Search results for: space time cube

Authors: Tuğçe Arslan

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In the dark and gloomy times of the Middle Ages, high, majestic, and frightening structures were revealed in the architectural field. Thus, gothic architecture began to find a place for itself in different areas and spread its influence. Gothic has found its place in almost every literary genre and manages to show itself as the dominant genre in the works it enters. It has exploited many concepts, such as a chest full of bad feelings, and creates a gloomy, scary, frightening, and pessimistic mood in the story with these concepts. One of the essential concepts it uses while creating these feelings is the concept of “Doppelganger.” With this concept, the authors make sense of the uncanny; at this point, they allow the spaces to act like characters, just like the uncanny feeling Edgar Allan Poe creates in his story “The Fall of the House of the Usher.” In this story by Edgar Allan Poe, attention should be paid to the symbolic link between the two, as “House of Usher” refers to the family and the building. And indeed, it is possible to see this minor rift as representative of a breakdown in family unity, specifically between Madeline and Roderick. Because although the home is not alive, it has some supernatural features that make it look like a living, breathing being. Therefore, the remainder of this paper will argue that apart from the apparent twins, the house should also qualify as a Doppelganger in the story. This study will first explore the physical and mental disorders of the twins and their journey to complement each other; next, in an attempt to demonstrate how the house as a non-living needs to be considered as a Doppelganger of the twins, a close reading on the house depictions will be scrutinized.

Keywords: Edgar Allan Poe, doppelganger, uncanny, gothic, space, home

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Authors: Romi Dey

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Introduction: One of the most important criteria for technology to sustain and grow is through it’s elaborate and intuitive design methodology and design thinking. Designing for enterprise applications that cater to Just in Time Technology is one of the most challenging and detailed processes any User Experience Designer would come across. Description: The basic principles of Design, when applied to tailor to these technologies, creates an immense challenge and that’s how a set of redefined and revised design principles that can be applied to designing any Just In Time manufacturing solution. Findings: The thorough process of understanding the end user, their existing pain points which they’ve faced in the real world, their responsibilities and expectations, the core needs and last but not the least the demands, creates havoc nurturing of the design methodologies for the Just in Time solutions. With respect to the business aspect, design and design principles play a strong role in any form of innovation. Conclusion: Innovation and knowledge about the latest technologies are the keywords in the manufacturing industry. It becomes crucial for the product development team to be precise in their understanding of the technology and being sure of end users expectation.

Keywords: design thinking, enterprise application, Just in Time, user experience design

Procedia PDF Downloads 170

Authors: M. Berrehail, F. Benamira

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We study the quantum motion of a particle in the presence of a time- dependent linear potential using an operator invariant that is quadratic in p and linear in q within the framework of the Lewis-Riesenfeld invariant, The special invariant operator proposed in this work is demonstrated to be an Hermitian operator which has an Airy wave packet as its Eigenfunction

Keywords: airy wave packet, ivariant, time-dependent linear potential, unitary transformation

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Authors: Hande Burcu Deniz

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People in the U.S. spend a considerable portion of their time indoors. This makes it crucial to provide environments that support the well-being of people. Restorative environments aim to help people recover their cognitive resources that were spent due to intensive use of directed attention. Spending time in nature and taking a nap are two of the best ways to restore these resources. However, they are not possible to do most of the time. The problem is that many studies have revealed how nature and spending time in natural contexts can help boost restoration, but there are fewer studies conducted to understand how cognitive resources can be restored in interior settings. This study aims to explore the answer to this question: which qualities of interiors increase the restorativeness of an interior setting and how do they mediate restorativeness of an interior. To do this, a phenomenological qualitative study was conducted. The study was interested in the definition of attention restoration and the experiences of the phenomena. As the themes emerged, they were analyzed to match with Attention Restoration Theory components (being away, extent, fascination, compatibility) to examine how interior design elements mediate the restorativeness of an interior. The data was gathered from semi-structured interviews with international residents of Minnesota. The interviewees represent young professionals who work in Minnesota and often experience mental fatigue. Also, they have less emotional connections with places in Minnesota, which enabled data to be based on the physical qualities of a space rather than emotional connections. In the interviews, participants were asked about where they prefer to be when they experience mental fatigue. Next, they were asked to describe the physical qualities of the places they prefer to be with reasons. Four themes were derived from the analysis of interviews. The themes are in order according to their frequency. The first, and most common, the theme was “connection to outside”. The analysis showed that people need to be either physically or visually connected to recover from mental fatigue. Direct connection to nature was reported as preferable, whereas urban settings were the secondary preference along with interiors. The second theme emerged from the analysis was “the presence of the artwork,” which was experienced differently by the interviewees. The third theme was “amenities”. Interviews pointed out that people prefer to have the amenities that support desired activity during recovery from mental fatigue. The last theme was “aesthetics.” Interviewees stated that they prefer places that are pleasing to their eyes. Additionally, they could not get rid of the feeling of being worn out in places that are not well-designed. When we matched the themes with the four art components (being away, extent, fascination, compatibility), some of the interior qualities showed overlapping since they were experienced differently by the interviewees. In conclusion, this study showed that interior settings have restorative potential, and they are multidimensional in their experience.

Keywords: attention restoration, fatigue, interior design, qualitative study, restorative environments

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Authors: Yulong Jia, Xiang Zhang, Ziyuan Wu, Shiji Hu

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The calculation of bilinear pairing is the core of the SM9 algorithm, which relies on the underlying prime domain algorithm and the quadratic extension domain algorithm. Among the field algorithms, modular multiplication operation is the most time-consuming part. Therefore, the underlying modular multiplication algorithm is optimized to maximize the operation speed of bilinear pairings. This paper uses a modular multiplication method based on non-least positive (NLP) combined with Karatsuba and schoolbook multiplication to improve the Montgomery algorithm. At the same time, according to the characteristics of multiplication operation in the quadratic extension domain, a quadratic extension domain FP2-NLP modular multiplication algorithm for bilinear pairings is proposed, which effectively reduces the operation time of modular multiplication in the quadratic extension domain. The sub-expanded domain Fp₂ -NLP modular multiplication algorithm effectively reduces the operation time of modular multiplication under the second-expanded domain. The multiplication unit in the quadratic extension domain is implemented using SMIC55nm process, and two different implementation architectures are designed to cope with different application scenarios. Compared with the existing related literature, The output latency of this design can reach a minimum of 15 cycles. The shortest time for calculating the (AB+CD)r⁻¹ mod form is 37.5ns, and the comprehensive area-time product (AT) is 11400. The final R-ate pairing algorithm hardware accelerator consumes 2670k equivalent logic gates and 1.8ms computing time in 55nm process.

Keywords: sm9, hardware, NLP, Montgomery

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Authors: Athina Thanou, Eirini Eleni Tsiropoulou, Symeon Papavassiliou

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The problem of museum visitor experience and congestion management – in various forms - has come increasingly under the spotlight over the last few years, since overcrowding can significantly decrease the quality of visitors’ experience. Evidence suggests that on busy days the amount of time a visitor spends inside a crowded house museum can fall by up to 60% compared to a quiet mid-week day. In this paper we consider the aforementioned problem, by treating museums as evolving social systems that induce constraints. However, in a cultural heritage space, as opposed to the majority of social environments, the momentum of the experience is primarily controlled by the visitor himself. Visitors typically behave selfishly regarding the maximization of their own Quality of Experience (QoE) - commonly expressed through a utility function that takes several parameters into consideration, with crowd density and waiting/visiting time being among the key ones. In such a setting, congestion occurs when either the utility of one visitor decreases due to the behavior of other persons, or when costs of undertaking an activity rise due to the presence of other persons. We initially investigate how visitors’ behavioral risk attitudes, as captured and represented by prospect theory, affect their decisions in resource sharing settings, where visitors’ decisions and experiences are strongly interdependent. Different from the majority of existing studies and literature, we highlight that visitors are not risk neutral utility maximizers, but they demonstrate risk-aware behavior according to their personal risk characteristics. In our work, exhibits are organized into two groups: a) “safe exhibits” that correspond to less congested ones, where the visitors receive guaranteed satisfaction in accordance with the visiting time invested, and b) common pool of resources (CPR) exhibits, which are the most popular exhibits with possibly increased congestion and uncertain outcome in terms of visitor satisfaction. A key difference is that the visitor satisfaction due to CPR strongly depends not only on the invested time decision of a specific visitor, but also on that of the rest of the visitors. In the latter case, the over-investment in time, or equivalently the increased congestion potentially leads to “exhibit failure”, interpreted as the visitors gain no satisfaction from their observation of this exhibit due to high congestion. We present a framework where each visitor in a distributed manner determines his time investment in safe or CPR exhibits to optimize his QoE. Based on this framework, we analyze and evaluate how visitors, acting as prospect-theoretic decision-makers, respond and react to the various pricing policies imposed by the museum curators. Based on detailed evaluation results and experiments, we present interesting observations, regarding the impact of several parameters and characteristics such as visitor heterogeneity and use of alternative pricing policies, on scalability, user satisfaction, museum capacity, resource fragility, and operation point stability. Furthermore, we study and present the effectiveness of alternative pricing mechanisms, when used as implicit tools, to deal with the congestion management problem in the museums, and potentially decrease the exhibit failure probability (fragility), while considering the visitor risk preferences.

Keywords: museum resource and visitor management, congestion management, propsect theory, cyber physical social systems

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Authors: Y. Fekir, K. Mederbal, M. A. Hamadouche, D. Anteur

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The soil erosion by water has become one of the biggest problems of the environment in the world, threatening the majority of countries. There are several models to evaluate erosion. These models are still a simplified representation of reality. They permit the analysis of complex systems, measurements are complementary to allow an extrapolation in time and space and may combine different factors. The empirical model of soil loss proposed by Wischmeier and Smith (Universal Soil Loss Equation), is widely used in many countries. He considers that erosion is a multiplicative function of five factors: rainfall erosivity (the R factor) the soil erodibility factor (K), topography (LS), the erosion control practices (P) and vegetation cover and agricultural practices (C). In this work, we tried to develop a tool based on Web GIS functionality to evaluate soil losses caused by erosion taking into account five factors. This tool allows the user to integrate all the data needed for the evaluation (DEM, Land use, rainfall ...) in the form of digital layers to calculate the five factors taken into account in the USLE equation (R, K, C, P, LS). Accordingly, and after treatment of the integrated data set, a map of the soil losses will be achieved as a result. We tested the proposed tool on a watershed basin located in the weste of Algeria where a dataset was collected and prepared.

Keywords: USLE, erosion, web gis, Algeria

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Authors: D. Bianco, A. Sollazzo, M. Barbarino, G. Elia, A. Smoraldi, N. Favaloro

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The activities, described in the present paper, have been conducted in the framework of the HYPROB-New Program, carried out by the Italian Aerospace Research Centre (CIRA) promoted and funded by the Italian Ministry of University and Research (MIUR) in order to improve the National background on rocket engine systems for space applications. The Program has the strategic objective to improve National system and technology capabilities in the field of liquid rocket engines (LRE) for future Space Propulsion Systems applications, with specific regard to LOX/LCH4 technology. The main purpose of the HYPROB program is to design and build a Propulsion Test Facility (HIMP) allowing test activities on Liquid Thrusters. The development of skills in liquid rocket propulsion can only pass through extensive test campaign. Following its mission, CIRA has planned the development of new testing facilities and infrastructures for space propulsion characterized by adequate sizes and instrumentation. The IMP test cell is devoted to testing articles representative of small combustion chambers, fed with oxygen and methane, both in liquid and gaseous phase. This article describes the activities that have been carried out for the evaluation of the acoustic impact, and its consequent mitigation. The impact of the simulated acoustic disturbance has been evaluated, first, using an approximated method based on experimental data by Baumann and Coney, included in “Noise and Vibration Control Engineering” edited by Vér and Beranek. This methodology, used to evaluate the free-field radiation of jet in ideal acoustical medium, analyzes in details the jet noise and assumes sources acting at the same time. It considers as principal radiation sources the jet mixing noise, caused by the turbulent mixing of jet gas and the ambient medium. Empirical models, allowing a direct calculation of the Sound Pressure Level, are commonly used for rocket noise simulation. The model named after K. Eldred is probably one of the most exploited in this area. In this paper, an improvement of the Eldred Standard model has been used for a detailed investigation of the acoustical impact of the Hyprob facility. This new formulation contains an explicit expression for the acoustic pressure of each equivalent noise source, in terms of amplitude and phase, allowing the investigation of the sources correlation effects and their propagation through wave equations. In order to enhance the evaluation of the facility acoustic impact, including an assessment of the mitigation strategies to be set in place, a more advanced simulation campaign has been conducted using both an in-house code for noise propagation and scattering, and a commercial code for industrial noise environmental impact, CadnaA. The noise prediction obtained with the revised Eldred-based model has then been used for formulating an empirical/BEM (Boundary Element Method) hybrid approach allowing the evaluation of the barrier mitigation effect, at the design. This approach has been compared with the analogous empirical/ray-acoustics approach, implemented within CadnaA using a customized definition of sources and directivity factor. The resulting impact evaluation study is reported here, along with the design-level barrier optimization for noise mitigation.

Keywords: acoustic impact, industrial noise, mitigation, rocket noise

Procedia PDF Downloads 146

Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

Procedia PDF Downloads 123

Authors: Z. Bouchama, N. Essounbouli, A. Hamzaoui, M. N. Harmas

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A new robust finite time synergetic controller is presented based on recently developed synergetic control methodology and a terminal attractor technique. A Fast Terminal Synergetic Control (FTSC) is proposed for controlling DC-DC buck converter. Unlike Synergetic Control (SC) and sliding mode control, the proposed control scheme has the characteristics of finite time convergence and chattering free phenomena. Simulation of stabilization and reference tracking for buck converter systems illustrates the approach effectiveness while stability is assured in the Lyapunov sense and converse Lyapunov results involving scalar differential inequalities are given for finite-time stability.

Keywords: dc-dc buck converter, synergetic control, finite time convergence, terminal synergetic control, fast terminal synergetic control, Lyapunov

Procedia PDF Downloads 459

Authors: Carlos Tapia Cortez, Serkan Saydam, Jeff Coulton, Claude Sammut

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Strategic mineral commodities (SMC) both energetic and metals have long been fundamental for human beings. There is a strong and long-run relation between the mineral resources industry and society's evolution, with the provision of primary raw materials, becoming one of the most significant drivers of economic growth. Due to mineral resources’ relevance for the entire economy and society, an understanding of the SMC market behaviour to simulate price fluctuations has become crucial for governments and firms. For any human activity, SMC price fluctuations are affected by economic, geopolitical, environmental, technological and psychological issues, where cognition has a major role. Cognition is defined as the capacity to store information in memory, processing and decision making for problem-solving or human adaptation. Thus, it has a significant role in those systems that exhibit dynamic equilibrium through time, such as economic growth. Cognition allows not only understanding past behaviours and trends in SCM markets but also supports future expectations of demand/supply levels and prices, although speculations are unavoidable. Technological developments may also be defined as a cognitive system. Since the Industrial Revolution, technological developments have had a significant influence on SMC production costs and prices, likewise allowing co-integration between commodities and market locations. It suggests a close relation between structural breaks, technology and prices evolution. SCM prices forecasting have been commonly addressed by econometrics and Gaussian-probabilistic models. Econometrics models may incorporate the relationship between variables; however, they are statics that leads to an incomplete approach of prices evolution through time. Gaussian-probabilistic models may evolve through time; however, price fluctuations are addressed by the assumption of random behaviour and normal distribution which seems to be far from the real behaviour of both market and prices. Random fluctuation ignores the evolution of market events and the technical and temporal relation between variables, giving the illusion of controlled future events. Normal distribution underestimates price fluctuations by using restricted ranges, curtailing decisions making into a pre-established space. A proper understanding of SMC's price dynamics taking into account the historical-cognitive relation between economic, technological and psychological factors over time is fundamental in attempting to simulate prices. The aim of this paper is to discuss the SMC market cognition hypothesis and empirically demonstrate its dynamic-cognitive capacity. Three of the largest and traded SMC's: oil, copper and gold, will be assessed to examine the economic, technological and psychological cognition respectively.

Keywords: commodity price simulation, commodity price uncertainties, dynamic-cognition, dynamic systems

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Authors: Shuen-Tai Wang, Ying-Chuan Chen, Hsi-Ya Chang

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Cloud computing is a paradigm of computing that shifts the way computing has been done in the past. The users can use cloud resources such as application software or storage space from the cloud without needing to own them. This paper is focused on solutions that are anticipated to introduce IaaS idea to build cloud base services and enable the individual remote user's applications in cloud environments, which appear as if they are running on the end user's local computer. The available features of application delivery solution have been developed based on our previous research on the virtualization technology to offer applications independent of location so that the users can work online, offline, anywhere, with appropriate device and at any time. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for cloud service. Users no longer need to burden the system managers and drastically reduces the overall cost of hardware and software licenses. Moreover, this flexible remote application virtualization service represents the next significant step to the mobile workplace, and it lets users access their applications remotely through cloud services anywhere. This is also made possible by the low administrative costs as well as relatively inexpensive end-user terminals and reduced energy expenses.

Keywords: cloud computing, IaaS, virtualization, application delivery

Procedia PDF Downloads 281

Authors: Jimena Alarcon, Andrea Llorens, Gabriel Hernandez, Maritza Palma, Lucia Navarrete

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The research has funding from the Government of Chile and is focused on defining the design of rural primary classroom that stimulates creativity. The relevance of the study consists of its capacity to define adequate educational spaces for the implementation of the design-based learning (DBL) methodology. This methodology promotes creativity and teamwork, generating a meaningful learning experience for students, based on the appreciation of their environment and the generation of projects that contribute positively to their communities; also, is an inquiry-based form of learning that is based on the integration of design thinking and the design process into the classroom. The main goal of the study is to define the design characteristics of rural primary school classrooms, associated with the implementation of the DBL methodology. Along with the change in learning strategies, it is necessary to change the educational spaces in which they develop. The hypothesis indicates that a change in the space and equipment of the classrooms based on the emotions of the students will motivate better learning results based on the implementation of a new methodology. In this case, the pedagogical dynamics require an important interaction between the participants, as well as an environment favorable to creativity. Methodologies from Kansei engineering are used to know the emotional variables associated with their definition. The study is done to 50 students between 6 and 10 years old (average age of seven years), 48% of men and 52% women. Virtual three-dimensional scale models and semantic differential tables are used. To define the semantic differential, self-applied surveys were carried out. Each survey consists of eight separate questions in two groups: question A to find desirable emotions; question B related to emotions. Both questions have a maximum of three alternatives to answer. Data were tabulated with IBM SPSS Statistics version 19. Terms referred to emotions are grouped into twenty concepts with a higher presence in surveys. To select the values obtained as part of the implementation of Semantic Differential, a number expected of 'chi-square test (x2)' frequency calculated for classroom space is considered lower limit. All terms over the N expected a cut point, are included to prepare tables for surveys to find a relation between emotion and space. Statistic contrast (Chi-Square) represents significance level ≥ 0, indicator that frequencies appeared are not random. Then, the most representative terms depend on the variable under study: a) definition of textures and color of vertical surfaces is associated with emotions such as tranquility, attention, concentration, creativity; and, b) distribution of the equipment of the rooms, with emotions associated with happiness, distraction, creativity, freedom. The main findings are linked to the generation of classrooms according to diverse DBL team dynamics. Kansei engineering is the appropriate methodology to know the emotions that students want to feel in the classroom space.

Keywords: creativity, design-based learning, education spaces, emotions

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Authors: Peter K. Galenko, Stefanie Koch, Markus Rettenmayr, Robert Wonneberger, Evgeny V. Kharanzhevskiy, Maria Zamoryanskaya, Vladimir Ankudinov

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We analyze the structure of undercooled melts, crystal growth kinetics and amorphous/crystalline microstructure of rapidly solidifying glass-forming Pd-based and CuZr-based alloys. A dendrite growth model is developed using a combination of the kinetic phase-field model and mesoscopic sharp interface model. The model predicts features of crystallization kinetics in alloys from thermodynamically controlled growth (governed by the Gibbs free energy change on solidification) to the kinetically limited regime (governed by atomic attachment-detachment processes at the solid/liquid interface). Comparing critical undercoolings observed in the crystallization kinetics with experimental data on melt viscosity, atomistic simulation's data on liquid microstructure and theoretically predicted dendrite growth velocity allows us to conclude that the dendrite growth kinetics strongly depends on the cluster structure changes of the melt. The obtained data of theoretical and experimental investigations are used for interpretation of microstructure of samples processed in electro-magnetic levitator on board International Space Station in the frame of the project "MULTIPHAS" (European Space Agency and German Aerospace Center, 50WM1941) and "KINETIKA" (ROSKOSMOS).

Keywords: dendrite, kinetics, model, solidification

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Authors: Reza Bazargan lari, Mohammad H. Fattahi

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Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for pre-processing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based pre-processing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.

Keywords: wavelet, de-noising, predictability, time series fractal analysis, valid length, ANN

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

Procedia PDF Downloads 866

Authors: Yountaik Leem, Sang Ho Lee

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In this paper, different goals and the ways to reach smart cities shown in many countries during planning and implementation processes will be discussed. Each country dealt with technologies which have been embedded into space as development of ICTs (information and communication technologies) for their own purposes and by their own ways. For example, European countries tried to adapt technologies to reduce greenhouse gas emission to overcome global warming while US-based global companies focused on the way of life using ICTs such as EasyLiving of Microsoft™ and CoolTown of Hewlett-Packard™ during last decade of 20th century. In the North-East Asian countries, urban space with ICTs were developed in large scale on the viewpoint of capitalism. Ubiquitous city, first introduced in Korea which named after Marc Weiser’s concept of ubiquitous computing pursued new urban development with advanced technologies and high-tech infrastructure including wired and wireless network. Japan has developed smart cities as comprehensive and technology intensive cities which will lead other industries of the nation in the future. Not only the goals and strategies but also new directions to which smart cities are oriented also suggested at the end of the paper. Like a Finnish smart community whose slogan is ‘one more hour a day for citizens,’ recent trend is forwarding everyday lives and cultures of human beings, not capital gains nor physical urban spaces.

Keywords: smart cities, urban strategy, future direction, comparative study

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: Chiranjit Das, Sanjay Jharkharia

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This study is focused on transport mode selection under lead time variability and emissions constraint. In order to reduce the carbon emissions generation due to transportation, organization has often faced a dilemmatic choice of transport mode selection since logistic cost and emissions reduction are complementary with each other. Another important aspect of transportation decision is lead-time variability which is least considered in transport mode selection problem. Thus, in this study, we provide a comprehensive mathematical based analytical model to decide transport mode selection under emissions constraint. We also extend our work through analysing the effect of lead time variability in the transport mode selection by a sensitivity analysis. In order to account lead time variability into the model, two identically normally distributed random variables are incorporated in this study including unit lead time variability and lead time demand variability. Therefore, in this study, we are addressing following questions: How the decisions of transport mode selection will be affected by lead time variability? How lead time variability will impact on total supply chain cost under carbon emissions? To accomplish these objectives, a total transportation cost function is developed including unit purchasing cost, unit transportation cost, emissions cost, holding cost during lead time, and penalty cost for stock out due to lead time variability. A set of modes is available to transport each node, in this paper, we consider only four transport modes such as air, road, rail, and water. Transportation cost, distance, emissions level for each transport mode is considered as deterministic and static in this paper. Each mode is having different emissions level depending on the distance and product characteristics. Emissions cost is indirectly affected by the lead time variability if there is any switching of transport mode from lower emissions prone transport mode to higher emissions prone transport mode in order to reduce penalty cost. We provide a numerical analysis in order to study the effectiveness of the mathematical model. We found that chances of stock out during lead time will be higher due to the higher variability of lead time and lad time demand. Numerical results show that penalty cost of air transport mode is negative that means chances of stock out zero, but, having higher holding and emissions cost. Therefore, air transport mode is only selected when there is any emergency order to reduce penalty cost, otherwise, rail and road transport is the most preferred mode of transportation. Thus, this paper is contributing to the literature by a novel approach to decide transport mode under emissions cost and lead time variability. This model can be extended by studying the effect of lead time variability under some other strategic transportation issues such as modal split option, full truck load strategy, and demand consolidation strategy etc.

Keywords: carbon emissions, inventory theoretic model, lead time variability, transport mode selection

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Authors: Mawarni Mohamed, Sharifah Shahira A. Hamid

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Leisure time is an important component to offset the sedentary lifestyle of the people. Women tend to benefit from leisure activities not only to reduce stress but also to provide opportunities for well-being and self-satisfaction. This study was conducted to investigate body composition and leisure time use among women in Selangor from the influences of community settlement. A total of 419 women aged 18-65 years were selected to participate in this study. Descriptive statistics, t-test and ANOVA were used to analyze the level of physical activity and the relationship between leisure-time use and body composition were made to analyze the physical lifestyles. The results showed that women with normal body composition seem to be involved in more passive activities than women with less weight gain and obesity. Thus, the study recommended that the government and other health and recreational agencies should develop more places and activities suitable for leisure preference for women in their community settlement so they become more interested to engage in more active recreational and physical activities.

Keywords: body composition, community settlement, leisure time, physical lifestyles

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Authors: Yamileth P. Herrera, Ronald R. Gutierrez, Carlos, Pacheco-Bustos

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This research aims at the numerical modeling of a rectangular contact tank in order to improve the hydrodynamic behavior and the retention time of the water to be treated with the disinfecting agent. The methodology to be followed includes a hydraulic analysis of the tank to observe the fluid velocities, which will allow evidence of low-speed areas that may generate pathogenic agent incubation or high-velocity areas, which may decrease the optimal contact time between the disinfecting agent and the microorganisms to be eliminated. Based on the results of the numerical model, the efficiency of the tank under the geometric and hydraulic conditions considered will be analyzed. This would allow the performance of the tank to be improved before starting a construction process, thus avoiding unnecessary costs.

Keywords: contact tank, numerical models, hydrodynamic modeling, residence time

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Authors: Sinisa Prvanov

Abstract:

This research is an attempt to analyze the existing urban form of outdoor public space of Duhok city and to give proposals for their improvements in terms of urban seating. The aim of this research is to identify the main urban furniture elements and behaviour of users of three central parks of Duhok city, recognizing their functionality and the most common errors. Citizens needs, directly related to the physical characteristics of the environment, are categorized in terms of contact with nature. Parks as significant urban environments express their aesthetic preferences, as well as the need for recreation and play. Citizens around the world desire to contact with nature and places where they can socialize, play and practice different activities, but also participate in building their community and feeling the identity of their cities. The aim of this research is also to reintegrate these spaces in the wider urban context of the city of Duhok, to develop new functions by designing new seating patterns, more improved urban furniture, and necessary supporting facilities and equipment. Urban furniture is a product that uses an enormous number of people in public space. It has a high level of wear and damage due to intense use, exposure to sunlight and weather conditions. Iraq has a hot and dry climate characterized by long, warm, dry summers and short, cold winters. The climate is determined by the Iraq location at the crossroads of Arab desert areas and the subtropical humid climate of the Persian Gulf. The second part of this analysis will describe the possibilities of traditional and contemporary materials as well as their advantages in urban furniture production, providing users protection from extreme local climate conditions, but also taking into account solidities and unwelcome consequences, such as vandalism. In addition, this research represents a preliminary stage in the development of IND307 furniture design course for needs of the Department of Interior design, at the American University in Duhok. Based on results obtained in this research, the course would present a symbiosis between people and technology, promotion of new street furniture design that perceives pedestrian activities in an urban setting, and practical use of anthropometric measurements as a tool for technical innovations.

Keywords: Furniture design, Street furniture, Social interaction, Public space

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Authors: Lu Zhao, Nadir Farhi, Zoi Christoforou, Nadia Haddadou

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The safety test of deploying intelligent connected vehicles (ICVs) on the road network is a critical challenge. Road traffic network simulation can be used to test the functionality of ICVs, which is not only time-saving and less energy-consuming but also can create scenarios with car collisions. However, the relationship between different human driver behaviors and the car-collision occurrences has been not understood clearly; meanwhile, the procedure of car-collisions generation in the traffic numerical simulators is not fully integrated. In this paper, we propose an approach to identify specific driver profiles from real driven data; then, we replicate them in numerical traffic simulations with the purpose of generating inter-vehicular collisions. We proposed three profiles: (i) 'aggressive': short time-headway, (ii) 'inattentive': long reaction time, and (iii) 'normal' with intermediate values of reaction time and time-headway. These three driver profiles are extracted from the NGSIM dataset and simulated using the intelligent driver model (IDM), with an extension of reaction time. At last, the generation of inter-vehicular collisions is performed by varying the percentages of different profiles.

Keywords: vehicular collisions, human driving behavior, traffic modeling, car-following models, microscopic traffic simulation

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Authors: Hankyu Lee, Seoung Bum Kim

Abstract:

The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: L. Edirisinghe, Zhihong Jin, A. W. Wijeratne, R. Mudunkotuwa

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The virtual container yard is an effective solution to the container inventory imbalance problem which is a global issue. It causes substantial cost to carriers, which inadvertently adds to the prices of consumer goods. The virtual container yard is rooted in the fundamentals of container interchange between carriers. If carriers opt to interchange their excess containers with those who are deficit, a substantial part of the empty reposition cost could be eliminated. Unlike in other types of ships, cargo cannot be directly loaded to a container ship. Slots and containers are supplementary components; thus, without containers, a carrier cannot ship cargo if the containers are not available and vice versa. Few decades ago, carriers recognized slot (the unit of space in a container ship) interchange as a viable solution for the imbalance of shipping space. Carriers interchange slots among them and it also increases the advantage of scale of economies in container shipping. Some of these service agreements between mega carriers have provisions to interchange containers too. However, the interchange mechanism is still not popular among carriers for containers. This is the paradox that prevails in the liner shipping industry. At present, carriers reposition their excess empty containers to areas where they are in demand. This research applied factor analysis statistical method. The paper reveals that five major components may influence the virtual container yard namely organisation, practice and culture, legal and environment, international nature, and marketing. There are 12 variables that may impact the virtual container yard, and these are explained in the paper.

Keywords: virtual container yard, shipping, imbalance, management, inventory

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Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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Authors: Md Al Masum Bhuiyan, Maria C. Mariani, Osei K. Tweneboah

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In the present work, we develop a technique for estimating the volatility of financial time series by using stochastic differential equation. Taking the daily closing prices from developed and emergent stock markets as the basis, we argue that the incorporation of stochastic volatility into the time-varying parameter estimation significantly improves the forecasting performance via Maximum Likelihood Estimation. While using the technique, we see the long-memory behavior of data sets and one-step-ahead-predicted log-volatility with ±2 standard errors despite the variation of the observed noise from a Normal mixture distribution, because the financial data studied is not fully Gaussian. Also, the Ornstein-Uhlenbeck process followed in this work simulates well the financial time series, which aligns our estimation algorithm with large data sets due to the fact that this algorithm has good convergence properties.

Keywords: financial time series, maximum likelihood estimation, Ornstein-Uhlenbeck type models, stochastic volatility model

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Authors: Xinyuan Y. Song, Kai Kang

Abstract:

Joint models for analyzing longitudinal and survival data are widely used to investigate the relationship between a failure time process and time-variant predictors. A common assumption in conventional joint models in the survival analysis literature is that all predictors are observable. However, this assumption may not always be supported because unobservable traits, namely, latent variables, which are indirectly observable and should be measured through multiple observed variables, are commonly encountered in the medical, behavioral, and financial research settings. In this study, a joint modeling approach to deal with this feature is proposed. The proposed model comprises three parts. The first part is a dynamic factor analysis model for characterizing latent variables through multiple observed indicators over time. The second part is a random coefficient trajectory model for describing the individual trajectories of latent variables. The third part is a proportional hazard model for examining the effects of time-invariant predictors and the longitudinal trajectories of time-variant latent risk factors on hazards of interest. A Bayesian approach coupled with a Markov chain Monte Carlo algorithm to perform statistical inference. An application of the proposed joint model to a study on the Alzheimer's disease neuroimaging Initiative is presented.

Keywords: Bayesian analysis, joint model, longitudinal data, time-to-event data

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Authors: Mohamad Javad Shakouri, Behshad Riahipour

Abstract:

Today, the use of medicinal herbs in architecture and green space has a significant effect on the process of calming human and increases the reliability coefficient of design and design flexibility. The current research was conducted with the aim to design green roof and investigate the effect of medicinal herbs such as cress, leek, fenugreek, beet, sweet fennel, green basil, purple basil, and purslane on reducing the number of environmental pollutants (copper, zinc, and cadmium). Finally, the weight of the dry plant and the concentration of elements zinc, lead, and cadmium in the herbs was measured. According to the results, the maximum dry weight (88.10 and 73.79 g) was obtained in beet and purslane respectively and the minimum dry weight (24.12 and 25.21) was obtained in purple basil, and green basil respectively. The maximum amount of element zinc (235 and 213 mg/kg) and the maximum amount of lead (143 mg/kg) were seen in sweet fennel and purple basil. In addition, the maximum amount of cadmium (13 mg/kg) was seen in sweet fennel and purple basil and the minimum amount of lead and cadmium (78 and 7 mg/kg) was seen in green basil, and the minimum amount of zinc (110 mg/kg) was seen in leek. On the other hand, the absorption amount of element lead in the herbs beet and purslane was the same and both absorbed 123 mg/kg lead. Environmentally, if green roofs are implemented extensively and in wide dimensions in urban spaces, they will purify and reduce pollution significantly by absorbing carbon dioxide and producing oxygen.

Keywords: medicinal herbs, green space, green roof, heavy metals, lead, green basil

Procedia PDF Downloads 163