MethodsX最新文献_第3页

An efficient method of modulo adder design for Digital Signal Processing applications

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-15 DOI: 10.1016/j.mex.2025.103216

Subodh Kumar Singhal , Sumit Kumar , Sujit Kumar Patel , K. Anjali Rao , Gaurav Saxena

Modulo adder is a widely used arithmetic component in many Digital Signal Processing (DSP) applications such as Finite Impulse Response (FIR), Infinite Impulse Response (IIR) filters, digital signal processors, image processing modules, discrete cosine transform, and cryptography. Therefore, in this paper, the critical path delay and area of modulo adder are analyzed. An optimized diminished-one modulo adder for

2^{n} + 1

is proposed based on the analysis results.

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Theoretical comparison shows that the suggested modulo adder involves 23.41 % less area (transistors count) and 31.64 % less delay than the best existing design for an average bit-width.
•
Synthesis result reveals that the proposed modulo adder involves 13.71 % less area and 14.5 % less delay compared to the best existing modulo adder structure design in the literature for an average bit-width.
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To observe the overall efficacy of the suggested modulo adder design, the area delay product (ADP) and power delay product (PDP) values of the proposed and existing modulo adder designs are computed using synthesis data. The values obtained for ADP and PDP reveal that the proposed design achieves a 26.2 % reduction in ADP and a 32.8 % improvement in PDP compared to the best available modulo-adder structure.

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引用次数: 0

“Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand” "利用堆叠式自动编码器神经网络优化仿生手的 sEMG 手势识别"

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-15 DOI: 10.1016/j.mex.2025.103207

Mr. Amol Pandurang Yadav , Dr. Sandip.R. Patil

This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robustness of gesture classification.

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Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components.
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Time Domain Parameters: A total of 28 features per subject were extracted from the time domain, including statistical and spectral features.
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Classifier Evaluation: Initial evaluations involved Autoencoder and LDA (Linear Discriminant Analysis) classifiers, with Autoencoder achieving an average accuracy of 77.96 % ± 1.24, outperforming LDA's 65.36 % ± 1.09.

Advanced Neural Network Approach: Stacked Autoencoder Neural Network: To address challenges in distinguishing similar gestures within grasp groups, a Stacked Autoencoder Neural Network was employed. This advanced neural network architecture improved classification accuracy to over 100 %, demonstrating its effectiveness in handling complex gesture recognition tasks. These findings emphasize the significant potential of deep learning models in enhancing prosthetic control and rehabilitation technologies. . To verify these findings, we developed a 3d hand module in ADAMS software that is simulated using Matlab-ADAMS cosimulation.

{"title":"“Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand”","authors":"Mr. Amol Pandurang Yadav , Dr. Sandip.R. Patil","doi":"10.1016/j.mex.2025.103207","DOIUrl":"10.1016/j.mex.2025.103207","url":null,"abstract":"<div><div>This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robustness of gesture classification.<ul><li><span>•</span><span><div>Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components.</div></span></li><li><span>•</span><span><div>Time Domain Parameters: A total of 28 features per subject were extracted from the time domain, including statistical and spectral features.</div></span></li><li><span>•</span><span><div>Classifier Evaluation: Initial evaluations involved Autoencoder and LDA (Linear Discriminant Analysis) classifiers, with Autoencoder achieving an average accuracy of 77.96 % ± 1.24, outperforming LDA's 65.36 % ± 1.09.</div></span></li></ul>Advanced Neural Network Approach: Stacked Autoencoder Neural Network: To address challenges in distinguishing similar gestures within grasp groups, a Stacked Autoencoder Neural Network was employed. This advanced neural network architecture improved classification accuracy to over 100 %, demonstrating its effectiveness in handling complex gesture recognition tasks. These findings emphasize the significant potential of deep learning models in enhancing prosthetic control and rehabilitation technologies. . To verify these findings, we developed a 3d hand module in ADAMS software that is simulated using Matlab-ADAMS cosimulation.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103207"},"PeriodicalIF":1.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-13 DOI: 10.1016/j.mex.2025.103219

D. Dhinakaran , L. Srinivasan , S. Edwin Raja , K. Valarmathi , M. Gomathy Nayagam

Feature selection and classification efficiency and accuracy are key to improving decision-making regarding medical data analysis. Since the medical datasets are large and complex, they give rise to certain problematic issues such as computational complexity, limited memory space, and a lesser number of correct classifications. In order to overcome these drawbacks, the new integrated algorithm is presented here: Synergistic Kruskal-RFE Selector and Distributed Multi-Kernel Classification Framework (SKR-DMKCF). The innovative architecture of SKR-DMKCF results in the reduction of dimensionality while preserving useful characteristics of the image utilizing recursive feature elimination and multi-kernel classification in a distributed environment. Detailed evaluations were performed on four broad medical datasets and established our performance advantage. The average feature reduction ratio was 89 % for the proposed method, SKR-DMKCF, which can outperform all the methods by achieving the best classification average accuracy of 85.3 %, precision of 81.5 %, and recall 84.7 %. On the efficiency calculations, it was seen that the memory usage is a 25 % reduction compared to the existing methods and the speed-up time was a significant improvement as well to assure scalability for resource-limited environments.

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Innovative Synergistic Kruskal-RFE Selector for efficient feature selection in medical datasets.
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Distributed Multi-Kernel Classification Framework achieving superior accuracy and computational efficiency.

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引用次数: 0

Heat4Future: A strategic planning tool for decarbonizing district heating systems

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-13 DOI: 10.1016/j.mex.2025.103222

Nina Kicherer , Pablo Benalcazar , Peter Lorenzen , Olessya Kozlenko , Sadi Tomtulu , Jan Trosdorff

Strategic planning of future heat supply, particularly in the context of district heating systems, is essential for achieving a viable and cost-efficient energy transition. However, existing planning tools for district heating systems often require detailed data, which is frequently unavailable in the early stages of the planning process. In response to this challenge, this paper presents Heat4Future, a new planning tool designed to provide insights into potential decarbonized district heating systems for a given location using minimal input data.

The tool demonstrates the implementation of an innovative methodology for strategic planning of district heating systems. It uses a snapshot simulation model to configure the supply for a particular district heating system, taking into account the annual heat demand and user-specified heat sources. The tool comprises of four modules for calculating the cost-effective generation profile of the system. It is designed to generate detailed hourly profiles over an entire year for key parameters essential to the operation and planning of a DHS using a specified set of renewable energy sources, including heat load, generation, and storage profiles.

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Heat4Future provides an overview of the possibilities for a decarbonized district heating supply in a specific location.
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The simulation tool contains four modules for calculating the system's generation profile: Weather and Environmental Data Module, Thermal Load Module, Buffer Thermal Energy Storage Module, and Strategic Heat Planning Module.
•
The tool is licensed under the MIT License and is available as an open-source repository on GitLab.

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引用次数: 0

Analytic study and statistical enforcement of extended beta functions imposed by Mittag-Leffler and Hurwitz-Lerch Zeta functions

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-13 DOI: 10.1016/j.mex.2025.103206

Faten F. Abdulnabi , Hiba F. Al-Janaby , Firas Ghanim

Special Function Theory is used in many mathematical fields to model scientific progress, from theoretical to practical. This helps efficiently analyze the newly expanded Beta class of functions on a complicated domain. We use Mittag-Leffler and Hurwitz Lerch zeta (HLZ) kernels to produce the Beta function using the convolution tool. This special function advances a statistical implementation research approach. This unique function also discusses and gives analytical benefits, including functional and summation relations, Mellin transformations, and integral representations. Additionally, many derivative formulae are obtained. The statistical implementation of expanded Beta distribution using the suggested beta function was also conducted. We use the extended Beta function to create the new extended ordinary hypergeometric function and Kummer function. Derivative formulae, integral representations, generating functions, and fractional derivatives are also investigated.

•
Developed utilizing Mittag-Leffler and Hurwitz Lerch Zeta functions as kernels, delivering increased analytical and computational capabilities.
•
Comprises derivative formulae, integral representations, Mellin transformations, and generating functions, offering a comprehensive mathematical foundation.
•
Illustrates the use of the extended Beta function inside the Beta distribution, highlighting its statistical importance.

特殊函数理论在许多数学领域都被用于建立从理论到实践的科学进步模型。这有助于高效分析复杂域上新扩展的 Beta 类函数。我们使用 Mittag-Leffler 和 Hurwitz Lerch zeta (HLZ) 核，利用卷积工具生成 Beta 函数。这一特殊函数推进了统计实现研究方法。这个独特的函数还讨论并给出了分析优势，包括函数和求和关系、梅林变换和积分表示。此外，还获得了许多导数公式。我们还利用建议的贝塔函数对扩展贝塔分布进行了统计实现。我们利用扩展 Beta 函数创建了新的扩展普通超几何函数和库默函数。利用 Mittag-Leffler 和 Hurwitz Lerch Zeta 函数作为内核进行开发，提高了分析和计算能力。

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引用次数: 0

Enhancing efficiency in photovoltaic hydrogen production: A comparative analysis of MPPT and electrolysis control strategies

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-13 DOI: 10.1016/j.mex.2025.103220

Shen Yuong Wong, Jiawei Li

With the rapid growth of photovoltaic installed capacity, photovoltaic hydrogen production can effectively solve the problem of electricity mismatch between new energy output and load demand. Photovoltaic electrolysis systems pose unique challenges due to their nonlinear, multivariable, and complex nature. This paper presents a thorough investigation into the control methodologies for such systems, focusing on both Maximum Power Point Tracking (MPPT) and electrolysis cell control strategies. Beginning with a comprehensive review of MPPT techniques, including classical, intelligent, optimization, and hybrid approaches, the study delves into the intricate dynamics of Proton Exchange Membrane Electrolysis Cells (PEMEL). Considering the nonlinear and time-varying characteristics of PEMEL, various control strategies such as Proportional-Integral-Derivative (PID), robust, Model Predictive Control (MPC), and Fault Tolerant Control (FTC) are analyzed. Evaluation metrics encompass stability, accuracy, computational complexity, and response speed. This paper provides a comparative analysis, encapsulating the strengths and limitations of each MPPT and PEM control technique.

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引用次数: 0

Accurate sampling of undisturbed top sediment from grab sampler collected using aluminum tube and stainless-steel containers for shallow and deep-sea applications

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-12 DOI: 10.1016/j.mex.2025.103213

Mutsumi Iizuka , Atsuko Amano , Takuya Itaki

This study describes a sediment sampling protocol using a Kinoshita-type grab (K-grab) sediment sampler to collect and analyze microplastics (<5 mm) and macroplastics (>5 mm) in marine sediments. During the GB24 geological survey cruise aboard the Bosei-maru, 133 surface sediment samples were collected from depths of 20–800 m. The K-grab, equipped with a head-slide weight mechanism, enhanced sampling efficiency across various sediment types. For microplastics, stainless steel containers and J-shaped aluminum tubes minimized contamination while maintaining sample integrity. Macroplastics were separated using a 5 mm mesh and analyzed on board. Method verification confirmed high-spatial-resolution sampling with minimal contamination. These results demonstrate that the K-grab is a reliable tool for microplastic and macroplastic analysis, providing valuable data on plastic pollution in marine sediments.

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This study describes a sediment sampling protocol using a grab sampler to collect and analyze microplastics (<5 mm) and macroplastics (>5 mm) in marine sediments.
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During the survey, 133 surface sediment samples were collected from depths of 20–800 m, with microplastics handled using J-shaped aluminum tubes and stainless steel containers to minimize contamination while maintaining sample integrity.
•
Macroplastics were separated using a 5 mm mesh and analyzed on board. Method verification confirmed high-spatial-resolution sampling with minimal contamination.

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引用次数: 0

PyPortOptimization: A portfolio optimization pipeline leveraging multiple expected return methods, risk models, and post-optimization allocation techniques PyPortOptimization：利用多种预期收益方法、风险模型和优化后配置技术的投资组合优化管道

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-07 DOI: 10.1016/j.mex.2025.103211

Rushikesh Nakhate , Harikrishnan Ramachandran , Amay Mahajan

This paper presents PyPortOptimization, an automated portfolio optimization library that incorporates multiple methods for expected returns, risk return modeling, and portfolio optimization. The library offers a flexible and scalable solution for constructing optimized portfolios by supporting various risk-return matrices, covariance and correlation matrices, and optimization methods. Users can customize the pipeline at every step, from data acquisition to post-processing of portfolio weights, using their own methods or selecting from predefined options. Built-in Monte Carlo simulations help assess portfolio robustness, while performance metrics such as return, risk, and Sharpe ratio are calculated to evaluate optimization results.

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The study compares various configured methods for each step of the portfolio optimization pipeline, including expected returns, risk-modeling and optimization techniques.
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Custom Designed Allocator outperformed. For example, the Proportional Allocator's sharpe ratio of out-performed the expected average.
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A caching system was implemented to optimize execution time.

{"title":"PyPortOptimization: A portfolio optimization pipeline leveraging multiple expected return methods, risk models, and post-optimization allocation techniques","authors":"Rushikesh Nakhate , Harikrishnan Ramachandran , Amay Mahajan","doi":"10.1016/j.mex.2025.103211","DOIUrl":"10.1016/j.mex.2025.103211","url":null,"abstract":"<div><div>This paper presents PyPortOptimization, an automated portfolio optimization library that incorporates multiple methods for expected returns, risk return modeling, and portfolio optimization. The library offers a flexible and scalable solution for constructing optimized portfolios by supporting various risk-return matrices, covariance and correlation matrices, and optimization methods. Users can customize the pipeline at every step, from data acquisition to post-processing of portfolio weights, using their own methods or selecting from predefined options. Built-in Monte Carlo simulations help assess portfolio robustness, while performance metrics such as return, risk, and Sharpe ratio are calculated to evaluate optimization results.<ul><li><span>•</span><span><div>The study compares various configured methods for each step of the portfolio optimization pipeline, including expected returns, risk-modeling and optimization techniques.</div></span></li><li><span>•</span><span><div>Custom Designed Allocator outperformed. For example, the Proportional Allocator's sharpe ratio of out-performed the expected average.</div></span></li><li><span>•</span><span><div>A caching system was implemented to optimize execution time.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103211"},"PeriodicalIF":1.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-07 DOI: 10.1016/j.mex.2025.103201

Jayapradha J , Su-Cheng Haw , Naveen Palanichamy , Kok-Why Ng , Senthil Kumar Thillaigovindhan

In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT). The model integrates two architectures, MobileNetV2 and U-NET, for classifying the input lung data. The input CT lung images are pre-processed using Z-score Normalization. The semantic features of lung images are extracted based on texture, intensity, and shape to provide information to the training network.

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In this work, the transfer learning technique is incorporated, and the pre-trained NN was used as an encoder for the U-NET model for segmentation. Furthermore, Support Vector Machine is used here to classify input lung data as benign and malignant.
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The results are measured based on the metrics such as, specificity, sensitivity, precision, accuracy and F-Score, using the data from benchmark datasets. Compared to the existing lung tumor segmentation and classification models, the proposed model provides better results and evidence for earlier disease diagnosis.

{"title":"IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT","authors":"Jayapradha J , Su-Cheng Haw , Naveen Palanichamy , Kok-Why Ng , Senthil Kumar Thillaigovindhan","doi":"10.1016/j.mex.2025.103201","DOIUrl":"10.1016/j.mex.2025.103201","url":null,"abstract":"<div><div>In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT). The model integrates two architectures, MobileNetV2 and U-NET, for classifying the input lung data. The input CT lung images are pre-processed using Z-score Normalization. The semantic features of lung images are extracted based on texture, intensity, and shape to provide information to the training network.<ul><li><span>•</span><span><div>In this work, the transfer learning technique is incorporated, and the pre-trained NN was used as an encoder for the U-NET model for segmentation. Furthermore, Support Vector Machine is used here to classify input lung data as benign and malignant.</div></span></li><li><span>•</span><span><div>The results are measured based on the metrics such as, specificity, sensitivity, precision, accuracy and F-Score, using the data from benchmark datasets. Compared to the existing lung tumor segmentation and classification models, the proposed model provides better results and evidence for earlier disease diagnosis.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103201"},"PeriodicalIF":1.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced load balancing techniques using MIMO fuzzy logic: A panel distribution case study at state polytechnic of Malang

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-06 DOI: 10.1016/j.mex.2025.103197

Ika Noer Syamsiana, Harry Hassidiqi, Wijaya Kusuma, Anang Dasa Novfowan, Arwin Datumaya Wahyudi Sumari, Chandra Wiharya

The issue of unbalance in electrical distribution systems is a significant challenge that requires effective management to ensure stability, reliability, and safety. Load imbalance can result in overheating of transformers and other electrical equipment, reducing their operational life and increasing the risk of failure, even leading to power outages.

A study employed fuzzy logic to address the problem, achieving load balancing through the utilization of the Sugeno Fuzzy Logic method. The objective of this research is to make a significant contribution to improving the efficiency, reliability, and scalability of the power distribution system, with the ultimate goal of maximizing the use of electrical equipment.

It will facilitate the implementation of more intelligent and adaptive decision-making processes. The method is as follows:

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The fuzzy approach used a multi-input multi-output (MIMO) system with rule-base 3 × 3 × 3 matrix.
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The Sugeno method was selected due to its utilization of a constant mathematical function. This approach offers the benefit of straightforward computation, which can enhance the system's speed and efficiency.
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The results showed that the initial load imbalance was 30.86 %, reduced to 5.59 % after the application of load balancing, this is following the IEEE std 446–1995 which allows the maximum load imbalance percentage to be 5–20 %.

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引用次数: 0