Two-layer Method Research Articles

Economic and environmental analysis of coupling waste-to-power technology to integrated energy system (IES) using a two-layer optimization method

Integrated energy system (IES) could improve the energy efficiency and reduce carbon emissions through multi-energy production, storage, transportation, and supply. However, the energy flow in traditional IES is usually one-way: from electricity to heat/cold, or from heat to cold. Coupling waste-to-power (WTP) may also convert waste heat to electricity, thereby further improving the flexibility and reducing pollutant emissions. Therefore, this work focuses on exploring the value and applicable scenario of IES-WTP, from the perspective of economics and carbon emissions. A two-layer optimization method is proposed to realize the multi-objective optimization of both the operation strategy and system design. Then this IES-WTP system is compared with traditional IES considering three different load profiles. Results indicate that coupling WTP could significantly improve the overall performance of IES. Specifically, the annual carbon emissions could be reduced by up to 13%, and the annual costs could also be reduced with the same emissions. As the WTP efficiency improves from 9% to 18%, the emission reduction will further increase by 6%. In addition, this IES-WTP system is found more applicable for the area where the electric load is higher or close to the heat/cold load, but is not suitable for the area where heat load is much higher than electric/cold load.

Optimal configuration for photovoltaic storage system capacity in 5G base station microgrids

Base station operators deploy a large number of distributed photovoltaics to solve the problems of high energy consumption and high electricity costs of 5G base stations. In this study, the idle space of the base station’s energy storage is used to stabilize the photovoltaic output, and a photovoltaic storage system microgrid of a 5G base station is constructed. Aiming at the capacity planning problem of photovoltaic storage systems, a two-layer optimal configuration method is proposed. The inner layer optimization considers the energy sharing among the base station microgrids, combines the communication characteristics of the 5G base station and the backup power demand of the energy storage battery, and determines an economic scheduling strategy for each photovoltaic storage system with the goal of minimizing the daily operation cost of the base station microgrid. The outer model aims to minimize the annual average comprehensive revenue of the 5G base station microgrid, while considering peak clipping and valley filling, to optimize the photovoltaic storage system capacity. The CPLEX solver and a genetic algorithm were used to solve the two-layer models. Considering the construction of the 5G base station in a certain area as an example, the results showed that the proposed model can not only reduce the cost of the 5G base station operators, but also reduce the peak load of the power grid and promote the local digestion of photovoltaic power.

Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic Algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

Optimal battery capacity of grid-connected PV-battery systems considering battery degradation

The optimal capacity of a battery energy storage system (BESS) is significant to the economy of energy systems and photovoltaic (PV) self-consumption. In this study, considering the long-term battery degradation, a mixed-integer nonlinear programming (MINLP) model was proposed for the PV-battery systems which aim to minimize the life cycle cost (LCC) and was solved by a novel two-layer optimization method. This study compared optimal battery capacities, LCCs, and self-consumption ratios (SCR) under two types of tariffs with and without considering battery degradation. The effects of feed-in tariffs, feed-in limits and PV degradation on the optimization results are discussed. The results showed that the battery degradation could cause an increase in operation cost. The cases considering battery degradation have larger battery capacity and more life cycle cost than the cases without considering battery degradation. Compared with the feed-in tariff profile of flat rate, the cases with feed-in tariff profile of time-of-use have smaller optimal battery capacity, higher SCR, and lower LCC. The feed-in tariff, feed-in limit and PV degradation have an important impact on optimal battery capacity and total life cycle cost. This study provides an optimization method and theoretical analysis for the capacity and operation optimization of PV-battery systems.

What is the evidence for oxygenation during kidney preservation for transplantation in 2021? A scoping review.

The main objective of static cold storage is to reduce cellular metabolic demands to extend the period of ischaemia prior to transplantation. Hypothermia does not halt metabolism and the absence of oxygen causes a cellular shift toward anaerobic respiratory pathways. There is emerging evidence that the introduction of oxygenation during organ preservation may help ameliorate the degree of ischaemia reperfusion injury and improve post-transplantation outcomes. This review aims to appraise and summarise all published literature that utilises oxygenation to improve kidney preservation for purposes of transplantation. We performed a scoping review of the literature using the bibliographic databases Embase and MEDLINE. The final date for searches was 20 March 2021. All research studies included were those that reported oxygen delivery during kidney preservation as well as providing a description of the oxygenation technique. 17 human and 48 animal studies met the inclusion criteria. The oxygen delivery methods investigated included hypothermic oxygenated machine perfusion (HOPE), oxygen carriers, two-layer method, venous systemic persufflation, hyperbaric oxygenation, normothermic machine perfusion and sub-normothermic machine perfusion. The COMPARE trial was the only study carried out with the most methodological robustness being a randomised, double blind, controlled, phase III trial that investigated the efficacy of HOPE versus HMP. A variety of studies reflect the evolution of oxygenation with useful lessons and encouraging outcomes. The first in human studies investigating HOPE and oxygen carriers are most robustly investigated strategies for oxygenation during kidney preservation and are, therefore, the best clinical references.

Two-Layer Collaborative Optimization Configuration Method for CCHP System with Wind-Solar-Storage

Two-Layer Collaborative Optimization Configuration Method for CCHP System with Wind-Solar-Storage

Multi-scenario analysis and collaborative optimization of a novel distributed energy system coupled with hybrid energy storage for a nearly zero-energy community

Coupling electric vehicles (EV) and hybrid energy storage (HES) with the distributed energy system (DES) can improve energy-saving and emission reduction efficiency and reduce system investment operating costs. However, the system modeling, optimization design, application site, and comprehensive performance analysis of the novel DES need to be further studied. Therefore, this paper combines photovoltaic technology, HES with P2G technology, and EVs with DESs to form a DES that combines HES. Taking the annual net energy import volume and annual cost as targets, a two-layer collaborative optimization method considering both system optimal configuration and operation strategy is used. The novel system will be used to supply energy to a nearly zero-energy community. The influence of various parameters is discussed through parameter analysis. The results show that under the operation strategy in charging during the daytime, the annual net energy import and annual value cost of the novel system are 2170772 kWh and 2272786 yuan, respectively; compared with the multi-parameter collaborative optimization method, it decreased by 3.1% and 5.0%, respectively. Finally, the two-layer collaborative optimization method of this paper is also applicable to the collaborative optimization design of other complex energy systems. The proposed operation strategy is also suitable for other energy systems with source, load, and storage.

Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles

This paper proposes a two-layer path-planning method, where an optimized artificial potential field (APF) method and an improved dynamic window approach (DWA) are used at the global and local layer, respectively. This method enables the robot to plan a better path under a multi-obstacle environment while avoiding the moving obstacles effectively. For the part of global path planning, a new repulsive field is proposed based on the APF method. The length and smoothness of the global path are taken as fitness functions of particle swarm optimization (PSO) to obtain the obstacle influence range, the coefficients of gravitation and repulsion in APF. At the level of local path planning, on the basis of DWA, a fuzzy control scheme is adopted to evaluate the danger level of moving obstacles via collision risk index and relative distance. In brief, compared with existing methods, the robot can reasonably plan a shorter and smoother path with the aid of PSO-based APF, meanwhile quickly react to the moving obstacles and avoid them by fuzzy-based DWA. Finally, a static multi-obstacle environment and two dynamic scenarios with moving obstacles are simulated to verify the effectiveness of the proposed path-planning method.

Research on Two-layer Cooperative Optimal Allocation Method of Integrated Energy System

Integrated energy system is one of the effective means to improve the operating economy of China’s energy system, reduce pollution emissions and improve energy utilization efficiency. The upper model solves the planning problem of the integrated energy system, and inputs the planning scheme into the lower model. The lower model realizes the operation optimization of the integrated energy system within the life cycle based on the given planning scheme. Through the iterative updating of the upper and lower models, the optimization of the plan-operation scheme is realized.

Two-layer material point method for modeling soil–water interaction in unsaturated soils and rainfall-induced slope failure

Two-layer material point method for modeling soil–water interaction in unsaturated soils and rainfall-induced slope failure

Research on solving strategy of structural reliability index based on probabilistic interval hybrid model

In this paper, a hybrid model is established in which the randomness of probabilistic parameters coexists with the uncertainty of interval parameters. Based on the reliability theory, the evaluation criteria of structural reliability under the hybrid model were proposed, and the two-layer cycle method and direct iterative algorithm were derived to calculate the structural reliability index. The correctness and effectiveness of the model and algorithm are verified by examples of a cantilever beam and a three-story frame structure. The hybrid model combines the probabilistic model with the interval model effectively and has practical significance for the engineering application of reliability theory.

Segmentation of the cardiac ventricle using two layer level sets with prior shape constraint

The cardiac Left Ventricle (LV) segmentation is still challengeable due to the complex anatomical structure surrounding the LV and intensity overlaps caused by intensity inhomogeneities, which widely exists in cardiac Magnetic Resonance (MR) images. To overcome these problems, a shape prior constrained two-layer level set method is proposed to simultaneously extract the endocardium and epicardium of the LV from cardiac short-axis MR images. The proposed method is validated on MR images from the LV challenge of MICCAI 2009. Quantitative and qualitative experiments and clinical metrics demonstrate that the proposed method has better performance than some representative methods in term of Dice Similarity Coefficient (DSC), Average Perpendicular Distance (APD), Ejection Fraction (EF), and LV mass. Experimental results demonstrate that the proposed method has the priority of preserving cardiac anatomical structure and improving segmentation accuracy.

Improving detection of BSA protein by applying atmospheric pressure plasma jets in MALDI-TOF mass spectrometry

To enhance protein identification in mass spectrometry systems, plasma can be applied in the sample preparation step in addition to conventional use, which improves the ionization process. In the present study, cold plasma technology is evaluated to achieve highly sensitive detection of low analyte concentrations.Plasma treatments are performed by two different low-temperature atmospheric-pressure sources. The protein ion signal after plasma treatment is characterized by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). The sample preparation method, exposure time and gas type for bovine serum albumin (BSA) are optimized. Plasma characteristics are determined by optical emission spectroscopy (OES) method. Also, the temperatures of plasma environment and sample are measured during the treatment with a non-contact infra-red digital camera.The water-soluble BSA protein is irradiated by cold atmospheric-pressure plasma jet (CAP jet) and radio-frequency plasma jet (RF jet). Mass spectra showed that argon plasma treatment with two-layer method induced a significant increase in ion intensity of MALDI-TOF MS. Argon treated water-soluble BSA and powder BSA lead to ten-times and three-times enhancement of protein ion signals as compared to the control, respectively. Also, treatment with argon RF jet, increases the ion signal more than two-times.The results indicate that ion intensity of BSA treated with CAP jet is significantly higher than that of BSA treated with RF jet. This finding, in addition to frequency and geometry parameters, depends on more analyte protonation, resulting in more ion flux toward BSA surface. Further, two-layer technique is considered as an optimal sampling method for MALDI.

Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping.

Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2–5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.

Free-floating bike-sharing green relocation problem considering greenhouse gas emissions

Abstract This paper introduces the problem of green bike relocation considering greenhouse gas emissions in free-floating bike-sharing systems (FFBSSs) and establishes a mathematical model of the problem. This model minimizes the total imbalance degree of bikes in the FFBSS and the greenhouse gas emissions generated by relocation in the FFBSS. Before the relocation phase, the FFBSS is divided into multiple relocation areas using a two-layer clustering method to reduce the scale of the relocation problem. In the relocation phase, the relocation route problem is converted into a pickup and delivery vehicle-routing problem. Then, an adaptive variable neighbourhood tabu search algorithm with a three-dimensional tabu list is proposed, which can simultaneously solve the relocation problem and the routing problem. A computational study based on the actual FFBSS used in Shanghai shows that this method can effectively solve the green relocation problem of FFBSSs.

Multi-task service composition model considering competitive resource constraint in cloud manufacturing

Service composition optimization is one of the core issues of cloud manufacturing research. At present, relevant researches mostly focus on single-task service composition, and there are few reports on the problem of multi-task service composition considering resource competition. However, in the real cloud manufacturing environment, multi-task service composition is common, so how to formulate a robust multi-task service composition model is a meaningful research direction. This paper first proposes a multi-task service composition optimization model considering resource competition constraints (MTSCOM-RCC) in the cloud manufacturing environment to fill the above-mentioned gap. The MTSCOM-RCC model considers the conflicts of interest and time between parallel subtasks and proposes an important service evaluation indicator. Secondly, a two-layer coding method, including task layer and subtask, is proposed based on studying the MTSCOM-RCC’s competition mechanism and characteristics. An improved hybrid genetic artificial bee colony algorithm model is proposed to solve the multi-task model. The model is combined with the global optimization method and task queue optimization method. Experimental results prove that the two-layer task optimization method model is more feasible and effective than the other two solution models.

Game theory based multi-task scheduling of decentralized 3D printing services in cloud manufacturing

To improve the multi-task scheduling competitivenesss of the distributed three-dimensional (3D) printing services with different types in cloud manufacturing, a non-cooperative game model of 3D printing services is proposed to reduce completion time, cost and to improve service quality. Moreover, the non-cooperative game consists of two kinds of sub-game to work together. Some service attributes, such as moving speed of nozzle, model dimension, 3D printing precision, 3D printing material, and pricing mode, are considered in the model. In order to obtain the expected solution, a two-layer nested method based on genetic algorithm is developed to improve scheduling efficiency. An industrial case is given to verify the feasibility and effectiveness of the proposed method. The results show that it has a better performance than traditional scheduling methods.

Ensemble of ML-KNN for classification algorithm recommendation

With the mountains of classification algorithms proposed in the literature, the study of how to select suitable classifier(s) for a given problem is important and practical. Existing methods rely on a single learner built on one type of meta-features or a simple combination of several types of meta-features to address this problem. In this paper, we propose a two-layer classification algorithm recommendation method called EML (Ensemble of ML-KNN for classification algorithm recommendation) to leverage the diversity of different sets of meta-features. The proposed method can automatically recommend different numbers of appropriate algorithms for different dataset, rather than specifying a fixed number of appropriate algorithm(s) as done by the ML-KNN, SLP-based and OBOE methods. Experimental results on 183 public datasets show the effectiveness of the EML method compared to the three baseline methods.

Laboratory investigation on the heat dissipation regularity and road performance of different pavement structure combinations by double-layer paving

In this study, the heat dissipation regularity and road performance of the asphalt pavement of traditional paving and double-layer paving methods are studied, and test pieces with different preparation methods are designed to simulate different paving methods. The temperature test is used to test the temperature dissipation law, and the interlayer shear test, the rutting test, and the low-temperature bending test are used to test the road performance. The results show that the two-layer paving method’s heat loss is less, which indicates that it has a longer construction time. The interlaminar bonding performance, high-temperature performance, and low-temperature performance of the double-layer paving method are higher than those of the traditional paving method.

A Two-layer Sequential State Estimation Method for electricity and heat Networks

As one of the main application forms of energy coupling utilization, the electric gas integrated energy system has been widely concerned. State estimation plays an important role in the operation monitoring and optimal scheduling of the electric gas integrated energy system. The state estimation is the basis of energy management of the electro-pneumatic coupling network, which can provide a high-precision global consistent solution for the subsequent optimal scheduling and safety evaluation of the electro-pneumatic coupling network. The steady state estimation model of gas network is established for grid connected operation or island mode, and the state estimation of complex gas network is realized. On this basis, the coupled state estimation model of electricity and gas is further established, and its performance in state estimation accuracy and bad data identification is tested.