Defined Objective Function Research Articles

Optimization of Daylighting Design Using Self-Shading Mechanism in Tropical School Classrooms with Bilateral Openings

Despite its potential, daylighting strategies in school classrooms in the tropical climate regions is little explored in the literature. The use of two-sided or bilateral daylight opening, as well as the self-shading mechanism using sloped walls, are currently seen as potential strategies to achieve good daylighting in tropical buildings. This study thus aims at exploring and optimizing self-shading design possibilities for creating daylight-friendly, tropical elementary school classrooms with bilateral openings, by means of sensitivity and uncertainty analyses. Design parameters such as façade orientation, window-to-wall ratio, window elevations, wall slopes, interior surface reflectance and glazing transmittance are considered in the model of a hypothetical, typical classroom without shading devices. Climate-based daylight metrics such as UDI250-750 lx, sDA300/50% and ASE1000,250 are utilized as the performance indicators. Computational modelling with Honeybee [+] in Rhinoceros/ Grasshopper platform is employed to conduct the annual daylight simulation. Multi-objective optimizations using genetic algorithms (GA) through Octopus with SPEA-2 algorithms are performed to determine the optimum design solutions following the sensitivity and uncertainty analysis. The slope and WWR of the wall that faces west, southwest, south, or southeast, has the strongest influence on the defined objective function that includes all daylight metrics. To achieve the optimum daylight performance, design of the opposing window façades of school classrooms with bilateral openings should not be identical.

Structure-based optimization problem for model predictive control in linear multi-input multi-value systems

Various technical and other realworld systems can be modelled with decent precision as linear systems. This approach is the core of the long established control theory, whose mathematical apparatus is ubiquitous when it comes to controlling some kind of system. While it is hard to underestimate importance of this approach, long history of research in this field showed some of its shortcomings which may hinder its application in various ways. For example, it does not allow to incorporate constraints on control signal’s magnitude into the system’s model. Thus, engineers are forced to manually tune controller’s parameters adhoc in order to satisfy these constraints. This paper is dedicated to development of an alternative control algorithm based on the model predictive control approach. Its core idea is to generate control sequences by solving an optimization problem which objective function depends on predicted future state. It allows to generate fast stabilization trajectories without additional tuning by using the classic linear system’s evolutionary equation as a future state predictor and constraints on controls as optimization problem’s constraints. Meaningfully defined objective function is crucial in order to make this control algorithm work properly. It appeared that defining an objective function with good enough properties in general case is not a trivial task. This paper leverages modern nonstandard analysis in order to achieve this feat.

Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-Resolution Electrogastrogram.

Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysiology of pediatric functional nausea from healthy controls would be an invaluable aid to support clinical decision-making in diagnosis and management of patient treatment methodology. The purpose of this paper is to present an innovative approach for objectively classifying pediatric functional nausea using cutaneous high-resolution electrogastrogram data. We present an Automated Electrogastrogram Data Analytics Pipeline framework and demonstrate its use in a 3x8 factorial design to identify an optimal classification model according to a defined objective function. Low-fidelity synthetic high-resolution electrogastrogram data were generated to validate outputs and determine SOBI-ICA noise reduction effectiveness. A 10 parameter support vector machine binary classifier with a radial basis function kernel was selected as the overall top-performing model from a pool of over 1000 alternatives via maximization of an objective function. This resulted in a 91.6% test ROC AUC score. Using an automated machine learning pipeline approach to process high-resolution electrogastrogram data allows for clinically significant objective classification of pediatric functional nausea. To our knowledge, this is the first study to demonstrate clinically significant performance in the objective classification of pediatric nausea patients from healthy control subjects using experimental high-resolution electrogastrogram data. These results indicate a promising potential for high-resolution electrogastrography to serve as a data-driven screening tool for the objective diagnosis of pediatric functional nausea.

Multi-objective optimization of lateral stability strength of transversely loaded laminated composite beams with varying I-section

In this research, the lateral buckling analysis and layup optimization of the laminated composite of web and flanges tapered thin-walled I-beams based on maximizing lateral-torsional stability strength and minimizing mass/cost of the structure are investigated. The classical lamination theory and Vlasov’s model for thin-walled cross-section are adopted to establish the total potential energy for thin-walled symmetric balanced laminated beams with varying I-section. By implementing the Ritz method, an explicit formulation for the lateral-torsional buckling load of a double-tapered beam subjected to transverse loading is then derived in terms of the load height parameter and stiffness quantities. Subsequently, the optimal arrangements of layer sequences are obtained using the non-dominated sorting genetic algorithm (NSGA-II) and properly defined objective function. The critical factors of fitness function as lateral buckling strength and the mass of the structure with critical limitations as ply angle, number of layers for the web and flanges, and the thickness of all section walls are considered in this study. Finally, the optimal layer arrangement for the web and flanges are separately determined and discussed. The results show that the presented optimization procedure and layups patterns lead to increasing the lateral-torsional buckling capacity about 52% compared to the conventional angle-ply and unidirectional layups for the web and flanges, respectively.

Selection of Sensors for Efficient Transmitter Localization

We address the problem of localizing an (unauthorized) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner, wherein the efficiency is defined in terms of the number of sensors used to localize. Localization of unauthorized transmitters is an important problem which arises in many important applications, e.g., in patrolling of shared spectrum systems for any unauthorized users. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors’ energy resources. In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function—and show that it delivers a provably approximate solution for the general case. We develop techniques to significantly reduce the time complexity of the designed algorithms by incorporating certain observations and reasonable assumptions. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques—our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations and up to 16% in small-scale testbeds.

Daily Operating Room Schedule Taking into Account the Preferences of Hospital Physicians (Case Study of Imam Khomeini Hospital)

The issue of allocating the capacity of operating rooms to different departments of surgery is done by observing the decisions made at the strategic level of hospital decisions. Therefore, operating room scheduling plays an important role in increasing their productivity and reducing hospital costs. On the other hand, due to various uncertainties in operating room activities, this issue can be very challenging. In this case, according to the defined objective function and also the existing limitations, the capacity of operating rooms is allocated to different surgical groups. Therefore, in this model, the daily schedule of surgeries in two stages of surgery and recovery and considering all the real limitations in hospitals has been investigated. This issue determines the sequence, start time of each surgery and allocation of resources required in each stage with the aim of minimizing the total cost of employing surgeons according to the differences in their skills, performance and preferences.

Methodology of Multicriterial Optimization of Geometric Features of an Orthopedic Implant

The main purpose of the article is to describe the methodology used for multi-criteria optimization of the geometric features of the orthopedic implant used for the reconstruction of the anterior cruciate ligament located in the knee joint. The methodology includes: 1. Method of development of the bones of the knee joint model; 2. Method of multi-criteria optimization of the geometric features of the orthopedic implant using an artificial immune system, the objective function and the Pareto front; 3. Expert evaluation method based on forms. The work confirmed that the assumed thesis, a multi-criteria optimization using an artificial immune system, which is a specially defined objective function, and the Pareto method, which allows to determine the geometrical features of the implant, will lead to fulfill optimal blood perfusion and sufficient strength properties of the implant simultaneously. We conclude that the described methodology allowed to achieve the optimal geometrical features of the orthopedic implant used for reconstruction of the anterior cruciate ligament located in the knee joint.

Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm.

The existence of speckle noise in ultrasound (US) image processing distorts the image quality and also hinders the development of systematic approaches for US images. Numerous de-speckling schemes were established to date that concern speckle reduction; however, the models suffer from demerits like computational time, computational complexity, etc., that are to be rectified as soon as possible. This compulsion takes to the introduction of a new de-speckling model via an adaptive hybrid filter model that includes four filters like guided filter (GF), speckle-reducing bilateral filter (SRBF), rotation invariant bilateral nonlocal means filter (RIBNLM), and median filter (MF) respectively. Moreover, the novelty goes under the selection of optimal filter coefficients that make the process effective. Bayesian-based neural network is used to predict the appropriate filter coefficients, where the training library is constructed with the optimal coefficients. Along with this, the selection of optimal filter coefficients is done under the defined objective function using a new hybrid algorithm termed as Randomized FireFly (FF) update in Lion Algorithm (RFU-LA) that hybrids the concept of both LA and FF, respectively. Finally, the performance of the proposed de-speckling model is compared over that of other conventional models with respect to different performance measures. Accordingly, from the analysis, the mean MAPE of the proposed method are 39.13% and 49.28% higher than those of the wavelet filtering and hybrid filtering schemes for a noise variance of 0.1.

W77. COMPARISON OF MACHINE LEARNING APPROACHES FOR PREDICTION OF SCHIZOPHRENIA USING GENETIC AND DEMOGRAPHIC FACTORS IN THE UK BIOBANK

We present a prototype system, OptiLoc, that aims at analysing consequences of medical capacity restrictions under the Covid-19 regime as well as at providing medical analysts and decision makers with medical procedure relocation plans for a given area in a given time period. This is achieved by first forecasting the demand of medical procedures of different types at the selected geographical granularity level and then finding relocations which are optimal according to a well defined objective function that takes into account procedure and relocation costs, under constraints imposed by Covid-19 restrictions and hospital capacities. Allocation plans are visualized in a user friendly web-based application. We demonstrate the effectiveness of the developed system on the data on urological procedures from Poland.

OPTIMIZATION DESIGN OF GRANULAR ACTIVAED CARBON USING GENETIC ALGORITHM

Integrated advanced wastewater treatment provides important fundamental solutions to problems associated with water scarcity prevailing in arid and semi arid climatic regions. This was accomplished through treated water with specific specification and characteristics suitable to be used for agricultural, domestic, and industrial purposes. This study is concerned with the use of genetic algorithm procedure for the optimum design of integrated advance wastewater treatment units, with their various types and characteristics. The aim of optimum wastewater treatment units design is to attain optimum values of certain pre defined objective function. Based on the results of applying genetic algorithm on activated carbon treatment plant, it was found that the optimum values of empty bed contact time, dose of activated carbon, diameter of contractor, and loading rate of multiple hearth furnaces are 10min, 25mg/l, 2.4m, and 30 m/min, respectively

Estimation of Functional Form of Time-Dependent Heat Transfer Coefficient Using an Accurate and Robust Parameter Estimation Approach: An Inverse Analysis

This paper presents a numerical method to address function estimation problems in inverse heat transfer problems using parameter estimation approach without prior information on the functional form of the variable to be estimated. Using an inverse analysis, the functional form of a time-dependent heat transfer coefficient is estimated efficiently and accurately. The functional form of the heat transfer coefficient is assumed unknown and the inverse heat transfer problem should be treated using a function estimation approach by solving sensitivity and adjoint problems during the minimization process. Based on proposing a new sensitivity matrix, however, the functional form can be estimated in an accurate and very efficient manner using a parameter estimation approach without the need for solving the sensitivity and adjoint problems and imposing extra computational cost, mathematical complexity, and implementation efforts. In the proposed sensitivity analysis scheme, all sensitivity coefficients can be computed in only one direct problem solution at each iteration. In this inverse heat transfer problem, the body shape is irregular and meshed using a body-fitted grid generation method. The direct heat conduction problem is solved using the finite-difference method. The steepest-descent method is used as a minimization algorithm to minimize the defined objective function and the termination of the minimization process is carried out based on the discrepancy principle. A test case with three different functional forms and two different measurement errors is considered to show the accuracy and efficiency of the used inverse analysis.

Determination of Economic Leakage Level with Optimization Algorithm in Water Distribution Systems

It is necessary to set the most appropriate targets in water loss management (WLM) and to choose the most economically and technically suitable method to achieve these targets. The aim of this work is to define the level of economic leakage with the optimization algorithm in sustainable WLM, considering the system components, requirements and costs of the methods. For this purpose, a new model based on discrete stochastic optimization has been developed to determine the most appropriate values of the pressure level and the number of teams. After the system components were entered into the model, the optimal values of parameters were determined according to the problem-specific defined objective function. It was calculated that the pressure should be reduced from 40[Formula: see text]m to 25.30[Formula: see text]m, and the number of teams was 4 for the sample system. The Economic Leakage Level for the system was calculated as 145.85[Formula: see text]m3/day in volume and 11.9% in percentage.

Differentiable Fluids with Solid Coupling for Learning and Control

We introduce an efficient differentiable fluid simulator that can be integrated with deep neural networks as a part of layers for learning dynamics and solving control problems. It offers the capability to handle one-way coupling of fluids with rigid objects using a variational principle that naturally enforces necessary boundary conditions at the fluid-solid interface with sub-grid details. This simulator utilizes the adjoint method to efficiently compute the gradient for multiple time steps of fluid simulation with user defined objective functions. We demonstrate the effectiveness of our method for solving inverse and control problems on fluids with one-way coupled solids. Our method outperforms the previous gradient computations, state-of-the-art derivative-free optimization, and model-free reinforcement learning techniques by at least one order of magnitude.

Markov decision processes with recursive risk measures

In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state and action spaces and unbounded cost. We treat both finite and infinite planning horizons. Our optimality criterion is based on the recursive application of static risk measures. This is motivated by recursive utilities in the economic literature. It has been studied before for the entropic risk measure and is extended here to general static risk measures. Under direct assumptions on the model data we derive a Bellman equation and prove the existence of optimal Markov policies. For an infinite planning horizon, the model is shown to be contractive and the optimal policy to be stationary. Our approach unifies results for a number of well-known risk measures. Moreover, we establish a connection to distributionally robust MDPs, which provides a global interpretation of the recursively defined objective function. Monotone models are studied in particular.

Building synergetic consensus for dynamic gas-plume tracking applications using UAV platforms

This article investigates the problem of deploying a swarm of UAVs equipped with gas sensors for industrial remote gas-plume sensing. This setup’s objective is to continuously adjust the swarm formation to maximize the combined perception for the dynamically evolved plume’s cloud, focusing around areas with the highest concentration/intensity. Initially, such a setup is formulated into an optimization problem, the solution of which could be acquired by the maximization of an appropriately defined objective function. Due to the model-free approach, this objective function’s analytical form is not available, prohibiting standard gradient descent methodologies. To this end, a tracking algorithm is developed and studied, which operates in a distributed manner and enables the UAV swarm to build a common consensus dynamically, during the evolution of the leakage phenomenon. The overall performance is tested in a simulative yet realistic environment using ANSYS Fluent suite, considering a simultaneous gas-leak incident at two different points. Aside from the standalone evaluation study, the proposed gas-plume tracking scheme is able to outperform a state-of-the-art alternative algorithm, namely Efficient Global Optimization (EGO), in various simulation setups, deploying a different number of UAVs on the field.

Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids

This paper proposes a novel adversarial scheme for learning from data under harsh learning conditions of partially labelled samples and skewed class distributions. This novel scheme integrates the generative ability of the state-of-the-art conditional generative adversarial network with the semi-supervised deep ladder network and semi-supervised deep auto-encoder. The proposed generative-adversarial based semi-supervised learning framework, named GBSS, is a triple network that aims to optimize a newly defined objective function to enhance the performance of the semi-supervised learner with the help of a generator and discriminator. The duel between the generator and discriminator results in the generation of more synthetic minority class samples that are very similar to the original minority samples (attacks and faults). Meanwhile, GBSS trains the semi-supervised model to learn the general distribution of the minority class samples including the newly generated samples in contrast to other classes and iteratively adjusts its weights. Moreover, a diagnostic framework is designed, in which GBSS and several state-of-the-art semi-supervised learners are used for learning and diagnosing attacks and faults in power grids. These methods are evaluated and compared for diagnosing attacks and faults in two different power grid cases. The attained results demonstrate the superiority of GBSS in diagnosing attacks and faults under the harsh conditions.

A robust topology optimization for enlarging working bandwidth of acoustic devices

AbstractWe propose a novel robust topology optimization for designing acoustic devices that are effective for broadband sound waves. Here, we define the objective function as the weighted sum of the acoustic response to an incident wave consisting of a single frequency and its standard deviation (SD) against the frequency perturbation. By approximating the SD, under the assumption that the incident frequency follows the normal distribution, with the high‐order Taylor expansion of the (conventional) objective function, we deal with significant frequency variations. To calculate such an approximation, we need to calculate the high‐order frequency derivatives of the state variable. Here, we define them by integral representations, which enables us to characterize them even when the state variable is defined in an unbounded domain as is often the case with wave scattering problems. We further show that, based on this definition, the high‐order derivatives can efficiently be computed by a combination of the boundary element method and automatic differentiation. We also present a derivation and calculation of the topological derivative for the newly defined objective function. We install all the proposed techniques into a topology optimization method based on the level‐set method to design wideband acoustic devices. The validity and effectiveness of the proposed topology optimization are confirmed through several numerical examples.

Optimal Relocation of Medical Procedures Under the Covid-19 Regime: an Urology Case Study

We present a prototype system, OptiLoc, that aims at analysing consequences of medical capacity restrictions under the Covid-19 regime as well as at providing medical analysts and decision makers with medical procedure relocation plans for a given area in a given time period. This is achieved by first forecasting the demand of medical procedures of different types at the selected geographical granularity level and then finding relocations which are optimal according to a well defined objective function that takes into account procedure and relocation costs, under constraints imposed by Covid-19 restrictions and hospital capacities. Allocation plans are visualized in a user friendly web-based application. We demonstrate the effectiveness of the developed system on the data on urological procedures from Poland.

A diversified shared latent variable model for efficient image characteristics extraction and modelling

An object can be consisting of various attributes, such as illuminance, appearance, shape, orientation, etc. Separately extract these attributes has enormous value in visual effects modeling, attribute-specific retrieval and recognition. Essentially, these attributes can be fairly abstract and thus need labels to extract. However, sometimes the labels of these attributes may not be available with training data. A solution to this problem is projecting the observed data into a lower dimension latent subspace, such that each observed data can be represented by a latent variable. After that, the dimensions of a latent variable can be segmented into different parts by weighting the kernel automatic relevance determination (ARD) parameters. Consequently, the latent variable is segmented into different parts each of which corresponds to the main attribute. In real life scenery, the attributes of an object may vary significantly from case to case. For instance, a single face can probably be under different illuminance conditions. Taking into account the diversity of these attribute variations, we propose the Diversified Shared Latent Variable Model (DSLVM) to extract and manipulate object attributes in an unsupervised way. More specifically, we initially set up two views that share the same latent variables. Then, two Diversity Encouraging (DE) priors are applied to the inducing points of each model view. Here, the inducing points are a small representative dataset that explains the observed data in its entirety. Meanwhile, the exploited diversity encouraging priors are able to cover more diverse characteristics of the attributes. The defined objective function is computed by variational inference. Extensive experiments on different datasets demonstrate that our method can accurately deal with various object.

Sustainable resource optimization under water-energy-food-carbon nexus

Pressures from growing demands and shrinking supplies have reached a critical junction in major global resources, particularly water, energy, and food (WEF). Recognizing the complex interaction across those highly interconnected resources, the nexus concept evolved to boost efficiencies across all nexus pillars. Several modeling efforts tried to capture the complexity of this problem, but most attempts captured only one or two nexus pillars, remained localized to fixed case-studies or applications, or used simulations to assess pre-defined scenarios rather than solving for optimum solutions under defined objective function and constraints. Here, we present an optimization model for water, energy, and food nexus resource management and allocation at a regional scale. The model was successfully validated using a hypothetical case study to test its efficiency under several resource availability scenarios and different policy targets. The results enhanced the understanding of the interlinkages among the nexus sectors by demonstrating the sensitivity of the WEF nexus to adopted strategies. For example, imposing food variety constraints changed water consumption by an order of magnitude and more than doubled energy requirements. Moreover, adopting renewable energy may cause increased demands for land, but can significantly cut CO2 emissions. The model serves as an effective decision-making tool that enables policy makers to assess multiple WEF sources and recommends the optimum resource allocation under various policy, technology, and resource constraints.