Search results for: tectono-stratigraphic architecture

Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Evridiki Papachristou, Nikolaos Bilalis

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Imagine an apparel industry where fashion design does not begin with a paper-and-pen drawing which is then translated into pattern and later to a 3D model where the designer tries out different fabrics, colours and contrasts. Instead, imagine a fashion designer in the future who produces that initial fashion drawing in a three-dimensional space and won’t leave that environment until the product is done, communicating his/her ideas with the entire development team in true to life 3D. Three-dimensional (3D) technology - while well established in many other industrial sectors like automotive, aerospace, architecture and industrial design, has only just started to open up a whole range of new opportunities for apparel designers. The paper will discuss the process of 3D simulation technology enhanced by high quality visualization of data and its capability to ensure a massive competitiveness in the market. Secondly, it will underline the most frequent problems & challenges that occur in the process chain when various partners in the production of textiles and apparel are working together. Finally, it will offer a perspective of how the Virtual Prototyping Technology will make the global textile and apparel industry change to a level where designs will be visualized on a computer and various scenarios modeled without even having to produce a physical prototype. This state-of-the-art 3D technology has been described as transformative and“disruptive”comparing to the process of the way apparel companies develop their fashion products today. It provides the benefit of virtual sampling not only for quick testing of design ideas, but also reducing process steps and having more visibility.A so called“digital asset” that can be used for other purposes such as merchandising or marketing.

Keywords: 3D visualization, apparel, virtual prototyping, prototyping technology

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Mohamed Dhaoui, Hakim Gabtni

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The migration of hydrocarbons is a fairly complicated process that depends on several parameters, both structural and sedimentological. In this study, we will try to determine secondary migration paths which convey hydrocarbon from their main source rock to the largest reservoir of the Paleozoic petroleum system of the Tunisian part of Ghadames basin. In fact, The Silurian source rock is the main source rock of the Paleozoic petroleum system of the Ghadames basin. However, the most solicited reservoir in this area is the Triassic reservoir TAGI (Trias Argilo-Gréseux Inférieur). Several geochemical studies have confirmed that oil products TAGI come mainly from the Tannezuft Silurian source rock. That being said that secondary migration occurs through the fault system which affects the post-Silurian series. Our study is based on analysis and interpretation of gravity data. The gravity modeling was conducted in the northern part of Ghadames basin and the Telemzane uplift. We noted that there is a close relationship between the location of producing oil fields and gravity gradients which separate the positive and negative gravity anomalies. In fact, the analysis and transformation of the Bouguer anomaly map, and the residual gravity map allowed as understanding the architecture of the Precambrian in the study area, thereafter gravimetric models were established allowed to determine the probable migration path.

Keywords: basement, Ghadames, gravity, hydrocarbon, migration path

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Monica Anumula

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Nowadays human beings attain comfort zone artificially for heating, cooling and lighting the spaces they live, and their main importance is given to aesthetics of building and they are not designed to protect themselves from climate. They depend on artificial sources of energy resulting in energy wastage. In order to reduce the amount of energy being spent in the construction industry and Energy Package goals by 2020, new ways of constructing houses is required. The larger part of energy consumption of a building is directly related to architectural aspects hence nature has to be integrated into the building design to attain comfort zone and reduce the dependency on artificial source of energy. The research is to develop bioclimatic design strategies and techniques for the walls and roofs of Vijayawada houses. Study and analysis of design strategies and techniques of various cases like Kerala, Mangalore etc. for similar kind of climate is examined in this paper. Understanding the vernacular architecture and modern techniques of that various cases and implementing in the housing of Vijayawada not only decreases energy consumption but also enhances socio cultural values of Vijayawada. This study focuses on the comparison of vernacular techniques and modern building bio climatic strategies to attain thermal comfort and energy reduction in hot and humid climate. This research provides further thinking of new strategies which include both vernacular and modern bioclimatic techniques.

Keywords: bioclimatic design, energy consumption, hot and humid climates, thermal comfort

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Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy

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This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.

Keywords: sign language, CNN, HCI, segmentation

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the Central Processing Unit (CPU), operational (RAM), and permanent (ROM) memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles

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Authors: Michael Gerges, Ograbe Ahiakwo, Martin Jaeger, Ahmad Asaad

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Recent advances of Building Information Modeling (BIM) especially in the Middle East have increased remarkably. Dubai has been taking a lead on this by making it mandatory for BIM to be adopted for all projects that involve complex architecture designs. This is because BIM is a dynamic process that assists all stakeholders in monitoring the project status throughout different project phases with great transparency. It focuses on utilizing information technology to improve collaboration among project participants during the entire life cycle of the project from the initial design, to the supply chain, resource allocation, construction and all productivity requirements. In view of this trend, the paper examines the extent of applying BIM in the State of Kuwait, by exploring practitioners’ perspectives on BIM, especially their perspectives on main barriers and main advantages. To this end structured interviews were carried out based on questionnaires and with a range of different construction professionals. The results revealed that practitioners perceive improved communication and mitigated project risks by encouraged collaboration between project participants. However, it was also observed that the full implementation of BIM in the State of Kuwait requires concerted efforts to make clients demanding BIM, counteract resistance to change among construction professionals and offer more training for design team members. This paper forms part of an on-going research effort on BIM and its application in the State of Kuwait and it is on this basis that further research on the topic is proposed.

Keywords: building information modeling, BIM, construction industry, Kuwait

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Authors: Işılay Tiarnagh Sheridan

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World EXPO, short version for the “exposition”, is a large universal public exhibition held since 1851. Within the 164 years, it was organized 34 times in 22 cities and as a result it has given birth to its very own culture unlike most of other international events. It has an outstanding power in transforming the places, in which it is held, into trademarks via changes in their urban tissues. For that, it is widely remembered with its cities instead of its countries. Within the scope of this change, some constructions were planned to be temporary, some planned to be permanent and some were thought to be temporary but kept afterwards becoming important monuments such as the Crystal Palace of London (though it was destroyed later by a fire) and the Eiffel Tower of Paris. These examples are the most prominent names upon considering World EXPOs. Yet, there are so many other legacies of these events within modern city fabric today that we don’t usually associate with its Expo history. Some of them are leading figures not only for the housing city but for other cities also, such as the first Metro line of Paris during 1900 World EXPO; some of them are listed as monuments of the cities such as Saint Louis Art Museum of 1904 World EXPO; some of them, like Melbourne Royal Exhibition Building of 1880 World’s EXPO, are among UNESCO World Heritage Sites and some of them are the masterpieces of modern architecture such as the famous Barcelona Pavilion, German pavilion of the 1929 World’s EXPO, of Ludwig Mies van der Rohe. Thus, the aim of this paper is to analyze the history of World’s EXPO and its eventual results in the birth of its own cultural heritage. Upon organizing these results, the paper aims to create a brief list of EXPO heritage monuments and sites so as to form a database for their further conservation needs.

Keywords: expo, heritage, world's fair, legacy

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Authors: M. Alwetaishi

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One of the most common and traditional strategies in architecture is to design buildings passively. This is a way to ensure low building energy reliance with respect to specific micro-building locations. There are so many ways where buildings can be designed passively, some of which are applying thermal insulation, thermal mass, courtyard and glazing to wall ratio. This research investigates the impact of each of these aspects with respect to the hot and dry climate of the capital of Riyadh. Thermal Analysis Simulation (TAS) will be utilized which is powered by Environmental Design Simulation Limited company (EDSL). It is considered as one of the most powerful tools to predict energy performance in buildings. There are three primary building designs and methods which are using courtyard, thermal mass and thermal insulation. The same building size and fabrication properties have been applied to all designs. Riyadh city which is the capital of the country was taken as a case study of the research. The research has taken into account various zone directions within the building as it has a large contribution to indoor energy and thermal performance. It is revealed that it is possible to achieve nearly zero carbon building in the hot and dry region in winter with minimum reliance on energy loads for building zones facing south, west and east. Moreover, using courtyard is more beneficial than applying construction materials into building envelope. Glazing to wall ratio is recommended to be 10% and not exceeding 30% in all directions in hot and arid regions.

Keywords: sustainable buildings, hot and arid climates, passive building design, Saudi Arabia

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Authors: R. C. Law, Shirley Kang, T. Y. Hin, M. Z. Abdullah

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LED technology has been evolving aggressively in recent years from incandescent bulb during older days to as small as chip scale package. It will continue to stay bright in future. As such, there is tremendous pressure to stay competitive in the market by optimizing products to next level of performance and reliability with the shortest time to market. This changes the conventional way of product design and development to virtual prototyping by means of Computer Aided Engineering (CAE). It comprises of the deployment of Finite Element Method (FEM) and Computational Fluid Dynamic (CFD). FEM accelerates the investigation for early detection of failures such as crack, improve the thermal performance of system and enhance solder joint reliability. CFD helps to simulate the flow pattern of molding material as a function of different temperature, molding parameters settings to evaluate failures like voids and displacement. This paper will briefly discuss the procedures and applications of FEM in thermal stress, solder joint reliability and CFD of compression molding in LED CSP. Integration of virtual prototyping in product development had greatly reduced the time to market. Many successful achievements with minimized number of evaluation iterations required in the scope of material, process setting, and package architecture variant have been materialized with this approach.

Keywords: LED, chip scale packaging (CSP), computational fluid dynamic (CFD), virtual prototyping

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Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: Eliziane Fernanda Navarro, Aparecida Eleonora Sitta

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Child of the Dark is the work of the Brazilian author Carolina Maria de Jesus, published at the first time by Ática and Francisco Alves in 1960. It is, mostly, a story of lack of rights. It lacks to men who live in the slums what is essential in order to take advantage of the privilege of rationality to develop themselves as civilized humans. It is, therefore, in the withholding of the basic rights that inequality finds space to build itself to be the main misery on Earth. Antonio Candido, a Brazilian sociologist claims that it is the right to literature has the ability to humanize men, once the aptitude to create fiction and fable is essential to the social balance. Hence, for the forming role that literature holds, it must be thought as the number of rights that assure human dignity, such as housing, education, health, freedom, etc. When talking about her routine, Carolina puts in evidence something that has great influence over the formation of human beings, contributing to the way they live: the slum. Even though it happens in a distinct way and using her own linguistics variation, Carolina writes about something that will only be discussed later on Brazil’s Cities Statute and Erminia Maricato: the right to the city, and how the slums are, although inserted in the city, an attachment, an illegal city, a dismissing room. It interests ourselves, for that matter, in this work, to analyse how the deprivation of the rights to the city and literature, detailed in Carolina’s journal, conditions human beings to a life where the instincts overcome the social values.

Keywords: Child of the Dark, slum, literature, architecture and urbanism, fundamental rights, Brazil

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Authors: Mohamad Javad Shakouri, Behshad Riahipour

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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

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Authors: Aratrika Sarkar, Jayita Guha Niyogi

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This paper focuses on the application of general healing design theories to modulate them into case-specific and contextual design considerations. Existing literature focuses on the relationship between architecture and mental health. Primary case studies are selected in India to focus on the effect of a specific location on design considerations. They are qualitatively analysed to further contextualise the inferences from the literature study. An academic project is cited as an example to apply the learnings from the study and understand the influence of various parameters on the design process for further conclusion. Literature studies, case studies and hypothetical design applications helped in finding the different ways of achieving the similar goal of a sensitive approach toward mental health. Along with salutogenic parameters, category of establishment, age group, location of the site and user preference plays a crucial role in the design process. Design of mental health establishments, especially in India, has to involve transparency between stakeholders and users. Owing to different climatic zones and diverse sociocultural traditions, the approach toward healing should adapt accordingly. It should be an effort towards striking a balance between contradictory elements of healing design and resolving the dilemmas with sensitivity and consensus. Lastly, the design should not force a person towards communication or companionship but rather let the person realise that naturally through the healing process.

Keywords: contextual healing design, deinstitutionalisation, Indian mental healthcare establishments, environmental psychology, salutogenesis, therapeutic design

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Nikita Katyal, Shaurya Uppal

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This paper tackles the significant challenge of accurately classifying customers within a retailer’s customer base. Accurate classification is essential for developing targeted marketing strategies that effectively engage this important demographic. To address this issue, we propose a method that utilizes Large Language Models (LLMs). By employing LLMs, we analyze the metadata associated with product purchases derived from historical data to identify key product categories that act as distinguishing factors. These categories, such as baby food, eldercare products, or family-sized packages, offer valuable insights into the likely household composition of customers, including families with babies, families with kids/teenagers, families with pets, households caring for elders, or mixed households. We segment high-confidence customers into distinct categories by integrating historical purchase behavior with LLM-powered product classification. This paper asserts that life stage segmentation can significantly enhance e-commerce businesses’ ability to target the appropriate customers with tailored products and campaigns, thereby augmenting sales and improving customer retention. Additionally, the paper details the data sources, model architecture, and evaluation metrics employed for the segmentation task.

Keywords: LLMs, segmentation, product tags, fine-tuning, target segments, marketing communication

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Feran Asur

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In cities, plants; with its visual and functional effects, it helps to provide balance between human and environmental system. It is possible to develop alternative solutions to eliminate visual pollution by evaluating the potential properties of plant materials with other inanimate materials such as color, texture, form, size, etc. characteristics and other inanimate materials such as highlighter, background forming, harmonizing and concealer. In cold climates, the number of ornamental plant species that grow in warmer climates is less. For this reason, especially in the landscaping works of urban spaces, it is difficult to create the desired visuality with aesthetically qualified plants that are suitable for the ecology of the area, without creating monotony, with color variety. In this study, the importance of plant and plant compositions in the solution of visual problems in urban environments in cold climatic conditions is emphasized. The potential of ornamental plants that can be used for this purpose in preventing visual pollution is given. It has been shown how to use prominent features of these ornamental plants such as size, form, texture, vegetation periods to improve visual landscape in urban spaces in a long time. In addition to the design group disciplines that have activity on planning or application basis in the city and its surroundings, landscape architecture discipline can provide visual improvement of the studies to be carried out in detail in terms of planting design.

Keywords: residential landscape, planting, urban space, visual improvement

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Authors: Li Guo, Jianhong Wang, Dian Huang, Shengzhong Feng

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Computing networks as a new network architecture has shown great promise in boosting the utilization of different resources, such as computing, caching, and communications. To maximise the efficiency of resource orchestration in computing network control systems (CNCSs), this work proposes a dynamic orchestration strategy of a different resource based on task requirements from computing power requestors (CPRs). Specifically, computing power providers (CPPs) in CNCSs could share information with each other through communication channels on the basis of blockchain technology, especially their current idle resources. This dynamic process is modeled as a cooperative game in which CPPs have the same target of maximising long-term rewards by improving the resource utilization ratio. Meanwhile, the task requirements from CPRs, including size, deadline, and calculation, are simultaneously considered in this paper. According to task requirements, the proposed orchestration strategy could schedule the best-fitting resource in CNCSs, achieving the maximum long-term rewards of CPPs and the best quality of experience (QoE) of CRRs at the same time. Based on the EdgeCloudSim simulation platform, the efficiency of the proposed strategy is achieved from both sides of CPRs and CPPs. Besides, experimental results show that the proposed strategy outperforms the other comparisons in all cases.

Keywords: computing network control systems, resource orchestration, dynamic scheduling, blockchain, cooperative game

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Authors: Excellent Hansda

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Over the past years, a lot of research has been on the ethnic enclaves from historical, sociological and economic point of view. However there exist a research gap in the built environment and spatial layout of these areas. When immigrants (People) assimilate in a different place, they struggle to preserve their original identity to maintain their heritage. Then there is the Place, which is the physical manifestation of the heritage, shown through streetscape and architecture. Together 'People and Place' form a relationship with the authenticity of the enclave. As immigrants come in the host country, they try to bring their culture into the place, but at the same time, the culture of the host country also affects the immigrants. This creates conflicts not only in the lifestyle and culture of the immigrants, but also the built characteristics of the place. In the midst of such conflicts, one may easily question the authenticity of an ethnic enclave. In Malaysia, a number of ethnic enclaves emerged due to trade during the medieval times. Little India is one among the other ethnic enclaves present in Chulia Street in Malaysia. The study investigates the factors of 'Place and People', affecting the authenticity of a little India, in the context of an evolving state of Penang in Malaysia. The study is carried through extensive literature review of existing data, followed by observations drawn by visual analysis, discussions and interviews with the stakeholders of the study area. The findings of this research suggest the contribution of 'people and places' in the process of place making in an ethnic enclave. The findings are essential for conservation and further development of ethnic enclaves.

Keywords: conservation, ethnic enclaves, heritage, identity

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

Abstract:

In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: H. Solomon, A. Abdul-Latif, R. Baleh, I. Deiab, K. Khanafer

Abstract:

Aluminum foams were developed and tested due to their high energy absorption abilities for multifunctional applications. The aim of this research work was to investigate experimentally the effect of quasi-static biaxial loading complexity (combined compression-torsion) on the energy absorption capacity of highly uniform architecture open-cell aluminum foam manufactured by kelvin cell model. The two generated aluminum foams have 80% and 85% porosities, spherical-shaped pores having 11mm in diameter. These foams were tested by means of several square-section specimens. A patented rig called ACTP (Absorption par Compression-Torsion Plastique), was used to investigate the foam response under quasi-static complex loading paths having different torsional components (i.e., 0°, 37° and 53°). The main mechanical responses of the aluminum foams were studied under simple, intermediate and severe loading conditions. In fact, the key responses to be examined were stress plateau and energy absorption capacity of the two foams with respect to loading complexity. It was concluded that the higher the loading complexity and the higher the relative density, the greater the energy absorption capacity of the foam. The highest energy absorption was thus recorded under the most complicated loading path (i.e., biaxial-53°) for the denser foam (i.e., 80% porosity).

Keywords: open-cell aluminum foams, biaxial loading complexity, foams porosity, energy absorption capacity, characterization

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Authors: Syed Ahsan, Saleh Alshomrani, Ishtiaq Rasool, Ali Hassan

Abstract:

In this oral presentation, we will provide an overview of the technical and semantic architecture of a desert border security and critical infrastructure protection security system. Modern border security systems are designed to reduce the dependability and intrusion of human operators. To achieve this, different types of sensors are use along with video surveillance technologies. Application of these technologies in a harsh desert environment of Saudi Arabia poses unique challenges. Environmental and geographical factors including high temperatures, desert storms, temperature variations and remoteness adversely affect the reliability of surveillance systems. To successfully implement a reliable, effective system in a harsh desert environment, the following must be achieved: i) Selection of technology including sensors, video cameras, and communication infrastructure that suit desert environments. ii) Reduced power consumption and efficient usage of equipment to increase the battery life of the equipment. iii) A reliable and robust communication network with efficient usage of bandwidth. Also, to reduce the expert bottleneck, an ontology-based intelligent information systems needs to be developed. Domain knowledge unique and peculiar to Saudi Arabia needs to be formalized to develop an expert system that can detect abnormal activities and any intrusion.

Keywords: border security, sensors, abnormal activity detection, ontologies

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Authors: Alessandra Marino

Abstract:

This paper thoroughly investigates the design characteristics necessary for intergenerational living and evaluates their applicability within the Italian social panorama in order to identify a model that can serve as a reference for subsequent regulatory adjustments of a new building typology. The applied methodology involves the collaboration of people with various background and architects, all representing the three main ages of life - childhood or youth, adulthood, seniority - through questionnaires aimed at researching the peculiar characteristics that contemporary intergenerational housing should include; the questionnaires are then compared with each other in order to identify any recurring patterns by age group and/or influenced by the specialist knowledge on the subject of the architects compared to the rest of the user sample. The results indicate that among specialist users in the field of architecture, young students identify home automation as the key to the inclusion of the weakest groups within the building, adult architects believe that the identification of intergenerational/community services within the building is the cornerstone, and senior architects focus on widespread spatial accessibility. At the same time, the results among non-specialist users do not identify a significantly diversified model by age group but are generally in agreement in the importance of separation between private environments and collective spaces. The interpretation of the results obtained leads to a compositional study of a new building typology with the future objective of channeling the subsequent outcomes within the regulatory adjustments of the sector.

Keywords: intergenerational living, social sustainability, health, lifestyle, well-being

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Authors: M. Devaki, K. B. Jayanthi

Abstract:

The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: N. Türkmenoğlu Bayraktar, E. Kishalı

Abstract:

Campus buildings are educational facilities where various amount of energy consumption for lighting, heating, cooling and ventilation occurs. Some of the new universities in Turkey, where this investigation takes place, still continue their educational activities in existing buildings primarily designed for different architectural programs and converted to campus buildings via changes of function, space organizations and structural interventions but most of the time without consideration of appropriate micro climatic conditions. Reducing energy consumption in these structures not only contributes to the national economy but also mitigates the negative effects on environment. Furthermore, optimum thermal comfort conditions should be provided during the refurbishment of existing campus structures and their building envelope. Considering this issue, the first step is to investigate the climatic performance of building elements regarding refurbishment process. In the context of the study Kocaeli University, Faculty of Design and Architecture building constructed in 1980s in Anıtpark campus located in the central part of Kocaeli, Turkey was investigated. Climatic factors influencing thermal conditions; the deteriorations on building envelope; temperature distribution; heat losses from façade elements observed by thermography were presented in order to improve strategies for retrofit process for the building envelope. Within the scope of the survey, refurbishment strategies towards providing optimum climatic comfort conditions, increasing energy efficiency of building envelope were proposed.

Keywords: building envelope, IRT, refurbishment, non-destructive test

Procedia PDF Downloads 384