Search results for: convolutional neural network topology

Authors: Asha Dukkipati

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Cognitive neuroscience explores neural functioning and aberrant brain activity during cognitive and perceptual tasks. Neurocinematics is a subfield of cognitive neuroscience that observes neural responses of individuals watching a film to see similarities and differences between individuals. This method is typically used for commercial use, allowing directors and filmmakers to produce better visuals and increasing their results in the box office. However, neurocinematics is increasingly becoming a common tool for neuroscientists interested in studying similar patterns of brain activity across viewers outside of the film industry. In this review, it argue that neurocinematics provides an easy, naturalistic approach for studying and diagnosing learning disorders. While the neural underpinnings of developmental learning disorders are traditionally assessed with well-established methods like EEG and fMRI that target particular cognitive domains, such as simple visual and attention tasks, there is initial evidence and theoretical background in support of neurocinematics as a biomarker for learning differences. By using ADHD, dyslexia, and autism as case studies, this literature review discusses the potential advantages of neurocinematics as a new tool for learning disorders research.

Keywords: behavioral and social sciences, neuroscience, neurocinematics, biomarkers, neurobehavioral disorders

Procedia PDF Downloads 98

Authors: Muthana Saleh Alalaki, Michael Opoku Agyeman

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The growth in the number of Intellectual Properties (IPs) or the number of cores on the same chip becomes a critical issue in System-on-Chip (SoC) due to the intra-communication problem between the chip elements. As a result, Network-on-Chip (NoC) has emerged as a system architecture to overcome intra-communication issues. This paper presents a study of recent contributions on simulation tools for NoC. Furthermore, an overview of NoC is covered as well as a comparison between some NoC simulators to help facilitate research in on-chip communication.

Keywords: WiNoC, simulation tool, network-on-chip, SoC

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Authors: Rakesh Kumar, A. K. Ghosh

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The paper presents the modeling of linear and nonlinear longitudinal aerodynamics using real flight data of Hansa-3 aircraft gathered at low and high angles of attack. The Neural-Gauss-Newton (NGN) method has been applied to model the linear and nonlinear longitudinal dynamics and estimate parameters from flight data. Unsteady aerodynamics due to flow separation at high angles of attack near stall has been included in the aerodynamic model using Kirchhoff’s quasi-steady stall model. NGN method is an algorithm that utilizes Feed Forward Neural Network (FFNN) and Gauss-Newton optimization to estimate the parameters and it does not require any a priori postulation of mathematical model or solving of equations of motion. NGN method was validated on real flight data generated at moderate angles of attack before application to the data at high angles of attack. The estimates obtained from compatible flight data using NGN method were validated by comparing with wind tunnel values and the maximum likelihood estimates. Validation was also carried out by comparing the response of measured motion variables with the response generated by using estimates a different control input. Next, NGN method was applied to real flight data generated by executing a well-designed quasi-steady stall maneuver. The results obtained in terms of stall characteristics and aerodynamic parameters were encouraging and reasonably accurate to establish NGN as a method for modeling nonlinear aerodynamics from real flight data at high angles of attack.

Keywords: parameter estimation, NGN method, linear and nonlinear, aerodynamic modeling

Procedia PDF Downloads 447

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

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

Keywords: graphs, mathematics, networks, urban studies

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis

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Authors: Tao Feng, Fei Xing, Ye Lu, Jun Li Fang

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NDN is a kind of future Internet architecture. Due to the NDN design introduces four privacy challenges,Many research institutions began to care about the privacy issues of naming data network(NDN).In this paper, we are in view of the major NDN’s privacy issues to investigate privacy protection,then put forwards more effectively anonymous transfer policy for NDN.Firstly,based on mutual anonymity communication for MP2P networks,we propose NDN mutual anonymity protocol.Secondly,we add interest package authentication mechanism in the protocol and encrypt the coding coefficient, security of this protocol is improved by this way.Finally, we proof the proposed anonymous transfer protocol security and anonymity.

Keywords: NDN, mutual anonymity, anonymous routing, network coding, authentication mechanism

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: G. Usha, S. Kannimuthu, K. Mahalakshmi

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Mobile Adhoc Network (MANET) is one of the most promising technologies that have applications ranging from various portable devices to military networks. MANET has no fixed infrastructure and the security of such network is a big concern. Therefore, in order to operate MANET’s securely, the misbehavior and intrusions should be detected before the attackers affect the network communication. In this article, we make a comprehensive survey against black hole attack that is a serious threat against MANET that exploits the routing behavior of the MANET. We have given broad survey solutions that detect black hole attacks in MANET. This is achieved by analyzing the techniques involved in detecting the attacks in each scheme. Furthermore, we examine about the challenges to the researchers for constructing an in-depth solution against black hole attack.

Keywords: AODV, cross layer security, mobile Adhoc network (MANET), packet delivery ratio, single layer security

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Authors: Hwayeon Song

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The long-term purpose of research and development (R&D) programs is to strengthen national competitiveness by developing new knowledge and technologies. Thus, it is important to determine a proper budget for government programs to maintain the vigor of R&D when the total funding is tight due to the national deficit. In this regard, a ripple effect analysis for the budgetary changes in R&D programs is necessary as well as an investigation of the current status. This study proposes a new approach using Artificial Neural Networks (ANN) for both tasks. It particularly focuses on R&D programs related to Construction and Transportation (C&T) technology in Korea. First, key factors in C&T technology are explored to draw impact indicators in three areas: economy, society, and science and technology (S&T). Simultaneously, ANN is employed to evaluate the relationship between data variables. From this process, four major components in R&D including research personnel, expenses, management, and equipment are assessed. Then the ripple effect analysis is performed to see the changes in the hypothetical future by modifying current data. Any research findings can offer an alternative strategy about R&D programs as well as a new analysis tool.

Keywords: Artificial Neural Networks, construction and transportation technology, Government Research and Development, Ripple Effect

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Authors: Xinyuan Zou, Qihui Tang, Shujian Wang, Yulin Huang, Jie Gui, Xiangping Liu, Gang Liu, Yanqiang Tao

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Childhood trauma can have a long-lasting influence on individuals and contribute to mental disorders, including depression and anxiety. The current study aimed to explore the symptomatic and developmental patterns of depression, anxiety, and stress among adolescents who have suffered from childhood trauma. A total of 3,598 college students (female = 1,617 (44.94%), Mean Age = 19.68, SD Age = 1.35) in China completed the Childhood Trauma Questionnaire (CTQ) and the Depression, Anxiety, and Stress Scales (DASS-21), and 2,337 participants met the selection standard based on the cut-off scores of the CTQ. The symptomatic network and directed acyclic graph (DAG) network approaches were used. The results revealed that males reported experiencing significantly more physical abuse, physical neglect, emotional neglect, and sexual abuse compared to females. However, females scored significantly higher than males on all items of DASS-21, except for “Worthless”. No significant difference between the two genders was observed in the network structure and global strength. Meanwhile, among all participants, “Down-hearted” and “Agitated” appeared to be the most interconnected symptoms, the bridge symptoms in the symptom network, as well as the most vital symptoms in the DAG network. Apart from that, “No-relax” also served as the most prominent symptom in the DAG network. The results suggested that intervention targeted at assisting adolescents in developing more adaptive coping strategies with stress and regulating emotion could benefit the alleviation of comorbid depression, anxiety, and stress.

Keywords: symptom network, childhood trauma, depression, anxiety, stress

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Authors: Yong-Jun Kim, Jae-Hyun Ro, Chang-Bin Ha, Hyoung-Kyu Song

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This paper proposes the cooperative transmission scheme with pre-coding because the cellular communication requires high reliability. The cooperative transmission scheme uses pre-coding method with limited feedback information among small cells. Particularly, the proposed scheme has adaptive mode according to the position of mobile station. Thus, demand of recent wireless communication is resolved by this scheme. From the simulation results, the proposed scheme has better performance compared to the conventional scheme in the cellular network.

Keywords: CDD, cellular network, pre-coding, SPC

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Authors: Smriti Agarwal, Ashish Payal, B. V. R. Reddy

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IEEE 802.15.4 is a Low Rate Wireless Personal Area Networks (LR-WPAN) standard combined with ZigBee, which is going to enable new applications in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) domain. In recent years, it has become a popular standard for WSNs. Wireless communication among sensor motes, enabled by IEEE 802.15.4 standard, is extensively replacing the existing wired technology in a wide range of monitoring and control applications. Researchers have proposed a routing framework and mechanism that interacts with the IEEE 802.15.4 standard using software platform. In this paper, we have designed and implemented MAC based routing (MBR) based on IEEE 802.15.4 standard using a hardware platform “SENSEnuts”. The experimental results include data through light and temperature sensors obtained from communication between PAN coordinator and source node through coordinator, MAC address of some modules used in the experimental setup, topology of the network created for simulation and the remaining battery power of the source node. Our experimental effort on a WSN Testbed has helped us in bridging the gap between theoretical and practical aspect of implementing IEEE 802.15.4 for WSNs applications.

Keywords: IEEE 802.15.4, routing, WSN, ZigBee

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Authors: S. M. Mahbobur Rahman

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Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control.  Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.

Keywords: ASHRAE case study, clear sky solar radiation model, energy modeling, thermal network model

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Authors: Okyu Kwon, Dongho Kang, Byungsik Kim, Seungkwon Jung

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We propose a technique to measure the impact of loss of traffic function in a particular area to surrounding areas. The proposed method is applied to the city of Seoul, which is the capital of South Korea, with a population of about ten million. Based on the actual road network in Seoul, we construct an abstract road network between 1kmx1km grid cells. The link weight of the abstract road network is re-adjusted considering traffic volume measured at several survey points. On the modified abstract road network, we evaluate the traffic vulnerability by calculating a network measure of betweenness centrality (BC) for every single grid cells. This study analyzes traffic impacts caused by road dysfunction due to heavy rainfall in urban areas. We could see the change of the BC value in all other grid cells by calculating the BC value once again when the specific grid cell lost its traffic function, that is, when the node disappeared on the grid-based road network. The results show that it is appropriate to use the sum of the BC variation of other cells as the influence index of each lattice cell on traffic. This research was supported by a grant (2017-MOIS31-004) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS).

Keywords: vulnerability, road network, beweenness centrality, heavy rainfall, road impact

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Authors: Zimu Li, Zheng Wan

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The Suez Canal plays an important role in global shipping networks and is one of the most frequently used waterways in the world. The 2021 canal obstruction by ship Ever Given in March 2021, however, completed blocked the Suez Canal for a week and caused significant disruption to world trade. Therefore, it is very important to quantitatively analyze the impact of the accident on the robustness of the global shipping network. However, the current research on maritime transportation networks is usually limited to local or small-scale networks in a certain region. Based on the complex network theory, this study establishes a global shipping complex network covering 2713 nodes and 137830 edges by using the real trajectory data of the global marine transport ship automatic identification system in 2018. At the same time, two attack modes, deliberate (Suez Canal Blocking) and random, are defined to calculate the changes in network node degree, eccentricity, clustering coefficient, network density, network isolated nodes, betweenness centrality, and closeness centrality under the two attack modes, and quantitatively analyze the actual impact of Suez Canal Blocking on the robustness of global shipping network. The results of the network robustness analysis show that Suez Canal blocking was more destructive to the shipping network than random attacks of the same scale. The network connectivity and accessibility decreased significantly, and the decline decreased with the distance between the port and the canal, showing the phenomenon of distance attenuation. This study further analyzes the impact of the blocking of the Suez Canal on Chinese ports and finds that the blocking of the Suez Canal significantly interferes withChina's shipping network and seriously affects China's normal trade activities. Finally, the impact of the global supply chain is analyzed, and it is found that blocking the canal will seriously damage the normal operation of the global supply chain.

Keywords: global shipping networks, ship AIS trajectory data, main channel, complex network, eigenvalue change

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: M. Kowalik, J. Masternak, K. Kazimierczuk, O. V. Khavryuchenko, B. Kupcewicz, B. Barszcz

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Recently, the growing interest of chemists in metal-organic coordination polymers (MOCPs) is primarily derived from their intriguing structures and potential applications in catalysis, gas storage, molecular sensing, ion exchanges, nonlinear optics, luminescence, etc. Currently, we are devoting considerable effort to finding the proper method of synthesizing new coordination polymers containing S- or N-heteroaromatic carboxylates as linkers and characterizing the obtained Pb(II) compounds according to their structural diversity, luminescence, and thermal properties. The choice of Pb(II) as the central ion of MOCPs was motivated by several reasons mentioned in the literature: i) a large ionic radius allowing for a wide range of coordination numbers, ii) the stereoactivity of the 6s2 lone electron pair leading to a hemidirected or holodirected geometry, iii) a flexible coordination environment, and iv) the possibility to form secondary bonds and unusual non-covalent interactions, such as classic hydrogen bonds and π···π stacking interactions, as well as nonconventional hydrogen bonds and rarely reported tetrel bonds, Pb(lone pair)···π interactions, C–H···Pb agostic-type interactions or hydrogen bonds, and chelate ring stacking interactions. Moreover, the construction of coordination polymers requires the selection of proper ligands acting as linkers, because we are looking for materials exhibiting different network topologies and fluorescence properties, which point to potential applications. The reaction of Pb(NO₃)₂ with 1H-pyrrole-2-carboxylic acid (2prCOOH) leads to the formation of a new four-nuclear Pb(II) polymer, [Pb4(2prCOO)₈(H₂O)]ₙ, which has been characterized by CHN, FT-IR, TG, PL and single-crystal X-ray diffraction methods. In view of the primary Pb–O bonds, Pb1 and Pb2 show hemidirected pentagonal pyramidal geometries, while Pb2 and Pb4 display hemidirected octahedral geometries. The topology of the strongest Pb–O bonds was determined as the (4·8²) fes topology. Taking the secondary Pb–O bonds into account, the coordination number of Pb centres increased, Pb1 exhibited a hemidirected monocapped pentagonal pyramidal geometry, Pb2 and Pb4 exhibited a holodirected tricapped trigonal prismatic geometry, and Pb3 exhibited a holodirected bicapped trigonal prismatic geometry. Moreover, the Pb(II) lone pair stereoactivity was confirmed by DFT calculations. The 2D structure was expanded into 3D by the existence of non-covalent O/C–H···π and Pb···π interactions, which was confirmed by the Hirshfeld surface analysis. The above mentioned interactions improve the rigidity of the structure and facilitate the charge and energy transfer between metal centres, making the polymer a promising luminescent compound.

Keywords: coordination polymers, fluorescence properties, lead(II), lone electron pair stereoactivity, non-covalent interactions

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Authors: Zineb Nougrara

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In this paper, we propose a novel methodology for extracting a road network and its nodes from satellite images of Algeria country. This developed technique is a progress of our previous research works. It is founded on the information theory and the mathematical morphology; the information theory and the mathematical morphology are combined together to extract and link the road segments to form a road network and its nodes. We, therefore, have to define objects as sets of pixels and to study the shape of these objects and the relations that exist between them. In this approach, geometric and radiometric features of roads are integrated by a cost function and a set of selected points of a crossing road. Its performances were tested on satellite images of Algeria country.

Keywords: satellite image, road network, nodes, image analysis and processing

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Authors: Nivedha Rajaram

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Hybrid Quantum solvers have been given prime focus in recent days by computation problem-solving domain industrial applications. D’Wave Quantum Computers are one such paragon of systems built using quantum annealing mechanism. Discrete Quadratic Models is a hybrid quantum computing model class supplied by D’Wave Ocean SDK - a real-time software platform for hybrid quantum solvers. These hybrid quantum computing modellers can be employed to solve classic problems. One such problem that we consider in this paper is finding a network connectivity knowledge hub in a huge network of systems. Using this quantum solver, we try to find out the prime system hub, which acts as a supreme connection point for the set of connected computers in a large network. This paper establishes an innovative problem approach to generate a connectivity system hub plot for a set of systems using DWave ocean SDK hybrid quantum solvers.

Keywords: quantum computing, hybrid quantum solver, DWave annealing, network knowledge graph

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Authors: Anton S. Perin, Vladimir M. Shandarov

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Compensation for the nonlinear diffraction of narrow laser beams with wavelength of 532 and the formation of photonic waveguides and waveguide circuits due to the contribution of pyroelectric effect to the nonlinear response of lithium niobate crystal have been experimentally demonstrated. Complete compensation for the linear and nonlinear diffraction broadening of light beams is obtained upon uniform heating of an undoped sample from room temperature to 55 degrees Celsius. An analysis of the light-field distribution patterns and the corresponding intensity distribution profiles allowed us to estimate the spacing for the channel waveguides. The observed behavior of bright soliton beams may be caused by their coherent interaction, which manifests itself in repulsion for anti-phase light fields and in attraction for in-phase light fields. The experimental results of this study showed a fundamental possibility of forming optically complex waveguide structures in lithium niobate crystals with pyroelectric mechanism of nonlinear response. The topology of these structures is determined by the light field distribution on the input face of crystalline sample. The optical induction of channel waveguide elements by interacting spatial solitons makes it possible to design optical systems with a more complex topology and a possibility of their dynamic reconfiguration.

Keywords: self-action, soliton, lithium niobate, piroliton, photorefractive effect, pyroelectric effect

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Authors: Omid Ataei Nia, Karim Salahshoor

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In this paper, a new precise and reliable sensor network methodology is introduced for unit processes and operations using the Constriction Coefficient Particle Swarm Optimization (CPSO) method. CPSO is introduced as a new search engine for optimal sensor network design purposes. Furthermore, a Square Root Unscented Kalman Filter (SRUKF) algorithm is employed as a new data reconciliation technique to enhance the stability and accuracy of the filter. The proposed design procedure incorporates precision, cost, observability, reliability together with importance-of-variables (IVs) as a novel measure in Instrumentation Criteria (IC). To the best of our knowledge, no comprehensive approach has yet been proposed in the literature to take into account the importance of variables in the sensor network design procedure. In this paper, specific weight is assigned to each sensor, measuring a process variable in the sensor network to indicate the importance of that variable over the others to cater to the ultimate sensor network application requirements. A set of distinct scenarios has been conducted to evaluate the performance of the proposed methodology in a simulated Continuous Stirred Tank Reactor (CSTR) as a highly nonlinear process plant benchmark. The obtained results reveal the efficacy of the proposed method, leading to significant improvement in accuracy with respect to other alternative sensor network design approaches and securing the definite allocation of sensors to the most important process variables in sensor network design as a novel achievement.

Keywords: constriction coefficient PSO, importance of variable, MRMSE, reliability, sensor network design, square root unscented Kalman filter

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Authors: Jessica Achebe, Susan Tighe

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The reduction of material consumption and greenhouse gas emission when maintain and rehabilitating road networks can achieve added benefits including improved life cycle performance of pavements, reduced climate change impacts and human health effect due to less air pollution, improved productivity due to an optimal allocation of resources and reduced road user cost. This is the essence of incorporating environmental sustainability into pavement management. The functionality of performance measurement approach has made it one of the most valuable tool to Pavement Management Systems (PMSs) to account for different criteria in the decision-making process. However measuring the environmental performance of road network is still a far-fetched practice in road network management, more so an ostensive agency-wide environmental sustainability or sustainable maintenance specifications is missing. To address this challenge, this present research focuses on the environmental sustainability performance of network-level pavement management. The ultimate goal is to develop a framework to incorporate environmental sustainability in pavement management systems for network-level maintenance programming. In order to achieve this goal, this paper present the first step, the intention is to review the previous studies that employed environmental performance measures, as well as the suitability of environmental performance indicators for the evaluation of the sustainability of network-level pavement maintenance strategies. Through an industry practice survey, this paper provides a brief forward regarding the pavement manager motivations and barriers to making more sustainable decisions, and data needed to support the network-level environmental sustainability. The trends in network-level sustainable pavement management are also presented, existing gaps are highlighted, and ideas are proposed for network-level sustainable maintenance and rehabilitation programming.

Keywords: pavement management, environment sustainability, network-level evaluation, performance measures

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Authors: Shadi Fathi, Moura Mehravar, Mujib Rahman

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The railwaytransportation network in the UK is over 100 years old and is known as one of the oldest mass transit systems in the world. This aged track network requires frequent closure for maintenance. One of the main reasons for closure is inadequate drainage due to the leakage in the buried drainage pipes. The leaking water can cause localised subgrade weakness, which subsequently can lead to major ground/substructure failure.Different condition assessment methods are available to assess the railway substructure. However, the existing condition assessment methods are not able to detect any local ground weakness/damageand provide details of the damage (e.g. size and location). To tackle this issue, a hybrid back-analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to predict the substructurelayers’ moduli and identify any soil weaknesses. At first, afinite element (FE) model of a railway track section under Falling Weight Deflection (FWD) testing was developed and validated against field trial. Then a drainage pipe and various scenarios of the local defect/ soil weakness around the buried pipe with various geometriesand physical properties were modelled. The impact of the soil local weaknesson the track surface deflection wasalso studied. The FE simulations results were used to generate a database for ANN training, and then a GA wasemployed as an optimisation tool to optimise and back-calculate layers’ moduli and soil weakness moduli (ANN’s input). The hybrid ANN-GA back-analysis technique is a computationally efficient method with no dependency on seed modulus values. The modelcan estimate substructures’ layer moduli and the presence of any localised foundation weakness.

Keywords: finite element (FE) model, drainage defect, falling weight deflectometer (FWD), hybrid ANN-GA

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Authors: Alireza Pour Ebrahimi, Vahid Abasi

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Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach.

Keywords: network, intrusion detection system, peer to peer, internal and external network

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Zhizhong Yuan, Lili Wang, Chenya Zheng, Wuqi Yang

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How to encourage and support peer-to-peer (P2P) network lending, and effectively monitor the risk of P2P network lending, has become the focus of the Chinese government departments, industrialists, experts and scholars in recent years. The reason is that this convenient online micro-credit service brings a series of credit risks and other issues. Avoiding the risks brought by the P2P network lending model, it can better play a benign role and help China's small and medium-sized private enterprises with vigorous development to solve the capital needs; otherwise, it will bring confusion to the normal financial order. As a form of financial services, P2P network lending has injected new blood into China's non-government finance in the past ten years, and has found a way out for idle funds and made up for the shortage of traditional financial services in China. However, it lacks feasible measures in credit evaluation and government supervision. This paper collects a large amount of data about P2P network lending of China. The data collection comes from the official media of the Chinese government, the public achievements of existing researchers and the analysis and collation of correlation data by the authors. The research content of this paper includes literature review; the current situation of China's P2P network lending development; the risk analysis of P2P network lending in China; the risk prevention strategy of P2P network lending in China. The focus of this paper is to try to find a specific program to strengthen supervision and avoid risks from the perspective of government regulators, operators of P2P network lending platform, investors and users of funds. These main measures include: China needs to develop self-discipline organization of P2P network lending industry and formulate self-discipline norms as soon as possible; establish a regular information disclosure system of P2P network lending platform; establish censorship of credit rating of borrowers; rectify the P2P network lending platform in compliance through the implementation of bank deposition. The results and solutions will benefit all the P2P network lending platforms, creditors, debtors, bankers, independent auditors and government agencies of China and other countries.

Keywords: peer-to-peer(P2P), regulation, risk prevention, supervision

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Authors: Krisana Chueachainat

Abstract:

The study is about the identity, typology and symbol using in communication through offline and online social network of each Thai football Premier League supporters. Also, study is about the factors that affected the sport to become the growing business and the communication factors that play the important role in the growth of the sport business. The Thai Premier League communicated with supporters in order to show the identity of each supporter and club in different ways. The expression of the identity was shown through online social network and offline told other people who they were. The study also about the factor that impacted the roles and communication factors that make football become the growing business. The factor that impact to the growth of football to the business, if clubs can action, the sport business would be to higher level and also push Thailand’s football to be effective and equal to other countries.

Keywords: online social network, offline social network, Thai football, supporters

Procedia PDF Downloads 299

Authors: Tuğçe Yücel

Abstract:

A Wireless Sensor Network(WSN) is a collection of sensor nodes which are deployed randomly in an area for surveillance. Efficient utilization of limited battery energy of sensors for increased network lifetime as well as data security are major design objectives for WSN. Moreover secure transmission of data sensed to a base station for further processing. Producing multiple copies of data packets and sending them on different paths is one of the strategies for this purpose, which leads to redundant energy consumption and hence reduced network lifetime. In this work we develop a restricted multi-copy multipath strategy where data move through ‘frequently’ or ‘heavily’ used sensors is copied by the sensor incident to such central nodes and sent on node-disjoint paths. We develop a mixed integer programing(MIP) model and heuristic approach present some preleminary test results.

Keywords: MIP, sensor, telecommunications, WSN

Procedia PDF Downloads 512

Authors: Feng Huang, Carlos G. Ochoa, Haitao Zhao

Abstract:

Poyang Lake, the largest freshwater lake in China, is located at the middle-lower reaches of the Yangtze River basin. It has great value in socioeconomic development and is internationally recognized as an important lacustrine and wetland ecosystem with abundant biodiversity. Impacted by ongoing climate change and anthropogenic activities, especially the regulation of the Three Gorges Reservoir since 2003, Poyang Lake has experienced significant water regime evolution, resulting in challenges for the management of water resources and the environment. Quantifying the contribution of hydrologic and topographic changes to water regime alteration is necessary for policymakers to design effective adaption strategies. Long term hydrologic data were collected and the back-propagation neural networks were constructed to simulate the lake water level. The impacts of hydrologic and topographic changes were differentiated through scenario analysis that considered pre-impact and post-impact hydrologic and topographic scenarios. The lake water regime was characterized by hydrologic indicators that describe monthly water level fluctuations, hydrologic features during flood and drought seasons, and frequency and rate of hydrologic variations. The results revealed different contributions of hydrologic and topographic changes to different features of the lake water regime.Noticeable changes were that the water level declined dramatically during the period of reservoir impoundment, and the drought was enhanced during the dry season. The hydrologic and topographic changes exerted a synergistic effect or antagonistic effect on different lake water regime features. The findings provide scientific reference for lacustrine and wetland ecological protection associated with water regime alterations.

Keywords: back-propagation neural network, scenario analysis, water regime, Poyang Lake

Procedia PDF Downloads 141

Authors: Amin Sedighfar, M. R. Moniri

Abstract:

Determination of state of charge (SOC) in today’s world becomes an increasingly important issue in all the applications that include a battery. In fact, estimation of the SOC is a fundamental need for the battery, which is the most important energy storage in Hybrid Electric Vehicles (HEVs), smart grid systems, drones, UPS and so on. Regarding those applications, the SOC estimation algorithm is expected to be precise and easy to implement. This paper presents an online method for the estimation of the SOC of Valve-Regulated Lead Acid (VRLA) batteries. The proposed method uses the well-known Kalman Filter (KF), and Neural Networks (NNs) and all of the simulations have been done with MATLAB software. The NN is trained offline using the data collected from the battery discharging process. A generic cell model is used, and the underlying dynamic behavior of the model has used two capacitors (bulk and surface) and three resistors (terminal, surface, and end), where the SOC determined from the voltage represents the bulk capacitor. The aim of this work is to compare the performance of conventional integration-based SOC estimation methods with a mixed algorithm. Moreover, by containing the effect of temperature, the final result becomes more accurate. 

Keywords: Kalman filter, neural networks, state-of-charge, VRLA battery

Procedia PDF Downloads 193