Search results for: inter-datacenters transport network

Authors: Rani Danavath, V. B. Narsimha

Abstract:

Cloud computing is emerging as a new paradigm of large scale distributed computing. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., three service models, and four deployment networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics models. Load balancing is one of the main challenges in cloud computing, which is required to distribute the dynamic workload across multiple nodes, to ensure that no single node is overloaded. It helps in optimal utilization of resources, enhancing the performance of the system. The goal of the load balancing is to minimize the resource consumption and carbon emission rate, that is the direct need of cloud computing. This determined the need of new metrics energy consumption and carbon emission for energy-efficiency load balancing techniques in cloud computing. Existing load balancing techniques mainly focuses on reducing overhead, services, response time and improving performance etc. In this paper we introduced a Technique for energy-efficiency, but none of the techniques have considered the energy consumption and carbon emission. Therefore, our proposed work will go towards energy – efficiency. So this energy-efficiency load balancing technique can be used to improve the performance of cloud computing by balancing the workload across all the nodes in the cloud with the minimum resource utilization, in turn, reducing energy consumption, and carbon emission to an extent, which will help to achieve green computing.

Keywords: cloud computing, distributed computing, energy efficiency, green computing, load balancing, energy consumption, carbon emission

Procedia PDF Downloads 445

Authors: Mathias U. Bonet, Pericles Pilidis, Georgios Doulgeris

Abstract:

An aero-derivative marine Gas Turbine engine model is simulated to be installed as the main propulsion prime mover to power a cruise ship which is designed and routed to transport intending Muslim pilgrims for the annual hajj pilgrimage from Nigeria to the Islamic port city of Jeddah in Saudi Arabia. A performance assessment of the Gas Turbine engine has been conducted by examining the effect of varying aerodynamic and hydrodynamic conditions encountered at various geographical locations along the scheduled transit route during the voyage. The investigation focuses on the overall behavior of the Gas Turbine engine employed to power and propel the ship as it operates under ideal and adverse conditions to be encountered during calm and rough weather according to the different seasons of the year under which the voyage may be undertaken. The variation of engine performance under varying operating conditions has been considered as a very important economic issue by determining the time the speed by which the journey is completed as well as the quantity of fuel required for undertaking the voyage. The assessment also focuses on the increased resistance caused by the fouling of the submerged portion of the ship hull surface with its resultant effect on the power output of the engine as well as the overall performance of the propulsion system. Daily ambient temperature levels were obtained by accessing data from the UK Meteorological Office while the varying degree of turbulence along the transit route and according to the Beaufort scale were also obtained as major input variables of the investigation. By assuming the ship to be navigating the Atlantic Ocean and the Mediterranean Sea during winter, spring and summer seasons, the performance modeling and simulation was accomplished through the use of an integrated Gas Turbine performance simulation code known as ‘Turbomach’ along with a Matlab generated code named ‘Poseidon’, all of which have been developed at the Power and Propulsion Department of Cranfield University. As a case study, the results of the various assumptions have further revealed that the marine Gas Turbine is a reliable and available alternative to the conventional marine propulsion prime movers that have dominated the maritime industry before now. The techno-economic and environmental assessment of this type of propulsion prime mover has enabled the determination of the effect of changes in weather and sea conditions on the ship speed as well as trip time and the quantity of fuel required to be burned throughout the voyage.

Keywords: ambient temperature, hull fouling, marine gas turbine, performance, propulsion, voyage

Procedia PDF Downloads 185

Authors: Hong Yu, Ion Matei

Abstract:

Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.

Keywords: carbon composite, fault detection, fault identification, particle filter

Procedia PDF Downloads 190

Authors: Brandon Ko

Abstract:

Whether it’s a kitchen staff or a law firm, many groups tend to have homogenous characteristics of race, gender, interests, and goals. Social groups are not typically random samples of the population and will usually have common identifiers. According to Blau, age, sex, and education all play salient roles in shaping relationships within members of society. So if there is some degree of homogeneity within groups, the question arises whether this is beneficial or harmful to a group’s effectiveness. There has been much disagreement in the scientific community as to whether the presence of homophily benefits or hinders an athletic team's cohesiveness. For this paper, a comparative study of research of soccer case studies that followed various, youth players was studied against examinations of the effects that such a culture has on athletes. The case studies were used as evidence to determine what kind of homophily existed within the soccer camps. One case study followed several European developmental clubs such as Bayern Munich and Barcelona. Another study followed eight different players, four of each gender, implementing a similar method of interviewing, observing, and questioning. The individual and team goals of each athlete were reviewed to see which teams and players were ego-oriented and which were team-oriented. Additionally, there had been little research done on the relationship between homophily and how it applies to the sport community, suggesting the need to develop this neglected problem in applied psychology. This paper argues that the benefits of an egalitarian culture and stronger relations with people of a similar socio-demographic outweigh the liabilities of cohesion like being stereotyped and a lack of network outside the group as produced by homophily in athletic competition.

Keywords: group cohesion, homophily, sports psychology, youth athletics

Procedia PDF Downloads 282

Authors: Sarah Alamolhoda, Kevin J. Smith, Xiaotao Bi, Naoko Ellis

Abstract:

For millennials, biomass has been the most important source of fuel used to produce energy. Energy derived from biomass is renewable by re-growth of biomass. Various technologies are used to convert biomass to potential renewable products including combustion, gasification, pyrolysis and fermentation. Gasification is the incomplete combustion of biomass in a controlled environment that results in valuable products such as syngas, biooil and biochar. Syngas is a combustible gas consisting of hydrogen (H₂), carbon monoxide (CO), carbon dioxide (CO₂), and traces of methane (CH₄) and nitrogen (N₂). Cleaned syngas can be used as a turbine fuel to generate electricity, raw material for hydrogen and synthetic natural gas production, or as the anode gas of solid oxide fuel cells. In this work, syngas as a product of woody biomass gasification in British Columbia, Canada, was introduced to two consecutive fixed bed reactors to perform a catalytic water gas shift reaction followed by a catalytic methanation reaction. The water gas shift reaction is a well-established industrial process and used to increase the hydrogen content of the syngas before the methanation process. Catalysts were used in the process since both reactions are reversible exothermic, and thermodynamically preferred at lower temperatures while kinetically favored at elevated temperatures. The water gas shift reactor and the methanation reactor were packed with Cu-based catalyst and Ni-based catalyst, respectively. Simulated syngas with different percentages of CO, H₂, CH₄, and CO₂ were fed to the reactors to investigate the effect of operating conditions in the unit. The water gas shift reaction experiments were done in the temperature of 150 ˚C to 200 ˚C, and the pressure of 550 kPa to 830 kPa. Similarly, methanation experiments were run in the temperature of 300 ˚C to 400 ˚C, and the pressure of 2340 kPa to 3450 kPa. The Methanation reaction reached 98% of CO conversion at 340 ˚C and 3450 kPa, in which more than half of CO was converted to CH₄. Increasing the reaction temperature caused reduction in the CO conversion and increase in the CH₄ selectivity. The process was designed to be renewable and release low greenhouse gas emissions. Syngas is a clean burning fuel, however by going through water gas shift reaction, toxic CO was removed, and hydrogen as a green fuel was produced. Moreover, in the methanation process, the syngas energy was transformed to a fuel with higher energy density (per volume) leading to reduction in the amount of required fuel that flows through the equipment and improvement in the process efficiency. Natural gas is about 3.5 times more efficient (energy/ volume) than hydrogen and easier to store and transport. When modification of existing infrastructure is not practical, the partial conversion of renewable hydrogen to natural gas (with up to 15% hydrogen content), the efficiency would be preserved while greenhouse gas emission footprint is eliminated.

Keywords: renewable natural gas, methane, hydrogen, gasification, syngas, catalysis, fuel

Procedia PDF Downloads 111

Authors: Deepa S N, Srinivasan a D, Veeramanju K T

Abstract:

The power transformer is a critical equipment in the transmission and distribution network that must be managed to ensure uninterrupted power service. The liquid insulation is essential for the proper functioning of the transformer, as it serves as both coolant and insulating medium, which influences the transformer’s durability. Further, the insulating state of a power transformer has a significant impact on its reliability. Mineral oil derived from petroleum crude oil has been employed as liquid dielectrics for decades due to its superior functional characteristics, however as a resource for the same are getting depleted over the years. Research is undertaken across the globe to identify a viable substitute for mineral oil. Further, alternate insulating oils are being investigated for better environmental impact, biodegradability and economics. Several combinations of vegetable oil derived natural esters are being inspected by researchers across the globe in these domains. In this work, mineral oil is blended with soyabean oil with various proportions and dielectric properties such as dielectric breakdown voltage, relative permittivity, dissipation factor, viscosity, flash and fire point have been investigated according to international standards. A quantitative comparison is made among various samples and is observed that the blended oil sample of equal proportion of mineral oil and soyabean oil, MO50+SO50 exhibits superior dielectric properties such as breakdown voltage of 65kV, dissipation factor of 0.0044, relative permittivity of 3.1680 that are closer to the range of values recommended for power transformer applications. Also, Breakdown voltage values of all the investigated oil samples obeyed the Weibull and Normal probability distribution.

Keywords: blended oil, dielectric breakdown, liquid insulation, power transformer

Procedia PDF Downloads 84

Authors: Richa Grover, Rahul Grover, V. Balaji Rao, Kavish Kejriwal

Abstract:

Selection of suppliers is a crucial problem in the supply chain management. On top of that, sustainable supplier selection is the biggest challenge for the organizations. Environment protection and social problems have been of concern to society in recent years, and the traditional supplier selection does not consider about this factor; therefore, this research work focuses on introducing sustainable criteria into the structure of supplier selection criteria. Sustainable Supply Chain Management (SSCM) is the management and administration of material, information, and money flows, as well as coordination among business along the supply chain. All three dimensions - economic, environmental, and social - of sustainable development needs to be taken care of. Purpose of this research is to maximize supply chain profitability, maximize social wellbeing of supply chain and minimize environmental impacts. Problem statement is selection of suppliers in a sustainable supply chain network by ranking the suppliers against sustainable criteria identified. The aim of this research is twofold: To find out what are the sustainable parameters that can be applied to the supply chain, and to determine how these parameters can effectively be used in supplier selection. Multicriteria decision making tools will be used to rank both criteria and suppliers. AHP Analysis will be used to find out ratings for the criteria identified. It is a technique used for efficient decision making. TOPSIS will be used to find out rating for suppliers and then ranking them. TOPSIS is a MCDM problem solving method which is based on the principle that the chosen option should have the maximum distance from the negative ideal solution (NIS) and the minimum distance from the ideal solution.

Keywords: sustainable supply chain management, sustainable criteria, MCDM tools, AHP analysis, TOPSIS method

Procedia PDF Downloads 321

Authors: Jean Berger, Nassirou Lo, Martin Noel

Abstract:

Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.

Keywords: search path planning, search and rescue, multi-agent, mixed-integer linear programming, optimization

Procedia PDF Downloads 366

Authors: Debalina Ghoshal

Abstract:

Nuclear security is the ‘prevention and detection of, and response to unauthorised removal, sabotage, unauthorised access, illegal transfer or other malicious acts involving nuclear or radiological material or their associated facilities.’ Ever since the end of Cold War, nuclear materials security has remained a concern for global security. However, with the increase in terrorist attacks not just in India especially, security of nuclear materials remains a priority. Therefore, India has made continued efforts to tighten its security on nuclear materials to prevent nuclear theft and radiological terrorism. Nuclear security is different from nuclear safety. Physical security is also a serious concern and India had been careful of the physical security of its nuclear materials. This is more so important since India is expanding its nuclear power capability to generate electricity for economic development. As India targets 60,000 MW of electricity production by 2030, it has a range of reactors to help it achieve its goal. These include indigenous Pressurised Heavy Water Reactors, now standardized at 700 MW per reactor Light Water Reactors, and the indigenous Fast Breeder Reactors that can generate more fuel for the future and enable the country to utilise its abundant thorium resource. Nuclear materials security can be enhanced through two important ways. One is through proliferation resistant technologies and diplomatic efforts to take non proliferation initiatives. The other is by developing technical means to prevent any leakage in nuclear materials in the hands of asymmetric organisations. New Delhi has already implemented IAEA Safeguards on their civilian nuclear installations. Moreover, the IAEA Additional Protocol has also been ratified by India in order to enhance its transparency of nuclear material and strengthen nuclear security. India is a party to the IAEA Conventions on Nuclear Safety and Security, and in particular the 1980 Convention on the Physical Protection of Nuclear Material and its amendment in 2005, Code of Conduct in Safety and Security of Radioactive Sources, 2006 which enables the country to provide for the highest international standards on nuclear and radiological safety and security. India's nuclear security approach is driven by five key components: Governance, Nuclear Security Practice and Culture, Institutions, Technology and International Cooperation. However, there is still scope for further improvements to strengthen nuclear materials and nuclear security. The NTI Report, ‘India’s improvement reflects its first contribution to the IAEA Nuclear Security Fund etc. in the future, India’s nuclear materials security conditions could be further improved by strengthening its laws and regulations for security and control of materials, particularly for control and accounting of materials, mitigating the insider threat, and for the physical security of materials during transport. India’s nuclear materials security conditions also remain adversely affected due to its continued increase in its quantities of nuclear material, and high levels of corruption among public officials.’ This paper would study briefly the progress made by India in nuclear and nuclear material security and the step ahead for India to further strengthen this.

Keywords: India, nuclear security, nuclear materials, non proliferation

Procedia PDF Downloads 349

Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

Abstract:

With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

Procedia PDF Downloads 129

Authors: Zeinab Haji Abolhasani, Romeo Marian, Lee Luong

Abstract:

This paper and its companions (Part II, Part III) will concentrate on optimizing a class of supply chain problems known as Multi- Commodities Consumer Supply Chain (MCCSC) problem. MCCSC problem belongs to production-distribution (P-D) planning category. It aims to determine facilities location, consumers’ allocation, and facilities configuration to minimize total cost (CT) of the entire network. These facilities can be manufacturer units (MUs), distribution centres (DCs), and retailers/end-users (REs) but not limited to them. To address this problem, three major tasks should be undertaken. At the first place, a mixed integer non-linear programming (MINP) mathematical model is developed. Then, system’s behaviors under different conditions will be observed using a simulation modeling tool. Finally, the most optimum solution (minimum CT) of the system will be obtained using a multi-objective optimization technique. Due to the large size of the problem, and the uncertainties in finding the most optimum solution, integration of modeling and simulation methodologies is proposed followed by developing new approach known as GASG. It is a genetic algorithm on the basis of granular simulation which is the subject of the methodology of this research. In part II, MCCSC is simulated using discrete-event simulation (DES) device within an integrated environment of SimEvents and Simulink of MATLAB® software package followed by a comprehensive case study to examine the given strategy. Also, the effect of genetic operators on the obtained optimal/near optimal solution by the simulation model will be discussed in part III.

Keywords: supply chain, genetic algorithm, optimization, simulation, discrete event system

Procedia PDF Downloads 310

Authors: Redouane Krini, Lutz Nuhn, Hicham El Mard Cheol Woo Ha, Yoondeok Han, Kwang-Sup Lee, Dong-Yol Yang, Jinsoo Joo, Rudolf Zentel

Abstract:

To use Quantum Dots (QDs) in the two photon initiated polymerization technique (TPIP) for 3D patternings, QDs were modified on the surface with photosensitive end groups which are able to undergo a photopolymerization. We were able to fabricate fluorescent 3D lattice structures using photopatternable QDs by TPIP for photonic devices such as photonic crystals and metamaterials. The QDs in different diameter have different emission colors and through mixing of RGB QDs white light fluorescent from the polymeric structures has been created. Metamaterials are capable for unique interaction with the electrical and magnetic components of the electromagnetic radiation and for manipulating light it is crucial to have a negative refractive index. In combination with QDs via TPIP technique polymeric structures can be designed with properties which cannot be found in nature. This makes these artificial materials gaining a huge importance for real-life applications in photonic and optoelectronic. Understanding of interactions between nanoparticles and biological systems is of a huge interest in the biomedical research field. We developed a synthetic strategy of polymer functionalized nanoparticles for biomedical studies to obtain hybrid systems of QDs and copolymers with a strong binding network in an inner shell and which can be modified in the end through their poly(ethylene glycol) functionalized outer shell. These hybrid systems can be used as models for investigation of cell penetration and drug delivery by using measurements combination between CryoTEM and fluorescence studies.

Keywords: biomedical study models, lithography, photo induced polymerization, quantum dots

Procedia PDF Downloads 520

Authors: R. Pussinen, V. Jankovic, U. Herzberg, M. Cerrada-Gimenez, T. Huhtala, A. Nurmi, T. Ahtoniemi

Abstract:

Targeted over-expression of human α-synuclein using viral-vector mediated gene delivery into the substantia nigra of rats and non-human primates has been reported to lead to dopaminergic cell loss and the formation of α-synuclein aggregates reminiscent of Lewy bodies. We have previously shown how AAV-mediated expression of α-synuclein is seen in the chronic phenotype of the rats over 16 week follow-up period. In the context of these findings, we attempted to further characterize this long term PD related functional and motor deficits as well as neurochemical and neuropathological changes in AAV-mediated α-synuclein transfection model in rats during chronic follow-up period. Different titers of recombinant AAV expressing human α-synuclein (A53T) were stereotaxically injected unilaterally into substantia nigra of Wistar rats. Rats were allowed to recover for 3 weeks prior to initial baseline behavioral testing with rotational asymmetry test, stepping test and cylinder test. A similar behavioral test battery was applied again at weeks 5, 9,12 and 15. In addition to traditionally used rat PD model tests, MotoRater test system, a high speed kinematic gait performance monitoring was applied during the follow-up period. Evaluation focused on animal gait between groups. Tremor analysis was performed on weeks 9, 12 and 15. In addition to behavioral end-points, neurochemical evaluation of dopamine and its metabolites were evaluated in striatum. Furthermore, integrity of the dopamine active transport (DAT) system was evaluated by using 123I- β-CIT and SPECT/CT imaging on weeks 3, 8 and 12 after AAV- α-synuclein transfection. Histopathology was examined from end-point samples at 3 or 12 weeks after AAV- α-synuclein transfection to evaluate dopaminergic cell viability and microglial (Iba-1) activation status in substantia nigra by using stereological analysis techniques. This study focused on the characterization and validation of previously published AAV- α-synuclein transfection model in rats but with the addition of novel end-points. We present the long term phenotype of AAV- α-synuclein transfected rats with traditionally used behavioral tests but also by using novel fine motor analysis techniques and tremor analysis which provide new insight to unilateral effects of AAV α-synuclein transfection. We also present data about neurochemical and neuropathological end-points for the dopaminergic system in the model and how well they correlate with behavioral phenotype.

Keywords: adeno-associated virus, alphasynuclein, animal model, Parkinson’s disease

Procedia PDF Downloads 291

Authors: Ajay Kumar Singh, Arvind Kumar, S. P. Singh

Abstract:

Cellulose nanofibrils (CNF) have shown potential of their extensive use in various fields, including papermaking, due to their unique characteristics. In this study, CNF’s were prepared by fibrillating the pulp obtained from raw materials e.g. bagasse, hardwood and softwood using enzymatic pretreatment followed by mechanical refining. These nanofibrils, when examined under FE-SEM, show that partial fibrillation on fiber surface has resulted in production of nanofibers. Mixing these nanofibers with the unrefined and normally refined fibers show their reinforcing effect. This effect is manifested in observing the improvement in the physical and mechanical properties e.g. tensile index and burst index of paper. Tear index, however, was observed to decrease on blending with nanofibers. The optical properties of paper sheets made from blended fibers showed no significant change in comparison to those made from only mechanically refined pulp. Mixing of normal pulp fibers with nanofibers show increase in ºSR and consequent decrease in drainage rate. These changes observed in mechanical, optical and other physical properties of the paper sheets made from nanofibrils blended pulp have been tried to explain considering the distribution of the nanofibrils alongside microfibrils in the fibrous network. Since usually, paper/boards with higher strength are observed to have diminished optical properties which is a drawback in their quality, the present work has the potential for developing paper/boards having improved strength alongwith undiminished optical properties utilising the concepts of nanoscience and nanotechnology.

Keywords: enzymatic pretreatment, mechanical refining, nanofibrils, paper properties

Procedia PDF Downloads 350

Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

Abstract:

Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

Procedia PDF Downloads 192

Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 52

Authors: Paula Quiterio, Arlete Apolinario, Celia T. Sousa, Joao Azevedo, Paula Dias, Adelio Mendes, Joao P. Araujo

Abstract:

Nowadays one of the mankind's greatest challenges has been the supply of low-cost and environmentally friendly energy sources as an alternative to non-renewable fossil fuels. Hydrogen has been considered a promising solution, representing a clean and low-cost fuel. It can be produced directly from clean and abundant resources, such as sunlight and water, using photoelectrochemical cells (PECs), in a process that mimics the nature´s photosynthesis. Hematite (alpha-Fe2O3) has attracted considerable attention as a promising photoanode for solar water splitting, due to its high chemical stability, nontoxicity, availability and low band gap (2.2 eV), which allows reaching a high thermodynamic solar-to-hydrogen efficiency of 16.8 %. However, the main drawbacks of hematite such as the short hole diffusion length and the poor conductivity that lead to high electron-hole recombination result in significant PEC efficiency losses. One strategy to overcome these limitations and to increase the PEC efficiency is to use 1D nanostructures, such as nanotubes (NTs) and nanowires (NWs), which present high aspect ratios and large surface areas providing direct pathways for electron transport up to the charge collector and minimizing the recombination losses. In particular, due to the ultrathin walls of the NTs, the holes can reach the surface faster than in other nanostructures, representing a key factor for the NTs photoresponse. In this work, we prepared hematite NWs and NTs, respectively by hydrothermal process and electrochemical anodization. For hematite NWs growing, we studied the effect of variable hydrothermal conditions, different annealing temperatures and time, and the use of Ti and Sn dopants on the morphology and PEC performance. The crystalline phase characterization by X-ray diffraction was crucial to distinguish the formation of hematite and other iron oxide phases, alongside its effect on the photoanodes conductivity and consequent PEC efficiency. The conductivity of the as-prepared NWs is very low, in the order of 10-5 S cm-1, but after doping and annealing optimization it increased by a factor of 105. A high photocurrent density of 1.02 mA cm-2 at 1.45 VRHE was obtained under simulated sunlight, which is a very promising value for this kind of hematite nanostructures. The stability of the photoelectrodes was also tested, presenting good stability after several J-V measurements over time. The NTs, synthesized by fast anodizations with potentials ranging from 20-100 V, presented a linear growth of the NTs pore walls, with very low thicknesses from 10 - 18 nm. These preliminary results are also very promising for the use of hematite photoelectrodes on PEC hydrogen applications.

Keywords: hematite, nanotubes, nanowires, photoelectrochemical cells

Procedia PDF Downloads 226

Authors: Qinglin Liu, Zhiyong Lin, Zongheng Wei, Jianfeng Wen, Congming Yi, Hai Liu

Abstract:

The multi-channel blind rendezvous problem in distributed cognitive radio networks (DCRNs) refers to how users in the network can hop to the same channel at the same time slot without any prior knowledge (i.e., each user is unaware of other users' information). The channel hopping (CH) technique is a typical solution to this blind rendezvous problem. In this paper, we propose a quinary coding and matrix structure-based CH algorithm called QCMS-CH. The QCMS-CH algorithm can guarantee the rendezvous of users using only one cognitive radio in the scenario of the asynchronous clock (i.e., arbitrary time drift between the users), heterogeneous channels (i.e., the available channel sets of users are distinct), and symmetric role (i.e., all users play a same role). The QCMS-CH algorithm first represents a randomly selected channel (denoted by R) as a fixed-length quaternary number. Then it encodes the quaternary number into a quinary bootstrapping sequence according to a carefully designed quaternary-quinary coding table with the prefix "R00". Finally, it builds a CH matrix column by column according to the bootstrapping sequence and six different types of elaborately generated subsequences. The user can access the CH matrix row by row and accordingly perform its channel, hoping to attempt rendezvous with other users. We prove the correctness of QCMS-CH and derive an upper bound on its Maximum Time-to-Rendezvous (MTTR). Simulation results show that the QCMS-CH algorithm outperforms the state-of-the-art in terms of the MTTR and the Expected Time-to-Rendezvous (ETTR).

Keywords: channel hopping, blind rendezvous, cognitive radio networks, quaternary-quinary coding

Procedia PDF Downloads 86

Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

Abstract:

Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

Procedia PDF Downloads 108

Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

Abstract:

The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

Procedia PDF Downloads 137

Authors: Hedi Dghim, Ahmed El-Naggar, Istvan Erlich

Abstract:

Power electronic converters are being introduced in low voltage (LV) grids at an increasingly rapid rate due to the growing adoption of power electronic-based home appliances in residential grid. Photovoltaic (PV) systems are considered one of the potential installed renewable energy sources in distribution power systems. This trend has led to high distortion in the supply voltage which consequently produces harmonic currents in the network and causes an inherent voltage unbalance. In order to investigate the effect of harmonic distortions, a case study of a typical LV grid configuration with high penetration of 3-phase and 1-phase rooftop mounted PV from southern Germany was first considered. Electromagnetic transient (EMT) simulations were then carried out under the MATLAB/Simulink environment which contain detailed models for power electronic-based loads, ohmic-based loads as well as 1- and 3-phase PV. Note that, the switching patterns of the power electronic circuits were considered in this study. Measurements were eventually performed to analyze the distortion levels when PV operating under different solar irradiance. The characteristics of the load-side harmonic impedances were analyzed, and their harmonic contributions were evaluated for different distortion levels. The effect of the high penetration of PV on the harmonic distortion of both positive and negative sequences was also investigated. The simulation results are presented based on case studies. The current distortion levels are in agreement with relevant standards, otherwise the Total Harmonic Distortion (THD) increases under low PV power generation due to its inverse relation with the fundamental current.

Keywords: harmonic distortion analysis, power quality, PV systems, residential distribution system

Procedia PDF Downloads 261

Authors: Mayank, Navneet Kaur, Narinder Singh

Abstract:

The V599 mutations in the BRAF protein are extremely oncogenic, responsible for countless of malignant conditions. Along with wild type, V599E, V599D, and V599R are the important mutated variants of the BRAF proteins. The BRAF inhibitory anticancer agents are continuously developing, and sorafenib is a BRAF inhibitor that is under clinical use. The crystal structure of sorafenib bounded to wild type, and V599 is known, showing a similar interaction pattern in both the case. The mutated 599th residue, in both the case, is also found not interacting directly with the co-crystallized sorafenib molecule. However, the IC50 value of sorafenib was found extremely different in both the case, i.e., 22 nmol/L for wild and 38 nmol/L for V599E protein. Molecular docking study and MMGBSA binding energy results also revealed a significant difference in the binding pattern of sorafenib in both the case. Therefore, to explore the role of distinctively situated 599th residue, we have further conducted comprehensive computational studies. The molecular dynamics simulation, residue interaction network (RIN) analysis, and residue correlation study results revealed the importance of the 599th residue on the therapeutic outcome and overall dynamic of the BRAF protein. Therefore, although the position of 599th residue is very much distinctive from the ligand-binding cavity of BRAF, still it has exceptional control over the overall functional outcome of the protein. The insight obtained here may seem extremely important and guide us while designing ideal BRAF inhibitory anticancer molecules.

Keywords: BRAF, oncogenic, sorafenib, computational studies

Procedia PDF Downloads 111

Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

Procedia PDF Downloads 129

Authors: V. Bezverbny

Abstract:

The actuality of the project deals with stable growing of deviant behavior’ statistics among Moscow citizens. During the recent years the socioeconomic health, wealth and life expectation of Moscow residents is regularly growing up, but the limits of crime and drug addiction have grown up seriously. Another serious Moscow problem has been economical stratification of population. The cost of identical residential areas differs at 2.5 times. The project is aimed at complex research and the development of methodology for main factors and reasons evaluation of deviant behavior growing in Moscow. The main project objective is finding out the links between the urban environment quality and dynamics of citizens’ deviant behavior in regional and municipal aspect using the statistical research methods and GIS modeling. The conducted research allowed: 1) to evaluate the dynamics of deviant behavior in Moscow different administrative districts; 2) to describe the reasons of crime increasing, drugs addiction, alcoholism, suicides tendencies among the city population; 3) to develop the city districts classification based on the level of the crime rate; 4) to create the statistical database containing the main indicators of Moscow population deviant behavior in 2010-2015 including information regarding crime level, alcoholism, drug addiction, suicides; 5) to present statistical indicators that characterize the dynamics of Moscow population deviant behavior in condition of expanding the city territory; 6) to analyze the main sociological theories and factors of deviant behavior for concretization the deviation types; 7) to consider the main theoretical statements of the city sociology devoted to the reasons for deviant behavior in megalopolis conditions. To explore the level of deviant behavior’ factors differentiation, the questionnaire was worked out, and sociological survey involved more than 1000 people from different districts of the city was conducted. Sociological survey allowed to study the socio-economical and psychological factors of deviant behavior. It also included the Moscow residents’ open-ended answers regarding the most actual problems in their districts and reasons of wish to leave their place. The results of sociological survey lead to the conclusion that the main factors of deviant behavior in Moscow are high level of social inequality, large number of illegal migrants and bums, nearness of large transport hubs and stations on the territory, ineffective work of police, alcohol availability and drug accessibility, low level of psychological comfort for Moscow citizens, large number of building projects.

Keywords: deviant behavior, megapolis, Moscow, urban environment, social stratification

Procedia PDF Downloads 188

Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 55

Authors: Aina Barcelona, Carolyn Oldham, Jordi Colomer, Teresa Serra

Abstract:

The loss of seagrass meadows worldwide is being tackled by formulating coastal restoration strategies. Seagrass loss results in a network of vegetated patches which are barely interconnected, and consequently, the ecological services they provide may be highly compromised. Hence, there is a need to optimize coastal management efforts in order to implement successful restoration strategies, not only through modifying the architecture of the canopies but also by gathering together information on the hydrodynamic conditions of the seabeds. To obtain information on the hydrodynamics within the patches of vegetation, this study deals with the scale analysis of the minimum lengths of patch management strategies that can be effectively used on. To this aim, a set of laboratory experiments were conducted in a laboratory flume where the plant densities, patch lengths, and hydrodynamic conditions were varied to discern the vegetated patch lengths that can provide optimal ecosystem services for canopy development. Two possible patch behaviours based on the turbulent kinetic energy (TKE) production were determined: one where plants do not interact with the flow and the other where plants interact with waves and produce TKE. Furthermore, this study determines the minimum patch lengths that can provide successful management restoration. A canopy will produce TKE, depending on its density, the length of the vegetated patch, and the wave velocities. Therefore, a vegetated patch will produce plant-wave interaction under high wave velocities when it presents large lengths and high canopy densities.

Keywords: seagrass, minimum patch size, turbulent kinetic energy, oscillatory flow

Procedia PDF Downloads 193

Authors: William C. Alexander

Abstract:

The oscillating water column (OSC) wave energy converter (WEC) with deep water reactance (DWR) consists of a large hollow sphere filled with seawater at the base, referred to as the ‘stabilizer’, a hollow cylinder at the top of the device, with a said cylinder having a bottom open to the sea and a sealed top save for an orifice which leads to an air turbine, and a long, narrow rod connecting said stabilizer with said cylinder. A small amount of ballast at the bottom of the stabilizer and a small amount of floatation in the cylinder keeps the device upright in the sea. The floatation is set such that the mean water level is nominally halfway up the cylinder. The entire device is loosely moored to the seabed to keep it from drifting away. In the presence of ocean waves, seawater will move up and down within the cylinder, producing the ‘oscillating water column’. This gives rise to air pressure within the cylinder alternating between positive and negative gauge pressure, which in turn causes air to alternately leave and enter the cylinder through said top-cover situated orifice. An air turbine situated within or immediately adjacent to said orifice converts the oscillating airflow into electric power for transport to shore or elsewhere by electric power cable. Said oscillating air pressure produces large up and down forces on the cylinder. Said large forces are opposed through the rod to the large mass of water retained within the stabilizer, which is located deep enough to be mostly free of any wave influence and which provides the deepwater reactance. The cylinder and stabilizer form a spring-mass system which has a vertical (heave) resonant frequency. The diameter of the cylinder largely determines the power rating of the device, while the size (and water mass within) of the stabilizer determines said resonant frequency. Said frequency is chosen to be on the lower end of the wave frequency spectrum to maximize the average power output of the device over a large span of time (such as a year). The upper portion of the device (the cylinder) moves laterally (surge) with the waves. This motion is accommodated with minimal loading on the said rod by having the stabilizer shaped like a sphere, allowing the entire device to rotate about the center of the stabilizer without rotating the seawater within the stabilizer. A full-scale device of this type may have the following dimensions. The cylinder may be 16 meters in diameter and 30 meters high, the stabilizer 25 meters in diameter, and the rod 55 meters long. Simulations predict that this will produce 1,400 kW in waves of 3.5-meter height and 12 second period, with a relatively flat power curve between 5 and 16 second wave periods, as will be suitable for an open-ocean location. This is nominally 10 times higher power than similar-sized WEC spar buoys as reported in the literature, and the device is projected to have only 5% of the mass per unit power of other OWC converters.

Keywords: oscillating water column, wave energy converter, spar bouy, stabilizer

Procedia PDF Downloads 103

Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

Abstract:

Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

Procedia PDF Downloads 521

Authors: Rajkumar Ghosh

Abstract:

Fly ash, a byproduct of coal combustion, has become a prevalent environmental concern due to its potential impact on both groundwater quality and local climate change. This study aims to provide an in-depth analysis of the various mechanisms through which fly ash contaminates groundwater, as well as the possible consequences of this contamination on local climate change. The presence of fly ash in groundwater not only poses a risk to human health but also has the potential to influence local climate change through complex interactions. Although fly ash has various applications in construction and other industries, improper disposal and lack of containment measures have led to its infiltration into groundwater systems. Through a comprehensive review of existing literature and case studies, the interactions between fly ash and groundwater systems, assess the effects on hydrology, and discuss the implications for the broader climate. This section reviews the pathways through which fly ash enters groundwater, including leaching from disposal sites, infiltration through soil, and migration from surface water bodies. The physical and chemical characteristics of fly ash that contribute to its mobility and persistence in groundwater. The introduction of fly ash into groundwater can alter its chemical composition, leading to an increase in the concentration of heavy metals, metalloids, and other potentially toxic elements. The mechanisms of contaminant transport and highlight the potential risks to human health and ecosystems. Fly ash contamination in groundwater may influence the hydrological cycle through changes in groundwater recharge, discharge, and flow dynamics. This section examines the implications of altered hydrology on local water availability, aquatic habitats, and overall ecosystem health. The presence of fly ash in groundwater may have direct and indirect effects on local climate change. The role of fly ash as a potent greenhouse gas absorber and its contribution to radiative forcing. Additionally, investigation of the possible feedback mechanisms between groundwater contamination and climate change, such as altered vegetation patterns and changes in local temperature and precipitation patterns. In this section, potential mitigation and remediation techniques to minimize fly ash contamination in groundwater are analyzed. These may include improved waste management practices, engineered barriers, groundwater remediation technologies, and sustainable fly ash utilization. This paper highlights the critical link between fly ash contamination in groundwater and its potential contribution to local climate change. It emphasizes the importance of addressing this issue promptly through a combination of preventive measures, effective management strategies, and continuous monitoring. By understanding the interconnections between fly ash contamination, groundwater quality, and local climate, towards creating a more resilient and sustainable environment for future generations. The findings of this research can assist policymakers and environmental managers in formulating sustainable strategies to mitigate fly ash contamination and minimize its contribution to climate change.

Keywords: groundwater, climate, sustainable environment, fly ash contamination

Procedia PDF Downloads 80

Authors: K. Hamdi, Z. Aboura, W. Harizi, K. Khellil

Abstract:

Improving the electrical properties of organic matrix composites has been investigated in several studies. Thus, to extend the use of composites in more varied application, one of the actual barrier is their poor electrical conductivities. In the case of carbon fiber composites, organic matrix are in charge of the insulating properties of the resulting composite. However, studying the properties of continuous carbon fiber nano-filled composites is less investigated. This work tends to characterize the effect of carbon black nano-fillers on the properties of the woven carbon fiber composites. First of all, SEM observations were performed to localize the nano-particles. It showed that particles penetrated on the fiber zone (figure1). In fact, by reaching the fiber zone, the carbon black nano-fillers created network connectivity between fibers which means an easy pathway for the current. It explains the noticed improvement of the electrical conductivity of the composites by adding carbon black. This test was performed with the four points electrical circuit. It shows that electrical conductivity of 'neat' matrix composite passed from 80S/cm to 150S/cm by adding 9wt% of carbon black and to 250S/cm by adding 17wt% of the same nano-filler. Thanks to these results, the use of this composite as a strain gauge might be possible. By the way, the study of the influence of a mechanical excitation (flexion, tensile) on the electrical properties of the composite by recording the variance of an electrical current passing through the material during the mechanical testing is possible. Three different configuration were performed depending on the rate of carbon black used as nano-filler. These investigation could lead to develop an auto-instrumented material.

Keywords: carbon fibers composites, nano-fillers, strain-sensors, auto-instrumented

Procedia PDF Downloads 406