Gain Parameters Research Articles

Enhancing Power Generation for Solar-Based Cardiac Pacemakers: A Novel Approach with FOPID and HCMKH-BS2

Biomedical implants, such as implantable cardioverter heart monitors, rely on limited battery life, which necessitates costly replacements and patient discomfort. To address these challenges, a novel solar-based pacemaker model is proposed that is integrated with a fractional-order proportional integral derivative (FOPID) controller and a Hybridized Chaotic multiverse Krill Herd-based Backtracking Search Strategy (HCMKH-BS2) to enhance power generation for pacemaker operation. Our approach considers the impact of partial shading conditions on solar power. To mitigate these conditions, a FOPID controller with an HCMKH-BS2 model is developed which also addresses environmental issues through the adoption of a DC-DC converter. The gain parameters of the FOPID controller are determined using the proposed HCMKH-BS2 algorithm. The proposed technique is implemented to evaluate its performance using various measures in terms of duty cycle, convergence analysis, and power. The performance validation is performed based on the analyses of constant irradiance and changed irradiance. The proposed method achieved a fitness value of 0.01 from the convergence analysis and the duty cycle of 0.48D. These results demonstrate the superiority of our proposed technique over previous methods, highlighting its potential for enhancing the performance of solar-based cardiac pacemakers.

Study of real-time parameter measurement of ring rolling pieces based on machine vision.

Real time parameter measurement cannot be carried out to dynamic ring parts during automation ring rolling processes so that rolling process parameters cannot be adjusted in time. Considering effects of shaping of ring rolling parts, a visual measurement platform was set up and a machine vision-based non-contact real -time measurement method was put forward. This article improves the subpixel level edge extraction algorithm to extract edge data information of circular rolling pieces. Based on the characteristics of circular rolling pieces, an RG-Hough transform method is proposed to fit the detected edge data information. The conversion relationship between pixel and actual sizes were determined in combination with the camera calibration to gain parameters of ring rolling parts. Measurements of ring parts (OD: 462.12mm; and ID: 315.53mm) were applied to verify the effectiveness of our method. Our measurement error is ±0.25mm and our average speed can be up to 104ms/frame. Our study can provide powerful technical supports for intelligent control of ring rolling pieces.

Normalization of the Suppression Head Impulse Test (SHIMP) and its correlation with the Head Impulse Test (HIMP) in healthy adults.

In our study, it was aimed to compare vestibulo-ocular reflex (VOR) gain and saccade parameters in HIMP and SHIMP tests between gender, right and left ears, and age groups in healthy adults and to examine the correlation between the tests regarding these parameters. The study included a total of 100 healthy participants aged 18-65 and without complaints of hearing loss, dizziness, lightheadedness, and/or imbalance. Participants underwent HIMP and SHIMP tests, respectively. No significant difference was found in HIMP and SHIMP VOR gain values according to gender and age groups. SHIMP duration was significantly longer in women. VOR gain values were lower in the right ear. HIMP amplitude values were higher and SHIMP amplitude values were lower with increasing age. In older age groups, SHIMP peak velocity and duration values were significantly decreased, while HIMP duration value increased and latency value was longer. In the 1st saccade, a significant difference was obtained between HIMP and SHIMP tests for all saccade parameters. There was a statistically significant positive correlation between the VOR gain values of HIMP and SHIMP tests. The present study showed that VOR gain and saccade parameters obtained in different age groups will be important in determining clinical outcomes in vestibular pathologies.

The GeoCarb greenhouse gas retrieval algorithm: simulations and sensitivity to sources of uncertainty

Abstract. The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb were to advance our knowledge of the carbon cycle, in particular, land–atmosphere fluxes of the greenhouse gases carbon dioxide (CO2) and methane (CH4) and the effects of these fluxes on the Earth's radiation budget. GeoCarb would retrieve column-integrated dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO), important for understanding tropospheric chemistry), in addition to solar-induced fluorescence (SIF), from hyperspectral resolution measurements in the O2 A-band at 0.76 µm, the weak CO2 band at 1.6 µm, the strong CO2 band at 2.06 µm, and a CH4/CO band at 2.32 µm. Unlike its predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb would be in a geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high-spatial-resolution imaging capabilities would provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land–atmosphere CO2 and CH4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the L2 retrieval algorithms which will be followed by simulation experiments to determine an error budget for each target gas. Several sources of uncertainty will be explored, including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to forward model parameters including meteorology and spectroscopy, although there are some other instrument-related sources of uncertainty that are left out for this study, including that from “smile”, the keystone effect, stray light, detector persistence, and scene inhomogeneity. The results indicate that the errors (1σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2, XCH4, and XCO, respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMSEs of 1.06 ppm for XCO2, 8.2 ppb for XCH4, and 2.5 ppb for XCO, respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO are dominated by measurement noise. It is important to note that the GeoCarb mission was canceled by NASA; however, the instrument is still in development and will be delivered to NASA, in full, with the hope that it will eventually be adopted in a future mission proposal.

Certain performance analysis of Islanded microgrid systems stability through biologically activated engineering optimization technique

The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model’s particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable.

The effect of humate as a feed additive on feed intake, production, and carcass parameters of Angus steers

Humate may be a valuable livestock feed additive, with potential effects on nutrient utilisation and animal performance. Thus, the aim of this study was to investigate the effect of K Humate S 100R supplementation on the feed intake, liveweight gain, and carcass parameters of Angus steers. Within individual pens, 40 weaned steers were allocated to four treatment groups (n = 10/potassium humate K Humate S100R, Omnia Specialities Australia) for 100 days. The treatment groups included Group 1, 35 g K Humate S100R/animal/day; Group 2, 70 g K Humate S100R/animal/day; Group 3, 140 g K Humate S100R/animal/day; and Control Group, which were not supplemented with K Humate S100R (0 g K Humate S100R/animal/day). Chemical and mineral composition of the feed ingredients, dry matter intake (DMI), and average daily weight gains were recorded. The steers were slaughtered as a single group at a commercial Australian abattoir. Standard measures for hot standard carcass weight, eye muscle area, fat depth and coverage, marbling, ossification, meat and fat colour, dressing percentage and loin pH values at 24‐hour postmortem were recorded. It was found that the steers allocated to Group 2 had higher DMI (P = 0.003) and feed conversion ratio (FCR) (P < 0.001) compared with those allocated to Group 1 and the Control Group. The MSA marbling score was lowest for steers allocated to the Control Group (P < 0.05) and comparable for those allocated to Groups 1, 2, and 3. Together, these results demonstrate that increased levels of K Humate S100R supplementation improved the carcass quality, via an increase in MSA. However, further research is warranted on the potential effects of humates supplementation on intramuscular fat associated qualities of beef.

Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC

The current paper presents an original approach into the microgrid control framework by incorporating LSTM-based optimization with specific emphasis on refining the gain parameters of a Proportional-Integral-Derivative (PID) controller. This integration represents a significant advancement in improving the overall efficiency of microgrid control systems. By creatively applying LSTM optimization, the paper achieves dynamic adjustments of the PID controller's parameters, resulting in more precise regulation of output power quality. Through the utilization of the Unified Power Quality Conditioner (UPQC) in conjunction with LSTM-based optimization, the paper establishes a compelling link between improved power quality and the resultant tariff rates. This highlights their combined influence on enhancing power quality and calibrating tariff rates, providing a fresh perspective on optimizing microgrid operations.

Load frequency control in deregulated power system with renewable energy sources: Hybrid GOA‐SNN technique

AbstractThis paper proposes a hybrid technique for load frequency control (LFC) in an inter‐connected deregulated power‐system. The proposed method is the combination of a gannet optimization algorithm (GOA) and spiking neural network (SNN), hence, it is named as GOA‐SNN technique. The objective of the proposed method is to minimize frequency deviations within the power system (PS). By lessening the frequency‐deviation and tie‐line power variation, this approach ensures system frequency‐control under the effect of load disturbances. The GOA method is utilized to generate the set of control signals of the controller. The SNN method is used to predict the optimum gain parameter of the controller. By then the proposed method is run in MATLAB software and evaluated their performance with various existing approaches. The proposed method shows better results than other existing methods, such as Ant Lion Optimization (ALO), particle swarm optimization (PSO), and Salp Swarm Algorithm (SSA). The GOA‐SNN approach shows a low Area control error is 0.48% and a high efficiency is 96% compared with other existing approaches.

Design and implementation of a 2-DOF 5R parallel mechanism with a coaxial-driven layout

In order to expand and improve the reachable workspace of planar parallel manipulators, a 2-DOF 5R parallel mechanism with a coaxial-driven layout is proposed. The configuration analysis and structural design are carried out, and the kinematic model is presented to conduct simulation analysis. The results indicate that the layout with the axes of the two driving devices overlapping has a larger workspace and a more compact structure. Meanwhile, a closed-loop PID control system is established to evaluate the accuracy of the proposed mechanism and analyse the kinematic error under different gain parameters. Simulation results indicate that the kinematic performance of the mechanism has improved. The control system is constructed based on using a motion control card as the primary control component and two servo motors as the drive device. Additionally, gain values are adjusted by the driver to reduce the deviation of the servo motors. Finally, the design of the prototype is deployed, and the experiment proved that the 2-DOF 5R planar parallel mechanism (PPM) with a coaxial-driven layout has the advantages of a large workspace, fast dynamic response, high stiffness, and easy control. The design of the 2-DOF 5R PPM solves the problem of single operation and high repeatability, which is increasingly utilized in the fields of electronics, food, daily chemicals, and pharmaceuticals.

A hybrid technique linked FOPID for a nonlinear system based on closed-loop settling time of plant

Wind and hydroelectric systems are more cost-effective and environmentally beneficial. A hybrid technique is proposed for the fractional-order proportional-integral-derivative (FOPID) controller to regulate the wind and hydro system. The proposed hybrid technique combines the feedback-artificial-tree (FAT), and atomic-orbital-search (AOS); together known as FAT-AOS approach. The proposed technique is utilized to decide the optimum controller parameters, and it guarantees system constancy in large disturbances using less computation and overshoot by restraining the parameter variation. The FAT is used to predict the optimum gain parameter of FOPID, and minimizing the system error is accomplished with the AOS approach. The performance metrics are peak time, rise time, settling time, and peak overshoot, are analyzed. The performance of the proposed method is done in the MATLAB platform. The simulation result of proposed approach for the rise time as 0.001 sec, settling time is 0.012 sec, and the overshoot percentage is 0.02 %. By comparing the existing methods, like Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Particle swarm optimization (PSO), the proposed approach rise time and settling time overshoot, is less. The comparison proves that the proposed system delivers improved outcome than existing systems.

Disorder-resistant fusion estimator design for nonlinear stochastic systems in the presence of measurement quantization

In this paper, we thoroughly study a new nonlinear disorder-resistant fusion estimation problem under both packet disorder and measurement quantization. Packet disorder, which occurs due mainly to random transmission delays (RTDs), are characterized via a series of integer-valued functions and random variables obeying certain distributions. Sensor measurements are logarithmically quantized before entering designated communication networks. The upper bounds on the filtering error covariances are acquired and later minimized by appropriately designing gain parameters of both local and fusion estimators. For fusion estimation, all local estimates and filtering covariances are integrated at a fusion center through the well-known federated criterion. Moreover, the fusion performance is examined by means of assessing the resistance of the designed estimator against the RTD-induced disorder. Simulation examples are presented to illustrate the applicability of the proposed disorder-resistant fusion estimator.

A Novel Hybrid Optimization-based Backstepping Fractional Order Sliding Mode Proportional-integral-derivative Controller for Nonlinear Biological System

Nowadays, the role of the control system in the bioengineering field plays a major part due to its significant improvement and demand. Usually, if there are any changes in the parameter of a biological system, then it tends to cause crucial diseases in humans. Previously, various control algorithms have been developed; however, the proper control process failed due to the high error rate, slow convergence, Computational Complexity, Tuning Complexity and inaccurate function. The proposed control strategy should ensure accurate tracking of the desired trajectory or reference signal, even in the presence of uncertainties and disturbances. The control system should be capable of adapting to changes in the system dynamics and maintaining stable operation. This complexity can lead to high computational requirements, making the controller computationally expensive and potentially unsuitable for real-time applications or systems with limited computing resources. To overcome these issues, in this work, the novel Hybrid Ant Lion Moth Flame Optimization (HALMFO) based Backstepping Fractional-Order Sliding mode Proportional-Integral-Derivative (BFO-SPID) controller is proposed for different nonlinear biological systems. The proposed method enhances the efficiency, improved performance, robustness, convergence speed, and solution quality of the optimization process. This paper addresses the challenge of controlling nonlinear biological systems by proposing a novel hybrid optimization-based backstepping approach combined with a fractional order sliding mode proportional-integral-derivative (PID) controller. This work’s essential contribution lies in addressing existing control strategies’ limitations by integrating multiple control techniques to achieve robust tracking performance and disturbance rejection. This system proposes predictive control with state estimation based on the backstepping sliding mode method. The gain parameters of the developed controller are optimized using the HALMFO method. The proposed control approach performance has been tested under biological systems such as Protein, Pancreas, and Genetic Regulatory Network (GRN). The execution of this proposed model is done in MATLAB/Simulink. Furthermore, the performance of the proposed control technique is compared with various state-of-the-art methods in terms of accuracy, Mean Average Error (MAE), Mean Square Error (MSE), settling time, overshoot, and rise time. The comparison illustrates that the projected controller has reached a steady condition with the least error rate in the control application. Thus the developed controller regulates the parameter variations in the biological system as per the desired level, providing significant effective outcomes to complex problems.

Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system

In order to reduce vibration and increase ride comfort, this article utilizes a system of quarter-car suspension integrated with a Fuzzy PID controller. To build and improve the Fuzzy PID controller for the semi-active suspension system used in quarter cars, using a novel meta-heuristic technique known as Grey Wolf Optimizer (GWO). Here the magnetorheological damper (MR) fluid with the Fuzzy PID controller was examined to optimize using the GWO algorithm. With the GWO technique and the integral of time absolute error (IAE) as a fitness function, the three gain parameters of the Fuzzy PID controller – Kp, Ki, and Kd– have been optimally set. The suggested approach has additional advantages for the optimization of functions with three variables, including simplicity in implementation, quick convergence traits, and superior computational capabilities. This work is significant, to the best of the author’s knowledge there is no optimization method using GWO to online tune a Fuzzy PID controller for a semi-active suspension system. The optimal output parameters of the controller can be updated online in real-time by GWO. The performance of the proposed controller was examined by assessing the root mean square (RMS) values and peak-to-peak (PTP) values of body displacement and body acceleration under various road profiles. To ensure that the intelligent controller was of the highest caliber, an online test rig was constructed. Results from simulations and online experiments demonstrated that the Fuzzy GWO PID controller significantly improved ride comfort under a variety of road conditions when compared to the Fuzzy PID controller and passive suspension system.

Controller design using PSO and WOA algorithm for enhanced performance of vector-controlled PMSM drive

ABSTRACT This study presents the tuning strategy of PI controllers gain parameters to improve the performance of vector-controlled permanent magnet synchronous motor drive. In the proposed work PI controller parameters are optimized using Particle Swarm Optimization (PSO) and Whale Optimisation Algorithm (WOA). Nonlinear programming techniques, namely PSO and WOA optimisation, are employed to enhance the convergence rate of speed, torque, and flux errors using an offline tuning method. The simulation results of speed, torque, and flux show that the transient and steady state responses have been greatly improved and verified for different operating conditions. It has been observed that the quality of stator current, developed output voltage of inverter, and ripples in electromagnetic torque are also improved, which shows the feasibility and accuracy of the proposed method.

Unidirectional ring laser operation and tunable single-frequency emission using differential parametric gain.

In this Letter, a novel approach for unidirectional operation of a 1064 nm solid-state ring laser is demonstrated based on difference frequency mixing. Unidirectional operation is achieved exploiting the directional parametric gain from a single-pass diode laser, facilitated through a periodically poled LiNbO3 crystal. In addition to achieving unidirectional operation, the nonlinear process further enables the generation of single-frequency mid-infrared light. Using a single-pass tapered diode laser, tunable in the range from 780 to 815 nm, the generated mid-infrared signal covers the 2.9 to 3.5 µm range while optimizing the phase-match condition of the difference frequency generation process.

Manipulation of neonatal ruminal populations at birth results in sustained effects on microbial populations and measures of health and production in merino and suffolk lambs

The efficiency of the microbial-driven fermentation process in the rumen is closely related to the efficiency of the ruminant host. This experiment examined the effect of manipulating the neonatal microbiota on postnatal development, immune function and productivity (liveweight gain and wool growth parameters). The trial examined the differences between naturally inoculated lambs (maternal control, n = 21) and those given ruminal fluid, which had been collected from ruminally-cannulated ewes, fed either a roughage diet (roughage, n = 14), or a grain-based diet (high grain, n = 14). At lambing, newborn lambs were tagged and weighed. Inoculation lambs received 10 ml of rumen fluid per inoculation, daily, for a total of 7 inoculations. Live weight, body condition scores, wool growth (greasy and clean wool weights), wool length, mean fibre diameter, and differential blood cell counts, and IgG and IgA in both blood and saliva) were quantified from birth until week 18 (slaughter). Total feed intake was recorded from weaning. Ruminal fluid was collected by stomach tube from 12 lambs from each treatment group and their dams (n = 7–8 per treatment, with the lower number of ewes coming about through ewes that had twins) at time of weaning (week 10). The pH of the ruminal fluid, the density of ciliated protozoa in ruminal fluid, and the composition of the ruminal bacterial populations were analysed. At slaughter (week 18), ileal samples were taken from the same focal lambs as were used for microbial analysis. The artificial inoculations resulted in significant differences in ruminal bacterial genera to those of naturally inoculated lambs at time of weaning (some 3 months post-lambing). Significant differences were also apparent in ileal bacterial communities at slaughter, between lambs which had received the inoculum from roughage-fed ewes and naturally inoculated lambs. Both the efficiency (growth, condition, feed conversion efficiency, carcass weight) and indices of health (mortalities, red blood cell count, blood haemoglobin concentration, haematocrit, and eosinophil counts) of the inoculated animals, were affected negatively by the inoculations ( < 0.05). There was a significant interaction between inoculum source and breed in terms of effects on all parameters measured. The effects of the high grain inoculation were significantly affected by breed, with high grain merinos found to have reduced mean fibre diameter (P < 0.05) and increased wool growth relative to the maternal control (P = 0.035). This effect was not noted in the suffolks. In summary, we have demonstrated that long-term effects on ruminal and intestinal microbiota can be generated by intervention in the normal process of ruminal inoculation in neonatal lambs. Moreover, there appeared to be genetic differences in response to artificial inoculation. Whilst the effects of artificial inoculation in this trial were negative in terms of indices of health and production, further studies of maternal diet and its effects on the natural establishment of the neonatal microbiota are warranted given the persistence of the effects and the interaction with breed.

Acute and 28‐Day Repeated Dose of Oral Polyphenol‐Rich Extract of Ocimum gratissimum Leaves Treatment on Adult Male Wistar Rats

Ocimum gratissimum (OG) Linn is a member of the Lamiaceae family and is widely distributed in the coastal and savannah areas of tropical countries such as Brazil, Nigeria, India, and Mexico. In this report, the impact of acute and 28‐day repeated oral polyphenol‐rich extract of Ocimum gratissimum (PREOG) treatment was investigated in adult male Wistar rats using Lorke and stipulated OECD (420 and 407) guidelines with little modifications. In the acute test, (1000, 2000, 3000, 4000, and 5000 mg/kg) PREOG was orally administered using gavage at a single dose, and for 14 days, the animals were scrutinized for any signs of toxicity. In the 28‐day oral PREOG test, the animals were allotted (50, 100, 200, 400, and 800 mg/kg) PREOG daily over a period of 28 days; Control Group A received only distilled water, while Group B received corn oil. At the end of these studies, semen, blood, and tissues were collected for semen, hormonal, hematological, biochemical, and histopathological analyses. No death and obvious signs of toxicity were recorded in both the acute and 28‐day oral PREOG treatment, and PREOG was well tolerated even at 5000 mg/kg. Body weight gain, histopathology (liver, testes, and kidney), and hematological and biochemical parameters were unperturbed by the acute and 28‐day oral PREOG treatment. Semen quality and hormonal and antioxidant capacity of the liver, testes, and kidney were also not impaired by the 28‐day oral PREOG treatment. These studies demonstrated tolerability of PREOG by adult male rats and the 28‐day oral PREOG treatment did not impair the reproductive and antioxidant capacity in adult male rats.

Dietary Nanozeolite Powder to Reduce Water Total Ammonia Nitrogen (TAN) and Improve Growth Performance and Plasma Biochemical Indices of Nile Tilapia (Oreochromis niloticus)

The effects of nanoscale synthetic zeolite powder (NZeP) levels in diet on water quality, growth performance, muscle proximate composition, and some blood biochemical indices of Nile tilapia (Oreochromis niloticus) fingerlings were evaluated in a 60‐day experiment. One hundred eighty fish with an average weight of 5.91 ± 0.45 g were distributed in three different groups of control (basal diet without NZeP), NZP0.5% (basal diet + 0.5% NZeP), and NZeP1% (basal diet + 1% NZeP) with triplicate. The zeolite particles were characterized via field emission scanning electron microscopy coupled with X‐ray energy dispersive spectroscopy (FE‐SEM/EDS), dynamic light scattering (DLS), and X‐ray diffraction (XRD) techniques. The results revealed that the total ammonia nitrogen (TAN) concentration in the NZeP dietary groups decreased compared to the control group after the 20th day of the experiment (p &lt; 0.05). The TAN concentration in the NZeP1% group was lower than in the NZeP0.5% group on days 50 and 60 (p &lt; 0.05). The final weight, weight gain, and average daily gain parameters were lower in the fish fed on NZeP1%‐supplemented diet than in other dietary groups (p &lt; 0.05). The feed conversion ratio value decreased in the NZeP dietary groups compared to the control (p &lt; 0.05). The lowest FCR value was recorded in the NZeP1% dietary group (p &lt; 0.05). The muscle crude protein content increased in the fish fed on diet supplemented with NZeP1% in comparison to the control (p &lt; 0.05). The aspartate aminotransferase (AST) concentration decreased in the plasma of fish fed on NZeP‐supplemented diets compared to the control (p &lt; 0.05). The plasma glucose concentration was lower in the NZeP1% dietary group than the control (p &lt; 0.05). In conclusion, the adding nanozeolite powder at a level of 1% to diet had positive effects on water quality, the growth performance, and muscle proximate composition of Nile tilapia fingerlings.

A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis

High-dimensional and incomplete (HDI) data are commonly encountered in various big data-related applications concerning the complex interactions among numerous nodes, such as the user-item iterations in a recommender system. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model can perform efficient representation learning to such HDI data, thereby extracting useful knowledge from them. However, a standard SGD algorithm updates a latent factor based on the current stochastic gradient only, without the considerations on the past information, making a resultant model suffer from slow convergence. To address this critical issue, this paper proposes an Adaptive Non-linear PID-incorporated SGD (ANPS) algorithm with two-fold ideas: 1) rebuilding the instant learning error when computing the stochastic gradient following the principle of a nonlinear PID controller to incorporate past update information into the learning scheme efficiently, and 2) implementing gain parameter adaptation following the principle of particle swarm optimization (PSO). Experiments on six widely-adopted HDI datasets demonstrate that compared with state-of-the-art LFA models, an ANPS-based LFA model achieves significant advantage in both efficiency and accuracy. Moreover, its flexible gain parameter adaptation mechanism greatly boosts its practicability for real issues. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In many industrial applications like recommender systems, social network systems, and cloud service systems, people usually encounter numerous nodes and their highly-incomplete relationships. An HDI matrix is commonly adopted to describe such specific relationships. One of the major challenges is to acquire useful knowledge from an HDI matrix efficiently and accurately for various data analysis tasks, e.g., accurate recommendation, community detection, and web service selection. An SGD-based LFA model has been widely adopted to tackle this issue. However, it suffers from slow convergence that leads to considerable time cost on large-scale datasets. This study proposes an ANPS algorithm following the principle of a nonlinear PID controller. With it, an ANPS-based LFA model is achieved, which possesses fast convergence rate on an industrial HDI matrix. The proposed ANPS algorithm can be leveraged for different types of various machine learning models, thereby improving their utility and scalability in practice.

Zonotopic Distributed Fusion Over Binary Sensor Networks With Bit Rate Allocation: A Coding-Decoding Approach.

In this article, the zonotopic distributed fusion estimation problem is investigated for a class of general nonlinear systems over binary sensor networks subject to unknown-but-bounded (UBB) noises. The network communication from nodes to the fusion center is confined to the limited bit rate. To alleviate the impact from less measurement information of the binary sensor, a modified innovation is constructed to improve the estimation accuracy. Then, a novel coding-decoding approach is proposed to ensure that the decoder has the ability to decode information from each node. Based on the matrix weighting fusion method, a distributed fusion algorithm is put forward under the zonotopic set-membership filtering framework, and the F -radius of the local zonotopic sets are derived and minimized by selecting the filtering gain parameters. Moreover, the bit rate allocation scheme and the weighting coefficients are determined by resolving two optimization problems. In addition, a sufficient condition is established to guarantee the uniform boundedness of the F -radius of the fused zonopotic. Finally, the ballistic object tracking systems is utilized to illustrate the availability of the presented algorithm.