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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Kontogiannis, Dimitrios Bargiotas, Dimitrios Daskalopulu, Aspassia Tsoukalas, Lefteri H. University of Thessaly Department of Electrical and Computer Engineering Volos Greece Purdue University Center for Intelligent Energy Systems West Lafayette United States

ISBN: (纸本)9798350318067

Short-term load forecasting tasks utilize a plethora of weather sequences in order to study meaningful features that describe complex relationships between environmental parameters and the variable of load as well as the occurrence of impactful events, reinforcing the predictive power of estimators. It is evident that the inclusion of those additional features increases the input dimensions and decreases the overall explain ability of forecasting models since not all of the extracted features are equally compatible with the assumptions of the regressors, resulting in models where the justification of predictive potency, the connection between the variance of the target variable and the variance of predictors as well as the explanation of the computational flow are made increasingly difficult due to ambiguity. Therefore, this research work studies the representativeness of weather variables and examines the explain ability of interpretable short-term load forecasting structures that utilize environmental sequences towards the selection of important feature subsets that maximize the explained variance of load time series as well as the seasonal, trend and residual load components during training. This project considers linear regression and prominent tree-based estimators coupled with model-dependent feature importance frameworks utilizing game theoretic, permutative and impurity-based approaches for the derivation of optimal feature sets and feature set combinations in terms of explained variance scores. Additionally, two structural approaches of explainable feature set generation from the joint feature importance scores through the utilization of hierarchical and maj ority inclusion are compared and the main observations from each stage of this analysis are discussed. © 2023 IEEE.

关键词： Machine learning

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Non-integer Order Control Scheme for Pressurized Water Reactor Core Power

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computers, Materials & Continua 2022年第7期72卷 651-662页

作者： Ibrahim M.Mehedi Maher H.AL-Sereihy Asmaa Ubaid Al-Saggaf Ubaid M.Al-Saggaf Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia

Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very *** reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of *** overcome the uncertainties,a non-integer-based fractional order control method is demonstrated to control the core power of *** available dynamic model of the reactor core is used in this *** power is controlled using a modified state feedback approach with a non-integer integral scheme through two different approximations,CRONE(Commande Robuste d’Ordre Non Entier,meaning Non-integer orderRobust Control)and FOMCON(non-integer order modeling and control).Simulation results are produced using MATLAB■*** non-integer results are compared with an integer order PI(Proportional Integral)algorithm to justify the effectiveness of the proposed ***-spacemodel Core power control Non-integer control Pressurized water reactor PI controller CRONE FOMCON.

关键词： Sate-space model core power control non-integer control pressurized water reactor PI controller CRONE FOMCON

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Monocular vision navigation system for UAV autonomous mission: a real-time window-based obstacle avoidance approach

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Neural Computing and Applications 2025年第16期37卷 9843-9860页

作者： Javaid, Abdulrahman Alnaser, Mustafa Baroudi, Uthman Alfaraj, Amjad Research and Development Department Yokogawa Saudi Arabia Company Al Khobar Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Center for Intelligent Secure Systems King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia

The need for autonomous unmanned aerial vehicles (UAVs) is rapidly increasing for various industrial applications today. However, the realization of UAV autonomy requires a careful mix of Artificial Intelligence (AI) and computer vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low-cost hardware components. We have infused state-of-the-art AI and CV technologies into our developed system architecture to improve autonomous navigational capabilities of UAVs. The proposed architecture has been implemented and tested on the DJI Tello UAV equipped only with a monocular camera on-board and an inertial measurement unit (IMU). We focused on solving multiple practical technical problems that have arisen during this study due to the hardware limitations of the UAV. The study investigated methods for representing obstacles within an environmental map, which are crucial for effective avoidance and path planning, such as their edges, width, and shape, to ensure accurate detection and navigation. The experiments were carried out in an environment that included dynamic obstacles that were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has a high potential to achieve autonomous UAV operation in the field with low hardware and energy requirements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Control of Linear Servo Carts with Integral-Based Disturbance Rejection

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computers, Materials & Continua 2022年第10期73卷 453-463页

作者： Ibrahim M.Mehedi Abdulah Jeza Aljohani Ubaid M.Al-Saggaf Ahmed I.Iskanderani Thangam Palaniswamy Mohamed Mahmoud Mohammed J.Abdulaal Muhammad Bilal Waleed Alasmary Department of Electrical and Computer Engineering(ECE) King Abdulaziz University5521589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz University6521589Saudi Arabia Electrical and Computer Engineering TN38505United States Computer and Information Systems Umm Al-Qura UniversityMakkahSaudi Arabia

This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control(ILADRC)scheme to detect and respond to *** upgrade in this control technique provides extensive immunity to uncertainties,attenuation,internal disturbances,and external sources of *** fundamental technology base of LADRC is Extended State Observer(ESO).LADRC,when combined with Integral action,becomes a hybrid control technique,namely *** tracking is based on Bode’s Ideal Transfer Function(BITF)in this proposed ILADRC *** proves to be a very robust and appropriate pole placement *** proposed LSC system has experimented with the hybrid ILADRC technique plotted the *** the results,it is evident that the proposed ILADRC scheme enhances the robustness of the LSC system with remarkable disturbance ***,the results of a linear quadratic regulator(LQR)and ILADRC schemes are comparatively *** analysis deduced the improved performance of ILADRC over the LQR control scheme.

关键词： Pole placement ADRC Active disturbance rejection robust control linear servo cart system

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OBSO Based Fractional PID for MPPT-Pitch Control of Wind Turbine systems

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computers, Materials & Continua 2022年第5期71卷 4001-4017页

作者： Ibrahim M.Mehedi Ubaid M.Al-Saggaf Mahendiran T.Vellingiri Ahmad H.Milyani Nordin Bin Saad Nor Zaihar Bin Yahaya Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical&Electronics Engineering Universiti Teknologi PETRONASMalaysia

In recent times,wind energy receives maximum attention and has become a significant green energy source *** wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and *** turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power *** pitch control angle is employed to effectively operate the WT at the above nominal wind ***,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating *** achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly *** this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT *** proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch *** OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID *** performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different *** simulation outcomes ensured the promising characteristics of the proposed model over the other methods.

关键词： Wind turbine wind energy pitch control brain storm optimization PID controller maximum power point

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From Simulation to Reality with Random Noise

From Simulation to Reality with Random Noise

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Luo, Steven Lipkis, Rory López-Francos, Ignacio Vlastos, Pavlo Agogino, Adrian Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Intelligent Systems Division NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at relatively low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is not straightforward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult to perform. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research center and train a small convolutional neural network to classify camera position of an ego vehicle relative to a center line. Further models are trained with different types of noise progressively added to the data. In our experiments, we find that naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Remote Aerial Vehicle Solutions for Weed Detection in Precision Agriculture

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IEEE Access 2025年 13卷 101440-101454页

作者： Shekhar S. Borah Aryan Anand Prabha Sundaravadivel Reginald Fletcher Krishna N. Reddy Department of Electrical and Computer Engineering Center for Robotics and Intelligent Systems The University of Texas at Tyler Tyler TX USA Crop Production Systems Research Unit USDA-Agricultural Research Service (ARS) Stoneville MS USA

This study presents a novel Remote Aerial Vehicle-based approach for detecting pigweeds in soybean (Glycine max) fields using a combination of deep learning and advanced image processing techniques. A custom, high-resolution dataset comprising RGB (red, green, and blue) and multispectral images was collected from USDA operated fields and manually annotated for target pigweed detection. Beyond incorporating YOLOv8 variants for real-time weed classification, this study integrates a comprehensive image processing pipeline, incorporating global thresholding, k-means clustering, 3D surface mapping, and spectral signature analysis to enhance interpretability and detection accuracy. A comparative evaluation of YOLOv8 nano, small, medium, and large models, along with Faster R-CNN, confirmed YOLOv8’s balance between detection accuracy and real-time performance for deployment in precision agriculture. The YOLOv8 nano model emerged as the most balanced in terms of precision (75.6%), recall (81.7%), and mAP@0.5 (81.5%), demonstrating effective weed detection performance under real field conditions. In addition, quadrant-level weed coverage and spatial heatmaps were generated to support targeted interventions. This study advances the current state of RAV based weed detection by providing a field-ready, explainable, and resource-efficient solution, contributing to sustainable farming and data-driven weed management practices.

关键词： Crops Deep learning Accuracy Autonomous aerial vehicles Agriculture Real-time systems Feature extraction Soil Precision agriculture Drones

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A Rigid-Flexible Coupling Recursive Formulation for Dynamic Modeling of Biped Robots 16th

A Rigid-Flexible Coupling Recursive Formulation for Dynamic...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Tang, Lingling Liang, Dingkun Wang, Xin Xie, Anhuan Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9789819964796

Biped robots are complex rigid-flexible coupling multibody systems that achieve the desired stable locomotion through foot-ground interactions. In the recent bio-inspired structure designs for biped robots, flexible parts are usually adopted to absorb the foot-ground impact and store the contact energy. However, most existing modeling formulations can only treat biped robots as rigid multibody systems, and the flexible parts are simply replaced by some linear or torsion springs. Since the deformations of the flexible parts have significant effects on the dynamic responses of the biped robot, the rigid multibody modeling method inevitably leads to a gap between the simulation model and the real prototype, and makes the sim-to-real transfer more difficult. To address this problem, a rigid-flexible coupling recursive modeling formulation is presented in this paper. In addition, the foot-ground interaction is modeled via a smooth normal contact model and a velocity-based Coulomb friction model. Finally, several numerical examples including inverse dynamics, kinematic closed-loop and frictional contact are given to verify the rigid-flexible coupling formulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine design

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