Search results for: network pinch analysis

Authors: Wen-Jye Shyr, Chia-Ming Lin, Hung-Yun Feng

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The purpose of this study was to develop an energy management system for university campuses based on the Internet of Things (IoT) technique. The proposed IoT technique based on WebAccess is used via network browser Internet Explore and applies TCP/IP protocol. The case study of IoT for lighting energy usage management system was proposed. Structure of proposed IoT technique included perception layer, equipment layer, control layer, application layer and network layer.

Keywords: energy management, IoT technique, sensor, WebAccess

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Authors: Natalia Strobel, Jan Kratzer

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In this paper, we test whether different types of research and development (R&D) alliances positively impact the radical innovation performance of firms. We differentiate between the R&D alliances without extern R&D orders and embeddedness in knowledge source network. We test the differences between the domestically diversified R&D alliances and R&D alliances diversified abroad. Moreover, we test how non-price competition influences the impact of domestically diversified R&D alliances, and R&D alliance diversified abroad on radical innovation performance. Our empirical analysis is based on the comprehensive Swiss innovation panel, which allowed us to study 3520 firms between the years between 1996 and 2011 in 3 years intervals. We analyzed the data with a linear estimation with Swamy-Aurora transformation using plm package in R software. Our results show as hypothesized a positive impact of R&D alliances diversity abroad as well as domestically on radical innovation performance. The effect of non-price interaction is in contrast to our hypothesis, not significant. This suggests that diversity of R&D alliances is highly advantageous independent of non-price competition.

Keywords: R&D alliances, partner diversity, knowledge source diversity, non-price competition, absorptive capacity

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Authors: Sarah Pfoser, Lisa-Maria Putz, Thomas Berger

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In the EU, the transport sector accounts for roughly one fourth of the total greenhouse gas emissions. In fact, the transport sector is one of the main contributors of greenhouse gas emissions. Climate protection targets aim to reduce the negative effects of greenhouse gas emissions (e.g. climate change, global warming) worldwide. Achieving a modal shift to foster environmentally friendly modes of transport such as rail and inland waterways is an important strategy to fulfill the climate protection targets. The present paper goes beyond these conventional transport modes and reflects upon currently emerging high-performance transport modes that yield the potential of complementing future transport systems in an efficient way. It will be defined which properties describe high-performance transport modes, which types of technology are included and what is their potential to contribute to a sustainable future transport network. The first step of this paper is to compile state-of-the-art information about high-performance transport modes to find out which technologies are currently emerging. A multidimensional impact analysis will be conducted afterwards to evaluate which of the technologies is most promising. This analysis will be performed from a spatial, social, economic and environmental perspective. Frequently used instruments such as cost-benefit analysis and SWOT analysis will be applied for the multidimensional assessment. The estimations for the analysis will be derived based on desktop research and discussions in an interdisciplinary team of researchers. For the purpose of this work, high-performance transport modes are characterized as transport modes with very fast and very high throughput connections that could act as efficient extension to the existing transport network. The recently proposed hyperloop system represents a potential high-performance transport mode which might be an innovative supplement for the current transport networks. The idea of hyperloops is that persons and freight are shipped in a tube at more than airline speed. Another innovative technology consists in drones for freight transport. Amazon already tests drones for their parcel shipments, they aim for delivery times of 30 minutes. Drones can, therefore, be considered as high-performance transport modes as well. The Trans-European Transport Networks program (TEN-T) addresses the expansion of transport grids in Europe and also includes high speed rail connections to better connect important European cities. These services should increase competitiveness of rail and are intended to replace aviation, which is known to be a polluting transport mode. In this sense, the integration of high-performance transport modes as described above facilitates the objectives of the TEN-T program. The results of the multidimensional impact analysis will reveal potential future effects of the integration of high-performance modes into transport networks. Building on that, a recommendation on the following (research) steps can be given which are necessary to ensure the most efficient implementation and integration processes.

Keywords: drones, future transport networks, high performance transport modes, hyperloops, impact analysis

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Jinan Fiaidhi, Sabah Mohammed

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Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.

Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning

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Authors: M. Nasri Nasrabadi, A. A. Hassani

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Karoon is one of the greatest and longest rivers of Iran, which because of the existence of numerous industrial, agricultural centers and drinking usage, has a strategic situation in the west and southwest parts of Iran, and the optimal monitoring of its water quality is an essential and indispensable national issue. Due to financial constraints, water quality monitoring network design is an efficient way to manage water quality. The most crucial part is to find appropriate locations for monitoring stations. Considering the objectives of water usage, we evaluate existing water quality sampling stations of this river. There are several methods for assessment of existing monitoring stations such as Sanders method, multiple criteria decision making and dynamic programming approach (DPA) which DPA opted in this study. The results showed that due to the drinking water quality index out of 20 existing monitoring stations, nine stations should be retained on the river, that include of Gorgor-Band-Ghir of A zone, Dez-Band-Ghir of B zone, Teir, Pole Panjom and Zargan of C zone, Darkhoein, Hafar, Chobade, and Sabonsazi of D zone. In additional, stations of Dez river have the best conditions.

Keywords: DPA, karoon river, network monitoring, water quality, sampling site

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Authors: Omar M. Elmabrouk

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The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

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Authors: G. Arockiasamy, B. Sujeevan Kumar, Surendheran

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Human progress and development are highly influenced by the power of information access and technology. A global and multi-national transformation all over the word is possible due to digitalization. In the process of exchanging information, experience, and resources, there is a radical shift in who controls them. Mass media has turned the world into a global village by strengthening communication network. As a result, a new digital culture has emerged as a social network commonly known as new media. Today the advancement of technology is at the doorstep of everyone linking to anywhere. The traditional social restrictions are broken down by the new type of virtual communication modality that transcends people beyond boundaries At the same time media empire has invaded every nook and corner of the world through great expansion. Media activism is growing stronger and stronger but the truth and true meaning lost in the process. This paper explores the peoples’ attitude to media activism and tracing its direction. The methodology employed is random sampling survey and content analysis method. Both qualitatively and quantitatively measured. The findings tend to show 60 percent indicate media activism as positive and others indicate as negative. As a conclusion, media activism has danger within but depends on nature of the development of human orientation.

Keywords: media activism, media industry, program, truth information, orientation and nature

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, artificial neural network, kinect, stereotypical motor movements

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Authors: Harvey Sanchez-Restrepo, Jorge Louca

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Equity and education are major focus of government policies around the world due to its relevance for addressing the sustainable development goals launched by Unesco. In this research, we developed a primary analysis of a data set of more than one hundred educational and non-educational factors associated with learning, coming from a census-based large-scale assessment carried on in Ecuador for 1.038.328 students, their families, teachers, and school directors, throughout 2014-2018. Each participating student was assessed by a standardized computer-based test. Learning outcomes were calibrated through item response theory with two-parameters logistic model for getting raw scores that were re-scaled and synthetized by a learning index (LI). Our objective was to develop a network for modelling educational deprivation and analyze the structure of inequality gaps, as well as their relationship with socioeconomic status, school financing, and student's ethnicity. Results from the model show that 348 270 students did not develop the minimum skills (prevalence rate=0.215) and that Afro-Ecuadorian, Montuvios and Indigenous students exhibited the highest prevalence with 0.312, 0.278 and 0.226, respectively. Regarding the socioeconomic status of students (SES), modularity class shows clearly that the system is out of equilibrium: the first decile (the poorest) exhibits a prevalence rate of 0.386 while rate for decile ten (the richest) is 0.080, showing an intense negative relationship between learning and SES given by R= –0.58 (p < 0.001). Another interesting and unexpected result is the average-weighted degree (426.9) for both private and public schools attending Afro-Ecuadorian students, groups that got the highest PageRank (0.426) and pointing out that they suffer the highest educational deprivation due to discrimination, even belonging to the richest decile. The model also found the factors which explain deprivation through the highest PageRank and the greatest degree of connectivity for the first decile, they are: financial bonus for attending school, computer access, internet access, number of children, living with at least one parent, books access, read books, phone access, time for homework, teachers arriving late, paid work, positive expectations about schooling, and mother education. These results provide very accurate and clear knowledge about the variables affecting poorest students and the inequalities that it produces, from which it might be defined needs profiles, as well as actions on the factors in which it is possible to influence. Finally, these results confirm that network analysis is fundamental for educational policy, especially linking reliable microdata with social macro-parameters because it allows us to infer how gaps in educational achievements are driven by students’ context at the time of assigning resources.

Keywords: complex network, educational deprivation, evidence-based policy, large-scale assessments, policy informatics

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Authors: Mehdi Radmehr, Amir Hamed Mashhadzadeh, Mehdi Jafari

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Traditional HVDC systems are tough to DC short circuits as they are current regulated with a large reactance connected in series with cables. Multi-terminal DC wind farm topologies are attracting increasing research attempt. With AC/DC converters on the generator side, this topology can be developed into a multi-terminal DC network for wind power collection, which is especially suitable for large-scale offshore wind farms. For wind farms, the topology uses high-voltage direct-current transmission based on voltage-source converters (VSC-HVDC). Therefore, they do not suffer from over currents due to DC cable faults and there is no over current to react to. In this study, the multi-terminal DC wind farm topology is introduced. Then, possible internal DC faults are analyzed according to type and characteristic. Fault over current expressions are given in detail. Under this characteristic analysis, fault detection and detailed protection methods are proposed. Theoretical analysis and PSCAD/EMTDC simulations are provided.

Keywords: DC short circuits, multi-terminal DC wind farm topologies, HVDC transmission based on VSC, fault analysis

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Authors: Moustafa Osman Mohammed

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The modeling of a spatial-economic database is crucial in recitation economic network structure to social development. Sustainability within the spatial-economic model gives attention to green businesses to comply with Earth’s Systems. The natural exchange patterns of ecosystems have consistent and periodic cycles to preserve energy and materials flow in systems ecology. When network topology influences formal and informal communication to function in systems ecology, ecosystems are postulated to valence the basic level of spatial sustainable outcome (i.e., project compatibility success). These referred instrumentalities impact various aspects of the second level of spatial sustainable outcomes (i.e., participant social security satisfaction). The sustainability outcomes are modeling composite structure based on a network analysis model to calculate the prosperity of panel databases for efficiency value, from 2005 to 2025. The database is modeling spatial structure to represent state-of-the-art value-orientation impact and corresponding complexity of sustainability issues (e.g., build a consistent database necessary to approach spatial structure; construct the spatial-economic-ecological model; develop a set of sustainability indicators associated with the model; allow quantification of social, economic and environmental impact; use the value-orientation as a set of important sustainability policy measures), and demonstrate spatial structure reliability. The structure of spatial-ecological model is established for management schemes from the perspective pollutants of multiple sources through the input–output criteria. These criteria evaluate the spillover effect to conduct Monte Carlo simulations and sensitivity analysis in a unique spatial structure. The balance within “equilibrium patterns,” such as collective biosphere features, has a composite index of many distributed feedback flows. The following have a dynamic structure related to physical and chemical properties for gradual prolong to incremental patterns. While these spatial structures argue from ecological modeling of resource savings, static loads are not decisive from an artistic/architectural perspective. The model attempts to unify analytic and analogical spatial structure for the development of urban environments in a relational database setting, using optimization software to integrate spatial structure where the process is based on the engineering topology of systems ecology.

Keywords: ecological modeling, spatial structure, orientation impact, composite index, industrial ecology

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Authors: William Wong Xiu Shun, Yoonjin Hyun, Mingyu Kim, Seongi Choi, Namgyu Kim

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Recently, the demand of extracting the R&D keywords from the issues and using them in retrieving R&D information is increasing rapidly. But it is hard to identify the related issues or to distinguish them. Although the similarity between the issues cannot be identified, but with the R&D lexicon, the issues that always shared the same R&D keywords can be determined. In details, the R&D keywords that associated with particular issue is implied the key technology elements that needed to solve the problem of the particular issue. Furthermore, the related issues that sharing the same R&D keywords can be showed in a more systematic way through the issue clustering constructed from the perspective of R&D. Thus, sharing of the R&D result and reusable of the R&D technology can be facilitated. Indirectly, the redundancy of investment on the same R&D can be reduce as the R&D information can be shared between those corresponding issues and reusability of the related R&D can be improved. Therefore, a methodology of constructing an issue clustering from the perspective of common R&D keywords is proposed to satisfy the demands mentioned.

Keywords: clustering, social network analysis, text mining, topic analysis

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Authors: A. Andreasyan, C. Connors

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The Elliptic Curve Digital Signature algorithm-based X509v3 certificates are becoming more popular due to their short public and private key sizes. Moreover, these certificates can be stored in Internet of Things (IoT) devices, with limited resources, using less memory and transmitted in network security protocols, such as Internet Key Exchange (IKE), Transport Layer Security (TLS) and Secure Shell (SSH) with less bandwidth. The proposed method gives another advantage, in that it increases the performance of the above-mentioned protocols in terms of key exchange by saving one scalar multiplication operation.

Keywords: cryptography, elliptic curve digital signature algorithm, key exchange, network security protocol

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Tatsuya Otoshi, Masayuki Murata

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With the development and diversification of applications on the Internet, applications that require high responsiveness, such as video streaming, are becoming mainstream. Application responsiveness is not only a matter of communication delay but also a matter of time required to grasp changes in network conditions. The tradeoff between accuracy and measurement time is a challenge in network control. We people make countless decisions all the time, and our decisions seem to resolve tradeoffs between time and accuracy. When making decisions, people are known to make appropriate choices based on relatively small samples. Although there have been various studies on models of human decision-making, a model that integrates various cognitive biases, called ”quantum decision-making,” has recently attracted much attention. However, the modeling of small samples has not been examined much so far. In this paper, we extend the model of quantum decision-making to model decision-making with a small sample. In the proposed model, the state is updated by value-based probability amplitude amplification. By analytically obtaining a lower bound on the number of samples required for decision-making, we show that decision-making with a small number of samples is feasible.

Keywords: quantum decision making, small sample, MPEG-DASH, Grover's algorithm

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Authors: Hadi Rouhi Belvirdi, Davoud Beheshtizadeh

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The increasing demand for sustainable construction materials has led to a growing interest in high-strength recycled concrete (HSRC). Utilizing recycled materials not only reduces waste but also minimizes the depletion of natural resources. This study explores the application of artificial intelligence (AI) techniques to model and predict the properties of HSRC. In the past two decades, the production levels in various industries and, consequently, the amount of waste have increased significantly. Continuing this trend will undoubtedly cause irreparable damage to the environment. For this reason, engineers have been constantly seeking practical solutions for recycling industrial waste in recent years. This research utilized the results of the compressive strength of 90-day high-strength recycled concrete. The method for creating recycled concrete involved replacing sand with crushed glass and using glass powder instead of cement. Subsequently, a feedforward artificial neural network was employed to model the compressive strength results for 90 days. The regression and error values obtained indicate that this network is suitable for modeling the compressive strength data.

Keywords: high-strength recycled concrete, feedforward artificial neural network, regression, construction materials

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Authors: M. Pilgun

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The report presents the results of a study of the specifics of speech perception by actors of video hosting services on the material of urban planning conflicts. To analyze the content, the multimodal approach using neural network technologies is employed. Analysis of word associations and associative networks of relevant stimulus revealed the evaluative reactions of the actors. Analysis of the data identified key topics that generated negative and positive perceptions from the participants. The calculation of social stress and social well-being indices based on user-generated content made it possible to build a rating of road transport construction objects according to the degree of negative and positive perception by actors.

Keywords: social media, speech perception, video hosting, networks

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Authors: Bander Alzahrani, Mohammed Alreshoodi

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Information-centric networking (ICN) using architectures such as the Publish-Subscribe Internet Technology (PURSUIT) has been proposed as a new networking model that aims at replacing the current used end-centric networking model of the Internet. This emerged model focuses on what is being exchanged rather than which network entities are exchanging information, which gives the control plane functions such as routing and host location the ability to be specified according to the content items. The forwarding plane of the PURSUIT ICN architecture uses a simple and light mechanism based on Bloom filter technologies to forward the packets. Although this forwarding scheme solve many problems of the today’s Internet such as the growth of the routing table and the scalability issues, it is vulnerable to brute force attacks which are starting point to distributed- denial-of-service (DDoS) attacks. In this work, we design and analyze a novel source-routing and information delivery technique that keeps the simplicity of using Bloom filter-based forwarding while being able to deter different attacks such as denial of service attacks at the ingress of the network. To achieve this, special forwarding nodes called Edge-FW are directly attached to end user nodes and used to perform a security test for malicious injected random packets at the ingress of the path to prevent any possible attack brute force attacks at early stage. In this technique, a core entity of the PURSUIT ICN architecture called topology manager, that is responsible for finding shortest path and creating a forwarding identifiers (FId), uses a cryptographically secure hash function to create a 64-bit hash, h, over the formed FId for authentication purpose to be included in the packet. Our proposal restricts the attacker from injecting packets carrying random FIds with a high amount of filling factor ρ, by optimizing and reducing the maximum allowed filling factor ρm in the network. We optimize the FId to the minimum possible filling factor where ρ ≤ ρm, while it supports longer delivery trees, so the network scalability is not affected by the chosen ρm. With this scheme, the filling factor of any legitimate FId never exceeds the ρm while the filling factor of illegitimate FIds cannot exceed the chosen small value of ρm. Therefore, injecting a packet containing an FId with a large value of filling factor, to achieve higher attack probability, is not possible anymore. The preliminary analysis of this proposal indicates that with the designed scheme, the forwarding function can detect and prevent malicious activities such DDoS attacks at early stage and with very high probability.

Keywords: forwarding identifier, filling factor, information centric network, topology manager

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Authors: Kavian Khosravinia, Mohd Khair Hassan, Ribhan Zafira Abdul Rahman, Syed Abdul Rahman Al-Haddad

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The CAN (controller area network) bus is introduced as a multi-master, message broadcast system. The messages sent on the CAN are used to communicate state information, referred as a signal between different ECUs, which provides data consistency in every node of the system. OBD-II Dongles that are based on request and response method is the wide-spread solution for extracting sensor data from cars among researchers. Unfortunately, most of the past researches do not consider resolution and quantity of their input data extracted through OBD-II technology. The maximum feasible scan rate is only 9 queries per second which provide 8 data points per second with using ELM327 as well-known OBD-II dongle. This study aims to develop and design a programmable, and latency-sensitive vehicle data acquisition system that improves the modularity and flexibility to extract exact, trustworthy, and fresh car sensor data with higher frequency rates. Furthermore, the researcher must break apart, thoroughly inspect, and observe the internal network of the vehicle, which may cause severe damages to the expensive ECUs of the vehicle due to intrinsic vulnerabilities of the CAN bus during initial research. Desired sensors data were collected from various vehicles utilizing Raspberry Pi3 as computing and processing unit with using OBD (request-response) and direct CAN method at the same time. Two types of data were collected for this study. The first, CAN bus frame data that illustrates data collected for each line of hex data sent from an ECU and the second type is the OBD data that represents some limited data that is requested from ECU under standard condition. The proposed system is reconfigurable, human-readable and multi-task telematics device that can be fitted into any vehicle with minimum effort and minimum time lag in the data extraction process. The standard operational procedure experimental vehicle network test bench is developed and can be used for future vehicle network testing experiment.

Keywords: CAN bus, OBD-II, vehicle data acquisition, connected cars, telemetry, Raspberry Pi3

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Authors: A. Hadadi, M. Abdollahi, A. Dustmohammadi

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The growing use of DGs in distribution networks provide many advantages and also cause new problems which should be anticipated and be solved with appropriate solutions. One of the problems is transient voltage drop and short circuit in the electrical network, in the presence of distributed generation - which can lead to instability. The appearance of the short circuit will cause loss of generator synchronism, even though if it would be able to recover synchronizing mode after removing faulty generator, it will be stable. In order to increase system reliability and generator lifetime, some strategies should be planned to apply even in some situations which a fault prevent generators from separation. In this paper, one fault current limiter is installed due to prevent DGs separation from the grid when fault occurs. Furthermore, an innovative objective function is applied to determine the impedance optimal amount of fault current limiter in order to improve transient stability of distributed generation. Fault current limiter can prevent generator rotor's sudden acceleration after fault occurrence and thereby improve the network transient stability by reducing the current flow in a fast and effective manner. In fact, by applying created impedance by fault current limiter when a short circuit happens on the path of current injection DG to the fault location, the critical fault clearing time improve remarkably. Therefore, protective relay has more time to clear fault and isolate the fault zone without any instability. Finally, different transient scenarios of connection plan sustainability of small scale synchronous generators to the distribution network are presented.

Keywords: critical clearing time, fault current limiter, synchronous generator, transient stability, transient states

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Authors: John Segars

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Built out of a real-world need to have better, more detailed, asset and infrastructure documentation, this project will lay out the case for using the database functionality of ArcGIS as a tool to track and maintain infrastructure location, status, maintenance and serviceability. Workflows and processes will be presented and detailed which may be applied to an organizations’ infrastructure needs that might allow them to make use of the robust tools which surround the ArcGIS platform. The end result is a value-added information system framework with a geographic component e.g., the spatial location of various I.T. assets, a detailed set of records which not only documents location but also captures the maintenance history for assets along with photographs and documentation of these various assets as attachments to the numerous feature class items. In addition to the asset location and documentation benefits, the staff will be able to log into the devices and pull SNMP (Simple Network Management Protocol) based query information from within the user interface. The entire collection of information may be displayed in ArcGIS, via a JavaScript based web application or via queries to the back-end database. The project is applicable to all organizations which maintain an IT infrastructure but specifically targets post-secondary educational institutions where access to ESRI resources is generally already available in house.

Keywords: ESRI, GIS, infrastructure, network documentation, PostgreSQL

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Simiao Ren, En Wei

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Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

Procedia PDF Downloads 101

Authors: N. R. Harris, J. Curry

Abstract:

This paper describes the construction and operation of an experimental LoRaWAN network surrounding the University of Southampton in the United Kingdom. Following successful installation, an experimental node design is built and characterised, with particular emphasis on radio range. Several configurations are investigated, including different data rates, and varying heights of node. It is concluded that although range can be great (over 8 km in this case), environmental topology is critical. However, shorter range implementations, up to about 2 km in an urban environment, are relatively insensitive although care is still needed. The example node and the relatively simple base station reported demonstrate that LoraWan can be a very low cost and practical solution to Internet of Things type applications for distributed monitoring systems with sensors spread over distances of several km.

Keywords: long-range, wireless, sensor, network

Procedia PDF Downloads 137

Authors: Tesfaye Fentaw Nigatu, Alexander Trupp, Teh Pek Yen

Abstract:

Research on museums and the experiences of visitors has flourished in recent years, especially after museums became centers of edutainment beyond preserving heritage resources. This paper aims to comprehensively understand the changes, continuities, and future research development directions of museum visitors’ experiences. To identify current research trends, the paper summarizes and analyses research article publications from 1986 to 2023 on museum visitors' experiences. Bibliometric analysis software VOSviewer and Harzing POP (Publish or Perish) were used to analyze 407 academic articles. The articles were generated from the Scopus database. The study attempted to map new insights for future scholars and academics to expand the scope of museum visitors’ experience studies by analyzing keywords, citation patterns, influential articles in the field, publication trends, collaborations between authors, institutions, and clusters of highly cited articles. Accessibility to museums, social media usage within museums, aesthetics in museum settings, mixed reality experiences, sustainability issues, and emotions have emerged as key research areas in the study of museum visitors' experiences. The results benefit stakeholders and researchers in advancing the collective progress of considering recent research trends to stay informed about the latest developments and breakthroughs in the global academic landscape and visitors’ experiences development in the museum.

Keywords: bibliometric analysis, museum, network analysis, visitors’ experiences, visual analysis

Procedia PDF Downloads 68

Authors: Zheng Zhihao

Abstract:

Recently, Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation, and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an new graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. wild range, and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures.

Keywords: knowledge graph, graph neural networks, structural adaptive receptive fields, information aggregation

Procedia PDF Downloads 33

Authors: Farshad Zeinali, Sajedeh Norouzi, Nader Mokari, Eduard Jorswieck

Abstract:

The capacity of fifth-generation (5G) vehicle-to-everything (V2X) networks poses significant challenges. To ad- dress this challenge, this paper utilizes New Radio (NR) and New Radio Unlicensed (NR-U) networks to develop a heterogeneous vehicular network (HetNet). We propose a new framework, named joint BS assignment and resource allocation (JBSRA) for mobile V2X users and also consider coexistence schemes based on flexible duty cycle (DC) mechanism for unlicensed bands. Our objective is to maximize the average throughput of vehicles while guaranteeing the WiFi users' throughput. In simulations based on deep reinforcement learning (DRL) algorithms such as deep deterministic policy gradient (DDPG) and deep Q network (DQN), our proposed framework outperforms existing solutions that rely on fixed DC or schemes without consideration of unlicensed bands.

Keywords: vehicle-to-everything (V2X), resource allocation, BS assignment, new radio (NR), new radio unlicensed (NR-U), coexistence NR-U and WiFi, deep deterministic policy gradient (DDPG), deep Q-network (DQN), joint BS assignment and resource allocation (JBSRA), duty cycle mechanism

Procedia PDF Downloads 103