Search results for: industrial wireless network (IWN)

Authors: Mohammad Momeenul Islam, U. Johnson Alengaram, Mohd Zamin Jumaat, Iftekhair Ibnul Bashar

Abstract:

Palm oil industry produces millions of tonnes of industrial wastes and these wastes create huge storage and environmental problems. In order to solve these problems various research works have been performed for past decades. The commonly available wastes are Oil palm shells (OPS) and Palm oil fuel ash (POFA). These materials have already acquired well recognition as alternate of conventional construction materials. OPS has been used as coarse aggregate and compressive strength was found up to 56 MPa for 56-day. It is said that 30 grade Oil Palm shell concrete (OPSC) is possible without adding any cementitious materials. The maximum modulus of elasticity for OPSC was found 18.6 GPa. The Oil palm shell concrete (OPSC) are used in country areas and nearby areas where the palm oil factories are located for houses, road-kerbs, drain blocks, etc. In case of superstructure like beams and slab are also produced by utilizing OPS. Many experimental works have been performed to establish POFA as a substituting binding material in replace of Ordinary Portland cement (OPC). Throughout the research it has been showed that up to 20% of cement by mass can be replaced by POFA. POFA is one of the most enriched pozzolanic materials. The main purpose of this review is to discuss the usage and opportunity of the palm oil industrial wastes as construction materials following the previous experimental research work.

Keywords: construction materials, oil palm shells (OPS), palm oil fuel ash (POFA), aggregates

Procedia PDF Downloads 324

Authors: Afaf Alharbi, Qianni Zhang

Abstract:

The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: attention multiple instance learning, MIL and transfer learning, histopathological slides, cancer tissue classification

Procedia PDF Downloads 83

Authors: Kwaku Damoah

Abstract:

This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

Procedia PDF Downloads 47

Authors: Mayank Mundhra, Chester Rebeiro

Abstract:

Cryptocurrencies are rapidly finding wide application in areas such as Real Time Gross Settlements and Payments Systems. Ripple is a cryptocurrency that has gained prominence with banks and payment providers. It solves the Byzantine General’s Problem with its Ripple Protocol Consensus Algorithm (RPCA), where each server maintains a list of servers, called Unique Node List (UNL) that represents the network for the server, and will not collectively defraud it. The server believes that the network has come to a consensus when members of the UNL come to a consensus on a transaction. In this paper we improve Ripple to achieve better speed, security, last mile connectivity and ease of use. We implement guidelines and automated systems for building and maintaining UNLs for resilience, robustness, improved security, and efficient information propagation. We enhance the system so as to ensure that each server receives information from across the whole network rather than just from the UNL members. We also introduce the paradigm of UNL overlap as a function of information propagation and the trust a server assigns to its own UNL. Our design not only reduces vulnerabilities such as eclipse attacks, but also makes it easier to identify malicious behaviour and entities attempting to fraudulently Double Spend or stall the system. We provide experimental evidence of the benefits of our approach over the current Ripple scheme. We observe ≥ 4.97x and 98.22x in speedup and success rate for information propagation respectively, and ≥ 3.16x and 51.70x in speedup and success rate in consensus.

Keywords: Ripple, Kelips, unique node list, consensus, information propagation

Procedia PDF Downloads 125

Authors: Awais Ahmed, Javed Iqbal

Abstract:

Pakistan has been for many years facing extreme challenges in energy deficit due to the shortage of power generation compared to increasing demand. A part of this energy deficit is also contributed by the power lost in transmission and distribution network. Unfortunately, distribution companies are not equipped with modern technologies and methods to identify and eliminate these losses. According to estimate, total energy lost in early 2000 was between 20 to 26 percent. To address this issue the present research study was designed with the objectives of developing a standalone GIS application for distribution companies having the capability of loss calculation as well as identification of overloaded transformers. For this purpose, Hilal Road feeder in Faisalabad Electric Supply Company (FESCO) was selected as study area. An extensive GPS survey was conducted to identify each consumer, linking it to the secondary pole of the transformer, geo-referencing equipment and documenting conductor sizes. To identify overloaded transformer, accumulative kWH reading of consumer on transformer was compared with threshold kWH. Technical losses of 11kV and 220V lines were calculated using the data from substation and resistance of the network calculated from the geo-database. To automate the process a standalone GIS application was developed using ArcObjects with engineering analysis capabilities. The application uses GIS database developed for 11kV and 220V lines to display and query spatial data and present results in the form of graphs. The result shows that about 14% of the technical loss on both high tension (HT) and low tension (LT) network while about 4 out of 15 general duty transformers were found overloaded. The study shows that GIS can be a very effective tool for distribution companies in management and planning of their distribution network.

Keywords: geographical information system, GIS, power distribution, distribution transformers, technical losses, GPS, SDSS, spatial decision support system

Procedia PDF Downloads 358

Authors: M. S. Mrudula, M. R. Gopinathan Nair

Abstract:

In order to utilize the natural rubber for developing new green polymeric materials for specialty applications, we have prepared natural rubber and polyethylene oxide based polymeric networks by two shot method. The polymeric networks thus formed have been used as chelating exchanger for metal ion binding. Chelating exchangers are, in general, coordinating copolymers containing one or more electron donor atoms such as N, S, O, and P that can form coordinate bonds with metals. Hydrogels are water- swollen network of hydrophilic homopolymer or copolymers. They acquire a great interest due to the facility of the incorporation of different chelating groups into the polymeric networks. Such polymeric hydrogels are promising materials in the field of hydrometallurgical applications and water purification due to their chemical stability. The current study discusses the swelling response of the polymeric networks as a function of time, temperature, pH and [NaCl] and sorption studies. Equilibrium swelling has been observed to depend on both structural aspects of the polymers and environmental factors. Metal ion sorption shows that these polymeric networks can be used for removal, separation, and enrichment of metal ions from aqueous solutions and can play an important role for environmental remediation of municipal and industrial wastewater.

Keywords: block copolymer, adsorption, chelating exchanger, swelling study, polymer, metal complexes

Procedia PDF Downloads 323

Authors: Semra Turkan

Abstract:

In this paper, we have examined the factors that affect the productivity of Turkey’s Top 500 Industrial Enterprises in 2014. The labor productivity of enterprises is taken as an indicator of productivity of industrial enterprises. When the relationships between some financial ratios and labor productivity, it is seen that there is a nonparametric relationship between labor productivity and return on sales. In addition, the distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, the quantile regression is more suitable for this data. Hence, the nonparametric relationship between labor productivity and return on sales by quantile smoothing splines.

Keywords: quantile regression, smoothing spline, labor productivity, financial ratios

Procedia PDF Downloads 285

Authors: Vikram Prabhu, Mohammad Shikh Bahaei

Abstract:

This paper depicts the implementation of a new infallible technique to protect an Unmanned Aerial Vehicle from cyber-attacks. An Unmanned Aerial Vehicle (UAV) could be vulnerable to cyber-attacks because of jammers or eavesdroppers over the network which pose as a threat to the security of the UAV. In the field of network security, there are quite a few protocols which can be used to establish a secure connection between UAVs and their Operators. In this paper, we discuss how the Interlock Protocol could be implemented to foil the Man-in-the-Middle Attack. In this case, Wireshark has been used as the sniffer (man-in-the-middle). This paper also shows a comparison between the Interlock Protocol and the TCP Protocols using cryptcat and netcat and at the same time highlights why the Interlock Protocol is the most efficient security protocol to prevent eavesdropping over the communication channel.

Keywords: interlock protocol, Diffie-Hellman algorithm, unmanned aerial vehicles, control station, man-in-the-middle attack, Wireshark

Procedia PDF Downloads 286

Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

Abstract:

In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

Procedia PDF Downloads 255

Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

Abstract:

The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

Procedia PDF Downloads 277

Authors: F. Lhafiane, A. Elbyed, M. Bouchoum

Abstract:

The design of Reverse logistics Network has attracted growing attention with the stringent pressures from both environmental awareness and business sustainability. Reverse logistical activities include return, remanufacture, disassemble and dispose of products can be quite complex to manage. In addition, demand can be difficult to predict, and decision making is one of the challenges tasks. This complexity has amplified the need to develop an integrated architecture for product return as an enterprise system. The main purpose of this paper is to design Multi agent system (MAS) architecture using the Prometheus methodology to efficiently manage reverse logistics processes. The proposed MAS architecture includes five types of agents: Gate keeping Agent, Collection Agent, Sorting Agent, Processing Agent and Disposal Agent which act respectively during the five steps of reverse logistics Network.

Keywords: reverse logistics, multi agent system, prometheus methodology

Procedia PDF Downloads 446

Authors: Usama Shahid

Abstract:

Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

Procedia PDF Downloads 63

Authors: Priyal Motwani

Abstract:

The current market for bamboo products in India is about Rs. 21000 crores and is highly unorganised and fragmented. In this study, we have closely analysed the structure and functions of a major bamboo craft based organisation in Kerela, India and elaborated about its value chain, product mix, pricing strategy and supply chain, collaborations and competitive landscape. We have identified six major bottlenecks that are prevalent in such organisations, based on the Indian context, in relation to their product mix, asset management, and supply chain- corresponding waste management and retail network. The study has identified that the target customers for the bamboo based products and alternative revenue streams (eco-tourism, microenterprises, training), by carrying out secondary and primary research (5000 sample space), that can boost the existing revenue by 150%. We have then recommended an optimum product mix-covering premium, medium and low valued processing, for medium sized bamboo based organisations, in accordance with their capacity to maximize their revenue potential. After studying such organisations and their counter parts, the study has established an optimum retail network, considering B2B, B2C physical and online retail, to maximize their sales to their target groups. On the basis of the results obtained from the analysis of the future and present trends, our study gives recommendations to improve the revenue potential of bamboo based organisation in India and promote sustainability.

Keywords: bamboo, bottlenecks, optimization, product mix, retail network, value chain

Procedia PDF Downloads 199

Authors: Mohammad Reza Fooladi

Abstract:

The patent system which fits with industrial and economic situation in the country, by strengthening research and development, technology transfer and increasing foreign investment can provide economic and industrial growth of the countries. However, the extent and manner of support should be commensurate with the country's conditions and comply with significant rules to have a positive effect on research and development, technology transfer and the amount of foreign investment. The present article tries to while reviewing the state of effectiveness of the patent system on economic growth, to illustrate the characteristics of the patent system fits Afghanistan and according to this matter provide the necessary recommendations about the improvement of laws and regulations related to the patent in Afghanistan.

Keywords: patent, economic growth, technology transfer, Afghanistan

Procedia PDF Downloads 388

Authors: Walailak Atthirawong

Abstract:

By the end of 2015, the Association of Southeast Asian Nations (ASEAN) countries proclaim to transform into the next stage of an economic era by having a single market and production base called ASEAN Economic Community (AEC). One objective of the AEC is to establish ASEAN as a single market and one production base making ASEAN highly competitive economic region and competitive with new mechanisms. As a result, it will open more opportunities to enterprises in both trade and investment, which offering a competitive market of US$ 2.6 trillion and over 622 million people. Location decision plays a key role in achieving corporate competitiveness. Hence, it may be necessary for enterprises to redesign their supply chains via enlarging a new production base which has low labor cost, high labor skill and numerous of labor available. This strategy will help companies especially for apparel industry in order to maintain a competitive position in the global market. Therefore, in this paper a generic model for location selection decision for Thai apparel industry using Fuzzy Analytical Network Process (FANP) is proposed. Myanmar, Vietnam and Cambodia are referred for alternative location decision from interviewing expert persons in this industry who have planned to enlarge their businesses in AEC countries. The contribution of this paper lies in proposing an approach model that is more practical and trustworthy to top management in making a decision on location selection.

Keywords: apparel industry, ASEAN Economic Community (AEC), Fuzzy Analytical Network Process (FANP), location decision

Procedia PDF Downloads 215

Authors: Chayma Bahar, Chokri Baccouch, Hedi Sakli, Nizar Sakli

Abstract:

Harvesting and transport of optical and radiofrequency signals are a topical subject with multiple challenges. In this paper, we present a Optical RECTENNA system. We propose here a hybrid system solar cell antenna for 5G mobile communications networks. Thus, we propose rectifying circuit. A parametric study is done to follow the influence of load resistance and input power on Optical RECTENNA system performance. Thus, we propose a solar cell antenna structure in the frequency band of future 5G standard in 2.45 GHz bands.

Keywords: antenna, IoT, optical rectenna, solar cell

Procedia PDF Downloads 161

Authors: Khaled Sellami, Lynda Sellami, Pierre F. Tiako

Abstract:

Ubiquitous computing helps make data and services available to users anytime and anywhere. This makes the cooperation of devices a crucial need. In return, such cooperation causes an overload of the devices and/or networks, resulting in network malfunction and suspension of its activities. Our goal in this paper is to propose an approach of devices reconfiguration in order to help to reduce the energy consumption in ubiquitous environments. The idea is that when high-energy consumption is detected, we proceed to a change in component distribution on the devices to reduce and/or balance the energy consumption. We also investigate the possibility to detect high-energy consumption of devices/network based on devices abilities. As a result, our idea realizes a reconfiguration of devices aimed at reducing the consumption of energy and/or load balancing in ubiquitous environments.

Keywords: ubiquitous computing, load balancing, device energy consumption, reconfiguration

Procedia PDF Downloads 253

Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj K. Biswas, Frank Ferrese

Abstract:

This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

Procedia PDF Downloads 498

Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj Biswas, Frank Ferrese

Abstract:

This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

Procedia PDF Downloads 443

Authors: Brandon Foggo, Nanpeng Yu

Abstract:

Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: distribution network, machine learning, network topology, phase identification, smart grid

Procedia PDF Downloads 280

Authors: Marziyeh Khavari

Abstract:

In recent decades, climate change, global warming, and the growing population worldwide face some challenges, such as increasing food consumption and shortage of resources. Assessing how climate change could disturb crops, especially hazelnut production, seems crucial for sustainable agriculture production. For hazelnut cultivation in the mid-warm condition, such as in Iran, here we present an investigation of climate parameters and how much they are effective on fertility and nut production of hazelnut trees. Therefore, the climate change of the northern zones in Iran has investigated (1960-2017) and was reached an uptrend in temperature. Furthermore, the descriptive analysis performed on six cultivars during seven years shows how this small-scale survey could demonstrate the effects of climate change on hazelnut production and stability. Results showed that some climate parameters are more significant on nut production, such as solar radiation, soil temperature, relative humidity, and precipitation. Moreover, some cultivars have produced more stable production, for instance, Negret and Segorbe, while the Mervill de Boliver recorded the most variation during the study. Another aspect that needs to be met is training and predicting an actual model to simulate nut production through a neural network and linear regression simulation. The study developed and estimated the ANN model's generalization capability with different criteria such as RMSE, SSE, and accuracy factors for dependent and independent variables (environmental and yield traits). The models were trained and tested while the accuracy of the model is proper to predict hazelnut production under fluctuations in weather parameters.

Keywords: climate change, neural network, hazelnut, global warming

Procedia PDF Downloads 115

Authors: A. T. Omarova, G. N. Nakipova

Abstract:

This article discusses the principles of object-oriented human capital development using the technology program. Also the article includes priorities of the strategy of industrial-innovative development of Kazakhstan in conditions of integration activity into the world community. The article shows the tasks of human resource management in the implementation of industrial and innovation development, particularities of Kazakhstan's theory of management staff, as well as due to the specificity of the Kazakhstan authorities. In the article, we have considered the factors which are affecting the people in the organization and also have considered mechanisms of HRM within organization in the conditions of innovative development in Kazakhstan.

Keywords: programming, management of human resources, innovation, investment, innovation process, HRD model, innovative development, integration, management, transformation, economic potential, competitiveness

Procedia PDF Downloads 384

Authors: Ahlem Dhahri, Waleed Omri, Audrey Becuwe, Abdelwahed Omri

Abstract:

In industrial processes, decision-making ranges across different scales, from process control to supply chain management. The purchasing decision-making process in the supply chain is presently gaining more attention as a critical contributor to the company's strategic success. Given the scarcity of thorough summaries in the prior studies, this bibliometric analysis aims to adopt a meticulous approach to achieve quantitative knowledge on the constantly evolving subject of purchasing decision-making in supply chain management. Through bibliometric analysis, we examine a sample of 358 peer-reviewed articles from the Scopus database. VOSviewer and Gephi software were employed to analyze, combine, and visualize the data. Data analytic techniques, including citation network, page-rank analysis, co-citation, and publication trends, have been used to identify influential works and outline the discipline's intellectual structure. The outcomes of this descriptive analysis highlight the most prominent articles, authors, journals, and countries based on their citations and publications. The findings from the research illustrate an increase in the number of publications, exhibiting a slightly growing trend in this field. Co-citation analysis coupled with content analysis of the most cited articles identified five research themes mentioned as follows integrating sustainability into the supplier selection process, supplier selection under disruption risks assessment and mitigation strategies, Fuzzy MCDM approaches for supplier evaluation and selection, purchasing decision in vendor problems, decision-making techniques in supplier selection and order lot sizing problems. With the help of a graphic timeline, this exhaustive map of the field illustrates a visual representation of the evolution of publications that demonstrate a gradual shift from research interest in vendor selection problems to integrating sustainability in the supplier selection process. These clusters offer insights into a wide variety of purchasing methods and conceptual frameworks that have emerged; however, they have not been validated empirically. The findings suggest that future research would emerge with a greater depth of practical and empirical analysis to enrich the theories. These outcomes provide a powerful road map for further study in this area.

Keywords: bibliometric analysis, citation analysis, co-citation, Gephi, network analysis, purchasing, SCM, VOSviewer

Procedia PDF Downloads 67

Authors: Maria M. Giannakou, Athanasios K. Ziliaskopoulos

Abstract:

Transmission pipelines carrying natural gas are often routed through populated cities, industrial and environmentally sensitive areas. While the need for these networks is unquestionable, there are serious concerns about the risk these lifeline networks pose to the people, to their habitat and to the critical infrastructures, especially in view of natural disasters such as earthquakes. This work presents an Integrated Pipeline Risk Management methodology (IPRM) for assessing the hazard associated with a natural gas pipeline failure due to natural or manmade disasters. IPRM aims to optimize the allocation of the available resources to countermeasures in order to minimize the impacts of pipeline failure to humans, the environment, the infrastructure and the economic activity. A proposed knapsack mathematical programming formulation is introduced that optimally selects the proper mitigation policies based on the estimated cost – benefit ratios. The proposed model is demonstrated with a small numerical example. The vulnerability analysis of these pipelines and the quantification of consequences from such failures can be useful for natural gas industries on deciding which mitigation measures to implement on the existing pipeline networks with the minimum cost in an acceptable level of hazard.

Keywords: cost benefit analysis, knapsack problem, natural gas distribution network, risk management, risk mitigation

Procedia PDF Downloads 273

Authors: Sumitra Nuanmeesri

Abstract:

The research focus on study, analyze and design the model of the infrastructure and computer networks for distance education, online learning via new media, e-learning and blended learning. The collected information from study and analyze process that information was evaluated by the index of item objective congruence (IOC) by 9 specialists to design model. The results of evaluate the model with the mean and standard deviation by the sample of 9 specialists value is 3.85. The results showed that the infrastructure and computer networks are designed to be appropriate to a great extent appropriate to a great extent.

Keywords: blended learning, new media, infrastructure and computer network, tele-education, online learning

Procedia PDF Downloads 387

Authors: A. M. Khourshid, A. M. El-Kassas, I. Sabry

Abstract:

The objective of this work was to investigate the mechanical properties in order to demonstrate the feasibility of friction stir welding for joining Al 6061 aluminium alloys. Welding was performed on pipe with different thickness (2, 3 and 4 mm), five rotational speeds (485, 710, 910, 1120 and 1400 rpm) and a traverse speed of 4mm/min. This work focuses on two methods which are artificial neural networks using software and Response Surface Methodology (RSM) to predict the tensile strength, the percentage of elongation and hardness of friction stir welded 6061 aluminium alloy. An Artificial Neural Network (ANN) model was developed for the analysis of the friction stir welding parameters of 6061 pipe. Tensile strength, the percentage of elongation and hardness of weld joints were predicted by taking the parameters tool rotation speed, material thickness and axial force as a function. A comparison was made between measured and predicted data. Response Surface Methodology (RSM) was also developed and the values obtained for the response tensile strength, the percentage of elongation and hardness are compared with measured values. The effect of FSW process parameters on mechanical properties of 6061 aluminium alloy has been analysed in detail.

Keywords: friction stir welding, aluminium alloy, response surface methodology, artificial neural network

Procedia PDF Downloads 273

Authors: Tatiana A. Dolenko, Sergey A. Burikov, Alexander O. Efitorov, Sergey A. Dolenko

Abstract:

In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average.

Keywords: inverse problems, multi-component solutions, neural networks, Raman spectroscopy

Procedia PDF Downloads 504

Authors: Rama Shanker, Anil Kumar Sachan

Abstract:

Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: aggregate proportions, artificial neural network, concrete grade, concrete mix design

Procedia PDF Downloads 372

Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

Abstract:

First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

Procedia PDF Downloads 370

Authors: Nader Khalafian, Mohsen Ghaderi

Abstract:

Field observations have revealed many examples of structures which were damaged due to ground surface deformation caused by the faulting phenomena. In this paper some efforts were made in order to estimate the length and location of the ground surface where large displacements were created due to the reverse faulting. This research has conducted in two steps; (1) in the first step, a 2D explicit finite element model were developed using ABAQUS software. A subroutine for Mohr-Coulomb failure criterion with strain softening model was developed by the authors in order to properly model the stress strain behavior of the soil in the fault rapture zone. The results of the numerical analysis were verified with the results of available centrifuge experiments. Reasonable coincidence was found between the numerical and experimental data. (2) In the second step, the effects of the fault dip angle (δ), depth of soil layer (H), dilation and friction angle of sand (ψ and φ) and the amount of fault offset (d) on the soil surface displacement and fault rupture path were investigated. An artificial neural network-based model (ANN), as a powerful prediction tool, was developed to generate a general model for predicting faulting characteristics. A properly sized database was created to train and test network. It was found that the length and location of the zone of displaced ground surface can be accurately estimated using the proposed model.

Keywords: reverse faulting, surface deformation, numerical, neural network

Procedia PDF Downloads 408