Search results for: internet optimization

Authors: Michael A. Sprayberry, Vincent C. Paquit

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Process parameter optimization in metal powder bed electron beam melting (MPBEBM) is crucial to ensure the technology's repeatability, control, and industry-continued adoption. Despite continued efforts to address the challenges via the traditional design of experiments and process mapping techniques, there needs to be more successful in an on-the-fly optimization framework that can be adapted to MPBEBM systems. Additionally, data-intensive physics-based modeling and simulation methods are difficult to support by a metal AM alloy or system due to cost restrictions. To mitigate the challenge of resource-intensive experiments and models, this paper introduces a Multi-Objective Reinforcement Learning (MORL) methodology defined as an optimization problem for MPBEBM. An off-policy MORL framework based on policy gradient is proposed to discover optimal sets of beam power (P) – beam velocity (v) combinations to maintain a steady-state melt pool depth and phase transformation. For this, an experimentally validated Eagar-Tsai melt pool model is used to simulate the MPBEBM environment, where the beam acts as the agent across the P – v space to maximize returns for the uncertain powder bed environment producing a melt pool and phase transformation closer to the optimum. The culmination of the training process yields a set of process parameters {power, speed, hatch spacing, layer depth, and preheat} where the state (P,v) with the highest returns corresponds to a refined process parameter mapping. The resultant objects and mapping of returns to the P-v space show convergence with experimental observations. The framework, therefore, provides a model-free multi-objective approach to discovery without the need for trial-and-error experiments.

Keywords: additive manufacturing, metal powder bed fusion, reinforcement learning, process parameter optimization

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Authors: Marc S. Paolella, Pawel Polak

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The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery.

Keywords: comfort, financial crises, portfolio optimization, risk monitoring

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Authors: Misha Teimouri

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Our world has been transformed by the use of the internet and the constant flow of information. While this transmission has its benefits, it has also added significant challenges to family relations, primarily in the field of parenting and children's digital lives. Screens, speed, and connectedness are the words that characterize the lives of today's digital generation; it's as if the entire world is in their pockets at all times. Parents attempt to regulate and control their children's internet use in the hopes of maximizing the advantages and minimizing the disadvantages of their children's internet use; however, given that children spend more time online, particularly ever since the pandemic, children's cyber-aggression has become an issue for them. Children may externalize their behavior online, bully others, send anger/hatred/resist messages, share violent and bloody content, and engage in sexting. These types of online aggression make parenting more difficult, especially for digital immigrant parents compared to digital native parents. In response to these challenges, this study investigated the level of cyber aggression among children, as well as the effects of digi-parenting (active, monitoring, restrictive, and warm and supportive) on children's cyber-aggression (sexual, verbal, visual) as victims or aggressors. The study also determined whether there were any differences in parenting styles between digital natives (DN) and digital immigrants. In accordance with the study, boys and older children are more likely to engage in cyber aggression as aggressors, whereas girls and younger children are more likely to engage as victims. Warmth and supportive digiparenting have a greater impact on children's cyber-aggression (sexual, verbal, and visual) as victims or aggressors. This study also found that, when compared to DI parents, DN parents are more successful at digi-parenting and reducing their children's exposure to cyber-aggression.

Keywords: digi-parenting, cyber-aggression, digital natives, digital immigrants, children's cyber-aggression (sexual, verbal, visual)

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Authors: Masoud Mohammadpour, Blair Carlson, Radovan Kovacevic

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The quality of laser welded-brazed (LWB) joints were strongly dependent on the main process parameters, therefore the effect of laser power (3.2–4 kW), welding speed (60–80 mm/s) and wire feed rate (70–90 mm/s) on mechanical strength and surface roughness were investigated in this study. The comprehensive optimization process by means of response surface methodology (RSM) and desirability function was used for multi-criteria optimization. The experiments were planned based on Box– Behnken design implementing linear and quadratic polynomial equations for predicting the desired output properties. Finally, validation experiments were conducted on an optimized process condition which exhibited good agreement between the predicted and experimental results. AlSi3Mn1 was selected as the filler material for joining aluminum alloy 6022 and hot-dip galvanized steel in coach peel configuration. The high scanning speed could control the thickness of IMC as thin as 5 µm. The thermal simulations of joining process were conducted by the Finite Element Method (FEM), and results were validated through experimental data. The Fe/Al interfacial thermal history evidenced that the duration of critical temperature range (700–900 °C) in this high scanning speed process was less than 1 s. This short interaction time leads to the formation of reaction-control IMC layer instead of diffusion-control mechanisms.

Keywords: laser welding-brazing, finite element, response surface methodology (RSM), multi-response optimization, cross-beam laser

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Authors: Henrik Prinzhorn, Peter Nyhuis, Johannes Wagner, Peter Burggräf, Torben Schmitz, Christina Reuter

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Assembling large-scale products, such as airplanes, locomotives, or wind turbines, involves frequent process interruptions induced by e.g. delayed material deliveries or missing availability of resources. This leads to a negative impact on the logistical performance of a producer of xxl-products. In industrial practice, in case of interruptions, the identification, evaluation and eventually the selection of an alternative order of assembly activities (‘assembly alternative’) leads to an enormous challenge, especially if an optimized logistical decision should be reached. Therefore, in this paper, an innovative, optimization model for the identification of assembly alternatives that addresses the given problem is presented. It describes make-to-order, large-scale product assembly processes as a resource constrained project scheduling (RCPS) problem which follows given restrictions in practice. For the evaluation of the assembly alternative, a cost-based definition of the logistical objectives (delivery reliability, inventory, make-span and workload) is presented.

Keywords: assembly scheduling, large-scale products, make-to-order, optimization, rescheduling

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Authors: Thiago Ribeiro de Alencar, Jacyro Gramulia Junior, Patricia Teixeira Leite

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The area of the electricity sector that deals with energy needs by the hydroelectric in a coordinated manner is called Operation Planning of Hydrothermal Power Systems (OPHPS). The purpose of this is to find a political operative to provide electrical power to the system in a given period, with reliability and minimal cost. Therefore, it is necessary to determine an optimal schedule of generation for each hydroelectric, each range, so that the system meets the demand reliably, avoiding rationing in years of severe drought, and that minimizes the expected cost of operation during the planning, defining an appropriate strategy for thermal complementation. Several optimization algorithms specifically applied to this problem have been developed and are used. Although providing solutions to various problems encountered, these algorithms have some weaknesses, difficulties in convergence, simplification of the original formulation of the problem, or owing to the complexity of the objective function. An alternative to these challenges is the development of techniques for simulation optimization and more sophisticated and reliable, it can assist the planning of the operation. Thus, this paper presents the development of a computational tool, namely Hydro-IA for solving optimization problem identified and to provide the User an easy handling. Adopted as intelligent optimization technique is Genetic Algorithm (GA) and programming language is Java. First made the modeling of the chromosomes, then implemented the function assessment of the problem and the operators involved, and finally the drafting of the graphical interfaces for access to the User. The results with the Genetic Algorithms were compared with the optimization technique nonlinear programming (NLP). Tests were conducted with seven hydroelectric plants interconnected hydraulically with historical stream flow from 1953 to 1955. The results of comparison between the GA and NLP techniques shows that the cost of operating the GA becomes increasingly smaller than the NLP when the number of hydroelectric plants interconnected increases. The program has managed to relate a coherent performance in problem resolution without the need for simplification of the calculations together with the ease of manipulating the parameters of simulation and visualization of output results.

Keywords: energy, optimization, hydrothermal power systems, artificial intelligence and genetic algorithms

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Orestis Κ. Efthymiou, Stavros T. Ponis

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In the last decade, a new industrial revolution seems to be emerging, supported -once again- by the rapid advancements of Information Technology in the areas of Machine-to-Machine (M2M) communication permitting large numbers of intelligent devices, e.g. sensors to communicate with each other and take decisions without any or minimum indirect human intervention. The advent of these technologies have triggered the emergence of a new category of hybrid (cyber-physical) manufacturing systems, combining advanced manufacturing techniques with innovative M2M applications based on the Internet of Things (IoT), under the umbrella term Industry 4.0. Even though the topic of Industry 4.0 has attracted much attention during the last few years, the attempts of providing a systematic literature review of the subject are scarce. In this paper, we present the authors’ initial study of the field with a special focus on the use and applications of Industry 4.0 principles in material handling automations and in-house logistics. Research shows that despite the vivid discussion and attractiveness of the subject, there are still many challenges and issues that have to be addressed before Industry 4.0 becomes standardized and widely applicable.

Keywords: Industry 4.0, internet of things, manufacturing systems, material handling, logistics

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Authors: Yang Xiaodong

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This article explores the application and structural characteristics of flexible and programmable systems in electronic systems, with a focus on analyzing their advantages and architectural differences in dealing with complex environments. By introducing mathematical models and simulation experiments, the performance of dynamic module combination in flexible systems and fixed path selection in programmable systems in resource utilization and performance optimization was demonstrated. This article also discusses the mutual transformation between the two in practical applications and proposes a solution to improve system flexibility and performance through dynamic reconfiguration technology. This study provides theoretical reference for the design and optimization of flexible and programmable systems.

Keywords: flexibility, programmable, electronic systems, system architecture

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Authors: Marilena Ianculescu, Adriana Alexandru

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Internet of Health Things (IoHT) has already proved to be a persuasive means to support a proper assessment of the living conditions by collecting a huge variety of data. For a customized health management of a senior patient, IoHT provides the capacity to build a dynamic solution for sustaining the shift inside the patient-physician relationship by allowing a real-time and continuous remote monitoring of the health status, well-being, safety and activities of the senior, especially in a non-clinical environment. Thus, is created a win-win solution in which both the patient and the physician enhance their involvement and shared decision-making, with significant outcomes. Health monitoring systems in smart environments are becoming a viable alternative to traditional healthcare solutions. The ongoing “Non-invasive monitoring and health assessment of the elderly in a smart environment (RO-SmartAgeing)” project aims to demonstrate that the existence of complete and accurate information is critical for assessing the health condition of the seniors, improving wellbeing and quality of life in relation to health. The researches performed inside the project aim to highlight how the management of IoHT devices connected to the RO-SmartAgeing platform in a secure way by using a role-based access control system, can allow the physicians to provide health services at a high level of efficiency and accessibility, which were previously only available in hospitals. The project aims to identify deficient aspects in the provision of health services tailored to a senior patient’s specificity and to offer a more comprehensive perspective of proactive and preventive medical acts.

Keywords: health management, internet of health things, remote monitoring, senior patient

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Authors: Mourad Hrizi

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In this paper, we study the inverse problem of reconstructing an interior interface D appearing in the elliptic partial differential equation: Δu+χ(D)u=0 from the knowledge of the boundary measurements. This problem arises from a semiconductor transistor model. We propose a new shape reconstruction procedure that is based on the Kohn-Vogelius formulation and the topological sensitivity method. The inverse problem is formulated as a topology optimization one. A topological sensitivity analysis is derived from a function. The unknown subdomain D is reconstructed using a level-set curve of the topological gradient. Finally, we give several examples to show the viability of our proposed method.

Keywords: inverse problem, topological optimization, topological gradient, Kohn-Vogelius formulation

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Authors: Karthikeyan Kalirajan, Ashok Joshi

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An optimization problem is to setup to maximize the terminal kinetic energy of a maneuverable reentry vehicle (MaRV). The target location, the impact angle is given as constraints. The MaRV uses an explicit guidance law called Vector guidance. This law has two gains which are taken as decision variables. The problem is to find the optimal value of these gains which will result in minimum miss distance and impact angle error. Using a simple 3DOF non-rotating flat earth model and Lockheed martin HP-MARV as the reentry vehicle, the nature of solutions of the optimization problem is studied. This is achieved by carrying out a parametric study for a range of closed loop gain values and the corresponding impact angle error and the miss distance values are generated. The results show that there are well defined lower and upper bounds on the gains that result in near optimal terminal guidance solution. It is found from this study, that there exist common permissible regions (values of gains) where all constraints are met. Moreover, the permissible region lies between flat regions and hence the optimization algorithm has to be chosen carefully. It is also found that, only one of the gain values is independent and that the other dependent gain value is related through a simple straight-line expression. Moreover, to reduce the computational burden of finding the optimal value of two gains, a guidance law called Diveline guidance is discussed, which uses single gain. The derivation of the Diveline guidance law from Vector guidance law is discussed in this paper.

Keywords: Marv guidance, reentry trajectory, trajectory optimization, guidance gain selection

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Authors: Leili Esmaeilani, Jafar Ghaisari, Mohsen Ahmadian

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Hammerstein–Wiener model is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output and could be used to model various processes. This paper contains an optimization approach method for analysing the problem of Hammerstein–Wiener systems identification. The method relies on reformulate the identification problem; solve it as constraint quadratic problem and analysing its solutions. During the formulation of the problem, effects of adding noise to both input and output signals of nonlinear blocks and disturbance to linear block, in the emerged equations are discussed. Additionally, the possible parametric form of matrix operations to reduce the equation size is presented. To analyse the possible solutions to the mentioned system of equations, a method to reduce the difference between the number of equations and number of unknown variables by formulate and importing existing knowledge about nonlinear functions is presented. Obtained equations are applied to an instance H–W system to validate the results and illustrate the proposed method.

Keywords: identification, Hammerstein-Wiener, optimization, quantization

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Authors: Jamaludin Sallim, Rozlina Mohamed, Roslina Abdul Hamid

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In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns.

Keywords: ant colony optimization algorithm, searching algorithm, protein functional module, protein interaction network

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Authors: Mohamed Ghorab

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Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO₂ optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO₂ emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO₂ emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.  

Keywords: distributed energy resources, network energy system, optimization, microgeneration system

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Authors: Charisse H. Tay

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Background: Schizophrenia is a severe chronic mental disorder that can result in hallucinations, delusions, reduced social engagement, and lack of motivation. While antipsychotic medications often provide the basis for treatment, psychosocial strategies complement the benefit of medications and can result in meaningful improvements in symptoms and functioning. The aim of the study was to investigate psychosocial strategies used by internet self-help forum participants to effectively manage symptoms caused by schizophrenia. Internet self-help forums are a resource for medical and psychological problems and are commonly used to share information about experiences with symptom management. Method: Three international self-help internet forums on schizophrenia were identified using a search engine. 1,181 threads regarding non-pharmacological, psychosocial self-management of schizophrenia symptoms underwent screening, resulting in the final identification and coding of 91 threads and 191 posts from 134 unique forum users that contained details on psychosocial strategies endorsed personally by users that allowed them to effectively manage symptoms of schizophrenia, including positive symptoms (e.g., auditory/visual/tactile hallucinations, delusions, paranoia), negative symptoms (e.g.., avolition, apathy, anhedonia), symptoms of distress, and cognitive symptoms (e.g., memory loss). Results: Effective symptom management strategies personally endorsed by online forum users were psychological skills (e.g., re-focusing, mindfulness/meditation, reality checking; n = 94), engaging in activities (e.g., exercise, working/volunteering, hobbies; n = 84), social/familial support (n = 48), psychotherapy (n = 33), diet (n = 18), and religion/spirituality (n = 14). 44.4% of users reported using more than one strategy to manage their symptoms. The most common symptoms targeted and effectively managed, as specified by users, were positive symptoms (n = 113), negative symptoms (n = 17), distress (n = 8), and memory loss (n = 6). 10.5% of users reported more than one symptom effectively targeted. 70.2% of users with positive symptoms reported that psychological skills were effective for symptom relief. 88% of users with negative symptoms and 75% with distress symptoms reported that engaging in activities was effective. Discussion: Individuals with schizophrenia rely on a variety of different psychosocial methods to manage their symptoms. Different symptomology appears to be more effectively targeted by different types of psychosocial strategies. This may help to inform treatment strategy and tailored for individuals with schizophrenia.

Keywords: psychosocial treatment, qualitative methods, schizophrenia, symptom management

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Authors: Ghita Benayad

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Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques.

Keywords: asset allocation, portfolio optimization, principle component analysis, multi-objective modelling, financial market

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Authors: Hamidoddin Yousife

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Drilling optimization requires a prediction of drilling rate of penetration (ROP) since it provides a significant reduction in drilling costs. There are several factors that can have an impact on the ROP, both controllable and uncontrollable. Numerous drilling penetration rate models have been considered based on drilling parameters. This papers considered the effect of proper drilling parameter selection such as bit, Mud Type, applied weight on bit (WOB), Revolution per minutes (RPM), and flow rate on drilling optimization and drilling cost reduction. A predicted analysis is used in real-time drilling performance to determine the optimal drilling operation. As a result of these modeling studies, the real data collected from three directional wells at Azadegan oil fields, Iran, was verified and adjusted to determine the drillability of a specific formation. Simulation results and actual drilling results show significant improvements in inaccuracy. Once simulations had been validated, optimum drilling parameters and equipment specifications were determined by varying weight on bit (WOB), rotary speed (RPM), hydraulics (hydraulic pressure), and bit specification for each well until the highest drilling rate was achieved. To evaluate the potential operational and economic benefits of optimizing results, a qualitative and quantitative analysis of the data was performed.

Keywords: drlling, cost, optimization, parameters

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Authors: Zongyan Li, Matt Best

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This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated.

Keywords: correlation analysis, F-ratio, levenberg-marquardt, MSE, NARX, neural network, optimisation

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Authors: Andrey A. Chernousov, Ben Y. B. Chan

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The use of the low-cost paraffinic phase change material can be rather effective in smart building envelopes in the South China region. Particular attention has to be paid to the PCM optimization as an exploitation conditions and the envelope insulation changes its thermal characteristics. The studied smart building envelope consists of a reinforced aluminum exterior, polymeric insulation foam, phase change material and reinforced interior gypsum board. A prototype sample was tested to validate the numerical scheme using EnergryPlus software. Three scenarios of insulation thermal resistance loss (ΔR/R = 0%, 25%, 50%) were compared with the different PCM thicknesses (tP=0, 1, 2.5, 5 mm). The comparisons were carried out for a west facing enveloped office building (50 storey). PCM optimization was applied to find the maximum efficiency for the different ΔR/R cases. It was found, during the optimization, that the PCM is an important smart component, lowering the peak energy demand up to 2.7 times. The results are not influenced by the insulation aging in terms of ΔR/R during long-term exploitation. In hot and humid climates like Hong Kong, the insulation core of the smart systems is recommended to be laminated completely. This can be very helpful in achieving an acceptable payback period.

Keywords: smart building envelope, thermal performance, phase change material, energy efficiency, large-scale sandwich panel

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Authors: Kevin Spencer G. Anglim, Zhenyu Zhang, Qingbin Gao

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Reusable launch vehicles (RLVs) present a more environmentally-friendly approach to accessing space when compared to traditional launch vehicles that are discarded after each flight. This paper studies the recyclable nature of RLVs by presenting a solution method for determining minimum-fuel optimal trajectories using principles from optimal control theory and particle swarm optimization (PSO). This problem is formulated as a minimum-landing error powered descent problem where it is desired to move the RLV from a fixed set of initial conditions to three different sets of terminal conditions. However, unlike other powered descent studies, this paper considers the highly nonlinear effects caused by atmospheric drag, which are often ignored for studies on the Moon or on Mars. Rather than optimizing the controls directly, the throttle control is assumed to be bang-off-bang with a predetermined thrust direction for each phase of flight. The PSO method is verified in a one-dimensional comparison study, and it is then applied to the two-dimensional cases, the results of which are illustrated.

Keywords: minimum-fuel optimal trajectory, particle swarm optimization, reusable rocket, SpaceX

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Authors: Jiwon Park, Jihae Hur, Kukjae Kim, Hansoo Kim

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In this study, outriggers, which are horizontal structures connecting a building core to distant columns to increase the lateral stiffness of a tall building, are used to reduce differential axial shortening in a tall building. Therefore, the outriggers in tall buildings are used to serve the dual purposes of reducing the lateral displacement and reducing the differential axial shortening. Since the location of the outrigger greatly affects the effectiveness of the outrigger in terms of the lateral displacement at the top of the tall building and the maximum differential axial shortening, the optimum locations of the dual-purpose outriggers can be determined by an optimization method. Because the floors where the outriggers are installed are given as integer numbers, the conventional gradient-based optimization methods cannot be directly used. In this study, a piecewise quadratic interpolation method is used to resolve the integrality requirement posed by the optimum locations of the dual-purpose outriggers. The optimal solutions for the dual-purpose outriggers are searched by linear scalarization which is a popular method for multi-objective optimization problems. It was found that increasing the number of outriggers reduced the maximum lateral displacement and the maximum differential axial shortening. It was also noted that the optimum locations for reducing the lateral displacement and reducing the differential axial shortening were different. Acknowledgment: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (NRF-2017R1A2B4010043) and financially supported by Korea Ministry of Land, Infrastructure and Transport(MOLIT) as U-City Master and Doctor Course Grant Program.

Keywords: concrete structure, optimization, outrigger, tall building

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Authors: Won Jung, Wan Emri

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Ferrite magnet is widely used in many automotive components such as motors and alternators. Magnets used inside the components must be in good quality to ensure the high level of performance. The purpose of this study is to design input parameters that optimize the ferrite magnet production process to ensure the quality and reliability of manufactured products. Design of Experiments (DOE) and Statistical Process Control (SPC) are used as mutual supplementations to optimize the process. DOE and SPC are quality tools being used in the industry to monitor and improve the manufacturing process condition. These tools are practically used to maintain the process on target and within the limits of natural variation. A mixed Taguchi method is utilized for optimization purpose as a part of DOE analysis. SPC with proportion data is applied to assess the output parameters to determine the optimal operating conditions. An example of case involving the monitoring and optimization of ferrite magnet process was presented to demonstrate the effectiveness of this approach. Through the utilization of these tools, reliable magnets can be produced by following the step by step procedures of proposed framework. One of the main contributions of this study was producing the crack free magnets by applying the proposed parameter design.

Keywords: ferrite magnet, crack, reliability, process optimization, Taguchi method

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Lotha Valan, Åsa Hörnsten, Ulf Isaksson

Abstract:

Introduction: Sweden has a national child health care program (CHCP) where all parents are offered support to raise their children and support them for lifelong health. A systematic review concludes that there is a request for guidance in using the internet effectively for the health purposes of their children. However, a study about internet use among young mothers means that the internet is not always easy to navigate for parents, and they may need support. To fill this gap and develop a digital channel to complement the child health care (CHC) for the support of parents of children within CHC, there is a demand to investigate parents' needs in relation to this purpose. Methods: The study had a qualitative approach using focus group interviews with parents. The interview data were analyzed using qualitative content analysis. Results: The main theme highlights that parents expected that a digital support channel would be something that might strengthen them toward independence concerning the care of their children in a positive way. However, they also felt that they needed personal support and that relationships with other parents and the child health care nurse were significant and meaningful. Another parental desire that emerged was that a future digital channel would facilitate and simplify access to care, and they suggested having both planned and urgent times available for parents to book. The digital channel was expected to make this possible and be a good complement to the physical contacts the traditional child healthcare currently offers. Discussion/conclusions: The parents in this study believed that digital solutions could increase their parental power in relation to the care of their children. Examples were given as nurse-led parent groups where parents with similar problems and experiences around their children could support each other and were expected to strengthen them over time. The parents stressed that a planned digital support channel also needs satisfactory solutions for both contact and response. It was suggested that there should be bookable times for both planned and urgent needs and also the possibility of rescheduling visits.

Keywords: child healthcare, parents, digital support, nursing

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Authors: Maruf Farhan, Abdul Salih, Sikandar Ali Tahir

Abstract:

Protecting patient information's confidentiality is paramount considering the widespread use of Internet of Things (IoT) gadgets in medical settings. This study's subjects are decentralized identifiers (DIDs) and verifiable credentials (VCs) in conjunction with an OAuth-based authorization framework, as they are the key to protecting IoT healthcare devices. DIDs enable autonomous authentication and trust formation between IoT devices and other entities. To authorize users and enforce access controls based on verified claims, VCs offer a secure and adaptable solution. Through the proposed method, medical facilities can improve the privacy and security of their IoT devices while streamlining access control administration. A Smart pill dispenser in a hospital setting is used to illustrate the advantages of this method. The findings demonstrate the value of DIDs, VCs, and OAuth-based delegation in protecting the IoT devices. Improved processes for authorizing and controlling access to IoT devices are possible thanks to the research findings, which also help ensure patient confidentiality in the healthcare sector.

Keywords: Iot, DID, authorization, verifiable credentials

Procedia PDF Downloads 76

Authors: Myeong-Gon Lee, Yang-Gyun Kim, Tae-Young Kim, Seung-Ho Han

Abstract:

The high-pressure valve uses one set of 2-way valves for the purpose of reversing fluid direction. If there is no accurate control device for the 2-way valves, lots of surging can be generated. The surging is a kind of pressure ripple that occurs in rapid changes of fluid motions under inaccurate valve control. To reduce the surging effect, a 3-way reversing valve can be applied which provides a rapid and precise change of water flow directions without any accurate valve control system. However, a cavitation occurs due to a complicated internal trim shape of the 3-way reversing valve. The cavitation causes not only noise and vibration but also decreasing the efficiency of valve-operation, in which the bubbles generated below the saturated vapor pressure are collapsed rapidly at higher pressure zone. The shape optimization of the 3-way reversing valve to minimize the cavitation effect is necessary. In this study, the cavitation index according to the international standard ISA was introduced to estimate macroscopically the occurrence of the cavitation effect. Computational fluid dynamic analysis was carried out, and the cavitation effect was quantified by means of the percent of cavitation converted from calculated results of vapor volume fraction. In addition, the shape optimization of the 3-way reversing valve was performed by taking into account of the percent of cavitation.

Keywords: 3-Way reversing valve, cavitation, shape optimization, vapor volume fraction

Procedia PDF Downloads 371

Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh

Abstract:

A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.

Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety

Procedia PDF Downloads 324

Authors: Chung To Kong, Siu Ming Yiu

Abstract:

Rubik's cube was invented in 1974. Since then, speedcubers all over the world try their best to break the world record again and again. The newest record is 3.47 seconds. There are many factors that affect the timing, including turns per second (tps), algorithm, finger trick, hardware of the cube. In this paper, the lower bound of the cube solving time will be discussed using convex optimization. Extended analysis of the world records will be used to understand how to improve the timing. With the understanding of each part of the solving step, the paper suggests a list of speed improvement techniques. Based on the analysis of the world record, there is a high possibility that the 3 seconds mark will be broken soon.

Keywords: Rubik's Cube, speed, finger trick, optimization

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Authors: Li Long, Thomas Ortlepp

Abstract:

A theoretical model for the optimization of thermopile sensor performance is developed for thermoelectric-based infrared radiation detection. It is shown that the performance of polycrystalline silicon film thermopile sensor can be optimized according to the thermoelectric quality factor, sensor layer structure factor, and sensor layout geometrical form factor. Based on the properties of electrons, phonons, grain boundaries, and their interactions, the thermoelectric quality factor of polycrystalline silicon is analyzed with the relaxation time approximation of the Boltzmann transport equation. The model includes the effect of grain structure, grain boundary trap properties, and doping concentration. The layer structure factor is analyzed with respect to the infrared absorption coefficient. The optimization of layout design is characterized by the form factor, which is calculated for different sensor designs. A double-layer polycrystalline silicon thermopile infrared sensor on a suspended membrane has been designed and fabricated with a CMOS-compatible process. The theoretical approach is confirmed by measurement results.

Keywords: polycrystalline silicon, relaxation time approximation, specific detectivity, thermal conductivity, thermopile infrared sensor

Procedia PDF Downloads 139