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The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.
Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.
A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption
(2018)
Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results.
This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models.
2018 American Institute of Chemical Engineers Environ Prog, 2018
https://doi.org/10.1002/ep.12934
As one of its primary objectives, Computer Graphics aims at the simulation of fabrics’ complex reflection behaviour. Characteristic surface reflectance of fabrics, such as highlights, anisotropy or retro-reflection arise the difficulty of synthesizing. This problem can be solved by using Bidirectional Texture Functions (BTFs), a 2D-texture under various light and view direction. But the acquisition of Bidirectional Texture Functions requires an expensive setup and the measurement process is very time-consuming. Moreover, the size of BTF data can range from hundreds of megabytes to several gigabytes, as a large number of high resolution pictures have to be used in any ideal cases. Furthermore, the three-dimensional textured models rendered through BTF rendering method are subject to various types of distortion during acquisition, synthesis, compression, and processing. An appropriate image quality assessment scheme is a useful tool for evaluating image processing algorithms, especially algorithms designed to leave the image visually unchanged. In this contribution, we present and conduct an investigation aimed at locating a robust threshold for downsampling BTF images without loosing perceptual quality. To this end, an experimental study on how decreasing the texture resolution influences perceived quality of the rendered images has been presented and discussed.
Next, two basic improvements to the use of BTFs for rendering are presented: firstly, the study addresses the cost of BTF acquisition by introducing a flexible low-cost step motor setup for BTF acquisition allowing to generate a high quality BTF database taken at user-defined arbitrary angles. Secondly, the number of acquired textures to the perceptual quality of renderings is adapted so that the database size is not overloaded and can fit better in memory when rendered.
Although visual attention is one of the essential attributes of HVS, it is neglected in most existing quality metrics. In this thesis an appropriate objective quality metric based on extracting visual attention regions from images and adequate investigation of the influence of visual attention on perceived image quality assessment, called Visual Attention Based Image Quality Metric (VABIQM), has been proposed. The novel metric indicates that considering visual saliency can offer significant benefits with regard to constructing objective quality metrics to predict the visible quality differences in images rendered by compressed and non-compressed BTFs and also outperforms straightforward existing image quality metrics at detecting perceivable differences.
This cumulative dissertation discusses - by the example of four subsequent publications - the various layers of a tangible interaction framework, which has been developed in conjunction with an electronic musical instrument with a tabletop tangible user interface. Based on the experiences that have been collected during the design and implementation of that particular musical application, this research mainly concentrates on the definition of a general-purpose abstraction model for the encapsulation of physical interface components that are commonly employed in the context of an interactive surface environment. Along with a detailed description of the underlying abstraction model, this dissertation also describes an actual implementation in the form of a detailed protocol syntax, which constitutes the common element of a distributed architecture for the construction of surface-based tangible user interfaces. The initial implementation of the presented abstraction model within an actual application toolkit is comprised of the TUIO protocol and the related computer-vision based object and multi-touch tracking software reacTIVision, along with its principal application within the Reactable synthesizer. The dissertation concludes with an evaluation and extension of the initial TUIO model, by presenting TUIO2 - a next generation abstraction model designed for a more comprehensive range of tangible interaction platforms and related application scenarios.
INTRODUCTION
The research field of sound landscape and public life, initially drew my attention during the master class of ‘Media of the Urban’, originally ‘Medien des Urbanen, which was given by Prof. Dr. Gabriele Schabacher in the 2015 summer semester. For the relevant class, I conducted an conceptual case study in Istanbul, Beyoglu District, with the intention of analysing the perception of the space by urban sound. During the summer 2015 I recorded various sounds of different spatial settings and developed the analysis by comparing the situations. By that time, I realized the inherent property of the sound as a medium for our perception in urban context.
In the 2015-2016 winter semester, I participated in the master class of the architectural project, named ‘Build Allegory’, which was given by Prof. Dipl.-Ing. Heike Büttner. The project was situated in Berlin Westkreuz, AVUS north curve, on the highway and was originally a race track from 1921. In this context, the aim of my project was to answer various questions, main of which was, how does the architectural form shape the sound of the place? And, how does the sound of the place shape the architectural from? Since the place is still serving mainly to the vehicles, although the function has differed, the sound objects and the context have remained. Through the existence of contextual references, I started with creating a computational tool for analysing the acoustic characteristics of this urban setting, which is fundamentally providing results as the sound cloud, driven from the sound ray tracing method. Regarding to this soundscape analysis method, which I developed, this computational tool assisted me to find an optimum reciprocal relation between architecture and sound.
Since I have been working on soundscape in the context of architecture, urban situations, public life and public space, I was determined to produce a comprehensive research in this field and propound the hypothesis; the existence of the reciprocity between the social behaviours in public space and the sound landscape. In which extent does this reciprocity exist? What are the effects of the public life on the sonic configurations of the space and the other way around?
This thesis suggests cooperation as a design paradigm for human-computer interaction. The basic idea is that the synergistic co-operation of interfaces through concurrent user activities enables increased interaction fluency and expressiveness. This applies to bimanual interaction and multi-finger input, e.g., touch typing, as well as the collaboration of multiple users. Cooperative user interfaces offer more interaction
flexibility and expressivity for single and multiple users.
Part I of this thesis analyzes the state of the art in user interface design. It explores limitations of common approaches and reveals the crucial role of cooperative action in several established user interfaces and research prototypes. A review of related research in psychology and human-computer interaction offers insights to the cognitive, behavioral, and ergonomic foundations of cooperative user interfaces. Moreover, this thesis suggests a broad applicability of generic cooperation patterns and contributes three high-level design principles.
Part II presents three experiments towards cooperative user interfaces in detail. A study on desktop-based 3D input devices, explores fundamental benefits of cooperative bimanual input and the impact of interface design on bimanual cooperative behavior. A novel interaction technique for multitouch devices is presented that follows the paradigm of cooperative user interfaces and demonstrates advantages over the status quo. Finally, this thesis introduces a fundamentally new display technology that provides up to six users with their individual perspectives of a shared 3D environment. The system creates new possibilities for the cooperative interaction of
multiple users.
Part III of this thesis builds on the research results described in Part II, in particular, the multi-user 3D display system. A series of case studies in the field of collaborative virtual reality provides exemplary evidence for the relevance and applicability of the suggested design principles.
Volumerendering ist eine Darstellungstechnik, um verschiedene räumliche Mess- und Simulationsdaten anschaulich, interaktiv grafisch darzustellen. Im folgenden Beitrag wird ein Verfahren vorgestellt, mehrere Volumendaten mit einem Architekturflächenmodell zu überlagern. Diese komplexe Darstellungsberechnung findet mit hardwarebeschleunigten Shadern auf der Grafikkarte statt. Im Beitrag wird hierzu der implementierte Softwareprototyp "VolumeRendering" vorgestellt. Neben dem interaktiven Berechnungsverfahren wurde ebenso Wert auf eine nutzerfreundliche Bedienung gelegt. Das Ziel bestand darin, eine einfache Bewertung der Volumendaten durch Fachplaner zu ermöglichen. Durch die Überlagerung, z. B. verschiedener Messverfahren mit einem Flächenmodell, ergeben sich Synergien und neue Auswertungsmöglichkeiten. Abschließend wird anhand von Beispielen aus einem interdisziplinären Forschungsprojekt die Anwendung des Softwareprototyps illustriert.
In this paper we introduce LUCI, a Lightweight Urban Calculation Interchange system, designed to bring the advantages of calculation and content co-ordination system to small planning and design groups by the means of an open source middle-ware. The middle-ware focuses on problems typical to urban planning and therefore features a geo-data repository as well as a job runtime administration, to coordinate simulation models and its multiple views. The described system architecture is accompanied by two exemplary use cases, that have been used to test and further develop our concepts and implementations.
This work presents a concept of interactive machine learning in a human design process. An urban design problem is viewed as a multiple-criteria optimization problem. The outlined feature of an urban design problem is the dependence of a design goal on a context of the problem. We model the design goal as a randomized fitness measure that depends on the context. In terms of multiple-criteria decision analysis (MCDA), the defined measure corresponds to a subjective expected utility of a user. In the first stage of the proposed approach we let the algorithm explore a design space using clustering techniques. The second stage is an interactive design loop; the user makes a proposal, then the program optimizes it, gets the user’s feedback and returns back the control over the application interface.