ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition. by David Koslicki, Saikat Chatterjee, Damon Shahrivar, Alan W Walker, Suzanna C Francis, Louise J Fraser, Mikko Vehkaperä, Yueheng Lan, Jukka Corander

“…The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. …”
Get full text

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals by Siddhant Arora, Sridharan Naveen Venkatesh, Vaithiyanathan Sugumaran, Anoop Prabhakaranpillai Sreelatha, Vetri Selvi Mahamuni

“…Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. …”
Get full text

A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD by Fengfeng Bie, Xueping Ding, Qianqian Li, Yuting Zhang, Xinyue Huang

“…Initially, the critical parameters (modal number n and filter length L) of FMD are optimized using an improved genetic algorithm (IGA), and the refined FMD is employed to decompose the vibration signals from the planetary gearbox. …”
Get full text

Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions by Y. Bo, H. Liang, T. Li, F. Zhou, F. Zhou, F. Zhou

“…Here, we propose an advancing framework that addresses these problems by integrating a process-based soil–crop model with vital physiological effects, a novel method for model upscaling, and the non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimization algorithm at a parallel computing platform. …”
Get full text

Real-Time Height Measurement for Moving Pedestrians by Wenju Zhou, Fulong Yao, Wei Feng, Haikuan Wang

“…Firstly, a normalization equation is presented to convert the depth image into the grey image for a lower time cost and better performance. Secondly, a difference-particle swarm optimization (D-PSO) algorithm is proposed to remove the complex background and reduce the noises. …”
Get full text

A lightweight lattice-based group signcryption authentication scheme for Internet of things by XU Chuan, AI Xinghao, WANG Shanshan, ZHAO Guofeng, HAN Zhenzhen

“…In the key generation stage, the improved trapdoor diagonal matrix was designed to optimize the original image sampling algorithm required for key generation and reduce the overall time required for generating a large number of keys. …”
Get full text

Adaptive DBP System with Long-Term Memory for Low-Complexity and High-Robustness Fiber Nonlinearity Mitigation by Mingqing Zuo, Huitong Yang, Yi Liu, Zhengyang Xie, Dong Wang, Shan Cao, Zheng Zheng, Han Li

“…In this paper, an improved A-DBP algorithm with long-term memory (LTM) is proposed, employing root mean square propagation (RMSProp) to achieve low-complexity and high-robustness compensation performances. …”
Get full text

Broad learning system based on attention mechanism and tracking differentiator by LIAO Lüchao, ZOU Weidong, YANG Jialong, LU Huihuang, XIA Yuanqing, GAO Jianlei

“…In terms of model structure, A-TD-BLS introduced self-attention mechanism to the original BLS, and further fused and transformed the extracted features through attention weighting to improve the feature learning ability.In terms of model training methods, a weight optimization algorithm based on tracking differentiator was designed.This method effectively alleviates the overfitting phenomenon of the original BLS by limiting the size of the weight values, significantly reduces the influence of the number of hidden layer nodes on model performance and makes the generalization performance more stable.Moreover, the training algorithm was extended to the BLS incremental learning framework, so that the model can improve performance by dynamically adding hidden layer nodes.Multiple experiments conducted on some benchmark datasets show that compared to the original BLS, the classification accuracy of A-TD-BLS is increased by 1.27% on average on classification datasets and the root mean square error of A-TD-BLS is reduced by 0.53 on average on regression datasets.Besides, A-TD-BLS is less affected by the number of hidden layer nodes and has more stable generalization performance. …”
Get full text

State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model by Jun Chen, Yan Liu, Jun Yong, Cheng Yang, Liqin Yan, Yanping Zheng

“…This optimization enhances the ELM’s performance, addressing instability issues in the standard algorithm. …”
Get full text

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

“…The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. …”
Get full text

A lightweight lattice-based group signcryption authentication scheme for Internet of things by XU Chuan, AI Xinghao, WANG Shanshan, ZHAO Guofeng, HAN Zhenzhen

“…In the key generation stage, the improved trapdoor diagonal matrix was designed to optimize the original image sampling algorithm required for key generation and reduce the overall time required for generating a large number of keys. …”
Get full text

Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties by Hong Zhang, Qianwei Xi, Lei Chen, Yong Min, Xiongxiong Fan, Wenjin Fang, Nan Tian, Fei Xu

“…Taking the lowest comprehensive operation cost as the economic objective, the smallest emissions of CO2 and atmospheric pollutants as environmental objective and the largest user’s comprehensive satisfaction with electricity as the social objective, based on the robust fuzzy theory, the multi-objective uncertainty optimal scheduling model is constructed, which is transformed into deterministic model and then solved by intelligent optimization algorithm. …”
Get full text

A Full-Profile Measurement Method for an Inner Wall with Narrow-Aperture and Large-Cavity Parts Based on Line-Structured Light Rotary Scanning by Zhengwen Li, Changshuai Fang, Xiaodong Zhang

“…Considering the structural constraints in the measurement of narrow-aperture and large-cavity parts, a structural optimization algorithm is designed to enable the sensor to achieve a high theoretical measurement resolution while satisfying the geometric constraints of the measured parts. …”
Get full text

DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions by Chao Cao, Mengli Li, Chunyu Wang, Lei Xu, Quan Zou, Yansu Wang, Wu Han

“…Next, we present a joint model that combines an improved neural graph collaborative filtering method with a feature extraction network for optimization. …”
Get full text

Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network by Zhichao ZHOU, Yi FENG, Xiaohan XIA, Yuyao FENG, Chao CAI, Jiahui QIU, Lihui YANG, Yunxiao WU

“…The positioning scheme based on mobile cellular network technology is one of the important technical approaches to provide network optimization, emergency rescue, police patrol and location services.The traditional positioning scheme based on cell base station location information has low positioning accuracy and large positioning error, so it cannot meet the requirements of some positioning applications.The scheme based on fingerprint location can greatly improve the location accuracy, save computational cost and enhance the usability based on the coarse location scheme of the cell and become the hotspot of the research.Rasterization and non-rasterization of outdoor fingerprint location scheme based on machine learning were studied and analyzed to meet the business requirements of outdoor fingerprint location.By means of parameter weighting, data fitting and other methods, large-scale fingerprint data were cleaned to improve the effectiveness of data sources.Through the realization of sub-modules such as demarcating research area, rasterizing, constructing fingerprint database, training model, correcting model, non-rasterizing, rough positioning coupling, matching parameter and training parameter, the operation efficiency and positioning accuracy of the algorithm were analyzed and optimized, and the key indexes affecting the algorithm performance were determined.Then, the performance of two fingerprint-based localization schemewas analyzed based on the simulation results.Finally, the typical scenarios of the fingerprint location scheme based on machine learning in practical application were presented.…”
Get full text

Comprehensive Evaluation and Trade‐Off of Top‐Level Requirements for BWB UAVs by Xinshi Suo, Zhouwei Fan, Yundong Guo, Tengzhou Xu

“…A parallelizable subset‐simulation optimization algorithm is implemented to iteratively refine the design, thereby maximizing overall system competitiveness. …”
Get full text

Anti-disturbance predictive control for path tracking of unmanned agricultural vehicles based on safety distance by HUANG Zhenzhen, SUN Jinlin, DING Shihong

“…Subsequently, an automatic optimization algorithm for the reference point of the agricultural vehicle is designed to prevent excessive steering during path tracking. …”
Get full text

Anti-disturbance predictive control for path tracking of unmanned agricultural vehicles based on safety distance by HUANG Zhenzhen, SUN Jinlin, DING Shihong

“…Subsequently, an automatic optimization algorithm for the reference point of the agricultural vehicle is designed to prevent excessive steering during path tracking. …”
Get full text

Heuristic Binary Search for Modulated Predictive Control by Rafael FIgueiredo, Igor Oliani, Angelo Lunardi, Alfeu J. Sguarezi Filho

“…Experimental results comparing three variants of the Predictive Torque Control (one vector, three vector and modulated) show improvements in torque and flux ripple and improvements of current THD up to 30% over classic Modulated Predictive Torque Control implementation with reduced or similar computational cost. …”
Get full text

Shoulder–Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration by Siyu Zong, Wei Li, Dawen Sun, Zhuoda Jia, Zhengwei Yue

“…To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. …”
Get full text