Search results for: social network size
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 18724

Search results for: social network size

17434 Toward an Understanding of the Neurofunctional Dissociation between Animal and Tool Concepts: A Graph Theoretical Analysis

Authors: Skiker Kaoutar, Mounir Maouene

Abstract:

Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from Mc Rae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-world, resilience to damage

Procedia PDF Downloads 547
17433 Nonlinear Modeling of the PEMFC Based on NNARX Approach

Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

Abstract:

Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

Procedia PDF Downloads 381
17432 Software-Defined Networking: A New Approach to Fifth Generation Networks: Security Issues and Challenges Ahead

Authors: Behrooz Daneshmand

Abstract:

Software Defined Networking (SDN) is designed to meet the future needs of 5G mobile networks. The SDN architecture offers a new solution that involves separating the control plane from the data plane, which is usually paired together. Network functions traditionally performed on specific hardware can now be abstracted and virtualized on any device, and a centralized software-based administration approach is based on a central controller, facilitating the development of modern applications and services. These plan standards clear the way for a more adaptable, speedier, and more energetic network beneath computer program control compared with a conventional network. We accept SDN gives modern inquire about openings to security, and it can significantly affect network security research in numerous diverse ways. Subsequently, the SDN architecture engages systems to effectively screen activity and analyze threats to facilitate security approach modification and security benefit insertion. The segregation of the data planes and control and, be that as it may, opens security challenges, such as man-in-the-middle attacks (MIMA), denial of service (DoS) attacks, and immersion attacks. In this paper, we analyze security threats to each layer of SDN - application layer - southbound interfaces/northbound interfaces - controller layer and data layer. From a security point of see, the components that make up the SDN architecture have a few vulnerabilities, which may be abused by aggressors to perform noxious activities and hence influence the network and its administrations. Software-defined network assaults are shockingly a reality these days. In a nutshell, this paper highlights architectural weaknesses and develops attack vectors at each layer, which leads to conclusions about further progress in identifying the consequences of attacks and proposing mitigation strategies.

Keywords: software-defined networking, security, SDN, 5G/IMT-2020

Procedia PDF Downloads 100
17431 Hydrodynamics of Wound Ballistics

Authors: Harpreet Kaur, Er. Arjun, Kirandeep Kaur, P. K. Mittal

Abstract:

Simulation of a human body from a 20% gelatin & 80% water mixture is examined from a wound ballistics point of view. Parameters such as incapacitation energy & temporary to permanent cavity size & tools of hydrodynamics have been employed to arrive at a model of the human body similar to the one adopted by NATO. Calculations using equations of motion yield a value of 339 µs in which a temporary cavity with maximum size settles down to a permanent cavity. This occurs for 10mm size bullets & settles down to a permanent cavity in the case of 4 different bullets, i.e., 5.45, 5.56, 7.62,10 mm sizes. The obtained results are in excellent agreement with the body as a right circular cylinder of 15 cm height & 10 cm diameter. An effort is made here in this work to present a sound theoretical base to parameters commonly used in wound ballistics from field experience discussed by Col Coats & Major Beyer.

Keywords: gelatine, gunshot, hydrodynamic model, oscillation time, temporary and permanent cavity, wound ballistics

Procedia PDF Downloads 75
17430 BlueVision: A Visual Tool for Exploring a Blockchain Network

Authors: Jett Black, Jordyn Godsey, Gaby G. Dagher, Steve Cutchin

Abstract:

Despite the growing interest in distributed ledger technology, many data visualizations of blockchain are limited to monotonous tabular displays or overly abstract graphical representations that fail to adequately educate individuals on blockchain components and their functionalities. To address these limitations, it is imperative to develop data visualizations that offer not only comprehensive insights into these domains but education as well. This research focuses on providing a conceptual understanding of the consensus process that underlies blockchain technology. This is accomplished through the implementation of a dynamic network visualization and an interactive educational tool called BlueVision. Further, a controlled user study is conducted to measure the effectiveness and usability of BlueVision. The findings demonstrate that the tool represents significant advancements in the field of blockchain visualization, effectively catering to the educational needs of both novice and proficient users.

Keywords: blockchain, visualization, consensus, distributed network

Procedia PDF Downloads 62
17429 Understanding and Improving Neural Network Weight Initialization

Authors: Diego Aguirre, Olac Fuentes

Abstract:

In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques.

Keywords: deep learning, image classification, supervised learning, weight initialization

Procedia PDF Downloads 135
17428 On the Optimality Assessment of Nano-Particle Size Spectrometry and Its Association to the Entropy Concept

Authors: A. Shaygani, R. Saifi, M. S. Saidi, M. Sani

Abstract:

Particle size distribution, the most important characteristics of aerosols, is obtained through electrical characterization techniques. The dynamics of charged nano-particles under the influence of electric field in electrical mobility spectrometer (EMS) reveals the size distribution of these particles. The accuracy of this measurement is influenced by flow conditions, geometry, electric field and particle charging process, therefore by the transfer function (transfer matrix) of the instrument. In this work, a wire-cylinder corona charger was designed and the combined field-diffusion charging process of injected poly-disperse aerosol particles was numerically simulated as a prerequisite for the study of a multi-channel EMS. The result, a cloud of particles with non-uniform charge distribution, was introduced to the EMS. The flow pattern and electric field in the EMS were simulated using computational fluid dynamics (CFD) to obtain particle trajectories in the device and therefore to calculate the reported signal by each electrometer. According to the output signals (resulted from bombardment of particles and transferring their charges as currents), we proposed a modification to the size of detecting rings (which are connected to electrometers) in order to evaluate particle size distributions more accurately. Based on the capability of the system to transfer information contents about size distribution of the injected particles, we proposed a benchmark for the assessment of optimality of the design. This method applies the concept of Von Neumann entropy and borrows the definition of entropy from information theory (Shannon entropy) to measure optimality. Entropy, according to the Shannon entropy, is the ''average amount of information contained in an event, sample or character extracted from a data stream''. Evaluating the responses (signals) which were obtained via various configurations of detecting rings, the best configuration which gave the best predictions about the size distributions of injected particles, was the modified configuration. It was also the one that had the maximum amount of entropy. A reasonable consistency was also observed between the accuracy of the predictions and the entropy content of each configuration. In this method, entropy is extracted from the transfer matrix of the instrument for each configuration. Ultimately, various clouds of particles were introduced to the simulations and predicted size distributions were compared to the exact size distributions.

Keywords: aerosol nano-particle, CFD, electrical mobility spectrometer, von neumann entropy

Procedia PDF Downloads 343
17427 Critical Evaluation and Analysis of Effects of Different Queuing Disciplines on Packets Delivery and Delay for Different Applications

Authors: Omojokun Gabriel Aju

Abstract:

Communication network is a process of exchanging data between two or more devices via some forms of transmission medium using communication protocols. The data could be in form of text, images, audio, video or numbers which can be grouped into FTP, Email, HTTP, VOIP or Video applications. The effectiveness of such data exchange will be proved if they are accurately delivered within specified time. While some senders will not really mind when the data is actually received by the receiving device, inasmuch as it is acknowledged to have been received by the receiver. The time a data takes to get to a receiver could be very important to another sender, as any delay could cause serious problem or even in some cases rendered the data useless. The validity or invalidity of a data after delay will therefore definitely depend on the type of data (information). It is therefore imperative for the network device (such as router) to be able to differentiate among the packets which are time sensitive and those that are not, when they are passing through the same network. So, here is where the queuing disciplines comes to play, to handle network resources when such network is designed to service widely varying types of traffics and manage the available resources according to the configured policies. Therefore, as part of the resources allocation mechanisms, a router within the network must implement some queuing discipline that governs how packets (data) are buffered while waiting to be transmitted. The implementation of the queuing discipline will regulate how the packets are buffered while waiting to be transmitted. In achieving this, various queuing disciplines are being used to control the transmission of these packets, by determining which of the packets get the highest priority, less priority and which packets are dropped. The queuing discipline will therefore control the packets latency by determining how long a packet can wait to be transmitted or dropped. The common queuing disciplines are first-in-first-out queuing, Priority queuing and Weighted-fair queuing (FIFO, PQ and WFQ). This paper critically evaluates and analyse through the use of Optimized Network Evaluation Tool (OPNET) Modeller, Version 14.5 the effects of three queuing disciplines (FIFO, PQ and WFQ) on the performance of 5 different applications (FTP, HTTP, E-Mail, Voice and Video) within specified parameters using packets sent, packets received and transmission delay as performance metrics. The paper finally suggests some ways in which networks can be designed to provide better transmission performance while using these queuing disciplines.

Keywords: applications, first-in-first-out queuing (FIFO), optimised network evaluation tool (OPNET), packets, priority queuing (PQ), queuing discipline, weighted-fair queuing (WFQ)

Procedia PDF Downloads 358
17426 Artificial Neural Network in Ultra-High Precision Grinding of Borosilicate-Crown Glass

Authors: Goodness Onwuka, Khaled Abou-El-Hossein

Abstract:

Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

Procedia PDF Downloads 305
17425 A New Graph Theoretic Problem with Ample Practical Applications

Authors: Mehmet Hakan Karaata

Abstract:

In this paper, we first coin a new graph theocratic problem with numerous applications. Second, we provide two algorithms for the problem. The first solution is using a brute-force techniques, whereas the second solution is based on an initial identification of the cycles in the given graph. We then provide a correctness proof of the algorithm. The applications of the problem include graph analysis, graph drawing and network structuring.

Keywords: algorithm, cycle, graph algorithm, graph theory, network structuring

Procedia PDF Downloads 386
17424 Social Networks And Social Complexity: The Southern Italian Drive For Trade Exchange During The Late Bronze Age

Authors: Sara Fioretti

Abstract:

During the Middle Bronze Age, southern Italy underwent a reorganisation of social structures where local cultures, such as the sub-Apennine and Nuragic, flourished and participated in maritime trade. This paper explores the socio-economic relationships, in both cross-cultural and potentially inter-regional settings, present within the archaeological repertoire of the southern Italian Late Bronze Age (LBA 1600 -1050 BCE). The emergence of economic relations within the connectivity of the regional settlements is explored through ceramic contexts found in the case studies Punta di Zambrone, Broglio di Trebisacce, and Nuraghe Antigori. This paper discusses the findings of a statistical and theoretical approach from an ongoing study in relation to the Mediterranean’s characterisation as a period dominated by Mycenaean influence. This study engages with a theoretical bricolage of Social Networks Entanglement, and Assertive Objects Theory to address the selective and assertive dynamics evident in the cross-cultural trade exchanges as well as consider inter-regional dynamics. Through this intersection of theory and statistical analysis, the case studies establish a small percentage of pottery as imported, whilst assertive productions have a relatively higher quantity. Overall, the majority still adheres to regional Italian traditions. Therefore, we can dissect the rhizomatic relationships cultivated by the Italian coasts and Mycenaeans and their roles within their networks through the intersection of theoretical and statistical analysis. This research offers a new perspective on the complex nature of the Late Bronze Age relational structures.

Keywords: late bronze age, mediterranean archaeology, exchanges and trade, frequency distribution of ceramic assemblages, social network theory, rhizomatic exchanges

Procedia PDF Downloads 47
17423 Influencer Endorsement: Consumer Purchase Intention in Social Media Marketing

Authors: Izian Idris, Melissa Ha, Mikkay Wong

Abstract:

Social media marketing, including influencer marketing, is an ongoing phenomenon, and most companies as well as industries, are finding it crucial to implement social media marketing in their marketing strategies. However, social media influencer marketing still needs to be explored, and further research on this area needs to be carried out to fully understand the importance of social media influencer marketing in impacting consumer purchase decisions. Influencer endorsement has become a trend to grab users’ attention these days. Thus, the aim of this research paper is to explore the attributes of social media influencers/influencer as the endorser that impact consumer purchase intentions. The attributes that will be investigated include attitude homophily, physical attractiveness, and social attractiveness. Following this, the elaboration likelihood model from the theory of persuasion is implemented in this research to further examine the influence of social media influencer attributes on consumer purchase intentions. This study will be able to help marketers, businesses, and researchers understand the attributes of social media influencers as endorsers that will impact consumer purchase intentions and allow businesses to enhance their strategies to better cater to their target market.

Keywords: influencer, endorsement, consumer purchase, social media

Procedia PDF Downloads 84
17422 Distribution of Synechococcus and Prochlorococcus in Southeastern Coast of Peninsular Malaysia

Authors: Roswati Md. Amin, Nurul Asmera Mudiman, Muhammad Faisal Abd. Rahman, Md-Suffian Idris, Noor Hazwani Mohd Azmi

Abstract:

Distribution of picophytoplankton from two genera, Synechococcus and Prochlorococcus at the surface water (0.5m) were observed from coastal to offshore area of the southeastern coast of Peninsular Malaysia, for a six day cruise in August 2014 during SouthWest monsoon. The picophytoplankton was divided into two different size fractions (0.7-2.7μm and <0.7 μm) by filtering through GF/D (2.7 μm) and GF/F (0.7 μm) filter papers and counted by using flow cytometer. Synechococcus and Prochlorococcus contribute higher at 0.7-2.7μm size range (ca. 90% and 95%, respectively) compared to <0.7 μm (ca. 10% and 5%, respectively). Synechococcus (>52%) dominated the total picophytoplankton compared to Prochlorococcus (<26%) for both size fractions in southeastern coast of Peninsular Malaysia. Total density (<2.7 μm) of Synechococcus was ranging between 1.72 x104 and 12.57 x104 cells ml-1, while Prochlorococcus varied from 1.50 x104 to 8.62 x104. Both Synechococcus and Prochlorococcus abundance showed a decreasing trend from coastal to offshore.

Keywords: Peninsular Malaysia, prochlorococcus, South China Sea, synechococcus

Procedia PDF Downloads 316
17421 Proposed Fault Detection Scheme on Low Voltage Distribution Feeders

Authors: Adewusi Adeoluwawale, Oronti Iyabosola Busola, Akinola Iretiayo, Komolafe Olusola Aderibigbe

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The complex and radial structure of the low voltage distribution network (415V) makes it vulnerable to faults which are due to system and the environmental related factors. Besides these, the protective scheme employed on the low voltage network which is the fuse cannot be monitored remotely such that in the event of sustained fault, the utility will have to rely solely on the complaint brought by customers for loss of supply and this tends to increase the length of outages. A microcontroller based fault detection scheme is hereby developed to detect low voltage and high voltage fault conditions which are common faults on this network. Voltages below 198V and above 242V on the distribution feeders are classified and detected as low voltage and high voltages respectively. Results shows that the developed scheme produced a good response time in the detection of these faults.

Keywords: fault detection, low voltage distribution feeders, outage times, sustained faults

Procedia PDF Downloads 543
17420 Facing Global Competition through Participation in Global Innovation Networks: The Case of Mechatronics District in the Veneto Region

Authors: Monica Plechero

Abstract:

Many firms belonging to Italian industrial districts faced a crisis starting from 2000 and upsurging during 2008-2014. To remain competitive in the global market, these firms and their local systems need to renovate their traditional competitive advantages, strengthen their link with global flows of knowledge. This may be particularly relevant in sectors such as the mechatronics, that combine traditional knowledge domain with new knowledge domains (e.g. mechanics, electronics, and informatics). This sector is nowadays one of the key sectors within the so-called ‘smart specialization strategy’ that can lead part of the Italian traditional industry towards new economic developmental opportunities. This paper, by investigating the mechatronics district of the Veneto region, wants to shed new light on how firms of a local system can gain from the globalization of innovation and innovation networks. Methodologically, the paper relies on primary data collected through a survey targeting firms of the local system, as well as on a number of qualitative case studies. The relevant role of medium size companies in the district emerges as evident, as they have wider opportunities to be involved in different processes of globalization of innovation. Indeed, with respect to small companies, the size of medium firms allows them to exploit strategically international markets and globally distributed knowledge. Supporting medium firms’ global innovation strategies, and incentivizing their role as district gatekeepers, may strengthen the competitive capability of the local system and provide new opportunities to positively face global competition.

Keywords: global innovation network, industrial district, internationalization, innovation, mechatronics, Veneto region

Procedia PDF Downloads 230
17419 GA3C for Anomalous Radiation Source Detection

Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

Abstract:

In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

Procedia PDF Downloads 114
17418 Leveraging Automated and Connected Vehicles with Deep Learning for Smart Transportation Network Optimization

Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

Procedia PDF Downloads 79
17417 Factors Influencing the Adoption of Social Media as a Medium of Public Service Broadcasting

Authors: Seyed Mohammadbagher Jafari, Izmeera Shiham, Masoud Arianfar

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The increased usage of Social media for different uses in turn makes it important to develop an understanding of users and their attitudes toward these sites, and moreover, the uses of such sites in a broader perspective such as broadcasting. This quantitative study addressed the problem of factors influencing the adoption of social media as a medium of public service broadcasting in the Republic of Maldives. These powerful and increasingly usable tools, accompanied by large public social media datasets, are bringing in a golden age of social science by empowering researchers to measure social behavior on a scale never before possible. This was conducted by exploring social responses on the use of social media. Research model was developed based on the previous models such as TAM, DOI and Trust combined model. It evaluates the influence of perceived ease of use, perceived usefulness, trust, complexity, compatibility and relative advantage influence on the adoption of social Media. The model was tested on a sample of 365 Maldivian people using survey method via questionnaire. The result showed that perceived usefulness, trust, relative advantage and complexity would highly influence the adoption of social media.

Keywords: adoption, broadcasting, maldives, social media

Procedia PDF Downloads 483
17416 Key Affecting Factors for Social Sustainability through Urban Green Space Planning

Authors: Raziyeh Teimouri, Sadasivam Karuppannan, Alpana Sivam, Ning Gu

Abstract:

Urban Green Space (UGS) is one of the most critical components of urban systems to create sustainable cities. UGS has valuable social benefits that closely correlate with people's life quality. Studying social sustainability factors that can be achieved by green spaces is required for optimal UGS planning to increase urban social sustainability. This paper aims to identify key factors that enhance urban social sustainability through UGS planning. To reach the goal of the study international experts’ survey has been conducted. According to the results of the survey analysis, factors of proper distribution, links to public transportation, walkable access, sense of place, social interactions, public education, safety and security, walkability and cyclability, physical activity and recreational facilities, suitability for all ages, disabled people, women, and children are among the key factors that should consider in UGS planning programs to promote urban social sustainability.

Keywords: UGS, planning, social sustainability, key factors

Procedia PDF Downloads 77
17415 Organization Structure of Towns and Villages System in County Area Based on Fractal Theory and Gravity Model: A Case Study of Suning, Hebei Province, China

Authors: Liuhui Zhu, Peng Zeng

Abstract:

With the rapid development in China, the urbanization has entered the transformation and promotion stage, and its direction of development has shifted to overall regional synergy. China has a large number of towns and villages, with comparative small scale and scattered distribution, which always support and provide resources to cities leading to urban-rural opposition, so it is difficult to achieve common development in a single town or village. In this context, the regional development should focus more on towns and villages to form a synergetic system, joining the regional association with cities. Thus, the paper raises the question about how to effectively organize towns and villages system to regulate the resource allocation and improve the comprehensive value of the regional area. To answer the question, it is necessary to find a suitable research unit and analysis of its present situation of towns and villages system for optimal development. By combing relevant researches and theoretical models, the county is the most basic administrative unit in China, which can directly guide and regulate the development of towns and villages, so the paper takes county as the research unit. Following the theoretical concept of ‘three structures and one network’, the paper concludes the research framework to analyse the present situation of towns and villages system, including scale structure, functional structure, spatial structure, and organization network. The analytical methods refer to the fractal theory and gravity model, using statistics and spatial data. The scale structure analyzes rank-size dimensions and uses the principal component method to calculate the comprehensive scale of towns and villages. The functional structure analyzes the functional types and industrial development of towns and villages. The spatial structure analyzes the aggregation dimension, network dimension, and correlation dimension of spatial elements to represent the overall spatial relationships. In terms of organization network, from the perspective of entity and ono-entity, the paper analyzes the transportation network and gravitational network. Based on the present situation analysis, the optimization strategies are proposed in order to achieve a synergetic relationship between towns and villages in the county area. The paper uses Suning county in the Beijing-Tianjin-Hebei region as a case study to apply the research framework and methods and then proposes the optimization orientations. The analysis results indicate that: (1) The Suning county is lack of medium-scale towns to transfer effect from towns to villages. (2) The distribution of gravitational centers is uneven, and the effect of gravity is limited only for nearby towns and villages. The gravitational network is not complete, leading to economic activities scattered and isolated. (3) The overall development of towns and villages system is immature, staying at ‘single heart and multi-core’ stage, and some specific optimization strategies are proposed. This study provides a regional view for the development of towns and villages and concludes the research framework and methods of towns and villages system for forming an effective synergetic relationship between them, contributing to organize resources and stimulate endogenous motivation, and form counter magnets to join the urban-rural integration.

Keywords: towns and villages system, organization structure, county area, fractal theory, gravity model

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17414 Characteristics of Sorghum (Sorghum bicolor L. Moench) Flour on the Soaking Time of Peeled Grains and Particle Size Treatment

Authors: Sri Satya Antarlina, Elok Zubaidah, Teti Istiana, Harijono

Abstract:

Sorghum bicolor (Sorghum bicolor L. Moench) has the potential as a flour for gluten-free food products. Sorghum flour production needs grain soaking treatment. Soaking can reduce the tannin content which is an anti-nutrient, so it can increase the protein digestibility. Fine particle size decreases the yield of flour, so it is necessary to study various particle sizes to increase the yield. This study aims to determine the characteristics of sorghum flour in the treatment of soaking peeled grain and particle size. The material of white sorghum varieties KD-4 from farmers in East Java, Indonesia. Factorial randomized factorial design (two factors), repeated three times, factor I were the time of grain soaking (five levels) that were 0, 12, 24, 36, and 48 hours, factor II was the size of the starch particles sifted with a fineness level of 40, 60, 80, and 100 mesh. The method of making sorghum flour is grain peeling, soaking peeled grain, drying using the oven at 60ᵒC, milling, and sieving. Physico-chemical analysis of sorghum flour. The results show that there is an interaction between soaking time of grain with the size of sorghum flour particles. Interaction in yield of flour, L* color (brightness level), whiteness index, paste properties, amylose content, protein content, bulk density, and protein digestibility. The method of making sorghum flour through the soaking of peeled grain and the difference in particle size has an important role in producing the physicochemical properties of the specific flour. Based on the characteristics of sorghum flour produced, it is determined the method of making sorghum flour through sorghum grain soaking for 24 hours, the particle size of flour 80 mesh. The sorghum flour with characteristic were 24.88% yield of flour, 88.60 color L* (brightness level), 69.95 whiteness index, 3615 Cp viscosity, 584.10 g/l of bulk density, 24.27% db protein digestibility, 90.02% db starch content, 23.4% db amylose content, 67.45% db amylopectin content, 0.22% db crude fiber content, 0.037% db tannin content, 5.30% db protein content, ash content 0.18% db, carbohydrate content 92.88 % db, and 1.94% db fat content. The sorghum flour is recommended for cookies products.

Keywords: characteristic, sorghum (Sorghum bicolor L. Moench) flour, grain soaking, particle size, physicochemical properties

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17413 Artificial Neural Network Reconstruction of Proton Exchange Membrane Fuel Cell Output Profile under Transient Operation

Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

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17412 Buffer Allocation and Traffic Shaping Policies Implemented in Routers Based on a New Adaptive Intelligent Multi Agent Approach

Authors: M. Taheri Tehrani, H. Ajorloo

Abstract:

In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

Procedia PDF Downloads 518
17411 The Using of Social Marketing Approach for Conducting Anti-Corruption Campaign: A Review of Literature

Authors: Rosidah Rosidah

Abstract:

The paper aims to identify and examine the effectiveness of social marketing as an approach for conducting anti-corruption campaign. Social marketing has been widely used to promote social change for the benefit of individual and society; such as for promoting healthy foods consumption, encouraging breastfeeding, reducing smoking, solving alcohol problem and drunk driving. Therefore, it is believed that this approach can be promising to be used in anti-corruption campaign. It is because social marketing can be useful of prompting people to act in accordance to the existing norms that denounce corruption, or help to establish new norms that more averse to corruption. It has established into evidence and insight based approaches to social campaign that focus on changing people’s behavior. Qualitative approach will be used in this study, with the using of literature review and secondary data analysis as the research methods. This paper is still on preliminary stage, which its results is expected to provide fundamental basis for designing model of intervention (anti-corruption campaign) using social marketing approaches.

Keywords: anti-corruption campaign, behavioral change, social influence, social marketing

Procedia PDF Downloads 331
17410 An Ensemble-based Method for Vehicle Color Recognition

Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

Abstract:

The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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17409 Analyzing the Perception of Students and Faculty Members on Social Media Use in Academic Activities: A Case Study of Beijing Normal University

Authors: Mcjerry A. Bekoe, Emile Uwamahoro

Abstract:

Social media has become the order of the day, in particular among the youth. It is widely used both formally and informally in the university communities with varied definitions both in the academic circles and in the public domain. In simple terms, it is a media upon which social interactions are carried. In this work social media denote mobile phones, and web-base applications use by students and institutions to construct, partake, and distribute both existing and new information in a digital setting through internet communication. The basic aim of conducting this study was to analyze the perception of students and faculty members Beijing Normal University on social media use in the academic setting and to contribute to the understanding of how university students use social media, the advantages and disadvantages of social media in education. The study was qualitative and employed open-ended interview questions developed to seek students’ perception of the effects of social media and administered based on purposive sampling. Document analysis was also done because of triangulation to ensure validity and reliability. The results show there are positive and negative impacts of social media use depending on how one uses it. Social media have the capability to become a priceless asset to aid their educational communication.

Keywords: academics, high education, interactions, social media

Procedia PDF Downloads 341
17408 Methaheuristic Bat Algorithm in Training of Feed-Forward Neural Network for Stock Price Prediction

Authors: Marjan Golmaryami, Marzieh Behzadi

Abstract:

Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 548
17407 A Custom Convolutional Neural Network with Hue, Saturation, Value Color for Malaria Classification

Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

Abstract:

Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

Procedia PDF Downloads 89
17406 Prediction of Product Size Distribution of a Vertical Stirred Mill Based on Breakage Kinetics

Authors: C. R. Danielle, S. Erik, T. Patrick, M. Hugh

Abstract:

In the last decade there has been an increase in demand for fine grinding due to the depletion of coarse-grained orebodies and an increase of processing fine disseminated minerals and complex orebodies. These ores have provided new challenges in concentrator design because fine and ultra-fine grinding is required to achieve acceptable recovery rates. Therefore, the correct design of a grinding circuit is important for minimizing unit costs and increasing product quality. The use of ball mills for grinding in fine size ranges is inefficient and, therefore, vertical stirred grinding mills are becoming increasingly popular in the mineral processing industry due to its already known high energy efficiency. This work presents a hypothesis of a methodology to predict the product size distribution of a vertical stirred mill using a Bond ball mill. The Population Balance Model (PBM) was used to empirically analyze the performance of a vertical mill and a Bond ball mill. The breakage parameters obtained for both grinding mills are compared to determine the possibility of predicting the product size distribution of a vertical mill based on the results obtained from the Bond ball mill. The biggest advantage of this methodology is that most of the minerals processing laboratories already have a Bond ball mill to perform the tests suggested in this study. Preliminary results show the possibility of predicting the performance of a laboratory vertical stirred mill using a Bond ball mill.

Keywords: bond ball mill, population balance model, product size distribution, vertical stirred mill

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17405 Survey of the Elimination of Red Acid Dye by Wood Dust

Authors: N. Ouslimani, T. Abadlia, M. Fadel

Abstract:

This work focused on the elimination of acid textile dye (red bermacide acid dye BN-CL-200), widely used for dyeing wool and polyamide fibers, by adsorption on a natural material, wood sawdust, in the static mode by keeping under continuous stirring, a specific mass of the adsorbent, with a dye solution of known concentration. The influence of various parameters is studied like the influence of particle size, mass, pH and time. The best results were obtained with 0.4 mm grain size, mass of 3g, Temperature of 20 °C, pH 2 and Time contact of 120 min.

Keywords: acid dye, environment, wood sawdust, wastewater

Procedia PDF Downloads 443