Search results for: network attack
3212 Participation in the Decision Making and Job Satisfaction in Greek Fish Farms
Authors: S. Anastasiou, C. Nathanailides
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There is considerable evidence to suggest that employees participation in the decision-making process of an organisation, has a positive effect on job satisfaction and work performance of the employees. The purpose of the present work was to examine the HRM practices, demographics and the level of job satisfaction of employees in Greek Aquaculture fish farms. A survey of employees (n=86) in 6 Greek Aquaculture Firms was carried out. The results indicate that HRM practices such as recruitment of the personnel and communication between the departments did not vary between different firms. The most frequent method of recruitment was through the professional network or the personal network of the managers. The preferred method of HRM communication was through the line managers and through group meeting. The level of job satisfaction increased with work experience participation and participation in the decision making process. A high percentage of the employees (81,3%±8.39) felt that they frequently participated in the decision making process. The Aquaculture employees exhibited high level of job satisfaction (88,1±6.95). The level of job satisfaction was related with participation in the decision making process (-0.633, P<0.05) but was not related with as age or gender. In terms of the working conditions, employees were mostly satisfied with their work itself, their colleagues and mostly dissatisfied with working hours, salary issues and low prospects of pay rises.Keywords: aquaculture, human resources, job satisfaction
Procedia PDF Downloads 4583211 Cerebrovascular Modeling: A Vessel Network Approach for Fluid Distribution
Authors: Karla E. Sanchez-Cazares, Kim H. Parker, Jennifer H. Tweedy
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The purpose of this work is to develop a simple compartmental model of cerebral fluid balance including blood and cerebrospinal-fluid (CSF). At the first level the cerebral arteries and veins are modelled as bifurcating trees with constant scaling factors between generations which are connected through a homogeneous microcirculation. The arteries and veins are assumed to be non-rigid and the cross-sectional area, resistance and mean pressure in each generation are determined as a function of blood volume flow rate. From the mean pressure and further assumptions about the variation of wall permeability, the transmural fluid flux can be calculated. The results suggest the next level of modelling where the cerebral vasculature is divided into three compartments; the large arteries, the small arteries, the capillaries and the veins with effective compliances and permeabilities derived from the detailed vascular model. These vascular compartments are then linked to other compartments describing the different CSF spaces, the cerebral ventricles and the subarachnoid space. This compartmental model is used to calculate the distribution of fluid in the cranium. Known volumes and flows for normal conditions are used to determine reasonable parameters for the model, which can then be used to help understand pathological behaviour and suggest clinical interventions.Keywords: cerebrovascular, compartmental model, CSF model, vascular network
Procedia PDF Downloads 2683210 Modeling of Power Network by ATP-Draw for Lightning Stroke Studies
Authors: John Morales, Armando Guzman
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Protection relay algorithms play a crucial role in Electric Power System stability, where, it is clear that lightning strokes produce the mayor percentage of faults and outages of Transmission Lines (TLs) and Distribution Feeders (DFs). In this context, it is imperative to develop novel protection relay algorithms. However, in order to get this aim, Electric Power Systems (EPS) network have to be simulated as real as possible, especially the lightning phenomena, and EPS elements that affect their behavior like direct and indirect lightning, insulator string, overhead line, soil ionization and other. However, researchers have proposed new protection relay algorithms considering common faults, which are not produced by lightning strokes, omitting these imperative phenomena for the transmission line protection relays behavior. Based on the above said, this paper presents the possibilities of using the Alternative Transient Program ATP-Draw for the modeling and simulation of some models to make lightning stroke studies, especially for protection relays, which are developed through Transient Analysis of Control Systems (TACS) and MODELS language corresponding to the ATP-Draw.Keywords: back-flashover, faults, flashover, lightning stroke, modeling of lightning, outages, protection relays
Procedia PDF Downloads 3033209 AS-Geo: Arbitrary-Sized Image Geolocalization with Learnable Geometric Enhancement Resizer
Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu
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Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature
Procedia PDF Downloads 2053208 Proposing an Algorithm to Cluster Ad Hoc Networks, Modulating Two Levels of Learning Automaton and Nodes Additive Weighting
Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi
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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power
Procedia PDF Downloads 4023207 Strengthening Farmer-to-farmer Knowledge Sharing Network: A Pathway to Improved Extension Service Delivery
Authors: Farouk Shehu Abdulwahab
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The concept of farmer-farmer knowledge sharing was introduced to bridge the extension worker-farmer ratio gap in developing countries. However, the idea was poorly accepted, especially in typical agrarian communities. Therefore, the study explores the concept of a farmer-to-farmer knowledge-sharing network to enhance extension service delivery. The study collected data from 80 farmers randomly selected through a series of multiple stages. The Data was analysed using a 5-point Likert scale and descriptive statistics. The Likert scale results revealed that 62.5% of the farmers are satisfied with farmer-to-farmer knowledge-sharing networks. Moreover, descriptive statistics show that lack of capacity building and low level of education are the most significant problems affecting farmer-farmer sharing networks. The major implication of these findings is that the concept of farmer-farmer knowledge-sharing networks can work better for farmers in developing countries as it was perceived by them as a reliable alternative for information sharing. Therefore, the study recommends introducing incentives into the concept of farmer-farmer knowledge-sharing networks and enhancing the capabilities of farmers who are opinion leaders in the farmer-farmer concept of knowledge-sharing to make it more sustainable.Keywords: agricultural productivity, extension, farmer-to-farmer, livelihood, technology transfer
Procedia PDF Downloads 533206 Intelligent Rainwater Reuse System for Irrigation
Authors: Maria M. S. Pires, Andre F. X. Gloria, Pedro J. A. Sebastiao
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The technological advances in the area of Internet of Things have been creating more and more solutions in the area of agriculture. These solutions are quite important for life, as they lead to the saving of the most precious resource, water, being this need to save water a concern worldwide. The paper proposes the creation of an Internet of Things system based on a network of sensors and interconnected actuators that automatically monitors the quality of the rainwater that is stored inside a tank in order to be used for irrigation. The main objective is to promote sustainability by reusing rainwater for irrigation systems instead of water that is usually available for other functions, such as other productions or even domestic tasks. A mobile application was developed for Android so that the user can control and monitor his system in real time. In the application, it is possible to visualize the data that translate the quality of the water inserted in the tank, as well as perform some actions on the implemented actuators, such as start/stop the irrigation system and pour the water in case of poor water quality. The implemented system translates a simple solution with a high level of efficiency and tests and results obtained within the possible environment.Keywords: internet of things, irrigation system, wireless sensor and actuator network, ESP32, sustainability, water reuse, water efficiency
Procedia PDF Downloads 1413205 Resting-State Functional Connectivity Analysis Using an Independent Component Approach
Authors: Eric Jacob Bacon, Chaoyang Jin, Dianning He, Shuaishuai Hu, Lanbo Wang, Han Li, Shouliang Qi
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Objective: Refractory epilepsy is a complicated type of epilepsy that can be difficult to diagnose. Recent technological advancements have made resting-state functional magnetic resonance (rsfMRI) a vital technique for studying brain activity. However, there is still much to learn about rsfMRI. Investigating rsfMRI connectivity may aid in the detection of abnormal activities. In this paper, we propose studying the functional connectivity of rsfMRI candidates to diagnose epilepsy. Methods: 45 rsfMRI candidates, comprising 26 with refractory epilepsy and 19 healthy controls, were enrolled in this study. A data-driven approach known as independent component analysis (ICA) was used to achieve our goal. First, rsfMRI data from both patients and healthy controls were analyzed using group ICA. The components that were obtained were then spatially sorted to find and select meaningful ones. A two-sample t-test was also used to identify abnormal networks in patients and healthy controls. Finally, based on the fractional amplitude of low-frequency fluctuations (fALFF), a chi-square statistic test was used to distinguish the network properties of the patient and healthy control groups. Results: The two-sample t-test analysis yielded abnormal in the default mode network, including the left superior temporal lobe and the left supramarginal. The right precuneus was found to be abnormal in the dorsal attention network. In addition, the frontal cortex showed an abnormal cluster in the medial temporal gyrus. In contrast, the temporal cortex showed an abnormal cluster in the right middle temporal gyrus and the right fronto-operculum gyrus. Finally, the chi-square statistic test was significant, producing a p-value of 0.001 for the analysis. Conclusion: This study offers evidence that investigating rsfMRI connectivity provides an excellent diagnosis option for refractory epilepsy.Keywords: ICA, RSN, refractory epilepsy, rsfMRI
Procedia PDF Downloads 643204 Advanced Simulation and Enhancement for Distributed and Energy Efficient Scheduling for IEEE802.11s Wireless Enhanced Distributed Channel Access Networks
Authors: Fisayo G. Ojo, Shamala K. Subramaniam, Zuriati Ahmad Zukarnain
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As technology is advancing and wireless applications are becoming dependable sources, while the physical layer of the applications are been embedded into tiny layer, so the more the problem on energy efficiency and consumption. This paper reviews works done in recent years in wireless applications and distributed computing, we discovered that applications are becoming dependable, and resource allocation sharing with other applications in distributed computing. Applications embedded in distributed system are suffering from power stability and efficiency. In the reviews, we also prove that discrete event simulation has been left behind untouched and not been adapted into distributed system as a simulation technique in scheduling of each event that took place in the development of distributed computing applications. We shed more lights on some researcher proposed techniques and results in our reviews to prove the unsatisfactory results, and to show that more work still have to be done on issues of energy efficiency in wireless applications, and congestion in distributed computing.Keywords: discrete event simulation (DES), distributed computing, energy efficiency (EE), internet of things (IOT), quality of service (QOS), user equipment (UE), wireless mesh network (WMN), wireless sensor network (wsn), worldwide interoperability for microwave access x (WiMAX)
Procedia PDF Downloads 1803203 Multi-Objective Electric Vehicle Charge Coordination for Economic Network Management under Uncertainty
Authors: Ridoy Das, Myriam Neaimeh, Yue Wang, Ghanim Putrus
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Electric vehicles are a popular transportation medium renowned for potential environmental benefits. However, large and uncontrolled charging volumes can impact distribution networks negatively. Smart charging is widely recognized as an efficient solution to achieve both improved renewable energy integration and grid relief. Nevertheless, different decision-makers may pursue diverse and conflicting objectives. In this context, this paper proposes a multi-objective optimization framework to control electric vehicle charging to achieve both energy cost reduction and peak shaving. A weighted-sum method is developed due to its intuitiveness and efficiency. Monte Carlo simulations are implemented to investigate the impact of uncertain electric vehicle driving patterns and provide decision-makers with a robust outcome in terms of prospective cost and network loading. The results demonstrate that there is a conflict between energy cost efficiency and peak shaving, with the decision-makers needing to make a collaborative decision.Keywords: electric vehicles, multi-objective optimization, uncertainty, mixed integer linear programming
Procedia PDF Downloads 1733202 Manufacturing Anomaly Detection Using a Combination of Gated Recurrent Unit Network and Random Forest Algorithm
Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene
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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest
Procedia PDF Downloads 1033201 Computational Model for Predicting Effective siRNA Sequences Using Whole Stacking Energy (ΔG) for Gene Silencing
Authors: Reena Murali, David Peter S.
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The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA
Procedia PDF Downloads 5183200 Heart Attack Prediction Using Several Machine Learning Methods
Authors: Suzan Anwar, Utkarsh Goyal
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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest
Procedia PDF Downloads 1313199 Other-Generated Disclosure: A Challenge to Privacy on Social Network Sites
Authors: Tharntip Tawnie Chutikulrungsee, Oliver Kisalay Burmeister, Maumita Bhattacharya, Dragana Calic
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Sharing on social network sites (SNSs) has rapidly emerged as a new social norm and has become a global phenomenon. Billions of users reveal not only their own information (self disclosure) but also information about others (other-generated disclosure), resulting in a risk and a serious threat to either personal or informational privacy. Self-disclosure (SD) has been extensively researched in the literature, particularly regarding control of individual and existing privacy management. However, far too little attention has been paid to other-generated disclosure (OGD), especially by insiders. OGD has a strong influence on self-presentation, self-image, and electronic word of mouth (eWOM). Moreover, OGD is more credible and less likely manipulated than SD, but lacks privacy control and legal protection to some extent. This article examines OGD in depth, ranging from motivation to both online and offline impacts, based upon lived experiences from both ‘the disclosed’ and ‘the discloser’. Using purposive sampling, this phenomenological study involves an online survey and in-depth interviews. The findings report the influence of peer disclosure as well as users’ strategies to mitigate privacy issues. This article also calls attention to the challenge of OGD privacy and inadequacies in the law related to privacy protection in the digital domain.Keywords: facebook, online privacy, other-generated disclosure, social networks sites (SNSs)
Procedia PDF Downloads 2423198 Relay Node Placement for Connectivity Restoration in Wireless Sensor Networks Using Genetic Algorithms
Authors: Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri
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Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.Keywords: connectivity restoration, genetic algorithms, multiple-node failure, relay nodes, wireless sensor networks
Procedia PDF Downloads 2283197 End-to-End Pyramid Based Method for Magnetic Resonance Imaging Reconstruction
Authors: Omer Cahana, Ofer Levi, Maya Herman
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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning
Procedia PDF Downloads 833196 Evaluation of Bollworm Tolerance in F1 and F2 BT Cotton under Unprotected Condition
Authors: N. K. Bhute, B. B. Bhosle
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Field experiment was conducted during kharif 2005, at the experimental farm of the Department of Genetics and Plant Breeding, College of Agriculture, Marathwada Agricultural University, Parbhani, Maharashtra. F1 and F2 hybrids of 23 Bt and 5 non-Bt hybrids were grown in a randomized block design with two replications. The results showed that among F1 hybrids, open boll damage due to bollworm complex was not noticed in 4233 Bt and 4247 Bt cotton hybrids which were found significantly superior over MECH 6301 Bt (3.2 %), 4255 Bt (3.28 %) and it was at par with rest of the hybrids. Among F2 hybrids minimum open boll damage (3.10 %) was noticed in Proagro 144 Bt, which was found significantly superior over rest of the hybrids except 4234 Bt (4.17 %) and 4254 Bt (4.98 %) which were at par with each other. In respect of seed cotton yield, among F1 hybrids maximum yield (15.51 q/ha) was recorded in 4233 Bt which was found significantly superior over rest of the hybrids except 4237 Bt (15.24 q/ha). Among F2 maximum yield (15.44 q/ha) was recorded in 4233 Bt which was found significantly superior over rest of the hybrids except 4258 Bt (15.41 q/ha), 4239 Bt (15.098 q/ha) which were at par with each other. Thus F2 Bt cotton express Bt protein in segregated pattern in which bollworm attack was more as compared to F1 which affects yield as well as quality of lint.Keywords: Bt cotton, bollworms, F1 and F2 generations, unprotected condition
Procedia PDF Downloads 2903195 Mixed Natural Adsorbents and Oxides for Oil Remediation
Authors: Cesar Maximo Oliva González, Javier Acevedo Cortez, Boris Kharisov, Thelma Serrano Quezada
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The importance of the crude oil refining process is due to the demand for petroleum products such as gasoline, kerosene, asphalt, etc., which are used in daily activities and have a high impact on the global economy. In the processes of oil obtaining and refining, it is common to find problems such as spills on seabed and high energy consumption in processing. In order to quickly and efficiently attack these problems, the use of adsorbents has taken on great importance due to its ease of implementation, as well as the possibility of their regeneration to be reused. In this work, the use of two types of adsorbents is proposed: the first is a natural adsorbent such as aloe vera or nopal, which were lyophilized and hydrophobized to achieve a selectivity in oil adsorption in oil / water mixtures. The second is a mixed iron/nickel oxide, which is specially designed to adsorb the asphaltenes in the heavy fractions of the oil; in addition, this type of adsorbents presents catalytic properties that manage to decompose the heavier fractions of the petroleum in light hydrocarbons, descending thus the energy required for the oil refining process.Keywords: nanomaterials, oil spills, remediation, natural adsorbents, mixed oxides
Procedia PDF Downloads 2273194 Performance Based Road Asset Evaluation
Authors: Kidus Dawit Gedamu
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Addis Ababa City Road Authority is responsible for managing and setting performance evaluation of the city’s road network using the International Roughness Index (IRI). This helps the authority to conduct pavement condition assessments of asphalt roads each year to determine the health status or Level of service (LOS) of the roadway network and plan program improvements such as maintenance, resurfacing and rehabilitation. For a lower IRI limit economical and acceptable maintenance strategy may be selected among a number of maintenance alternatives. The Highway Development and Management (HDM-4) tool can do such measures to help decide which option is the best by evaluating the economic and structural conditions. This paper specifically addresses flexible pavement, including two principal arterial streets under the administration of the Addis Ababa City Roads Authority. The roads include the road from Megenagna Interchange to Ayat Square and from Ayat Square to Tafo RA. First, it was assessed the procedures followed by the city's road authority to develop the appropriate road maintenance strategies. Questionnaire surveys and interviews are used to collect information from the city's road maintenance departments. Second, the project analysis was performed for functional and economic comparison of different maintenance alternatives using HDM-4.Keywords: appropriate maintenance strategy, cost stream, road deterioration, maintenance alternative
Procedia PDF Downloads 513193 Strengthening by Assessment: A Case Study of Rail Bridges
Authors: Evangelos G. Ilias, Panagiotis G. Ilias, Vasileios T. Popotas
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The United Kingdom has one of the oldest railway networks in the world dating back to 1825 when the world’s first passenger railway was opened. The network has some 40,000 bridges of various construction types using a wide range of materials including masonry, steel, cast iron, wrought iron, concrete and timber. It is commonly accepted that the successful operation of the network is vital for the economy of the United Kingdom, consequently the cost effective maintenance of the existing infrastructure is a high priority to maintain the operability of the network, prevent deterioration and to extend the life of the assets. Every bridge on the railway network is required to be assessed every eighteen years and a structured approach to assessments is adopted with three main types of progressively more detailed assessments used. These assessment types include Level 0 (standardized spreadsheet assessment tools), Level 1 (analytical hand calculations) and Level 2 (generally finite element analyses). There is a degree of conservatism in the first two types of assessment dictated to some extent by the relevant standards which can lead to some structures not achieving the required load rating. In these situations, a Level 2 Assessment is often carried out using finite element analysis to uncover ‘latent strength’ and improve the load rating. If successful, the more sophisticated analysis can save on costly strengthening or replacement works and avoid disruption to the operational railway. This paper presents the ‘strengthening by assessment’ achieved by Level 2 analyses. The use of more accurate analysis assumptions and the implementation of non-linear modelling and functions (material, geometric and support) to better understand buckling modes and the structural behaviour of historic construction details that are not specifically covered by assessment codes are outlined. Metallic bridges which are susceptible to loss of section size through corrosion have largest scope for improvement by the Level 2 Assessment methodology. Three case studies are presented, demonstrating the effectiveness of the sophisticated Level 2 Assessment methodology using finite element analysis against the conservative approaches employed for Level 0 and Level 1 Assessments. One rail overbridge and two rail underbridges that did not achieve the required load rating by means of a Level 1 Assessment due to the inadequate restraint provided by U-Frame action are examined and the increase in assessed capacity given by the Level 2 Assessment is outlined.Keywords: assessment, bridges, buckling, finite element analysis, non-linear modelling, strengthening
Procedia PDF Downloads 3023192 Performance Investigation of UAV Attitude Control Based on Modified PI-D and Nonlinear Dynamic Inversion
Authors: Ebrahim Hassan Kapeel, Ahmed Mohsen Kamel, Hossan Hendy, Yehia Z. Elhalwagy
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Interest in autopilot design has been raised intensely as a result of recent advancements in Unmanned Aerial vehicles (UAVs). Due to the enormous number of applications that UAVs can achieve, the number of applied control theories used for them has increased in recent years. These small fixed-wing UAVs are suffering high non-linearity, sensitivity to disturbances, and coupling effects between their channels. In this work, the nonlinear dynamic inversion (NDI) control lawisdesigned for a nonlinear small fixed-wing UAV model. The NDI is preferable for varied operating conditions, there is no need for a scheduling controller. Moreover, it’s applicable for high angles of attack. For the designed flight controller validation, a nonlinear Modified PI-D controller is performed with our model. A comparative study between both controllers is achieved to evaluate the NDI performance. Simulation results and analysis are proposed to illustrate the effectiveness of the designed controller based on NDI.Keywords: UAV dynamic model, attitude control, nonlinear PID, dynamic inversion
Procedia PDF Downloads 973191 Automatic Product Identification Based on Deep-Learning Theory in an Assembly Line
Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez
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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.Keywords: deep-learning, image classification, image identification, industrial engineering.
Procedia PDF Downloads 1523190 Reading and Writing Memories in Artificial and Human Reasoning
Authors: Ian O'Loughlin
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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.Keywords: artificial reasoning, human memory, machine learning, neural networks
Procedia PDF Downloads 2623189 Using Hidden Markov Chain for Improving the Dependability of Safety-Critical Wireless Sensor Networks
Authors: Issam Alnader, Aboubaker Lasebae, Rand Raheem
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Wireless sensor networks (WSNs) are distributed network systems used in a wide range of applications, including safety-critical systems. The latter provide critical services, often concerned with human life or assets. Therefore, ensuring the dependability requirements of Safety critical systems is of paramount importance. The purpose of this paper is to utilize the Hidden Markov Model (HMM) to elongate the service availability of WSNs by increasing the time it takes a node to become obsolete via optimal load balancing. We propose an HMM algorithm that, given a WSN, analyses and predicts undesirable situations, notably, nodes dying unexpectedly or prematurely. We apply this technique to improve on C. Lius’ algorithm, a scheduling-based algorithm which has served to improve the lifetime of WSNs. Our experiments show that our HMM technique improves the lifetime of the network, achieved by detecting nodes that die early and rebalancing their load. Our technique can also be used for diagnosis and provide maintenance warnings to WSN system administrators. Finally, our technique can be used to improve algorithms other than C. Liu’s.Keywords: wireless sensor networks, IoT, dependability of safety WSNs, energy conservation, sleep awake schedule
Procedia PDF Downloads 953188 DTI Connectome Changes in the Acute Phase of Aneurysmal Subarachnoid Hemorrhage Improve Outcome Classification
Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens
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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage
Procedia PDF Downloads 1803187 An Evaluation of Impact of Video Billboard on the Marketing of GSM Services in Lagos Metropolis
Authors: Shola Haruna Adeosun, F. Adebiyi Ajoke, Odedeji Adeoye
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Video billboard advertising by networks and brand switching was conceived out of inquisition at the huge billboard advertising expenditures made by the three major GSM network operators in Nigeria. The study was anchored on Lagos State Metropolis with a current census population over 1,000,000. From this population, a purposive sample of 400 was adopted, and the questionnaire designed for the survey was carefully allocated to members of this ample in the five geographical zones of the city so that each rung of the society was well represented. The data obtained were analyzed using tables and simple percentages. The results obtained showed that subscribers of these networks were hardly influenced by the video billboard advertisements. They overwhelmingly showed that rather than the slogans of the GSM networks carried on the video billboards, it was the incentives to subscribers as well as the promotional strategies of these organizations that moved them to switch from one network to another. These switching lasted only as long as the incentives and promotions were in effect. The results of the study also seemed to rekindle the age-old debate on media effects, by the unyielding schools of the theory of ‘all-powerful media’, ‘the limited effects media’, ‘the controlled effects media’ and ‘the negotiated media influence’.Keywords: evaluation, impact, video billboard, marketing, services
Procedia PDF Downloads 2433186 Analyzing and Predicting the CL-20 Detonation Reaction Mechanism Based on Artificial Intelligence Algorithm
Authors: Kaining Zhang, Lang Chen, Danyang Liu, Jianying Lu, Kun Yang, Junying Wu
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In order to solve the problem of a large amount of simulation and limited simulation scale in the first-principle molecular dynamics simulation of energetic material detonation reaction, we established an artificial intelligence model for analyzing and predicting the detonation reaction mechanism of CL-20 based on the first-principle molecular dynamics simulation of the multiscale shock technique (MSST). We employed principal component analysis to identify the dominant charge features governing molecular reactions. We adopted the K-means clustering algorithm to cluster the reaction paths and screen out the key reactions. We introduced the neural network algorithm to construct the mapping relationship between the charge characteristics of the molecular structure and the key reaction characteristics so as to establish a calculation method for predicting detonation reactions based on the charge characteristics of CL-20 and realize the rapid analysis of the reaction mechanism of energetic materials.Keywords: energetic material detonation reaction, first-principle molecular dynamics simulation of multiscale shock technique, neural network, CL-20
Procedia PDF Downloads 973185 Investigation of Oscillation Mechanism of a Large-scale Solar Photovoltaic and Wind Hybrid Power Plant
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
Procedia PDF Downloads 293184 F-VarNet: Fast Variational Network for MRI Reconstruction
Authors: Omer Cahana, Maya Herman, Ofer Levi
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Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.Keywords: MRI, deep learning, variational network, computer vision, compress sensing
Procedia PDF Downloads 1453183 The Role of Regional Economic Communities in Fighting Terrorism in Africa: The Case of Inter-Governmental Authority on Development (IGAD)
Authors: Memar Ayalew Demeke, Solomon Gebreyohans Gebru
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In Africa, Regional Economic Communities (RECs) were initially established to tackle the economic challenges of the continent. However, overtime, they expanded their mandate to deal with the security threats of the continent such as terrorism. In fact, the fight against terrorism has been internationalized following the September 9/11 terrorist attack in the U.S.A. Since then, RECs have been giving considerable attention to preventing and combating terrorism in their respective regions. Similarly, IGAD has been involved in preventing and combating terrorism. So far, however, little has been done with regard to what IGAD has performed in fighting terrorism. Therefore, this study was intended to describe and analyze the legal and practical activities carried out by IGAD in its fight against terrorism in the region general and in Somalia in particular. Both descriptive and analytical methods were employed and data were analyzed through qualitative approach. Finally, based on the findings, the study argues that, instead of over-reliance on hard power as a means of fighting terrorism, IGAD should invest more on the political and socio-economic problems of its member states so as to address the root causes.Keywords: regional economic communities, IGAD, terrorism, treaties, conventions
Procedia PDF Downloads 413