj.72.17663.
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System identification methods have extensive application in the aerospace industry’s experimental stability and control studies. Accurate aerodynamic modeling and system identification are necessary because they enable performance evaluation, flight simulation, control system design, fault detection, and model aircraft’s complex non-linear behavior. Various estimation methods yield different levels of accuracies with varying complexity and computational time requirements. The primary motivation of such studies is the accurate quantification of process noise. This research evaluates the performance of two recursive parameter estimation methods, viz.; First is the Fourier Transform Regression (FTR). The second approach describes the Extended version of Recursive Least Square (EFRLS), where E.F. refers to the Extended Forgetting factor. Also, the computational viability of these methods was analyzed for real-time application in aerodynamic parameter estimation for both linear and non-linear systems. While the first method utilizes the frequency domain to evaluate aerodynamic parameters, the second method works when noise covariances are unknown. The performance of both methods was assessed by benchmarking against parameter estimates from established methods like Extended Kalman Filter (EKF), Unscented Kalman Filter (UNKF), and Output Error Method (OEM).
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Dichter, Martin Nikolaus, Diana Trutschel, Christian Günter Georg Schwab, Burkhard Haastert, Tina Quasdorf, and Margareta Halek. "Dementia care mapping in nursing homes: effects on caregiver attitudes, job satisfaction, and burnout. A quasi-experimental trial." International Psychogeriatrics 29, no. 12 (August 30, 2017): 1993–2006. http://dx.doi.org/10.1017/s104161021700148x.
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ABSTRACTBackground:The Dementia Care Mapping (DCM) method is an internationally recognized complex intervention in dementia research and care for implementing person-centered care. The Leben-QD II trial aimed to evaluate the effectiveness of DCM with regard to caregivers.Methods:The nine participating nursing home units were allocated to three groups: (1) DCM method experienced ≥ 1 year, (2) DCM newly introduced during this trial, and (3) regular rating of residents’ quality of life (control group). Linear mixed models were fit to cluster-aggregated data after 0, 6, and 18 months, adjusting for repeated measurements and confounders. The primary outcome was the Approaches to Dementia Questionnaire (ADQ) score; the secondary outcomes were the Copenhagen Psychosocial Questionnaire (COPSOQ) and the Copenhagen Burnout Inventory (CBI).Results:The analysis included 201 caregivers with 290 completed questionnaires (all three data collection time points). The ADQ showed a significant time and time*intervention effect. At baseline, the estimated least-square means for the ADQ were 71.98 (group A), 72.46 (group B), and 71.15 (group C). The non-linear follow-up of group A indicated an estimated-least square means of 69.71 (T1) and 68.97 (T2); for group B, 72.80 (T1) and 72.29 (T2); and for group C, 66.43 (T1) and 70.62 (T2).Conclusions:The DCM method showed a tendency toward negatively affecting the primary and secondary outcomes; this finding could be explained by the substantial deviation in adherence to the intervention protocol.
Book chapters on the topic "Complex non-linear least-square (CNLS)"
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Chung, William, and Iris M. H. Yeung. "Convex Nonparametric Least Squares for Predictive Maintenance." In Encyclopedia of Data Science and Machine Learning, 2636–52. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch158.
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A nonparametric regression method, convex nonparametric least square (CNLS), as a machine learning approach is introduced and discussed to the application of predictive maintenance. With the industrial internet of things sensors, predictive maintenance (PdM) becomes viable to identify maintenance issues in real time by predicting the next error of the system. Machine learning (ML) methods, such as support vector machine (SVM), are popular to develop the prediction model for PdM. On the other hand, regression-based methods are considered inappropriate due to their pre-defined function forms and the non-linear nature of the PdM models. The convex nonparametric least squares (CNLS) method can overcome the above shortcomings of the regression-based methods. One of the attractive properties of the CNLS method is its regression-based analysis without pre-defined non-linear function forms. In addition, the CNLS method does not need to find the appropriate Kernel function like the SVM. Hence, the CNLS method is introduced for developing a predicting model for PdM.
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P. James, Steven, and Dena Bondugji. "Gamma-Aminobutyric Acid (GABA) and the Endocannabinoids: Understanding the Risks and Opportunities." In Gamma-Aminobutyric Acid - Neuropsychiatric and Therapeutic Implications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99242.
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The Gamma-aminobutyric acid (GABA) system is the main inhibitory neurotransmitter system in the central nervous system (CNS) of vertebrates and is involved in critical cellular communication and brain function. The endocannabioid system (ECS) was only recenty discovered and quickly recognized to be abundantly expressed in GABA-rich areas of the brain. The strong relationship between the GABA system and ECS is supported both by studies of the neuraoanatomy of mammalian nervous systems and the chemical messaging between neurons. The ECS is currently known to consist of two endocannabinoids, Anandamide (AEA) and 2-Arachidonyl Glycerol (2-AG), that function as chemical messengers between neurons, at least two cannabinoid receptors (CB1 and CB2), and complex synthetic and degradative metabolic systems. The ECS differs from the GABA system and other neurotransmitter systems in multiple ways including retrograde communication from the activated post-synaptic neuron to the presynaptic cell. Together, this molecular conversation between the ECS and GABA systems regulate the homeostasis and the chemical messaging essential for higher cortical functions such as learning and memory and may play a role in several human pathologies. Phytocannabinoids are synthesized in the plant Cannabis sativa (C. sativa). Within the family of phytocannabinoids at least 100 different cannabinoid molecules or derivatives have been identified and share the properties of binding to the endogenous cannabinoid receptors CB1 and CB2. The well-known psychoactive phytocannabinoid Δ9-tetrahydrocannabinol (THC) and the non-psychoactive cannabidiol (CBD) are just two of the many substances synthesized within C. sativa that act on the body. Although the phytocannabinoids THC and CBD bind to these endogenous receptors in the mammalian CNS, these plant derived molecules have little in common with the endocannabinoids in structure, distribution and metabolism. This overlap in receptor binding is likely coincidental since phytocannabinoids evolved within the plant kingdom and the ECS including the endocannabinoids developed within animals. The GABA and ECS networks communicate through carefully orchestrated activities at localized synaptic level. When phytocannabinoids become available, the receptor affinities for CB1 and CB2 may compete with the naturally occurring endocannabinoid ligands and influence the GABA-ECS communication. In some instances this addition of phytocannabinoids may provide some therapeutic benefit while in other circumstances the presence of these plant derived ligands for the CB1 and CB2 receptors binding site may lead to disruption of important functions within the CNS. The regulatory approval of several THC products for nausea and vomiting and anorexia and CBD for rare pediatric seizure disorders are examples of some of the benefits of phytocannabinoids. Concerns regarding cannabis exposure in utero and in the child and adolescence are shrill warnings of the hazards associated with disrupting the normal maturation of the developing CNS.
Conference papers on the topic "Complex non-linear least-square (CNLS)"
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Chen, Kai, and Richard A. Foulds. "The Mechanics of Perturbed Upper Limb Movement Control." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37201.
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The dependence of muscle force on muscle length gives rise to a “spring - like” behavior which has been shown to play an important role during movement. This study extended this concept and incorporated the influential factors of the mechanical behavior of the neural, muscular and skeletal system on the control of elbow movement. A significant question in motor control is determining how information about movement is used to modify control signals to achieve desired performance. One theory proposed and supported by Feldman et. is the equilibrium point hypothesis (EPH). In it the central nervous system (CNS) reacts to movement as a shift of the limb’s equilibrium posture. The EPH drastically simplified the requisite computations for multi-joint movements and mechanical interactions with complex dynamic objects in the context. Because the neuromuscular system is spring-like, the instantaneous difference between the arm’s actual position and the equilibrium position specified by the neural activity can generate the requisite torques, avoiding the complex “inverse dynamic” of computing the torques at the joints. Moreover, this instantaneous difference serves as a potential source of movement control related to limb dynamics and associated movement-dependent torques when perturbations are added. In this paper, we have used an EPH model to examine changes to control signals for arm movements in the context of adding perturbations in format of forces or torques. The mechanical properties and reflex actions of muscles crossing the elbow joint were examined during a planned 1 radian voluntary elbow flexion movement. Brief unexpected torque/force pulses of identical magnitude and time duration (4.5 N flexion switching to 50 N extension within 120ms) were introduced at various points of a movement in randomly selected trials. Single perturbation was implemented in different trials during early, mid, stages of the movement by pre-programmed 6DOF robotic arm (MOOG FCS HapticMaster). Changes in movement trajectory induced by a torque/ force perturbation determined over the first 120 ms by a position prediction formulation, and then a modified and optimization K-B-I (stiffness-damping-inertia) model was fit to the responses for predicting both non-perturbed and perturbed movement of elbow. The stiffness and damping coefficients estimate during voluntary movements were compared to values recorded of different subjects during trials. A least square nonlinear optimization model was designed to help determine the optimized impedance a subject could generate, and the identified of adapted of K-B-I in perturbed upper limb movements confirmed our assumption.
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Ngatu, Grum T., Wei Hu, Curt S. Kothera, and Norman M. Wereley. "A Semi-Active Magnetorheological Fluid Snubber Lag Damper for a Hingeless/Bearingless Helicopter." In ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2008. http://dx.doi.org/10.1115/smasis2008-494.
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A magnetorheolocial fluid elastomeric (snubber) lag damper is developed to provide adaptive lead-lag damping augmentation for a hingeless helicopter rotor. The MR fluid elastomeric (MRFE) lag damper consists of a flow valve, a flexible snubber body, and a flexible center wall separating the body into two fluid chambers. MR fluid enclosed in the snubber body can flow through the valve and be activated by a magnetic field in the valve. Consistent with the loading conditions for a helicopter lag damper, the MRFE damper is tested under single frequency (lag/rev) sinusoidal excitation. The complex modulus method was used to compare the MRFE damper damping characteristics with a baseline passive snubber type Fluidlastic® damper. The field-off damping of MRFE damper is smaller than the baseline damper. A significant controllable damping range is also observed as current was applied to a magnetic valve in the MRFE damper. Furthermore, the non-linear behavior of the MRFE damper is not sufficiently modeled using the complex modulus method. Thus, to account for the hysteresis behavior of the MRFE damper and estimate the damping force, a hydro-mechanical model is formulated based on lumped parameters. Parameters for the model are established using experimental data and a least-mean-square error minimization technique. The model is then applied to reconstruct the force time history under lag/rev excitation frequency. The average error between the predicted and measured damping forces is also evaluated to assess model accuracy.
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Li, C. James, and C. Jansuwan. "Projection Network for Unsupervised Pattern Classification." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79603.
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Projection network, being a non-linear dynamic system itself, has been shown to be superior to static classifiers such as neural networks in some applications where noise is significant. However it is a supervised classifier by nature. To extend its utility for unsupervised classification, this study proposes an unsupervised pattern classifier integrating a clustering algorithm based on DBSCAN and a dynamic classifier based on the projection network. The former is used to form clusters out of un-labeled data and eliminate outliers. Then, significant clusters in terms of size are identified. Subsequently, a system of projection networks is established to recognize all the significant clusters. The unsupervised classifier is tested with three well-known benchmark data sets (by ignoring data labels during training) including the Fisher’s iris data, the heart disease data and the credit screening data and the results are compared to those of supervised classifiers based on the projection network. The difference in performance is small. However, the ability of unsupervised classification comes at a price of a more complex classifier system and the need of data pre-conditioning. The former is because more than one cluster could be formed for a class and therefore more computational units are needed for the classifier, and the latter is because increased similarity of data after clustering increases the chances of numerical instability in the least square algorithm used to initialize the classifier.