Search results for: fuzzy network project

Authors: Rishabh Nagpal, Riya Uchagaonkar, Venkata Naga Narasimha Ashish Mernedi, Ahmed Hambaba

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The rapid evolution of technology in the communication sector continually seeks to bridge the gap between different communities, notably between the deaf community and the hearing world. This project develops a comprehensive American Sign Language (ASL) recognition system, leveraging the advanced capabilities of convolutional neural networks (CNNs) and vision transformers (ViTs) to interpret and translate ASL in real-time. The primary objective of this system is to provide an effective communication tool that enables seamless interaction through accurate sign language interpretation. The architecture of the proposed system integrates dual networks -VGG16 for precise spatial feature extraction and vision transformers for contextual understanding of the sign language gestures. The system processes live input, extracting critical features through these sophisticated neural network models, and combines them to enhance gesture recognition accuracy. This integration facilitates a robust understanding of ASL by capturing detailed nuances and broader gesture dynamics. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing diverse ASL signs, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced ASL recognition system and lays the groundwork for future innovations in assistive communication technologies.

Keywords: sign language, computer vision, vision transformer, VGG16, CNN

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Authors: Thilini M. Yatanwala

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CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos.

Keywords: CCTV surveillance, human detection and identification, image processing, inter-process communication, security, suspicious detection

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Authors: Sandesh Achar

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Personalized advertising holds greater potential for higher conversion rates compared to generic advertisements. However, its widespread application in the retail industry faces challenges due to complex implementation processes. These complexities impede the swift adoption of personalized advertisement on a large scale. Personalized advertisement, being a data-driven approach, necessitates consumer-related data, adding to its complexity. This paper introduces an innovative data-driven decision-making framework, D3Advert, which personalizes advertisements by analyzing personalities using a BiLSTM network. The framework utilizes the Myers–Briggs Type Indicator (MBTI) dataset for development. The employed BiLSTM network, specifically designed and optimized for D3Advert, classifies user personalities into one of the sixteen MBTI categories based on their social media posts. The classification accuracy is 86.42%, with precision, recall, and F1-Score values of 85.11%, 84.14%, and 83.89%, respectively. The D3Advert framework personalizes advertisements based on these personality classifications. Experimental implementation and performance analysis of D3Advert demonstrate a 40% improvement in impressions. D3Advert’s innovative and straightforward approach has the potential to transform personalized advertising and foster widespread personalized advertisement adoption in marketing.

Keywords: personalized advertisement, deep Learning, MBTI dataset, BiLSTM network, NLP.

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Authors: Sachin Sharma

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A wireless sensor network (WSN) consists of a large number of sensor nodes which are deployed over an area to perform local computations based on information gathered from the surroundings. With the increasing demand for real-time applications in WSN, real-time critical events anticipate an efficient quality-of-service (QoS) based routing for data delivery from the network infrastructure. Hence, maximizing the lifetime of the network through minimizing the energy is an important challenge in WSN; sensors cannot be easily replaced or recharged due to their ad-hoc deployment in a hazardous environment. Considerable research has been focused on developing robust energy efficient QoS based routing protocols. The main focus of this article is primarily on periodical cycling schemes which represent the most compatible technique for energy saving and we also focus on the data-driven approaches that can be used to improve the energy efficiency. Finally, we will make a review on some communication protocols proposed for sensor networks.

Keywords: energy efficient, quality of service, wireless sensor networks, MAC

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Authors: Rosina U. Ndukwe, Diane Crone, Nick Cavill

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Active transport (i.e. walking to school, cycle to work schemes etc.) is an effective approach with multiple social and environmental benefits for transforming urban environments into active urban environments. Although walking and cycling often remain on the margins of urban planning and infrastructure, there are new approaches emerging, along with policy intervention relevant for the creation of sustainable urban active environments conductive to active travel, increasing physical activity levels of involved communities and supporting social inclusion through more active participation. SPAcE - Supporting Policy and Action for Active Environments is a 3 year Erasmus+ project that aims to integrate active transport programmes into public policy across the EU. SPAcE focuses on cities/towns with recorded low physical activity levels to support the development of active environments in 5 sites: Latvia [Tukums], Italy [Palermo], Romania [Brasov], Spain [Castilla-La Mancha] and Greece [Trikala]. The first part of the project involved a review of good practice including case studies from across the EU and project partner countries. This has resulted in the first output from the project, an evidence of good practice summary with case study examples. In the second part of the project, working groups across the 5 sites have carried out co-production to develop Urban Active Environments (UActivE) Action Plans aimed at influencing policy and practice for increasing physical activity primarily through the use of cycling and walking. Action plans are based on international evidence and guidance for healthy urban planning. Remaining project partners include Universities (Gloucestershire, Oxford, Zurich, Thessaly) and Fit for Life programme (National physical activity promotion program, Finland) who provide support and advice incorporating current evidence, healthy urban planning and mentoring. Cooperation and co-production with public health professionals, local government officers, education authorities and transport agencies has been a key approach of the project. The third stage of the project has involved training partners in the WHO HEAT tool to support the implementation of the Action Plans. Project results show how multi-agency, transnational collaboration can produce real-life Action Plans in five EU countries, based on published evidence, real-life experience, consultation and collaborative working with other organisations across the EU. Learning from the processes adopted within this project will demonstrate how public health, local government and transport agencies across the EU, can work together to create healthy environments that have the aim of facilitating active behaviour, even in times of constrained public budgets. The SPAcE project has captured both the challenges and solutions for increasing population physical activity levels, health and wellness in urban spaces and translating evidence into policy and practice ensuring innovation at policy level. Funding acknowledgment: SPAcE (www.activeenvironments.eu) is co-funded by the Sport action of the ERASMUS+ programme.

Keywords: action plans, active transport, SPAcE, UActivE urban active environments, walking and cycling

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Authors: Maher Aburrous, Adel Khelifi, Manar Abu Talib

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Transactions performed by financial institutions on daily basis require XML encryption on large scale. Encrypting large volume of message fully will result both performance and resource issues. In this paper a novel approach is presented for securing financial XML transactions using classification data mining (DM) algorithms. Our strategy defines the complete process of classifying XML transactions by using set of classification algorithms, classified XML documents processed at later stage using element-wise encryption. Classification algorithms were used to identify the XML transaction rules and factors in order to classify the message content fetching important elements within. We have implemented four classification algorithms to fetch the importance level value within each XML document. Classified content is processed using element-wise encryption for selected parts with "High", "Medium" or “Low” importance level values. Element-wise encryption is performed using AES symmetric encryption algorithm and proposed modified algorithm for AES to overcome the problem of computational overhead, in which substitute byte, shift row will remain as in the original AES while mix column operation is replaced by 128 permutation operation followed by add round key operation. An implementation has been conducted using data set fetched from e-banking service to present system functionality and efficiency. Results from our implementation showed a clear improvement in processing time encrypting XML documents.

Keywords: XML transaction, encryption, Advanced Encryption Standard (AES), XML classification, e-banking security, fuzzy classification, cryptography, intelligent encryption

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Authors: Klas Elmquist, Anders Larsson

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Until the year 2000, almost all short pulse modulators in the accelerator world were made with the pulse forming network (PFN) technique. The pulse forming network systems have since then been replaced with solid state modulators that have better efficiency, better stability, and lower cost of ownership, and they are much smaller. In this paper, it is shown that it is possible to replace a pulse forming network system with a solid-state system without changing the klystron tank and the klystron transformer. The solid-state modulator uses semiconductors switching at 1 kV level. A first pulse transformer transforms the voltage up to 10 kV. The 10 kV pulse is finally fed into the original transformer that is placed under the klystron. A flatness of 0.8 percent and stability of 100 PPM is achieved. The test is done with a CPI 8262 type of klystron. It is also shown that it is possible to run such a system with long cables between the transformers. When using this technique, it will be possible to keep original sub-systems like filament systems, vacuum systems, focusing solenoid systems, and cooling systems for the klystron. This will substantially reduce the cost of an upgrade and prolong the life of the klystron system.

Keywords: modulator, solid-state, PFN-system, thyratron

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Authors: Syed Tahir Shah, Fazal Muhammad, Syed Kashif Shah, Maleeha Gul

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In recent days, natural gas has become a relatively clean and quality source of energy, which is recovered from deep wells by expensive drilling activities. The recovered substance is purified by processing in multiple stages to remove the unwanted/containments like dust, dirt, crude oil and other particles. Mostly, gas utilities are concerned with essential objectives of quantity/quality of natural gas delivery, financial outcome and safe natural gas volumetric inventory in the transmission gas pipeline. Gas quantity and quality are primarily related to standards / advanced metering procedures in processing units/transmission systems, and the financial outcome is defined by purchasing and selling gas also the operational cost of the transmission pipeline. SNGPL (Sui Northern Gas Pipelines Limited) Pakistan has a wide range of diameters of natural gas transmission pipelines network of over 9125 km. This research results in answer a few of the issues in accuracy/metering procedures via multiple advanced gadgets for gas flow attributes after being utilized in the transmission system and research. The effects of good pressure management in transmission gas pipeline network in contemplation to boost the gas volume deposited in the existing network and finally curbing gas losses UFG (Unaccounted for gas) for financial benefits. Furthermore, depending on the results and their observation, it is directed to enhance the maximum allowable working/operating pressure (MAOP) of the system to 1235 PSIG from the current round about 900 PSIG, such that the capacity of the network could be entirely utilized. In gross, the results depict that the current model is very efficient and provides excellent results in the minimum possible time.

Keywords: natural gas, pipeline network, UFG, transmission pack, AGA

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Authors: S. S. Dol

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Improvement in vehicle efficiency can reduce global fossil fuels consumptions. For that sole reason, Shell Global Corporation introduces Shell Eco-marathon where student teams require to design, build and test energy-efficient vehicles. Hence, this paper will focus on design processes and the development of a fuel economic vehicle which satisfying the requirements of the competition. In this project, three components are designed and analyzed, which are the body, chassis and powertrain of the vehicle. Optimum design for each component is produced through simulation analysis and theoretical calculation in which improvement is made as the project progresses.

Keywords: energy efficient, drag force, chassis, powertrain

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Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

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Authors: Zhidong Zhang

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This study established a mixed method model in assessing statistics learning with Bayesian network models. There are three variants in exploratory sequential designs. There are three linked steps in one of the designs: qualitative data collection and analysis, quantitative measure, instrument, intervention, and quantitative data collection analysis. The study used a scoring model of analysis of variance (ANOVA) as a content domain. The research study is to examine students’ learning in both semantic and performance aspects at fine grain level. The ANOVA score model, y = α+ βx1 + γx1+ ε, as a cognitive task to collect data during the student learning process. When the learning processes were decomposed into multiple steps in both semantic and performance aspects, a hierarchical Bayesian network was established. This is a theory-driven process. The hierarchical structure was gained based on qualitative cognitive analysis. The data from students’ ANOVA score model learning was used to give evidence to the hierarchical Bayesian network model from the evidential variables. Finally, the assessment results of students’ ANOVA score model learning were reported. Briefly, this was a mixed method research design applied to statistics learning assessment. The mixed methods designs expanded more possibilities for researchers to establish advanced quantitative models initially with a theory-driven qualitative mode.

Keywords: exploratory sequential design, ANOVA score model, Bayesian network model, mixed methods research design, cognitive analysis

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Authors: Sneha Leslie

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The current situation in the cyber world is that crimes performed by Botnets are increasing and the masterminds (botmaster) are not detectable easily. The botmaster in the botnet compromises the legitimate host machines in the network and make them bots or zombies to initiate the cyber-attacks. This paper will focus on the live detection of the botmaster in the network by using the strong framework 'metasploit', when distributed denial of service (DDOS) attack is performed by the botnet. The affected victim machine will be continuously monitoring its incoming packets. Once the victim machine gets to know about the excessive count of packets from any IP, that particular IP is noted and details of the noted systems are gathered. Using the vulnerabilities present in the zombie machines (already compromised by botmaster), the victim machine will compromise them. By gaining access to the compromised systems, applications are run remotely. By analyzing the incoming packets of the zombies, the victim comes to know the address of the botmaster. This is an effective and a simple system where no specific features of communication protocol are considered.

Keywords: bonet, DDoS attack, network security, detection system, metasploit framework

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Authors: Tas Yong Koh, Steve Rowlinson, Yuzhong Shen

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In construction safety management, safety climate has long been linked to workers' safety behaviors and performance. For this reason, safety climate concept and tools have been used as heuristics to diagnose a range of safety-related issues by some progressive contractors in Hong Kong and elsewhere. However, as a diagnostic tool, safety climate tends to treat the different components of the climate construct in a linear fashion. Safety management in construction projects, in reality, is a multi-faceted and multilevel phenomenon that resembles a complex system. Hence, understanding safety management in construction projects requires not only the understanding of safety climate but also the organizational-systemic nature of the phenomenon. Our involvement, diagnoses, and interpretations of a range of safety climate-related issues which culminated in the project’s sub-optimum safety performance in an infrastructure construction project have brought about such revelation. In this study, a range of data types had been collected from various hierarchies of the project site organization. These include the frontline workers and supervisors from the main and sub-contractors, and the client supervisory personnel. Data collection was performed through the administration of safety climate questionnaire, interviews, observation, and document study. The findings collectively indicate that what had emerged in parallel of the seemingly linear climate-based exploration is the exposition of the organization-systemic nature of the phenomenon. The results indicate the negative impacts of climate perceptions mismatch, insufficient work planning, and risk management, mixed safety leadership, workforce negative attributes, lapsed safety enforcement and resources shortages collectively give rise to the project sub-optimum safety performance. From the dynamic causation and multilevel perspective, the analyses show that the individual, group, and organizational levels issues are interrelated and these interrelationships are linked to negative safety climate. Hence the adoption of both perspectives has enabled a fuller understanding of the phenomenon of safety management that point to the need for an organizational-systemic intervention strategy. The core message points to the fact that intervention at an individual level will only meet with limited success if the risks embedded in the higher levels in group and project organization are not addressed. The findings can be used to guide the effective development of safety infrastructure by linking different levels of systems in a construction project organization.

Keywords: construction safety management, dynamic causation, multilevel analysis, safety climate

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Authors: María-Dolores Afonso-Suárez, Nayra Pumar-Carreras, Juan Ruiz-Alzola

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This research addresses the use of an e-Learning creation methodology for learning objects. Throughout the process, indicators are being gathered, to determine if it responds to the main objectives of an engineering discipline. These parameters will also indicate if it is necessary to review the creation cycle and readjust any phase. Within the project developed for this study, apart from the use of structured methods, there has been a central objective: the establishment of a learning atmosphere. A place where all the professionals involved are able to collaborate, plan, solve problems and determine guides to follow in order to develop creative and innovative solutions. It has been outlined as a blended learning program with an assessment plan that proposes face to face lessons, coaching, collaboration, multimedia and web based learning objects as well as support resources. The project has been drawn as a long term task, the pilot teaching actions designed provide the preliminary results object of study. This methodology is been used in the creation of learning content for the African countries of Senegal, Mauritania and Cape Verde. It has been developed within the framework of the MACbioIDi, an Interreg European project for the International cooperation and development. The educational area of this project is focused in the training and advice of professionals of the medicine as well as engineers in the use of applications of medical imaging technology, specifically the 3DSlicer application and the Open Anatomy Browser.

Keywords: teaching contents engineering, e-learning, blended learning, international cooperation, 3dslicer, open anatomy browser

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Authors: Shashank Bhatnagar, W. Wilfred Godfrey

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We develop in this paper, an approach to find the trendy topic, which not only considers the user-topic interaction but also considers the community, in which user belongs. This method modifies the previous approach of user-topic interaction to user-community-topic interaction with better speed-up in the range of [1.1-3]. We assume that trend detection in a social network is dependent on two things. The one is, broadcast of messages in social network governed by self-exciting point process, namely called Hawkes process and the second is, Community Rank. The influencer node links to others in the community and decides the community rank based on its PageRank and the number of users links to that community. The community rank decides the influence of one community over the other. Hence, the Hawkes process with the kernel of user-community-topic decides the trendy topic disseminated into the social network.

Keywords: community detection, community rank, Hawkes process, influencer node, pagerank, trend detection

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Mohammad Ali Oghabian, Reza Reiazi, Esmaeel Parsai, Mehdi Aghili, Ramin Jaberi

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In this project a new procedure has been introduced for utilizing digitally reconstructed radiograph from MRI images in Brachytherapy treatment planning. This procedure enables us to localize the tumor volume and delineate the extent of critical structures in vicinity of tumor volume. The aim of this project was to improve the accuracy of dose delivered to targets of interest in 2D treatment planning system.

Keywords: brachytherapy, cervix, digitally reconstructed radiographs, lymph node

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Authors: Ajoy Kumar Das, Prasenjit Dey

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Heat transfer due to forced convection of copper water based nanofluid has been predicted by Artificial Neural network (ANN). The present nanofluid is formed by mixing copper nano particles in water and the volume fractions are considered here are 0% to 15% and the Reynolds number are kept constant at 100. The back propagation algorithm is used to train the network. The present ANN is trained by the input and output data which has been obtained from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software Ansys Fluent. The numerical simulation based results are compared with the back propagation based ANN results. It is found that the forced convection heat transfer of water based nanofluid can be predicted correctly by ANN. It is also observed that the back propagation ANN can predict the heat transfer characteristics of nanofluid very quickly compared to standard CFD method.

Keywords: forced convection, square cylinder, nanofluid, neural network

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Authors: Eugenija Strazdiene, Violeta Bytautaite, Daivute Krisciuniene

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The object of the investigation is the suit for chemical protection, which totally covers human body together with breathing apparatus, breathing mask and helmet (JSC Ansell Protective Solutions Lithuania). The end users of such clothing are the members of rescue team – firefighters. During the presentation, the results of 3D scanning with stationary Human Solutions scanner and portable Artec Eva scanner will be compared on the basis of the efficiency of scanning procedure and scanning accuracy. Also, the possibilities to exporting scanned bodies into specialized CAD systems for suit design development and material consumption calculation will be analyzed. The necessity to understand and to implement corresponding clothing material properties during 3D visualization of garment on CAD systems will be presented. During the presentation, the outcomes of the project ‘Smart and Safe Work Wear Clothing SWW’ will be discussed. The project is carried out under the Interreg Baltic Sea Region Program as 2014-2020 European territorial cooperation objective. Thematic priority is Capacity for Innovation. The main goal of the project is to improve competitiveness and to increase business possibilities for work wear enterprises in the Baltic Sea Region. The project focuses on mass customization of products for various end users. It engages textile and clothing manufacturing technology researchers, work wear producers, end users, as well as national textile and clothing branch organizations in Finland, Lithuania, Latvia, Estonia and Poland.

Keywords: CAD systems, mass customization, 3D scanning, safe work wear

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Authors: Hanna Lu, Sandra S. M. Chan, Linda C. W. Lam

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Objectives: Intra-individual variability (IIV) has been considered as a biomarker of healthy ageing. However, the composite role of IIV in attention, as an impending indicator for neurocognitive disorders warrants further exploration. This study aims to investigate the IIV, as well as their relationships with attention network functions in adults with neurocognitive disorders (NCD). Methods: 36adults with NCD due to Alzheimer’s disease(NCD-AD), 31adults with NCD due to vascular disease (NCD-vascular), and 137 healthy controls were recruited. Intraindividual standard deviations (iSD) and intraindividual coefficient of variation of reaction time (ICV-RT) were used to evaluate the IIV. Results: NCD groups showed greater IIV (iSD: F= 11.803, p < 0.001; ICV-RT:F= 9.07, p < 0.001). In ROC analyses, the indices of IIV could differentiateNCD-AD (iSD: AUC value = 0.687, p= 0.001; ICV-RT: AUC value = 0.677, p= 0.001) and NCD-vascular (iSD: AUC value = 0.631, p= 0.023;ICV-RT: AUC value = 0.615, p= 0.045) from healthy controls. Moreover, the processing speed could distinguish NCD-AD from NCD-vascular (AUC value = 0.647, p= 0.040). Discussion: Intra-individual variability in attention provides a stable measure of cognitive performance, and seems to help distinguish the senior adults with different cognitive status.

Keywords: intra-individual variability, attention network, neurocognitive disorders, ageing

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Authors: Mirza Ahsan Baig, Paul Evans

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Due to the high demand for automobiles, manufacturers have to run their production lines non-stop for long periods. At such a scale, unplanned downtime could cost up to $50 billion annually. Improper training of new hires could lead to safety issues causing unplanned downtimes. The project developed a virtual reality simulation that could train personnel for key operations on an automobile manufacturing floor, reducing the risk of unplanned downtime while eliminating any disruption (due to training) on the assembly line. An automation firm sponsored it to explore the possibilities the likes of Jaguar and BMW are yet to achieve! The project was inspired by Bentley, the only company that succeeded in simulation training scenarios within virtual space. The project aims to inspire other significant firms to do the same. It was increasing employee safety, eliminating downtimes, and improving efficiency. The developed prototype uncovered various limitations within existing simulation providers and was able to simulate state-of-the-art training scenarios. It also explored the possibility of a multiuser interface and resolved complex issues such as lack of movement space within the training area.

Keywords: virtual reality, scenario training, limited movement, multiuser

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Authors: Lassaad Smirani

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In this paper we propose a Neurosymbolic Learning System (NLS) as a channel estimator for Long Term Evolution Advanced (LTE-A) uplink. The proposed system main idea based on Neural Network has modules capable of performing bidirectional information transfer between symbolic module and connectionist module. We demonstrate various strengths of the NLS especially the ability to integrate theoretical knowledge (rules) and experiential knowledge (examples), and to make an initial knowledge base (rules) converted into a connectionist network. Also to use empirical knowledge witch by learning will have the ability to revise the theoretical knowledge and acquire new one and explain it, and finally the ability to improve the performance of symbolic or connectionist systems. Compared with conventional SC-FDMA channel estimation systems, The performance of NLS in terms of complexity and quality is confirmed by theoretical analysis and simulation and shows that this system can make the channel estimation accuracy improved and bit error rate decreased.

Keywords: channel estimation, SC-FDMA, neural network, hybrid system, BER, LTE-A

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Authors: Maria Areias, Diogo Simões, Ana Figueiredo, Anishur Rahman, Filipa Figueiredo, João Nunes

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It is expected that close to 7 billion people will live in urban areas by 2050. In order to improve the sustainability of the territories and its transition towards circular economy, it’s necessary to understand its metabolism and promote and guide the entrepreneurship answer. The study of a macroeconomic metabolism involves the quantification of the inputs, outputs and storage of energy, water, materials and wastes for an urban region. This quantification and analysis representing one opportunity for the promotion of green entrepreneurship. There are several methods to assess the environmental impacts of an urban territory, such as human and environmental risk assessment (HERA), life cycle assessment (LCA), ecological footprint assessment (EF), material flow analysis (MFA), physical input-output table (PIOT), ecological network analysis (ENA), multicriteria decision analysis (MCDA) among others. However, no consensus exists about which of those assessment methods are best to analyze the sustainability of these complex systems. Taking into account the weaknesses and needs identified, the CiiM - Circular Innovation Inter-Municipality project aims to define an uniform and globally accepted methodology through the integration of various methodologies and dynamic approaches to increase the efficiency of macroeconomic metabolisms and promoting entrepreneurship in a circular economy. The pilot territory considered in CiiM project has a total area of 969,428 ha, comprising a total of 897,256 inhabitants (about 41% of the population of the Center Region). The main economic activities in the pilot territory, which contribute to a gross domestic product of 14.4 billion euros, are: social support activities for the elderly; construction of buildings; road transport of goods, retailing in supermarkets and hypermarkets; mass production of other garments; inpatient health facilities; and the manufacture of other components and accessories for motor vehicles. The region's business network is mostly constituted of micro and small companies (similar to the Central Region of Portugal), with a total of 53,708 companies identified in the CIM Region of Coimbra (39 large companies), 28,146 in the CIM Viseu Dão Lafões (22 large companies) and 24,953 in CIM Beiras and Serra da Estrela (13 large companies). For the construction of the database was taking into account data available at the National Institute of Statistics (INE), General Directorate of Energy and Geology (DGEG), Eurostat, Pordata, Strategy and Planning Office (GEP), Portuguese Environment Agency (APA), Commission for Coordination and Regional Development (CCDR) and Inter-municipal Community (CIM), as well as dedicated databases. In addition to the collection of statistical data, it was necessary to identify and characterize the different stakeholder groups in the pilot territory that are relevant to the different metabolism components under analysis. The CIIM project also adds the potential of a Geographic Information System (GIS) so that it is be possible to obtain geospatial results of the territorial metabolisms (rural and urban) of the pilot region. This platform will be a powerful visualization tool of flows of products/services that occur within the region and will support the stakeholders, improving their circular performance and identifying new business ideas and symbiotic partnerships.

Keywords: circular economy tools, life cycle assessment macroeconomic metabolism, multicriteria decision analysis, decision support tools, circular entrepreneurship, industrial and regional symbiosis

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Authors: Karin Schakib-Ekbatan, Annette Roser

Abstract:

According to the Federal Government’s plans, the German building stock should be virtually climate neutral by 2050. Thus, the “EnEff.Gebäude.2050” funding initiative was launched, complementing the projects of the Energy Transition Construction research initiative. Beyond the construction and renovation of individual buildings, solutions must be found at the neighborhood level. The subject of the presented pilot project is a building ensemble from the Wilhelminian period in Munich, which is planned to be refurbished based on a socially compatible, energy-saving, innovative-technical modernization concept. The building ensemble, with about 200 apartments, is part of the building cooperative. To create an optimized network and possible synergies between researchers and projects of the funding initiative, a Scientific Accompanying Research was established for cross-project analyses of findings and results in order to identify further research needs and trends. Thus, the project is characterized by an interdisciplinary approach that combines constructional, technical, and socio-scientific expertise based on a participatory understanding of research by involving the tenants at an early stage. The research focus is on getting insights into the tenants’ comfort requirements, attitudes, and energy-related behaviour. Both qualitative and quantitative methods are applied based on the Technology-Acceptance-Model (TAM). The core of the refurbishment strategy is a wall heating system intended to replace conventional radiators. A wall heating provides comfortable and consistent radiant heat instead of convection heat, which often causes drafts and dust turbulence. Besides comfort and health, the advantage of wall heating systems is an energy-saving operation. All apartments would be supplied by a uniform basic temperature control system (around perceived room temperature of 18 °C resp. 64,4 °F), which could be adapted to individual preferences via individual heating options (e. g. infrared heating). The new heating system would affect the furnishing of the walls, in terms of not allowing the wall surface to be covered too much with cupboards or pictures. Measurements and simulations of the energy consumption of an installed wall heating system are currently being carried out in a show apartment in this neighborhood to investigate energy-related, economical aspects as well as thermal comfort. In March, interviews were conducted with a total of 12 people in 10 households. The interviews were analyzed by MAXQDA. The main issue of the interview was the fear of reduced self-efficacy within their own walls (not having sufficient individual control over the room temperature or being very limited in furnishing). Other issues concerned the impact that the construction works might have on their daily life, such as noise or dirt. Despite their basically positive attitude towards a climate-friendly refurbishment concept, tenants were very concerned about the further development of the project and they expressed a great need for information events. The results of the interviews will be used for project-internal discussions on technical and psychological aspects of the refurbishment strategy in order to design accompanying workshops with the tenants as well as to prepare a written survey involving all households of the neighbourhood.

Keywords: energy efficiency, interviews, participation, refurbishment, residential buildings

Procedia PDF Downloads 117

Authors: Carole A. El Raheb, Hassan K. Abdel-Salam, Ingi Elcherif

Abstract:

An architect carrying the design process alone is the main reason for the deterioration of the quality of the architectural product as the complexity of the projects makes it a multi-disciplinary work; then, the Integrative Design Process (IDP) must be applied in the architectural firm especially from the early design phases to improve the product’s quality and to eliminate the ignorance of the principles of design causing the occurrence of low-grade buildings. The research explores the Integrative Design (ID) principles that fit in the architectural practice. Constraints facing this application are presented with strategies and solutions to overcome them. A survey questionnaire was conducted to collect data from a number of recognized Egyptian Architecture, Engineering and Construction (AEC) firms that explores their opinions on using the IDP. This survey emphasizes the importance of the IDP in firms and presents the reasons preventing the firms from applying the IDP. The aim here is to investigate the potentials of integrating this approach into architectural firms emphasizing the importance of this application which ensures the realization of the project’s goal and eliminates the reduction in the project’s quality.

Keywords: application, architectural firms, integrative design principles, integrative design process, the project quality

Procedia PDF Downloads 220

Authors: Rania Jradi, Christophe Marvillet, Mohamed Razak Jeday

Abstract:

One of the most frequently encountered problems in industrial heat exchangers is fouling, which degrades the thermal and hydraulic performances of these types of equipment, leading thus to failure if undetected. And it occurs due to the accumulation of undesired material on the heat transfer surface. So, it is necessary to know about the heat exchanger fouling dynamics to plan mitigation strategies, ensuring a sustainable and safe operation. This paper proposes an Artificial Neural Network (ANN) approach to estimate the fouling resistance in a cross-flow heat exchanger by the collection of the operating data of the phosphoric acid concentration loop. The operating data of 361 was used to validate the proposed model. The ANN attains AARD= 0.048%, MSE= 1.811x10⁻¹¹, RMSE= 4.256x 10⁻⁶ and r²=99.5 % of accuracy which confirms that it is a credible and valuable approach for industrialists and technologists who are faced with the drawbacks of fouling in heat exchangers.

Keywords: cross-flow heat exchanger, fouling, estimation, phosphoric acid concentration loop, artificial neural network approach

Procedia PDF Downloads 192

Authors: Yu-Cheng Lin, Yu-Chih Su

Abstract:

Generally, there are many numerous existing problems during the construction phase special in civil engineering. The problems communication and management (PCM) of civil engineering are important and necessary to enhance the performance of construction management. The civil information modelling (CIM) approach is used to retain information with digital format and assist easy updating and transferring of information in the 3D environment for all related civil and infrastructure projects. When the application of CIM technology is adopted in infrastructure projects, all the related project participants can discuss problems and obtain feedback and responds among project participants integrated with the assistance of CIM models 3D illustration. Usually, electronic mail (e-mail) is one of the most popular communication tools among all related participants for rapid transit system (MRT), also known as a subway or metro, construction project in Taiwan. Furthermore, all interfaces should be traced and managed effectively during the process. However, there are many problems with the use of e-mail for communication of all interfaces. To solve the above problems, this study proposes a CIM-based Problem Communication and Management (CPCM) system to improve performance of problem communication and management. The CPCM system is applied to a case study of an MRT project in Taiwan to identify its CPCM effectiveness. Case study results show that the proposed CPCM system and Markup-enabled CIM Viewer are effective CIM-based communication tools in CIM-supported PCM work of civil engineering. Finally, this study identifies conclusion, suggestion, benefits, and limitations for further applications.

Keywords: building information modeling, civil information modeling, infrastructure, general contractor

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Authors: Bahareh Golchin, Nooshin Riahi

Abstract:

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

Procedia PDF Downloads 129

Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

Abstract:

Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

Procedia PDF Downloads 286

Authors: G. M. Komba

Abstract:

The Internet Gateway (IGW) has extra ability than a simple Mesh Router (MR) and the responsibility to route mostly the all traffic from Mesh Clients (MCs) to the Internet backbone however, IGWs are more expensive. Choosing strategic locations for the Internet Gateways (IGWs) best location in Backbone Wireless Mesh (BWM) precarious to the Wireless Mesh Network (WMN) and the location of IGW can improve a quantity of performance related problem. In this paper, we propose a novel algorithm, namely New Gateway Location Algorithm (NGLA), which aims to achieve four objectives, decreasing the network cost effective, minimizing delay, optimizing the throughput capacity, Different from existing algorithms, the NGLA increasingly recognizes IGWs, allocates mesh routers (MRs) to identify IGWs and promises to find a feasible IGW location and install minimum as possible number of IGWs while regularly conserving the all Quality of Service (QoS) requests. Simulation results showing that the NGLA outperforms other different algorithms by comparing the number of IGWs with a large margin and it placed 40% less IGWs and 80% gain of throughput. Furthermore the NGLA is easy to implement and could be employed for BWM.

Keywords: Wireless Mesh Network, Gateway Location Algorithm, Quality of Service, BWM

Procedia PDF Downloads 357