Search results for: home network
5030 Maximum Power Point Tracking for Small Scale Wind Turbine Using Multilayer Perceptron Neural Network Implementation without Mechanical Sensor
Authors: Piyangkun Kukutapan, Siridech Boonsang
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The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.Keywords: maximum power point tracking, multilayer perceptron netural network, optimal duty cycle, DC generator
Procedia PDF Downloads 3265029 In Life: Space as Doppelganger in “The House of Usher”
Authors: Tuğçe Arslan
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In the dark and gloomy times of the Middle Ages, high, majestic, and frightening structures were revealed in the architectural field. Thus, gothic architecture began to find a place for itself in different areas and spread its influence. Gothic has found its place in almost every literary genre and manages to show itself as the dominant genre in the works it enters. It has exploited many concepts, such as a chest full of bad feelings, and creates a gloomy, scary, frightening, and pessimistic mood in the story with these concepts. One of the essential concepts it uses while creating these feelings is the concept of “Doppelganger.” With this concept, the authors make sense of the uncanny; at this point, they allow the spaces to act like characters, just like the uncanny feeling Edgar Allan Poe creates in his story “The Fall of the House of the Usher.” In this story by Edgar Allan Poe, attention should be paid to the symbolic link between the two, as “House of Usher” refers to the family and the building. And indeed, it is possible to see this minor rift as representative of a breakdown in family unity, specifically between Madeline and Roderick. Because although the home is not alive, it has some supernatural features that make it look like a living, breathing being. Therefore, the remainder of this paper will argue that apart from the apparent twins, the house should also qualify as a Doppelganger in the story. This study will first explore the physical and mental disorders of the twins and their journey to complement each other; next, in an attempt to demonstrate how the house as a non-living needs to be considered as a Doppelganger of the twins, a close reading on the house depictions will be scrutinized.Keywords: Edgar Allan Poe, doppelganger, uncanny, gothic, space, home
Procedia PDF Downloads 1255028 Recognition of Tifinagh Characters with Missing Parts Using Neural Network
Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui
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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN
Procedia PDF Downloads 3355027 A Survey on Traditional Mac Layer Protocols in Cognitive Wireless Mesh Networks
Authors: Anusha M., V. Srikanth
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Maximizing spectrum usage and numerous applications of the wireless communication networks have forced to a high interest of available spectrum. Cognitive Radio control its receiver and transmitter features exactly so that they can utilize the vacant approved spectrum without impacting the functionality of the principal licensed users. The Use of various channels assists to address interferences thereby improves the whole network efficiency. The MAC protocol in cognitive radio network explains the spectrum usage by interacting with multiple channels among the users. In this paper we studied about the architecture of cognitive wireless mesh network and traditional TDMA dependent MAC method to allocate channels dynamically. The majority of the MAC protocols suggested in the research are operated on Common-Control-Channel (CCC) to handle the services between Cognitive Radio secondary users. In this paper, an extensive study of Multi-Channel Multi-Radios or frequency range channel allotment and continually synchronized TDMA scheduling are shown in summarized way.Keywords: TDMA, MAC, multi-channel, multi-radio, WMN’S, cognitive radios
Procedia PDF Downloads 5635026 Water Demand Modelling Using Artificial Neural Network in Ramallah
Authors: F. Massri, M. Shkarneh, B. Almassri
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Water scarcity and increasing water demand especially for residential use are major challenges facing Palestine. The need to accurately forecast water consumption is useful for the planning and management of this natural resource. The main objective of this paper is to (i) study the major factors influencing the water consumption in Palestine, (ii) understand the general pattern of Household water consumption, (iii) assess the possible changes in household water consumption and suggest appropriate remedies and (iv) develop prediction model based on the Artificial Neural Network to the water consumption in Palestinian cities. The paper is organized in four parts. The first part includes literature review of household water consumption studies. The second part concerns data collection methodology, conceptual frame work for the household water consumption surveys, survey descriptions and data processing methods. The third part presents descriptive statistics, multiple regression and analysis of the water consumption in the two Palestinian cities. The final part develops the use of Artificial Neural Network for modeling the water consumption in Palestinian cities.Keywords: water management, demand forecasting, consumption, ANN, Ramallah
Procedia PDF Downloads 2205025 A Highly Efficient Broadcast Algorithm for Computer Networks
Authors: Ganesh Nandakumaran, Mehmet Karaata
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A wave is a distributed execution, often made up of a broadcast phase followed by a feedback phase, requiring the participation of all the system processes before a particular event called decision is taken. Wave algorithms with one initiator such as the 1-wave algorithm have been shown to be very efficient for broadcasting messages in tree networks. Extensions of this algorithm broadcasting a sequence of waves using a single initiator have been implemented in algorithms such as the m-wave algorithm. However as the network size increases, having a single initiator adversely affects the message delivery times to nodes further away from the initiator. As a remedy, broadcast waves can be allowed to be initiated by multiple initiator nodes distributed across the network to reduce the completion time of broadcasts. These waves initiated by one or more initiator processes form a collection of waves covering the entire network. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using waves with multiple concurrent initiators. In this paper, we propose the first stabilizing multi-wave sequence algorithm implementing waves started by multiple initiator processes such that every process in the network receives at least one sequence of broadcasts. Due to being stabilizing, the proposed algorithm can withstand transient faults and do not require initialization. We view a fault as a transient fault if it perturbs the configuration of the system but not its program.Keywords: distributed computing, multi-node broadcast, propagation of information with feedback and cleaning (PFC), stabilization, wave algorithms
Procedia PDF Downloads 5055024 The Omicron Variant BA.2.86.1 of SARS- 2 CoV-2 Demonstrates an Altered Interaction Network and Dynamic Features to Enhance the Interaction with the hACE2
Authors: Taimur Khan, Zakirullah, Muhammad Shahab
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The SARS-CoV-2 variant BA.2.86 (Omicron) has emerged with unique mutations that may increase its transmission and infectivity. This study investigates how these mutations alter the Omicron receptor-binding domain's interaction network and dynamic properties (RBD) compared to the wild-type virus, focusing on its binding affinity to the human ACE2 (hACE2) receptor. Protein-protein docking and all-atom molecular dynamics simulations were used to analyze structural and dynamic differences. Despite the structural similarity to the wild-type virus, the Omicron variant exhibits a distinct interaction network involving new residues that enhance its binding capacity. The dynamic analysis reveals increased flexibility in the RBD, particularly in loop regions crucial for hACE2 interaction. Mutations significantly alter the secondary structure, leading to greater flexibility and conformational adaptability compared to the wild type. Binding free energy calculations confirm that the Omicron RBD has a higher binding affinity (-70.47 kcal/mol) to hACE2 than the wild-type RBD (-61.38 kcal/mol). These results suggest that the altered interaction network and enhanced dynamics of the Omicron variant contribute to its increased infectivity, providing insights for the development of targeted therapeutics and vaccines.Keywords: SARS-CoV-2, molecular dynamic simulation, receptor binding domain, vaccine
Procedia PDF Downloads 275023 Deniplant Nutraceuticals for Endometriosis Pain
Authors: Gheorghe Giurgiu, Manole Cojocaru, Mihnea Andrei Nicodin
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Background: Inflammation has the main role in the progression of endometriosis. The mechanisms by which endometriosis induces a chronic pain state remain poorly understood. Unfortunately, there is no known cure for endometriosis. But you can manage it with medication and at-home treatments. Some findings have highlighted the main role of inflammation in endometriosis by acting on proliferation, apoptosis, and angiogenesis. The introduction of new agents can be effective in improving the condition of patients; for example, plants are promising sources of bioactive natural components. Objectives: These natural compounds could be interesting strategies in therapy. While there is no absolute cure for this condition, some home remedies can relieve the pain and discomfort it brings. The purpose of this study is to summarize the potential action of Deniplant nutraceuticals in endometriosis by acting on inflammation. Materials and Methods: The primary symptoms of endometriosis are pelvic pain and infertility. The use of Deniplant nutraceuticals could be interesting in disease management for women. Results: Treating pain-related aspects of endometriosis would contribute to the improvement of mental health and daytime function. Because the microbiome can influence inflammation, new therapies can develop through its natural modulation. There are other options, including natural remedies, herbs like cinnamon twigs or licorice root, or supplements such as thiamine, magnesium, or omega-3 fatty acids. Conclusion: Deniplant nutraceuticals can downregulate inflammation in endometriosis. Nevertheless, the limited number of studies focusing on the different interactions of Deniplant nutraceuticals in endometriosis restricts its clear and immediate use in a therapeutic strategy.Keywords: endometriosis, diet, Deniplant nutraceuticals, pain
Procedia PDF Downloads 775022 DCDNet: Lightweight Document Corner Detection Network Based on Attention Mechanism
Authors: Kun Xu, Yuan Xu, Jia Qiao
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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.Keywords: document detection, corner detection, attention mechanism, lightweight
Procedia PDF Downloads 3555021 Comparison of Home Ranges of Radio Collared Jaguars (Panthera onca L.) in the Dry Chaco and Wet Chaco of Paraguay
Authors: Juan Facetti, Rocky McBride, Karina Loup
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The Chaco Region of Paraguay is a key biodiverse area for the conservation of jaguars (Panthera onca), the largest feline of the Americas. It comprises five eco-regions, which holds important but decreasing populations of this species. The last decades, the expansion of soybean over the Atlantic Forest, forced the translocation of cattle-ranches towards the Chaco. Few studies of Jaguar's population densities in the American hemisphere were done until now. In the region, the specie is listed as vulnerable or threatened and more information is needed to implement any conservation policy. Among the factors that threaten the populations are land-use change, habitat fragmentation, prey depletion and illegal hunting. Two largest eco-regions were studied: the Wet Chaco and the Dry Chaco. From 2002 more than 20 jaguars were captured and fitted with GPS-collar. Data collected from 11 GPS-collars were processed, transformed numerically and finally converted into maps for analyzing. 8.092 locations were determined for four adult females (AF) and one adult male (AM) in the Wet Chaco, and one AF, one juvenile male (JM) and four AM in the Dry Chaco, during 1,867 days. GIS and kernel methodology were used to calculate daily distance of movement, home range-HR (95% isopleth), and core area (considered as 50% isopleth). In the Wet Chaco HR were 56 Km2 and 238 km2 for females and males respectively; while in the Dry Chaco HR were 685 Km2 and 844.5 km2 for females and males respectively, and 172 Km2 for a juvenile. Core areas of individual activity for each jaguar, were on average 11.5 Km2 and 33.55 km2 for AF and AM respectively in the Wet Chaco, while in the Dry Chaco were larger: 115 km2 for five AM and 225 Km2 for an AF and 32.4 Km2 for a JM. In both ecoregions, only one relevant overlap of HR of adults was reported. During the reproduction season, the HR (95% K) of one AM overlapped 49.83% with that of one AF. At the Wet Chaco, the maximum daily distance moved by an AF was 14.5 Km and 11.6 Km for the AM, while the Maximum Mean Daily Moved (MMDM) distance was 5.6 km for an AF and 3.1 km for an AM. At the Dry Chaco, the maximum daily distance for an AF was 61.7Km., 50.9Km for the AM and 6.6 Km for the JM, while the MMDM distance was 13.2 km for an AM and 8.4 km for an AF. This study confirmed that, as the invasion to jaguar habitat increased, it resulted in fragmented landscapes that influence spacing patterns of jaguars. Males used largest HR that of the smaller females and males covers largest distances that of the females. There appeared to be important spatial segregation between not only females but also males. It is likely that the larger areas used by males are partly caused by the sexual dimorphism in body size that entails differences in prey requirements. These could explain the larger distances travelled daily by males.Keywords: Chaco ecoregions, Jaguar, home range, Panthera onca, Paraguay
Procedia PDF Downloads 3025020 Introduce a New Model of Anomaly Detection in Computer Networks Using Artificial Immune Systems
Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi
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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks
Procedia PDF Downloads 3565019 A 5G Architecture Based to Dynamic Vehicular Clustering Enhancing VoD Services Over Vehicular Ad hoc Networks
Authors: Lamaa Sellami, Bechir Alaya
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Nowadays, video-on-demand (VoD) applications are becoming one of the tendencies driving vehicular network users. In this paper, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different cars included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over VANET. The proposed algorithm takes advantage of the concept of small cells and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)- Advanced network. The proposed clustering algorithm considers multiple characteristics such as the vehicle’s position and acceleration to reduce latency and packet loss. Therefore, each cluster is counted as a small cell containing vehicular nodes and an access point that is elected regarding some particular specifications.Keywords: video-on-demand, vehicular ad-hoc network, mobility, vehicular traffic load, small cell, wireless backhaul, LTE-advanced, latency, packet loss
Procedia PDF Downloads 1425018 Citation Analysis of New Zealand Court Decisions
Authors: Tobias Milz, L. Macpherson, Varvara Vetrova
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The law is a fundamental pillar of human societies as it shapes, controls and governs how humans conduct business, behave and interact with each other. Recent advances in computer-assisted technologies such as NLP, data science and AI are creating opportunities to support the practice, research and study of this pervasive domain. It is therefore not surprising that there has been an increase in investments into supporting technologies for the legal industry (also known as “legal tech” or “law tech”) over the last decade. A sub-discipline of particular appeal is concerned with assisted legal research. Supporting law researchers and practitioners to retrieve information from the vast amount of ever-growing legal documentation is of natural interest to the legal research community. One tool that has been in use for this purpose since the early nineteenth century is legal citation indexing. Among other use cases, they provided an effective means to discover new precedent cases. Nowadays, computer-assisted network analysis tools can allow for new and more efficient ways to reveal the “hidden” information that is conveyed through citation behavior. Unfortunately, access to openly available legal data is still lacking in New Zealand and access to such networks is only commercially available via providers such as LexisNexis. Consequently, there is a need to create, analyze and provide a legal citation network with sufficient data to support legal research tasks. This paper describes the development and analysis of a legal citation Network for New Zealand containing over 300.000 decisions from 125 different courts of all areas of law and jurisdiction. Using python, the authors assembled web crawlers, scrapers and an OCR pipeline to collect and convert court decisions from openly available sources such as NZLII into uniform and machine-readable text. This facilitated the use of regular expressions to identify references to other court decisions from within the decision text. The data was then imported into a graph-based database (Neo4j) with the courts and their respective cases represented as nodes and the extracted citations as links. Furthermore, additional links between courts of connected cases were added to indicate an indirect citation between the courts. Neo4j, as a graph-based database, allows efficient querying and use of network algorithms such as PageRank to reveal the most influential/most cited courts and court decisions over time. This paper shows that the in-degree distribution of the New Zealand legal citation network resembles a power-law distribution, which indicates a possible scale-free behavior of the network. This is in line with findings of the respective citation networks of the U.S. Supreme Court, Austria and Germany. The authors of this paper provide the database as an openly available data source to support further legal research. The decision texts can be exported from the database to be used for NLP-related legal research, while the network can be used for in-depth analysis. For example, users of the database can specify the network algorithms and metrics to only include specific courts to filter the results to the area of law of interest.Keywords: case citation network, citation analysis, network analysis, Neo4j
Procedia PDF Downloads 1105017 Influence of the Refractory Period on Neural Networks Based on the Recognition of Neural Signatures
Authors: José Luis Carrillo-Medina, Roberto Latorre
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Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network
Procedia PDF Downloads 4945016 Supporting Densification through the Planning and Implementation of Road Infrastructure in the South African Context
Authors: K. Govender, M. Sinclair
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This paper demonstrates a proof of concept whereby shorter trips and land use densification can be promoted through an alternative approach to planning and implementation of road infrastructure in the South African context. It briefly discusses how the development of the Compact City concept relies on a combination of promoting shorter trips and densification through a change in focus in road infrastructure provision. The methodology developed in this paper uses a traffic model to test the impact of synthesized deterrence functions on congestion locations in the road network through the assignment of traffic on the study network. The results from this study demonstrate that intelligent planning of road infrastructure can indeed promote reduced urban sprawl, increased residential density and mixed-use areas which are supported by an efficient public transport system; and reduced dependence on the freeway network with a fixed road infrastructure budget. The study has resonance for all cities where urban sprawl is seemingly unstoppable.Keywords: compact cities, densification, road infrastructure planning, transportation modelling
Procedia PDF Downloads 1795015 Experimental Study and Neural Network Modeling in Prediction of Surface Roughness on Dry Turning Using Two Different Cutting Tool Nose Radii
Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi
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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.Keywords: full factorial design, neural network, nose radius, surface finish
Procedia PDF Downloads 3695014 Application of Neural Network in Portfolio Product Companies: Integration of Boston Consulting Group Matrix and Ansoff Matrix
Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari
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This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.Keywords: artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix
Procedia PDF Downloads 1445013 Real-Time Pedestrian Detection Method Based on Improved YOLOv3
Authors: Jingting Luo, Yong Wang, Ying Wang
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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3
Procedia PDF Downloads 1435012 The Study of ZigBee Protocol Application in Wireless Networks
Authors: Ardavan Zamanpour, Somaieh Yassari
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ZigBee protocol network was developed in industries and MIT laboratory in 1997. ZigBee is a wireless networking technology by alliance ZigBee which is designed to low board and low data rate applications. It is a Protocol which connects between electrical devises with very low energy and cost. The first version of IEEE 802.15.4 which was formed ZigBee was based on 2.4GHZ MHZ 912MHZ 868 frequency band. The name of system is often reminded random directions that bees (BEES) traversing during pollination of products. Such as alloy of the ways in which information packets are traversed within the mesh network. This paper aims to study the performance and effectiveness of this protocol in wireless networks.Keywords: ZigBee, protocol, wireless, networks
Procedia PDF Downloads 3705011 Impact of Integrated Signals for Doing Human Activity Recognition Using Deep Learning Models
Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo
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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps
Procedia PDF Downloads 1015010 RBF Neural Network Based Adaptive Robust Control for Bounded Position/Force Control of Bilateral Teleoperation Arms
Authors: Henni Mansour Abdelwaheb
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This study discusses the design of a bounded position/force feedback controller developed to ensure position and force tracking for bilateral teleoperation arms operating with variable delay, and actuator saturation. Also, an adaptive robust Radial Basis Function (RBF) neural network is used to estimate the environment torque. The parameters of the environment torque are then sent from the slave site to the master site as a non-power signal to avoid passivity problems. Moreover, a nonlinear function is applied to each controller term as a smooth saturation function, providing a bounded control signal and preserving the system’s actuators. Lastly, the Lyapunov approach demonstrates the global stability of the controlled system, and numerical experiment results further confirm the validity of the presented strategy.Keywords: teleoperation manipulators system, time-varying delay, actuator saturation, adaptive robust rbf neural network approximation, uncertainties
Procedia PDF Downloads 795009 Designing an Integrated Platform for Real-Time Recommendations Sharing among the Aged and People Living with Cancer
Authors: Adekunle O. Afolabi, Pekka Toivanen
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The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.Keywords: recommendation systems, Internet of Things, healthcare, homecare, real-time
Procedia PDF Downloads 1555008 Classification of Manufacturing Data for Efficient Processing on an Edge-Cloud Network
Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson
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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0
Procedia PDF Downloads 1835007 Developing a Cloud Intelligence-Based Energy Management Architecture Facilitated with Embedded Edge Analytics for Energy Conservation in Demand-Side Management
Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li
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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification
Procedia PDF Downloads 2515006 Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network
Authors: Jia Xin Low, Keng Wah Choo
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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification
Procedia PDF Downloads 3505005 Investigations into Effect of Neural Network Predictive Control of UPFC for Improving Transient Stability Performance of Multimachine Power System
Authors: Sheela Tiwari, R. Naresh, R. Jha
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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.Keywords: identification, neural networks, predictive control, transient stability, UPFC
Procedia PDF Downloads 3755004 Health Trajectory Clustering Using Deep Belief Networks
Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour
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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.Keywords: health trajectory, clustering, deep learning, DBN
Procedia PDF Downloads 3715003 Mutiple Medical Landmark Detection on X-Ray Scan Using Reinforcement Learning
Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V
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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network
Procedia PDF Downloads 1445002 Performance Comparison of Resource Allocation without Feedback in Wireless Body Area Networks by Various Pseudo Orthogonal Sequences
Authors: Ojin Kwon, Yong-Jin Yoon, Liu Xin, Zhang Hongbao
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Wireless Body Area Network (WBAN) is a short-range wireless communication around human body for various applications such as wearable devices, entertainment, military, and especially medical devices. WBAN attracts the attention of continuous health monitoring system including diagnostic procedure, early detection of abnormal conditions, and prevention of emergency situations. Compared to cellular network, WBAN system is more difficult to control inter- and inner-cell interference due to the limited power, limited calculation capability, mobility of patient, and non-cooperation among WBANs. In this paper, we compare the performance of resource allocation scheme based on several Pseudo Orthogonal Codewords (POCs) to mitigate inter-WBAN interference. Previously, the POCs are widely exploited for a protocol sequence and optical orthogonal code. Each POCs have different properties of auto- and cross-correlation and spectral efficiency according to its construction of POCs. To identify different WBANs, several different pseudo orthogonal patterns based on POCs exploits for resource allocation of WBANs. By simulating these pseudo orthogonal resource allocations of WBANs on MATLAB, we obtain the performance of WBANs according to different POCs and can analyze and evaluate the suitability of POCs for the resource allocation in the WBANs system.Keywords: wireless body area network, body sensor network, resource allocation without feedback, interference mitigation, pseudo orthogonal pattern
Procedia PDF Downloads 3545001 Construction of the Large Scale Biological Networks from Microarrays
Authors: Fadhl Alakwaa
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One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.Keywords: gene regulatory network, biclustering, denoising, system biology
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