Quadratic Algorithm Research Articles

Two-set inequalities for the binary knapsack polyhedra

This paper presents two-set inequalities, a class of valid inequalities for knapsack and multiple knapsack problems. Two-set inequalities are generated from two arbitrary sets of variables from a knapsack constraint. This class of cutting planes is not a traditional type of lifting since a valid inequality over a restricted space is not required to start. Furthermore, they cannot be derived using any existing lifting technique. The paper presents a quadratic algorithm to efficiently generate many two-set inequalities. Conditions for facet-defining two-set inequalities are also derived. Computational experiments tested these inequalities as pre-processing cuts versus CPLEX, a high-performance mathematical programming solver, at default settings. Overall, two-set inequalities reduced the time to solve some benchmark multiple knapsack instances to up to 80%. Computational results also showed the potential of this new class of cutting planes to solve computationally challenging binary integer programs.

Assessment of food safety risk using machine learning-assisted hyperspectral imaging: Classification of fungal contamination levels in rice grain

A rapid and nondestructive assessment of food safety risk using machine learning-assisted hyperspectral imaging was developed for classification of fungal contamination in brown rice grain. Brown rice was inoculated with Penicillium. The fungal infected rice was then mixed with healthy rice to obtain 0 %, 5 %, 25 %, 50 % and 100 % (w/w) contamination of infected rice. Volatile compounds including pentamethyl-heptane, decane, dodecane, 3-octanone, and 1-octen-3-ol were found in fungal infected rice, as analyzed using gas chromatography-mass spectrometry. The HSI system was used to collect spectral reflectance and spatial data of the samples covering the wavelength range of 400–1000 nm. The hypercubed data were analyzed using machine learning algorithms, including principal component analysis (PCA), discriminant factor analysis (DFA) and support vector machine (SVM). Using PCA for data reduction, 3 principal components were extracted with a cumulative variance of 90.53 %. DFA (linear and quadratic algorithms) and SVM (linear, quadratic, cubic, and Gaussian algorithms) were then used to classify the samples. HSI integrated with Gaussian SVM gave 93.4% accuracy which was best for classifying rice with different percentages of contamination. The image analysis gave a pseudo-color distribution map which facilitated the visualization of the contaminated rice by presenting data in an uncomplicated image. The machine learning-assisted HSI can be used as a rapid, nondestructive and chemical-free tool for an assessment of food safety risk for rice grain.

Enhancing the performance of quantum reservoir computing and solving the time-complexity problem by artificial memory restriction

We propose a scheme that can enhance the performance and reduce the computational cost of quantum reservoir computing. Quantum reservoir computing is a computing approach which aims at utilizing the complexity and high dimensionality of small quantum systems, together with the fast trainability of reservoir computing, in order to solve complex tasks. The suitability of quantum reservoir computing for solving temporal tasks is hindered by the collapse of the quantum system when measurements are made. This leads to the erasure of the memory of the reservoir. Hence, for every output, the entire input signal is needed to reinitialize the reservoir, leading to quadratic time complexity. Another critical issue for the hardware implementation of quantum reservoir computing is the need for an experimentally accessible means of tuning the nonlinearity of the quantum reservoir. We present an approach which addresses both of these issues. We propose artificially restricting the memory of the quantum reservoir by only using a small number inputs to reinitialize the reservoir after measurements are performed. This strongly influences the nonlinearity of the reservoir response due to the influence of the initial reservoir state, while also substantially reducing the number of quantum operations needed to perform time-series prediction tasks due to the linear rather than quadratic time complexity. The reinitialization length therefore provides an experimental accessible means of tuning the nonlinearity of the response of the reservoir, which can lead to significant task-specific performance improvement. We numerically study the linear and quadratic algorithms for a fully connected transverse Ising model and a quantum processor model. Published by the American Physical Society 2024

Algorithmic strategies for a fast exploration of the TSP 4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4$$\\end{document}-OPT neighborhood

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Predictor-based model predictive control for maglev planar motor with a 2D Halbach array mover

Magnetic levitated planar motors are widely employed in flexible manufacturing and photolithography due to their characteristics of high precision, high acceleration, and low maintenance. In this paper, a novel predictor-based model predictive control is proposed to improve the tracking performance of a maglev planar motor which consists of a 2D Halbach array mover and stationary square coils. A Kalman filter and finite spectrum assignment based on an augmented system model are employed to forecast mover’s location by compensating for the system delay due to data transmission and processing. The predicted location is fed back to model predictive control for control optimization under the limit of coil currents. Hildreth’s quadratic algorithm is then applied to solve for the optimal control effort within the constraints. Comparative experiments demonstrate the effectiveness of the proposed control strategy, the total tracking error of which is much smaller than the result of a proportional–integral–derivative controller and standard model predictive control in all tests. Additionally, experimental results show that the proposed predictor-based model predictive control can tolerate higher speeds of the mover while retaining mover’s steadiness. The experimental results suggest the proposed predictor-based model predictive control is an effective path-tracking strategy for magnetic levitated planar motors subject to delays.

A Regression Analysis on Steam Gasification of Polyvinyl Chloride Waste for an Efficient and Environmentally Sustainable Process.

Over the last few years, researchers have shown a growing interest in polyvinyl chloride (PVC) gasification and have conducted several studies to evaluate and enhance the process. These studies have recognized that processing parameters have a crucial impact on the assessment of PVC gasification. Despite this, there has been limited exploration of the use of machine learning techniques, particularly regression models, to optimize PVC waste gasification. This study aims to investigate the effectiveness of regression models as machine learning algorithms in predicting the performance of PVC waste gasification. The study uses data collected through a validated thermodynamic model, and three different regression models are tested and compared in detail. Cold gas efficiency and normalized carbon dioxide emission are predicted using linear, quadratic, and quadratic with interaction algorithms. The outcomes for emission algorithms reveal that the linear emission algorithm possesses a high R-square value of 97.49%, which indicates its strong predictive capability. Nevertheless, the quadratic algorithm outperforms it, exhibiting an R-square value of 99.81%. The quadratic algorithm with an interaction term, however, proves to be the best among them all, displaying a perfect R-square value of 99.90%. A similar observation is detected for the cold gas efficiency algorithms. These findings suggest that the quadratic algorithm with an interaction term is superior and has a greater predictive accuracy. This research is expected to provide valuable insight into how regression algorithms can be used to maximize the efficiency of PVC waste gasification and reduce its associated environmental concerns.

Increasing global oceanic wind speed partly counteracted water clarity management effectiveness: A case study of Hainan Island coastal waters

A sustainable coastal “blue economy” is one of the most significant opportunities and challenges in the new era. However, the management and conservation of marine ecosystems must recognize the interdependence in the coupled human and natural systems. In this study, we employed satellite remote sensing to map the spatial and temporal distribution of Secchi disk depth (SDD) in Hainan coastal waters, China for the first time, and quantitatively revealed the impacts of environmental investments on the coastal water environment in the context of global climate change. Based on the moderate resolution imaging spectroradiometer (MODIS) in situ concurrent matchups (N = 123), a simple green band (555 nm)-based quadratic algorithm was first developed to estimate the SDD for the coastal waters of Hainan Island in China (R2 = 0.70, root mean square error (RMSE) = 1.74 m). The long time-series SDD dataset (2001–2021) for Hainan coastal waters was reconstructed from MODIS observations. Spatially, SDD showed a pattern of high water clarity in eastern and southern coastal waters and low water clarity in the western and northern coastal areas. This pattern is attributed to unbalanced distributions of bathymetry and pollution from seagoing rivers. Seasonally, the humid tropical monsoon climate drove the SDD into a general pattern of high in the wet season and low in the dry season. Annually, the SDD in Hainan coastal waters improved significantly (p < 0.1), benefiting from environmental investments over the last 20 years. However, the increasing global oceanic wind speed in recent years has exacerbated sediment resuspension and deep ocean mixing, counteracting approximately 14.14% of the remedial management's effectiveness in protecting and restoring the coastal ecosystem. This study offers ways to improve the ecological and environmental regulations under global changes and to strengthen the public service capacity for aquatic management authorities with methods that support the sustainable development of coastal areas.

Efficiency Improvement of Induction Motors Based on Rotor Slot and Tooth Structures

Due to simple structure, easy maintenance and low cost, induction motors (IMs) are widely applied in various industries, accounting for 60-80% alternating current (AC) motors used in industry. However, the efficiency of IMs is very low, and even small improvement can result in significant energy saving. For instance, 1% efficiency increase saves billions of kilowatt hours. Therefore, this paper aimed to improve the efficiency of IMs, thus reducing energy consumption and greenhouse gas emissions. For an IM with 7.5kW rated power and IE3 energy efficiency, the efficiency is improved by making various changes. Sequential quadratic algorithm and fmincon function are proposed to change the rotor slot and teeth structures, realizing nearly 91% motor efficiency, which is a significant improvement over the original efficiency. It is worth noting that improving the efficiency of IMs saves a lot of energy, especially in cases where IMs account for a large proportion of AC motors.

Adaptive cruise control method based on hierarchical control and multi-objective optimization

Considering the perception of a driver, driving safety, and riding comfort, an adaptive cruise control (ACC) method based on a multi-objective (MO) real-time optimization control algorithm is proposed. The ACC system can be divided into various working modes according to the driver’s perception, as well as the relative state of the host vehicle and the preceding vehicle. To distinguish the driver’s intention in the rapid acceleration, strong deceleration, steady, and larger clearance modes, a fuzzy tool is proposed to solve the problem. To maintain the inter-clearance, velocity, and jerking action in a reasonable region, an MO control algorithm based on model predictive control was used to obtain the optimal control vectors. Moreover, the control vector increment is restricted to suppressing the fluctuation caused by switching among various modes. Simulations were conducted for various scenarios to verify the effectiveness of the ACC method. The simulation results indicated that the driver’s comfort and fuel economy improved by 35.3% and 16.6%, respectively, compared with the linear quadratic algorithm in a complicated driving environment. Various simulation results demonstrated that the MO-ACC controller can reduce fuel consumption while barely sacrificing riding comfort or tracking performance, as compared to the linear quadratic controller.

Quadratic vector support machine algorithm, applied to prediction of university student satisfaction

This study aims to identify the most optimal supervised learning algorithm to be applied to the prediction of satisfaction of university students. In this study, the IBM SPSS - 25.0 software was used to test the reliability of the satisfaction questionnaire and the MATLAB R2021b software through the classification learner technique to determine the supervised learning algorithm. The experimental results determine a Cronbach's Alpha reliability of 0.979, in terms of the classification algorithm, it is validate d that the quadratic vector support machine (SVM) has better performance metrics, being correct in 97.8% (a ccuracy) in the predictions of satisfaction of university students, with a r ecall (sensitivity) of 96.5% and an F1 score of 0.968. Likewise, when eva luating the classification model by means of the receiver operating characteristic curve (ROC) technique, it is identified that for the three expected classes of satisfaction the value of the area under the curve (AUC) is equal to 1, in such sense the pred ictive model through the SVM Quadratic algorithm, has a high capacity to distinguish between the 3 classes ; i) d issatisfied, ii) s atisfied and iii) v ery satisfied of satisfaction of university students.

Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test

The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1–L1) and in-depth neuropsychological battery (level 2–L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB (p < 0.0001, p = 0.028 and p = 0.0007, respectively). Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81; and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms.This study has been registered on ClinicalTrials.gov (NCT04858893).

Linear Time Dynamic Programming for Computing Breakpoints in the Regularization Path of Models Selected From a Finite Set

Many learning algorithms are formulated in terms of finding model parameters which minimize a data-fitting loss function plus a regularizer. When the regularizer involves the pseudo-norm, the resulting regularization path consists of a finite set of models. The fastest existing algorithm for computing the breakpoints in the regularization path is quadratic in the number of models, so it scales poorly to high-dimensional problems. We provide new formal proofs that a dynamic programming algorithm can be used to compute the breakpoints in linear time. Our empirical results include analysis of the proposed algorithm in the context of various learning problems (regression, changepoint detection, clustering, and matrix factorization). We use a detailed analysis of changepoint detection problems to demonstrate the improved accuracy and speed of our approach relative to grid search and a previous quadratic time algorithm.

Nonlinear Detector Response of Aqua MODIS Land Imaging Bands

The detectors in the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) reflective solar bands (RSBs) are calibrated assuming a linear relationship between the observed top-of-atmosphere radiance and the instrument output response. Any nonlinear behavior in the system response can contribute to errors in NASA's Level 1B MODIS reflectance and radiance products, which are used in a wide variety of Earth science applications. While most RSB detectors continue to have very linear behavior even after more than 18 years of operation, we present evidence that the detectors in Aqua MODIS bands 1 (645 nm) and 2 (858 nm) have deviations from gain linearity that change over the mission by up to 3%. We show a clear divergence between gain measurements made with the onboard solar diffuser (SD) at two different radiance levels, achieved by collecting data both with and without an attenuation screen. Radiance trends from lunar calibrations and typical desert and ocean scenes are also compared. We present a simple method to characterize the response nonlinearity using a quadratic function by combining the results of the two sets of SD calibrations. Applying this quadratic calibration algorithm to the desert and lunar data results in improved agreement in their long-term radiance trends. A clear understanding of the magnitude of the gain nonlinearity could be useful to help improve the quality of numerous MODIS science products, including the land imaging applications that rely heavily on these bands.

SDTS: Security Driven Task Scheduling Algorithm for Real-Time Applications Using Fog Computing

Nowadays, security and scheduling have become more critical issues of rapidly increasing applications running in real-time systems. In this work, we tried to solve these intricate issues and develop a security-driven task scheduling algorithm for real-time applications using Fog computing, which is abbreviated as SDTS. It works for the enhancement of both the security and scheduling services of real-time applications. SDTS is the integration of various proposed algorithms that are modified quadratic search algorithm, Min_Heap algorithm, and modified Earliest Deadline First (m-EDF). In which, modified quadratic search, Min Heap algorithm, and m-EDF algorithms are used for evaluating the average security value of tasks, optimal nodes using Fog computing, and task selection according to their absolute deadlines, respectively. The quantitative results analysis shows that the proposed model outperforms the existing models in terms of guarantee ratio (rate of success), average security value, and overall performance, i.e. 7.4% for success ratio, 7.69% for average security value, and 1.02% for overall performance in comparison to traditional methods.

A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems

Voice over Internet Protocol (VoIP) is a recent voice communication technology and due to its variety of calling capabilities, the system is expected to fuel the market value even further in the next five years. However, there are serious concerns since VoIP systems are frequently been attacked. According to recent security alliance reports, malicious activities have increased largely during the current pandemic against VoIP and other vulnerable networks. This hence implies that existing models are not sufficiently reliable since most of them do not have a hundred percent detection rate. In this paper, a review of our most recent Intrusion Detection &amp; Prevention Systems (IDPS) developed is proposed together with a comparative analysis. The final work consisted of ten models which addressed flood intentional attacks to mitigate VoIP attacks. The methodological approaches of the studies included the quantitative and scientific paradigms, for which several instruments (comparative analysis and experiments) were used. Six prevention models were developed using three sorting methods combined with either a modified galloping algorithm or an extended quadratic algorithm. The seventh IDPS was designed by improving an existing genetic algorithm (e-GAP) and the eighth model is a novel deep learning method known as the Closest Adjacent Neighbour (CAN). Finally, for a better comparative analysis of AI-based algorithms, a Deep Analysis of the Intruder Tracing (DAIT) model using a bottom-up approach was developed to address the issues of processing time, effectiveness, and efficiency which were challenges when addressing very large datasets of incoming messages. This novel method prevented intruders to access a system without authorization and avoided any anomaly filtering at the firewall with a minimum processing time. Results revealed that the DAIT and the e-GAP models are very efficient and gave better results when benchmarking with models. These two models obtained an F-score of 98.83%, a detection rate of 100%, a false rate of 0%, an accuracy of 98.7%, and finally a processing time per message of 0.092 ms and 0.094 ms respectively. When comparing with previous models in the literature from which it is specified that detection rates obtained are 95.5% and false-positive alarm of around 1.8%, except for one recent machine learning-based model having a detection rate of 100% and a processing time of 0.53 ms, the DAIT and the e-GAP models give better results.

Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.

S-NPP VIIRS Thermal Emissive Bands 10-Year On-Orbit Calibration and Performance

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership Program (S-NPP) satellite, launched in late 2011, has reached the decade landmark under successful operations. VIIRS has 22 spectral bands, 7 of which are thermal emissive bands (TEB) that cover the 3.70 to 11.84 μm wavelength range. Over the years, VIIRS TEB observations have been used to generate several data products (e.g., surface/cloud/atmospheric temperatures, cloud top altitude, and water vapor properties). The VIIRS TEB calibration uses a quadratic algorithm and is referenced to an on-board blackbody with temperature measurements traceable to the National Institute of Standards and Technology standard. This manuscript provides an overview of the VIIRS instrument operations and TEB calibration activities and algorithms used in the level 1B data and describes the TEB on-orbit performance for S-NPP VIIRS. The 10-year on-orbit performance of the S-NPP VIIRS TEB has generally been stable, and the degradations in the S-NPP TEB detector responses are minor after a decade in orbit. The noise characterization performance repeatedly meets the design requirements for all TEB detectors as well. On-orbit changes in the TEB response-versus-scan-angle, based on pitch maneuver observations, have been demonstrated to be extremely small. Moreover, multiple time series over select ground targets have shown that the sensor’s on-orbit performance is quite stable.

Dynamic positioning of an oceanographic research vessel using fuzzy logic controller in different sea states

A dynamic positioning (DP) system is a computer-controlled system which maintains the positioning and heading of ship by means of active thrust. A DP system consist of sensors, observer, controller and thrust allocation algorithm. The purpose of this paper is to investigate the performance of proportional derivative type fuzzy controller with Mamdani interface scheme for dynamic positioning of an oceanographic research vessel (ORV) by numerical simulation. Nonlinear passive observer is used to filter the noise from the position and orientation. A nonlinear mathematical model of the ORV is subjected to the wave disturbance ranging from calm to phenomenal sea. Robustness and efficiency of the fuzzy logic controller is analysed in comparison with the multivariable proportional integral derivative (PID) and the linear quadratic regulator (LQR) controller. A simplified constrained linear quadratic algorithm is used for thrust allocation. The frequency response of the closed loop system with different controllers is analysed using the bode plot. The stability of controller is established using the Lyapunov criteria.

Multi-Tier 3D IC Physical Design with Analytical Quadratic Partitioning Algorithm Using 2D P&amp;R Tool

In this study, we developed a complete flow for the design of monolithic 3D ICs. We have taken the register-transfer level netlist of a circuit as the input and synthesized it to construct the gate-level netlist. Next, we partitioned the circuit using custom-made partitioning algorithms and implemented the place and route flow of the entire 3D IC by repurposing 2D electronic design automation tools. We implemented two different partitioning algorithms, namely the min-cut and the analytical quadratic (AQ) algorithms, to assign the cells in different tiers. We applied our flow on three different benchmark circuits and compared the total power dissipation of the 3D designs with their 2D counterparts. We also compared our results with that of similar works and obtained significantly better performance. Our two-tier 3D flow with AQ partitioner obtained 37.69%, 35.06%, and 12.15% power reduction compared to its 2D counterparts on the advanced encryption standard, floating-point unit, and fast Fourier transform benchmark circuits, respectively. Finally, we analyzed the type of circuits that are more applicable for a 3D layout and the impact of increasing the number of tiers of the 3D design on total power dissipation.

Design and experiment of multi-mode steering system of agricultural machinery for hilly and mountainous area

In order to improve the steering flexibility of agricultural machinery in hilly and mountainous areas, a multi-mode steering system with front wheel steering, rear wheel steering, and four-wheel steering has been developed. The hydraulic steering system based on load sensitivity principle and proportion-integration-differentiation (PID) controlling algorithm was designed, which overcomes the negative impact of external load changes on flow control accuracy. The mechanical-hydraulic-controlling coupling model established in the AMESim and the sequential quadratic combinatorial optimization algorithm (SQCOA) was adopted to obtain the optimal combination of PID parameters. The simulation results demonstrate that the parameters such as pressure, speed, displacement of hydraulic cylinders, etc. in different steering modes meet the design requirements. To examine and verify the system performance, the test platform was researched and developed for conducting steering radius and displacement measurement. The experimental data illustrated that the front and rear hydraulic cylinders have good synchronization accuracy in four-wheel steering mode, and the fast switch of steering mode can be realized. The maximum error rate of is steering radius 4.21% and 3.77%, respectively, in two-wheel steering and four-wheel steering modes. The research methods and conclusions can provide a theoretical basis and reference for the other steering system development.