Search results for: chaotic neural networks

Authors: Vladimir I. Ponomarenko, Mikhail D. Prokhorov, Anatoly S. Karavaev

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The interaction between respiration and low-frequency rhythms of the cardiovascular system is studied. The obtained results count in favor of the hypothesis that low-frequency rhythms in blood pressure and R-R intervals are generated in different central neural structures involved in the autonomic control of the cardiovascular systems.

Keywords: Cardiovascular system, R-R intervals, blood pressure, synchronization.

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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.

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Authors: Fatma El-zahraa El-taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition  problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset are examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of detection of intersections in satellite images is evaluated.

Keywords: Satellite images, remote sensing images, data acquisition, autonomous vehicles, robot navigation, route planning, road intersections.

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Authors: J. P. Dubois, H. M. Chiu

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In this paper, we study statistical multiplexing of VBR video in ATM networks. ATM promises to provide high speed realtime multi-point to central video transmission for telemedicine applications in rural hospitals and in emergency medical services. Video coders are known to produce variable bit rate (VBR) signals and the effects of aggregating these VBR signals need to be determined in order to design a telemedicine network infrastructure capable of carrying these signals. We first model the VBR video signal and simulate it using a generic continuous-data autoregressive (AR) scheme. We carry out the queueing analysis by the Fluid Approximation Model (FAM) and the Markov Modulated Poisson Process (MMPP). The study has shown a trade off: multiplexing VBR signals reduces burstiness and improves resource utilization, however, the buffer size needs to be increased with an associated economic cost. We also show that the MMPP model and the Fluid Approximation model fit best, respectively, the cell region and the burst region. Therefore, a hybrid MMPP and FAM completely characterizes the overall performance of the ATM statistical multiplexer. The ramifications of this technology are clear: speed, reliability (lower loss rate and jitter), and increased capacity in video transmission for telemedicine. With migration to full IP-based networks still a long way to achieving both high speed and high quality of service, the proposed ATM architecture will remain of significant use for telemedicine.

Keywords: ATM, multiplexing, queueing, telemedicine, VBR.

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Authors: Malinwo Estone Ayikpa

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Electric power systems are likely to operate with minimum losses and voltage meeting international standards. This is made possible generally by control actions provide by automatic voltage regulators, capacitors and transformers with on-load tap changer (OLTC). With the development of photovoltaic (PV) systems technology, their integration on distribution networks has increased over the last years to the extent of replacing the above mentioned techniques. The conventional analysis and simulation tools used for electrical networks are no longer able to take into account control actions necessary for studying distributed PV generation impact. This paper presents an unbalanced optimal power flow (OPF) model that minimizes losses with association of active power generation and reactive power control of single-phase and three-phase PV systems. Reactive power can be generated or absorbed using the available capacity and the adjustable power factor of the inverter. The unbalance OPF is formulated by current balance equations and solved by primal-dual interior point method. Several simulation cases have been carried out varying the size and location of PV systems and the results show a detailed view of the impact of PV distributed generation on distribution systems.

Keywords: Distribution system, losses, photovoltaic generation, primal-dual interior point method, reactive power control.

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: Data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional.

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Authors: B. Malarkodi, S. K. Riyaz Hussain, B. Venkataramani

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Power consumption of nodes in ad hoc networks is a critical issue as they predominantly operate on batteries. In order to improve the lifetime of an ad hoc network, all the nodes must be utilized evenly and the power required for connections must be minimized. In this project a link layer algorithm known as Power Aware medium Access Control (PAMAC) protocol is proposed which enables the network layer to select a route with minimum total power requirement among the possible routes between a source and a destination provided all nodes in the routes have battery capacity above a threshold. When the battery capacity goes below a predefined threshold, routes going through these nodes will be avoided and these nodes will act only as source and destination. Further, the first few nodes whose battery power drained to the set threshold value are pushed to the exterior part of the network and the nodes in the exterior are brought to the interior. Since less total power is required to forward packets for each connection. The network layer protocol AOMDV is basically an extension to the AODV routing protocol. AOMDV is designed to form multiple routes to the destination and it also avoid the loop formation so that it reduces the unnecessary congestion to the channel. In this project, the performance of AOMDV is evaluated using PAMAC as a MAC layer protocol and the average power consumption, throughput and average end to end delay of the network are calculated and the results are compared with that of the other network layer protocol AODV.

Keywords: AODV, PAMAC, AOMDV, Power consumption.

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Authors: Moneeb Gohar, Seok Joo Koh, Tae-Won Um, Hyun-Woo Lee

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This paper proposes a fast tree join scheme to provide seamless multicast handover in the mobile networks based on the Fast Mobile IPv6 (FMIPv6). In the existing FMIPv6-based multicast handover scheme, the bi-directional tunnelling or the remote subscription is employed with the packet forwarding from the previous access router (AR) to the new AR. In general, the remote subscription approach is preferred to the bi-directional tunnelling one, since in the remote subscription scheme we can exploit an optimized multicast path from a multicast source to many mobile receivers. However, in the remote subscription scheme, if the tree joining operation takes a long time, the amount of data packets to be forwarded and buffered for multicast handover will increase, and thus the corresponding buffer may overflow, which results in severe packet losses. In order to reduce these costs associated with packet forwarding and buffering, this paper proposes the fast join to multicast tree, in which the new AR will join the multicast tree as fast as possible, so that the new multicast data packets can also arrive at the new AR, by which the packet forwarding and buffering costs can be reduced. From numerical analysis, it is shown that the proposed scheme can give better performance than the existing FMIPv6-based multicast handover schemes in terms of the multicast packet delivery costs.

Keywords: Mobile Multicast, FMIPv6, Seamless Handover, Fast Tree Join.

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Authors: Y. Harold Robinson, M. Rajaram, E. Golden Julie, S. Balaji

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In wireless sensor networks, locality and positioning information can be captured using Global Positioning System (GPS). This message can be congregated initially from spot to identify the system. Users can retrieve information of interest from a wireless sensor network (WSN) by injecting queries and gathering results from the mobile sink nodes. Routing is the progression of choosing optimal path in a mobile network. Intermediate node employs permutation of device nodes into teams and generating cluster heads that gather the data from entity cluster’s node and encourage the collective data to base station. WSNs are widely used for gathering data. Since sensors are power-constrained devices, it is quite vital for them to reduce the power utilization. A tree-based data fusion clustering routing algorithm (TBDFC) is used to reduce energy consumption in wireless device networks. Here, the nodes in a tree use the cluster formation, whereas the elevation of the tree is decided based on the distance of the member nodes to the cluster-head. Network simulation shows that this scheme improves the power utilization by the nodes, and thus considerably improves the lifetime.

Keywords: WSN, TBDFC, LEACH, PEGASIS, TREEPSI.

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Authors: K. P. Valdés García, E. I. Rodríguez Fonseca, L. G. Juárez Cantú

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Suicide is a global, multidimensional and dynamic problem of mental health, which requires a constant study for its understanding and prevention. When research of this phenomenon is done, it is necessary to consider the different characteristics it may have because of the individual and sociocultural variables, the importance of this consideration is related to the generation of effective treatments and interventions. Adolescents are a vulnerable population due to the characteristics of the development stage. The investigation was carried out with the objective of identifying and describing the conceptualization of adolescents of suicide, and in this process, we find possible differences between men and women. The study was carried out in Saltillo, Coahuila, Mexico. The sample was composed of 418 volunteer students aged between 11 and 18 years. The ethical aspects of the research were reviewed and considered in all the processes of the investigation with the participants, their parents and the schools to which they belonged, psychological attention was offered to the participants and preventive workshops were carried in the educational institutions. Natural semantic networks were the instrument used, since this hybrid method allows to find and analyze the social concept of a phenomenon; in this case, the word suicide was used as an evocative stimulus and participants were asked to evoke at least five words and a maximum 10 that they thought were related to suicide, and then hierarchize them according to the closeness with the construct. The subsequent analysis was carried with Excel, yielding the semantic weights, affective loads and the distances between each of the semantic fields established according to the words reported by the subjects. The results showed similarities in the conceptualization of suicide in adolescents, men and women. Seven semantic fields were generated; the words were related in the discourse analysis: 1) death, 2) possible triggering factors, 3) associated moods, 4) methods used to carry it out, 5) psychological symptomatology that could affect, 6) words associated with a rejection of suicide, and finally, 7) specific objects to carry it out. One of the necessary aspects to consider in the investigations of complex issues such as suicide is to have a diversity of instruments and techniques that adjust to the characteristics of the population and that allow to understand the phenomena from the social constructs and not only theoretical. The constant study of suicide is a pressing need, the loss of a life from emotional difficulties that can be solved through psychiatry and psychological methods requires governments and professionals to pay attention and work with the risk population.

Keywords: Adolescents, semantic networks, speech analysis, suicide.

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Authors: Ricardo A. Martins, Matheus S. da Silva, Gabriel H. F. Iarossi, Helen C. M. Senefonte, Cinthyan R. S. C. de Barbosa

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This paper is the development of a concept of the man/robot interaction. More accurately in developing of an autistic child that have more troubles with interaction, here offers an efficient solution, even though simple; however, less studied for this public. This concept is based on code applied thought out the Lego NXT kit, built for the interpretation of the robot, thereby can create this interaction in a constructive way for children suffering with Autism.

Keywords: Lego NXT, autism, ANN (Artificial Neural Network), Backpropagation.

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the CPU, RAM, and ROM memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles.

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: Metagenomics, phenotype prediction, deep learning, embeddings, multiple instance learning.

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Authors: Sung Ho Ha, Eun Kyoung Kwon, Jong Sik Jin, Hyun Sun Park

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This article outlines a hybrid method, incorporating multiple techniques into an evaluation process, in order to select competitive suppliers in a supply chain. It enables a purchaser to do single sourcing and multiple sourcing by calculating a combined supplier score, which accounts for both qualitative and quantitative factors that have impact on supply chain performance.

Keywords: Analytic hierarchy process, Data envelopment analysis, Neural network, Supply chain management.

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Authors: Xiang Zhang, Daohong Yang, Ziyuan Wu, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) uses cameras to gather information in unknown environments to achieve simultaneous localization and mapping of the environment. This technology has a wide range of applications in autonomous driving, virtual reality, and other related fields. Currently, the research advancements related to VSLAM can maintain high accuracy in static environments. But in dynamic environments, the presence of moving objects in the scene can reduce the stability of the VSLAM system, leading to inaccurate localization and mapping, or even system failure. In this paper, a robust VSLAM method was proposed to effectively address the challenges in dynamic environments. We proposed a dynamic region removal scheme based on a semantic segmentation neural network and geometric constraints. Firstly, a semantic segmentation neural network is used to extract the prior active motion region, prior static region, and prior passive motion region in the environment. Then, the lightweight frame tracking module initializes the transform pose between the previous frame and the current frame on the prior static region. A motion consistency detection module based on multi-view geometry and scene flow is used to divide the environment into static regions and dynamic regions. Thus, the dynamic object region was successfully eliminated. Finally, only the static region is used for tracking thread. Our research is based on the ORBSLAM3 system, which is one of the most effective VSLAM systems available. We evaluated our method on the TUM RGB-D benchmark and the results demonstrate that the proposed VSLAM method improves the accuracy of the original ORBSLAM3 by 70%˜98.5% under a high dynamic environment.

Keywords: Dynamic scene, dynamic visual SLAM, semantic segmentation, scene flow, VSLAM.

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Authors: P. S. Bhowmik, D. V. Rajan, S. P. Bose

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The load flow study in a power system constitutes a study of paramount importance. The study reveals the electrical performance and power flows (real and reactive) for specified condition when the system is operating under steady state. This paper gives an overview of different techniques used for load flow study under different specified conditions.

Keywords: Load Flow Studies, Y-matrix and Z-matrix iteration, Newton-Raphson method, Fast Decoupled method, Fuzzy logic, Artificial Neural Network.

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Authors: Jong-Moon Chung, Hyung-Weon Cho, Ki-Yong Jin, Min-Hee Cho

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In this paper, we present an analytical framework for the evaluation of the uplink performance of multihop cellular networks based on dynamic time division duplex (TDD). New wireless broadband protocols, such as WiMAX, WiBro, and 3G-LTE apply TDD, and mobile communication protocols under standardization (e.g., IEEE802.16j) are investigating mobile multihop relay (MMR) as a future technology. In this paper a novel MMR TDD scheme is presented, where the dynamic range of the frame is shared to traffic resources of asymmetric nature and multihop relaying. The mobile communication channel interference model comprises of inner and co-channel interference (CCI). The performance analysis focuses on the uplink due to the fact that the effects of dynamic resource allocation show significant performance degradation only in the uplink compared to time division multiple access (TDMA) schemes due to CCI [1-3], where the downlink results to be the same or better.The analysis was based on the signal to interference power ratio (SIR) outage probability of dynamic TDD (D-TDD) and TDMA systems,which are the most widespread mobile communication multi-user control techniques. This paper presents the uplink SIR outage probability with multihop results and shows that the dynamic TDD scheme applying MMR can provide a performance improvement compared to single hop applications if executed properly.

Keywords: Co-Channel Interference, Dynamic TDD, MobileMultihop Reply, Cellular Network, Time Division Multiple Access.

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of big data technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centres or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through VADER and RoBERTa model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and Term Frequency – Inverse Document Frequency (TFIDF) Vectorization and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide if the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: Counter vectorization, Convolutional Neural Network, Crawler, data technology, Long Short-Term Memory, LSTM, Web Scraping, sentiment analysis.

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Authors: Justinian Anatory, Ekael Stephen Manase

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In recent years, the adoption of mobile phones has been exceptionally rapid in many parts of the world, and Tanzania is not exceptional. We are witnessing a number of new mobile network operators being licensed from time to time by Tanzania Communications Regulatory Authority (TCRA). This makes competition in the telecommunications market very stiff. All mobile phone companies are struggling to earn more new customers into their networks. This trend courses a stiff competition. The various measures are being taken by different companies including, lowering tariff, and introducing free short messages within and out of their networks, and free calls during off-peak periods. This paper is aimed at investigating the influence of tariffs on students’ mobile customers in selecting their mobile network operators. About seventy seven students from high learning institutions in Dodoma Municipality, Tanzania, participated in responding to the prepared questionnaires. The sought information was aimed at determining if tariffs influenced students into selection of their current mobile operators. The results indicate that tariffs were the major driving factor in selection of mobile operators. However, female mobile customers were found to be more easily attracted into subscribing to a mobile operator due to low tariffs, a bigger number of free short messages or discounted call charges than their fellow male customers.

Keywords: Consumer Buying, mobile operators, tariff.

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Authors: Konstantinos C. Giotopoulos, Christos E. Alexakos, Grigorios N. Beligiannis, Spiridon D.Likothanassis

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In this contribution an innovative platform is being presented that integrates intelligent agents and evolutionary computation techniques in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting: I) various assessment agents for e-learning environments, II) a specific resource retrieval agent for the provision of additional information from Internet sources matching the needs and profile of the specific user and III) a genetic algorithm designed to extract efficient information (classifying rules) based on the students- answering input data. The agents are implemented in order to provide intelligent assessment services based on computational intelligence techniques such as Bayesian Networks and Genetic Algorithms. The proposed Genetic Algorithm (GA) is used in order to extract efficient information (classifying rules) based on the students- answering input data. The idea of using a GA in order to fulfil this difficult task came from the fact that GAs have been widely used in applications including classification of unknown data. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

Keywords: Bayesian Networks, Computational Intelligencetechniques, E-learning legacy systems, Service Oriented Integration, Intelligent Agents, Genetic Algorithms.

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Authors: K. Nirmala Devi, V. Murali Bhaskaran

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Traditional document representation for classification follows Bag of Words (BoW) approach to represent the term weights. The conventional method uses the Vector Space Model (VSM) to exploit the statistical information of terms in the documents and they fail to address the semantic information as well as order of the terms present in the documents. Although, the phrase based approach follows the order of the terms present in the documents rather than semantics behind the word. Therefore, a semantic concept based approach is used in this paper for enhancing the semantics by incorporating the ontology information. In this paper a novel method is proposed to forecast the intraday stock market price directional movement based on the sentiments from Twitter and money control news articles. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, political events and investor’s sentiment etc. The stock market series are generally dynamic, nonparametric, noisy and chaotic by nature. The sentiment analysis along with wisdom of crowds can automatically compute the collective intelligence of future performance in many areas like stock market, box office sales and election outcomes. The proposed method utilizes collective sentiments for stock market to predict the stock price directional movements. The collective sentiments in the above social media have powerful prediction on the stock price directional movements as up/down by using Granger Causality test.

Keywords: Bag of Words, Collective Sentiments, Ontology, Semantic relations, Sentiments, Social media, Stock Prediction, Twitter, Vector Space Model and wisdom of crowds.

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Authors: Angelo Lerro, Manuela Battipede, Piero Gili, Alberto Brandl

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Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.

Keywords: Neural network, aerodynamic angles, virtual sensor, unmanned aerial vehicle, air data system, flight test.

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Authors: Sakshi Kausha, R.K Sharma

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High speed networks provide realtime variable bit rate service with diversified traffic flow characteristics and quality requirements. The variable bit rate traffic has stringent delay and packet loss requirements. The burstiness of the correlated traffic makes dynamic buffer management highly desirable to satisfy the Quality of Service (QoS) requirements. This paper presents an algorithm for optimization of adaptive buffer allocation scheme for traffic based on loss of consecutive packets in data-stream and buffer occupancy level. Buffer is designed to allow the input traffic to be partitioned into different priority classes and based on the input traffic behavior it controls the threshold dynamically. This algorithm allows input packets to enter into buffer if its occupancy level is less than the threshold value for priority of that packet. The threshold is dynamically varied in runtime based on packet loss behavior. The simulation is run for two priority classes of the input traffic – realtime and non-realtime classes. The simulation results show that Adaptive Partial Buffer Sharing (ADPBS) has better performance than Static Partial Buffer Sharing (SPBS) and First In First Out (FIFO) queue under the same traffic conditions.

Keywords: Buffer Management, Consecutive packet loss, Quality-of-Service, Priority based packet discarding, partial buffersharing.

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Authors: V. T. S. Dao, T. G. Etoh, C. Vo Le, H. D. Nguyen, K. Takehara, T. Akino, K. Nishi

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Since 2004, we have been developing an in-situ storage image sensor (ISIS) that captures more than 100 consecutive images at a frame rate of 10 Mfps with ultra-high sensitivity as well as the video camera for use with this ISIS. Currently, basic research is continuing in an attempt to increase the frame rate up to 100 Mfps and above. In order to suppress electro-magnetic noise at such high frequency, a digital-noiseless imaging transfer scheme has been developed utilizing solely sinusoidal driving voltages. This paper presents highly efficient-yet-accurate expressions to estimate attenuation as well as phase delay of driving voltages through RC networks of an ultra-high-speed image sensor. Elmore metric for a fundamental RC chain is employed as the first-order approximation. By application of dimensional analysis to SPICE data, we found a simple expression that significantly improves the accuracy of the approximation. Similarly, another simple closed-form model to estimate phase delay through fundamental RC networks is also obtained. Estimation error of both expressions is much less than previous works, only less 2% for most of the cases . The framework of this analysis can be extended to address similar issues of other VLSI structures.

Keywords: Dimensional Analysis, ISIS, Digital-noiseless, RC network, Attenuation, Phase Delay, Elmore model

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Authors: O. S. Ebrahim, K. O. Shawky, M. A. Badr, P. K. Jain

Abstract:

This paper proposes an intelligent, supervisory, hysteresis liquid-level control with three-state energy saving mode (ESM) for induction motor (IM) in fluid transportation system (FTS) including storage tank. The IM pump drive comprises a modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to the computer’s 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. Considering the motor’s thermal capacity used (TCU) and grid-compatible tariff structure, a logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction. Fuzzy-logic (FL) based availability assessment is designed and deployed on cloud, in order to provide mobilized service for the star/delta switch and highly reliable contactors. Moreover, 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 demonstrate the validity and effectiveness of the proposed control system in terms of reliability, power quality and operational cost reduction with a motivation of power factor correction.

Keywords: Artificial Neural Network, ANN, Contactor Health Assessment, Energy Saving Mode, Induction Machine, IM, Supervisory Control, Fluid Transportation, Fuzzy Logic, FL, cloud computing, pumped storage.

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Authors: Kejun Zhuang, Zhaohui Wen

Abstract:

In this paper, a tri–neuron network model with time delay is investigated. By using the Bendixson-s criterion for high– dimensional ordinary differential equations and global Hopf bifurcation theory for functional differential equations, sufficient conditions for existence of periodic solutions when the time delay is sufficiently large are established.

Keywords: Delay, global Hopf bifurcation, neural network, periodicsolutions.

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Authors: Satyasen Panda, Urmila Bhanja

Abstract:

In this paper, we have presented and analyzed three-dimensional (3-D) matrices of wavelength/time/space code for optical code division multiple access (OCDMA) networks with NAND subtraction detection technique. The 3-D codes are constructed by integrating a two-dimensional modified quadratic congruence (MQC) code with one-dimensional modified prime (MP) code. The respective encoders and decoders were designed using fiber Bragg gratings and optical delay lines to minimize the bit error rate (BER). The performance analysis of the 3D-OCDMA system is based on measurement of signal to noise ratio (SNR), BER and eye diagram for a different number of simultaneous users. Also, in the analysis, various types of noises and multiple access interference (MAI) effects were considered. The results obtained with NAND detection technique were compared with those obtained with OR and AND subtraction techniques. The comparison results proved that the NAND detection technique with 3-D MQC\MP code can accommodate more number of simultaneous users for longer distances of fiber with minimum BER as compared to OR and AND subtraction techniques. The received optical power is also measured at various levels of BER to analyze the effect of attenuation.

Keywords: Cross correlation, three-dimensional optical code division multiple access, spectral amplitude coding optical code division multiple access, multiple access interference, phase induced intensity noise, three-dimensional modified quadratic congruence/modified prime code.

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Authors: V. Pizarro-Carmona, S. Castano-Solis, M. Cortés-Carmona, J. Fraile-Ardanuy, D. Jimenez-Bermejo

Abstract:

The purpose of this article is to optimize the Equivalent Electric Circuit Model (EECM) of different orders to obtain greater precision in the modeling of Li-ion battery packs. Optimization includes considering circuits based on 1RC, 2RC and 3RC networks, with a dependent voltage source and a series resistor. The parameters are obtained experimentally using tests in the time domain and in the frequency domain. Due to the high non-linearity of the behavior of the battery pack, Genetic Algorithm (GA) was used to solve and optimize the parameters of each EECM considered (1RC, 2RC and 3RC). The objective of the estimation is to minimize the mean square error between the measured impedance in the real battery pack and those generated by the simulation of different proposed circuit models. The results have been verified by comparing the Nyquist graphs of the estimation of the complex impedance of the pack. As a result of the optimization, the 2RC and 3RC circuit alternatives are considered as viable to represent the battery behavior. These battery pack models are experimentally validated using a hardware-in-the-loop (HIL) simulation platform that reproduces the well-known New York City cycle (NYCC) and Federal Test Procedure (FTP) driving cycles for electric vehicles. The results show that using GA optimization allows obtaining EECs with 2RC or 3RC networks, with high precision to represent the dynamic behavior of a battery pack in vehicular applications.

Keywords: Li-ion battery packs modeling optimized, EECM, GA, electric vehicle applications.

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Authors: Ashok Kumar, Vinod Kumar

Abstract:

Wireless Sensor Networks (WSNs) are used to monitor/observe vast inaccessible regions through deployment of large number of sensor nodes in the sensing area. For majority of WSN applications, the collected data needs to be combined with geographic information of its origin to make it useful for the user; information received from remote Sensor Nodes (SNs) that are several hops away from base station/sink is meaningless without knowledge of its source. In addition to this, location information of SNs can also be used to propose/develop new network protocols for WSNs to improve their energy efficiency and lifetime. In this paper, range free localization protocols for WSNs have been proposed. The proposed protocols are based on weighted centroid localization technique, where the edge weights of SNs are decided by utilizing fuzzy logic inference for received signal strength and link quality between the nodes. The fuzzification is carried out using (i) Mamdani, (ii) Sugeno, and (iii) Combined Mamdani Sugeno fuzzy logic inference. Simulation results demonstrate that proposed protocols provide better accuracy in node localization compared to conventional centroid based localization protocols despite presence of unintentional radio frequency interference from radio frequency (RF) sources operating in same frequency band.

Keywords: localization, range free, received signal strength, link quality indicator, Mamdani fuzzy logic inference, Sugeno fuzzy logic inference.

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Authors: Ling-fei Xu, Qi Zhao, Yan-ru Chen, Mu-chun Zhou, Meng Zhang, Shi-xue Xu

Abstract:

SVM ( Support Vector Machine ) is a new method in the artificial neural network ( ANN ). In the steel making, how to use computer to predict the end point of BOF accuracy is a great problem. A lot of method and theory have been claimed, but most of the results is not satisfied. Now the hot topic in the BOF end point predicting is to use optical way the predict the end point in the BOF. And we found that there exist some regular in the characteristic curve of the flame from the mouse of pudding. And we can use SVM to predict end point of the BOF, just single spectrum intensity should be required as the input parameter. Moreover, its compatibility for the input space is better than the BP network.

Keywords: SVM, predict, BOF, single spectrum intensity.

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