A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles by Yasin Abdolahi, Sajad Yousefi, Jafar Tavoosi

“…The model predictive controller (MPC) is one of the efficient approaches by which the speed and direction of the near future of an automobile could be predicted and controlled. …”
Get full text

Systematic review railway infrastructure monitoring: From classic techniques to predictive maintenance by Giovanni Bianchi, Chiara Fanelli, Francesco Freddi, Felice Giuliani, Aldo La Placa

“…Recent Artificial Intelligence (AI) algorithms, which enable the use of digital tools such as Data-Driven models that can automatically adapt system operation, make decisions and suggest strategies based on collected data, form the basis of modern Predictive Maintenance (PdM). …”
Get full text

Development and Optimization of the Internet of Things Network for Product Quality Monitoring by U. А. Vishniakou, Hu Zhifeng

Get full text

Parametric Optimization Approach to Evaluate Dynamic Shading Within Double-Skin Insulated Glazed Units for Multi-Criteria Daylighting Performance in Tropics by Ali Ahmed Bahdad, Nooriati Taib, Fahad Saud Allahaim, Ali Mohammed Ajlan

Get full text

Effective Facial Expression Recognition System Using Artificial Intelligence Technique by Imad S. Yousif, Tarik A. Rashid, Ahmed S. Shamsaldin, Sabat A. Abdulhameed, Abdulhady Abas Abdullah

“…This paper presents an improved performance of the Facial Expression Recognition (FER) systems via augmentation in Artificial Neural Networks and Genetic Algorithms, two renowned artificial intelligence techniques possessing disparate strengths. …”
Get full text

Motion Planning and Tracking Control of Autonomous Vehicle Based on Improved A∗ Algorithm by Yunlong Bai, Gang Li, Ning Li

“…To overcome these potential limitations, an improved A∗ algorithm-based motion planning algorithm and a tracking control strategy based on model predictive control theory were proposed in this work. …”
Get full text

A new classification algorithm for low concentration slurry based on machine vision by Chuanzhen Wang, Xinyi Wang, Andile Khumalo, Fengcheng Jiang, Jintao Lv

“…Subsequently, a new low concentration classification model was systematically developed, encompassing aspects such as original image acquisition, data augmentation, dataset partitioning, classification algorithm design, and model evaluation. DCGAN was employed for image generation, achieving favorable outcomes with generator learning rate set at 5 × 10− 5, discriminator at 1 × 10− 6, and iteration number at 2000. …”
Get full text

Predictive diagnostics of computer systems logs using natural language processing techniques by Vladislav A. Kiriachek, Soltan I. Salpagarov

“…This study aims to develop and validate a method for predictive diagnostics and anomaly detection in computer system logs, using the Vertica database as a case study. …”
Get full text

The Artificial Intelligence and the dispute for different ways in its predictive use in the criminal process by Rodrigo Régnier Chemim Guimarães

“…Given the delicate cooperation that must exist between experts in criminal procedure and knowledge engineers in the selection and construction of the algorithms that will teach the machine for the preparation of the predictive research, which one would be appropriate? …”
Get full text

Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States. by Mehdi Mourali, Dallas Novakowski, Ruth Pogacar, Neil Brigden

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? …”
Get full text

An Ensemble Learning-Based Predictive Parameterization Approach for Permanent Magnet Synchronous Machines by Sema Nur Ipek, Nur Bekiroglu, Murat Taskiran

“…An averaging voting ensemble model is developed by integrating the two highest-performing algorithms, LRNN and TRF, leveraging the strengths of both algorithms. …”
Get full text

ML-Based Control Strategy for PHEV Under Predictive Vehicle Usage Behaviour by Aleksandr Doikin, Aleksandr Korsunovs, Felician Campean, Oscar García-Afonso, Enrico Agostinelli

“…This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. In particular, the RusBoosted ensemble classifier performed consistently well across the heterogeneous dataset (volume of data for training and variable imbalance in the datasets, reflecting the natural variability in the vehicle usage profiles), providing sufficiently accurate predictions for the proposed EMS strategy. …”
Get full text

A Decreasing Horizon Model Predictive Control for Landing Reusable Launch Vehicles by Guillermo Zaragoza Prous, Enric Grustan-Gutierrez, Leonard Felicetti

“…A novel approach to model predictive control (MPC) with a decreasing horizon is analysed for guiding and controlling reusable launch vehicles (RLVs) during powered descent phases. …”
Get full text

Population-level predictive variation in machine learning diagnosis of symptomatic bacterial vaginosis by Diandra P. Ojo, Cameron Celeste, Dion Ming, Ruogu Fang, Ivana K. Parker

“…To determine the ability of ML models to perform equitably, this study evaluates the performance of ML algorithms in predicting symptomatic BV across different ethnic groups using 16S rRNA sequencing data. …”
Get full text

A Simplified Predictive Control of Constrained Markov Jump System with Mixed Uncertainties by Yanyan Yin, Yanqing Liu, Hamid R. Karimi

“…A simplified model predictive control algorithm is designed for discrete-time Markov jump systems with mixed uncertainties. …”
Get full text

Variable convergence train noise active control algorithm based on feedforward reference by LI Tao, HE Yuyao, WANG Ning, LI Zhuang, SHEN Zhaoyuan, XIAO Gang

“…Compared with the fixed-convergence FxLMS, variable-convergence NLMS, and SVSLMS control algorithms, it has the advantage of noise reduction up to about 3.6 dB.…”
Get full text

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models by Oluseyi Rotimi Taiwo, Helen Onyeaka, Elijah K. Oladipo, Julius Kola Oloke, Deborah C. Chukwugozie

“…Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.…”
Get full text

Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…The experimental results show that predictive performances of three predictive models are ranked from high to low: SVR, XGBoost, LSTM, among which the predictive performance of SVR is the most stable. …”
Get full text

Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis by M. Y. Arafat, M. J. Hossain, Li Li

“…This paper presents advanced frameworks for microgrid predictive maintenance by performing a comprehensive correlative analysis of advanced recurrent neural network (RNN) architectures, i.e., RNNs, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) for photovoltaic (PV) based DC microgrids (MGs). …”
Get full text

Methodological Aspects of Predictive Mineragenic Studies Using Earth Remote Sensing Data by Petrov Vladislav, Ustinov Stepan, Minaev Vasilii

“…Of the entire range of areas of fundamental and exploratory scientific research, the main attention within the framework of predictive and mineragenic studies is paid to solving the following problems: 1) allocation of lineaments (fault zones) based on processing of digital elevation models; 2) determination of hydraulically active fault structures for the period of ore formation based on tectonophysical reconstructions; 3) analysis of multispectral characteristics of pre-ore, ore-accompanying and post-ore metasomatites based on statistical processing of Landsat-8 satellite data; 4) assessment of fluid-dynamic settings of deposit formation based on data on the composition, properties and genesis of mineral-forming fluids. 5) creation of weight of evidence models based on statistical algorithms for processing data on the dynamics of ore-genetic processes. …”
Get full text