Search results for: Feed-Forward Neural Network
2241 Performance of a Connected Random Covered Energy Efficient Wireless Sensor Network
Authors: M. Mahdavi, M. Ismail, K. Jumari, Z. M. Hanapi
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For the sensor network to operate successfully, the active nodes should maintain both sensing coverage and network connectivity. Furthermore, scheduling sleep intervals plays critical role for energy efficiency of wireless sensor networks. Traditional methods for sensor scheduling use either sensing coverage or network connectivity, but rarely both. In this paper, we use random scheduling for sensing coverage and then turn on extra sensor nodes, if necessary, for network connectivity. Simulation results have demonstrated that the number of extra nodes that is on with upper bound of around 9%, is small compared to the total number of deployed sensor nodes. Thus energy consumption for switching on extra sensor node is small.
Keywords: Wireless sensor networks, energy efficient network, performance analysis, network coverage.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12972240 Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks
Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia
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PH, temperature and time of extraction of each stage, agitation speed and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.
Keywords: Zinc extraction, Efficiency, Neural networks, Operating condition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15772239 Performance Analysis of ERA Using Fuzzy Logic in Wireless Sensor Network
Authors: Kamalpreet Kaur, Harjit Pal Singh, Vikas Khullar
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In Wireless Sensor Network (WSN), the main limitation is generally inimitable energy consumption during processing of the sensor nodes. Cluster head (CH) election is one of the main issues that can reduce the energy consumption. Therefore, discovering energy saving routing protocol is the focused area for research. In this paper, fuzzy-based energy aware routing protocol is presented, which enhances the stability and network lifetime of the network. Fuzzy logic ensures the well-organized selection of CH by taking four linguistic variables that are concentration, energy, centrality, and distance to base station (BS). The results show that the proposed protocol shows better results in requisites of stability and throughput of the network.
Keywords: ERA, fuzzy logic, network model, WSN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8662238 3D Object Model Reconstruction Based on Polywogs Wavelet Network Parametrization
Authors: Mohamed Othmani, Yassine Khlifi
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This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets.Keywords: 3D object, optimization, parametrization, Polywog wavelets, reconstruction, wavelet networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14882237 Copper Price Prediction Model for Various Economic Situations
Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin
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Copper is an essential raw material used in the construction industry. During 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two hybrid price prediction models using artificial neural network and long short-term memory (ANN-LSTM), by Python, that can forecast the average monthly copper prices, traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022 and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices, and economic indicators of the three major exporting countries of copper depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation, and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-month prediction model is better than the 1-month prediction model; but still, both models can act as predicting tools for diverse economic situations.
Keywords: Copper prices, prediction model, neural network, time series forecasting.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1622236 Genetic Algorithm Based Wavelength Division Multiplexing Networks Planning
Authors: S.Baskar, P.S.Ramkumar, R.Kesavan
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This paper presents a new heuristic algorithm useful for long-term planning of survivable WDM networks. A multi-period model is formulated that combines network topology design and capacity expansion. The ability to determine network expansion schedules of this type becomes increasingly important to the telecommunications industry and to its customers. The solution technique consists of a Genetic Algorithm that allows generating several network alternatives for each time period simultaneously and shortest-path techniques to deduce from these alternatives a least-cost network expansion plan over all time periods. The multi-period planning approach is illustrated on a realistic network example. Extensive simulations on a wide range of problem instances are carried out to assess the cost savings that can be expected by choosing a multi-period planning approach instead of an iterative network expansion design method.Keywords: Wavelength Division Multiplexing, Genetic Algorithm, Network topology, Multi-period reliable network planning
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14292235 Deep Learning Based, End-to-End Metaphor Detection in Greek with Recurrent and Convolutional Neural Networks
Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos
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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.Keywords: Metaphor detection, deep learning, representation learning, embeddings.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5292234 Intelligent Modeling of the Electrical Activity of the Human Heart
Authors: Lambros V. Skarlas, Grigorios N. Beligiannis, Efstratios F. Georgopoulos, Adam V. Adamopoulos
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The aim of this contribution is to present a new approach in modeling the electrical activity of the human heart. A recurrent artificial neural network is being used in order to exhibit a subset of the dynamics of the electrical behavior of the human heart. The proposed model can also be used, when integrated, as a diagnostic tool of the human heart system. What makes this approach unique is the fact that every model is being developed from physiological measurements of an individual. This kind of approach is very difficult to apply successfully in many modeling problems, because of the complexity and entropy of the free variables describing the complex system. Differences between the modeled variables and the variables of an individual, measured at specific moments, can be used for diagnostic purposes. The sensor fusion used in order to optimize the utilization of biomedical sensors is another point that this paper focuses on. Sensor fusion has been known for its advantages in applications such as control and diagnostics of mechanical and chemical processes.Keywords: Artificial Neural Networks, Diagnostic System, Health Condition Modeling Tool, Heart Diagnostics Model, Heart Electricity Model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18182233 Information Theoretical Analysis of Neural Spiking Activity with Temperature Modulation
Authors: Young-Seok Choi
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This work assesses the cortical and the sub-cortical neural activity recorded from rodents using entropy and mutual information based approaches to study how hypothermia affects neural activity. By applying the multi-scale entropy and Shannon entropy, we quantify the degree of the regularity embedded in the cortical and sub-cortical neurons and characterize the dependency of entropy of these regions on temperature. We study also the degree of the mutual information on thalamocortical pathway depending on temperature. The latter is most likely an indicator of coupling between these highly connected structures in response to temperature manipulation leading to arousal after global cerebral ischemia.Keywords: Spiking activity, entropy, mutual information, temperature modulation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16162232 Analysis of Network Performance Using Aspect of Quantum Cryptography
Authors: Nisarg A. Patel, Hiren B. Patel
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Quantum cryptography is described as a point-to-point secure key generation technology that has emerged in recent times in providing absolute security. Researchers have started studying new innovative approaches to exploit the security of Quantum Key Distribution (QKD) for a large-scale communication system. A number of approaches and models for utilization of QKD for secure communication have been developed. The uncertainty principle in quantum mechanics created a new paradigm for QKD. One of the approaches for use of QKD involved network fashioned security. The main goal was point-to-point Quantum network that exploited QKD technology for end-to-end network security via high speed QKD. Other approaches and models equipped with QKD in network fashion are introduced in the literature as. A different approach that this paper deals with is using QKD in existing protocols, which are widely used on the Internet to enhance security with main objective of unconditional security. Our work is towards the analysis of the QKD in Mobile ad-hoc network (MANET).
Keywords: QKD, cryptography, quantum cryptography, network performance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9242231 A Survey of Attacks and Security Requirements in Wireless Sensor Networks
Authors: Vishnu Pratap Singh Kirar
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Wireless sensor network (WSN) is a network of many interconnected networked systems, they equipped with energy resources and they are used to detect other physical characteristics. On WSN, there are many researches are performed in past decades. WSN applicable in many security systems govern by military and in many civilian related applications. Thus, the security of WSN gets attention of researchers and gives an opportunity for many future aspects. Still, there are many other issues are related to deployment and overall coverage, scalability, size, energy efficiency, quality of service (QoS), computational power and many more. In this paper we discus about various applications and security related issue and requirements of WSN.
Keywords: Wireless Sensor Network (WSN), Wireless Network Attacks, Wireless Network Security.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29862230 A Review in Recent Development of Network Threats and Security Measures
Authors: Roza Dastres, Mohsen Soori
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Networks are vulnerable devices due to their basic feature of facilitating remote access and data communication. The information in the networks needs to be kept secured and safe in order to provide an effective communication and sharing device in the web of data. Due to challenges and threats of the data in networks, the network security is one of the most important considerations in information technology infrastructures. As a result, the security measures are considered in the network in order to decrease the probability of accessing the secured data by the hackers. The purpose of network security is to protect the network and its components from unauthorized access and abuse in order to provide a safe and secured communication device for the users. In the present research work a review in recent development of network threats and security measures is presented and future research works are also suggested. Different attacks to the networks and security measured against them are discussed in order to increase security in the web of data. So, new ideas in the network security systems can be presented by analyzing the published papers in order to move forward the research field.
Keywords: Network threats, network security, security measures, firewalls.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8152229 A New Recognition Scheme for Machine- Printed Arabic Texts based on Neural Networks
Authors: Z. Shaaban
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This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.
Keywords: Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14692228 Analysis of Detecting Wormhole Attack in Wireless Networks
Authors: Khin Sandar Win
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In multi hop wireless systems, such as ad hoc and sensor networks, mobile ad hoc network applications are deployed, security emerges as a central requirement. A particularly devastating attack is known as the wormhole attack, where two or more malicious colluding nodes create a higher level virtual tunnel in the network, which is employed to transport packets between the tunnel end points. These tunnels emulate shorter links in the network. In which adversary records transmitted packets at one location in the network, tunnels them to another location, and retransmits them into the network. The wormhole attack is possible even if the attacker has not compromised any hosts and even if all communication provides authenticity and confidentiality. In this paper, we analyze wormhole attack nature in ad hoc and sensor networks and existing methods of the defending mechanism to detect wormhole attacks without require any specialized hardware. This analysis able to provide in establishing a method to reduce the rate of refresh time and the response time to become more faster.Keywords: Ad hoc network, Sensor network, Wormhole attack, defending mechanism.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22862227 Periodic Solutions of Recurrent Neural Networks with Distributed Delays and Impulses on Time Scales
Authors: Yaping Ren, Yongkun Li
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In this paper, by using the continuation theorem of coincidence degree theory, M-matrix theory and constructing some suitable Lyapunov functions, some sufficient conditions are obtained for the existence and global exponential stability of periodic solutions of recurrent neural networks with distributed delays and impulses on time scales. Without assuming the boundedness of the activation functions gj, hj , these results are less restrictive than those given in the earlier references.
Keywords: Recurrent neural networks, global exponential stability, periodic solutions, distributed delays, impulses, time scales.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15792226 Retaining Users in a Commercially-Supported Social Network
Authors: Sasiphan Nitayaprapha
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A commercially-supported social network has become an emerging channel for an organization to communicate with and provide services to customers. The success of the commercially-supported social network depends on the ability of the organization to keep the customers in participating in the network. Drawing from the theories of information adoption, information systems continuance, and web usability, the author develops a model to explore how a commercially-supported social network can encourage customers to continue participating and using the information in the network. The theoretical model will be proved through an online survey of customers using the commercially-supported social networking sites of several high technology companies operating in the same sector. The result will be compared with previous studies to learn about the explanatory power of the research model, and to identify the main factors determining users’ intention to continue using a commercially-supported social network. Theoretical and practical implications and limitations are discussed.
Keywords: Social network, Information adoption, Information systems continuance, Web usability, User satisfaction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18872225 Almost Periodicity in a Harvesting Lotka-Volterra Recurrent Neural Networks with Time-Varying Delays
Authors: Yongzhi Liao
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By using the theory of exponential dichotomy and Banach fixed point theorem, this paper is concerned with the problem of the existence and uniqueness of positive almost periodic solution in a delayed Lotka-Volterra recurrent neural networks with harvesting terms. To a certain extent, our work in this paper corrects some result in recent years. Finally, an example is given to illustrate the feasibility and effectiveness of the main result.
Keywords: positive almost periodic solution, Lotka-Volterra, neural networks, Banach fixed point theorem, harvesting
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16082224 Virtualization Technology as a Tool for Teaching Computer Networks
Authors: Dalibor Dobrilovic, Borislav Odadžic
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In this paper is being described a possible use of virtualization technology in teaching computer networks. The virtualization can be used as a suitable tool for creating virtual network laboratories, supplementing the real laboratories and network simulation software in teaching networking concepts. It will be given a short description of characteristic projects in the area of virtualization technology usage in network simulation, network experiments and engineering education. A method for implementing laboratory has also been explained, together with possible laboratory usage and design of laboratory exercises. At the end, the laboratory test results of virtual laboratory are presented as well.Keywords: Computer network simulation software, teaching networking concepts, virtual network laboratory, virtualization technology.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22732223 Virtual Routing Function Allocation Method for Minimizing Total Network Power Consumption
Authors: Kenichiro Hida, Shin-Ichi Kuribayashi
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In a conventional network, most network devices, such as routers, are dedicated devices that do not have much variation in capacity. In recent years, a new concept of network functions virtualisation (NFV) has come into use. The intention is to implement a variety of network functions with software on general-purpose servers and this allows the network operator to select their capacities and locations without any constraints. This paper focuses on the allocation of NFV-based routing functions which are one of critical network functions, and presents the virtual routing function allocation algorithm that minimizes the total power consumption. In addition, this study presents the useful allocation policy of virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy takes the ratio of the power consumption of a routing function to that of a circuit and traffic distribution between areas into consideration. Furthermore, the present paper shows that there are cases where the use of NFV-based routing functions makes it possible to reduce the total power consumption dramatically, in comparison to a conventional network, in which it is not economically viable to distribute small-capacity routing functions.
Keywords: Virtual routing function, NFV, resource allocation, minimum power consumption.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12932222 Bi-Criteria Latency Optimization of Intra-and Inter-Autonomous System Traffic Engineering
Authors: K. Vidya, V.Rhymend Uthariaraj
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Traffic Engineering (TE) is the process of controlling how traffic flows through a network in order to facilitate efficient and reliable network operations while simultaneously optimizing network resource utilization and traffic performance. TE improves the management of data traffic within a network and provides the better utilization of network resources. Many research works considers intra and inter Traffic Engineering separately. But in reality one influences the other. Hence the effective network performances of both inter and intra Autonomous Systems (AS) are not optimized properly. To achieve a better Joint Optimization of both Intra and Inter AS TE, we propose a joint Optimization technique by considering intra-AS features during inter – AS TE and vice versa. This work considers the important criterion say latency within an AS and between ASes. and proposes a Bi-Criteria Latency optimization model. Hence an overall network performance can be improved by considering this jointoptimization technique in terms of Latency.Keywords: Inter-Domain Routing , Measurement, OptimizationPerformance, Traffic Engineering.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15072221 A Comparative Study of Novel Opportunistic Routing Protocols in Mobile Ad Hoc Networks
Authors: R. Poonkuzhali, M. Y. Sanavullah, M. R. Gurupriya
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Opportunistic routing is used, where the network has the features like dynamic topology changes and intermittent network connectivity. In Delay tolerant network or Disruption tolerant network opportunistic forwarding technique is widely used. The key idea of opportunistic routing is selecting forwarding nodes to forward data packets and coordination among these nodes to avoid duplicate transmissions. This paper gives the analysis of pros and cons of various opportunistic routing techniques used in MANET.
Keywords: Expected Transmission Count (ETX), Opportunistic routing, Proactive Source Routing (PSR), throughput.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24042220 A General Model for Amino Acid Interaction Networks
Authors: Omar Gaci, Stefan Balev
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In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the protein-s amino acids and whose edges are the interactions between them. Using a graph theory approach, we identify a number of properties of these networks. We compare them to the general small-world network model and we analyze their hierarchical structure.Keywords: interaction network, protein structure, small-world network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15672219 Unveiling the Mathematical Essence of Machine Learning: A Comprehensive Exploration
Authors: Randhir Singh Baghel
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In this study, the fundamental ideas guiding the dynamic area of machine learning—where models thrive and algorithms change over time—are rooted in an innate mathematical link. This study explores the fundamental ideas that drive the development of intelligent systems, providing light on the mutually beneficial link between mathematics and machine learning.
Keywords: Machine Learning, deep learning, Neural Network, optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1252218 Neural Networks Approaches for Computing the Forward Kinematics of a Redundant Parallel Manipulator
Authors: H. Sadjadian , H.D. Taghirad Member, A. Fatehi
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In this paper, different approaches to solve the forward kinematics of a three DOF actuator redundant hydraulic parallel manipulator are presented. On the contrary to series manipulators, the forward kinematic map of parallel manipulators involves highly coupled nonlinear equations, which are almost impossible to solve analytically. The proposed methods are using neural networks identification with different structures to solve the problem. The accuracy of the results of each method is analyzed in detail and the advantages and the disadvantages of them in computing the forward kinematic map of the given mechanism is discussed in detail. It is concluded that ANFIS presents the best performance compared to MLP, RBF and PNN networks in this particular application.Keywords: Forward Kinematics, Neural Networks, Numerical Solution, Parallel Manipulators.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19132217 Specialized Reduced Models of Dynamic Flows in 2-Stroke Engines
Authors: S. Cagin, X. Fischer, E. Delacourt, N. Bourabaa, C. Morin, D. Coutellier, B. Carré, S. Loumé
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The complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).
Keywords: Diesel engine, Design optimization, Model reduction, Neural network, NTF algorithm, Scavenging.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13172216 Offline Handwritten Signature Recognition
Authors: Gulzar A. Khuwaja, Mohammad S. Laghari
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Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capability to reliably distinguish between an authorized person and an imposter. Signature verification systems can be categorized as offline (static) and online (dynamic). This paper presents a neural network based recognition of offline handwritten signatures system that is trained with low-resolution scanned signature images.Keywords: Pattern Recognition, Computer Vision, AdaptiveClassification, Handwritten Signature Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28922215 Estimating Localization Network Node Positions with a Multi-Robot System
Authors: Mikko Elomaa, Aarne Halme
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A novel method using bearing-only SLAM to estimate node positions of a localization network is proposed. A group of simple robots are used to estimate the position of each node. Each node has a unique ID, which it can communicate to a robot close by. Initially the node IDs and positions are unknown. A case example using RFID technology in the localization network is introduced.
Keywords: Localization network, Multi-robot, RFID, SLAM
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11192214 Supervisory Controller with Three-State Energy Saving Mode for Induction Motor in Fluid Transportation
Authors: O. S. Ebrahim, K. O. Shawky, M. O. Ebrahim, P. K. Jain
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Induction Motor (IM) driving pump is the main consumer of electricity in a typical fluid transportation system (FTS). Changing the connection of the stator windings from delta to star at no load can achieve noticeable active and reactive energy savings. This paper proposes a supervisory hysteresis liquid-level control with three-state energy saving mode (ESM) for IM in FTS including storage tank. The IM pump drive comprises modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to computer ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. A logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction, considering the motor thermal capacity used. An artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and, computer simulations are performed to show the effectiveness of the proposed control in terms of reliability, power quality and energy/coenergy cost reduction with the suggestion of power factor correction.
Keywords: Artificial Neural Network, ANN, Energy Saving Mode, ESM, Induction Motor, IM, star/delta switch, supervisory control, fluid transportation, reliability, power quality.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3592213 Overload Control in a SIP Signaling Network
Authors: Masataka Ohta
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The Internet telephony employs a new type of Internet communication on which a mutual communication is realized by establishing sessions. Session Initiation Protocol (SIP) is used to establish sessions between end-users. For unreliable transmission (UDP), SIP message should be retransmitted when it is lost. The retransmissions increase a load of the SIP signaling network, and sometimes lead to performance degradation when a network is overloaded. The paper proposes an overload control for a SIP signaling network to protect from a performance degradation. Introducing two thresholds in a queue of a SIP proxy server, the SIP proxy server detects a congestion. Once congestion is detected, a SIP signaling network restricts to make new calls. The proposed overload control is evaluated using the network simulator (ns-2). With simulation results, the paper shows the proposed overload control works well.
Keywords: SIP signalling congestion overload control retransmission throughput simulation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17512212 Quality and Quantity in the Strategic Network of Higher Education Institutions
Authors: Juha Kettunen
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The study analyzes the quality and the size of the strategic network of higher education institutions and the concept of fitness for purpose in quality assurance. It also analyses the transaction costs of networking that have consequences on the number of members in the network. Empirical evidence is presented from the Consortium on Applied Research and Professional Education, which is a European strategic network of six higher education institutions. The results of the study support the argument that the number of members in the strategic network should be relatively small to provide high-quality results. The practical importance is that networking has been able to promote international research and development projects. The results of this study are important for those who want to design and improve international networks in higher education.
Keywords: Higher education, network, research and development, strategic management.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 613