Set Of Outputs Research Articles

New inertial self-adaptive algorithms for the split feasibility problem with multiple output sets

New inertial self-adaptive algorithms for the split feasibility problem with multiple output sets

Measuring efficiency in tourism: A problem of shared factors and multiple attributes in DEA

The current research makes three main contributions to the DEA (Data Envelopment Analysis) literature. First, when using DEA to derive an efficiency score for a given DMU, it is normally assumed that each and every DMU has its own unique set of inputs and outputs, there are situations whereby a DMU can have a factor that is shared with other DMUs. This means that one must view efficiency from the perspective of groups of DMUs rather than from the perspective of the individual DMU. Second, two stage problems can, in the presence of shared factors, result in different groupings of DMUs in one stage than in another. Third, in certain circumstances efficiency can be viewed from the perspective of multiple attributes (e.g. different types of tourism). Herein, we develop a model to cater for these features and illustrate the model using a data set on tourism in Mexico.

Two-step inertial accelerated algorithms for solving split feasibility problem with multiple output sets

In this paper, we present and study two new two-step inertial accelerated algorithms for finding an approximate solution of split feasibility problems with multiple output sets. Our methods are extensions of CQ algorithms previously studied in the literature. In contrast to the related iterative methods for solving SFP, our methods incorporate a two-step inertial technique that speeds up the convergence rate of the generated sequences to the unique solution of SFP studied in this work. Furthermore, we present weak and strong convergence results where the strong convergence result of our method is obtained without the on-line rule, a feature absent in related algorithms in the literature. Finally, we demonstrate the applicability and performance of our algorithms through numerical experiments. Our numerical results reveal that our methods perform better than existing related methods in the literature.

Relaxed inertial self-adaptive algorithm for the split feasibility problem with multiple output sets and fixed-point problem in the class of demicontractive mappings

The purpose of this paper is to study the split feasibility problem with multiple output sets and fixed point problem in the class of demicontractive mappings and propose relaxed inertial self-adaptive algorithms that do not use the least squares method. Under some appropriate assumptions, we establish a strong convergence result for the sequence generated by the proposed algorithm. Our result generalizes and extends several results existing in the literature. Finally, we illustrate the convergence of the proposed algorithm by using a numerical example.

A Relaxed Tseng’s Method-based Algorithm for Solving Split Variational Inequalities with Multiple Output Sets

In this paper, the split variational inequality problem with multiple output sets is considered in a more general framework of reflexive Banach spaces. We introduce a relaxed Tseng extragradient method for approximating the solution of this problem when the cost operators are pseudomonotone and non-Lipschitz. Moreover, our proposed algorithm employs the inertial technique and a new self-adaptive step size to guarantee high rate of convergence. Strong convergence of the algorithm is proved in this case without the need for the sequential weak continuity condition. Finally, some numerical experiments are given to show the applicability of our proposed algorithm.

Image based Modeling and Control for Batch Processes

This manuscript addresses the problem of leveraging thermal images for modeling and feedback control, specifically tailored for terminal quality control of batch processes. The primary objective, common in many batch processes, is to produce products with quality variables aligning with user specifications, available for measurement only at batch termination, precluding the direct use of classical control strategies. Furthermore, in many instances, traditional online sensors such as thermocouples may not be available, but instead spectral inputs like thermal images or acoustic data may be more readily available for feedback control. The challenge is to not only use the non-traditional sensor data for building a dynamic model but also to use that model for terminal quality control. The proposed approach involves a multi-layered modeling strategy. Initially, a dimensionality reduction technique is employed to condense the high-dimensional image into a set of representative outputs. Subsequently, subspace identification (SSID) is applied to develop a Linear Time-Invariant (LTI) State Space (SS) model between the inputs and the reduced outputs. Finally, a Partial Least Squares (PLS) model is constructed linking the terminal states of a batch (identified using SSID) with the product qualities obtained for that specific batch. This model is then incorporated into a Model Predictive Control (MPC) formulation. The effectiveness of the MPC is illustrated by showcasing its capability to generate products of high quality by deploying the MPC on a bi-axial lab-scale rotational molding setup.

Properties of sunspot light bridges on a geometric height scale

Context. Investigating light bridges (LBs) helps us comprehend key aspects of sunspots. However, few studies have analyzed the properties of LBs in terms of the geometric height, which is a more realistic perspective given the corrugation of the solar atmosphere. Aims. We aim to shed light on LBs by studying the variation in their physical properties with geometric height. Methods. We used the SICON code to infer the physical quantities in terms of the optical depth and the Wilson depression values of three LBs hosted by a sunspot observed with Hinode/SP in the Fe I 630 nm pair lines. We also used SIR inversions to cross-check the height variation of the field inclination in the LBs. In both output sets, we performed linear interpolation to convert the physical parameters from optical depth into a geometric height scale in each pixel. Results. Depending on their general appearance, we classified each LB as filamentary, grainy, or umbral. They appear as ridges that reach different maximum heights, with the umbral LB being the deepest. While the filamentary LB hosts a plasma inflow from the penumbra, the results for the grainy LB are compatible with an injection of hot plasma through convective cells of reduced field strength. Only a few positions reveal hints suggesting a cusp-like magnetic canopy. Moreover, strong gradients in the magnetic field strength and inclination usually exhibit enhanced electric currents, with the filamentary LB having remarkably strong currents that appear to be related to chromospheric events. Conclusions. The height stratification in filamentary and grainy LBs differ, indicating diverse mechanisms at work. Our results are in general incompatible with a magnetic canopy scenario, and further analysis is needed to confirm whether it exists along the entire LB or only at specific locations. Furthermore, this work assesses the usefulness of the SICON code when determining the height stratification of solar structures.

On the difference set of two transductions

The difference set Δs,t of two (nondeterministic, in general) transducers s,t is the set of all input words for which the output sets of the two transducers are not equal. When the two transducers realize homomorphisms, their difference set is the complement of the well known equality set of the two homomorphisms. However, we show that transducer difference sets result in Chomsky-like classes of languages that are different than the classes resulting from equality sets. We also consider the following word problem: given transducers s,t and input w, tell whether the output sets s(w) and t(w) are different. In general the problem is PSPACE-complete, but it becomes NP-complete when at least one of the given transducers has finite outputs. We also provide a PRAX (polynomial randomized approximation) algorithm for the word problem as well as for the NFA (in)equivalence problem. Our presentation of PRAX algorithms improves the original presentation.

Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis

Considering the seasonal and cyclical fluctuation of loads and the complexity of multi-energy coupling, this paper proposes a novel load forecasting model based on sequence decomposition fusion and factors correlation analysis. Firstly, the variational mode decomposition (VMD) is used to decompose the highly complex load sequences and the novel influencing factors correlation analysis (ICA) is proposed to select strong factors and remove weak feature variables to construct the input and output set. Secondly, this paper proposes the combined forecasting framework MTL-CNN-BiGRU-Attention to simultaneously forecast the cooling, heat, and electricity loads, along with BiGRU used as the hard sharing layer to deeply explore the coupling information between different types of loads. Meanwhile, the gray wolf algorithm (GWO) is improved to accurately and quickly search for the optimal hyperparameters of the model. Finally, the dataset of a comprehensive energy station in Shanghai is used to test our model, and the results show that the MAPE of the cooling and electricity loads forecasting achieve 5.501% and 5.821% in summer and 5.921%, 7.899% and 7.541% for the cooling, heat and electricity loads in transition season and winter, which confirms the effectiveness and superiority of our model.

Using Data Envelopment Analysis to Analyze Academic Programs in a Business College

Business colleges must efficiently manage academic programs which lead to degrees in various majors. In this paper we illustrate how data envelopment analysis (DEA) can be applied to analyze the relative efficiency of how different academic programs transform students into graduates. Assuming a set of common inputs and outputs, we use this optimization technique to compare the programs, and identify those which assume a maximum level of relative efficiency. Our DEA analysis also enables the analyses of why some programs are relatively inefficient and provide specific, quantitative guidance on how to make them more efficient.

Free disposal hull models of multicomponent technologies

AbstractFree disposal hull (FDH) is a nonparametric model of production technology based on the single assumption of free disposability of all inputs and outputs. In this paper, we consider multicomponent production technologies in which every decision making unit (DMU) consists of several parallel component processes that can in principle operate independently of each other, provided they have sufficient resources. An example is universities viewed as DMUs, with their departments or groups of departments viewed as component processes. Each component process uses its own set of inputs and an unknown part of the shared inputs in order to produce its own set of outputs and an unknown part of the shared outputs. We allow combinations of component processes taken from different DMUs in order to construct new hypothetical DMUs, and refer to the resulting model of technology as the multicomponent FDH (MFDH) model. We further develop a larger, and mathematically nontrivial, variant of MFDH for the case in which we can specify certain bounds on the proportions in which shared inputs and outputs are allocated to component processes. We use an illustrative example in the context of universities to demonstrate the increasing discriminatory power of the new MFDH models over the standard FDH models in the multicomponent setting.

Efficient targets and reference sets in selectively convex technologies

Conventional models of data envelopment analysis (DEA) typically assume that the underlying production technology is a convex set. It is known that such assumption may be clearly unsubstantiated in certain cases. Examples include studies in which some inputs or outputs are stated as proportions or percentages, or are represented by categorical measures. Excluding such “problematic” inputs and outputs from the assumption of convexity while assuming the latter for the remaining measures leads to the notion of selective convexity. Further examples of selectively convex technologies include technologies parameterized by an environmental factor and technologies in which only the input or output sets are convex. In this paper, we consider the identification of efficient targets and reference sets of decision making units in a selectively convex technology, which has not yet been explored in the literature. We show that, for such technologies, the conventional method based on the solution of the additive DEA model may not correctly identify the reference sets and needs an adjustment.

RELAXED DOUBLE INERTIA AND VISCOSITY ALGORITHMS FOR THE MULTIPLE-SETS SPLIT FEASIBILITY PROBLEM WITH MULTIPLE OUTPUT SETS

In this paper, we investigate a multiple-sets split feasibility problem with multiple output sets in infinite-dimensional Hilbert spaces. To address this problem, we propose relaxed inertial self-adaptive algorithms that do not use the least squares method and prove strong convergence results for the sequences generated by these algorithms. Finally, we validate the performances of the proposed algorithms by using numerical examples.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Active Distribution Network Expansion Planning Based on Wasserstein Distance and Dual Relaxation

In the future, a high proportion of distributed generations (DG) will be integrated into the distribution network. The existing active distribution network (ADN) planning methods have not fully considered multiple uncertainties, differentiated regulation modes or the cost of multiple types of interconnection switches. Meanwhile, it is difficult to solve large-scale problems at small granularity. Therefore, an expansion planning method of ADN considering the selection of multiple types of interconnection switches is proposed. Firstly, a probability distribution ambiguity set of DG output and electrical-load consumption based on the Wasserstein distance is established for dealing with the issue of source-load uncertainty. Secondly, a distributionally robust optimization model for collaborative planning of distribution network lines and multiple types of switches based on the previously mentioned ambiguity set is established. Then, the original model is transformed into a mixed integer second-order cone programming (SOCP) model by using the convex relaxation method, the Lagrangian duality method and the McCormick relaxation method. Finally, the effectiveness of the proposed method is systematically verified using the example of Portugal 54. The results indicate that the proposed method raises the annual net profit by nearly 5% compared with the traditional planning scheme and improves the reliability and low-carbon nature of the planning scheme.

Convergence Result for Solving the Split Fixed Point Problem with Multiple Output Sets in Nonlinear Spaces

Convergence Result for Solving the Split Fixed Point Problem with Multiple Output Sets in Nonlinear Spaces

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks.

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

Model based fault detection diagnosis and classification in shell and tube heat exchanger using intelligent techniques

A method for Model-Based Approach for Fault Detection and Diagnosis has been put forth in this research. The purpose of this study is to build a heat exchanger model and to locate, categorize faults in the heat exchanger system. One of the known methods for fault control in industrial processes is fault detection and diagnosis (FDD). Both linear and non-linear systems can benefit from the FDD technique; this work describes fault identification and diagnosis for the non-linear system, the heat exchanger. A realistic mathematical model of the heat exchanger with its set of inputs and outputs has therefore been constructed, as the fault detection of the heat exchanger is related to the mathematical model for its operation. Once the mathematical model has been realized using the formulated equation, the process passes on to fault detection. Residual, which defines the failure symptoms of the system, is used to detect faults. In order to prevent failures and defective circumstances from occurring in existing systems, observer design is employed as one of the most powerful and reliable approaches residual generation. Fault diagnosis is carried out using a Fuzzy logic controller (FLC), which is designed based on the residuals obtained, and fault classification is made using the FLC via a fixed threshold. The paper's primary goals are to reduce errors and enhance non-linear systems' overall performance in industrial processes. According to the findings of the simulation, errors in sensors, processes, and actuators can be classified according to a set threshold, and they can be controlled using a traditional PID controller.

Waste-Heat Renewable Gasifier Design through Taguchi's Method and MANFIS

Ensuring a global average temperature increase of no more than 1.5°C is crucial for aligning with the objectives of the Paris Agreement. The Intergovernmental Panel on Climate Change (IPCC) estimates that a 45% reduction in carbon dioxide emissions by 2030 or a 25% reduction by the same year is necessary to cap the temperature rise at 2°C. Failure to achieve this target by 2030 would necessitate substantial subsequent reductions to compensate for the delayed progress toward net zero emissions, potentially incurring higher costs. Elevating the focus on enhancing energy efficiency has emerged as a key strategy in expediting progress toward these climate goals. Hydrogen, renowned for its high efficiency and emission-free combustion, is anticipated to play a pivotal role in this endeavor. Traditionally, gasifiers have served as the conduit for transforming inexpensive fuels like coal or biomass into hydrogen. However, their energy-intensive nature poses a challenge.Addressing this inherent issue, an innovative approach known as the external-heating gasifier has been proposed and designed. The design of the external-heating gasifier represents a quintessential nonlinear and multiple-input multiple-output (MIMO) problem. To achieve optimal performance, the integration of Taguchi's method and inverse-model technology has been employed, with a simulation platform developed using the COMSOL Multiphysics software.Initially, Taguchi's method was utilized to determine the minimum number of experiments required for a full-factorial design and identify the most critical factors influencing product performance. Subsequently, an adaptive neuro-fuzzy inference system (ANFIS) was developed with a MIMO–ANFIS architecture. This system was employed to train an inverse model, facilitating the mapping of relationships between each input and output set. In essence, this approach enables the identification of manufacturing parameters that result in optimal performance. To illustrate the efficacy of the proposed method, a simulation study on an existing gasifier is presented.

West African Monsoon Dynamics and Its Control on the Stable Oxygen Isotopic Composition of Precipitation in the Late Cenozoic

AbstractThis study presents an overview of the Late Cenozoic evolution of the West African Monsoon (WAM), and the associated changes in atmospheric dynamics and oxygen isotopic composition of precipitation (δ18Op). This evolution is established by using the high‐resolution isotope‐enabled GCM ECHAM5‐wiso to simulate the climatic responses to paleoenvironmental changes during the Mid‐Holocene (MH), Last Glacial Maximum (LGM), and Mid‐Pliocene (MP). The simulated responses are compared to a set of GCM outputs from Paleoclimate Model Intercomparison Project Phase 4 (PMIP4) to assess the added value of a high resolution and model consistency across different time periods. Results show WAM magnitudes and pattern changes that are consistent with PMIP4 models and proxy reconstructions. ECHAM5‐wiso estimates the highest WAM intensification in the MH, with a precipitation increase of up to 150 mm/month reaching 25°N during the monsoon season. The WAM intensification in the MP estimated by ECHAM5‐wiso (up to 80 mm/month) aligns with the mid‐range of the PMIP4 estimates, while the LGM dryness magnitude matches most of the models. Despite an enhanced hydrological cycle in MP, MH simulations indicate a ∼50% precipitation increase and a greater northward extent of WAM than the MP simulations. Strengthened conditions of the WAM in the MH and MP result from a pronounced meridional temperature gradient driving low‐level westerly, Sahel‐Sahara vegetation expansion, and a northward shift of the Africa Easterly Jet. The simulated δ18Op values patterns and their relationship with temperature and precipitation are non‐stationarity over time, emphasizing the implications of assuming stationarity in proxy reconstruction transfer functions.