Search results for: passive optical network

Authors: Ravneil Nand

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Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

Procedia PDF Downloads 335

Authors: Farheen Tabassum, Shoab Ahmed Khan

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The brutality of attacks on networks and decisive infrastructures are on the climb over recent years and appears to continue to do so. Distributed Denial of service attack is the most prevalent and easy attack on the availability of a service due to the easy availability of large botnet computers at cheap price and the general lack of protection against these attacks. Application layer DDoS attack is DDoS attack that is targeted on wed server, application server or database server. These types of attacks are much more sophisticated and challenging as they get around most conventional network security devices because attack traffic often impersonate normal traffic and cannot be recognized by network layer anomalies. Conventional techniques of single-hosted security systems are becoming gradually less effective in the face of such complicated and synchronized multi-front attacks. In order to protect from such attacks and intrusion, corporation among all network devices is essential. To overcome this issue, a collaborative intrusion detection system (CIDS) is proposed in which multiple network devices share valuable information to identify attacks, as a single device might not be capable to sense any malevolent action on its own. So it helps us to take decision after analyzing the information collected from different sources. This novel attack detection technique helps to detect seemingly benign packets that target the availability of the critical infrastructure, and the proposed solution methodology shall enable the incident response teams to detect and react to DDoS attacks at the earliest stage to ensure that the uptime of the service remain unaffected. Experimental evaluation shows that the proposed collaborative detection approach is much more effective and efficient than the previous approaches.

Keywords: Distributed Denial-of-Service (DDoS), Collaborative Intrusion Detection System (CIDS), Slowloris, OSSIM (Open Source Security Information Management tool), OSSEC HIDS

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Authors: Linlong Mu, Maosong Huang

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Considering excavations in urban areas, the soil deformation induced by the excavations usually causes damage to the surrounding structures. Displacement control becomes a critical indicator of foundation design in order to protect the surrounding structures. Evaluation, the damage potential of the surrounding structures induced by the excavations, usually depends on the finite element method (FEM) because of the complexity of the excavation and the variety of the surrounding structures. Besides, evaluation the influence of the excavation on surrounding structures is a three-dimensional problem. And it is now well recognized that small strain behaviour of the soil influences the responses of the excavation significantly. Three-dimensional FEM considering small strain behaviour of the soil is a very complex method, which is hard for engineers to use. Thus, it is important to obtain a simplified method for engineers to predict the influence of the excavations on the surrounding structures. Based on large-scale finite element calculation with small-strain based soil model coupling with inverse analysis, an empirical method is proposed to calculate the three-dimensional soil movement induced by braced excavation. The empirical method is able to capture the small-strain behaviour of the soil. And it is suitable to be used in layered soil. Then the free-field soil movement is applied to the pile to calculate the responses of the pile in both vertical and horizontal directions. The asymmetric solutions for problems in layered elastic half-space are employed to solve the interactions between soil points. Both vertical and horizontal pile responses are solved through finite difference method based on elastic theory. Interactions among the nodes along a single pile, pile-pile interactions, pile-soil-pile interaction action and soil-soil interactions are counted to improve the calculation accuracy of the method. For passive piles, the shadow effects are also calculated in the method. Finally, the restrictions of the raft on the piles and the soils are summarized as: (1) the summations of the internal forces between the elements of the raft and the elements of the foundation, including piles and soil surface elements, is equal to 0; (2) the deformations of pile heads or of the soil surface elements are the same as the deformations of the corresponding elements of the raft. Validations are carried out by comparing the results from the proposed method with the results from the model tests, FEM and other existing literatures. From the comparisons, it can be seen that the results from the proposed method fit with the results from other methods very well. The method proposed herein is suitable to predict the responses of the pile-raft foundation induced by braced excavation in layered soil in both vertical and horizontal directions when the deformation is small. However, more data is needed to verify the method before it can be used in practice.

Keywords: excavation, pile-raft foundation, passive piles, deformation control, soil movement

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Authors: Walid Tawfik

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The interaction of intense short nano- and picosecond laser pulses with plasma leads to reach variety of important applications, including time-resolved laser induced breakdown spectroscopy (LIBS), soft x-ray lasers, and laser-driven accelerators. The progress in generating of femtosecond down to sub-10 fs optical pulses has opened a door for scientists with an essential tool in many ultrafast phenomena, such as femto-chemistry, high field physics, and high harmonic generation (HHG). The advent of high-energy laser pulses with durations of few optical cycles provided scientists with very high electric fields, and produce coherent intense UV to NIR radiation with high energy which allows for the investigation of ultrafast molecular dynamics with femtosecond resolution. In this work, we could experimentally achieve the generation of a two-octave-wide supercontinuum ultrafast pulses extending from ultraviolet at 3.5 eV to the near-infrared at 1.3 eV in neon-filled capillary fiber. These pulses are created due to nonlinear self-phase modulation (SPM) in neon as a nonlinear medium. The measurements of the generated pulses were performed using spectral phase interferometry for direct electric-field reconstruction. A full characterization of the output pulses was studied. The output pulse characterization includes the pulse width, the beam profile, and the spectral bandwidth. Under optimization conditions, the reconstructed pulse intensity autocorrelation function was exposed for the shorts possible pulse duration to achieve transform-limited pulses with energies up to 600µJ. Furthermore, the effect of variation of neon pressure on the pulse-width was studied. The nonlinear SPM found to be increased with the neon pressure. The obtained results may give an opportunity to monitor and control ultrafast transit interaction in femtosecond chemistry.

Keywords: femtosecond laser, ultrafast, supercontinuum, ultra-broadband

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Authors: C. Benjbara, A. Habbani

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Wireless networks are getting more and more used in every new technology or feature, especially those without infrastructure (Ad hoc mode) which provide a low cost alternative to the infrastructure mode wireless networks and a great flexibility for application domains such as environmental monitoring, smart cities, precision agriculture, and so on. These application domains present a common characteristic which is the need of coexistence and intercommunication between modules belonging to different types of ad hoc networks like wireless sensor networks, mesh networks, mobile ad hoc networks, vehicular ad hoc networks, etc. This vision to bring to life such heterogeneous networks will make humanity duties easier but its development path is full of challenges. One of these challenges is the communication complexity between its components due to the lack of common or compatible protocols standard. This article proposes a new patented routing protocol based on the OLSR standard in order to resolve the heterogeneous ad hoc networks communication issue. This new protocol is applied on a specific network architecture composed of MANET, VANET, and FANET.

Keywords: Ad hoc, heterogeneous, ID-Node, OLSR

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Authors: Radu Soflau, Lia-Ecaterina Oltean

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Background: Increasing evidence supports the efficacy of psychological interventions for psychosis. However, it is unclear which one of these interventions is most likely to address negative psychotic symptoms and related outcomes. We aimed to determine the relative efficacy of psychological and psychosocial interventions for negative symptoms, overall psychotic symptoms, and related outcomes. Methods: To attain this goal, we conducted a systematic review and network meta-analysis. We searched for potentially eligible trials in PubMed, EMBASE, PsycInfo, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases up until February 08, 2022. We included randomized controlled trials that investigated the efficacy of psychological for adults with psychosis. We excluded interventions for prodromal or “at risk” individuals, as well as patients with serious co-morbid medical or psychiatric conditions (others than depressive and/or anxiety disorders). Two researchers conducted study selection and performed data extraction independently. Analyses were run using STATA network and mvmeta packages, applying a random effect model under a frequentist framework in order to compute standardized mean differences or risk ratio. Findings: We identified 47844 records and screened 29466 records for eligibility. The majority of eligible interventions were delivered in addition to pharmacological treatment. Treatment as usual (TAU) was the most frequent common comparator. Theoretically driven psychological interventions generally outperformed TAU at post-test and follow-up, displaying small and small-to-medium effect sizes. A similar pattern of results emerged in sensitivity analyses focused on studies that employed an inclusion criterion for relevant negative symptom severity. Conclusion: While the efficacy of some psychological interventions is promising, there is a need for more high-quality studies, as well as more trials directly comparing psychological treatments for negative psychotic symptoms.

Keywords: psychosis, network meta-analysis, psychological interventions, efficacy, negative symptoms

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Authors: Rabindranath Jana, Plabani Basu, Keka Rana

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Nucleating agents are widely used to modify the properties of various polymers. The rate of crystallization and the size of the crystals have a strong impact on mechanical and optical properties of a polymer. The addition of nucleating agents to the semi-crystalline polymers provides a surface on which the crystal growth can start easily. As a consequence, fast crystal formation will result in many small crystal domains so that the cycle times for injection molding may be reduced. Moreover, the mechanical properties e.g., modulus, tensile strength, heat distortion temperature and hardness may increase. In the present work, multi-walled carbon nanotubes (MWNTs) as nucleating agents for the crystallization of poly (e-caprolactone)diol (PCL). Thus nanocomposites of PCL filled with MWNTs were prepared by solution blending. Differential scanning calorimetry (DSC) tests were carried out to study the effect of CNTs on on-isothermal crystallization of PCL. The polarizing optical microscopy (POM), and wide-angle X-ray diffraction (WAXD) were used to study the morphology and crystal structure of PCL and its nanocomposites. It is found that MWNTs act as effective nucleating agents that significantly shorten the induction period of crystallization and however, decrease the crystallization rate of PCL, exhibiting a remarkable decrease in the Avrami exponent n, surface folding energy σe and crystallization activation energy ΔE. The carbon-based fillers act as templates for hard block chains of PCL to form an ordered structure on the surface of nanoparticles during the induction period, bringing about some increase in equilibrium temperature. The melting process of PCL and its nanocomposites are also studied; the nanocomposites exhibit two melting peaks at higher crystallization temperature which mainly refer to the melting of the crystals with different crystal sizes however, PCL shows only one melting temperature.

Keywords: poly(e-caprolactone)diol, multiwalled carbon nanotubes, composite materials, nonisothermal crystallization, crystal structure, nucleation

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Authors: Hassan M. H. Mustafa

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This piece of research addresses an interesting comparative analytical study. Which considers two concepts of diverse algorithmic computational intelligence approaches related tightly with Neural and Non-Neural Systems. The first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems’ activities. Namely, they are Pavlov’s, and Thorndike’s experimental work. Besides a mouse’s trial during its movement inside figure of eight (8) maze, to reach an optimal solution for reconstruction problem. Conversely, second algorithmic intelligent approach originated from observed activities’ results for Non-Neural Ant Colony System (ACS). These results obtained after reaching an optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance shown to be similar for both introduced systems. Finally, performance of both intelligent learning paradigms shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.

Keywords: artificial neural network modeling, animal learning, ant colony system, traveling salesman problem, computational biology

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Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

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Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: agent communication, introspective agent, isolation of agent, policy enforcement system

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

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The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: Raida Zouari, Iness Ahriz, Rafik Zayani, Ali Dziri, Ridha Bouallegue

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This paper proposes a room level indoor localization algorithm based on the use Multi-Layer Neural Network (MLNN) classifiers and one versus one strategy. Seven parameters of the Channel Impulse Response (CIR) were used and Gram-Shmidt Orthogonalization was performed to study the relevance of the extracted parameters. Simulation results show that when relevant CIR parameters are used as position fingerprint and when optimal MLNN architecture is selected good room level localization score can be achieved. The current study showed also that some of the CIR parameters are not correlated to the location and can decrease the localization performance of the system.

Keywords: mobile indoor localization, multi-layer neural network (MLNN), channel impulse response (CIR), Gram-Shmidt orthogonalization

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Authors: Wael M. Bazzi, Amir Rastegarnia, Azam Khalili

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In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition.

Keywords: adaptive filter, distributed estimation, sensor network, IDLMS algorithm

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.

Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards

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Authors: Mbida Mohamed, Ezzati Abdellah

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A strong challenge in the wireless sensor networks (WSN) is to save the energy and have a long life time in the network without having a high rate of loss information. However, topology control (TC) protocols are designed in a way that the network is divided and having a standard system of exchange packets between nodes. In this article, we will propose a clustering based and centralized routing table protocol of TC (CBCRT) which delegates a leader node that will encapsulate a single routing table in every cluster nodes. Hence, if a node wants to send packets to the sink, it requests the information's routing table of the current cluster from the node leader in order to root the packet.

Keywords: mobile wireless sensor networks, routing, topology of control, protocols

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Authors: Rashi Jain

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The Wireless Body Area Networks (WBANs) is an emerging interdisciplinary area where small sensors are placed on/within the human body. These sensors monitor the physiological activities and vital statistics of the body. The data from these sensors is aggregated and communicated to a remote doctor for immediate attention or to a database for records. On 6 Feb 2012, the IEEE 802.15.6 task group approved the standard for Body Area Network (BAN) technologies. The standard proposes the physical and MAC layer for the WBANs. The work provides an introduction to WBANs and overview of the physical and MAC layers of the standard. The physical layer specifications have been covered. A comparison of different protocols used at MAC layer is drawn. An introduction to the network layer and security aspects of the WBANs is made. The WBANs suffer certain limitations such as regulation of frequency bands, minimizing the effect of transmission and reception of electromagnetic signals on the human body, maintaining the energy efficiency among others. This has slowed down their implementation.

Keywords: vehicular networks, sensors, MicroController 8085, LTE

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Authors: Chien Y. Lin, Jung Y. Huang, Leu-Wei Lo

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A growing body of data reveals that the membrane cholesterol molecules can alter the signaling pathways of living cells. However, the understanding about how membrane cholesterol modulates receptor proteins is still lacking. Single-molecule tracking can effectively probe into the microscopic environments and thermal fluctuations of receptor proteins in a living cell. In this study we applies single-molecule optical tracking on ligand-induced dimerization process of EGFRs in the plasma membranes of two cancer cell lines (HeLa and A431) and one normal endothelial cell line (MCF12A). We tracked individual EGFR and dual receptors, diffusing in a correlated manner in the plasma membranes of live cells. We developed an energetic model by integrating the generalized Langevin equation with the Cahn-Hilliard equation to help extracting important information from single-molecule trajectories. From the study, we discovered that ligand-bound EGFRs move from non-raft areas into lipid raft domains. This ligand-induced motion is a common behavior in both cancer and normal cells. By manipulating the total amount of membrane cholesterol with methyl-β-cyclodextrin and the local concentration of membrane cholesterol with nystatin, we further found that the amount of cholesterol can affect the stability of EGFR dimers. The EGFR dimers in the plasma membrane of normal cells are more sensitive to the local concentration changes of cholesterol than EGFR dimers in the cancer cells. Our method successfully captures dynamic interactions of receptors at the single-molecule level and provides insight into the functional architecture of both the diffusing EGFR molecules and their local cellular environment.

Keywords: membrane proteins, single-molecule tracking, Cahn-Hilliard equation, EGFR dimers

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Authors: Wilfred Fritz

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Grid connected solar PV inverters need to be compliant to standard regulations regarding unwanted harmonic generation. This paper gives an introduction to harmonics, solar PV inverter voltage regulation and balancing through compensation and investigates the behaviour of harmonic generation at different power levels. Practical measurements of harmonics and power levels with a power quality data logger were made, on a test network at a university in Germany. The test setup and test results are discussed. The major finding was that between the morning and afternoon load peak windows when the PV inverters operate under low solar insolation and low power levels, more unwanted harmonics are generated. This has a huge impact on the power quality of the grid as well as capital and maintenance costs. The design of a single-tuned harmonic filter towards harmonic mitigation is presented.

Keywords: harmonics, power quality, pulse width modulation, total harmonic distortion

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Authors: Klaus Froehlich-Gildhoff, Ullrich Boettinger, Katharina Rauh, Angela Schickler

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The Prevention Network Ortenaukreis, PNO, funded by the German Ministry of Education and Research, aims to promote physical and mental health as well as the social inclusion of 3 to 10 years old children and their families in the Ortenau district. Within a period of four years starting 11/2014 a community network will be established. One regional and five local prevention representatives are building networks with stakeholders of the prevention and health promotion field bridging the health care, educational and youth welfare system in a multidisciplinary approach. The regional prevention representative implements regularly convening prevention and health conferences. On a local level, the 5 local prevention representatives implement round tables in each area as a platform for networking. In the setting approach, educational institutions are playing a vital role when gaining access to children and their families. Thus the project will offer 18 month long organizational development processes with specially trained coaches to 25 kindergarten and 25 primary schools. The process is based on a curriculum of prevention and health promotion which is adapted to the specific needs of the institutions. Also to ensure that the entire region is reached demand oriented advanced education courses are implemented at participating day care centers, kindergartens and schools. Evaluation method: The project is accompanied by an extensive research design to evaluate the outcomes of different project components such as interview data from community prevention agents, interviews and network analysis with families at risk on their support structures, data on community network development and monitoring, as well as data from kindergarten and primary schools. The latter features a waiting-list control group evaluation in kindergarten and primary schools with a mixed methods design using questionnaires and interviews with pedagogues, teachers, parents, and children. Results: By the time of the conference pre and post test data from the kindergarten samples (treatment and control group) will be presented, as well as data from the first project phase, such as qualitative interviews with the prevention coordinators as well as mixed methods data from the community needs assessment. In supporting this project, the Federal Ministry aims to gain insight into efficient components of community prevention and health promotion networks as it is implemented and evaluated. The district will serve as a model region, so that successful components can be transferred to other regions throughout Germany. Accordingly, the transferability to other regions is of high interest in this project.

Keywords: childhood research, health promotion, physical health, prevention network, psychological well-being, social inclusion

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Authors: Z. Derikvand, M. Dusek, V. Eigner

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One dimensional coordination polymer of Cdᴵᴵ based on pyrazine (pz) and 3-nitrophthalic acid (3-nphaH₂), namely poly[[diaqua bis(3-nitro-2-carboxylato-1-carboxylic acid)(µ₂-pyrazine) cadmium(II)]dihydrate], {[Cd(3-nphaH)2(pz)(H₂O)₂]. 2H₂O}ₙ was prepared and characterized. The asymmetric unit consists of one Cdᴵᴵ center, two (3-nphaH)– anions, two halves of two crystallographically distinct pz ligands, two coordinated and two uncoordinated water molecules. The Cdᴵᴵ cation is surrounded by four oxygen atoms from two (3-nphaH)– and two water molecules as well as two nitrogen atoms from two pz ligands in distorted octahedral geometry. Complicated hydrogen bonding network accompanied with N–O···π and C–O···π stacking interactions leads to formation of a 3D supramolecular network. Commonly, this kind of C–O–π and N–O···π interaction is detected in electron-rich CO/NO groups of (3-nphaH)– ligand and electron-deficient π-system of pyrazine.

Keywords: supramolecular chemistry, Cd coordination polymer, crystal structure, 3-nithrophethalic acid

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Emanuele A. Solagna, Ricardo S, Tozetto, Roberto dos S. Rabello

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Nowadays, physical processes are becoming digitized by the evolution of communication, sensing and storage technologies which promote the development of smart cities. The evolution of this technology has generated multiple challenges related to the generation of big data and the active participation of electronic devices in society. Thus, devices can send information that is captured and processed over large areas, but there is no guarantee that all the obtained data amount will be effectively stored and correctly persisted. Because, depending on the technology which is used, there are parameters that has huge influence on the full delivery of information. This article aims to characterize the project, currently under development, of a platform that based on data science will perform a performance and effectiveness evaluation of an industrial network that implements LoRaWAN technology considering its main parameters configuration relating these parameters to the information loss.

Keywords: Internet of Things, LoRa, LoRaWAN, smart cities

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Jan Schmidt, Pierre Köhring

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Permanently excited machines are set up with magnets that are made of highly energetic magnetic materials. Inherently, the permanent magnets warm up while the machine is operating. With an increasing temperature, the electromotive force and hence the degree of efficiency decrease. The reasons for this are slot harmonics and distorted armature currents arising from frequency inverter operation. To prevent or avoid demagnetizing of the permanent magnets it is necessary to ensure that the magnets do not excessively heat up. Demagnetizations of permanent magnets are irreversible and a breakdown of the electrical machine is inevitable. For the design of an electrical machine, the knowledge of the behavior of heating under operating conditions of the permanent magnet is of crucial importance. Therefore, a calculation model is presented with which the machine designer can easily calculate the eddy current losses in the magnetic material.

Keywords: analytical model, eddy current, losses, lattice network, permanent magnet

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Authors: Shahriyar Keshtgar, Mahmoud Reza Heyhat, Sasan Bagheri, Ebrahim Gholami, Seyed Naser Raiisosadat

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Various tectonic interpretations have been presented by different researchers to explain the geological evolution of eastern Iran, but there are still many ambiguities and many disagreements about the geodynamic nature of the Paleogene mountain range of eastern Iran. The purpose of this research is to clarify and discuss the tectonic position of the foreland and hinterland regions of eastern Iran from the tectonic perspective of sedimentary basins. In the tectonic model of oceanic subduction crust under the Afghan block, the hinterland is located to the east and on the Afghan block, and the foreland is located on the passive margin of the Sistan open ocean in the west. After the collision of the two microcontinents, the foreland basin must be located somewhere on the passive margin of the Lut block. This basin can deposit thick Paleocene to Oligocene sediments on the Cretaceous and older sediments. Thrust faults here will move towards the west. If we accept the subduction model of the Sistan Ocean under the Lut Block, the hinterland is located to the west towards the Lut Block, and the foreland basin is located towards the Sistan Ocean in the east. After the collision of the two microcontinents, the foreland basin with Paleogene sediments should expand on the Sefidaba basin. Thrust faults here will move towards the east. If we consider the two-sided subduction model of the ocean crust under both Lut and Afghan continental blocks, the tectonic position of the foreland and hinterland basins will not change and will be similar to the one-sided subduction models. After the collision of two microcontinents, the foreland basin should develop in the central part of the eastern Iranian orogen. In the oroclinic buckling model, the foreland basin will continue not only in the east and west but continuously in the north as well. In this model, since there is practically no collision, the foreland basin is not developed, and the remnants of the Sistan Ocean ophiolites and their deep turbidite sediments appear in the axial part of the mountain range, where the Neh and Khash complexes are located. The structural data from this research in the northern border of the Sistan belt and the Lut block indicate the convergence of the tectonic vergence directions towards the interior of the Sistan belt (in the Ahangaran area towards the southwest, in the north of Birjand towards the south-southeast, in the Sechengi area to the southeast). According to this research, not only the general movement of thrust sheets do not follow the linear orogeny models, but the expected active foreland basins have not been formed in the mentioned places in eastern Iran. Therefore, these results do not follow previous tectonic models for eastern Iran (i.e., rifting of eastern Iran continental crust and subsequent linear collision of the Lut and Afghan blocks), but it seems that was caused by buckling model in the Late Eocene-Oligocene.

Keywords: foreland, hinterland, tectonic vergence, orocline buckling, eastern Iran

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Authors: Santhosh Srinivasan, Vipul Garhiya, Shahul Hamid Khan

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Environmental performance along with social performance is becoming vital factors for industries to achieve global standards. With a good environmental policy global industries are differentiating them from their competitors. This paper concentrates on multi stage, multi product and multi period manufacturing network. Single objective mathematical models for a total cost for the entire forward supply chain and reverse chain are considered. Here five different problems are considered by varying the number of facilities for illustration. M-MOGA, Shuffle Frog Leaping algorithm (SFLA) and CPLEX are used for finding the optimal solution for the mathematical model.

Keywords: closed loop supply chain, genetic algorithm, random search, multi period, green supply chain

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Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

Abstract:

Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

Procedia PDF Downloads 128

Authors: Susmita Panda, Arnab Banerjee, Ajinkya Baxy, Bappaditya Manna

Abstract:

The design of short to medium-span high-speed bridges in critical locations is an essential aspect of vehicle-bridge interaction. Due to dynamic interaction between moving load and bridge, mathematical models or finite element modeling computations become time-consuming. Thus, to reduce the computational effort, a universal approximator using an artificial neural network (ANN) has been used to evaluate the dynamic response of the bridge. The data set generation and training of surrogate models have been conducted over the results obtained from mathematical modeling. Further, the robustness of the surrogate model has been investigated, which showed an error percentage of less than 10% with conventional methods. Additionally, the dependency of the dynamic response of the bridge on various load and bridge parameters has been highlighted through a parametric study.

Keywords: artificial neural network, mode superposition method, moving load analysis, surrogate models

Procedia PDF Downloads 100

Authors: Rajeev Kumar, Angad Singh Kushwaha, Amritanshu Pandey, S. K. Srivastava

Abstract:

Photonic crystals (PCs) have attracted much attention due to its electromagnetic properties and potential applications. In PCs, there is certain range of wavelength where electromagnetic waves are not allowed to pass are called photonic band gap (PBG). A localized defect mode will appear within PBG, due to change in the interference behavior of light, when we create a defect in the periodic structure. We can also create different types of defect structures by inserting or removing a layer from the periodic layered structure in two and three-dimensional PCs. We can design microcavity, waveguide, and perfect mirror by creating a point defect, line defect, and palanar defect in two and three- dimensional PC structure. One-dimensional and two-dimensional PCs with defects were reported theoretically and experimentally by Smith et al.. in conventional photonic band gap structure. In the present paper, we have presented the defect mode tunability in tilted non-graded photonic crystal (NGPC) and linearly graded photonic crystal (LGPC) using lead sulphide (PbS) and titanium dioxide (TiO2) in the infrared region. A birefringent defect layer is created in NGPC and LGPC using potassium titany phosphate (KTP). With the help of transfer matrix method, the transmission properties of proposed structure is investigated for transverse electric (TE) and transverse magnetic (TM) polarization. NGPC and LGPC without defect layer is also investigated. We have found that a photonic band gap (PBG) arises in the infrared region. An additional defect layer of KTP is created in NGPC and LGPC structure. We have seen that an additional transmission mode appers in PBG region. It is due to the addition of defect layer. We have also seen the effect, linear gradation in thickness, angle of incidence, tilt angle, and thickness of defect layer, on PBG and additional transmission mode. We have observed that the additional transmission mode and PBG can be tuned by changing the above parameters. The proposed structure may be used as channeled filter, optical switches, monochromator, and broadband optical reflector.

Keywords: defect modes, graded photonic crystal, photonic crystal, tilt angle

Procedia PDF Downloads 376

Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

Abstract:

This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

Procedia PDF Downloads 215

Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

Abstract:

The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

Procedia PDF Downloads 435