Proposed Hybrid Technique Research Articles

Enhanced LA-HBO technique for optimal position of PMU with complete prominence

This manuscript proposes a hybrid technique for determining the optimal positioning of phasor measurement units (PMUs) in power systems. The PMUs play a crucial role in power system control, wide-area monitoring, and protection. The proposed hybrid method is the joint execution of the Lichtenberg algorithm (LA) and the heap-based optimization (HBO) technique. Hence, it is named the LA-HBO technique. The objective of the proposed method is to place the PMUs in the power system for observability. The goal is to enhance the efficiency and accuracy of PMU placement, ensuring optimal positioning for improved grid monitoring capabilities. The Lichtenberg Algorithm (LA) enhances PMU placement by addressing system observability challenges and ensuring that selected locations provide comprehensive coverage of the power grid. The heap-based approach optimizes PMU placement by efficiently managing the selection process, considering factors like computational efficiency and scalability. The proposed hybrid technique is implemented in IEEE-30 and -14 bus systems. The MATLAB-based simulation results are compared with the various existing methods, such as Sea Lion Optimization (SLO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC). By then, the outcome reveals the efficacy of the proposed method for defining the optimum PMU locations. The proposed method shows a low computational time of 0.02348 sec for the IEEE-14 bus, and 0.03565 sec for the IEEE-30 bus compared with other existing methods.

Distributed arithmetic-FIR filter design using Approximate Karatsuba Multiplier and VLCSA

In this manuscript, a High Throughput and Low Latency DA-FIR filter design is integrated with Approximate Karatsuba Multiplier (AKM) and Variable Latency Carry Skip Adder (VLCSA) is proposed for the noise removal application in SDR. In this design, an AKM and VLCSA are considered to decrease the count of partial products on DA framework, although no multiplication is clearly performed. Thus, an important reduction is accomplished under accumulation circuits. The main execution problem of the DA system is the size of lookup table (LUT) increases exponentially through the length of inner product. To further diminish the memory complexity, approximate DA architectures depending truncation approach are needed. Partial products on DA are created through truncating least significant bits (LSBs) of inputs. The proposed hybrid technique lessens a count of LUTs by truncating the least significant bits of input operands. To further reduce the latency, this manuscript deals with one of the fastest multipliers, namely Approximate Karatsuba Multiplier employed for accumulation of partial products. The proposed design is performed in Verilog using Xilinx 14.5 ISE simulation. The experimental performances of the proposed DA-FIR-Hyb AKE-VLCSA filter is evaluated under lower delay, lower static power and compared with the existing filters.

Fault Diagnosis of Three-Phase Induction Motor (IM) Using a Hybrid ELSE-RNN Technique

This manuscript proposes a hybrid technique for fault detection and classification in a three-phase induction motor (IM). The proposed hybrid technique combines enhanced ladder spherical evaluation (ELSE) and recurrent neural networks (RNN); hence, it is called the ELSE–RNN method. The main goal of the manuscript is to detect and categorize the faults that occur in the IM. The key objective of the proposed method is to enhance the performance of supercapacitor (SC) storage technology and fuzzy-tuned proportional–integral (PI) supervision over conventional control. The main contribution of this paper is developing an effective fault diagnosis method for three-phase IMs. The presence of stator, rotor, winding, and bearing faults is employed using signal processing techniques, such as the Hilbert transform and SIFT. The proposed ELSE–RNN technique is utilized with the end goal of detecting and classifying faults. Here, the proposed ELSE–RNN technique recognizes motors’ healthy or unhealthy conditions in many situations to distinguish faults for protection. The proposed ELSE–RNN technique reduces the complexity of detecting and classifying faults with a validated system and increases the system's accuracy. The ELSE–RNN technique is implemented in MATLAB, and its performance is compared to existing techniques.

Prediction of nano metal matrix composites based on hybrid approach

AbstractThis manuscript proposes a hybrid method to predict the optimal nano‐metal matrix composites. The proposed hybrid technique is the wrapper of the Fire‐Hawk Optimizer (FHO) and Spiking Neural Network (SNN). Commonly it is known as FHO‐SNN method. The main objective of the proposed method is to improve the method parameters for better enhancement in mechanical properties. FHO approach is used to improve the process parameters of stirring squeeze casting method. The SNN predicts optimal parameters. Moreover, the problem based on the casting is reduced. By then the proposed hybrid technique performance is performed in the MATLAB platform and associated with various existing approaches. The proposed system shows the high tensile strength, impact energy and hardness compared with other existing methods.

Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)—a retinal biomarker for Alzheimer’s disease—to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer’s disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer’s disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer’s disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of 72.2±4.2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$72.2\\pm 4.2$$\\end{document}%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer’s diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning

Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique.

Hybrid KNN-SVM machine learning approach for solar power forecasting

Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several conventional techniques like long short-term memory (LSTM), support vector machine (SVM), etc. are available, but due to some restrictions, their application is limited. To enhance the precision of forecasting solar power from solar farms, a hybrid machine learning model that includes blends of the K-Nearest Neighbor (KNN) machine learning technique with the SVM to increase reliability for power system operators is proposed in this investigation. The conventional LSTM technique is also implemented to compare the performance of the proposed hybrid technique. The suggested hybrid model is improved by the use of structural diversity and data diversity in KNN and SVM, respectively. For the solar power predictions, the suggested method was tested on the Jodhpur real-time series dataset obtained from the data centers of weather stations using Meteonorm. The data set includes metrics such as Hourly Average Temperature (HAT), Hourly Total Sunlight Duration (HTSD), Hourly Total Global Solar Radiation (HTGSR), and Hourly Total Photovoltaic Energy Generation (HTPEG). The collated data has been segmented into training data, validation data, and testing data. Furthermore, the proposed technique performed better when evaluated on the three performance indices, viz., accuracy, sensitivity, and specificity. Compared with the conventional LSTM technique, the hybrid technique improved the prediction with 98 % accuracy.

Experimental Investigation and Soft Computing-based Assessment using ANN-MOGWO-A Hybrid Approach for Inconel(825)

In this paper, a strategy has been set for minimizing the corner error in the modification of pulse and non-pulse parameters. Taguchi’s philosophy has been used to design the experiment by varying process parameters (i.e. Spark-on Time (STon), Wire Tension (WT), Servo-Voltage (Sv), Discharge Current (DC) and Wire-speed (Sw)), to explore the machining outcomes. The response characteristics have been measured in terms of cutting speed (Cs), Corner Error (CD) and surface Roughness (RA) using Topas plus X wire of [Formula: see text] 0.25[Formula: see text]mm diameter. The machining performance characteristics were analyzed using main effect plots and analysis of variance (ANOVA). Furthermore, a soft computing-based hybrid optimization technique (Artifical Neural Network (ANN)-based Multi-Objective Grey Wolf Optimizer (MOGWO)) has been utilized to search the multi-optimum parameter setting for superior machining results. The most significant parameter observed is DC for Cs, which is determined to be 34.81%. Moreover, WT found 26.29% and 34.10% for CD and RA, respectively. The confirmation test shows that the maximum absolute percentage errors are observed as 3.89%, 6.3% and 9.7% for Cs, CD and RA, respectively. The proposed hybrid technique can generate superior solutions compared to the existing algorithms. Notably, the outcomes obtained on new instances exhibit potential, purposeful, and efficacy.

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

Proper planning for electric vehicle (EV) charging stations, as well as the necessary network development, is critical for the continued growth of EVs and conventional loads. This article proposes a hybrid method for the allocation of fast‐charging stations (FCSs) and photovoltaic (PV) with battery energy storage (BES) and scheduling. The proposed hybrid technique is the wrapper of golden jackal optimization (GJO) and random forest algorithms (RFA), commonly named the GJO–RFA technique. Using the GJO approach, the allocation and EV assignment problems are resolved. RFA is used to address EV, traffic flow, and PV‐related uncertainties. The objective of the proposed approach is to minimize the loss of energy, investments, index of voltage deviation, and operation and maintenance costs of FCS, PV, and BES. The associated important factors are also assessed, including the number of charging ports, FCS capacity, and EV flow caught using a FCS. As a test example, a 33‐node radial distribution network with the appropriate traffic network is used. For the final problem, the proposed approach to minimal energy loss is 2622.7270 kWh. By then, the performance of the proposed approach is executed in MATLAB, and it is contrasted with other existing techniques. The result shows that the proposed method‐related energy loss is less than existing approaches.

A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

AbstractIn this paper, a novel hybrid technique is proposed for transient stability analysis on grid connected Wind-Diesel-PV hybrid system. The proposed hybrid methodology is combination of the dwarf mongoose optimization algorithm (DMO) and the recalling enhanced recurrent neural network (RERNN) named DMO-RERNN. The main purpose of this work is to consider various elements on hybrid system for the analysis of transient stability according to different conditions. The voltage profile of hybrid system is enhanced using the proposed unified power flow controller (UPFC), which also has higher performance improving transient performance compared to the conventional ANN, PI and fuzzy-sliding mode controller. Considering the proposed technique, DMO is used to find the optimal global solution for the fault predicted by the RERNN approach. The proposed system is executed on MATLAB work platform; its performance with existing systems is analyzed. The result proves that the proposed hybrid technique based UPFC controller provides better results compared with other existing technique. The efficiency of the PI is 82.136, ANN is 77, Fuzzy Sliding Mode is 65.097% and proposed technique is 97.99038%.

The Choquet integral supported by a hedonic approach for modelling preferences in hotel selection

This paper presents a methodology for modelling the hotel selection process by combining the hedonic pricing method and the Choquet integral, a multi-criteria decision-making approach. The hedonic pricing method is a valuable tool in revealed preference theory, enabling the acquisition of preferential information in a straightforward manner. In this study, we use hedonic pricing to identify the significant criteria that influence the tourism selection process and to evaluate their importance. We combine the hedonic methodology with the Choquet integral to consider not only the importance of each criterion but also the importance of each subset of criteria. This proposed hybrid technique – the Choquet integral supported by the hedonic price method – offers an effective methodology for hotel selection by taking into account the market valuation of the criteria. We apply this methodology to a database of Spanish hotels and demonstrate the validity of our proposal in introducing valuable preferential information into the hotel classification process. This approach holds potential benefits for both tourism companies and travellers.

The Implicit Multistep Block Method with An Off-step Point for Initial Value Problems of Neutral Delay Volterra Integro-differential Equations

The aim of this manuscript is to solve the initial-value problems of neutral delay Volterra integro-differential equations with constant or proportional delays. Hence, a proposed hybrid technique named as an implicit multistep block method with an off-step point (1OBM4) is formulated for the numerical solution of NDVIDE. A LMM associated with an off-point is known as hybrid LMM. The proposed technique, 1OBM4, attempts to solve the problem synchronously in a block manner. Moreover, a Taylor expansion is implemented to develop 1OBM4 in predictor-corrector mode. Two different approaches are presented in order to solve both integral and differential parts of the problem. Some analyses on 1OBM4 are considered in terms of order and convergence of the method. A stability polynomial is also obtained for the stability regions to be constructed. In the last section, some numerical results are demonstrated to show the applicability of 1OBM4 in solving NDVIDE with constant or proportional delays.

Hyperelastic constitutive model parameters identification using optical-based techniques and hybrid optimisation

AbstractIn this paper we propose a new optical-based technique to identify the constitutive relation coefficients of the hyperelastic material using a hybrid optimisation approach. This technique can be used in place of traditional mechanical testing of elastomers for applications that involve inhomogeneous deformation. The purpose of the proposed method is to identify the incompressible hyperelastic material constitutive relation coefficients using a single experiment under different loading cases. The method comprises sample surface 3D reconstruction and uses finite element simulations to replicate the experiments, and uses a hybrid optimisation technique to minimise the error between actual 3D deformations and FE simulation results. The proposed hybrid technique predicts the hyperelastic constitutive relation coefficients more accurately than other optimisation methods. This study introduces a novel approach by employing a subpixel image registration algorithm for 3D reconstruction. The method requires a single experiment with diverse loading cases to accurately determine the coefficients of hyperelastic constitutive relations. The setup is portable and can be accommodated in a small suitcase. For this purpose, an apparatus was constructed comprising a stereoscopic system with eight cameras and a six-degree-of-freedom force-torque sensor to measure the induced forces and torques during the experiments. We identified the constitutive relation coefficients of Ogden N1, Ogden N3, Yeoh, and Arruda-Boyce relations which are commonly used models for silicone materials, using a traditional uniaxial test, optical uniaxial test (experiments performed using a constructed optical system), and inhomogeneous deformations tests. The study demonstrated that the coefficients obtained from inhomogeneous deformation tests provided the most accurate FE predictions. It was also shown that hyperelastic constitutive relation coefficients obtained from traditional uniaxial tests are insufficient to describe the material behaviour when the material undergoes inhomogeneous deformations.

Intelligent Hybrid Trading Strategies Based on Quantum-Inspired Algorithm

Investing in stocks is a common choice for financial management. Technical indicators (TIs) assist investors in determining the best trading time to make a fortune. Moving average (MA) and relative strength index (RSI) are the most common TIs. The proposed hybrid technique maximizes the capabilities of these two indicators. This study utilizes the quantum-inspired algorithm to assist effectively in searching for the optimal solution in the vast solution space. The proposed trading system contains four innovative features. First, the traditional usage restriction of MA and RSI is removed to increase their potential effectiveness and identify the most profitable trading strategy. Second, this research proposes an innovative hybrid indicator (HI) that combines MA and RSI to simultaneously achieve both benefits. HI eliminates the restriction of employing a single indicator at once. Third, an efficient quantum-inspired algorithm, the Global-best-guided Quantum-inspired Tabu Search Algorithm with Quantum NOT Gate (GNQTS), effectively and efficiently explores optimized parameters. Fourth, this study proposes 60 sliding windows to determine the optimal period for training and testing. The investment targets include well-known indices: DJIA, S&P 500, NASDAQ Composite Index, and NYA, as well as high-reputation companies on the US stock market: DJIA components. By removing the restrictions imposed by these two indicators and the use of HI, the experiment results demonstrate that GNQTS can discover optimal parameters to generate higher returns than state-of-the-art methods and buy-and-hold (B&H) strategies. The proposed hybrid strategy provides for the promising prospect of quantum-inspired applications and the utilization of multiple indicators.

Electric vehicle parameter identification and state of charge estimation of Li-ion​ batteries: Hybrid WSO-HDLNN method

This manuscript proposes a hybrid method for measuring the battery’s dynamic electrical response as it is compressed by an external-force. The proposed hybrid technique is the wrapper of the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network, commonly called as WSO-HDLNN technique. The main aim of the proposed method is to lessen the battery-voltage error. The War Strategy Optimization method detects the parameters of the battery method. The Hierarchical Deep Learning Neural Network is used to predict the dynamic-electrical-response of the battery when it deforms during external-force. By using the proposed method, the estimated voltage and measured voltage error are reduced, and identifies the parameter effectively. Finally, the proposed method is done in the MATLAB platform and it is compared with different existing approaches. The error of the proposed method is 4 mV, the Jellyfish Search Optimizer method error is 6 mV, the Heap-based Optimizer method error is 12 mV, and the Grey Wolf Optimizer method error is 14 mV. The proposed method time is 0.7 s The proposed method shows better results in all methods, like Jellyfish Search Optimizer, Heap-based Optimizer, and Grey Wolf Optimizer, The proposed method provides less computation time and error than the existing one is proved from the simulation outcome.

Wide Dual-Band Circularly Polarized Diecletric Resonator: Innovative Integration of a Single Hybrid Feed and Thin Grounded Metasurface.

This article presents an application of a grounded substrate-based metasurface for hosting dielectric resonators (DRs), enabling a wide dual-band circularly polarized (CP) operation. The antenna structure comprises centrally positioned rectangular DRs, one above the other, along with a 7 × 7 square-slotted metasurface. The metasurface and DRs are hosted above a grounded substrate, which is fed through a single coaxial feed placed at a specific angle, employing a modified upper probe of the coaxial feed. The proposed hybrid technique utilizes the combined benefits of the feed angle and a well-matched metasurface, resulting in performance improvement. Notably, a measured impedance bandwidth of 88.1% for |S11| is achieved within the frequency range of 4.0 GHz to 10.3 GHz. Furthermore, the antenna design exhibits two overlapping measured 3-dB axial ratio (AR) bandwidths: 23.62% from 4.25 GHz to 5.4 GHz and 5.12% from 7.6 GHz to 8 GHz. The peak gain of the antenna is measured at 8.4 dBic. Consequently, this innovative single-feed antenna design, characterized by its compact profile, holds significant potential for realizing multi-band operations. Furthermore, the developed antenna is well-suited for deployment in indoor radio links and INSAT applications.

A novel hybrid assembly method to reduce operational costs of selective assembly

Selective assembly is a technique whereby components are smartly matched depending on their measured feature values. Although it can reduce scrap by allowing more relaxed tolerances on components when compared to traditional random assembly, it is often not adopted due to the high related operational costs. Therefore, this study proposes a novel hybrid strategy whereby a number of traditional assembly rounds are performed before switching to selective assembly. At each round, the components which did not lead to a feasible assembly are put into their respective unmatched piles and are used for the next traditional assembly round. When N such rounds have been performed, the remaining unmatched components will finally be assembled via selective assembly.To determine the optimal number of traditional assembly rounds before moving on to selective assembly, a cost function depending on the matching probabilities at each round and the relative costs of selective and traditional assembly is developed together with an optimality condition. Since the analytic calculation of the matching probabilities is in most of the practical cases infeasible, these are estimated using simulation. Particular attention is paid to reducing the variance of the estimators for the matching probabilities, and to their impact on the total variability of the cost function. The application of this innovative method to two case studies demonstrates that the proposed hybrid technique can significantly reduce operational costs of selective assembly, while still taking advantage of its typical benefits.

HSDL-based intelligent threat detection framework for IoT network

Many Internet of Things (IoT) devices are susceptible to cyber-attacks. Attackers can exploit these flaws using the internet and remote access. An efficient Intelligent threat detection framework is proposed for IoT networks. This paper considers four key layout ideas while building a deep learning-based intelligent threat detection system at the edge of the IoT. Based on these concepts, the Hybrid Stacked Deep Learning (HSDL) model is presented. Raw IoT traffic data is pre-processed with spark. Deep Vectorized Convolution Neural Network (VCNN) and Stacked Long Short Term Memory Network build the classification model (SLSTM). VCNN is used for extracting meaningful features of network traffic data, and SLSTM is used for classification and prevents the DL model from overfitting. Three benchmark datasets (NBaIoT-balanced, UNSW-NB15 & UNSW_BOT_IoT- imbalanced) are used to test the proposed hybrid technique. The results are compared with state-of-the-art models.

High-efficiency regenerative shock absorbers considering twin ball screws transmission for increasing the vehicles’ driving range using a hybrid approach

This article proposes a hybrid system for increasing the electric vehicles’ (EVs) driving range considering regenerative shock absorbers. The proposed hybrid approach is a combination of atomic orbital search (AOS) and wild horse optimizer (WHO); later, it was called AOSWHO method. The major objectives of the proposed work are to extend vehicle range, reduce the charging time of the vehicle, reduce the battery size, and increase the efficiency of vehicle. In this proposed work, linear Permanent magnet (PM) generators are used as the sole instrument and on-board source is utilized to absorb vibration energy of the moving vehicle. Here, the proposed method has four key modules: (a) suspension vibration input, (b) transmission mechanism, (c) generator module, and (d) power storage. A suspension vibration is mostly pretentious by road roughness and speed vibration, which is optimally handled by the proposed hybrid technique. The conversion of the alternating linear vibration into unidirectional rotation of generator shaft is processed by proposed hybrid technique. Based on the volume and efficiency of the dampers, a small volume, brushless DC motor with low rotor inertia is selected to produce electricity. By then, the proposed AOSWHO run on MATLAB site and its performance is related to several existing systems. From the simulation result, it is concluded that proposed system delivers an State of Charge (SOC) of 40.33, which is high compared to existing approaches.

Improvement of Power Quality Using Reduced Switch Count Eleven-Level Inverter in Smart Grid

This article proposed a hybrid control topology to cascaded multilevel-inverter (CMLI) using 11-level for the grid-tied photovoltaic generation system. The proposed hybrid technique combines an archerfish hunting optimiser (AHO) and Spike Neural Network (SNN); hence, it is called the AHO-SNN technique. The proposed control technique is to maintain the regulation of power or maximal energy conversion of the solar subsystem and reduce total harmonic distortion (THD). Here, the AHO is utilised to construct the optimum control signal dataset. The best control signals are estimated using a data set performed by SNN. The resultant control signals will regulate insulated-gate-bi-polar-switches (IGBT) of Cascaded MLI. The proposed AHO-SNN control topology determines the converter switching states by constructing the operating modes of the generation system. The system parameter changes and outside disturbances are optimally minimised with this control strategy. The proposed AHO-SNN control is done in MATLAB platform, and it evaluated their performance by using existing methods, like War Strategy Optimization Algorithm (WSO) Fuzzy Wavelet Neural Network (FWNN), Side-Blotched Lizard Algorithm (SBLA), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Memetic Fire-Fly Algorithm (MFA). The result shows that the proposed approach based on THD is less than existing approaches.