The performance of neural image coders is heavily dependent on their architecture and, hence, on the selection of hyperparameters. Such performance, for a given architecture, is often ascertained by trial, that is, after training and inference, so that many trials may be conducted to select the hyperparameters. We propose a multi-objective hyperparameter optimization (MOHPO) method for neural image compression based on rate-distortion-complexity (RDC) analysis, which drastically reduces the number of networks to try (train and test), thereby saving resources. We validate it on well-established benchmark problems and demonstrate its use with popular autoencoders, measuring their complexities in terms of the number of parameters and floating-point operations. Our method, which we refer to as the greedy lower convex hull (GLCH), aims to track the lower convex hull of a cloud of hyperparameter possibilities. We compare our method with other well-established state-of-the-art MOHPO methods in terms of log-hypervolume difference as a function of the number of trained networks. The results indicate that the proposed method is highly competitive, particularly with fewer trained networks, which is a critical scenario in practice. Furthermore, it is deterministic, that is, it remains consistent across different runs.
CrMnFeCoNi quinary complex concentrated alloys (CCAs) exhibit excellent mechanical properties due to the complexity of their atomic environment, attracting significant attention as potential structural materials. However, to effectively utilized CCAs in structural applications, a comprehensive understanding of their plasticity under various loading conditions is imperative for CCAs with various deformation mechanisms. In this study, quinary CCAs were systematically designed to exhibit constant yield strength by controlling the electronegativity difference and tailoring deformation mechanisms through the Gibbs energy difference between γ-austenite and ε-martensite. Uniaxial tensile tests and limit dome height tests were conducted to evaluate plasticity under both uniaxial and biaxial loading conditions. The normalized strain and displacement values revealed a significant reduction in plasticity for TRIP and TADP CCAs under biaxial loading. To elucidate this phenomenon, we compared the maximum Schmid factors of dislocation glide and martensitic transformation for random orientations. As a result, from a geometrical perspective, TRIP is not effectively activated under biaxial loading condition. These findings offer novel insights into the role of the Schmid factor in plasticity of CCAs, focusing on the critical impact of loading conditions on martensitic transformation. Consequently, our results establish effective guidelines for designing CCAs with enhanced plasticity under various stress states.
Materials of engineering and construction. Mechanics of materials
Fast charging is a critical factor for the widespread adoption of electric vehicles (EVs), yet it poses challenges to the durability of lithium-ion batteries (LiBs). This study develops an aging model for a 10 Ah LiB cell using COMSOL Multiphysics, integrating a one-dimensional electrochemical model with a zero-dimensional thermal model. The model is constructed using cell design and electrochemical parameters derived from existing literature, tailored to the specific electrode and electrolyte materials utilized in this study. Key aging phenomena, such as the Solid Electrolyte Interface (SEI) growth and lithium plating, are embedded within the model to simulate the aging patterns and the interplay between these factors across various C-rates and temperatures. Simulation results suggest that while higher charging rates can reduce charging time, they also lead to increased degradation, with lithium plating becoming more severe at these elevated rates. The model also indicates that temperature management during cell operation can influence cycle life, with strategic temperature increases potentially mitigating some negative effects of fast charging. The study also explores the concept of total charging time as an alternative metric for battery wear, examining its relationship with the charging current profile and temperature. Results indicate that while fast charging offers convenience, it significantly impacts cycle life, underscoring the need for optimized charging strategies. The insights from this work enhance the understanding of Li-ion cell aging mechanisms, shedding light on the complex trade-offs between charging speed, temperature management, and lifespan. This contributes to the development of more sophisticated battery management systems and charging protocols that aim to extend the operational life of LiBs while accommodating the demands of fast charging.
To solve the problem of insufficient melt pool width feature extraction accuracy caused by splash, arc light, and other interferences in the metal deposition process, a melt pool width extraction method based on the variable step size erosion model is proposed according to the characteristics of the spatial distribution of the melt pool size features. To achieve accurate measurement of the melt pool width, the melt pool image is first denoised using mathematical morphology and then segmented roughly using manual thresholding. Subsequently, the melt pool contour is iterated using an erosion model to obtain precise point localization information after fine segmentation, followed by the calculation of the melt pool width. Comparison experiments demonstrate that the method exhibits excellent accuracy and robustness in extracting melt pool width, while also showcasing high efficiency in fulfilling the requirements for closed-loop control. These findings lay the groundwork for the closed-loop control of the melt pool size.
BackgroundExtraction of carbon from water is a crucial preprocessing step for measuring 14C in environmental waters using liquid scintillation spectrometry.PurposeThis study aims to explore the optimal technological conditions for extracting carbon from water using wet oxidation method.MethodsA wet oxidation system combining sodium persulfate and Fenton's reagent, along with phosphoric acid acidification and nitrogen bubbling, were employed for the wet oxidation carbon extraction experiments on two types of water samples with known (deionized water + sucrose) and unknown carbon components, each with a volume of 10 L. Simultaneously, carbon extraction experiments were conducted on the water samples having unknown carbon component, using a combination of wet oxidation and 185 nm ultraviolet (UV) oxidation so as to determine the optimal timing and sequence of reagent addition, as well as the optimized reagent dosage and ratio. Further experiments under optimized conditions were conducted to obtain more results for deep analysis.ResultsUnder the optimized conditions, after a 3-h reaction at 90 °C, the organic carbon extraction rate for the known carbon component (deionized water + sucrose) exceeds 96%. The total carbon extraction rate from the unknown carbon component water is (96.8±0.3)%, with an inorganic carbon extraction rate >98.5%, and an organic carbon extraction rate of (93.4±0.2)%, while the oxidation rate of tannic acid-type organic compounds is only (88±0.2)%. After the combination of wet oxidation and 185 nm UV oxidation, the total carbon extraction rate for the unknown carbon component increases to (98.3±0.5)%, with an inorganic carbon extraction rate ≥99% and an organic carbon extraction rate that can reach (95.6±1.4)%.ConclusionsResults of this study indicate that wet oxidation alone cannot represent the carbon recovery rate in actual water samples using typical organic compound carbon recovery rates. The combination of wet oxidation and 185 nm UV oxidation proves to be a more effective method for carbon extraction from water.
Noise radars, as well as certain types of quantum radar, can be understood in terms of a correlation coefficient which characterizes their detection performance. Although most results in the noise radar literature are stated in terms of the signal-to-noise ratio (SNR), we show that it is possible to carry out performance prediction in terms of the correlation coefficient. To this end, we derive the range dependence of the correlation coefficient under the assumption that all external noise is additive white Gaussian noise. We then combine our result with a previously-derived expression for the receiver operating characteristic (ROC) curve of a coherent noise radar, showing that we can obtain ROC curves for varying ranges. A comparison with corresponding results for a conventional radar employing coherent integration shows that our results are sensible. The aim of our work is to show that the correlation coefficient is a viable adjunct to SNR in understanding noise radar performance.
Aiming at the problems of large workload and low intelligence of the current infrared image-based overheating defect detection techniques for composite insulators, and the poor accuracy and poor generalization performance of the traditional image segmentation methods in complex backgrounds, an overheating defect detection method is proposed for composite insulators based on instance segmentation network Mask R-CNN. Firstly, in order to improve the accuracy of segmentation, the Mask R-CNN network is improved according to the idea of Cascade R-CNN, and the data augmentation and transfer learning methods are used for model training to improve the network performance. Secondly, the result obtained by deep segmentation network is further optimized by using traditional image processing methods such as skeletonization, so that the final segmentation result only covers the core rod of the composite insulators. Finally, the temperature data in the infrared image is directly read and converted into the actual temperature value, and the grade of overheating defects is judged according to the relevant methods and criteria provided in DL / T664—2016 Infrared Diagnostic Application Specification for Live Equipment. The results show that the algorithm proposed in this paper has a high detection accuracy of 100% for the infrared images of composite insulators with serious and urgent defects, but has false detection occurrence for the infrared images without overheating defects or with general defects. On the whole, the accuracy rate of 93% is achieved in defect detection of test sets.
Electricity, Production of electric energy or power. Powerplants. Central stations
Mohamed Krarouch, Said Lamghari, Hassan Hamdi
et al.
The integration of hybrid renewable energy systems, such as hybrid solar-biomass, to address thermal energy needs in various applications is a promising, efficient and eco-friendly solution that can be successfully applied for traditional baths in Morocco. Indeed, these systems are not widespread in Morocco, since traditional firewood furnaces are still commonly used for heating purposes in many different applications. Accordingly, the main objective of the study is to introduce a new hybrid solar-biomass system designed for space heating and hot water supply in a public bathhouse in Marrakesh, Morocco. This hybrid system includes small-scale pellet boilers and small dimension parabolic trough solar collectors (PTC), which is the first small-size PTC facility proposed for low temperature applications (less than 100 °C) in Morocco. Moreover, the preliminary simulation results of this hybrid solar-biomass system for space heating – case study – using TRNSYS software are presented. The use of such a hybrid system as a new design to retrofit conventional firewood boilers that are widely used for space and water heating in public bathhouses will lead to significant savings in firewood, which will contribute in particular to reducing deforestation in Morocco and will also result in substantial reductions in CO2 emissions.
A method of correcting erasures based on the identification of the multiplicity of errors in two channels (forward and reverse) decoding is proposed. Found that the proposed method can reduce the decoding complexity in more than one order when compared to conventional linear search methods of correction of erasures.
The regular replacement of lubricating oil plays a key role in improving machine reliability and reducing unexpected failures of an oil lubricated system. This paper proposes a condition-based maintenance problem with selected oil field data to determine the optimal time of the lubricating oil replacement. The selected oil field data contain health information about the lubricating oil, so the degradation state of the oil can be predicted and the future health condition can be evaluated. The proposed lubricating oil replacement problem is modeled with the evaluated oil health condition in a Markov decision process framework and then, a method for constructing a health index for the lubricating oil is proposed based on information theory to fuse the multiple oil field data and build a degradation progression prediction model. Finally, the proposed method for condition-based lubricating oil replacement is illustrated in a practical case study. The possible applications of an optimal policy for lubricating oil replacement are much wider. For instance, the method can be used as an input to optimize an operational plan and further reduce the maintenance costs.
Novel Fe68(MoWCrVCoNiAlCu)32 (at.%) medium-entropy high-speed steel (ME-HSS) coatings, containing various carbon contents from 0.65 to 1.05 wt%, are prepared by laser rapid solidification. The newly prepared ME-HSS coatings are characterized by a hard martensitic matrix enhanced by secondary hardening, and specifically by coherent nano-sized M2C. The secondary hardening effect is enhanced with the increasing carbon content. The high amount of alloying elements in ME-HSS coatings results in excellent oxidative wear resistance, without leading to serious compositional segregation and coarsening of carbides. Keywords: High speed steel, Coating, Medium-entropy alloy, Secondary hardening, Martensite
Materials of engineering and construction. Mechanics of materials
In the present research, polyaniline is used as a conducting polymer and polyvinyl alcohol is also used as a biopolymer, because of its mechanical properties and suitable processability. Also, silver nanoparticles are considered as a reinforcing agent of thermal stability, mechanical and antibacterial properties to prepare polyaniline-polyvinyl alcohol-silver nanocomposite. The synthesis of polyaniline-polyvinyl alcohol composite and polyaniline-polyvinyl alcohol-silver nanocomposite is performed through addition of polyaniline and silver in polyvinyl alcohol solution. In order to review thermal, mechanical and antibacterial properties of synthesized composite and nanocomposites, components with different weight rates are used. The obtained results from thermogravimetric analysis (TGA) tests also indicate promotion of thermal stability of polyaniline-polyvinyl alcohol-silver nanocomposite compared with pure polyvinyl alcohol in temperatures above 400°C. The results of Fourier-transform infrared (FTIR) spectroscopy revealed the presence of polyaniline, polyvinyl alcohol and silver in the structure of polyaniline-polyvinyl alcohol-silver triple nanocomposite film. The obtained results from a review of antibacterial properties showed that polyaniline-polyvinyl alcohol-silver nanocomposites have antibacterial effects on two different types of Gram-positive and Gram-negative bacteria. The obtained results from a review of mechanical properties of nanocomposites showed that the greatest value of tensile strength (13.8 MPa) belonged to polyaniline-polyvinyl alcohol-silver (88%/9%/3% w/w) nanocomposites. Therefore, this is determined as an optimal triple nanocomposite. In addition, scanning electron microscopy (SEM) coupled with an energy dispersive X-ray (EDX) system was used to characterize the composition and structure of polyaniline-polyvinyl alcohol-silver nanocomposite film.
Materials of engineering and construction. Mechanics of materials
Praveen Damacharla, Ahmad Y. Javaid, Jennie J. Gallimore
et al.
A significant amount of work is invested in human-machine teaming (HMT) across multiple fields. Accurately and effectively measuring system performance of an HMT is crucial for moving the design of these systems forward. Metrics are the enabling tools to devise a benchmark in any system and serve as an evaluation platform for assessing the performance, along with the verification and validation, of a system. Currently, there is no agreed-upon set of benchmark metrics for developing HMT systems. Therefore, identification and classification of common metrics are imperative to create a benchmark in the HMT field. The key focus of this review is to conduct a detailed survey aimed at identification of metrics employed in different segments of HMT and to determine the common metrics that can be used in the future to benchmark HMTs. We have organized this review as follows: identification of metrics used in HMTs until now, and classification based on functionality and measuring techniques. Additionally, we have also attempted to analyze all the identified metrics in detail while classifying them as theoretical, applied, real-time, non-real-time, measurable, and observable metrics. We conclude this review with a detailed analysis of the identified common metrics along with their usage to benchmark HMTs.
A number of large underground oil storage spaces will be constructed in deep salt mines in China in the coming years. According to the general geological survey, the first salt cavern oil storage base of China is planned to be built in Jintan salt mine. In this research, the geological feasibility of the salt mine for oil storage is identified in detail as follows. (1) The characteristics of regional structure, strata sediment, and impermeable layer distribution of Jintan salt mine were evaluated and analyzed. (2) The tightness of cap rock was evaluated in reviews of macroscopic geology and microscopic measuring. (3) According to the geological characteristics of Jintan salt mine, the specific targeted formation for building underground oil storage was chosen, and the sealing of nonsalt interlayers was evaluated. (4) Based on the sonar measuring results of the salt caverns, the characteristics of solution mining salt caverns were analyzed. In addition, the preferred way of underground oil storage construction was determined. (5) Finally, the results of closed well observation in solution mining salt caverns were assessed. The research results indicated that Jintan salt mine has the basic geological conditions for building large-scale underground oil storage.
Materials of engineering and construction. Mechanics of materials
Power and energy minimization is a critical concern for the battery life, reliability, and yield of many minimum-sized SRAMs. In this paper, we extend our previously proposed hybrid analytical-empirical model for minimizing and predicting the delay and delay variability of SRAMs, VAR-TX, to a new enhanced version, exVAR-TX, to minimize and predict the power/energy and power/energy variability of a 16-nm 6T-SRAM under the influence of the three major types of variations: Fabrication, Operation, and Implementation. Using exVAR-TX for architectural optimization [exhaustively computing and comparing the range of feasible architectures subject to interdie (die-to-die/D2D) and intradie (within-die/WID) process and operation variations (PVT), electromigration (EM), negative bias temperature instability (NBTI), and soft-errors, among others] on top of deploying the most recent state of the art effective mitigation techniques we show that energy and energy-delay-product (EDP) of 64KB 16-nm 6T-SRAM could be reduced by ~12.5X and ~33%, respectively, as compared to the existing conventional designs.
Ahmed Hussain Qureshi, Saba Mumtaz, Yasar Ayaz
et al.
Rapidly-exploring Random Tree (RRT)-based algorithms have become increasingly popular due to their lower computational complexity as compared with other path planning algorithms. The recently presented RRT * motion planning algorithm improves upon the original RRT algorithm by providing optimal path solutions. While RRT determines an initial collision-free path fairly quickly, RRT * guarantees almost certain convergence to an optimal, obstacle-free path from the start to the goal points for any given geometrical environment. However, the main limitations of RRT * include its slow processing rate and high memory consumption, due to the large number of iterations required for calculating the optimal path. In order to overcome these limitations, we present another improvement, i.e, the Triangular Geometerized-RRT * (TG-RRT * ) algorithm, which utilizes triangular geometrical methods to improve the performance of the RRT * algorithm in terms of the processing time and a decreased number of iterations required for an optimal path solution. Simulations comparing the performance results of the improved TG-RRT * with RRT * are presented to demonstrate the overall improvement in performance and optimal path detection.