bluuebunny/crossref_metadata_2025_split huggingface dataset:
A hybrid particle swarm optimisation for flexible casting job shop scheduling problem with batch processing machine
Authors: Zhang Wei , Zhuang Mengzhen , Tang Hongtao , Li Xinyu , Guo Shunsheng 
Published: October 2024

AbstractA flexible casting job shop scheduling problem (FCJSP) with batch processing machines is proposed based on the analysis of the flexible job shop scheduling problem (FJSP) and the study of the expendable casting process. Considering the makespan under the influence of the energy consumption, the authors apply the time execution window to the FCJSP model in conjunction with the characteristics of casting production. A hybrid particle swarm optimisation algorithm (HPSO) is developed to solve the FCJSP. The HPSO employs a block integration decoding rule to address scheduling integration. Particle swarm optimisation is used for global search, employing both discrete and continuous search strategies. Furthermore, the local search employs tabu search with neighbourhood operations based on knowledge‐driven techniques. Simulation experiments demonstrate the feasibility of the proposed optimisation model. In the end, the HPSO algorithm has been successfully applied to the real expendable casting scheduling. The results demonstrate that it is more efficient and robust than previously reported algorithms.
Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems
Authors: Fan Jiaxin 
Published: October 2024

AbstractMatheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial optimisation problems, where a common framework is to insert mixed integer linear programming (MILP) models as local search functions for evolutionary algorithms. However, since a mathematical programming formulation only tries to find the solution with the best objective value, matheuristics are rarely adopted to multi‐objective scenarios asking for a set of Pareto optimal solutions, for example, vehicle routing problems and production scheduling problems. In this situation, the ɛ‐constraint, which transforms multi‐objective problems into single‐objective formulations by considering selected objectives as constraints, seems to be a promising approach. First, an augmented ɛ‐constraint‐based matheuristic methodology (ɛ‐MH) is proposed to apply the idea of ɛ‐constraint to embedded MILP models, so that Pareto fronts obtained by meta‐heuristics can be further improved by solving a set of MILP models. Afterwards, four speed‐up strategies are developed to alleviate the computational burden resulting from repeatedly solving mathematical formulations, which also imply preferable scenarios for taking advantages of the ɛ‐MH. Finally, several real‐world bi‐objective scheduling problems are discussed to present potential applications for the proposed methodology.
Resource‐adaptive and OOD‐robust inference of deep neural networks on IoT devices
Authors: Robertson Cailen , Tong Ngoc Anh , Nguyen Thanh Toan , Hung Nguyen Quoc Viet , Jo Jun 
Published: October 2024

AbstractEfficiently executing inference tasks of deep neural networks on devices with limited resources poses a significant load in IoT systems. To alleviate the load, one innovative method is branching that adds extra layers with classification exits to a pre‐trained model, enabling inputs with high‐confidence predictions to exit early, thus reducing inference cost. However, branching networks, not originally tailored for IoT environments, are susceptible to noisy and out‐of‐distribution (OOD) data, and they demand additional training for optimal performance. The authors introduce BrevisNet, a novel branching methodology designed for creating on‐device branching models that are both resource‐adaptive and noise‐robust for IoT applications. The method leverages the refined uncertainty estimation capabilities of Dirichlet distributions for classification predictions, combined with the superior OOD detection of energy‐based models. The authors propose a unique training approach and thresholding technique that enhances the precision of branch predictions, offering robustness against noise and OOD inputs. The findings demonstrate that BrevisNet surpasses existing branching techniques in training efficiency, accuracy, overall performance, and robustness.
Finite‐time lag bipartite synchronization of double‐layer networks with non‐linear coupling strength and random coupling delays via T‐S fuzzy logic theory
Authors: Yang Degang , Liang Tao , Zhang Wanli 
Published: October 2024

AbstractThis article discusses finite‐time lag bipartite (FETLB) synchronization of double‐layer networks with non‐linear coupling strength and multiple time delays. The cooperative and competitive interactions between nodes are considered based on signed graphs. To address the non‐linear couplings strength, the T‐S fuzzy logic theory is used. An intermittent control approach is introduced to achieve FETLB synchronization, effectively minimizing control costs. Moreover, by the Lyapunov functional method, we derive criteria for achieving FETLB synchronization and provide estimations for the synchronization settling time. In addition, numerical simulation affirms the validity of the theoretical findings, showcasing the practical application of the synchronization results in secure communication.
A learning‐based hierarchical energy management control strategy for hybrid electric vehicles
Authors: Chen Yanfang , Li Xuefang 
Published: October 2024

AbstractIn this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car‐following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real‐time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.
A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators
Authors: Roy Kingshuk , Debbarma Sanjoy , Roy Siddhartha Deb , Debbarma Liza 
Published: October 2024

AbstractThe consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.
Determining the optimal bid direction of a generation company using the gradient vector of the profit function in the network constraints of the electricity market
Authors: Hajiabadi Mohammad Ebrahim , Samadi Mahdi , Nikkhah Mohammad Hassan , Lotfi Hossein , Li Li 
Published: October 2024

AbstractOne of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.
A lightweight object detection approach based on edge computing for mining industry
Authors: Hanif Muhammad Wahab , Li Zhanli , Yu Zhenhua , Bashir Rehmat 
Published: October 2024

AbstractCoal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations.
Surface defect detection method of aluminium alloy castings based on data enhancement and CRT‐DETR
Authors: Li Long , Gao Mingyang , Tian Xing , Wang Chengjun , Yu Jiangli 
Published: October 2024

AbstractThe surface quality of aluminium alloy castings is crucial to quality control. Aiming to address the challenges of limited samples and extensive computation in deep learning‐based surface defect detection for aluminium alloy castings, this paper proposes a surface defect detection method based on data enhancement and the Casting Real‐Time DEtection TRansformer. First, to tackle the issue of small sample sizes and uneven distribution in surface defect data sets of aluminium alloy castings, ECA‐MetaAconC Deep Convolution Generative Adversarial Networks is proposed for generating defects with fewer samples and employ the image augmentation (IMGAUG) library for sample enhancement. Second, building upon the Real‐Time DEtection TRansformer (RT‐DETR), a lightweight partial‐rep convolution is designed to decrease the network's parameter count. Simultaneously, the Deformable attention module and the DRBC3 module are introduced to enhance the neck network, thereby improving the model's capability to capture information and enhancing its detection performance. Compared to RT‐DETR, this method reduces the number of model parameters by 38.7%, increases mAP by 1.5%, and achieves a frame rate that is 1.58 times higher than the original model. The experimental results demonstrate that this method can effectively and accurately detect surface defects in aluminium alloy castings, satisfying industrial requirements.
Comparison of space vector and switching frequency optimal pulse width modulation for diode free F‐type multi level inverter
Authors: Madhavan Meenakshi , Nallaperumal Chellammal , Hosseinpour Alireza 
Published: October 2024

AbstractThis study presents a comprehensive examination of space vector pulse width modulation (SVPWM) and switching frequency optimal PWM (SFOPWM) for an F‐type multilevel inverter (FTMLI). SVPWM offers a simple, digital implementation for three‐phase, three‐level inverter structures, and carrier‐based SFOPWM allows for maximum use of switching frequency with a simple construction. This approach reveals the underlying relationship between the above two PWM techniques with various amplitude and frequency modulation indices to ensure the performance of FTMLI. A suitable comparative study was carried out to verify the PWM techniques based on the inverter output voltage, total harmonic distortion, switching stress, and filter size. The analytical conclusions are supported by the simulation results and their experimental validation.
Effect of temperature on seasonal wind power and energy potential estimates in Nordic climates
Authors: Salmelin Markus , Karjunen Hannu , Lassila Jukka 
Published: October 2024

AbstractA major obstacle standing in the way of full‐scale adoption of renewable energy sources is their intermittency and seasonal variability. To better understand the power generation dynamics, the effect of air density due to temperature on power and energy generation figures was modelled. The model uses historical ERA5 data and considers changes in weather patterns in a subarctic climate where seasonal changes are most pronounced. The power generation figures of using a mean and a dynamic air density value were compared and the results show that power generation estimates may be under‐ and overestimated by on average 5% up to 10% in winter and summer, respectively. This can have implications for the sizing of power transmission infrastructure and energy storage in both on‐grid and off‐grid applications as well as power availability. The topic is highly relevant in the Nordic countries where roughly 10% of new global added wind capacity is installed annually.
Chemical Evolution of R-process Elements in Stars (CERES)
Authors: Fernandes de Melo Raphaela , Lombardo Linda , Alencastro Puls Arthur , Romano Donatella , Hansen Camilla Juul , Tsiatsiou Sophie , Meynet Georges 
Published: October 2024

Context. Carbon, nitrogen, and oxygen are the most abundant elements throughout the universe, after hydrogen and helium. Studying these elements in low-metallicity stars can provide crucial information on the chemical composition in the early Galaxy and possible internal mixing processes that can alter the surface composition of the stars.
Aims. This work aims to investigate the chemical abundance patterns for CNO elements and Li in a homogeneously analyzed sample of 52 metal-poor halo giant stars. From these results, we have been able to determine whether internal mixing processes have taken place in these stars.
Methods. We used high-resolution spectra with a high signal-to-noise ratio (S/N) to carry out a spectral synthesis to derive detailed C, N, O, and Li abundances for a sample of stars with metallicities in the range of −3.58 ≤ [Fe/H] ≤ −1.79 dex. Our study was based on the assumption of one-dimensional (1D) local thermodynamic equilibrium (LTE) atmospheres.
Results. Based on carbon and nitrogen abundances, we investigated the deep mixing taking place within stars along the red giant branch (RGB). The individual abundances of carbon decrease towards the upper RGB while nitrogen shows an increasing trend, indicating that carbon has been converted into nitrogen. No signatures of ON-cycle processed material were found for the stars in our sample. We computed a set of galactic chemical evolution (GCE) models, implementing different sets of massive star yields, both with and without including the effects of stellar rotation on nucleosynthesis. We confirm that stellar rotation is necessary to explain the highest [N/Fe] and [N/O] ratios observed in unmixed halo stars. The predicted level of N enhancement varies sensibly in dependence of the specific set of yields that are adopted. For stars with stellar parameters similar to those of our sample, heavy elements such as Sr, Y, and Zr appear to have unchanged abundances despite the stellar evolution mixing processes.
Conclusions. The unmixed RGB stars provide very useful constraints on chemical evolution models of the Galaxy. As they are more luminous than unevolved (main sequence and turnoff) stars, they also allow for stars to be probed at greater distances. The stellar CN-cycle clearly changes the atmospheric abundances of the lighter elements, but no changes were detected with respect to the heavy elements.
CHEMOUT: CHEMical complexity in star-forming regions of the OUTer Galaxy
Authors: Fontani F. , Vermariën G. , Viti S. , Gigli D. , Colzi L. , Beltrán M. T. , Caselli P. , Rivilla V. M. , Sánchez-Monge A. 
Published: October 2024

Context. Single-dish observations suggest that the abundances of organic species in star-forming regions of the outer Galaxy, which are characterised by sub-solar metallicities, are comparable to those found in the local Galaxy.
Aims. To understand this counter-intuitive result and avoid a misleading interpretation due to beam dilution effects at these large distances, spatially resolved molecular emission maps are needed to correctly link the measured abundances and local physical properties.
Methods. We observed several organic molecules with the Atacama Large Millimeter Array towards WB89-671, the source with the largest galactocentric distance (23.4 kpc) of the project CHEMical complexity in star-forming regions of the OUTer Galaxy (CHEMOUT) at a resolution of ~15 000 au. We compared the observed molecular abundances with chemical model predictions.
Results. We detected emission of c-C3H2, C4H, CH3OH, H2CO, HCO, H13CO+, HCS+, CS, HN13C, and SO. The emission morphology is complex, extended, and different in each tracer. In particular, the most intense emission in H13CO+, H2CO and c-C3H2 arises from two millimeter-continuum infrared-bright cores. The most intense CH3OH and SO emission predominantly arises from the part of the filament that lacks continuum sources. The narrow line widths across the filament indicate quiescent gas in spite of the two embedded protostars. The derived molecular column densities are comparable with those in local star-forming regions, and they suggest an anti-correlation between hydrocarbons, ions, HCO, and H2CO on the one hand, and CH3OH and SO on the other.
Conclusions. The static chemical models that match the observed column densities best favour low-energy conditions that are expected at large galactocentric radii, but they also favour carbon elemental abundances that exceed those derived by extrapolating the [C/H] galactocentric gradient at 23 kpc by three times. This would indicate a flatter [C/H] trend at large galactocentric radii, which is in line with a flat abundance of organics. However, to properly reproduce the chemical composition of each region, models should include dynamical evolution.
Effects of the internal temperature on vertical mixing and on cloud structures in ultra-hot Jupiters
Authors: Noti Pascal A. , Lee Elspeth K. H. 
Published: October 2024

Context. The vertical mixing in hot-Jupiter atmospheres plays a critical role in the formation and spacial distribution of cloud particles in their atmospheres. This affects the observed spectra of a planet through cloud opacity, which can be influenced by the degree of cold trapping of refractory species in the deep atmosphere.
Aims. We aim to isolate the effects of the internal temperature on the mixing efficiency in the atmospheres of ultra-hot Jupiters (UHJs) and the spacial distribution of cloud particles across the planet.
Methods. We combined a simplified tracer-based cloud model, a picket fence radiative-transfer scheme, and a mixing length theory to the Exo-FMS general circulation model. We ran the model for five different internal temperatures at typical UHJ atmosphere system parameters.
Results. Our results show the convective eddy diffusion coefficient remains low throughout the vast majority of the atmosphere, with mixing dominated by advective flows. However, some regions can show convective mixing in the upper atmosphere for colder interior temperatures. The vertical extent of the clouds is reduced as the internal temperature is increased. Additionally, a global cloud layer gets formed below the radiative-convective boundary (RCB) in the cooler cases.
Conclusions. Convection is generally strongly inhibited in UHJ atmospheres above the RCB due to their strong irradiation. Convective mixing plays a minor role compared to advective mixing in keeping cloud particles aloft in UHJs with warm interiors. Higher vertical turbulent heat fluxes and the advection of potential temperature inhibit convection in warmer interiors. Our results suggest that isolated upper atmosphere regions above cold interiors may exhibit strong convective mixing in isolated regions around Rossby gyres, allowing aerosols to be better retained in these areas.