Elimination Method Research Articles

Design of smoke detection system using deep learning and sensor fusion with recursive feature elimination cross-validation

The fire safety system is an important component that controls material and immaterial losses. Fire disasters are generally indicated by the appearance of excess smoke and changes in temperature, pressure, and changes in other parameters in the environment. Conventional smoke sensors are limited in reading parameter changes around their environment, making them less effective in early fire detection. This study aims to design a smoke detection system as an early fire detection system, using sensor fusion based on deep learning using the recursive feature elimination method with cross-validation (RFECV) using a random forest classifier used to select optimal parameters from public datasets as the basis for determining the sensor to be used. Based on the RFECV optimal feature, a deep learning algorithm was performed and obtained an accuracy of 0.99, a precision of 0.99, a recall of 1.00, and an F1 score of 0.99, with a latency time of 34.02 μs, which is 71.76% times faster than the original model.

Formation mechanism and elimination method of joint defects in lattice structures printed by metal droplet deposition manufacturing

Benefiting from the dense inner microstructure and smooth bead-like surface morphology, metal droplet deposition manufacturing (MDDM) is advantageous in manufacturing small metallic lattice structures, which have great potential in advanced aerospace, energy, and robotics applications. However, the improper top-layer droplet distance leads to joint defects in the printed lattices and deteriorates the forming accuracy. Moreover, if the substrate temperature and inclined angle increase, joint defects may occur even if the top-layer droplets are at an ideal distance due to droplet oscillation. This work proposes a novel method to eliminate joint defects by re-placing the joint region droplets to an ideal center distance. To this end, the dynamics of top-layer droplets (impact, spread, recoil, and oscillation) were investigated by combining morphology analysis and high-speed photography (Ts = 673 K, θ = 45°). A theoretical model was established to obtain the parameter range of defect-free joints through a series of bifurcate lattice printing experiments. Then, the morphology and generation mechanism of cumulative joint defects in the lattices at higher temperatures and angles (Ts = 773 K, θ = 60°) were analyzed. A parameter window of joint morphology with different values of inclined angle, substrate temperature, and top-layer droplet distance was established for predicting potential defects in the printing trace. Next, a novel method was proposed to remove redundant droplets and re-place top-layer droplet coordinates into the ideal center distance. Furthermore, the printing strategy was expanded to multi-truss lattices using three-dimensional transformation matrixes. Finally, multiple cells bifurcate, triangular, and pyramid lattices were successfully printed with perfect joints. The height deviation of joint regions is <5 % of the droplet diameter, verifying the feasibility of the proposed strategy. This work represents a further step towards MDDM printing of high-quality truss lattice structures and other complex 3D architectures.

Waveform Optimization Control of an Active Neutral Point Clamped Three-Level Power Converter System

Currently, the escalating integration of renewable energy sources is causing a steady weakening of grid strength. When grid strength is weak, interactions between inverters or those between inverters and grid line impedance can provoke widespread oscillations in the power system. Additionally, the diverse DC voltage application characteristics of power converter systems (PCS) may lead to over-modulation, generating narrow pulse issues that further impact control of the midpoint potential balance. Existing dead-time elimination methods are highly susceptible to current polarity judgments, rendering them ineffective in practical use. PCS, due to inherent dead-time effects, midpoint potential imbalances in three-level topologies, and narrow pulses, can elevate low-order harmonic content in the output voltage, ultimately distorting grid-connected currents. This is particularly susceptible to causing resonance in weak grids. To enhance the output voltage waveform of PCS, this article introduces a comprehensive compensation control strategy that combines dead-time elimination, midpoint potential balance, and narrow pulse suppression, all based on an active neutral point clamped (ANPC) three-level topology. This strategy gives precedence to dead-time elimination and calculates the upper and lower limits of the zero-sequence available for midpoint potential balance while fully compensating for narrow pulses. By prioritizing dead-time elimination, followed by narrow pulse suppression and finally midpoint potential balance, this method decouples the coupling between these three factors. The effectiveness of the proposed method is validated through semi-physical simulations.

The Bhargava Greedoid as a Gaussian Elimination Greedoid

Inspired by Manjul Bhargava's theory of generalized factorials, Grinberg and Petrov have defined the Bhargava greedoid - a greedoid (a matroid-like set system on a finite set) assigned to any "ultra triple" (a somewhat extended variant of a finite ultrametric space). Here we show that the Bhargava greedoid of a finite ultra triple is always a Gaussian elimination greedoid over any sufficiently large (e.g., infinite) field; this is a greedoid analogue of a representable matroid. We find necessary and sufficient conditions on the size of the field to ensure this.

Thermal radiation and Soret/Dufour effects on amplitude and oscillating frequency of Darcian mixed convective heat and mass rate of nanofluid along porous plate

The influence of thermal radiations and Soret/Dufour significantly increases the mass and heat transport in material science, geothermal process, chemical rectors and heat exchangers due to temperature and concentration variation of nanoparticles. Main objective is to enhance the amplitude, oscillation and frequency of heat transfer using the Soret and Dufour characteristics in Darcian radiating flow of thermophoretic nanoparticles along vertical porous surface. The dimensionless analysis is performed to develop the convenient form of governing thermodynamic Darcian nanofluid formulation. To develop the programming algorithm in FORTRAN language, the primitive variable formulation is used for both time-dependent and steady models. The numerical and graphical outcomes of primitive type equations under boundary conditions are explored by using implicit finite difference approach with Gaussian elimination scheme. Various pertinent parameters are used to elaborate the steady fluid velocity, surface temperature and steady concentrated nanoparticle volume fraction. Valuable novelty of current research is to use steady results for fluctuating shear stress, fluctuation heating rate and fluctuating mass rate. It is found that the influence of Soret and Dufour parameter enhances the fluid velocity and temperature distribution with maximum variations. It is noticed that the temperature distribution enhances as thermal radiation parameter increases. The oscillation and transient stability of heating rate enhances with prominent variations as thermal radiation and Soret/Dufour parameter increases. It is depicted that amplitude in shear stress and mass rate enhances as Soret and Dufour parameter enhances.

Solving Least-Squares Problems via a Double-Optimal Algorithm and a Variant of the Karush–Kuhn–Tucker Equation for Over-Determined Systems

A double optimal solution (DOS) of a least-squares problem Ax=b,A∈Rq×n with q≠n is derived in an m+1-dimensional varying affine Krylov subspace (VAKS); two minimization techniques exactly determine the m+1 expansion coefficients of the solution x in the VAKS. The minimal-norm solution can be obtained automatically regardless of whether the linear system is consistent or inconsistent. A new double optimal algorithm (DOA) is created; it is sufficiently time saving by inverting an m×m positive definite matrix at each iteration step, where m≪min(n,q). The properties of the DOA are investigated and the estimation of residual error is provided. The residual norms are proven to be strictly decreasing in the iterations; hence, the DOA is absolutely convergent. Numerical tests reveal the efficiency of the DOA for solving least-squares problems. The DOA is applicable to least-squares problems regardless of whether q&lt;n or q&gt;n. The Moore–Penrose inverse matrix is also addressed by adopting the DOA; the accuracy and efficiency of the proposed method are proven. The m+1-dimensional VAKS is different from the traditional m-dimensional affine Krylov subspace used in the conjugate gradient (CG)-type iterative algorithms CGNR (or CGLS) and CGRE (or Craig method) for solving least-squares problems with q&gt;n. We propose a variant of the Karush–Kuhn–Tucker equation, and then we apply the partial pivoting Gaussian elimination method to solve the variant, which is better than the original Karush–Kuhn–Tucker equation, the CGNR and the CGNE for solving over-determined linear systems. Our main contribution is developing a double-optimization-based iterative algorithm in a varying affine Krylov subspace for effectively and accurately solving least-squares problems, even for a dense and ill-conditioned matrix A with q≪n or q≫n.

Data driven damping separation method for the Mega-offshore wind turbine tower

This study proposes a novel damping separation method based on operating state-measured data collected from a 6.7 MW offshore wind turbine tower to provide various damping ratios caused by different loads. First, the operating frequencies and total damping ratios for various operating states are identified using the dynamic responses under normal operating conditions. Subsequently, the natural and harmonic frequencies caused by the rotation of the blades are distinguished using rotor speed data and the Campbell diagram. Finally, to determine the various damping ratios of different modal orders of offshore wind turbines under multiple load conditions using only actual response data, a new damping equation based on different damping sources is proposed. Filtering strategies for total damping ratios corresponding to different damping sources with varying vibration amplitudes and wind speed thresholds were proposed. The outlier elimination method was introduced to remove error-scattered points, and the average value calculation was used to identify various damping ratios. The actual measured data of a 6.7 MW offshore wind turbine tower was studied, and the results show that the proposed method can effectively separate various damping ratios.

Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis

The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red–green–blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) – recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R2 values for the four ML models were in the range of 0.54–0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations; the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat.

Bifurcation Analysis of a Holling–Tanner Model with Generalist Predator and Constant-Yield Harvesting

In this paper, we introduce constant-yield prey harvesting into the Holling–Tanner model with generalist predator. We prove that the unique positive equilibrium is a cusp of codimension 4. As the parameter values change, the system exhibits degenerate Bogdanov–Takens bifurcation of codimension 4. Using the resultant elimination method, we show that the positive equilibrium is a weak focus of order 2, and the system undergoes degenerate Hopf bifurcation of codimension 2 and has two limit cycles. By numerical simulations, we demonstrate that the system exhibits homoclinic bifurcation and saddle–node bifurcation of limit cycles as the parameters are varied. The main results show that constant-yield prey harvesting and generalist predator can lead to complex dynamic behavior of the model.

An Effective Charging Torque Elimination Method for Dual-Channel Electric-Drive-Reconstructed Onboard Chargers

The idea of electric-drive-reconstructed onboard charger (EDROC) systems, along with the concept of dual-channel charging, offers a novel design, thought to enhance the integration and fault tolerance of the charging system of electric vehicles (EVs). This article investigates a dual-channel EDROC incorporating an asymmetrical six-phase permanent magnet synchronous machine (ASPMSM). A unique operation mode, called the unbalanced charging voltage operation mode, exists in this topology, in case the voltages of the two batteries are unequal. This unbalance results in different winding currents following through two channels, leading to an undesired charging torque in the machine. To ensure the safety of the system, an effective charging torque elimination method, based on dual-channel winding current balance, is proposed, which achieves a dot-shaped current path of torque generation-associated subspace (i.e., α–β subspace) by balancing the dual-channel charging power. Eventually, a controller is designed for the system and a prototype is created, to validate the effectiveness of the proposed method.

A Comprehensive Approach to Azo Dichlorotriazine Dye Treatment: Assessing the Impact of Physical, Chemical, and Biological Treatment Methods through Statistical Analysis of Experimental Data

This exploration investigates integrated treatment systems combining advanced oxidation processes (Fenton and photo-Fenton) with biological methods for the effective elimination of stubborn organic compounds in simulated textile wastewater composed of azo Dichlorotriazine dye. A comprehensive optimization of key process factors including catalyst dosage, hydrogen peroxide quantity, irradiation duration, etc. was systematically conducted for both Fenton and photo-Fenton processes to realize maximum COD and color removal. Under ideal conditions (0.4 g/L photocatalyst, 1 mL/L H2O2, and 75-Watt UV intensity for 60 min), the photo-Fenton process realized 80% COD elimination and complete decolorization, meeting industrial discharge limits without needing extra biological treatment. Statistical models correlating process parameters to treatment efficiency were developed, giving important design insights. For Fenton, effluent COD exceeded discharge thresholds, so a post-biological treatment using activated sludge was essential to comply with regulations. This integrated Fenton–biological scheme utilizes synergism between chemical and biological processes for enhanced overall treatment. Notable economic benefits were achieved by photo-Fenton over conventional UV-only and UV/H2O2 methods regarding energy consumption and operating costs. Overall, this pioneering work successfully proves integrated advanced oxidation–biological systems as a superior, sustainable alternative to traditional techniques for economically removing obstinate pollutants, such as azo Dichlorotriazine dye, as it is a simulated textile wastewater treatment used to satisfy environmental standards.

Prediction through quantum dynamics simulations: Photo-excited cyclobutanone.

Quantum dynamics simulations are becoming a standard tool for simulating photo-excited molecular systems involving a manifold of coupled states, known as non-adiabatic dynamics. While these simulations have had many successes in explaining experiments and giving details of non-adiabatic transitions, the question remains as to their predictive power. In this work, we present a set of quantum dynamics simulations on cyclobutanone using both grid-based multi-configuration time-dependent Hartree and direct dynamics variational multi-configuration Gaussian methods. The former used a parameterized vibronic coupling model Hamiltonian, and the latter generated the potential energy surfaces on the fly. The results give a picture of the non-adiabatic behavior of this molecule and were used to calculate the signal from a gas-phase ultrafast electron diffraction (GUED) experiment. Corresponding experimental results will be obtained and presented at a later stage for comparison to test the predictive power of the methods. The results show that over the first 500fs after photo-excitation to the S2 state, cyclobutanone relaxes quickly to the S1 state, but only a small population relaxes further to the S0 state. No significant transfer of population to the triplet manifold is found. It is predicted that the GUED experiments over this time scale will see signals related mostly to the C-O stretch motion and elongation of the molecular ring along the C-C-O axis.

Non-adiabatic direct quantum dynamics using force fields: Toward solvation.

Quantum dynamics simulations are becoming a powerful tool for understanding photo-excited molecules. Their poor scaling, however, means that it is hard to study molecules with more than a few atoms accurately, and a major challenge at the moment is the inclusion of the molecular environment. Here, we present a proof of principle for a way to break the two bottlenecks preventing large but accurate simulations. First, the problem of providing the potential energy surfaces for a general system is addressed by parameterizing a standard force field to reproduce the potential surfaces of the molecule's excited-states, including the all-important vibronic coupling. While not shown here, this would trivially enable the use of an explicit solvent. Second, to help the scaling of the nuclear dynamics propagation, a hierarchy of approximations is introduced to the variational multi-configurational Gaussian method that retains the variational quantum wavepacket description of the key quantum degrees of freedom and uses classical trajectories for the remaining in a quantum mechanics/molecular mechanics like approach. The method is referred to as force field quantum dynamics (FF-QD), and a two-state ππ*/nπ* model of uracil, excited to its lowest bright ππ* state, is used as a test case.

Aquatic vegetation mapping with UAS-cameras considering phenotypes

Aquatic vegetation species at the genus level in an oxbow lake were identified in Hungary based on a multispectral Uncrewed Aerial System (UAS) survey within an elongated oxbow lake area of the Tisza River under continental climate. Seven and 13 classes were discriminated using three different classification methods (Support Vector Machine [SVM], Random Forest [RF], and Multivariate Adaptive Regression Splines [MARS]) using different input data in ten combinations: original spectral bands, spectral indices, Digital Surface Model (DSM), and Haralick texture indices. We achieved a high (97.1%) overall accuracies (OAs) by applying the SVM classifier, but the RF performed only <1% worse, as it was represented in the first places of the classification rank before the MARS. The highest classification accuracies (>84% OA) were obtained using the most important variables derived by the Recursive Feature Elimination (RFE) method. The best classification required DSM as an input variable. The poorest classification performance belonged to the model that used only texture indices or spectral indices. On the class level, Stratiotes aloides exhibit the lowest degree of separability compared to the other classes. Accordingly, we recommend using supplementary input data for the classifications besides the original spectral bands, for example, DSM, spectral, and texture indices, as these variables significantly improve the classification accuracies in the proper combinations of the input variables.

LRFE: A NOVEL LOCAL RESPONSE FEATURE ELIMINATION PROCESS FOR IDENTIFICATION OF LUNG CANCER CELLS

One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

Bioassay of Root Exudates, their Impact in Susceptible Species and their Degradation in Hydroponics: A Review

Plants grown in hydroponics exhibit superior characters and yield compared to those grown in soil. However, root exudates accumulated particularly in renewed nutrient solution (RNS) hinders development of crops in hydroponics. These exudates are secondary metabolites released by the roots as a result of physiological processes, exhibiting autotoxic effects in plants. They are allelochemicals encompassing various chemical groups such as phenolic compounds, terpenoids, alkaloids, benzoxazinoids or other organic acids. The quantity and type of allelochemical released by plants varies depending on multiple factors such as temperature, light, nutrient deficiency, stress, physiological status of plant, pH and environment. The combined effect of more than one allelochemical is often additive or synergistic thus elevating the impact caused by individual allelochemical. These phytotoxic exudates are known to have inhibited the growth and development of plants by diverse pathways unique to their characteristic. There are several methods developed for degradation of exudates including AC (Activated Charcoal), O3/H2O2 treatment or other. This review discusses the bioassay of secondary metabolites causing autotoxicity, mechanism, impact in horticultural and ornamental crops, factors affecting their release and methods for elimination in hydroponics.

New bandgap analysis method for metamaterial structures using variational principle

This paper presents an efficient bandgap analysis method for metamaterial structures based on variational principle and linear expression technique. This is in view of the challenges in constructing a displacement function that satisfies the Bloch boundary when applying the traditional energy method to analyze the dispersion relations of metamaterial structures. Also, the inclusion of the wave numbers in the displacement function leads to inefficient computation. While there have been methods, such as virtual springs, have been proposed to deal with such problems, issues remain to be resolved. The main idea of the current paper is the solution of linear uncorrelated coefficients by processing the constraints. In this way, the unknown coefficients in the equations of motion can be linearly expressed, thereby allowing the equations to become variational. Both Gaussian elimination and null-space techniques are used to implement the method. The solution procedures of the two methods are detailed and demonstrated by using one-dimensional and two-dimensional metamaterial structures. The accuracy of the method is verified by comparing the results with those from using finite element method. The calculation efficiency is compared with that of the traditional energy method in terms of both the matrix dimension and the number of the points of wave numbers. The results show that the method has a much higher calculation efficiency than that of the traditional energy method. The method proposed in this paper is free of convergence problems in dealing with boundary problems. Furthermore, in the discussion of combined periodic structures, it is shown that the method still has good geometrical applicability in structures containing multiple boundary conditions, showing great potential in solving complex engineering problems.

الحلول العددية لمعادلات أبيل التكاملية الخطية بطريقة متعددات حدود بوبكر

في هذه المقالة، تم تقديم طريقة عددية تستند الى عديدات حدود بوبكر لحل معادلات آبيل التكاملية الخطية من النوع الاول والثاني. تم تحويل معادلات آبيل التكاملية الى نظام جبري من المعادلات الخطية باستخدام المصفوفات. للحصول على معالم بوبكر، تم حل هذا النظام باستخدام طريقة كاوس للحذف. ولتوضيح نتائج هذه الطريقة تم تقديم اربعة امثلة ومقارنتها مع نتائج العديد من الطرق المذكرة في البحوث السابقة. تم استخدام برنامج الماتلاب R2018b لأجراء جميع الحسابات والرسوم البيانية.

Study on the heat treatment of multispectral ZnS for residual stress reduction

Multispectral ZnS (M − ZnS) will inevitably introduce stresses during the chemical vapor deposition (CVD) polycrystalline growth and hot isostatic pressing (HIPping) stages. In this paper, the generation mechanism and elimination method of residual stresses in M − ZnS are analyzed by means of SEM, transmittance, XRD analysis and mechanical property characterization, and the effects of heat treatment temperature, time and material annealing structure on the apparent morphology, optical and mechanical properties of M − ZnS materials are investigated. The results show that the internal stresses of M − ZnS mainly originate from the CVD ZnS growth and HIPping stages, and the most effective method to reduce the stresses is heat treatment. The key factors affecting the effect of heat treatment include temperature, time, and annealing structure. Selecting a GZ(G) annealing structure combined with a suitable cooling rate for heat treatment of M − ZnS materials will significantly improve the heat treatment effect. More annealing time has a significant effect on the transmittance in the 0.38–3 μm band, and insignificant effect on the transmittance in the 3–5 μm and 8–10 μm bands. Heat treatment will bring some degree of structural changes to the material, often manifested including a trace of hexagonal phase structure, which will affect the optical imaging quality of the window material. Heat treatment contributes to secondary grain growth, with an increase in grain size and a decrease in grain boundaries. Excessive heat treatment accelerates the loss of hardness and bending strength, and also leads to the generation of micro-cracks and macro-cracks in the material, reducing the mechanical properties of the material. Heat treatment holding 200 h and 100 h compared to the unannealed, Vickers hardness decreased by 13.518 and 7.96, respectively, bending strength decreased by 4.77 MPa and 1.73 MPa, respectively. Therefore, in actual production, should not be selected excessive heat treatment time.

Cyclone Intensity Estimation System using Satellite Images

Tropical cyclones are big storms that can cause a lot of damage. Cyclone intensity estimation plays a vital role in disaster preparedness, response, and mitigation strategies. This paper introduces a novel approach to estimating cyclone intensity using satellite images through a Convolutional Neural Network (CNN) model. Unlike previous methods, we employ advanced techniques such as histogram analysis for feature extraction and adaptive thresholding for image segmentation using mean, Gaussian, and Otsu methods. The model also predicts potential coverage distance. Additionally, we present a user-friendly visualization portal, a pioneering effort in this field, which displays the deep learning output along with contextual information for end-users