Two-layer Method Research Articles

Comparison of measurement of integrated relaxation pressure by esophageal manometry with analysis of swallowing sounds with artificial intelligence in patients with achalasia.

Esophageal motility disorders are mainly evaluated with high-resolution manometry (HRM) which is a time-consuming and uncomfortable procedure with potential adverse events. Acoustic characterization of the swallowing has the potential to be an alternative noninvasive procedure. We compared the findings on HRM and swallowing sounds in 43 patients who were referred for evaluation of dysphagia. The sound analysis was done with empirical mode decomposition method and with artificial intelligence (AI) and the estimated integrated relaxation pressure (IRP) from a two-layer neural network method was compared to measured IRP on HRM. The model then was tested in five patients. IRP was estimated with high accuracy using the model developed with two-layer neural network method. The analysis of acoustic properties of swallowing has the potential to be used for evaluation of esophageal motility disorders, this needs to be further evaluated in larger studies.

A two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub

Building a low-carbon park is crucial for achieving the carbon neutrality goals. However, most research on low-carbon economic planning methods for park-level integrated energy systems (PIES) has focused on multi-energy flow interactions, neglecting the “carbon perspective” and the impact of the dynamic coupling characteristics between multi-energy flows and carbon emission flow (CEF) on carbon reduction and planning schemes. Therefore, this paper proposes a two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub (CESH). Firstly, this paper establishes a CESH model for PIES to describe the synergistic relationship between CEF and multi-energy flows from input, conversion, storage, to output. Secondly, a PIES two-layer low-carbon economic planning model with CESH is proposed. The upper model determines the optimal device types and capacities during the planning cycle. The lower model employs the CESH model to promote carbon energy friendly interactions, optimize the daily operation scheme of PIES. The iterative process between the two layers, initiated by a genetic algorithm (GA), ensures the speed and accuracy. Finally, case studies show that, compared to planning methods without the CESH model, the proposed method is effective in reducing carbon emissions and total costs during the planning cycle. From a dual “carbon-energy” perspective, it enhances investment effectiveness and carbon reduction sensitivity by deeply exploring the energy conservation and carbon reduction potential of PIES.

Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load

A two-layer scheduling method of energy storage that considers the uncertainty of both source and load is proposed to coordinate thermal power with composite energy storage to participate in the peak regulation of power systems. Firstly, considering the characteristics of thermal power deep peak regulation, a cost model of thermal power deep peak regulation is constructed and fuzzy parameters are used to manage the uncertainty of wind, photovoltaics, and load. Secondly, based on the peaking characteristics and operating costs of composite energy storage, a two-layer optimal scheduling model of energy storage is constructed. The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. Finally, we verify the effectiveness of the proposed strategy based on an IEEE 39-node system.

The Color Harmony Estimation Model Construction Based on Two Layers of MLE and BPNN in the Color Matching Field

In the field of art, color matching is widely used in various art designs, such as images, posters, clothing, and interior home design. Among them, harmonious color matching is the decisive factor in whether a design is popular or not. To solve the problem of estimating color harmony, this study analyzes from the perspective of color pairs and uses the two-layer maximum likelihood estimation method to make preliminary predictions of color harmony by statistically modeling paired color preferences in existing datasets. After obtaining the preliminary estimation of color harmony, multiple linear regression is selected for denoising processing. Subsequently, the preliminary prediction results were refined using a backpropagation neural network, extracting various color features in different color spaces, and ultimately obtaining accurate harmony estimates. The results indicate that, compared with existing methods, the proposed method can simulate the aesthetic cognition of different users towards different color themes. Under the same statistical method, the model can maintain good harmony estimation and experimental results. This method can promote the development of related research fields, such as quickly evaluating the color harmony of an image, and one click color changing in scenes such as clothing, home, 3D models, etc. according to different user needs.

A two-layer planning method for location and capacity determination of public electric vehicle charging stations

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

Two-layer optimal scheduling method for regional integrated energy system considering flexibility characteristics of CHP system

A regional integrated energy system (RIES) guarantees the fulfillment of a diversified load demand of users by coordinating all types of energy equipment and is a two-layer optimal scheduling method that takes into consideration the flexibility of the characteristics of the combined heat and power (CHP) system is proposed in this paper. First, the thermoelectric output characteristics of hydrogen fuel cell (HFC) and CHP units are analyzed to establish an energy equipment model that takes into consideration the variability of the operating conditions. Subsequently, a two-layer optimal scheduling model of the RIES is established, where the upper layer takes into consideration the operating costs, carbon emission penalties, and renewable energy consumption capacity to determine the strategy for allocating the energy flow, and the lower layer considers the energy efficiency as the optimization objective to determine the output modes of the HFC and CHP units. Finally, an adaptive closed-loop control strategy is used to avoid the continuous startup and shutdown of the CHP unit. The simulation results show that the proposed method can effectively reduce operating costs and carbon emissions, improve energy efficiency and renewable energy consumption, and promote the efficient utilization of integrated energy resources.

Two-layer Optimization-Based Capacity Allocation Method of Integrated Energy Systems

Abstract In this paper, the coupling relationship between energy sources is used to carry out analysis and establish the capacity allocation and optimization model of the integrated energy system. Then a two-layer optimization algorithm is used to establish a linear programming model, which is simulated and analyzed according to the data of a certain region, and solved by CPLEX. The simulation results show that the multi-energy complementary has a good economy and can improve the cleanliness compared with the traditional energy system, which well proves the effectiveness of the proposed capacity optimization model and operation strategy of the integrated energy system.

Prolonged Lifespan of Superhydrophobic Thin Films and Coatings Using Recycled Polyethylene.

High-density polyethylene (HDPE) waste poses a significant environmental challenge due to its non-biodegradable nature and the vast quantities generated annually. However, conventional recycling methods are energy-intensive and often yield low-quality products. Herein, HDPE waste is upcycled into anti-aging, superhydrophobic thin films suitable for outdoor applications. A two-layer spin-casting method combined with heating-induced crosslinking is utilized to produce an exceptionally rough superhydrophobic surface, featuring a root mean square (RMS) roughness of 50 nm, an average crest height of 222 nm, an average trough depth of -264 nm, and a contact angle (CA) of 148°. To assess durability, weathering tests were conducted, revealing the films' susceptibility to degradation under harsh conditions. The films' resistance to environmental factors is improved by incorporating a UV absorber, maintaining their hydrophobic properties and mechanical strength. Our research demonstrates a sustainable method for upcycling waste into high-performance, weather-resistant, superhydrophobic films.

Co-optimization method for injection strategy of underground natural gas storage integrating aboveground and underground parts

Underground natural gas storage (UNGS) is an important infrastructure to overcome the imbalance between supply and demand of natural gas. During the gas injection period, gas is compressed aboveground and then transported to injection wells for injection into the underground reservoir. Reasonably formulating the compressor operation scheme and injection scheduling scheme is a crucial step. However, “Compressor-pipeline-injection well-reservoir" constitutes a synergistic whole. More efficient and practical operation schemes can only be proposed through the establishment of an optimization method that coordinates the entire gas injection process. A novel two-layer multi-objective collaborative optimization method is proposed to optimize the monthly operation scheme during the injection period. The upper-layer model focuses on optimizing injection volume allocation to minimize reservoir pressure deviation and total compressor energy consumption. The under-layer model optimizes the compressor operation scheme by considering the multi-stage compression (MC) and clearance adjustment (CA) equipped in reciprocating compressors. The synergistic relationship during the injection process is integrated into the traditional model through the interlayer iteration method. Furthermore, a high accuracy and efficiency proxy model based on machine learning algorithms (ML) is developed to predict compressor performance. The results of the case study indicate that, compared with traditional optimization method and the two-stage co-optimization method, the proposed method achieves an average energy consumption reduction of 9.54 × 106 kWh (10.79%) and 2.12 × 106 kWh (2.4%) respectively. The effective utilization of CA and MC can significantly alleviate the deviations between displacement and processing requirements, eliminating 22,120.5 × 104 Nm3(85.62%). The two-layer co-optimization method effectively integrates the aboveground and underground parts of UNGS, providing a solution for safe and efficient operation during the injection period.

Formulation and evaluation of ivermectin-loaded dissolving microarray patches for rosacea disease

PurposeThis investigation aims to develop and characterise dissolving microarray patches (MAPs) loaded with ivermectin (IVM) for rosacea therapy.MethodsTween® 80 and Soluplus® were evaluated to enhance the water solubility of IVM powder. Three dissolving MAPs were fabricated using a two-layer casting method, pure IVM-loaded (F1), IVM-Tween® 80 (F2), and IVM-Soluplus® (F3) loaded patches. Formulations were evaluated for drug content, in vitro and ex vivo mechanical performances, ex vivo skin dissolution time, dermatokinetics, in vitro biocompatibility and activity against rosacea.ResultsIVM solubility in water was improved with surfactants, reaching 1206.42 ± 53.78 and 130.78 ± 12.78 µg/mL in Tween® 80 and Soluplus® solutions, respectively. The MAPs, featuring bubble-free, perfectly shaped pyramidal needles of approximately 800 μm, exhibited considerably higher IVM content in F2 and F3 than in F1 (2.31 ± 0.26 mg for F1, 3.58 ± 0.15 mg for F2, and 3.19 ± 0.22 mg for F3). All formulations demonstrated mechanical robustness and penetrated the skin to a depth of 650 μm. The highest IVM deposition in the skin at 24 h was achieved by F2, selected as the lead formulation (F1 = 1456.35 ± 266.90 µg; F2 = 2165.24 ± 130.13 µg; F3 = 1684.74 ± 212.09 µg). Furthermore, F2 and F3 provided faster IVM deposition, most likely due to the quicker dissolution rate of microneedles in the skin. F2 proved biocompatible to skin cells in vitro and effectively inhibited the inflammatory cascade associated with rosacea diseases.ConclusionThis study encourages further investigation into IVM-loaded dissolving MAPs formulated with Tween® 80 for rosacea therapy.

Reversible data hiding based on global and local automatic contrast enhancement of low-light color images

Currently, many scholars have investigated reversible data hiding (RDH) based on automatic contrast enhancement (ACE). For RDH based on ACE (ACERDH), various methods have been proposed on how to better improve image quality. Preserving brightness can prevent the image from being over-enhanced. However, some ACERDH methods that can preserve brightness well cannot sufficiently enhance low-light images. Although some ACERDH methods that cannot preserve brightness well can more sufficiently enhance low-light color images, they lack the consideration of the local region, which is not conducive to enhancing the contrast of the local region. Therefore, a novel RDH method based on global ACE and local ACE is proposed. The global contrast is first improved by equalizing the global histogram. Then the high complexity region is optimized by the proposed RDH method based on double-layer ACE to further enhance the local contrast. The low-light color image can be well enhanced by applying the proposed method to enhance the R, G, and B channels sequentially. Experimental results demonstrated that the proposed method is superior to some existing advanced methods in obtaining better image quality and hiding more secret data.

Optimization of Integrated Energy Systems Based on Two-Step Decoupling Method

An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very complicated, and most of the current literature focuses on optimization of a specific system. In this work, a simulation-based two-step decoupling method is proposed to simplify the optimization of an IES. The generalized IES is split into four subsystems, and a two-layer optimization method is applied for optimization of the capacity of each piece of equipment. The proposed method enables fast comparison among abundant optional configurations of an IES, and it is applied to a hospital in Beijing, China. The optimized coupling system includes the gas-fired trigeneration system, the GSHP, and the electric chiller. Compared with the traditional distributed systems, the emission reduction rate of CO2 and NOX for the coupling system reaches 153.8% and 314.5%, respectively. Moreover, the primary energy consumption of the coupling system is 82.67% less than that of the traditional distributed energy system, while the annual cost is almost at the same level.

A Two-Layer Communication Relay Planning Method for a Fixed-Wing UAVs Swarm

A Two-Layer Communication Relay Planning Method for a Fixed-Wing UAVs Swarm

Two-Layer Optimization Method for Sharing Energy Storage and Energy considering Subjectivity

The high level of integration of distributed generation systems (DGSs), especially distributed wind and solar, significantly affects the flexibility and controllability of the power system. Aggregating local DGSs and shared energy storage systems (ESSs) within an energy community offers an economically and environmentally viable solution. However, the coupling of shared ESSs with the energy community, while considering subjectivity, is often overlooked. Therefore, this study introduces a two-layer optimization framework that enables DGSs to trade energy freely, voluntarily, and independently and to share ESSs within the energy community, considering participants’ subjectivity. The upper layer optimizes the size of shared ESSs, while the lower layer, structured as a two-layer model, simulates participant interactions. The numerical case shows that, compared to DGSs operating individually, the shared ESS case indicates that community self-sufficiency and self-consumption rates increase by 16.22% and 21.98%, respectively. Additionally, the annual operating cost is reduced by approximately 27.10%, and CO2 emissions are decreased by about 33.24%. Considering DGS’ subjectivity, the self-sufficiency and self-consumption rates are 3.04% lower, and the total operating costs and CO2 emissions are 3.26% and 6.86% higher, respectively.

Data-driven real-time predictive control for industrial heating loads

Uncertainties and computational complexity are two growing challenges in scheduling industrial heating loads. In this paper, a data-driven real-time predictive control approach is proposed to deal with these challenges in the industrial scheduling of bitumen tanks. Specifically, predictive control technology is utilized to leverage the updated information to mitigate the negative impact of past uncertainties in equipment parameters and external environmental factors, which may lead to temperature constraint violations in the bitumen tank operation processes. Meanwhile, a data-driven method using artificial neural networks (ANN) is developed to ensure efficient computation for real-time predictive control. Moreover, a two-layer control method is devised to reduce the calculation time for day-ahead optimal scheduling of a large scale of bitumen tanks, aiming to generate sufficient high-quality data for training ANN. In the two-layer control method, the clustered temperature transfer processes of bitumen tanks are analyzed and modeled for the first time. Simulation results indicate that the two-layer control method can significantly reduce the computational time required for the day-ahead optimal scheduling of bitumen tanks, facilitating the generation of a large amount of high-quality data for training ANN. Subsequently, the application of ANN enables real-time predictive control, helping to eliminate the negative impact of uncertainties.

Two-Layer Optimization Strategy of Electric Vehicle and Air Conditioning Load Considering the Benefit of Peak-to-Valley Smoothing

To satisfy the interests of multiple agents and those of comprehensive indicators such as peak-to-valley differences and load fluctuations occurring on the network side, this paper presents a flexible load demand-side response optimization method that considers the benefits of peak-to-valley smoothing. First, load aggregation modelling of air conditioning and electric vehicles was conducted, and the complementarity of the power consumption behavior of different types of flexible loads was used to improve the responsiveness of the load aggregator. Second, considering demand-side responses and taking into account the interests of both supply and demand, the load fluctuation and peak-to-valley difference on the network side are reduced, and a flexible load double-layer optimization model incorporating the peak-to-valley smoothing benefit is established. Finally, the effectiveness of the proposed optimization model is verified by using the KKT condition and the big M method to evaluate this two-layer optimization problem as a single-layer optimization problem. Comparative examples show that the proposed two-layer optimization method can take advantage of the complementarity of air conditioning and electric vehicles to improve the income of load aggregators. Moreover, the proposed method can effectively reduce the load peak-to-valley difference and load fluctuation of the distribution network by introducing the peak-to-valley smoothing benefit model.

Automation of superconducting cavity cooldown process using two-layer surrogate model and model predictive control method

Superconducting cavity is the key equipment of the superconducting accelerator, which provides higher acceleration voltage and higher frequency power per unit length, and saves equipment space. Superconducting cavities need to be gradually cooled from ambient temperature (300 K) to the superconducting temperature (4.2 K or below) during the test and operation. The temperature difference on the cavity must be strictly limited during the cooldown process to prevent excessive thermal stress on the surface of the superconducting cavity. Since this cooldown process for the superconducting cavity is a typical large hysteresis, non-linear process that is difficult to control automatically using decoupled proportion integral derivative (PID) methods directly, a less efficient manual control scheme is normally adopted. In this paper, 3D numerical simulation, 1D pipe and 0D tank model with artificial neural network (ANN) were combined to generate a two-layer surrogate model that can balance computational accuracy and speed, to improve the automation and cooling efficiency of the superconducting cavity cooldown process. In order to achieve automatic control of the cooling procedure for the superconducting cavity, a model predictive control (MPC) approach was also built on the basis of this two-layer surrogate model. According to the results of the experiment test, the improved method could realize a quick and smooth cooldown process of the superconducting cavity, during which the temperature difference on the cavity could satisfy the requirements. Additionally, the improved automatic cooldown method was more adaptable and saved 29 % more time than the original manual control method. The foundation for a more intelligent automated control of future large cryogenic systems or other system with the large hysteresis, non-linear properties, was laid.

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

In order to improve the operating benefits of the distribution network (DN) and reduce the energy consumption costs of small-micro industrial parks (SMIPs), a two-layer optimal electricity trading method for DN with SMIPs is proposed. First, based on the Stackelberg game, a multi-objective two-layer optimal trading model for DN and SMIP is established. In the upper layer, the DN agent is regarded as the leader, and a trading model is established with the goal of maximizing the profits of agents. In the lower layer, an energy optimization model is proposed for the SMIP operators, which are regarded as the followers, with the goal of minimizing the operating costs. According to the buying and selling electricity prices at the upper and lower layers, a dynamic pricing strategy is formulated. The Karush–Kuhn–Tucker condition (KKT) is introduced to transform the two-layer model into a single-layer model, and based on linear transformations, the model is further converted into a mixed-integer linear programming model. The transformations aim to address the non-linear issues arising from multivariable coupling between the upper and lower-layer trading models. The simulation results show that the trading strategy proposed in this paper can effectively increase the profit of DNs while reducing the operating costs of SMIPs and can provide a reference for decision-making in the electricity market (EM) with the participation of SMIP.

Predicting verbal and performance intelligence quotients from multimodal data in individuals with attention deficit/hyperactivity disorder.

Despite the importance of understanding how intelligence is ingrained in the function and structure of the brain in some neurological disorders, the alterations of intelligence-associated neurological factors in atypical neurodevelopmental disorders, such as attention deficit/hyperactivity disorder (ADHD), are limited. Therefore, we aimed to explore the relationship between the brain functional and morphological characteristics and the intellectual performance of 139 patients with ADHD. Resting-state functional and T1-weighted structural magnetic resonance imaging (MRI) data and intellectual-performance data of the patients were collected. The MRI data were preprocessed to extract four indicators characterizing the participants' brain features: fractional amplitude of low-frequency fluctuation, regional homogeneity, and gray and white matter volumes. Then, we used a two-layer feature-selection method with support vector regression models based on three kernel functions to predict the verbal and performance intelligent quotients of the patients, along with ten fold cross-validation to evaluate the models' predictive performance. All models showed good performance; the correlation coefficients between the predicted and observed values for each predictive phenotypic variable were >0.41, with statistical significance. The brain features that could best predict the intellectual performance of the patients were concentrated in the superior and inferior frontal gyrus of the prefrontal areas, the angular gyrus and precuneus of the parietal lobe, the inferior and middle temporal gyrus of the temporal lobe, and part of the cerebellar regions. Thus, the voxel-based brain-feature indicators could adequately predict the intellectual performance of patients with ADHD, providing a foundation for future neuroimaging studies of this disorder.

A two-layer optimization method for maintenance task scheduling considering multiple priorities

Timely and effective maintenance scheduling is the key to the safe and stable operation in oil and gas fields. Large-scale maintenance tasks with multiple priorities are difficult to complete in an acceptable time. To accelerate computation, a two-layer strategy is proposed. At the upper layer, a lumped general task for each well cluster is generated. This lumping allows the upper layer model to concentrate on the urgent and important tasks, and thus a significantly reduced-scale mixed integer linear programming (MILP) model can be resulted to determine the dispatch and time length assigned for each technician. At the lower layer, a small-scale MILP model is established for each technician, utilizing the reserved time in the upper layer as the constraint. The results demonstrate that our approach significantly outperforms the complete MILP model in efficiency. The method is presently being implemented in real-world scenarios through our collaboration with the industry.