Search results for: design of network governance

Authors: Rima Al-Awadhi, Abdul Jaleel Tharayil

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On a regular basis, KOC engages into various types of Projects. However, due to very nature and complexity involved, each project experience generates a lot of ‘learnings’ that need to be factored into while drafting a new contract and thus avoid repeating the same mistakes. But, many a time these learnings are localized and remain as tacit leading to scope re-work, larger cycle time, schedule overrun, adjustment orders and claims. Also, these experiences are not readily available to new employees leading to steep learning curve and longer time to competency. This is to share our experience in designing and implementing a process driven architecture for the ‘lessons learnt’ knowledge sharing framework in KOC. It high-lights the ‘lessons learnt’ sharing process adopted, integration with the organizational processes, governance framework, the challenges faced and learning from our experience in implementing a ‘lessons learnt’ framework.

Keywords: lessons learnt, knowledge transfer, knowledge sharing, successful practices, Lessons Learnt Workshop, governance framework

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Authors: Grace Titilayo, Kolawole-Amao

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Influence and authority of law within society should be respected by all and sundry regardless of individual status. Rule of law implies that every citizen is subject to the law. In a society governed by the rule of law, government and its officials and agents are also held subject to and accountable under the law. Law should not be employed to suit individual tenets. Where the rule of law operates, the government is the government of law and not of men. Corruption is a factor that kills the growth of the rule of law. Where corruption flourishes, the rule of law fails, simply put, corruption is a threat to the rule of law. It bastardized and undermines the rule of law and good governance principles - where men rule at their discretion rather than the use of the rule of law which makes governance processes ineffective. Corruption is prevalent all over the world, and has extremely far reaching effects. Many of the world’s greatest challenges have been amplified by corruption, for example poverty, unequal distribution of wealth and resources, and world hunger and it weakens the application and the entrenchment of the rule of law. It saps citizens' trust in their governments and undercuts government credibility. This paper will discuss the rule of law in the present democratic system in Nigeria, the impact of corruption on the rule of law in Nigeria and how corruption undermines and subverts the entrenchment of the rule of law in the present day Nigeria.

Keywords: rule of law, corruption, Nigeria, influence, authority

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Authors: Mahdiar Hosseinghadiry, Razali Ismail, F. Fotovati

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One of the most important and challenging tasks in testing network-on-chip based system-on-chips (NoC based SoCs) is to verify the communication entity. It is important because of its usage for transferring both data packets and test patterns for intellectual properties (IPs) during normal and test mode. Hence, ensuring of NoC reliability is required for reliable IPs functionality and testing. On the other hand, it is challenging due to the required time to test it and the way of transferring test patterns from the tester to the NoC components. In this paper, two testing techniques for mesh-based NoC interconnections are proposed. The first one is based on one-by-one testing and the second one divides NoC interconnects into three parts, horizontal links of switches in even columns, horizontal links of switches in odd columns and all vertical. A design for testability (DFT) architecture is represented to send test patterns directly to each switch under test and also support the proposed testing techniques by providing a loopback path in each switch. The simulation results shows the second proposed testing mechanism outperforms in terms of test time because this method test all the interconnects in only three phases, independent to the number of existed interconnects in the network, while test time of other methods are highly dependent to the number of switches and interconnects in the NoC.

Keywords: on chip, interconnection testing, horizontal-vertical testing, enhanced unicast

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Yusuf Adebayo Oduwole

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The evaluation of blockchain technology's potential effects on the accounting and auditing fields is the main objective of this essay. It also adds to the existing body of work by examining how these practices alter technological concerns, including cryptocurrency accounting, regulation, governance, accounting practices, and technical challenges. Examples of this advancement include the growth of the concept of blockchain and its application in accounting. This technology is being considered one of the digital revolutions that could disrupt the world and civilization as it can transfer large volumes of virtual currencies like cryptocurrencies with the help of a third party. The basis for this research is a systematic review of the articles using Vosviewer to display and reflect on the bibliometric information of the articles accessible on the Scopus database. Also, as the practice of using blockchain technology in the field of accounting and auditing is still in its infancy, it may be useful to carry out a more thorough analysis of any implications for accounting and auditing regarding aspects of governance, regulation, and cryptocurrency that have not yet been discussed or addressed to any significant extent. The main findings on the relationship between blockchain and accounting show that the application of smart contracts, such as triple-entry accounting, has increased the quality of accounting records as well as reliance on the information available. This results in fewer cyclical assignments, no need for resolution, and real-time accounting, among others. Thereby, to integrate blockchain through a computer system, one must continuously learn and remain naive when using blockchain-integrated accounting software. This includes learning about how cryptocurrencies are accounted for and regulated. In this study, three original and contributed efforts are presented. To offer a transparent view of the state of previous relevant studies and research works in accounting and auditing that focus on blockchain, it begins by using bibliographic visibility analysis and a Scopus narrative analysis. Second, it highlights legislative, governance, and ethical concerns, such as education, where it tackles the use of blockchain in accounting and auditing. Lastly, it examines the impact of blockchain technologies on the accounting recognition of cryptocurrencies. Users of the technology should, therefore, take their time and learn how it works, as well as keep abreast of the different developments. In addition, the accounting industry must integrate blockchain certification and practice, most likely offline or as part of university education for those intending to become auditors or accountants.

Keywords: blockchain, crypto assets, governance, regulation & smart contracts

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Authors: Cheng Fang, Lingwei Quan, Cunyue Lu

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Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.

Keywords: computer vision, pose estimation, pose tracking, Siamese network

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Authors: Claude Nsobya, Alice Moncaster, Karen Potter, Jed Ramsay

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The shift in Flood Risk Governance (FRG) has moved away from traditional approaches that solely relied on centralized decision-making and structural flood defenses. Instead, there is now the adoption of integrated flood risk management measures that involve various actors and stakeholders. This new approach emphasizes people-centered approaches, including adaptation and learning. This shift to a diversity of FRG approaches has been identified as a significant factor in enhancing resilience. Resilience here refers to a community's ability to withstand, absorb, recover, adapt, and potentially transform in the face of flood events. It is argued that if the FRG merely focused on the conventional 'fighting the water' - flood defense - communities would not be resilient. The move to these people-centered approaches also implies that communities will be more involved in FRG. It is suggested that effective flood risk governance influences resilience through meaningful community involvement, and effective community engagement is vital in shaping community resilience to floods. Successful community participation not only uses context-specific indigenous knowledge but also develops a sense of ownership and responsibility. Through capacity development initiatives, it can also raise awareness and all these help in building resilience. Recent Flood Risk Management (FRM) projects have thus had increasing community involvement, with varied conceptualizations of such community engagement in the academic literature on FRM. In the context of overland floods, there has been a substantial body of literature on Flood Risk Governance and Management. Yet, groundwater flooding has gotten little attention despite its unique qualities, such as its persistence for weeks or months, slow onset, and near-invisibility. There has been a little study in this area on how successful community involvement in Flood Risk Governance may improve community resilience to groundwater flooding in particular. This paper focuses on a case study of a flood risk management project in the United Kingdom. Buckinghamshire Council is leading Project Groundwater, which is one of 25 significant initiatives sponsored by England's Department for Environment, Food and Rural Affairs (DEFRA) Flood and Coastal Resilience Innovation Programme. DEFRA awarded Buckinghamshire Council and other councils 150 million to collaborate with communities and implement innovative methods to increase resilience to groundwater flooding. Based on a literature review, this paper proposes a new paradigm for effective community engagement in Flood Risk Governance (FRG). This study contends that effective community participation can have an impact on various resilience capacities identified in the literature, including social capital, institutional capital, physical capital, natural capital, human capital, and economic capital. In the case of social capital, for example, successful community engagement can influence social capital through the process of social learning as well as through developing social networks and trust values, which are vital in influencing communities' capacity to resist, absorb, recover, and adapt. The study examines community engagement in Project Groundwater using surveys with local communities and documentary analysis to test this notion. The outcomes of the study will inform community involvement activities in Project Groundwater and may shape DEFRA policies and guidelines for community engagement in FRM.

Keywords: flood risk governance, community, resilience, groundwater flooding

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Monica Violeta Achim, Sorin Nicolae Borlea

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This aim of this work is to stress the needs for enhancing the role of forensic accounting in fighting economic and financial crime, in the context of the new international regulation movements in this area enhanced by the International Federation of Accountants (IFAC). Corruption, money laundering, tax evasion and other frauds significant hamper the economic growth and human development and, ultimately, the UN Sustainable Development Goals. The present paper also stresses the role of good governance in fighting the frauds, in order to achieve the most suitable sustainable development of the society. In this view, we made a bibliometric systematic review on forensic accounting and its contribution towards fraud detection and prevention and theirs relationship with good governance and Sustainable Developments Goals (SDGs). In this view, two powerful bibliometric visual software tools, VosViewer and CiteSpace are used in order to analyze published papers identifies in Scopus and Web of Science databases over the time. Our findings reveal the main red flags identified in literature as used tools by forensic accounting, the evolution in time of the interest of the topic, the distribution in space among world countries and connectivity with patterns of a good governance. Visual designs and scientific maps are useful to show these findings, in a visual way. Our findings are useful for managers and policy makers to provide important avenues that may help in reaching the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, in the area of using forensic accounting in preventing frauds.

Keywords: forensic accounting, frauds, red flags, SDGs

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Authors: Y. B. Kirankumar, J. D. Mallapur

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Wireless Sensor Network (WSN) is an emerging technology, which has great invention for various low cost applications both for mass public as well as for defence. The wireless sensor communication technology allows random participation of sensor nodes with particular applications to take part in the network, which results in most of the uncovered simulation area, where fewer nodes are located at far distances. The drawback of such network would be that the additional energy is spent by the nodes located in a pattern of dense location, using more number of nodes for a smaller distance of communication adversely in a region with less number of nodes and additional energy is again spent by the source node in order to transmit a packet to neighbours, thereby transmitting the packet to reach the destination. The proposed work is intended to develop Energy Efficient Node Placement Algorithm (EENPA) in order to place the sensor node efficiently in simulated area, where all the nodes are equally located on a radial path to cover maximum area at equidistance. The total energy consumed by each node compared to random placement of nodes is less by having equal burden on fewer nodes of far location, having distributed the nodes in whole of the simulation area. Calculating the network lifetime also proves to be efficient as compared to random placement of nodes, hence increasing the network lifetime, too. Simulation is been carried out in a qualnet simulator, results are obtained on par with random placement of nodes with EENP algorithm.

Keywords: energy, WSN, wireless sensor network, energy approach

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Authors: Gaya Bin Noon, Thoko Hanjahanja-Phiri, Laura Xavier Fadrique, Plinio Pelegrini Morita, Hélène Vaillancourt, Jennifer Teague, Tania Donovska

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Active Assisted Living (AAL) refers to systems designed to improve the quality of life, aid in independence, and create healthier lifestyles for care recipients. As the population ages, there is a pressing need for non-intrusive, continuous, adaptable, and reliable health monitoring tools to support aging in place. AAL has great potential to support these efforts with the wide variety of solutions currently available, but insufficient efforts have been made to address concerns arising from the integration of AAL into care. The purpose of this research was to (1) explore the integration of AAL technologies and data into the clinical pathway, and (2) map data access and governance for AAL technology in order to develop standards for use by policy-makers, technology manufacturers, and developers of smart communities for seniors. This was done through four successive research phases: (1) literature search to explore existing work in this area and identify lessons learned; (2) modeling of the continuum of care; (3) adapting a framework for data governance into the AAL context; and (4) interviews with stakeholders to explore the applicability of previous work. Opportunities for standards found in these research phases included a need for greater consistency in language and technology requirements, better role definition regarding who can access and who is responsible for taking action based on the gathered data, and understanding of the privacy-utility tradeoff inherent in using AAL technologies in care settings.

Keywords: active assisted living, aging in place, internet of things, standards

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Lee Soo-Kyoung, Kim Young-Su

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This study established a network related to metabolic syndrome intervention by conducting a social network analysis of titles, keywords, and abstracts, and it identified emerging topics of research. It visualized an interconnection between critical keywords and investigated their frequency of appearance to construe the trends in metabolic syndrome intervention measures used in studies conducted over 38 years (1979–2017). It examined a collection of keywords from 8,285 studies using text rank analyzer, NetMiner 4.0. The analysis revealed 5 groups of newly emerging keywords in the research. By examining the relationship between keywords with reference to their betweenness centrality, the following clusters were identified. Thus if new researchers refer to existing trends to establish the subject of their study and the direction of the development of future research on metabolic syndrome intervention can be predicted.

Keywords: intervention, metabolic syndrome, network analysis, research, the trend

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Authors: Sanat Ramesh, Tarun Dutt, Abhilasha Aswal, Anushka Chandrababu, G. N. Srinivasa Prasanna

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Optimal Schedule Generation for a large national railway network operating thousands of passenger trains with tens of thousands of kilometers of track is a grand computational challenge in itself. We present heuristics based on a Mixed Integer Program (MIP) formulation for local optimization. These methods provide flexibility in scheduling new trains with varying speed and delays and improve utilization of infrastructure. We propose methods that provide a robust solution with hundreds of trains being scheduled over a portion of the railway network without significant increases in delay. We also provide techniques to validate the nominal schedules thus generated over global correlated variations in travel times thereby enabling us to detect conflicts arising due to delays. Our validation results which assume only the support of the arrival and departure time distributions takes an order of few minutes for a portion of the network and is computationally efficient to handle the entire network.

Keywords: mixed integer programming, optimization, railway network, train scheduling

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Nopsarun Vannasirikul

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The purposes of this research paper were to study the current situations of community development, women’s potentials, women’s participation, network, and government policy as well as to study the factors influencing women’s potentials, women’s participation, network, and government policy that have on the community development. The population included the women age of 18 years old who were living in the communities of Bangkok areas. This study was a mix research method of quantitative and qualitative method. A simple random sampling method was utilized to obtain 400 sample groups from 50 districts of Bangkok and to perform data collection by using questionnaire. Also, a purposive sampling method was utilized to obtain 12 informants for an in-depth interview to gain an in-sight information for quantitative method.

Keywords: community development, participation, network, women’s right, management

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Authors: Hanbhin Ryu, Ilkwon Yun

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Automotive industry targets to provide an improvement in safety and convenience through realizing fully autonomous vehicle. For partially realizing fully automated driving, Current vehicles already feature varieties of advanced driver assistance system (ADAS) for safety and infotainment systems for the driver’s convenience. This paper presents Cloud Connected Vehicle (CCV) which connected vehicles with cloud data center via the access network to control the vehicle for achieving next autonomous driving form and describes its features. This paper also describes the shortcoming of the existing In-Vehicle Network (IVN) to be a next generation IVN of CCV and organize the 802.3 Ethernet, the next generation of IVN, related research issue to verify the feasibility of using Ethernet. At last, this paper refers to additional considerations to adopting Ethernet-based IVN for CCV.

Keywords: autonomous vehicle, cloud connected vehicle, ethernet, in-vehicle network

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Authors: Amrah Maryam

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The emergence of online social networks (OSNs) has transformed the way we pursue and share information. On the one hand, OSNs provide great ease for the spreading of positive information while, on the other hand, they may also become a channel for the spreading of malicious rumors and misinformation throughout the social network. Thus, to assure the trustworthiness of OSNs to its users, it is of vital importance to detect the misinformation propagation in the network by placing network monitors. In this paper, we aim to place monitors near the suspected nodes with the intent to limit the diffusion of misinformation in the social network, and then we also detect the most significant nodes in the network for propagating true information in order to minimize the effect of already diffused misinformation. Thus, we initiate two heuristic monitor placement using articulation points and truth propagation using eigenvector centrality. Furthermore, to provide real-time workings of the system, we integrate both the monitor placement and truth propagation entities as well. To signify the effectiveness of the approaches, we have carried out the experiment and evaluation of Stanford datasets of online social networks.

Keywords: online social networks, monitor placement, independent cascade model, spread of misinformation

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Htay Htay Yi, Zin May Aye

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Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: false negative rate, intrusion detection system, machine learning methods, performance

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Authors: Dongjae Kang, Sokho Son, Jinmee Kim

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Cloud service brokering is a new service paradigm that provides interoperability and portability of application across multiple Cloud providers. In this paper, we designed cloud service brokerage system, any broker, supporting integrated service provisioning and SLA based service life cycle management. For the system design, we introduce the system concept and whole architecture, details of main components and use cases of primary operations in the system. These features ease the Cloud service provider and customer’s concern and support new Cloud service open market to increase cloud service profit and prompt Cloud service echo system in cloud computing related area.

Keywords: cloud service brokerage, multiple Clouds, Integrated service provisioning, SLA, network service

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Authors: H. Gorine, M. Ramadan Elmezughi

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In this paper, we investigate security issues and challenges facing researchers in wireless sensor networks and countermeasures to resolve them. The broadcast nature of wireless communication makes Wireless Sensor Networks prone to various attacks. Due to resources limitation constraint in terms of limited energy, computation power and memory, security in wireless sensor networks creates different challenges than wired network security. We will discuss several attempts at addressing the issues of security in wireless sensor networks in an attempt to encourage more research into this area.

Keywords: wireless sensor networks, network security, light weight encryption, threats

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Atefeh Omidkhah Kharashtomi, Rasoul Hedayat Nejad, Saeed Bakhtiyari

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Circulation paths and indoor connection network of the building play an important role both in the daily operation of the building and during evacuation in emergency situations. The degree of legibility of the paths for navigation inside the building has a deep connection with the perceptive and cognitive system of human, and the way the surrounding environment is being perceived. Human perception of the space is based on the sensory systems in a three-dimensional environment, and non-linearly, so it is necessary to avoid reducing its representations in architectural design as a two-dimensional and linear issue. Today, the advances in the field of virtual reality (VR) technology have led to various applications, and architecture and building science can benefit greatly from these capabilities. Especially in cases where the design solution requires a detailed and complete understanding of the human perception of the environment and the behavioral response, special attention to VR technologies could be a priority. Way-finding in the indoor circulation network is a proper example for such application. Success in way-finding could be achieved if human perception of the route and the behavioral reaction have been considered in advance and reflected in the architectural design. This paper discusses the VR technology applications for the way-finding improvements in indoor engineering of the building. In a systematic review, with a database consisting of numerous studies, firstly, four categories for VR applications for circulation way-finding have been identified: 1) data collection of key parameters, 2) comparison of the effect of each parameter in virtual environment versus real world (in order to improve the design), 3) comparing experiment results in the application of different VR devices/ methods with each other or with the results of building simulation, and 4) training and planning. Since the costs of technical equipment and knowledge required to use VR tools lead to the limitation of its use for all design projects, priority buildings for the use of VR during design are introduced based on case-studies analysis. The results indicate that VR technology provides opportunities for designers to solve complex buildings design challenges in an effective and efficient manner. Then environmental parameters and the architecture of the circulation routes (indicators such as route configuration, topology, signs, structural and non-structural components, etc.) and the characteristics of each (metrics such as dimensions, proportions, color, transparency, texture, etc.) are classified for the VR way-finding experiments. Then, according to human behavior and reaction in the movement-related issues, the necessity of scenario-based and experiment design for using VR technology to improve the design and receive feedback from the test participants has been described. The parameters related to the scenario design are presented in a flowchart in the form of test design, data determination and interpretation, recording results, analysis, errors, validation and reporting. Also, the experiment environment design is discussed for equipment selection according to the scenario, parameters under study as well as creating the sense of illusion in the terms of place illusion, plausibility and illusion of body ownership.

Keywords: virtual reality (VR), way-finding, indoor, circulation, design

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Authors: Mostafa Sefidgar, Kaamran Raahemifar, Hossein Bazmara, Madjid Soltani

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The computational methods provide condition for investigation related to the process of drug delivery, such as convection and diffusion of drug in extracellular matrices, and drug extravasation from microvascular. The information of this process clarifies the mechanisms of drug delivery from the injection site to absorption by a solid tumor. In this study, an advanced numerical method is used to solve fluid flow and solute transport equations simultaneously to show how capillary network structure induced by tumor affects drug delivery. The effect of heterogeneous capillary network induced by tumor on interstitial fluid flow and drug delivery is investigated by this multi scale method. The sprouting angiogenesis model is used for generating capillary network induced by tumor. Fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network and fluid flow in normal and tumor tissues. The Starling’s law is used for closing this system of equations and coupling the intravascular and extravascular flows. Finally, convection-diffusion-reaction equation is used to simulate drug delivery. The dynamic approach which changes the capillary network structure based on signals sent by hemodynamic and metabolic stimuli is used in this study for more realistic assumption. The study indicates that drug delivery to solid tumors depends on the tumor induced capillary network structure. The dynamic approach generates the irregular capillary network around the tumor and predicts a higher interstitial pressure in the tumor region. This elevated interstitial pressure with irregular capillary network leads to a heterogeneous distribution of drug in the tumor region similar to in vivo observations. The investigation indicates that the drug transport properties have a significant role against the physiological barrier of drug delivery to a solid tumor.

Keywords: solid tumor, physiological barriers to drug delivery, angiogenesis, microvascular network, solute transport

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Authors: Jeaha Yang, Ahmed Khan, Donna L. Rodela, Mohammed A. Qaudeer

Abstract:

Traditional supply chain silos still exist today due to the difficulty of establishing trust between various partners and technological barriers across industries. Companies lose opportunities and revenue and inadvertently make poor business decisions resulting in further challenges. Blockchain technology can bring a new level of transparency through sharing information with a distributed ledger in a decentralized manner that creates a basis of trust for business. Blockchain is a loosely coupled, hub-style communication network in which trading partners can work indirectly with each other for simpler integration, but they work together through the orchestration of their supply chain operations under a coherent process that is developed jointly. A Blockchain increases efficiencies, lowers costs, and improves interoperability to strengthen and automate the supply chain management process while all partners share the risk. Blockchain ledger is built to track inventory lifecycle for supply chain transparency and keeps a journal of inventory movement for real-time reconciliation. State design patterns are used to capture the life cycle (behavior) of inventory management as a state machine for a common, transparent and coherent process which creates an opportunity for trading partners to become more responsive in terms of changes or improvements in process, reconcile discrepancies, and comply with internal governance and external regulations. It enables end-to-end, inter-company visibility at the unit level for more accurate demand planning with better insight into order fulfillment and replenishment.

Keywords: supply chain management, inventory trace-ability, perpetual inventory system, inventory lifecycle, blockchain, inventory consignment, supply chain transparency, digital thread, demand planning, hyper ledger fabric

Procedia PDF Downloads 87

Authors: Wilfred Fritz

Abstract:

Grid connected solar PV inverters need to be compliant to standard regulations regarding unwanted harmonic generation. This paper gives an introduction to harmonics, solar PV inverter voltage regulation and balancing through compensation and investigates the behaviour of harmonic generation at different power levels. Practical measurements of harmonics and power levels with a power quality data logger were made, on a test network at a university in Germany. The test setup and test results are discussed. The major finding was that between the morning and afternoon load peak windows when the PV inverters operate under low solar insolation and low power levels, more unwanted harmonics are generated. This has a huge impact on the power quality of the grid as well as capital and maintenance costs. The design of a single-tuned harmonic filter towards harmonic mitigation is presented.

Keywords: harmonics, power quality, pulse width modulation, total harmonic distortion

Procedia PDF Downloads 234

Authors: Sayed Hadi Sadeghi

Abstract:

This research study examines the current state of support services for e-network practice in an Australian and an American university. It identifies information that will be of assistance to Australian and American universities to improve their existing online programs. The study investigated the two universities using a quantitative methodological approach. Participants were students, lecturers and admins of universities engaged with online courses and learning management systems. The support services for e-network practice variables, namely academic support services, administrative support and technical support, were investigated for e-practice. Evaluations of e-network support service and its sub factors were above average and excellent in both countries, although the American admins and lecturers tended to evaluate this factor higher than others did. Support practice was evaluated higher by all participants of an American university than by Australians. One explanation for the results may be that most suppliers of the Australian university e-learning system were from eastern Asian cultural backgrounds with a western networking support perspective about e-learning.

Keywords: support services, e-Network practice, Australian universities, United States universities

Procedia PDF Downloads 160

Authors: Jan Schmidt, Pierre Köhring

Abstract:

Permanently excited machines are set up with magnets that are made of highly energetic magnetic materials. Inherently, the permanent magnets warm up while the machine is operating. With an increasing temperature, the electromotive force and hence the degree of efficiency decrease. The reasons for this are slot harmonics and distorted armature currents arising from frequency inverter operation. To prevent or avoid demagnetizing of the permanent magnets it is necessary to ensure that the magnets do not excessively heat up. Demagnetizations of permanent magnets are irreversible and a breakdown of the electrical machine is inevitable. For the design of an electrical machine, the knowledge of the behavior of heating under operating conditions of the permanent magnet is of crucial importance. Therefore, a calculation model is presented with which the machine designer can easily calculate the eddy current losses in the magnetic material.

Keywords: analytical model, eddy current, losses, lattice network, permanent magnet

Procedia PDF Downloads 416