bluuebunny/crossref_metadata_2025_split huggingface dataset:
A dynamic target tracking model for uavs based on the fusion of twin networks and deep learning
Authors: Yang Xiaocong , Huang Xilong , Huang Ziqun , Zhu Lanchong , Li Xuwei 
Published: August 2024

Abstract
In this paper, a deep learning model incorporating twin networks is proposed to address the challenge of dynamic target tracking by UAVs in complex environments. By exploring the combination of twin networks and convolutional neural networks (CNNs) in detail, this study aims to develop an efficient algorithm capable of accurately tracking dynamic targets under various environmental conditions. The design of the model focuses on utilizing the strong feature-matching capability of twin networks and the advantages of CNNs in image processing in order to improve tracking accuracy and response speed. Experimental simulations show that the model in this paper exhibits better accuracy and response time than the existing popular algorithm YOLOv3 in dynamic target tracking tasks at different difficulty levels. In particular, the present model demonstrates higher robustness and adaptability under different conditions. In addition, the practical application potential of the model is discussed in this paper, pointing out its wide application prospects in the fields of unmanned aerial surveillance and environment sensing of self-driving vehicles.
Multi-Agent DRL for task offloading with delay reliability assurances in ris-aided MEC in UDN
Authors: Cai Yunlong , Dong Chongwu , Qiao Cheng , Wen Wushao 
Published: August 2024

Abstract
This paper focuses on the scenario where Reconfigurable Intelligent Surfaces (RISs) are introduced in ultra-dense networks (UDN) to guarantee the transmission performance of task offloading of users in Mobile Edge Computing (MEC). Considering users’ task arrival and wireless channel stochasticity, we analyze the delay violation probability bound for each user’s two-hop tandem queueing system with the Martingale theory, i.e., the task offloading and edge computing queues. By taking the delay violation probability bounds as the service reliability constraint, we further propose a MADRL-based algorithm, which co-trains each user and BS as independent and heterogeneous agents and maximizes the total energy efficiency of the system by jointly optimizing the users’ offloading ratio, the RISs’ phase-shift, and the BSs’ resource allocation policy. Experimental results show that our algorithm can improve the EE by about 17.9% compared to other alternative methods under different reliability requirements.
Research and comparison of algorithms based on multi-modal fusion
Authors: Li Cheng Yang 
Published: August 2024

Abstract
A multi-modal fusion algorithm is an important method for information fusion on multi-modal data provided by different sensors. It can make full use of the advantages of multiple sensors and improve the accuracy and robustness of data processing and decision-making. This paper aims to study the performance difference between the extended Kalman filter (EKF) algorithm and other algorithms in multi-modal fusion and explore a method to fuse multiple algorithms further to improve the accuracy of fusion results. The author uses the classic test set data set for experiments to evaluate the performance of different algorithms. By comparing and analyzing the performance of each algorithm in data fusion tasks, we can get their advantages and disadvantages in different scenarios. Among them, the extended Kalman filter algorithm is a classical algorithm based on Bayesian filtering, which can estimate the system state through recursive state estimation and covariance update. In addition, the author also uses linear regression, random forest, and other algorithms to compare. Then, the author uses several test sets to evaluate the performance of each algorithm. Through a comprehensive analysis of the MSE and MAE errors output by the algorithm, the applicability, advantages, and disadvantages of each algorithm in different scenarios are obtained.
A UAV communication strategy based on user distribution time series prediction
Authors: Cheng Hao , Liang Dong 
Published: August 2024

Abstract
To maximize the role of unmanned aerial vehicles (UAVs) as mobile relays to assist communication, while meeting the requirements of service quality and energy constraints, it is necessary to fully consider the motion trajectories and spatiotemporal distribution of ground users in UAV wireless communication systems. Therefore, an algorithm that combines Long Short-Term Memory (LSTM) and Gaussian Mixture Model (GMM) is proposed to predict user distribution. The LSTM, with its capability of handling temporal data, enables the UAV to refer to the preceding information during flight decision-making and make action outputs based on predictions of the dynamic environment. Simulation results demonstrate that the proposed approach can accurately predict the position information of mobile users, achieve faster convergence, and obtain better performance in dynamic scenarios. The system performance has improved by 5.8 percent.
Inverse Kinematics Analysis of a 2-DOF Stabilized Platform using Simulink and Simscape
Authors: Koraaa Mahmoud M. , Ghamry Khaled A. , Abo-Elnor Mootaz E. , Elsherif Ibrahim A. 
Published: August 2024

Abstract
This paper presents a parallel manipulator with two universal-prismatic-universal (UPU)-type active limbs and one revolute-spherical (RS)-type passive limb which is used as a stabilizing platform based on the shipboard helicopter’s landing process and its kinematics are studied systematically. First, a 3D model of this stabilized platform is constructed, and its degrees of freedom are analyzed. Second, analytic formulas for solving the inverse kinematics and calculating the required lengths of each limb are derived and modeled using Simulink®. Third, a Simscape™ model is constructed for the proposed design, and finally, the comparison of the results between the two models is done.
Kalman filter-based sensor fusion for Ackermann steering mobile robots
Authors: Peiris M , Lang H , El-Gindy M 
Published: August 2024

Abstract
Autonomous navigation technologies are continuously improving in terms of performance and safety. A key subfield of autonomous navigation for mobile robots is localization or precise positioning, involving the robot understanding its position relative to key points of interest in the environment. Traditionally, localization is purely dependent on the quality of input received from onboard sensors and therefore localization quality degrades in the presence of high sensor error or other faults. Sensor fusion is useful to combat this. Sensor fusion is used to combine various data types from different sensors to gain a lower overall uncertainty or error in the resulting data, when compared to the separate data from each individual sensor. In this study a four-wheel mobile robot with front-wheel driving and steering is localized during navigation. The investigation is performed in simulation and with a physical prototype. The mobile robot has several sensors including a wheel encoder on each wheel, an inertial measurement unit as well as an indoor GPS system. A designed Kalman Filter called The Combined Kalman Filter has been designed to fuse the sensor input data in order to output the position data required to localize the robot in (X, Y) cartesian coordinates. This is completed in the presence of sensor bias error and noise with the output path compared against a pre-selected ground truth trajectory. The proposed filter is based on the Extended Kalman Filter to contend with the non-linear model arising from the four-wheel steerable robot with the steering system following the Ackermann steering condition. The robot model and sensors are simulated in MATLAB for a chosen ground truth trajectory with results highlighting any detected faults and final accurate position information. It was seen that the estimated filter output had high accuracy when compared with the ground truth. Compared with other investigations the proposed filter has reduced mean error and minimal deviations compared with the ground truth path. In addition, this filtering model can be used in general for the localization of other actuated steering vehicles and aims to widen the research in this field as the majority of research in this area leverages differential drive robots which require less complex modeling.
The Impact of Exhaust Louvres and Emission Rates on The Effectiveness of Parking Lot Ventilation
Authors: Omar M H , Kamal M M , Abotaleb H A 
Published: August 2024

Abstract
This research investigated a couple of aspects of CO emissions from car vehicles. Firstly, it explored the influence of varying the number of vehicles in operation. Secondly, it analysed the significance of exhaust vents affecting the concentration of CO through parking space lots. The conducted analysis relied on simulation data obtained using ANSYS-Software, as opposed to experimental data. The accuracy of the natural ventilation model was validated. The impact of various running vehicles was examined, including those with half, partially, and fully-loaded. The objective was to determine the correlation between the number of running vehicles and the increase in CO concentration. In addition, several configurations of exhaust vents were examined for the fully loaded scenario. As a result, it was shown that the architectural layout has an impact on the natural ventilation system, which may not be suitable for human health, especially when dealing with greater car emission rates.
On the microstructure and hardness evaluation of aluminum metal matrix composites reinforced with in situ (Fe-Al-Cr-Ni) intermetallic compounds
Authors: Khalifa M , Anas O , Shamekh M , Osman M 
Published: August 2024

Abstract
In this study, Aluminum metal matrix composites (AMMCs) were produced through stir casting technique, using in situ intermetallic compounds as reinforcement. The in situ synthesis of these reinforcement phases involved the addition of stainless-steel particles (type 316L) at various weight percentages of 5, 10, and 15 wt. %. The impact of varying the amount of stainless particles added on the hardness and microstructure of the resulting AMMCs was examined. X-ray diffraction patterns (XRD) and energy dispersive spectroscopy (EDS) analysis reveal the formation of two types of intermetallic compounds: iron aluminide intermetallic compounds in irregular plate and needle-like morphologies and another one with higher percentage of Cr in a hexagonal-plate like morphology. Microstructural characterization shows a reputable distribution of the newly formed intermetallic phases. The findings indicate that the hardness of the composites improves as the stainless particles content increases. Hardness of the AMMC fabricated by adding 15 wt.% stainless steel was tripled compared with that of pure aluminium.
Advancing Machinability of Ti-6Al-4V Alloy: A Sustainable Integrated Approach
Authors: Omer Mohamed A.E. , Ibrahim Ahmed. M.M. , Abu-Qudeiri Jaber , Mourad Abdel-Hamid , Hegab Hussien 
Published: August 2024

Abstract
Titanium alloys are indispensable materials in numerous industries, including aerospace, medical, and dental fields. This prevalence is due to their unique properties, such as their high strength-to-weight ratio, corrosion resistance, low thermal expansion, and biocompatibility. On the other hand, the machinability of these materials faces a dual challenge: their low thermal conductivity, which confines heat within the cutting zone, and their chemical reactivity with the tool material. This work aims to explore and comprehensively evaluate the advancement in cooling and lubrication approaches that have been introduced to enhance the machinability of Ti-6Al-V alloys. Despite the numerous positive outcomes observed with recently employed techniques such as reduced friction, lowered temperatures, and prolonged tool life. These methods and techniques are either not accepted yet or limitedly accepted within the industry due to the inherent weaknesses and limitations of each technique. This work introduces a new integrated and sustainable concept that can surmount the challenges associated with previous approaches, aiming for more sustainable machining of Ti-6Al-4V alloys. The suggested integrated concept may overcome the lubrication, cooling, cost, and environmental problems of the recently used approaches.
A centralized fleet management system for electrified transportation
Authors: Lashin Yehia A. , Ali Ahmed M. , Aiad Ahmed F. , Asfoor Mostafa Sh. 
Published: August 2024

Abstract
Hybrid Electric Vehicles are a promising alternative to Conventional and Electric Vehicles, as they offer better fuel economy, lower emissions, and long drive range without requiring extensive charging infrastructure. Limited driving range and battery degradation in electric vehicles are the main challenges in electrified fleets working in large metropolitan. For these challenges, the hybrid electric vehicle fleet management system is an effective solution to optimize power consumption and mitigate electric vehicle and conventional vehicle challenges. This paper proposes a hybrid electric vehicle fleet management system for public transportation, to analyze and investigate the performance and energy consumption rate of conventional, electric, and hybrid electric vehicles on different routes using representative driving cycles. The hybrid electric vehicle fleet management system enhances operational efficiency for the fleet management system and reduces power consumption in transportation. The proposed work comprises a hybrid electric vehicle model that is based on object-oriented programming. The proposed work offers the ability to add the input of the initial state of charge, power split ratio, and route data for each bus, and the hybrid electric vehicle model computes the state of charge and fuel consumption rate along the road. Finally, The proposed work shows how a hybrid electric vehicle fleet management system has a significant potential to be a solution for public transportation in Egyptian urban areas at this time compared with conventional vehicle and electric vehicle fleet management systems.
Active Resonance Fitting Energy Harvesting from Spinning Objects
Authors: Hedaya Mohamed , Elhadidi Mohamed , Ibrahim Mahmoud Z. 
Published: August 2024

Abstract
Energy harvesting is an emerging topic in the recent energy global problem. Energy can be harvested from different resources such as sea waves, wind, solar, etc. In this research, energy harvesting from spinning objects using vibrations will be addressed in addition to how to conduct active fitting procedures to allow the vibrating elements at all time work under resonance condition. Any spinning object has a centrifugal force that affects the vibrating element and cause more stiffness to its normal condition, which helps increasing the vibrating element natural frequency keeping with the increasing driving/spinning frequency until a certain limit. Thus, a new methodology by actively customizing the vibrating element beam length is proposed to manipulate its natural frequency according the spinning frequency.
Photovoltaic MPPT based on improved Cuckoo algorithm
Authors: Liu Xiaodong , Zou Hairong 
Published: August 2024

Abstract
Photovoltaic power generation is susceptible to external factors such as light, resulting in a reduction in the actual efficiency of photovoltaic power generation. However, the traditional maximum power point tracking technology has the characteristics of slow convergence speed and poor accuracy. In order to improve the above problems, a new photovoltaic maximum power control algorithm with an improved cuckoo algorithm is proposed. By adjusting the iterative step size in the cuckoo algorithm and changing the probability of finding the bird nest, the iterative convergence speed is accelerated, and the global optimization ability is increased. Finally, the algorithm is applied to the maximum power tracking, and the simulation model is built on the Matlab/Simulink platform. After comparing the standard Cuckoo algorithm with the improved Cuckoo algorithm under static and dynamic conditions, the simulation results show that the improved Cuckoo algorithm has more advantages in maximum power tracking speed and accuracy.
Analysis of the resonant converter for multi-terminal collection of DC offshore wind farms
Authors: Yue Yuge , Hao Quanrui , Ma Ningyi 
Published: August 2024

Abstract
With the rapid development of power electronics technology, the offshore wind power system using DC collection and DC transmission is the best solution to increasing offshore distance. Resonant converters achieving soft switching have become a common choice in the DC/DC power conversion system. This paper proposed a design scheme for the multi-terminal collection of DC offshore wind farms based on the resonant converter. The operating principles are analyzed, and variable-frequency control of the converter is utilized to control the output voltage. Simulation verifies the effectiveness of the multi-terminal DC collection system and its control strategy.
A real-time estimation method of carbon emission for cement corporations based on load identification
Authors: Pan Juntao , Yang Zhou , Wei Xingqiu , Zhou Zhenglei 
Published: August 2024

Abstract
Accurate carbon emission estimation is the basis for assessing the carbon emissions of various regions and industries and formulating reasonable and feasible emission reduction programs. This paper presents a generalized real-time corporate carbon footprint measurement method. The total carbon emissions are categorized into direct emissions and indirect emissions. For the former, a load identification method based on CNN-BLSTM is proposed to monitor the state of the devices, and the carbon emissions are directly calculated in combination with the carbon emission intensity of the devices; for the latter, accurate estimation of carbon emissions is achieved through the application of marginal carbon emission factor. By utilizing this analytical model, the estimated carbon emissions are more accurate than those of other methods, which can better grasp the carbon emissions of enterprises and promote energy saving and emission reduction.
Future scenario generation and reduction methods for solar photovoltaic electricity production analysis
Authors: Wang Jing , Zhao Yuming , Ye Cantao , Chen Jiongcong , Li Huashan 
Published: August 2024

Abstract
A morphing-clustering-based method of generation and reduction future scenario for solar photovoltaic electricity production analysis is studied. Present historical annual weather data are transformed into future scenarios and timeframes by a morphing method with a general circulation model. The Copula methodology is applied to model the forthcoming decade’s annual meteorological data and generate abundant intra-day hourly scenario samples. The comparative analysis of the photovoltaic power output variations between future and present in reduction typical scenarios is conducted by the K-means clustering method with appropriate clustering parameter optimization. The simulation results show that the annual mean variations in global horizontal radiation, dry bulb temperature, and wind speed are projected to increase by approximately from 20.1 to 36.5 W/m2, 1.6 to 2.3°C, and 0.1 to 0.6 m/s, respectively in the forthcoming scenario of shared socioeconomic pathways 2-4.5. It is seen that solar photovoltaic electricity production anticipates a positive increase from 2.5% to 17.9%.
Optimization of the deep extreme learning machine using the seagull algorithm for state of health prediction of lithium batteries
Authors: Li Qingwei , Fu Xiaoxiong 
Published: August 2024

Abstract
The ability to predict the State of Health (SOH) of lithium-ion batteries with precision is essential for guaranteeing their safe and consistent performance. Notwithstanding, measuring internal resistance and capacity accurately during live battery operation is an ongoing challenge. Existing SOH prediction models often fall short in terms of precision. To tackle this issue, a novel SOH estimation technique for lithium-ion batteries has been proposed. This technique uses electrochemical impedance spectroscopy and the Seagull Optimization Algorithm simultaneously with a Deep Extreme Learning Machine (SOA-DELM). The impedance characteristics of lithium-ion batteries are taken into account, with real and imaginary components and the modulus extracted as the health factor (HF). Then, the seagull algorithm ascertains the optimal structural parameter solutions for the DELM network. An estimation model is subsequently created by using the Seagull algorithm-optimized DELM, which aids in accurate SOH prediction for aging batteries. The proposed methodology is assessed by using an aged electrochemical impedance dataset, namely, 45 mAh commercial button batteries made with active materials composed of lithium cobaltate and graphite for positive and negative ends respectively. This openly accessible dataset is available in Nature Communications. Upon validation, the methodology proves to be highly precise.
Multi-stage planning and calculation method of power system considering energy storage selection
Authors: Yang Guoshan , Liu Xumin , Zhang Wei , Ma Zhengxiong 
Published: August 2024

Abstract
Under new power system flexibility, this text proposes a multi-stage random generation-transmission-energy storage, integrated programming method that considers the short-period flexibility demand. The model can accurately reflect the relationship between short-period operation and long-period uncertainty within an extraordinary method and can also correctly evaluate the value of investment flexibility. Based on this, this text proposes a parallel processing solution, which can be separated into subproblems through column generation and shared decomposition and enables simulation calculation using a distributed measuring platform in long-period programming. The calculation proves the optimal solution speed of the proposed method and can effectively solve problems that cannot be dealt with by the known methods.
Expanded virtual vector-based model forecastable control strategy for three-phase two-level inverters
Authors: Weng Liguo , Zhang Lin , Chen Yaoming , Shen Hongda 
Published: August 2024

Abstract
A three-phase, two-level inverter based on model forecastable control has good transient performance but poor steady-state performance. Considering the limitation of a limited number of discrete voltage vectors in a two-level inverter topology, this paper proposes a virtual vector model prediction method based on the analysis of a time-stepped forecasting model for a three-phase grid-connected inverter. First, three fixed virtual voltage vectors are constructed in each sector, and the action time of the required fundamental vector is found. Then, the cost function is constructed, and the optimum is selected among the virtual and fundamental voltage vectors in each sector. Finally, a hardware-in-the-loop experiment was carried out based on the PE-Expert4 experimental platform developed by Myway Co., LTD. The results show that the proposed virtual vector model forecastable control strategy can effectively improve the transient and steady-state performance of three-phase two-level inverters.