Search results for: disaster relief networks

Authors: B. Zerouali, M. Kara, B. Hamaidi, H. Mahdjoub, S. Rouabhia

Abstract:

In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios.

Keywords: bayesian networks, crude oil tank, fault tree, prediction, safety

Procedia PDF Downloads 640

Authors: Jiaqi Huang, Yuheng Wang

Abstract:

Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

Procedia PDF Downloads 168

Authors: Brenden M. Shutt, Lubjana Beshaj, Paul L. Goethals, Ambrose Kam

Abstract:

Communication security is of particular interest to military data networks. A relatively novel approach to network security is blockchain, a cryptographically secured distribution ledger with a decentralized consensus mechanism for data transaction processing. Recent advances in blockchain technology have proposed new techniques for both data validation and trust management, as well as different frameworks for managing dataflow. The purpose of this work is to test the feasibility of different blockchain architectures as applied to military command and control networks. Various architectures are tested through discrete-event simulation and the feasibility is determined based upon a blockchain design’s ability to maintain long-term stable performance at industry standards of throughput, network latency, and security. This work proposes a consortium blockchain architecture with a computationally inexpensive consensus mechanism, one that leverages a Proof-of-Identity (PoI) concept and a reputation management mechanism.

Keywords: blockchain, consensus mechanism, discrete-event simulation, fog computing

Procedia PDF Downloads 122

Authors: Abdelazziz Aouiche, El Moundher Aouiche, Mouhamed Salah Soudani

Abstract:

Renewable energy sources now significantly contribute to the replacement of traditional fossil fuel energy sources. One of the most potent types of renewable energy that has developed quickly in recent years is photovoltaic energy. We all know that solar energy, which is sustainable and non-depleting, is the best knowledge form of energy that we have at our disposal. Due to changing weather conditions, the primary drawback of conventional solar PV cells is their inability to track their maximum power point. In this study, we apply artificial neural networks (ANN) to automatically track and measure the maximum power point (MPP) of solar panels. In MATLAB, the complete system is simulated, and the results are adjusted for the external environment. The results are better performance than traditional MPPT methods and the results demonstrate the advantages of using neural networks in solar PV systems.

Keywords: modeling, photovoltaic panel, artificial neural networks, maximum power point tracking

Procedia PDF Downloads 70

Authors: A. A. Behroozpoor, M. M. Mazarei

Abstract:

In this paper, a fuzzy recurrent neural network is proposed for solving the classical quadratic control problem subject to linear equality and bound constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed.

Keywords: REFERENCES [1] Xia, Y, A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks, 7(6), 1996, pp.1544–1548. [2] Xia, Y., & Wang, J, A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks, 16(2), 2005, pp. 379–386. [3] Xia, Y., H, Leung, & J, Wang, A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I, 49(4), 2002, pp.447–458.B. [4] Q. Liu, Z. Guo, J. Wang, A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks, 26, 2012, pp. 99-109.

Procedia PDF Downloads 627

Authors: Saurabh Shukla, G. N. Pandey

Abstract:

The fires have become the most serious disasters to forest resources and the human environment. In recent years, due to climate change, human activities and other factors the frequency of forest fires has increased considerably. The monitoring and prevention of forest fires have now become a global concern for forest fire prevention organizations. Currently, the methods for forest fire prevention largely consist of patrols, observation from watch towers. Thus, software like deployment of the wireless sensor network to prevent forest fire is being developed to get a better estimate of the temperature and humidity prospects. Now days, wireless sensor networks are beginning to be deployed at an accelerated pace. It is not unrealistic to expect that in coming years the world will be covered with wireless sensor networks. This new technology has lots of unlimited potentials and can be used for numerous application areas including environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces.

Keywords: deployment, sensors, wireless sensor networks, forest fires

Procedia PDF Downloads 415

Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

Abstract:

IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

Procedia PDF Downloads 42

Authors: Vathanachannbo Veth, Ilan Ich, Sophea Rom Phy, Ty Sok, Layheang Song, Sophal Try, Chantha Oeurng

Abstract:

Climate change is one of the major global challenges inducing disaster risks and threatening livelihoods and communities through adverse impacts on food and water security, ecosystems, and services. Prek Thnot River Basin of Cambodia is one of the largest tributaries in the Lower Mekong that has been exposed to hazards and disasters, particularly floods and is said to be the effect of climate change. Therefore, the assessment of precipitation and streamflow changes under the effect of climate change was proposed in this river basin using Soil Water Assessment Tool (SWAT) model and different flow indices under baseline (1997 to 2011) and climate change scenarios (RCP2.6 and RCP8.5 with three General Circulation Models (GCMs): GFDL, GISS, and IPSL) in two time-horizons: near future (the 2030s: 2021 to 2040) and medium future (2060s: 2051 to 2070). Both intensity and frequency indices compared with the historical extreme rainfall indices significantly change in the GFDL under the RCP8.5 for both 2030s and 2060s. The average rate change of Rx1day, Rx10day, SDII, and R20mm in the 2030s and 2060s of both RCP2.6 and RCP8.5 was found to increase in GFDL and decrease in both GISS and IPSL. The mean percentage change of the flow analyzed in the IHA tool (Group1) indicated that the flow in the Prek Thnot River increased in GFDL for both RCP2.6 and RCP8.5 in both 2030s and 2060s, oppositely in GISS, the flow decreases. Moreover, the IPSL affected the flow by increasing in five months (January, February, October, November, and December), and in the other seven months, the flow decreased accordingly. This study provides water resources managers and policymakers with a wide range of precipitation and water flow projections within the Prek Thnot River Basin in the context of plausible climate change scenarios.

Keywords: IHA, climate change, disaster risk, Prek Thnot River Basin, Cambodia

Procedia PDF Downloads 84

Authors: Gizelle K. Vianna, Gustavo S. Oliveira, Gabriel V. Cunha

Abstract:

The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action.

Keywords: artificial neural networks, cellular automata, decision support system, pattern recognition

Procedia PDF Downloads 432

Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

Abstract:

This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: transportation networks, freight delivery, data flow, monitoring, e-services

Procedia PDF Downloads 111

Authors: Mkhokheli Sithole, Brenda N. Muchapondwa

Abstract:

Food security is becoming a critical issue for people residing mainly in the rural areas where frequent droughts interrupt food production, reduce income, compromise the ability to save and erode livelihoods. This tends to increase the vulnerability of poor households to food and income insecurity, hence, malnutrition. There is an emerging need for DRR strategies to complement the existing rain fed crop production based livelihoods. One of such strategies employed by the community of Nerumedzo in Bikita district is the harvesting of Encosternum delegorguei insect. This article analyses the livelihood impacts of Encosternum delegorguei insect as a DRR strategy. The research used a combination of qualitative and quantitative approaches. The insect samples were tested in the laboratory for their nutritional composition while surveys were done on a sample of 40 community members. Participatory observations and 5 focus group discussions were also done. The results revealed that harvesting the Encosternum delegorguei insects provides a livelihood for the locals by complementing crop production thereby mitigating potential negative effects of frequent droughts. The insects are now a significant source of income to poor households in the community.

Keywords: disaster risk reduction, livelihoods, human, social sciences

Procedia PDF Downloads 183

Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

Abstract:

Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non-real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

Procedia PDF Downloads 267

Authors: YingWei Tan, XueFeng Ding

Abstract:

Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

Procedia PDF Downloads 51

Authors: Alberto Alcalá-Alvarez, Pablo Padilla-Longoria

Abstract:

The present paper describes a framework for constructing mathematical networks encoding relevant musical information from a music score for structural analysis. These graphs englobe statistical information about music elements such as notes, chords, rhythms, intervals, etc., and the relations among them, and so become helpful in visualizing and understanding important stylistic features of a music fragment. In order to build such networks, musical data is parsed out of a digital symbolic music file. This data undergoes different analytical procedures from Graph Theory, such as measuring the centrality of nodes, community detection, and entropy calculation. The resulting networks reflect important structural characteristics of the fragment in question: predominant elements, connectivity between them, and complexity of the information contained in it. Music pieces in different styles are analyzed, and the results are contrasted with the traditional analysis outcome in order to show the consistency and potential utility of this method for music analysis.

Keywords: computational musicology, mathematical music modelling, music analysis, style classification

Procedia PDF Downloads 80

Authors: Teles de Sales Bezerra, Saulo Aislan da Silva Eleuterio, José Anderson Rodrigues de Souza, Jeronimo Silva Rocha

Abstract:

Propagation in radio frequency is a constant concern in the application of Wireless Sensor Networks (WSN), the behavior of an environment determines how good the quality of signal reception. The objective of this paper is to analyze the behavior of a WSN in an environment for agriculture where environmental variables are present and correlate the capture of values received signal strength (RSSI) with a propagation model.

Keywords: propagation, WSN, agriculture, quality

Procedia PDF Downloads 736

Authors: Oyatayo Kehinde Taofik, Ndabula Christopher

Abstract:

Drainage basins are a central theme in the green economy. The rising challenges in flooding, erosion or sediment transport and sedimentation threaten the green economy. This has led to increasing emphasis on quantitative analysis of drainage basin parameters for better understanding, estimation and prediction of fluvial responses and, thus associated hazards or disasters. This can be achieved through direct measurement, characterization, parameterization, or modeling. This study applied the Remote Sensing and Geographic Information System approach of parameterization and characterization of the morphometric variables of Katsina – Ala basin using a 30 m resolution Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM). This was complemented with topographic and hydrological maps of Katsina-Ala on a scale of 1:50,000. Linear, areal and relief parameters were characterized. The result of the study shows that Ala and Udene sub-watersheds are 4th and 5th order basins, respectively. The stream network shows a dendritic pattern, indicating homogeneity in texture and a lack of structural control in the study area. Ala and Udene sub-watersheds have the following values for elongation ratio, circularity ratio, form factor and relief ratio: 0.48 / 0.39 / 0.35/ 9.97 and 0.40 / 0.35 / 0.32 / 6.0. They also have the following values for drainage texture and ruggedness index of 0.86 / 0.011 and 1.57 / 0.016. The study concludes that the two sub-watersheds are elongated, suggesting that they are susceptible to erosion and, thus higher sediment load in the river channels, which will dispose the watersheds to higher flood peaks. The study also concludes that the sub-watersheds have a very coarse texture, with good permeability of subsurface materials and infiltration capacity, which significantly recharge the groundwater. The study recommends that efforts should be put in place by the Local and State Governments to reduce the size of paved surfaces in these sub-watersheds by implementing a robust agroforestry program at the grass root level.

Keywords: erosion, flood, mitigation, morphometry, watershed

Procedia PDF Downloads 70

Authors: Efendi Zaenudin, Chien-Hung Huang, Ka-Lok Ng

Abstract:

Essential genes play an important role in the survival of an organism. It has been shown that cancer-associated essential genes are genes necessary for cancer cell proliferation, where these genes are potential therapeutic targets. Also, it was demonstrated that mutations of the cancer-associated essential genes give rise to the resistance of immunotherapy for patients with tumors. In the present study, we focus on studying the biological effects of the essential genes from a network perspective. We hypothesize that one can analyze a biological molecular network by decomposing it into both three-node and four-node digraphs (subgraphs). These network subgraphs encode the regulatory interaction information among the network’s genetic elements. In this study, the frequency of occurrence of the subgraph-associated essential genes in a molecular network was quantified by using the statistical parameter, odds ratio. Biological effects of subgraph-associated essential genes are discussed. In summary, the subgraph approach provides a systematic method for analyzing molecular networks and it can capture useful biological information for biomedical research.

Keywords: biological molecular networks, essential genes, graph theory, network subgraphs

Procedia PDF Downloads 140

Authors: Seyed Mahdi Jameii, Arash Nikdel, Seyed Mohsen Jameii

Abstract:

Energy efficiency is an important issue in the field of Wireless Sensor Networks (WSNs). So, minimizing the energy consumption in this kind of networks should be an essential consideration. Sleep/wake scheduling mechanism is an efficient approach to handling this issue. In this paper, we propose a Genetic Algorithm-based Sleep-Wake up Area Coverage protocol called GA-SWAC. The proposed protocol puts the minimum of nodes in active mode and adjusts the sensing radius of each active node to decrease the energy consumption while maintaining the network’s coverage. The proposed protocol is simulated. The results demonstrate the efficiency of the proposed protocol in terms of coverage ratio, number of active nodes and energy consumption.

Keywords: wireless sensor networks, genetic algorithm, coverage, connectivity

Procedia PDF Downloads 498

Authors: Sumit Banerjee, Chandan Kumar Chanda

Abstract:

The paper presents allocation of active power losses and energy losses to consumers connected to radial distribution networks in a deregulated environment for loads of composite types. A detailed comparison among four algorithms, namely quadratic loss allocation, proportional loss allocation, pro rata loss allocation and exact loss allocation methods are presented. Quadratic and proportional loss allocations are based on identifying the active and reactive components of current in each branch and the losses are allocated to each consumer, pro rata loss allocation method is based on the load demand of each consumer and exact loss allocation method is based on the actual contribution of active power loss by each consumer. The effectiveness of the proposed comparison among four algorithms for composite load is demonstrated through an example.

Keywords: composite type, deregulation, loss allocation, radial distribution networks

Procedia PDF Downloads 271

Authors: Chenlu Zhang, Wanni Zhang, Florian Schaule

Abstract:

Indoor environment in office buildings directly affects comfort, productivity, health, and well-being of building occupants. Wireless environmental sensor networks have been deployed in many modern offices to monitor and control the indoor environments. However, indoor environmental variables are not strong enough predictors of comfort and productivity levels of every occupant due to personal differences, both physiologically and psychologically. This study proposes human-centric sensor networks that integrate wireless environmental sensors, body area network sensors and participatory sensing technologies to collect data from both environment and human and support building operations. The sensor networks have been tested in one small-size and one medium-size office rooms with 22 participants for five months. Indoor environmental data (e.g., air temperature and relative humidity), physiological data (e.g., skin temperature and Galvani skin response), and physiological responses (e.g., comfort and self-reported productivity levels) were obtained from each participant and his/her workplace. The data results show that: (1) participants have different physiological and physiological responses in the same environmental conditions; (2) physiological variables are more effective predictors of comfort and productivity levels than environmental variables. These results indicate that the human-centric sensor networks can support human-centric building control and improve comfort and productivity in offices.

Keywords: body area network, comfort and productivity, human-centric sensors, internet of things, participatory sensing

Procedia PDF Downloads 126

Authors: Alamgir Naushad, Ghulam Abbas, Shehzad Ali Shah, Ziaul Haq Abbas

Abstract:

Unlike conventional networks, it is particularly challenging to manage resources efficiently in Wireless Sensor Networks (WSNs) due to their inherent characteristics, such as dynamic network topology and limited bandwidth and battery power. To ensure energy efficiency, this paper presents a routing protocol for WSNs, namely, Enhanced Hybrid Multipath Routing (EHMR), which employs hierarchical clustering and proposes a next hop selection mechanism between nodes according to a maximum residual energy metric together with a minimum hop count. Load-balancing of data traffic over multiple paths is achieved for a better packet delivery ratio and low latency rate. Reliability is ensured in terms of higher data rate and lower end-to-end delay. EHMR also enhances the fast-failure recovery mechanism to recover a failed path. Simulation results demonstrate that EHMR achieves a higher packet delivery ratio, reduced energy consumption per-packet delivery, lower end-to-end latency, and reduced effect of data rate on packet delivery ratio when compared with eminent WSN routing protocols.

Keywords: energy efficiency, load-balancing, hierarchical clustering, multipath routing, wireless sensor networks

Procedia PDF Downloads 64

Authors: Rasha Waheeb, Bjorn Andersen, Rafa Shakir

Abstract:

Purpose The purpose of this study is to avoid delays that occur in emergency reconstruction projects especially in post disaster circumstances whether if they were natural or manmade due to their particular national and humanitarian importance. We presented a theoretical and practical concepts for projects management in the field of construction industry that deal with a range of global and local trails. This study aimed to identify the factors of effective delay in construction projects in Iraq that affect the time and the specific quality cost, and find the best solutions to address delays and solve the problem by setting parameters to restore balance in this study. 30 projects were selected in different areas of construction were selected as a sample for this study. Design/methodology/approach This study discusses the reconstruction strategies and delay in time and cost caused by different delay factors in some selected projects in Iraq (Baghdad as a case study).A case study approach was adopted, with thirty construction projects selected from the Baghdad region, of different types and sizes. Project participants from the case projects provided data about the projects through a data collection instrument distributed through a survey. Mixed approach and methods were applied in this study. Mathematical data analysis was used to construct models to predict delay in time and cost of projects before they started. The artificial neural networks analysis was selected as a mathematical approach. These models were mainly to help decision makers in construction project to find solutions to these delays before they cause any inefficiency in the project being implemented and to strike the obstacles thoroughly to develop this industry in Iraq. This approach was practiced using the data collected through survey and questionnaire data collection as information form. Findings The most important delay factors identified leading to schedule overruns were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. Some of these are quite in line with findings from similar studies in other countries/regions, but some are unique to the Iraqi project sample, such as security issues and low-price bid selection. Originality/value we selected ANN’s analysis first because ANN’s was rarely used in project management , and never been used in Iraq to finding solutions for problems in construction industry. Also, this methodology can be used in complicated problems when there is no interpretation or solution for a problem. In some cases statistical analysis was conducted and in some cases the problem is not following a linear equation or there was a weak correlation, thus we suggested using the ANN’s because it is used for nonlinear problems to find the relationship between input and output data and that was really supportive.

Keywords: construction projects, delay factors, emergency reconstruction, innovation ANN, post disasters, project management

Procedia PDF Downloads 152

Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot

Abstract:

Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

Procedia PDF Downloads 357

Authors: Pavan K. Rallabandi, Kailash C. Patidar

Abstract:

In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

Procedia PDF Downloads 370

Authors: Mehmet Karaata

Abstract:

Given two distinct vertices (nodes) source s and target t of a graph G = (V, E), the two node-disjoint paths problem is to identify two node-disjoint paths between s ∈ V and t ∈ V . Two paths are node-disjoint if they have no common intermediate vertices. In this paper, we present an algorithm with O(m)-time complexity for finding two node-disjoint paths between s and t in arbitrary graphs where m is the number of edges. The proposed algorithm has a wide range of applications in ensuring reliability and security of sensor, mobile and fixed communication networks.

Keywords: disjoint paths, distributed systems, fault-tolerance, network routing, security

Procedia PDF Downloads 426

Authors: Hsu-Yung Cheng, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, Chih-Chang Yu, Ya-Sheng Liu

Abstract:

Photovoltaics modules are usually not installed horizontally to avoid water or dust accumulation. However, the measured irradiance data on tilted surfaces are rarely available since installing pyranometers with various tilt angles induces high costs. Therefore, estimating solar irradiance on tilted surfaces is an important research topic. In this work, artificial neural networks (ANN) are utilized to construct the transfer model to estimate solar irradiance on tilted surfaces. Instead of predicting tilted irradiance directly, the proposed method estimates the differences between the horizontal irradiance and the irradiance on a tilted surface. The outputs of the ANNs in the proposed design are differential values. The experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy compared to ANNs that estimate the titled irradiance directly.

Keywords: photovoltaics, artificial neural networks, tilted irradiance, solar energy

Procedia PDF Downloads 376

Authors: Zsofia Nagy, Tibor Dessewffy

Abstract:

The relationship between social movements and the media that represents them comprises both of the media representation of movements on the one hand, and the media strategies employed by movements on the other. A third possible approach is to connect the two and look at the interactions connecting the two sides. This relationship has been affected by the emergence of social networking sites (SNS) that have a transformative effect on both actors. However, the extent and direction of these changes needs to be investigated. Empirical case studies that focus on newly enabled forms of social movements can contribute to these debates in an analytically fruitful way. Therefore in our study, we use the case of Migration Aid, a Hungarian Facebook-based grassroots relief organization that gained prominence during the refugee crisis that unfolded in Hungary in 2015. Migration Aid formed without the use of traditional mobilizational agents, and that took over roles traditionally occupied by formal NGOs or the state. Analyzing different movement strategies towards the media - we find evidence that while effectively combining these strategies, SNSs also create affordances for movements to shift their strategy towards creating alternatives, their own micromedia. Beyond the practical significance of this – the ability to disseminate alternative information independently from traditional media – it also allowed the group to frame the issue in their own terms and to replace vertical modes of communication with horizontal ones. The creation of micromedia also shifts the relationship between social movements and the media away from an asymmetrical and towards a more symbiotic co-existence. We provide four central factors – project identity, the mobilization potential of SNSs, the disruptiveness of the event and selectivity in the construction of social knowledge – that explain this shift. Finally, we look at the specific processes that contribute to the creation of the movement’s own micromedia. We posit that these processes were made possible by the rhizomatic structure of the group and a function of SNSs we coin the Social Information Thermostat function. We conclude our study by positioning our findings in relation with the broader context.

Keywords: social networking sites, social movements, micromedia, media strategies

Procedia PDF Downloads 245

Authors: Jayantha Withanaarachchi, Sujeeva Setunge, Sara Moridpour

Abstract:

Gaps in strategic thinking and planning lead to critical transport infrastructure resilience. These gaps in strategic transport and land use development planning have an impact on communities and cities. Natural and man-induced disasters can be catastrophic to communities. After a disaster, many types of critical infrastructure, including transport infrastructure gets un-usable or gets damaged. This paper examines strategic thinking behind the resilience and protection of Critical Transport Infrastructure (CI) within transport networks by investigating the impact of disasters such as bushfires, hurricanes and earthquakes. A detailed analysis of three case studies have been conducted to identify the gaps in strategic transport planning and strategic decision making processes required to mitigate the impacts of disasters. Case studies will be analysed to identify existing gaps in road design, transport planning and decision making. This paper examines the effect of road designing, transport corridors and decision making during transport planning stages and how it impacts transport infrastructure as well as community resilience. A set of recommendations to overcome the shortcomings of existing strategic planning and designing process are presented. This research paper reviews transport infrastructure planning issues and presents the common approach suitable for future strategic thinking and planning which could be adopted in practices.

Keywords: community resilience, decision making , infrastructure resilience, strategic transport planning, transport infrastructure

Procedia PDF Downloads 269

Authors: Yu Zhang, Ye Tian, Fang Ye Yixuan Kang

Abstract:

The capacity of conventional cellular networks has reached its upper bound and it can be well handled by introducing femtocells with low-cost and easy-to-deploy. Spectrum interference issue becomes more critical in peace with the value-added multimedia services growing up increasingly in two-tier cellular networks. Spectrum allocation is one of effective methods in interference mitigation technology. This paper proposes a game-theory-based on OFDMA downlink spectrum allocation aiming at reducing co-channel interference in two-tier femtocell networks. The framework is formulated as a non-cooperative game, wherein the femto base stations are players and frequency channels available are strategies. The scheme takes full account of competitive behavior and fairness among stations. In addition, the utility function reflects the interference from the standpoint of channels essentially. This work focuses on co-channel interference and puts forward a negative logarithm interference function on distance weight ratio aiming at suppressing co-channel interference in the same layer network. This scenario is more suitable for actual network deployment and the system possesses high robustness. According to the proposed mechanism, interference exists only when players employ the same channel for data communication. This paper focuses on implementing spectrum allocation in a distributed fashion. Numerical results show that signal to interference and noise ratio can be obviously improved through the spectrum allocation scheme and the users quality of service in downlink can be satisfied. Besides, the average spectrum efficiency in cellular network can be significantly promoted as simulations results shown.

Keywords: femtocell networks, game theory, interference mitigation, spectrum allocation

Procedia PDF Downloads 142

Authors: Gaurav Sancheti

Abstract:

This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

Procedia PDF Downloads 204