Optimal design of mesostructured materials under uncertainty

The main objective of the topology optimization is to fulfill the objective function with the minimum amount of material. This reduces the overall cost of the structure and at the same time reduces the assembly, manufacturing and maintenance costs because of the reduced number of parts in the final structure. The concept of reliability analysis can be incorporated into the deterministic topology optimization method; this incorporated scheme is referred to as Reliability-based Topology Optimization (RBTO). In RBTO, the statistical nature of constraints and design problems are defined in the objective function and probabilistic constraint. The probabilistic constraint can specify the required reliability level of the system. In practical applications, however, finding global optimum in the presence of uncertainty is a difficult and computationally intensive task, since for every possible design a full stochastic analysis has to be performed for estimating various statistical parameters. Efficient methodologies are therefore required for the solution of the stochastic part and the optimization part of the design process. This research will explore a reliability-based synthesis method which estimates all the statistical parameters and finds the optimum while being less computationally intensive. The efficiency of the proposed method is achieved with the combination of topology optimization and stochastic approximation which utilizes a sampling technique such as Latin Hypercube Sampling (LHS) and surrogate modeling techniques such as Local Regression and Classification using Artificial Neural Networks (ANN). Local regression is comparatively less computationally intensive and produces good results in case of low probability of failures whereas Classification is particularly useful in cases where the reliability of failure has to be estimated with disjoint failure domains. Because classification using ANN is comparatively more computationally demanding than Local regression, classification is only used when local regression fails to give the desired level of goodness of fit. Nevertheless, classification is an indispensible tool in estimating the probability of failure when the failure domain is discontinuous. Representative examples will be demonstrated where the method is used to design customized meso-scale truss structures and a macro-scale hydrogen storage tank. The final deliverable from this research will be a less computationally intensive and robust RBTO procedure that can be used for design of truss structures with variable design parameters and force and boundary conditions.M.S.Committee Chair: Choi, Seung-Kyum; Committee Member: Muhanna, Rafi; Committee Member: Rosen, Davi

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Consumer behavior modeling for electrical energy systems : a complex systems approach

Orientador: Alexandre Rasi AokiCoorientador: Germano Lambert-TorresTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 27/02/2019Inclui referências: p. 141-154Resumo: Um sistema complexo é um sistema composto de muitas partes que interagem entre si, de modo que o comportamento coletivo emergente dessas partes é mais do que a soma de seus comportamentos individuais. O sistema elétrico de potência pode ser considerado um sistema complexo devido à sua diversidade de agentes heterogêneos inter-relacionados e a emergência de comportamento complexo. Sistemas de potência estão aumentando em complexidade com novos avanços relacionados à redes elétricas inteligentes tais como tecnologia de informação e comunicação, geração distribuída, veículos elétricos, armazenamento de energia e, especialmente, uma crescente interação e participação de um grande número de consumidores heterogêneos dispersos geograficamente. O sistema elétrico de potência pode ser estudado como um sistema técnico-socioeconômico complexo com múltiplas facetas, e a teoria de sistemas complexos pode fornecer uma base teórica sólida para seus desafios de modelagem e análise. O presente trabalho trata da aplicação da teoria de sistemas complexos em sistemas de potência, focando a análise no consumidor e no seu comportamento relacionado ao consumo de eletricidade, utilizando técnicas do campo da economia comportamental. Comportamentos complexos e emergentes sobre o consumo de eletricidade, bem como seu impacto nas redes elétricas, são analisados através da modelagem do comportamento dos cliente em uma simulação baseada em agentes, considerando quatro categorias de consumidores. A análise da simulação, aplicada a um estudo de caso em uma rede de distribuição de média tensão radial com dados reais, mostrou que premissas ligeiramente diferentes sobre o comportamento do consumidor no nível micro levam a resultados macro muito distintos e com comportamento não linear. Entender e modelar adequadamente o comportamento dos consumidores é de grande importância para o planejamento e operação de redes de energia, e a economia comportamental serve como uma base teórica promissora para modelar o comportamento no consumo de eletricidade. Os resultados deste trabalho mostraram que a teorias de sistemas complexos fornece ferramentas adequadas para lidar com sistemas de potência cada vez mais complexos, considerando-os não mais como um sistema independente agregado, mas como um sistema complexo integrado. Palavras-chave: distribuição de energia; consumo de eletricidade; teoria de sistemas complexos; simulação baseada em agentes; economia comportamental.Abstract: A complex system is a system composed of many interacting parts, such that the collective emergent behavior of those parts is more than the sum of their individual behaviors. Electrical energy systems may be considered a complex system due to its diversity of interrelated heterogeneous agents and emergent complex behavior. Energy systems are increasing in complexity with new advances related to the smart grid such as information and communication technology, distributed generation, electric vehicles, energy storage, and, especially, increasing interaction and participation of a large number of geographically distributed heterogeneous consumers. Power systems can be studied as a complex techno-socio-economical system with multiple facets, and Complex System Theory (CST) may provide a solid theoretical background for these modeling and analysis challenges. The present work deals with the application of CST into electrical energy systems, focusing the analysis on the consumer and their behavior on electricity consumption, using insights from the field of behavioral economics. Emergent complex behaviors on electricity consumption as well as its impact on power grids are analyzed by modeling customer behavior on an agent-based simulation, considering four different consumer categories. The analysis of the simulation, applied on a case study on a radial medium voltage distribution grid with real-world data, showed that slightly different assumptions on consumer behavior at the micro-level lead to very different and non-linear macro outcomes. To properly understand and model consumer behavior is of great importance to the planning and operation of electrical grids, and behavioral economics serves as a promising theoretical background to model behavior on electricity consumption. The results of this work showed that CST provides suitable tools to tackle electrical energy systems' increasing complexity, by considering the electrical power systems not as an aggregated independent system anymore, but as an integrated complex system. Keywords: power distribution; electricity consumption; complex systems theory; agent-based simulation; behavioral economics

SciTech News- 68(4)-2014

Columns and Reports From the Editor 5 SciTech News Call for Articles 5 Conference Report, Diane K. Foster International Student Travel Award Recipient 8 Conference Report, S. Kirk Cabeen Travel Stipend Award Recipient 9 Conference Report, Bonnie Hilditch International Librarian Award Recipient 11 Conference Report, IEEE Continuing Education Award Recipient 19 Division News Science-Technology Division 6 Chemistry Division 14 Engineering Division 17 Call for Nominations & Applications Bonnie Hilditch International Librarian Award 13 IEEE Continuing Education Stipend 20 Engineering Librarian of the Year Award 21 SPIE Digital Library Student Travel Stipend 22 Reviews Sci-Tech Book News Reviews 23 Advertisements Annual Reviews 3 IEEE

Fake Malware Generation Using HMM and GAN

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results

Crowdfunding Non-fungible Tokens on the Blockchain

Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs

The Application of Machine Learning to At-Risk Cultural Heritage Image Data

This project investigates the application of Convolutional Neural Network (CNN) methods and technologies to problems related to At-Risk cultural heritage object recognition. The primary aim for this work is the use of developmental software combining the disciplines of computer vision and artefact studies, developing applications in the field of heritage protection specifically related to the illegal antiquities market. To accomplish this digital image data provided by the Durham University Oriental Museum was used in conjunction with several different implementations of pre-trained CNN software models, for the purposes of artefact Classification and Identification. Testing focused on data capture using a variety of digital recording devices, guided by the developmental needs of a heritage programme seeking to create software solutions to heritage threats in the Middle East and North Africa (MENA) region. Quantitative data results using information retrieval metrics is reported for all model and test sets, and has been used to evaluate the models predictive results