Application Study of Sigmoid Regularization Method in Coke Quality Prediction by Shaohong Yan, Hailong Zhao, Liangxu Liu, Qiaozhi Sang, Peng Chen, Jie Li

“…A regularized network training method based on Sigmoid function is designed considering that redundancy of network structure may lead to the learning of undesired noise, in which weights having little impact on performance and leading to overfitting are removed in terms of computational complexity and training errors. The cascade forward neural network with validation is found to be the most suitable one for coke quality prediction, with errors around 5%, followed by feedforward neural network structure and radial basis neural networks. …”
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Optimizing Hybrid Electric Vehicle Performance: A Detailed Overview of Energy Management Strategies by Álvaro Gómez-Barroso, Iban Vicente Makazaga, Ekaitz Zulueta

“…A thorough and comprehensive overview of rule-based, optimization-based, and learning-based energy management strategies is presented, highlighting their main attributes and providing a comparative analysis in terms of fuel economy improvements, real-time implementation feasibility, and computational complexity, while simultaneously identifying and uncovering areas requiring further research in the field. …”
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Measurement and Statistical Analysis of Distinguishable Multipaths in Underground Tunnels by Junchang Sun, Shuai Ma, Hui Zhou, Chun Du, Shiyin Li

“…The commonly used method is the support vector machine (SVM) method with high computational complexity. To tackle this problem, this paper adopts the SVM classifier based on fewer selected features of the normalized power delay profile (PDP). …”
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Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output syst... by Xiaoping Zhou, Haichao Liu, Bin Wang, Qian Zhang, Yang Wang

“…This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.…”
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Charbonnier Quasi Hyperbolic Momentum Spline Based Incremental Strategy for Nonlinear Distributed Active Noise Control by Rajapantula Kranthi, Vasundhara, Asutosh Kar, Mads Grasboll Christensen

“…Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. …”
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Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand by Yongjian Yang, Xintao Wang, Yuanbo Xu, Qiuyang Huang

“…Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. …”
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Binocular stereo vision-based relative positioning algorithm for drone swarm by Qing Cheng, Yazhe Wang

“…Abstract To address the challenges of high computational complexity and poor real-time performance in binocular vision-based Unmanned Aerial Vehicle (UAV) formation flight, this paper introduces a UAV localization algorithm based on a lightweight object detection model. …”
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Block-Based Mode Decomposition in Few-Mode Fibers by Chenyu Wang, Jianyong Zhang, Baorui Yan, Shuchao Mi, Guofang Fan, Muguang Wang, Peiying Zhang

“…A block-based mode decomposition (BMD) algorithm is proposed in this paper, which reduces computational complexity and enhances noise resistance. The BMD uses randomly selected sample blocks of the beam images to restore mode coefficients instead of all pixels in the beam images. …”
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A lightweight power quality disturbance recognition model based on CNN and Transformer by ZHANG Bide, QIU Jie, LOU Guangxin, ZHOU Can, LUO Qingqing, LI Tianqian

“…A lightweight power quality disturbances (PQDs) recognition model that integrates convolutional neural network (CNN) and Transformer (CaT) is proposed to address the high number of parameters and computational complexity in existing deep learning-based models. …”
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Intelligent methods for natural data analysis: application to space weather by O.V. Mandrikova

“…But these methods have high computational complexity, failing to provide accurate estimates when the signal-to-noise ratio is low. …”
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DBnet: A Lightweight Dual-Backbone Target Detection Model Based on Side-Scan Sonar Images by Quanhong Ma, Shaohua Jin, Gang Bian, Yang Cui, Guoqing Liu

“…Due to the large number of parameters and high computational complexity of current target detection models, it is challenging to perform fast and accurate target detection in side-scan sonar images under the existing technical conditions, especially in environments with limited computational resources. …”
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Fast binary logistic regression by Nurdan Ayse Saran, Fatih Nar

“…Furthermore, to address the common problem of collinear features, we apply singular value decomposition (SVD), resulting in a low-rank representation commonly used to reduce computational complexity while preserving essential features and mitigating noise. …”
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FDK-Type Algorithms with No Backprojection Weight for Circular and Helical Scan CT by A. V. Narasimhadhan, Kasi Rajgopal

“…s algorithm in terms of computational complexity.…”
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Considerations for ethics review of big data health research: A scoping review. by Marcello Ienca, Agata Ferretti, Samia Hurst, Milo Puhan, Christian Lovis, Effy Vayena

“…The methodological novelty and computational complexity of big data health research raises novel challenges for ethics review. …”
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A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks by Shukui Zhang, Hao Chen, Qiaoming Zhu, Juncheng Jia

“…The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic.…”
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Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography by Chun-Chi Chen, Song-Xian Lin, Hyundoo Jeong

“…Through a comparative analysis, this study offers insights into efficient timing correction techniques for enhancing HR estimation from rPPG, particularly suitable for edge-computing applications where low computational complexity is essential. Cubic interpolation can provide robust performance in reconstructing signals but requires higher computational resources, while linear and filter interpolation offer more efficient solutions. …”
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Large-scale multiobjective competitive swarm optimizer algorithm based on regional multidirectional search by Xuenan Zhang, Debao Chen, Fangzhen Ge, Feng Zou, Lin Cui

“…Considering the algorithm’s computational complexity, the strategy selects the optimal individual within each subregion and constructs four-directional search vectors based on the lower limit of the global decision variables and the upper limit of the individual decision variables within the subregion. …”
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A Novel Efficient Passive Spatial Orientation Detection Method of UMT Enabled by ISB by Zhengqiang Xiong, Tao Sun, Zhengxing Wang, Yuhao Wu, Jie Yin

“…Without high computational complexity in any Transform Domain, the time consumption of ECOD is largely reduced, which is especially critical for underwater intrusion detection, territorial waters protection, and many other real-time underwater applications. …”
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A novel graph convolution and frequency domain filtering approach for hyperspectral anomaly detection by Yang Ding, Hao Yan, Jingyuan He, Juanjuan Yin, A. Ruhan

“…The key contributions of this work include: 1) the use of a graph-based model for HSI that effectively integrates both spatial and spectral dimensions, 2) employing KNN for edge construction to include distant pixels and mitigate noise, 3) spatial feature extraction via graph convolution to provide detailed insights into spatial interconnections and variations, enhancing the detection process, and 4) leveraging the beta wavelet filter to handle the ’right-shift’ spectral phenomenon and reduce computational complexity. Experimental evaluations on four benchmark datasets show that the proposed method achieves outstanding performance with AUC scores of 0.9986, 0.9975, 0.9859, and 0.9988, significantly outperforming traditional and state-of-the-art anomaly detection techniques.…”
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Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis by G. Geetha, P. Geethanjali

“…In industrial applications, it is necessary to identify the optimal number of features to differentiate various types of fault characteristics with less computational complexity and cost. However, motor fault diagnosis for real-time applications has challenges in capturing characteristics due to variations in speed, load, force, run-to-failure state as well as the type of the motor and its parts. …”
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