Search results for: distributed sensor networks

Authors: Abdullah A. AlShaher

Abstract:

In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords: character recognition, regression curves, handwritten Arabic letters, expectation maximization algorithm

Procedia PDF Downloads 150

Authors: Artur Krukowski, Emmanouela Vogiatzaki

Abstract:

The article presents results from the AF3 project “Advanced Forest Fire Fighting” focused on Unmanned Aircraft Systems (UAS)-based 3D surveillance and 3D area mapping using high-resolution photogrammetric methods from multispectral imaging, also taking advantage of the 3D scanning techniques from the SCAN4RECO project. We also present a proprietary embedded sensor system used for the detection of fire ignitions in the forest using near-infrared based scanner with weight and form factors allowing it to be easily deployed on standard commercial micro-UAVs, such as DJI Inspire or Mavic. Results from real-life pilot trials in Greece, Spain, and Israel demonstrated added-value in the use of UAS for precise and reliable detection of forest fires, as well as high-resolution 3D aerial modeling for accurate quantification of human resources and equipment required for firefighting.

Keywords: forest wildfires, surveillance, fuel volume estimation, firefighting, ignition detectors, 3D modelling, UAV

Procedia PDF Downloads 144

Authors: P. C. Chiu, Jehnming Lin

Abstract:

In this study, a 50-W CO₂ laser was used for the clad of 304L powders on the stainless steel substrate with a temperature sensor and image monitoring system. The laser power and cladding speed and focal position were modified to achieve the requirement of the workpiece flatness and mechanical properties. The numerical calculation is based on ANSYS to analyze the temperature change of the moving heat source at different surface positions when coating the workpiece, and the effect of the process parameters on the bath size was discussed. The temperature of stainless steel powder in the nozzle outlet reacting with the laser was simulated as a process parameter. In the experiment, the difference of the thermal conductivity in three-dimensional space is compared with single-layer cladding and multi-layer cladding. The heat dissipation pattern of the single-layer cladding is the steel plate and the multi-layer coating is the workpiece itself. The relationship between the multi-clad temperature and the profile was analyzed by the temperature signal from an IR pyrometer.

Keywords: laser cladding, temperature, profile, microstructure

Procedia PDF Downloads 227

Authors: Carole Salis, Marie F. Wilson, Fabrizio Murgia, Cristian Lai, Franco Atzori, Giulia M. Orrù

Abstract:

ANDASA is a knowledge management platform for the capitalization of knowledge and cultural assets for the artistic and cultural sectors. It was built based on the priorities expressed by the participating artists. Through mapping artistic activities and specificities, it enables to highlight various aspects of the artistic research and production. Such instrument will contribute to create networks and partnerships, as it enables to evidentiate who does what, in what field, using which methodology. The platform is accessible to network participants and to the general public.

Keywords: cultural promotion, knowledge representation, cultural maping, ICT

Procedia PDF Downloads 430

Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

Abstract:

Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

Procedia PDF Downloads 429

Authors: Abdullah A. Al Orainy

Abstract:

Small cell backhaul solutions need to be cost-effective, scalable, and easy to install. This paper presents an overview of small cell backhaul technologies. Wireless solutions including TV white space, satellite, sub-6 GHz radio wave, microwave and mmWave with their backhaul characteristics are discussed. Recent research on issues like beamforming, backhaul architecture, precoding and large antenna arrays, and energy efficiency for dense small cell backhaul with mmWave communications is reviewed. Recent trials of 5G technologies are summarized.

Keywords: backhaul, small cells, wireless, 5G

Procedia PDF Downloads 518

Authors: Joy Cao, Min Zhou

Abstract:

Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

Procedia PDF Downloads 94

Authors: S. M. Khairul Halim, Mojtaba Ghodsi, Morteza Mohammadzaheri

Abstract:

Gait analysis is essential for diagnosing musculoskeletal and neurological conditions. However, current methods are often complex and expensive. This paper introduces a methodology for analysing gait parameters using a smart insole with a built-in accelerometer. The system measures stance time, swing time, step count, and cadence and wirelessly transmits data to a user-friendly IoT dashboard for centralized processing. This setup enables remote monitoring and advanced data analytics, making it a versatile tool for medical diagnostics and everyday usage. Integration with IoT enhances the portability and connectivity of the device, allowing for secure, encrypted data access over the Internet. This feature supports telemedicine and enables personalized treatment plans tailored to individual needs. Overall, the approach provides a cost-effective (almost 25 GBP), accurate, and user-friendly solution for gait analysis, facilitating remote tracking and customized therapy.

Keywords: gait analysis, IoT, smart insole, accelerometer sensor

Procedia PDF Downloads 26

Authors: Asma Ameur, Dhafer Malouche

Abstract:

Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

Procedia PDF Downloads 138

Authors: J. L. Oliver, T. Agryzkov, L. Tortosa, J. Vicent, J. Santacruz

Abstract:

This paper aims to demonstrate how the use of Network Theory can be applied to a very interesting and complex urban situation: The parts of a city which may have some patrimonial value, but because of their lack of relevant architectural elements, they are not considered to be historic in a conventional sense. In this paper, we use the suburb of La Villaflora in the city of Quito, Ecuador as our case study. We first propose a system of indicators as a tool to characterize and quantify the historic value of a geographic area. Then, we apply these indicators to the suburb of La Villaflora and use Network Theory to understand and propose actions.

Keywords: graphs, mathematics, networks, urban studies

Procedia PDF Downloads 373

Authors: Farhan Siddiqui

Abstract:

The increasingly complex challenges related to smog require the latest real-time monitoring and mitigation solutions. This paper describes a distributed IoT-edge architecture to improve smog detection, analysis, and policymaking. IoT sensors collect information related to critical air quality indicators while edge nodes perform local analytics with low latency for swift intervention. The system uses predictive algorithms to generate actionable insights to inform adaptive urban management strategies. Field implementations show dramatic improvements, including a 45 percent reduction in processing latency and improved predictive accuracy (R² = 0.92). These results show the potential of the framework to transform urban environmental management and policy making.

Keywords: Internet of Things, edge computing, Smog management, air quality, policy making

Procedia PDF Downloads 16

Authors: Li Li, Li-Ru Xia, He-Ye Wang, Xiao-Dong Bi

Abstract:

In this paper, we presented a highly sensitive immune-affinity monolithic array for detection of α-fetoprotein (AFP) and carcinoembryonic antigen (CEA). Firstly, the epoxy functionalized monolith arrays were fabricated using UV initiated copolymerization method. Scanning electron microscopy (SEM) image showed that the poly(BABEA-co-GMA) monolith exhibited a well-controlled skeletal and well-distributed porous structure. Then, AFP and CEA immune-affinity monolithic arrays were prepared by immobilization of AFP and CEA antibodies on epoxy functionalized monolith arrays. With a non-competitive immune response format, the presented AFP and CEA immune-affinity arrays were demonstrated as an inexpensive, flexible, homogeneous and stable array for detection of AFP and CEA.

Keywords: chemiluminescent detection, immune-affinity, monolithic copolymer array, UV-initiated copolymerization

Procedia PDF Downloads 343

Authors: Sanghoon Bae, Hanju Cha

Abstract:

Failed fuel detector (FFD) in research reactor is a very crucial instrument to detect the anomaly from failed fuels in the early stage around primary cooling system (PCS) outlet prior to the decay tank. FFD is considered as a mandatory sensor to ensure the integrity of fuel assemblies and mitigate the consequence from a failed fuel accident. For the effective function of FFD, the location of them should be determined by contemplating the effect from coolant flow around two outlets. For this, the analysis on computational flow dynamics (CFD) should be first performed how the coolant outlet flow including radioactive materials from failed fuels are mixed and discharged through the outlet plenum within certain seconds. The analysis result shows that the outlet flow is well mixed regardless of the position of failed fuel and ultimately illustrates the effect of detector location.

Keywords: computational flow dynamics (CFD), failed fuel detector (FFD), fresh fuel assembly (FFA), spent fuel assembly (SFA)

Procedia PDF Downloads 244

Authors: Carly Madge, Melanie Zurba, Ryan Bullock

Abstract:

The Boreal Forest has experienced some of the most drastic climate change-induced temperature rises in Canada, with average winter temperatures increasing by 3°C since 1948. One of the main concerns of the province of Saskatchewan, and particularly wildfire managers, is the increased risk of wildfires due to climate change. With these concerns in mind Sakaw Askiy Management Inc., a forestry corporation located in Prince Albert, Saskatchewan with operations in the Boreal Forest biome, is developing wildfire risk reduction strategies that are supported by the shareholders of the corporation as well as the stakeholders of the Prince Albert Forest Management Area (which includes citizens, hunters, trappers, cottage owners, and outfitters). In the past, wildfire management strategies implemented through harvesting have been received with skepticism by some community members of Prince Albert. Engagement of the stakeholders of the Prince Albert Management Area through the development of the wildfire risk reduction strategies aims to reduce this skepticism and rebuild some of the trust that has been lost between industry and community. This research project works with the framework of social learning, which is defined as the learning that occurs when individuals come together to form a group with the purpose of understanding environmental challenges and determining appropriate responses to them. The project evaluates the social learning processes that occur through the development of the risk reduction strategies and how the learning has allowed Sakaw to work towards implementing the strategies into their forest harvesting plans. The incorporation of wildfire risk reduction strategies works to increase the adaptive capacity of Sakaw, which in this case refers to the ability to adjust to climate change, moderate potential damages, take advantage of opportunities, and cope with consequences. Using semi-structured interviews and wildfire workshop meetings shareholders and stakeholders shared their knowledge of wildfire, their main wildfire concerns, and changes they would like to see made in the Prince Albert Forest Management Area. Interviews and topics discussed in the workshops were inductively coded for themes related to learning, adaptive capacity, areas of concern, and preferred methods of wildfire risk reduction strategies. Analysis determined that some of the learning that has occurred has resulted through social interactions and the development of networks oriented towards wildfire and wildfire risk reduction strategies. Participants have learned new knowledge and skills regarding wildfire risk reduction. The formation of wildfire networks increases access to information on wildfire and the social capital (trust and strengthened relations) of wildfire personnel. Both factors can be attributed to increases in adaptive capacity. Interview results were shared with the General Manager of Sakaw, where the areas of concern and preferred strategies of wildfire risk reduction will be considered and accounted for in the implementation of new harvesting plans. This research also augments the growing conceptual and empirical evidence of the important role of learning and networks in regional wildfire risk management efforts.

Keywords: adaptive capacity, community-engagement, social learning, wildfire risk reduction

Procedia PDF Downloads 153

Authors: Rong-Tsorng Wang

Abstract:

In this paper, we derive a pricing formula for catastrophe equity put options (or CatEPut) with non-homogeneous loss and approximated compound distributions. We assume that the loss claims arrival process is a nonhomogeneous Poisson process (NHPP) representing the clustering occurrences of loss claims, the size of loss claims is a sequence of independent and identically distributed random variables, and the accumulated loss distribution forms a compound distribution and is approximated by a heavy-tailed distribution. A numerical example is given to calibrate parameters, and we discuss how the value of CatEPut is affected by the changes of parameters in the pricing model we provided.

Keywords: catastrophe equity put options, compound distributions, nonhomogeneous Poisson process, pricing model

Procedia PDF Downloads 171

Authors: Rishabh Garg

Abstract:

Traditionally, business parties build trust with a centralized operating mechanism, such as payment by letter of credit. However, the increase in cyber-attacks and malicious hacking has jeopardized business operations and finance practices. Emerging markets, owing to their higher banking risks and bigger presence of digital financing, are looking forward to technology-driven solutions, financial inclusion and innovative working paradigms. Blockchain has the potential to enhance transaction transparency and supply chain traceability. It has captured a vast landscape with 200 million crypto users worldwide. Fintech and blockchain products are popping up across brokerage, digital wallets, exchanges, post-trade clearance, settlement, middleware, infrastructure, and base protocols.

Keywords: blockchain, distributed ledger technology, decentralized applications, ethereum, smart contracts, trade finance

Procedia PDF Downloads 160

Authors: Junghwa An, Sungkyoung Choi, Eun Ye, San Hoon Han, Young-Gun Choi, Chul Oun Jung

Abstract:

The copper-winged bat (Myotis rufoniger) is the widely distributed medium body-sized bat in Asia, including Korea. This bat population has been decreasing because of habitat loss. This study reported the isolation and characterization of ten polymorphic microsatellite loci in endangered M. rufoniger. To do genetic studies, we use saliva DNA of bats during winter sleep period. The number of alleles per locus ranged from 2 to 9, and the observed and expected heterozygosities ranged from 0.063 to 0.750 and from 0.063 to 0.865, respectively. The average polymorphic information content (PIC) value of these markers was 0.37. Two loci of M. rufoniger showed departure from Hardy-Weinberg equilibrium(HWE). This demonstrated that the ten microsatellite loci can be used as genetic markers for further investigation of the copper-winged bat.

Keywords: copper-winged bat, microsatellite, population genetics, South Korea

Procedia PDF Downloads 378

Authors: Rizoyeva Marjona Rustam Qizi

Abstract:

Transnational organized crime (TOC) represents one of the most significant threats to global security and social stability in the 21st century. Criminal networks operating across borders engage in activities such as drug trafficking, human trafficking, arms smuggling, and cybercrime, exploiting legal loopholes and jurisdictional challenges. In response, criminal law has evolved to address the complexities of TOC through enhanced international cooperation, harmonization of legal standards, and stronger enforcement mechanisms. This abstract explores the role of criminal law in combating TOC, focusing on three key areas: legislative frameworks, international collaboration, and enforcement practices. First, the development of international legal instruments such as the United Nations Convention against Transnational Organized Crime (UNTOC) has facilitated the standardization of legal definitions and procedures. Countries have adopted these frameworks to ensure a coordinated response to TOC, closing jurisdictional gaps and simplifying extradition processes. Second, international collaboration among law enforcement agencies, facilitated by organizations like INTERPOL and Europol, has enhanced information sharing and joint operations, making it more difficult for criminal organizations to evade detection. Finally, advancements in technology have improved investigative techniques, enabling law enforcement to track and dismantle criminal networks more effectively. However, despite these achievements, challenges remain. Issues such as corruption, insufficient resources, and differing legal systems continue to hinder the global fight against TOC. In conclusion, criminal law plays a pivotal role in the global effort to combat transnational organized crime by providing a legal framework for prosecution and fostering international cooperation. Ongoing reforms and technological innovations are essential for overcoming existing challenges and ensuring a more secure future. This abstract highlights the need for continuous adaptation of criminal law to address the evolving nature of TOC and underscores the importance of a unified international approach in achieving long-term success.

Keywords: transnational organized crime, criminal law, international cooperation, legal frameworks

Procedia PDF Downloads 17

Authors: Zhikal Qadir Salih, Hanife Bensen

Abstract:

As the advent of modern technology continues to make towering impacts on everything, its relevance permeates to all spheres, language learning, and writing skills in particular not an exception. This study aimed at finding out how EFL learners perceive online tools to improve their writing skills. The study was carried out at Tishk University. Copies of the questionnaire were distributed to the participants, in order to elicit their perceptions. The collected data were subjected to descriptive and inferential statistics. The outcome revealed that the participants have positive perceptions about online tools in using them to enhance their writing skills. The study however found out that both gender and the class level of the participants do not make any significant difference in their perceptions about the use of online tools, as far as writing skill is concerned. Based on these outcomes, relevant recommendations were made.

Keywords: online tools, writing skills, EFL learners, language learning

Procedia PDF Downloads 107

Authors: Amritesh Srivastava, Gaurav Sharma

Abstract:

The purpose of this project is to develop an open source object storage system that is highly durable, scalable and reliable. There are two representative systems in cloud computing: Google and Amazon. Their storage systems for Google GFS and Amazon S3 provide high reliability, performance and stability. Our proposed system is highly inspired from Amazon S3. We are using Hadoop Distributed File System (HDFS) Java API to implement our system. We propose the architecture of object storage system based on Hadoop. We discuss the requirements of our system, what we expect from our system and what problems we may encounter. We also give detailed design proposal along with the abstract source code to implement it. The final goal of the system is to provide REST based access to our object storage system that exists on top of HDFS.

Keywords: Hadoop, HBase, object storage, REST

Procedia PDF Downloads 342

Authors: Bayu Dwi Cahyo, Ekohariadi, Theodorus Wiyanto Wibowo, I. G. P. Asto Budithahjanto, Eppy Yundra

Abstract:

The objective of this study is to examine the effects of self-confidence, adversity quotient, and self-efficacy variables on critical thinking. This research's participants are 137 cadets of Aviation Polytechnics of Surabaya with the sampling technique that was purposive sampling. In this study, the data collection method used a questionnaire with Linkert-scale and distributed or given to respondents by the specified number of samples. The SPSS AMOS v23 was used to test a number of a priori multivariate growth curve models and examining relationships between the variables via path analysis. The result of path analysis was (χ² = 88.463, df= 71, χ² /df= 1.246, GFI= .914, CFI= .988, P= .079, AGFI= .873, TLI= .985, RMSEA= .043). According to the analysis, there is a positive and significant relationship between self-confidence, adversity quotient, and self-efficacy variables on critical thinking.

Keywords: self-confidence, adversity quotient, self-efficacy variables, critical thinking

Procedia PDF Downloads 150

Authors: Gabriel P. Fife, Saeyong Lee, David M. O'Sullivan

Abstract:

Introduction: Although investigations into injury characteristics are represented well in the literature, few have investigated the biomechanical characteristics associated with head impacts in Taekwondo. Therefore, the purpose of this study was to identify the kinematic characteristics of head impacts due to taekwondo kicks among non-elite practitioners. Participants: Male participants (n= 11, 175 + 5.3 cm, 71 + 8.3 kg) with 7.5 + 3.6 years of taekwondo training volunteered for this study. Methods: Participants were asked to perform five repetitions of each technique (i.e., turning kick, spinning hook kick, spinning back kick, front axe kick, and clench axe kick) aimed at the Hybrid III head with their dominant kicking leg. All participants wore a protective foot pad (thickness = 12 mm) that is commonly used in competition and training. To simulate head impact in taekwondo, the target consisted of a Hybrid III 50th Percentile Crash Test Dummy (Hybrid III) head (mass = 5.1 kg) and neck (fitted with taekwondo headgear) secured to an aluminum support frame and positioned to each athlete’s standing height. The Hybrid III head form was instrumented with a 500 g tri-axial accelerometer (PCB Piezotronics) mounted to the head center of gravity to obtain resultant linear accelerations (RLA). Rotational accelerations were collected using three angular rate sensors mounted orthogonally to each other (Diversified Technical Systems ARS-12 K Angular Rate Sensor). The accelerometers were interfaced via a 3-channel, battery-powered integrated circuit piezoelectric sensor signal conditioner (PCB Piezotronics) and connected to a desktop computer for analysis. Acceleration data were captured using LABVIEW Signal Express and processed in accordance with SAE J211-1 channel frequency class 1000. Head injury criteria values (HIC) were calculated using the VSRSoftware. A one-way analysis of variance was used to determine differences between kicks, while the Tukey HSD test was employed for pairwise comparisons. The level of significance was set to an effect size of 0.20. All statistical analyses were done using R 3.1.0. Results: A statistically significant difference was observed in RLA (p = 0.00075); however, these differences were not clinically meaningful (η² = 0.04, 95% CI: -0.94 to 1.03). No differences were identified with ROTA (p = 0.734, η² = 0.0004, 95% CI: -0.98 to 0.98). A statistically significant difference (p < 0.001) between kicks in HIC was observed, with a medium effect (η2= 0.08, 95% CI: -0.98 to 1.07). However, the confidence interval of this difference indicates uncertainty. Tukey HSD test identified differences (p < 0.001) between kicking techniques in RLA and HIC. Conclusion: This study observed head impact levels that were comparable to previous studies of similar objectives and methodology. These data are important as impact measures from this study may be more representative of impact levels experienced by non-elite competitors. Although the clench axe kick elicited a lower RLA, the ROTA of this technique was higher than levels from other techniques (although not large differences in reference to effect sizes). As the axe kick has been reported to cause severe head injury, future studies may consider further study of this kick important.

Keywords: Taekwondo, head injury, biomechanics, kicking

Procedia PDF Downloads 44

Authors: Sayed Isahaq Hossain, K. T. Chang, Moustapha Ndour

Abstract:

Inference of the phreatic line through earth dams is of paramount importance because it could be directly associated with piping phenomena which may lead to the dam failure. Normally in the field, the instrumentations such as ‘diver’ and ‘standpipe’ are to be used to identify the seepage conditions which only provide point data with a fair amount of interpolation or assumption. Here in this paper, we employed high-resolution fiber optic cable sensors (FOCS) based on Raman Scattering in order to obtain a very accurate phreatic line and seepage profile. Unlike the above-mention devices which pinpoint the water level location, this kind of Distributed Fiber Optics Sensing gives us more reliable information due to its inherent characteristics of continuous measurement.

Keywords: standpipe, diver, FOCS, monitoring, Raman scattering

Procedia PDF Downloads 360

Authors: Saeed Khandan Siar, Stefan Tenbohlen, Christian Breuer, Raphael Lebreton

Abstract:

The goal of this article is to investigate the detailed temperature distribution and the fluid flow of an oil cooled winding of a power transformer by means of computational fluid dynamics (CFD). The experimental setup consists of three passes of a zig-zag cooled disc type winding, in which losses are modeled by heating cartridges in each winding segment. A precise temperature sensor measures the temperature of each turn. The laboratory setup allows the exact control of the boundary conditions, e.g. the oil flow rate and the inlet temperature. Furthermore, a simulation model is solved using the open source computational fluid dynamics solver OpenFOAM and validated with the experimental results. The model utilizes the laminar and turbulent flow for the different mass flow rate of the oil. The good agreement of the simulation results with experimental measurements validates the model.

Keywords: CFD, conjugated heat transfer, power transformers, temperature distribution

Procedia PDF Downloads 426

Authors: Duncan Wallace, M-Tahar Kechadi

Abstract:

In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

Procedia PDF Downloads 134

Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

Abstract:

Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

Procedia PDF Downloads 322

Authors: Sabah Khan

Abstract:

The idea of universal Islam tends to exaggerate the extent of homogeneity in Islamic beliefs and practices across Muslim communities. In the age of migration, various Muslim communities are in diaspora. The immediate implication of this is what happens to Islam in diaspora? How Islam gets represented in new forms? Such pertinent questions need to be dealt with. This paper shall draw on the idea of religious transnationalism, primarily transnational Islam. There are multiple ways to conceptualize transnational phenomenon with reference to Islam in terms of flow of people, transnational organizations and networks; Ummah oriented solidarity and the new Muslim public sphere. This paper specifically deals with the new Muslim public sphere. It primarily refers to the space and networks enabled by new media and communication technologies, whereby Muslim identity and Islamic normativity are rehearsed, debated by people in different locales. A new sense of public is emerging across Muslim communities, which needs to be contextualized. This paper uses both primary and secondary data. Primary data elicited through content analysis of audio-visuals on social media and secondary sources of information ranging from books, articles, journals, etc. The basic aim of the paper is to focus on the emerging Muslim public sphere and the role of media in expanding public spheres of Islam. It also explores how Muslims in diaspora negotiate Islam and Islamic practices through media and the new Muslim public sphere. This paper cogently weaves in discussions firstly, of re-intellectualization of Islamic discourse in the public sphere. In other words, how Muslims have come to reimagine their collective identity and critically look at fundamental principles and authoritative tradition. Secondly, the emerging alternative forms of Islam by young Muslims in diaspora. In other words, how young Muslims search for unorthodox ways and media for religious articulation, including music, clothing and TV. This includes transmission and distribution of Islam in diaspora in terms of emerging ‘media Islam’ or ‘soundbite Islam’. The new Muslim public sphere has offered an arena to a large number of participants to critically engage with Islam, which leads not only to a critical engagement with traditional forms of Islamic authority but also emerging alternative forms of Islam and Islamic practices.

Keywords: Islam, media, Muslims, public sphere

Procedia PDF Downloads 275

Authors: Masood Hussain, Erol Pehlivan, Ahmet Avci, Ecem Guder

Abstract:

Green synthesis of silver nanoparticles (AgNPs) was carried out by using echinacea flower extract as reducing/protecting agent. The effects of various operating parameters and additives on the dimensions such as stirring rate, temperature, pH of the solution, the amount of extract and concentration of silver nitrate were optimized in order to achieve monodispersed spherical and small size echinacea protected silver nanoparticles (echinacea-AgNPs) through biosynthetic method. The surface roughness and topography of synthesized metal nanoparticles were confirmed by using Atomic Force Microscopy (AFM). High-Resolution Transmission Electron Microscopic (HRTEM) results elaborated the formation of uniformly distributed Echinacea protected AgNPs (Echinacea-AgNPs) having an average size of 30.2±2nm.

Keywords: Echinacea flower extract, green synthesis, silver nanoparticles, morphology

Procedia PDF Downloads 426

Authors: Somayeh Pashaee

Abstract:

The ever-increasing growth of the Internet and the development of information and communication technology have prompted the politicians of different countries to use virtual networks as an efficient tool for their foreign policy. The communication of governments and countries, even in the farthest places from each other, through electronic networks, has caused vast changes in the way of statecraft and governance. Importantly, in the meantime, diplomacy, which is always based on information and communication, has been affected by the new prevailing conditions and new technologies more than other areas and has faced greater changes. The emergence of virtual space and the formation of new communication tools in the field of public diplomacy has led to the redefinition of the framework of diplomacy and politics in the international arena and the appearance of a new aspect of diplomacy called digital diplomacy. Digital diplomacy is in the concept of changing relations from a face-to-face and traditional way to a non-face-to-face and new way, and its purpose is to solve foreign policy issues using virtual space. Digital diplomacy, by affecting diplomatic procedures and its change, explains the role of technology in the visualization and implementation of diplomacy in different ways. The purpose of this paper is to investigate the position of digital diplomacy in the public diplomacy of the Islamic Republic of Iran. The paper tries to answer these two questions in a descriptive-analytical way, considering the progress of communication and the role of virtual space in the service of diplomacy, what is the approach of the Islamic Republic of Iran towards digital diplomacy and the use of a new way of establishing foreign relations in public diplomacy? What capacities and damages are facing the country after the use of this type of new diplomacy? In this paper, various theoretical concepts in the field of public diplomacy and modern diplomacy, including Geoff Berridge, Charles Kegley, Hans Tuch and Ronald Peter Barston, as well as the theoretical framework of Marcus Holmes on digital diplomacy, will be used as a conceptual basis to support the analysis. As a result, in order to better achieve the political goals of the country, especially in foreign policy, the approach of the Islamic Republic of Iran to public diplomacy with a focus on digital diplomacy should be strengthened and revised. Today, only emphasizing on advancing diplomacy through traditional methods may weaken Iran's position in the public opinion level from other countries.

Keywords: digital diplomacy, public diplomacy, islamic republic of Iran, foreign policy, opportunities and challenges

Procedia PDF Downloads 122

Authors: Xiping Wang，Shuran Yao, Liqin Dai

Abstract:

Many studies report the winter is getting warmer and the minimum air temperature is obviously rising as the important climate warming evidences. The exacerbated air temperature fluctuation tending to bring more severe weather variation is another important consequence of recent climate change which induced more disasters to crop growth in quite a certain regions. Hebei Province is an important winter wheat growing province in North of China that recently endures more winter freezing injury influencing the local winter wheat crop management. A winter wheat freezing injury assessment Bayesian Network framework was established for the objectives of estimating, assessing and predicting winter wheat freezing disasters in Hebei Province. In this framework, the freezing disasters was classified as three severity degrees (SI) among all the three types of freezing, i.e., freezing caused by severe cold in anytime in the winter, long extremely cold duration in the winter and freeze-after-thaw in early season after winter. The factors influencing winter wheat freezing SI include time of freezing occurrence, growth status of seedlings, soil moisture, winter wheat variety, the longitude of target region and, the most variable climate factors. The climate factors included in this framework are daily mean and range of air temperature, extreme minimum temperature and number of days during a severe cold weather process, the number of days with the temperature lower than the critical temperature values, accumulated negative temperature in a potential freezing event. The Bayesian Network model was evaluated using actual weather data and crop records at selected sites in Hebei Province using real data. With the multi-stage influences from the various factors, the forecast and assessment of the event-based target variables, freezing injury occurrence and its damage to winter wheat production, were shown better scoped by Bayesian Network model.

Keywords: bayesian networks, climatic change, freezing Injury, winter wheat

Procedia PDF Downloads 411