Proposed Aggregation Model Research Articles

Aggregate scheduling potential evaluation of large-scale air conditioning loads

As an adjustable load, the air conditioning (AC) load is a high-quality resource for power system operation regulation. However, due to the large number of AC loads and the heterogeneity among individual users, the scheduling potential of AC loads is difficult to accurately calculate. An aggregate scheduling potential evaluation of large-scale AC loads is proposed. Firstly, according to the operation characteristics of ACs, the aggregation models of central ACs and household split-type ACs are constructed by using the equivalent thermal parameter model and Monte Carlo sampling method respectively. Secondly, based on the proposed aggregation models of large-scale AC loads, the AC loads are rescheduled by resetting the set temperature, and the scheduling potential calculation model of central ACs and residential ACs is obtained. Finally, case studies show that AC loads have considerable scheduling potential when the indoor temperature is maintained within the range of human comfort.

Feasible operation region estimation of virtual power plant considering heterogeneity and uncertainty of distributed energy resources

The concept of the virtual power plant (VPP) has attracted extensive attention due to its distinguished capability of integrating various types of distributed energy resources (DERs). Defining the feasible operation region (FOR) is a crucial prerequisite for VPP's participation in the provision of power system services. However, considering the heterogeneity and uncertainty of DERs, it is always a challenging task to calculate the accurate FOR of VPP. In this paper, we propose a novel internal resource aggregation approach to estimate the FOR of VPP. The design of the proposed aggregation model consists of two stages. In the first stage, all the operational constraints of various DERs at the same node are expressed with a unified polytope form, and then these constraints are aggregated into one single polytope operation constraint by the computational geometry method. In the second stage, based on the aggregated polytope operation constraint at each node, combined with the dynamic line rating (DLR) and network constraints, a data-driven distributionally robust FOR estimation problem is formulated to determine the boundaries of the FOR. Numerical experiments have been conducted to validate the feasibility and superiority of the proposed approach and its applicability in handling multi-period FOR estimation.

An Extended State Space Model for Aggregation of Large-Scale EVs Considering Fast Charging

This article presents an extended state space model for aggregation of large-scale electric vehicles (EVs) for frequency regulation and peak load shaving in power systems. The proposed model systematically deals with the fast charging of EVs as an effective solution for immediate charging requirements. Furthermore, the proposed extended state space model increases the flexibility of the EV aggregator (EVA) by enabling the EVs to participate in ancillary services with both regular and fast charging/discharging rates. This will help the EVA to provide a prompt and efficient response to severe generation-consumption imbalances. A probabilistic control approach is developed which reduces the communication burden of the EVA. Furthermore, the uncertainties related to EV users' behavior are modeled in real-time. The simulations are conducted for a typical power system including a large population of EVs, a conventional generator (CG), and a wind generation system. It is shown that the proposed aggregation model can accurately describe the aggregated behavior of a large population of EVs enabling them to efficiently participate in frequency regulation and peak load shaving services. Finally, the performance of EVA is evaluated for different driving behaviors and state of charge (SOC) levels of the EV population.

Cluster-Based Secure Aggregation for Federated Learning

In order to protect each node’s local learning parameters from model inversion attacks, secure aggregation has become the essential technique for federated learning so that the federated learning server knows only the combined result of all local parameters. In this paper, we introduced a novel cluster-based secure aggregation model that effectively deals with dropout nodes while reducing communicational and computational overheads. Specifically, we considered a federated learning environment with heterogeneous devices deployed across the country. The computing power of each node and the amount of training data can be heterogeneous. Because of this, each node had a different local processing time, and the response time to the server is also different. To clearly determine the dropout nodes in this environment, our model clusters nodes with similar response times based on each node’s local processing time and location and then performs the aggregation on a pre-cluster basis. In addition, we propose a new practical additive sharing-based masking protocol to hide the actual local updates of nodes during aggregation. The new masking protocol makes it easy to remove the share of dropout nodes from the aggregation without using a (t, n) threshold scheme, and updates from dropout nodes are still secure even if they are delivered to the server after the dropout shares have been revealed. In addition, our model provides mask verification for reliable aggregation. Nodes can publicly verify the correctness and integrity of the masks received from others using a discrete logarithm problem before the aggregation. As a result, the proposed aggregation model is robust to dropout nodes and ensures the privacy of local updates if at least three honest nodes are alive in each cluster. Since the masking process is performed on a cluster basis, our model effectively reduces the overhead of generating and sharing the masking value. For an average cluster size C and a total number of nodes N, the computation and communication cost of each node is O(C), the computation cost of the server is O(N), and the communication cost is O(NC). We analyzed the security and efficiency of our protocol by simulating diverse dropout scenarios. The simulated results showed that our cluster-based secure aggregation outputs about a 91% learning accuracy regardless of dropout rate with four clusters for one hundred nodes.

Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning.

Rheumatic heart disease (RHD) affects an estimated 39 million people worldwide and is the most common acquired heart disease in children and young adults. Echocardiograms are the gold standard for diagnosis of RHD, but there is a shortage of skilled experts to allow widespread screenings for early detection and prevention of the disease progress. We propose an automated RHD diagnosis system that can help bridge this gap. Experiments were conducted on a dataset with 11646 echocardiography videos from 912 exams, obtained during screenings in underdeveloped areas of Brazil and Uganda. We address the challenges of RHD identification with a 3D convolutional neural network (C3D), comparing its performance with a 2D convolutional neural network (VGG16) that is commonly used in the echocardiogram literature. We also propose a supervised aggregation technique to combine video predictions into a single exam diagnosis. The proposed approach obtained an accuracy of 72.77% for exam diagnosis. The results for the C3D were significantly better than the ones obtained by the VGG16 network for videos, showing the importance of considering the temporal information during the diagnostic. The proposed aggregation model showed significantly better accuracy than the majority voting strategy and also appears to be capable of capturing underlying biases in the neural network output distribution, balancing them for a more correct diagnosis. Automatic diagnosis of echo-detected RHD is feasible and, with further research, has the potential to reduce the workload of experts, enabling the implementation of more widespread screening programs worldwide.

Solvent controlled aggregation behavior of peptide bond simulant N-methylacetamide and vibrational coupling splitting theory

Quantum state of C = O streching presents as two entangled quantum states, one is in phase and another is out phase when adjacent C = O coupled in aggregated dimers form. As the simplest model molecules of peptide, N-methylacetamide in binary mixture were chosen to investigate the intermolecular C = O coupling. The isotropic and anisotropic Raman spectra of N-methylacetamide/CCl4, N-methylacetamide/ methanol, N-methylacetamide/ acetonitrile, N-methylacetamide/ methanol and N-methylacetamide/ water binary mixture at different concentrations have been obtained. It was found that the quantum frequencies and strength of quantum entanglement could be continuously tuned span < 35 cm−1 by adjusting concentrations or selecting solvent. According to ΔrGm = ΔrHm-TΔrSm, only the solvent having good solvation or forming H-bond to solute could lower the ΔrHm or ΔrGm sufficiently so as to favor the entangled dimers breaking. The geometries of solvated and H-bonded monomers and dimers of N-methylacetamide were calculated at the B3LYP-D3/6-311G (d,p) level of theory. The isotropic and anisotropic Raman splitting phenomenon of the N-methylacetamide C = O vibration and their concentrations effects can be explained by presently proposed aggregation model and the calculations. This study is expected to be applied to the structural identification, the solvent controlled physical and chemical processes of molecules in solution phase, quantum communication and basic thermodynamics teaching experiments.

Robust Cumulative Crowdsourcing Framework Using New Incentive Payment Function and Joint Aggregation Model.

In recent years, crowdsourcing has gained tremendous attention in the machine learning community due to the increasing demand for labeled data. However, the labels collected by crowdsourcing are usually unreliable and noisy. This issue is mainly caused by: 1) nonflexible data collection mechanisms; 2) nonincentive payment functions; and 3) inexpert crowd workers. We propose a new robust crowdsourcing framework as a comprehensive solution for all these challenging problems. Our unified framework consists of three novel components. First, we introduce a new flexible data collection mechanism based on the cumulative voting system, allowing crowd workers to express their confidence for each option in multi-choice questions. Second, we design a novel payment function regarding the settings of our data collection mechanism. The payment function is theoretically proved to be incentive-compatible, encouraging crowd workers to disclose truthfully their beliefs to get the maximum payment. Third, we propose efficient aggregation models, which are compatible with both single-option and multi-option crowd labels. We define a new aggregation model, called simplex constrained majority voting (SCMV), and enhance it by using the probabilistic generative model. Furthermore, fast optimization algorithms are derived for the proposed aggregation models. Experimental results indicate higher quality for the crowd labels collected by our proposed mechanism without increasing the cost. Our aggregation models also outperform the state-of-the-art models on multiple crowdsourcing data sets in terms of accuracy and convergence speed.

Measuring performance with common weights: network DEA

In conventional data envelopment analysis (DEA), a production system has been seen as a black box for measuring the efficiency without any attention to what is happening inside the system. However, in practice, performance improvement often requires observing the internal structure of the producing system in order to find the sources of inefficiencies. In addition, weight flexibility as a key property of the multiplier DEA models allows a system to totally disregard an assessment factor, either input or output, from the evaluation process by assigning a value of zero or epsilon to its weight. This paper contributes to the existing literature by proposing a common-weights DEA model when the production system includes a number of interrelated processes. To this end, we propose an aggregate DEA model to calculate the most favourable common weights for determining the efficiency of all production systems and their processes at the same time. Our proposed aggregate model not only is linear for equitably evaluating the producing units on the same scale, but also enables us to deal with the mixed network structures. Furthermore, the network system is decomposed into a series system to build a relational network DEA model that emphasises separate relatedness. This greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in network DEA models including the zero value and fluctuating weights and multiple solutions. Managerially speaking, this paper aims to provide the top management team of a production system with an integrated framework to shape a better strategic decision process about firm performance, which is to treat the sources of inefficiencies and ultimately take corrective actions over the long run. Put differently, the proposed framework helps top managers make proper decisions in complex situations with a view of improving a firm’s efficiency in all production divisions, which can be identified as a core competency leading to competitive advantages of the organisation. In the context of performance management, our study is equipped with a simple numerical example and a case study of the non-life insurance companies to demonstrate the applicability of the proposed common-weights network model.

Modeling the Separation of Microorganisms in Bioprocesses by Flotation

Bioprocesses for the production of renewable energies and materials lack efficient separation processes for the utilized microorganisms such as algae and yeasts. Dissolved air flotation (DAF) and microflotation are promising approaches to overcome this problem. The efficiency of these processes depends on the ability of microorganisms to aggregate with microbubbles in the flotation tank. In this study, different new or adapted aggregation models for microbubbles and microorganisms are compared and investigated for their range of suitability to predict the separation efficiency of microorganisms from fermentation broths. The complexity of the heteroaggregation models range from an algebraic model to a 2D population balance model (PBM) including the formation of clusters containing several bubbles and microorganisms. The effect of bubble and cell size distributions on the flotation efficiency is considered by applying PBMs, as well. To determine the sensitivity of the results on the model assumptions, the modeling approaches are compared, and suggestions for their range of applicability are given. Evaluating the computational fluid dynamics (CFD) of a dissolved air flotation (DAF) system shows the heterogeneity of the fluid dynamics in the flotation tank. Since analysis of the streamlines of the tank show negligible back mixing, the proposed aggregation models are coupled to the CFD data by applying a Lagrangian approach.

Modeling Single Machine Preemptive Scheduling to Minimize the Cost of Tardiness and Overtime

We propose two alternative mathematical modeling approaches for the problem of finding a preemptive schedule that minimizes the total cost of tardiness for a set of jobs on a single processor under variable workload requirements. The first model follows from the conventional scheduling approach and the second borrows from the aggregate planning paradigm. We demonstrate with numerical experimentation and analytical methods that the latter modeling approach is more efficient and computes better lower bounds than the conventional model. We expand the study by analyzing special cases where overtime capacity is allowed and cases with equal work requirements where the advantages of the proposed aggregate model are fully exploded; finally, we validate the experimental results by applying the aggregate modeling paradigm to a real industry case where we compute well under four seconds the optimal overhaul schedule for 82 jobs under four different customers. Overall findings show that the proposed aggregate model is an effective tool for generating optimal or near-optimal schedules reasonably quick for real life applications.

Evaluation of Achievable Vehicle-to-Grid Capacity Using Aggregate PEV Model

Large-scale plug-in electric vehicles (PEVs) utilizing vehicle-to-grid (V2G) technology can collectively behave as a storage system under the control of an aggregator, e.g., arbitraging in the energy market and providing ancillary services to the grid. Quantitatively evaluating V2G capacity, i.e., charging and discharging power ranges, for a PEV fleet utilizing V2G technology (which is referred to as a V2G fleet in this paper) ahead of time is of fundamental importance for V2G implementation. However, because of the stochastic characteristics of PEV driving behaviors, charging demands are difficult to forecast, which makes evaluating V2G capacity technically difficult. This paper first establishes an aggregate model of a V2G fleet that employs aggregated parameters to represent energy and power constraints of the entire V2G fleet and, therefore, reduces the difficulty of forecasting. Then, an evaluation method for V2G capacity of large-scale PEVs is developed based on the proposed aggregate model. To make the V2G capacity evaluated in advance achievable while guaranteeing charging demands during real-time operation, a heuristic smart charging strategy is designed. The application of the evaluation method in optimal charge and discharge scheduling for a V2G fleet providing power reserves is illustrated. Numerical simulations are conducted to validate the proposed method.

A feature selection model based on genetic rank aggregation for text sentiment classification

Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model. Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time. In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance. This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained. In order to aggregate the individual feature lists, a genetic algorithm has been utilized. Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification.

An Aggregate Model of Plug-in Electric Vehicles Including Distribution Network Characteristics for Primary Frequency Control

In the future, the number of plug-in electric vehicles (PEVs) that will participate in the primary frequency control (PFC) is likely to increase. In our previous research, the computational complexity of the PFC problem for a large number of PEVs was reduced using aggregate models of PEVs. However, in the literature on the PFC, the distribution network characteristics have not been included in the aggregate models of PEVs for the PFC, despite the fact that PEVs will be dispersedly connected to the distribution network. This paper proposes an aggregate model of PEVs for the PFC that further incorporates distribution network characteristics, i.e., the distribution network power loss (DNPL) and the maximum allowed current (MAC) of the lines and transformers. The DNPL variation is formulated according to the line and transformer impedance, spatial distribution of PEVs and loads, and active power variation of PEVs. Then, DNPL variation together with the MAC of the lines and transformers are incorporated in the proposed model of PEVs. Finally, the simulation results show an excellent agreement of 98% between the detailed model and the proposed aggregate model of PEVs.

THE AGGREGATION BEHAVIOR OF SHORT CHAIN HYDROPHILIC IONIC LIQUIDS IN AQUEOUS SOLUTIONS

In this paper, aggregation behaviors of aqueous solutions of short chain hydrophilic Ionic Liquids (ILs), 1 - allyl - 3 - methylimidazolium chloride ((Amim)Cl), 1 - ethyl - 3 - methylimidazolium chloride ((Emim)Cl), 1 - butyl - 3 - methylimidazolium chloride ((Bmim)Cl), 1 - butyl - 3 - methylimidazolium tetrafluoroborate ((Bmim)BF4) were investigated using conductivity and laser light scattering. The measurements of conductivity and light scattering were conducted over the whole liquid range, and the Critical Aggregation Concentration (CAC) of aqueous ILs solution determined with the conductivity, was in the order of 2.35mol/L((Amim)Cl) > 2.32mol/L((Emim)Cl) > 2.29mol/L((Bmim)Cl) < 2.54mol/L((Bmim)BF 4 ). The CAC vaule of (Bmim)BF 4 determined with conductivity was consistent with the result of light scattering. In addition, we proposed aggregation model for illustrating the structural change in the aqueous ILs solutions.

A Study on Aggregation of TOPSIS Ideal Solutions for Group Decision-Making

The technique for order preference by similarity to ideal solution (TOPSIS) has become a popular multi-criteria decision making (MCDM) technique, since it has a comprehensible theoretical structure and is able to provide an exact model for decision making. For the use of TOPSIS in group decisions, the common approaches in aggregating individual decision makers’ judgments are the geometric and the arithmetic mean methods, although these are too intuitive and do not consider either preference levels or preference priorities among alternatives for individual decision makers. In this paper, a TOPSIS group decision aggregation model is proposed in which the construction consists of three stages: (1) The weight differences are calculated first as the degrees of preferences among different alternatives for each decision maker; (2) The alternative priorities are then derived, and the highest one can be denoted as the degree to which a decision maker wants his most favorite alternative to be chosen; (3) The group ideal solutions approach in TOPSIS is used for the aggregation of similarities obtained from different decision makers. A comparative analysis is performed, and the proposed aggregation model seems to be more satisfactory than the traditional aggregation model for solving compromise-oriented decision problems.

Data aggregation in constructing composite indicators: A perspective of information loss

Composite indicators (CIs) have been widely accepted as a useful tool for performance comparisons, public communication and decision support in a wide spectrum of fields, e.g. economy, environment and knowledge/information/innovation. The quality and reliability of a CI depend heavily on the underlying construction scheme where data aggregation is a major step. This paper analyzes the data aggregation problem in CI construction from the point of view of information loss. Based on the “minimum information loss” principle, the distance-based and entropy-based aggregation models for constructing CIs are presented. The entropy-based aggregation model has also been extended to deal with qualitative data. It is shown that the proposed aggregation models have close relationships with several popular MCDA aggregation methods in CI construction, although our proposed models seem to be more flexible while more complex in application. Two case studies are presented to illustrate the use of the proposed aggregation models.

Numerical Simulation of Aggregate Shapes of Two-Dimensional Concrete and Its Application

The plane extension method was used to model aggregate shapes of three-dimensional (3D) concrete. In this method, the convexity condition requires that the volume formed by all the extension points and border planes be positive. A test method is adopted that involved summing the aggregate volumes to judge the intersection and overlap of aggregates. In the 3D random aggregate simulation (3D-RAS) structure, the shape, size, and spatial distribution of the aggregate particles resemble real concrete in the statistical sense, and fundamental aggregate diameters within the same gradation are assumed to follow random Gaussian distributions. The fracture process test of a three-point notched beam considering the effects of inhomogeneous material was used to validate the proposed aggregate model. Numerical illustrations show that aggregate shapes play an important role in the failure process of beams. Parameter studies also show that, although the homogeneity index has only a small influence on the elastic modulus, it plays an important role in the nonlinear softening behavior. The aggregate distribution has a little influence on the overall mechanical response of the concrete composite. The interfacial transition zone (ITZ) thickness has a great effect on the ultimate load of the concrete. An optimal aggregate volume fraction should exist for the concrete composites.

On the application of scintillometry over heterogeneous grids

Summary In this paper the applicability of the Monin–Obukhov similarity theory (MOST) over heterogeneous terrain below the blending height is investigated. This is tested using two large aperture scintillometers (LAS), in conjunction with aggregation schemes to infer area-averaged refractive index structure parameters. The two LAS were operated simultaneously over the oliveyard of Agdal, located near Marrakech (Morocco). The Agdal olive yard is made up of two contrasted fields, or patches. The two sites are relatively homogeneous, but differ strongly in characteristics (mainly soil moisture status, and, to a lesser extent, vegetation cover). The higher soil moisture in the northern site creates heterogeneity at the scale of the entire olive yard (i.e. at grid scale). At patch scale, despite the complexity of the surface (tall, sparse trees), a good agreement was found between the sensible heat fluxes obtained from eddy-covariance systems and those estimated from the LAS. At grid scale, the aggregated structure parameter of the refractive index, simulated using the proposed aggregation model, behaves according to MOST. This aggregated structure parameter of the refractive index is obtained from measurements made below the grid scale blending height, and shows that MOST applies here. Consequently, scintillometers can be used at levels below the blending height. This is of interest, since strictly respecting the height requirements poses tremendous practical problems, especially if one is aiming to derive surface fluxes over large areas.

An Adaptive Differentiated Service Multicast Gateway

This paper proposes a DiffServ-based multicast media gateway, called Adaptive Differentiated Service Multicast Gateway (ADMG), to guarantee QoS and scalability for multimedia presentation. The two goals of the ADMG system are (1) to increase bandwidth utilization by reserving statistical multiplexing bandwidth based on layered-media aggregation and (2) to improve the quality of presentation of the received layered media streams by dynamically modifying the sending rates of various service queues when the network passes heavy traffic. To reach these two goals, the proposed statistical layered aggregation model exploits the traffic-burst regularity of each layered aggregating flow, including I-frame, P-frame and B-frame flows, to determine the approximate reserved bandwidth. Additionally, the ADMG system dynamically changes the sending rates of various service queues to ensure that layered media packets with higher priority are always sent before those with lower priority. The proposed aggregation model, which is inconsistent with unaware GOP-based layered-media aggregation methods, has been experimentally tested on several layered-video streams. The results reveal that the proposed scheme outperforms others with a substantial reduction of bandwidth reservation even given tight delay bounds. The experimental results of the ADMG system on DiffServ networks also reveal the capability for dynamic bandwidth adjustment in various networking situations.