Search results for: network model

Authors: Ahmidah Elgali

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SCADA is the abbreviation for "Administrative Control And Data Acquisition." SCADA frameworks are generally utilized in industry for administrative control and information securing of modern cycles. Regular SCADA frameworks use PC, journal, slim client, and PDA as a client. In this paper, a Java-empowered cell phone has been utilized as a client in an example SCADA application to show and regulate the place of an example model crane. The paper presents a genuine execution of the online controlling of the model crane through a cell phone. The remote correspondence between the cell phone and the SCADA server is performed through a base station by means of general parcel radio assistance GPRS and remote application convention WAP. This application can be used in industrial sites in areas that are likely to be exposed to a security emergency (like terrorist attacks) which causes the sudden exit of the operators; however, no time to perform the shutdown procedures for the plant. Hence this application allows shutting down units and equipment remotely by mobile and so avoids damage and losses.

Keywords: control, industrial, mobile, network, remote, SCADA

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Authors: Aziz Ahmad, Guillermo Ramirez-Prado

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Governments around the world encourage the use of solar water heating in residential houses due to the low maintenance requirements and efficiency of the solar collector water heating systems. The aim of this work is to study a domestic solar water heating system in a residential building to develop a model of the entire solar water heating system including flat-plate solar collector and storage tank. The proposed model is adaptable to any households and location. The model can be used to test different types of controllers and can provide efficiency as well as economic analysis. The proposed model is based on the heat and mass transfer equations along with assumptions applied in the model which can be modified for a variety of different solar water heating systems and sizes. Simulation results of the model were compared with the actual system which shows similar trends.

Keywords: solar thermal systems, solar water heating, solar collector model, hot water tank model, solar controllers

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Authors: Chakib Jerry, Mounir Jerry

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In this paper, a mathematical model of infested honey bees colonies is formulated in order to investigate Colony Collapse Disorder in a honeybee colony. CCD, as it is known, is a major problem on honeybee farms because of the massive decline in colony numbers. We introduce to the model a control variable which represents forager protection. We study the controlled model to derive conditions under which the bee colony can fight off epidemic. Secondly we study the problem of minimizing prevention cost under model’s dynamics constraints.

Keywords: honey bee, disease transmission model, disease control honeybees, optimal control

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Authors: M. A. T. AlSudairi, T. G. K. Vasista

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The concept of social media is becoming the top of the agenda for many business executives and public sector executives today. Decision makers as well as consultants, try to identify ways in which firms and enterprises can make profitable use of social media and network related applications such as Wikipedia, Face book, YouTube, Google+, Twitter. While it is fun and useful to participating in this media and network for achieving the communication effectively and efficiently, semantic and sentiment analysis and interpretation becomes a crucial issue. So, the objective of this paper is to provide literature review on social media, network and related technology related to semantics and sentiment or opinion analysis covering business and governance perspectives. In this regard, a case study on the use and adoption of Social media in Saudi Arabia has been discussed. It is concluded that semantic web technology play a significant role in analyzing the social networks and social media content for extracting the interpretational knowledge towards strategic decision support.

Keywords: CRASP methodology, formative assessment, literature review, semantic web services, social media, social networks

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Authors: C. B. Masera, M. Imprialou, L. Budd, C. Morton

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Even though signalised intersections are necessary for urban road traffic management, they can act as bottlenecks and disrupt traffic operations. Interrupted traffic flow causes congestion, delays, stop-and-go conditions (i.e. excessive acceleration/deceleration) and longer journey times. Vehicle and infrastructure connectivity offers the potential to provide improved new services with additional functions of assisting drivers. This paper focuses on one of the applications of vehicle-to-infrastructure communication namely Green Light Optimal Speed Advisory (GLOSA). To assess the effectiveness of GLOSA in the urban road network, an integrated microscopic traffic simulation framework is built into VISSIM software. Vehicle movements and vehicle-infrastructure communications are simulated through the interface of External Driver Model. A control algorithm is developed for recommending an optimal speed that is continuously updated in every time step for all vehicles approaching a signal-controlled point. This algorithm allows vehicles to pass a traffic signal without stopping or to minimise stopping times at a red phase. This study is performed with all connected vehicles at 100% penetration rate. Conventional vehicles are also simulated in the same network as a reference. A straight road segment composed of two opposite directions with two traffic lights per lane is studied. The simulation is implemented under 150 vehicles per hour and 200 per hour traffic volume conditions to identify how different traffic densities influence the benefits of GLOSA. The results indicate that traffic flow is improved by the application of GLOSA. According to this study, vehicles passed through the traffic lights more smoothly, and waiting times were reduced by up to 28 seconds. Average delays decreased for the entire network by 86.46% and 83.84% under traffic densities of 150 vehicles per hour per lane and 200 vehicles per hour per lane, respectively.

Keywords: connected vehicles, GLOSA, intelligent transport systems, vehicle-to-infrastructure communication

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Authors: Vangelis Papadimitropoulos

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This paper is part of the research program “Techno-Social Innovation in the Collaborative Economy”, funded by the Hellenic Foundation for Research and Innovation (H.F.R.I.) for the years 2022-2024. The paper showcases the Open Food Network (OFN) as an open-sourced digital platform supporting short food supply chains in local agricultural production and consumption. The paper outlines the research hypothesis, the theoretical framework, and the methodology of research as well as the findings and conclusions. Research hypothesis: The model of open cooperativism as a vehicle for systemic change in the agricultural sector. Theoretical framework: The research reviews the OFN as an illustrative case study of the three-zoned model of open cooperativism. The OFN is considered a paradigmatic case of the model of open cooperativism inasmuch as it produces commons, it consists of multiple stakeholders including ethical market entities, and it is variously supported by local authorities across the globe, the latter prefiguring the mini role of a partner state. Methodology: Research employs Ernesto Laclau and Chantal Mouffe’s discourse analysis -elements, floating signifiers, nodal points, discourses, logics of equivalence and difference- to analyse the breadth of empirical data gathered through literature review, digital ethnography, a survey, and in-depth interviews with core OFN members. Discourse analysis classifies OFN floating signifiers, nodal points, and discourses into four themes: value proposition, governance, economic policy, and legal policy. Findings: OFN floating signifiers align around the following nodal points and discourses: “digital commons”, “short food supply chains”, “sustainability”, “local”, “the elimination of intermediaries” and “systemic change”. The current research identifies a lack of common ground of what the discourse of “systemic change” signifies on the premises of the OFN’s value proposition. The lack of a common mission may be detrimental to the formation of a common strategy that would be perhaps deemed necessary to bring about systemic change in agriculture. Conclusions: Drawing on Laclau and Mouffe’s discourse theory of hegemony, research introduces a chain of equivalence by aligning discourses such as “agro-ecology”, “commons-based peer production”, “partner state” and “ethical market entities” under the model of open cooperativism, juxtaposed against the current hegemony of neoliberalism, which articulates discourses such as “market fundamentalism”, “privatization”, “green growth” and “the capitalist state” to promote corporatism and entrepreneurship. Research makes the case that for OFN to further agroecology and challenge the current hegemony of industrial agriculture, it is vital that it opens up its supply chains into equivalent sectors of the economy, civil society, and politics to form a chain of equivalence linking together ethical market entities, the commons and a partner state around the model of open cooperativism.

Keywords: sustainability, the digital commons, open cooperativism, innovation

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Authors: T. Benazzouz, K. Auhmani

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This paper presents a Hospital 4.0 Maturity Assessment Model based on the Industry 4.0 concepts. The self-assessment model defines current and target states of digital transformation by considering multiple aspects of a hospital and a healthcare supply chain. The developed model was validated and evaluated on real-life cases. The resulting model consisted of 5 domains: Technology, Strategy 4.0, Human resources 4.0 & Culture 4.0, Supply chain 4.0 management, and Patient journeys management. Each domain is further divided into several sub-domains, totally 34 sub-domains are identified, that reflect different facets of a hospital 4.0 mature organization.

Keywords: hospital 4.0, Industry 4.0, maturity assessment model, supply chain 4.0, patient

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Authors: Rakesh Kumar, A. K. Ghosh

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The paper presents the modeling of linear and nonlinear longitudinal aerodynamics using real flight data of Hansa-3 aircraft gathered at low and high angles of attack. The Neural-Gauss-Newton (NGN) method has been applied to model the linear and nonlinear longitudinal dynamics and estimate parameters from flight data. Unsteady aerodynamics due to flow separation at high angles of attack near stall has been included in the aerodynamic model using Kirchhoff’s quasi-steady stall model. NGN method is an algorithm that utilizes Feed Forward Neural Network (FFNN) and Gauss-Newton optimization to estimate the parameters and it does not require any a priori postulation of mathematical model or solving of equations of motion. NGN method was validated on real flight data generated at moderate angles of attack before application to the data at high angles of attack. The estimates obtained from compatible flight data using NGN method were validated by comparing with wind tunnel values and the maximum likelihood estimates. Validation was also carried out by comparing the response of measured motion variables with the response generated by using estimates a different control input. Next, NGN method was applied to real flight data generated by executing a well-designed quasi-steady stall maneuver. The results obtained in terms of stall characteristics and aerodynamic parameters were encouraging and reasonably accurate to establish NGN as a method for modeling nonlinear aerodynamics from real flight data at high angles of attack.

Keywords: parameter estimation, NGN method, linear and nonlinear, aerodynamic modeling

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Authors: Huilan Yao, Huaixin Zhang

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Simulations of the transition flow of model propellers are important for predicting hydrodynamic performance and studying scale effects. In this paper, the transition flow of a model propeller under different loadings are simulated using a transition model provided by STAR-CCM+, and the influence of turbulence intensity (TI) on the transition, especially friction and pressure components of propeller performance, was studied. Before that, the transition model was applied to simulate the transition flow of a flat plate and an airfoil. Predicted transitions agree well with experimental results. Then, the transition model was applied for propeller simulations in open water, and the influence of TI was studied. Under the heavy and moderate loadings, thrust and torque of the propeller predicted by the transition model (different TI) and two turbulence models are very close and agree well with measurements. However, under the light loading, only the transition model with low TI predicts the most accurate results. Above all, the friction components of propeller performance predicted by the transition model with different TI have obvious difference.

Keywords: transition flow, model propellers, hydrodynamic performance, numerical simulation

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Authors: Alaa Eddien Abdallah, Bajes Yousef Alskarnah

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Ant colony based routing algorithms are known to grantee the packet delivery, but they suffer from the huge overhead of control messages which are needed to discover the route. In this paper we utilize the network nodes positions to group the nodes in connected clusters. We use clusters-heads only on forwarding the route discovery control messages. Our simulations proved that the new algorithm has decreased the overhead dramatically without affecting the delivery rate.

Keywords: ad-hoc network, MANET, ant colony routing, position based routing

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Authors: Mohamed Jama Madar, Oso Wilis

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E-learning is the application of information technology in the teaching and learning process. This paper presents the Funnel model as a solution for the problems of implementation of e-learning in tertiary education institutions. While existing models such as TAM, theory-based e-learning and pedagogical model have been used over time, they have generally been found to be inadequate because of their tendencies to treat materials development, instructional design, technology, delivery and governance as separate and isolated entities. Yet it is matching components that bring framework of e-learning strategic implementation. The Funnel model enhances all these into one and applies synchronously and asynchronously to e-learning implementation where the only difference is modalities. Such a model for e-learning implementation has been lacking. The proposed Funnel model avoids ad-ad-hoc approach which has made other systems unused or inefficient, and compromised educational quality. Therefore, the proposed Funnel model should help tertiary education institutions adopt and develop effective and efficient e-learning system which meets users’ requirements.

Keywords: e-learning, pedagogical, technology, strategy

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Authors: Hatthaphone Silimanotham, Michael Henry

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The effective maintenance of road bridge infrastructure is becoming a widely researched topic in the civil engineering field. Deterioration is one of the main issues in bridge performance, and it is necessary to understand how bridges deteriorate to optimally plan budget allocation for bridge maintenance. In Laos, many bridges are in a deteriorated state, which may affect the performance of the bridge. Due to bridge deterioration, the Ministry of Public Works and Transport is interested in the deterioration model to allocate the budget efficiently and support the bridge maintenance planning. A deterioration model can be used to predict the bridge condition in the future based on the observed behavior in the past. This paper analyzes the available inspection data of road bridges on the road classifications network to build deterioration prediction models for the main bridge type found at the provincial level (concrete slab, concrete girder, and steel truss) using probabilistic deterioration modeling by linear regression method. The analysis targets there has three bridge types in the 18 provinces of Laos and estimates the bridge deterioration rating for evaluating the bridge's remaining life. This research thus considers the relationship between the service period and the bridge condition to represent the probability of bridge condition in the future. The results of the study can be used for a variety of bridge management tasks, including maintenance planning, budgeting, and evaluating bridge assets.

Keywords: deterioration model, bridge condition, bridge management, probabilistic modeling

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Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

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Authors: Zbigniew Olczykowski

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The paper presents the impact of work on the electric arc furnace power grid. The arc furnace operating will be modeled at different power conditions of steelworks. The paper will describe how to determine the increase in voltage fluctuations caused by working in parallel arc furnaces. The analysis of indicators characterizing the quality of electricity recorded during several cycles of measurement made at the same time at three points grid, with different power and different short-circuit rated voltage, will be carried out. The measurements analysis presented in this paper were conducted in the mains of one of the Polish steel. The indicators characterizing the quality of electricity was recorded during several cycles of measurement while making measurements at three points of different power network short-circuit power and various voltage ratings. Measurements of power quality indices included the one-week measurement cycles in accordance with the EN-50160. Data analysis will include the results obtained during the simultaneous measurement of three-point grid. This will determine the actual propagation of interference generated by the device. Based on the model studies and measurements of quality indices of electricity we will establish the effect of a specific arc on the mains. The short-circuit power network’s minimum value will also be estimated, this is necessary to limit the voltage fluctuations generated by arc furnaces.

Keywords: arc furnaces, long-term flicker, measurement and modeling of power quality, voltage fluctuations

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Authors: Jessica Raven Gates, Gemma Wilson-Menzfeld, Professor Alison Steven

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In 2019, the Primary Care system in the UK National Health Service (NHS) was subject to reform and restructuring. Primary Care Networks (PCNs) were established, which aligned with a trend towards integrated care both within the NHS and internationally. The introduction of PCNs brought groups of GP practices in a locality together, to operate as a network, build on existing services and collaborate at a larger scale. PCNs were expected to bring a range of benefits to patients and address some of the workforce pressures in the NHS, through an expanded and collaborative workforce. The early establishment of PCNs was disrupted by the emerging COVID-19 pandemic. This study, set in the context of the pandemic, aimed to explore experiences of the PCN workforce, and their perceptions of the establishment of PCNs. Specific objectives focussed on examining factors perceived as enabling or hindering the success of a PCN, the impact on day-to-day work, the approach to implementing change, and the influence of the COVID-19 pandemic upon PCN development. This study is part of a three-phase PhD project that utilized qualitative approaches and was underpinned by social constructionist philosophy. Phase 1: a systematic narrative review explored the provision of preventative healthcare services in UK primary settings and examined facilitators and barriers to delivery as experienced by the workforce. Phase 2: informed by the findings of phase 1, semi-structured interviews were conducted with fifteen participants (PCN workforce). Phase 3: follow-up interviews were conducted with original participants to examine any changes to their experiences and perceptions of PCNs. Three main themes span across phases 2 and 3 and were generated through a Framework Analysis approach: 1) working together at scale, 2) network infrastructure, and 3) PCN leadership. Findings suggest that through efforts to work together at scale and collaborate as a network, participants have broadly accepted the concept of PCNs. However, the workforce has been hampered by system design and system complexity. Operating against such barriers has led to a negative psychological impact on some PCN leaders and others in the PCN workforce. While the pandemic undeniably increased pressure on healthcare systems around the world, it also acted as a disruptor, offering a glimpse into how collaboration in primary care can work well. Through the integration of findings from all phases, a new theoretical model has been developed, which conceptualises the findings from this Ph.D. study and demonstrates how the workforce has experienced change associated with the establishment of PCNs. The model includes a contextual component of the COVID-19 pandemic and has been informed by concepts from Complex Adaptive Systems theory. This model is the original contribution to knowledge of the PhD project, alongside recommendations for practice, policy and future research. This study is significant in the realm of health services research, and while the setting for this study is the UK NHS, the findings will be of interest to an international audience as the research provides insight into how the healthcare workforce may experience imposed policy and service changes.

Keywords: health services research, qualitative research, NHS workforce, primary care

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Authors: Amir Deljoo

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The endeavor to comprehend the existence have been the center of thought for human in form of different disciplines and now basically in physics as the theory of everything. Here, after a brief review of the basic frameworks of thought, and a history of thought since ancient up to present, a logical methodology is presented based on a core axiom after which a function, a proto-field and then a coordinates are explained. Afterwards a generalization to Standard Model is proposed as General Standard Model which is believed to be the base of the Unified Theory of Everything.

Keywords: general relativity, grand unified theory, quantum mechanics, standard model, theory of everything

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

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

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

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

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Authors: Vanessa Matias Leite, Jandira Guenka Palma, Flávio Henrique de Oliveira

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This paper presents a model-oriented development approach to software development in the Model-View-Controller (MVC) architectural standard. This approach aims to expose a process of extractions of information from the models, in which through rules and syntax defined in this work, assists in the design of the initial model and its future conversions. The proposed paper presents a syntax based on the natural language, according to the rules agreed in the classic grammar of the Portuguese language, added to the rules of conversions generating models that follow the norms of the Object Management Group (OMG) and the Meta-Object Facility MOF.

Keywords: BNF Syntax, model driven architecture, model-view-controller, transformation, UML

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Authors: R. Rezaeipour, A. R. Mehrabi

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Recent increase of the DGs and microgrids in distribution systems, disturbs the tradition structure of the system. Coordination between protection devices in such a system becomes the concern of the network operators. This paper presents a new method for fault location detection in the active distribution networks, independent of the fault type or its resistance. The method uses synchronized voltage and current measurements at the interconnection of DG units and is able to adapt to changes in the topology of the system. The method has been tested on a 38-bus distribution system, with very encouraging results.

Keywords: fault location detection, active distribution system, micro grids, network operators

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

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Authors: Anusha M., V. Srikanth

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Maximizing spectrum usage and numerous applications of the wireless communication networks have forced to a high interest of available spectrum. Cognitive Radio control its receiver and transmitter features exactly so that they can utilize the vacant approved spectrum without impacting the functionality of the principal licensed users. The Use of various channels assists to address interferences thereby improves the whole network efficiency. The MAC protocol in cognitive radio network explains the spectrum usage by interacting with multiple channels among the users. In this paper we studied about the architecture of cognitive wireless mesh network and traditional TDMA dependent MAC method to allocate channels dynamically. The majority of the MAC protocols suggested in the research are operated on Common-Control-Channel (CCC) to handle the services between Cognitive Radio secondary users. In this paper, an extensive study of Multi-Channel Multi-Radios or frequency range channel allotment and continually synchronized TDMA scheduling are shown in summarized way.

Keywords: TDMA, MAC, multi-channel, multi-radio, WMN’S, cognitive radios

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Authors: Ting Kai Chia, Ruifeng Yan, Feifei Bai, Tapan Saha

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This research presents a real-world power system oscillation incident in 2022 originated by a hybrid solar photovoltaic (PV) and wind renewable energy farm with a rated capacity of approximately 300MW in Australia. The voltage and reactive power outputs recorded at the point of common coupling (PCC) oscillated at a sub-synchronous frequency region, which sustained for approximately five hours in the network. The reactive power oscillation gradually increased over time and reached a recorded maximum of approximately 250MVar peak-to-peak (from inductive to capacitive). The network service provider was not able to quickly identify the location of the oscillation source because the issue was widespread across the network. After the incident, the original equipment manufacturer (OEM) concluded that the oscillation problem was caused by the incorrect setting recovery of the hybrid power plant controller (HPPC) in the voltage and reactive power control loop after a loss of communication event. The voltage controller normally outputs a reactive (Q) reference value to the Q controller which controls the Q dispatch setpoint of PV and wind plants in the hybrid farm. Meanwhile, a feed-forward (FF) configuration is used to bypass the Q controller in case there is a loss of communication. Further study found that the FF control mode was still engaged when communication was re-established, which ultimately resulted in the oscillation event. However, there was no detailed explanation of why the FF control mode can cause instability in the hybrid farm. Also, there was no duplication of the event in the simulation to analyze the root cause of the oscillation. Therefore, this research aims to model and replicate the oscillation event in a simulation environment and investigate the underlying behavior of the HPPC and the consequent oscillation mechanism during the incident. The outcome of this research will provide significant benefits to the safe operation of large-scale renewable energy generators and power networks.

Keywords: PV, oscillation, modelling, wind

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Authors: Ganesh Nandakumaran, Mehmet Karaata

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A wave is a distributed execution, often made up of a broadcast phase followed by a feedback phase, requiring the participation of all the system processes before a particular event called decision is taken. Wave algorithms with one initiator such as the 1-wave algorithm have been shown to be very efficient for broadcasting messages in tree networks. Extensions of this algorithm broadcasting a sequence of waves using a single initiator have been implemented in algorithms such as the m-wave algorithm. However as the network size increases, having a single initiator adversely affects the message delivery times to nodes further away from the initiator. As a remedy, broadcast waves can be allowed to be initiated by multiple initiator nodes distributed across the network to reduce the completion time of broadcasts. These waves initiated by one or more initiator processes form a collection of waves covering the entire network. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using waves with multiple concurrent initiators. In this paper, we propose the first stabilizing multi-wave sequence algorithm implementing waves started by multiple initiator processes such that every process in the network receives at least one sequence of broadcasts. Due to being stabilizing, the proposed algorithm can withstand transient faults and do not require initialization. We view a fault as a transient fault if it perturbs the configuration of the system but not its program.

Keywords: distributed computing, multi-node broadcast, propagation of information with feedback and cleaning (PFC), stabilization, wave algorithms

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Authors: Dolgormaa Munkhbaatar, Munkhsaikhan Adiya, Tseedulam Khuut

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We performed an inversion method to determine the 1D-velocity model with station corrections of the Gobi-Altai area in the southern part of Mongolia using earthquake data collected in the National Data Center during the last 10 years. In this study, the concept of the new 1D model has been employed to minimize the average RMS of a set of well-located earthquakes, recorded at permanent (between 2006 and 2016) and temporary seismic stations (between 2014 and 2016), compute solutions for the coupled hypocenter and 1D velocity model. We selected 4800 events with RMS less than 0.5 seconds and with a maximum GAP of 170 degrees and determined velocity structures. Also, we relocated all possible events located in the Gobi-Altai area using the new 1D velocity model and achieved constrained hypocentral determinations for events within this area. We concluded that the estimated new 1D velocity model is a relatively low range compared to the previous velocity model in a significant improvement intend to, and the quality of the information basis for future research center locations to determine the earthquake epicenter area with this new transmission model.

Keywords: 1D velocity model, earthquake, relocation, Velest

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Authors: Prathik Ranka, Sridhar K, Vasanth Daniel, Mithun Shankar

Abstract:

With growing global food needs and climate uncertainties, informed crop choices are critical for increasing agricultural productivity. Here we propose a machine learning-based crop recommendation system to help farmers in choosing the most proper crops according to their geographical regions and soil properties. We can deploy algorithms like Decision Trees, Random Forests and Support Vector Machines on a broad dataset that consists of climatic factors, soil characteristics and historical crop yields to predict the best choice of crops. The approach includes first preprocessing the data after assessing them for missing values, unlike in previous jobs where we used all the available information and then transformed because there was no way such a model could have worked with missing data, and normalizing as throughput that will be done over a network to get best results out of our machine learning division. The model effectiveness is measured through performance metrics like accuracy, precision and recall. The resultant app provides a farmer-friendly dashboard through which farmers can enter their local conditions and receive individualized crop suggestions.

Keywords: crop recommendation, precision agriculture, crop, machine learning

Procedia PDF Downloads 12

Authors: Taimur Khan, Zakirullah, Muhammad Shahab

Abstract:

The SARS-CoV-2 variant BA.2.86 (Omicron) has emerged with unique mutations that may increase its transmission and infectivity. This study investigates how these mutations alter the Omicron receptor-binding domain's interaction network and dynamic properties (RBD) compared to the wild-type virus, focusing on its binding affinity to the human ACE2 (hACE2) receptor. Protein-protein docking and all-atom molecular dynamics simulations were used to analyze structural and dynamic differences. Despite the structural similarity to the wild-type virus, the Omicron variant exhibits a distinct interaction network involving new residues that enhance its binding capacity. The dynamic analysis reveals increased flexibility in the RBD, particularly in loop regions crucial for hACE2 interaction. Mutations significantly alter the secondary structure, leading to greater flexibility and conformational adaptability compared to the wild type. Binding free energy calculations confirm that the Omicron RBD has a higher binding affinity (-70.47 kcal/mol) to hACE2 than the wild-type RBD (-61.38 kcal/mol). These results suggest that the altered interaction network and enhanced dynamics of the Omicron variant contribute to its increased infectivity, providing insights for the development of targeted therapeutics and vaccines.

Keywords: SARS-CoV-2, molecular dynamic simulation, receptor binding domain, vaccine

Procedia PDF Downloads 18

Authors: Jie Bai, Yongsheng Gao, Shengxin Wang, Jie Zhao

Abstract:

Through the establishment of the elbow biomechanical model, it can provide theoretical guide for rehabilitation therapy on the upper limb of the human body. A biomechanical model of the elbow joint can be built by the connection of muscle force model and elbow dynamics. But there are many undetermined coefficients in the model like the optimal joint angle and optimal muscle force which are usually specified as the experimental parameters of other workers. Because of the individual differences, there is a certain deviation of the final result. To this end, the RMS value of the deviation between the actual angle and calculated angle is considered. A set of coefficients which lead to the minimum RMS value will be chosen to be the optimal parameters. The direct search method and the conjugacy search method are used to get the optimal parameters, thus the model can be more accurate and mode adaptability.

Keywords: elbow biomechanical model, RMS, direct search, conjugacy search

Procedia PDF Downloads 546

Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

Abstract:

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

Procedia PDF Downloads 333