Original Method Research Articles

A deep semantics-aware data augmentation method for fault localization

Context:Fault localization (FL) techniques are employed to identify the relationship between program statements and failures by analyzing runtime information. They rely on the statistics of input data to explore the underlying correlation rooted in it. Consequently, the quality of input data is of utmost importance for FL. However, in practice, passing tests significantly outnumber failing tests regarding a fault. This leads to a class imbalance challenge that can adversely affect the effectiveness of FL. Objective:To tackle the issue of imbalanced data in fault localization, we propose PRAM: a deeP semantic-awaRe dAta augmentation Method to improve the effectiveness of FL methods. Method:PRAM utilizes program dependencies to enhance the semantic context, thus showing how a failure is caused. Then, PRAM employs mixup method to synthesize new failing test samples by merging two real failing test cases with a random ratio to balance the input data. Finally, PRAM feeds the balanced data consisting of synthesized failing test cases and original test cases to FL techniques. To evaluate the effectiveness of PRAM, we conducted large-scale experiments on 330 versions of nine large-sized real programs for six state-of-the-art FL methods, two data optimization methods and two data augmentation methods. Results:Our experimental results show that PRAM outperforms in most cases for Top-K metrics and reduces the number of checked statements from 40.38% to 80.04% compared with the original FL methods. Furthermore, PRAM reduces the checked statements from 16.92% to 56.98% for data optimization methods and from 12.48% to 26.82% for data augmentation methods. Conclusion:The experimental results show that PRAM is not only more effective than the original FL methods but also more effective than two representative data optimization methods and two data augmentation methods, which indicates that PRAM is a universal effective data augmentation method for various FL methods.

Assessing the spatial distribution of positional error associated to dense point cloud measurements using regional Gaussian random fields

Being able to assess the amount of uncertainty locally associated to dense point clouds generated by measurement can help investigate the relations between the metrological performance of a chosen measuring technology, and the local geometric and surface properties of the measurand geometry. In previous research it was demonstrated that spatial statistics based on Gaussian Random Fields and measurement repeats could be used to obtain spatial maps capturing both local dispersion and local bias associated to the position of points within measured clouds. However, the previous method had scalability limitations when handling very dense point clouds, due to it requiring the resolution of a global, increasingly larger, covariance matrix in order to solve the random field fitting problem. This work presents a variant to the previous method, where the covariance matrix is solved only locally, making the method better scalable to handle denser point clouds. Despite the new method not being able to return an equally rich information content in relation to spatial covariance, it still allows to obtain almost equally accurate information on local bias and variance, with significant gains in terms of processing speed and, importantly, making it now possible to handle very dense clouds which would be unviable to process with the original method.

Visual Re-Initialization Model Development Methodology for Solving Problems Regarding Metaheuristic Algorithm-Based MPPT Applications

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and “Embedded Coder Support Package for TI C2000 Processors” allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency.

Experimental investigation of the CaO-Al2O3-TiO2 system: Isothermal section of 1500 °C and liquidus projection

The CaO-Al2O3-TiO2 phase diagram is of great guiding significance for the comprehensive utilization of Ti-bearing mineral resources and the design of metallurgical slag for Al-Ti alloy steel. In this paper, the high-temperature phase equilibrium experimental method was adopted; the types and compositions of phases were identified by Electron Probe Micro Analysis (EPMA) and X-ray diffraction (XRD). A total of 12 equilibrium phases were determined, including CaO, Al2O3, TiO2, 3CaO2TiO2, 4CaO3TiO2, CaOTiO2, Al2O3TiO2, 3CaOAl2O3, CaOAl2O3, CaO2Al2O3, CaO6Al2O3, and Liquid; they formed a total of 16 phase equilibrium relations. The isothermal section of CaO-Al2O3-TiO2 system at 1500 °C in atmosphere was constructed for the first time. There are 10 three-phase fields (Liquid+CaO+3CaO2TiO2, Liquid+3CaO2TiO2+4CaO3TiO2, CaOTiO2+CaOAl2O3+CaO2Al2O3, CaO2Al2O3+CaOTiO2+CaO6Al2O3, Liquid+CaOTiO2+ CaO6Al2O3, Liquid+CaO6Al2O3+Al2O3, Liquid+Al2O3+Al2O3TiO2, Liquid+TiO2+Al2O3TiO2, Liquid+CaOAl2O3+4CaO3TiO2, 4CaO3TiO2(ss)+CaOTiO2(ss)+CaOAl2O3), 13 two-phase fields (Liquid+CaO, CaO+3CaO2TiO2, Liquid+3CaO2TiO2, Liquid+4CaO3TiO2, Liquid+TiO2, Liquid+CaOTiO2, Liquid+Al2O3TiO2, Liquid+Al2O3, Liquid+CaO6Al2O3, Liquid+CaOAl2O3, CaOTiO2(ss)+CaO2Al2O3, 4CaO3TiO2(ss)+CaOTiO2(ss), 4CaO3TiO2(ss)+3CaO2TiO2(ss)), 2 single-phase solid solution fields (4CaO3TiO2(ss), 3CaO2TiO2(ss)), and 2 disconnected single liquid phase fields. On this basis, the liquidus projection of the CaO-Al2O3-TiO2 system was revised. The types of relevant invariant reactions were identified by the Lever Rule; the types of relevant univariant reactions were determined by the original method proposed in this paper.

Possibilities of Therapeutic Angiogenesis in Patients with Critical Lower Limb Ischemia

Two clinical observations of effective treatment of patients with critical ischemia of the lower extremities using plasma-free lysate autologous platelets. Both patients were male, 73 and 53 years old. Both were diagnosed with total damage to the arteries of the infrainguinal zone. One of them had previously undergone endarterectomy from the femoral artery with subsequent thrombosis. After examination and joint discussion with x-ray endovascular surgeons, they were found inoperable. Due to the ineffectiveness of standard conservative therapy, the patient was offered local administration of plasma-free lysate autologous platelets. After receiving written informed consent, the procedure for collecting venous blood and obtaining plasma-free lysate autologous platelets and the introduction of this drug into the muscles of the leg from the side of ischemia according to the original method. The method for assessing microcirculation was three-phase scintigraphy : before treatment, both patients showed a significant decrease in the inclusion of osteotropic radiopharmaceutical in the soft tissues of the legs. For areas of radiopharmaceutical hyperfixation, relative accumulation coefficients were calculated: for one patient, the calculation results demonstrated the formation of foci of aseptic necrosis. When patients were re-hospitalized after 6 months, there was an improvement in local status and relief of rest pain. According to scintigraphy, a significant improvement in microcirculation was noted; no foci of aseptic necrosis were identified. Thus, the use of plasma-free lysate autologous platelets in combination with complex conservative therapy has demonstrated positive results; this method can be considered as an alternative revascularization ischemic limb.

AROFRAG─A Systematic Approach for Fragmentation of Aromatic Molecules.

We present a new systematic fragmentation scheme of polycyclic aromatic hydrocarbons (PAHs), including fullerenes and nanotubes, based on an idea to treat a sextet ring as a single unbreakable unit so that the basic unit of aromaticity remains preserved upon fragmentation. In the approach, denoted as AROFRAG (from aromatic fragmentation), a set of predefined elementary subsystems, such as naphthalene and biphenyl in the first model and larger PAHs in the second and third models, is generated with appropriate weights with the aim of reproducing the structure of the original molecule. The energies of the molecules are approximated as weighted sums of the energies of these subsystems. For symmetric cases, e.g., fullerenes, the point-group symmetry is preserved during the decomposition. The AROFRAG is used in conjunction with the molecule-in-molecule (MIM) technique to obtain an accurate description of the electronic energies. The new approach has been applied for selected graphene structures and fullerene doped with boron and nitrogen atoms, for which isomerization energies were calculated, as well as for several nanotubes and regular fullerene molecules. The combination of the third AROFRAG model and the MIM approach leads to the reproduction of electronic energies with a few milli-hartree accuracy at a fraction of the computational cost of the original method.

A novel approach to tailoring the microstructure and electrophysical properties of Ni/GDC-based anodes by combining 3D-inkjet printing and layer-by-layer laser treatment

This work proposes an original method for tailoring the microstructure and electrical properties of Ni/GDC anodes by using 3D inkjet printing of high-viscosity functional compositions with intermediate laser treatment of each layer after each printing pass. Based on a mixture of highly dispersed oxides NiO and CeO2 doped with gadolinium (GDC), paste formulations for 3D inkjet printing have been developed. The effects of 3D printing and laser treatment conditions on the porosity and shrinkage ratio of the NiO/GDC anodes during sintering were investigated. The reduction of the anode support workpieces to produce Ni/GDC cermet was carried out and the effect of laser exposure on the morphology and electrical characteristics of the resulting samples was studied. It was found that an increase in the laser exposure values during layer-by-layer laser treatment of printing compositions leads to a two-fold increase in the porosity of the solid oxide fuel cell components, an increase in the average pore size from 0.2 to 0.3 to 2–5 μm, and to a five-fold decrease in the sintering shrinkage ratio. It was also found that the electrical conductivity of the Ni/GDC cermet, produced by the reduction of the laser treated NiO/GDC, increased by 5–10 times as the laser exposure values increased. The influence mechanism of the layer-by-layer laser treatment of the original NiO/GDC samples on the electrical conductivity of their reduced counterparts, Ni/GDC cermet anodes, has been proposed. An increase in the electrical conductivity was shown to be due to in situ reduction of NiO, partial sintering of Ni nanoparticles and the formation of a percolation network. This new method of hybrid 3D printing opens up great prospects for tailoring the morphological and electrical characteristics of SOFC anode supports.

Comparative analysis of the use of original and standard methods of rehabilitation treatment of dorsopathy

Introduction. The increase in the number of requests for medical help for pain in dorsopathy determines the relevance of this problem. Among all the conditions included in the Health Expenditure Survey, non-specific musculoskeletal back pain ranks first in the world. A special place belongs to cervicothoracic dorsopathy (CТD), which are the most common in developed countries and are characterized by a variety of clinical forms. There is a need to develop safe and effective complexes for the medical rehabilitation of patients with СТD. The complex method of СТD treating with the obligatory use of drug and non-drug methods of treatment prescribed together or sequentially in one complex, in our opinion, is the most optimal.Aim. To conduct a comparative evaluation of the therapeutic efficacy of restorative treatment methods in patients with cervicothoracic dorsopathy.Materials and methods. Patients with cervicothoracic dorsopathy (n = 220), stratified into 2 comparable groups, were studied, to whom the original (n = 155) and standard (n = 65) methods of restorative treatment were applied. The original method of treatment assumed a combined therapeutic effect with the use of drug therapy and a rehabilitation complex of physiotherapeutic and psychotherapeutic sessions. Statistical analysis included a comparative assessment of the intensity of the pain syndrome, static-dynamic and reflex disorders before and after the therapy.Results. The original and standard methods used in the course of the study statistically significantly (p < 0.05) reduced the intensity of the pain syndrome and reduced the frequency of reflex disorders. At the same time, the use of the original method of therapy was more effective (p < 0.05) in reducing the severity of the pain syndrome and reducing the frequency of reflex disorders in subgroups of patients, both with uncomplicated and complicated forms of cervicothoracic dorsopathy. The use of a standard method of therapeutic effect significantly reduced the frequency of manifestation of static-dynamic disorders in the group with uncomplicated forms of dorsopathy, while in patients with complicated forms of cervicothoracic dorsopathy, no therapeutic effect was achieved.Conclusions. The original method of therapy has confirmed its effectiveness, which indicates the need and justification for the use of combined restorative treatment of patients with cervicothoracic dorsopathy.

Deep unfolding as iterative regularization for imaging inverse problems

Deep unfolding methods have gained significant popularity in the field of inverse problems as they have driven the design of deep neural networks (DNNs) using iterative algorithms. In contrast to general DNNs, unfolding methods offer improved interpretability and performance. However, their theoretical stability or regularity in solving inverse problems remains subject to certain limitations. To address this, we reevaluate unfolded DNNs and observe that their algorithmically-driven cascading structure exhibits a closer resemblance to iterative regularization. Recognizing this, we propose a modified training approach and configure termination criteria for unfolded DNNs, thereby establishing the unfolding method as an iterative regularization technique. Specifically, our method involves the joint learning of a convex penalty function using an input-convex neural network to quantify distance to a real data manifold. Then, we train a DNN unfolded from the proximal gradient descent algorithm, incorporating this learned penalty. Additionally, we introduce a new termination criterion for the unfolded DNN. Under the assumption that the real data manifold intersects the solutions of the inverse problem with a unique real solution, even when measurements contain perturbations, we provide a theoretical proof of the stable convergence of the unfolded DNN to this solution. Furthermore, we demonstrate with an example of magnetic resonance imaging reconstruction that the proposed method outperforms original unfolding methods and traditional regularization methods in terms of reconstruction quality, stability, and convergence speed.

Constrained numerical deconvolution using orthogonal polynomials

In this article, we present an enhanced version of Cutler's deconvolution method to address the limitations of the original algorithm in estimating realistic input and output parameters. Cutler's method, based on orthogonal polynomials, suffers from unconstrained solutions, leading to the lack of realism in the deconvolved signals in some applications. Our proposed approach incorporates constraints using a ridge factor and Lagrangian multipliers in an iterative fashion, maintaining Cutler's iterative projection-based nature. This extension avoids the need for external optimization solvers, making it particularly suitable for applications requiring constraints on inputs and outputs. We demonstrate the effectiveness of the proposed method through two practical applications: the estimation of COVID-19 curves and the study of mavoglurant, an experimental drug. Our results show that the extended method presents a sum of squared residuals in the same order of magnitude of that of the original Cutler's method and other widely known unconstrained deconvolution techniques, but obtains instead physically plausible solutions, correcting the errors introduced by the alternative methods considered, as illustrated in our case studies.

Integrated image and sensor-based food intake detection in free-living

The first step in any dietary monitoring system is the automatic detection of eating episodes. To detect eating episodes, either sensor data or images can be used, and either method can result in false-positive detection. This study aims to reduce the number of false positives in the detection of eating episodes by a wearable sensor, Automatic Ingestion Monitor v2 (AIM-2). Thirty participants wore the AIM-2 for two days each (pseudo-free-living and free-living). The eating episodes were detected by three methods: (1) recognition of solid foods and beverages in images captured by AIM-2; (2) recognition of chewing from the AIM-2 accelerometer sensor; and (3) hierarchical classification to combine confidence scores from image and accelerometer classifiers. The integration of image- and sensor-based methods achieved 94.59% sensitivity, 70.47% precision, and 80.77% F1-score in the free-living environment, which is significantly better than either of the original methods (8% higher sensitivity). The proposed method successfully reduces the number of false positives in the detection of eating episodes.

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

A nonlinear repair technique for the MPFA-D scheme in single-phase flow problems and heterogeneous and anisotropic media

A novel Flux Limited Splitting (FLS) non-linear Finite Volume (FV) method for families of linear Control Volume Distributed Multi Point Flux Approximation (CVD-MPFA) schemes is presented. The new formulation imposes a local discrete maximum principal (LDMP) which ensures that the discrete solution is free of spurious oscillations. The FLS scheme can be seen as a natural extension of the M-Matrix Flux Splitting method that splits the MPFA flux components in terms of the Two-Point Flux Approximation (TPFA) flux and Cross Diffusion Terms (CDT), with the addition of a dynamically computed relaxation parameter to the CDT that identifies and locally corrects the regions where the LDMP is violated. Moreover, the whole non-linear procedure was devised as a series of simple straightforward matrix operations. The methodology is presented considering the Multi-Point Flux Approximation with a Diamond (MPFA-D) in what we call the FLS + MPFA-D formulation which is tested using a series of challenging benchmark problems. For all test cases, the FLS repair technique imposes the LDMP and eliminates the spurious oscillations induced by the original MPFA-D method.

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

Monitoring and understanding the development of agricultural management requires fine information on multiple agricultural land use classes. According to the main data bulletin of China’s third national land survey released in 2021, the proportion of arable land in China accounts for 16% of the total land area. After excluding areas that are not suitable for development (slope greater than 25 degrees), the remaining area represents arable land affected by human activities, constituting 96.69% of the total arable land area in the country. Artificially irrigated areas are products of the intertwined interactions between the natural environment and human society. However, the original remote sensing image (China’s land use/cover datasets, CLUDs), which is designed to depict the land use and cover patterns in mainland China, categorizes arable land into two main types: paddy fields and dryland. It lacks a subdivision of artificially irrigated areas. We also found a data discrepancy of more than 10% between the statistical data of rice and the paddy field data in CLUDs. To refine land use data, and then improve the simulation accuracy of the hydrological model, this article proposes a dual-source datasets fusion algorithm, learning based on big data, namely the LUCC statistical data fusion (LUSF) algorithm for integrating the remote sensing-based cropland area dataset and statistics dataset. The runoff simulation results show that, in the Yangtze River Basin, using LUSF datasets, the mean absolute percentage error value of monthly simulated runoff decreased by 0.74%, and the root mean square error value decreased by 0.22 million m3. At the basin scale, the absolute error of the simulated runoff is reduced by an average of 433 million m3 per year, and 36 million m3 every month. The LUSF datasets corrected the canopy interception coefficient effectively; the runoff simulation error was reduced by 2.96 billion m3/a. In the past 40 years, the runoff variation in Dongting Lake and Hanjiang River has been most strongly impacted by changes in the underlying surface. These results reveal that the new data fusion method has some significant improvement over the original method, applicable to the runoff simulation disturbed by strong human activities.

Absolute radiance calibration in the UV and visible spectral range using atmospheric observations during twilight

Abstract. We present an improved radiance calibration method for UV–Vis spectroscopic instruments with a narrow field of view (up to a few degrees) based on the calibration method by Wagner et al. (2015). The updated method uses only measurements during the twilight period instead of several hours as for the original method. The calibration is based on the comparison of measurements and simulations of the radiance of zenith-scattered sunlight. The main advantage of our method compared to radiance calibration methods in the laboratory is that the calibration can be directly applied in the field. This allows routine radiance calibrations whenever the sky is clear during twilight. The calibration can also be performed retrospectively and will thus be applicable for the large number of existing data sets. Also, potential changes in the instrument properties during transport from the laboratory to the field are avoided. The new version of the calibration method presented here has two main advantages. First, the required measurement period can be rather short (only a few minutes during twilight for cloud-free conditions). Second, even without knowledge of the aerosol optical depth (AOD), the errors in the calibration method are rather small, especially in the UV spectral range where they range from about 4 % at 340 nm to 8 % at 420 nm. If the AOD is known, the uncertainties are even smaller (about 3 % at 340 nm to 4 % at 420 nm). For visible wavelengths, good accuracy is only obtained if the AOD is approximately known with uncertainties from about 4 % at 420 nm to 10 % between about 550 and 700 nm (generally the AOD is nevertheless smaller in the visible than in the UV spectral range). One shortcoming of the method is that it is not possible to determine the AOD exactly at the time of the (twilight) measurements because AOD observations from sun photometer measurements or the MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) measurements are usually not meaningful for such high solar zenith angle (SZA). But the related uncertainty can be minimised by repeating the radiance calibrations during the twilight periods of several days.

A novel hydro-pneumatic fluid percussion device for inducing traumatic brain injury: assessment of sensory, motor, cognitive, molecular, and morphological outcomes in rodents.

The fluid percussion method is widely used to induce brain injury in rodents. However, this approach has several limitations, including variability in the resulting damage, which is attributed to factors such as manual control of the mass used to generate the desired pressure. To address these issues, several modifications to the original method have been proposed. In this study, we present a novel device called the Hydro-pneumatic Fluid Percussion Device, which delivers fluid directly to a lateral region of the brain to induce injury. To validate this model, three groups of male and female rats were subjected to lateral fluid percussion using our device, and the resulting damage was evaluated using sensory, motor, and cognitive tests, measurements of serum injury biomarkers, and morphological analysis via cresyl violet staining. Our results demonstrate that this new approach induced significant alterations in all parameters evaluated. This novel device for inducing TBI may be a valuable alternative for modeling brain injury and studying its consequences.

Improvement of MC method in SAE J2601 hydrogen refuelling protocol using dual-zone dual-temperature model

The MC method has been published in the SAE J2601 refuelling protocol to safely and quickly refuel hydrogen fuel cell vehicles. This article adopts a dual-zone dual-temperature model that distinguishes the hydrogen temperature from the tank wall temperature to replace the dual-zone single-temperature model of the original MC method. A model (0D1D) of lumped parameter gas (0D) and one-dimensional tank wall (1D) is established simultaneously to verify the modified MC method and generate simulation data. A modified formula to the original MC method for calculating final hydrogen temperature is derived, which contains a correction factor K. The formula and coefficients of the correction factor K are determined by fitting the simulated data from the 0D1D model. The correction factor K is expressed in a piecewise function. The formulas of K are relatively complicated, so another simpler correction factor k is derived, which is a linear function. Research shows that the modified MC method extends its applicability to situations of refuelling times <30 s, while the original MC method is unsuitable. In the conditions set in this article of ambient temperature 0 to 40 °C, precooling temperature −40 to −20 °C and initial pressure 2 to 60 MPa, the modified MC method reduces the errors of final hydrogen temperature between the 0D1D model by 2 to 7 °C compared to the original one, thus improving the calculation accuracy of the pressure target and reaching a more accurate state of charge. In a word, the modified MC method improves the accuracy of refuelling control and has positive significance for the operational safety of hydrogen refuelling stations and the driver's refuelling experience.

Configuration size optimization of gas-electric hybrid power systems on ships considering energy density and engine load response

System component sizes and their energy densities impact the long-term ship energy efficiency and economics, whereas power source load response influences system transient behavior, posing challenges to system design and energy management when clean fuels are used onboard. In this paper, a novel component sizing method is proposed for integrating reciprocating gas engines and energy storage systems (ESS) on ships, considering energy density and load response simultaneously. By specifying gas engine powers and ESS capacities as normalized optimization variables by ship scales, this method optimizes them based on the A. J. Klee multi-index evaluation and single-objective particle swarm optimization (PSO). Energy densities of engines and ESS were introduced to demonstrate the trade-off between the power-splitting and increased system mass and hull resistance. The engine load response was incorporated into the rule-based energy management strategy (EMS) to limit power ramps. This method was examined in the case study by taking four cruise ships as examples. It can be found that implementing the proposed evaluation-based optimization can improve cruise ship energy efficiency while maintaining reliability over the voyage. It is possible to reduce fuel consumption and carbon emissions by up to 17.1% and 14.8%, respectively. The minimum system reliability can be increased from 0.025 to 0.475 during transients. Furthermore, ship life-cycle costs can be reduced compared to the original design method, and the payback period for gas engines, ESS, and fuel tanks can be shortened to less than six years. Among the various cases, the definition of normalized optimization variables broadens the applicability of this method away from a specific ship, allowing it to be applied to ships of various tonnages and types.

Generalized modulus-based matrix splitting algorithm with Anderson acceleration strategy for vertical linear complementarity problems

The vertical linear complementarity problem (VLCP) with an arbitrary number l of matrices is related to many practical problems, of which the state-of-the-art modulus-based matrix splitting (MMS) method is proved to be an efficient solver. Enlightened by the Anderson acceleration which is a well-established and simple technique for speeding up fixed point iteration solvers with countless applications, we propose an Anderson accelerated generalized modulus-based matrix splitting (AA+GMMS) method for solving the VLCP. We particularly analyze the AA+GMMS method for the problem with l=2 and then generalize the method to any l. More importantly, the convergence theorems and theoretical optimal parameters of the MMS, AA+GMMS methods with any l are obtained in the positive definite case. Eventually, numerical experiments are given to demonstrate the effectiveness of the AA+GMMS method which significantly accelerates the original MMS method. In particular, we explore the parameters involved in the AA+GMMS method, and they have a small extent of impact on the suggested method, reinforcing that the AA+GMMS method is highly efficient.

A transformation method evaluate for near singular boundary integrals in the structural analysis of thin structure

The computational accuracy of near singular integrals is the key to determine the overall accuracy and stability of BEM in solving elastic variables, and even the success or failure of BEM analysis. In this paper, an alterative sinh transformation method is developed for solving the near singular boundary integrals in the structural analysis of thin structure. Firstly, the original sinh method is modified into the form include minimum distance and approximate expansion of local coordinates, and Newton-Raphson and cubic equation solving methods are used to obtain the position of the near singular projection point. To facilitate the implementation of the near singular integral, the integral element in the local coordinate space is mapped to the integral parameter space, and then the element integration is performed directly by sinh transformation in this space. The results of several numerical examples show that the proposed method can be used to regularize the nearly singular boundary integral effectively, and to accurately solve the elastic physical variables in the analysis of thin structures.