Search results for: RBF neural network modelling

Authors: Mahshid Arabi

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With the rapid expansion of digital technologies and the internet, information security has become a critical priority for organizations and individuals. The widespread use of digital tools such as smartphones and internet networks facilitates the storage of vast amounts of data, but simultaneously, vulnerabilities and security threats have significantly increased. The aim of this study is to examine and analyze modern methods of information security management and to develop a comprehensive model to counteract threats and information misuse. This study employs a mixed-methods approach, including both qualitative and quantitative analyses. Initially, a systematic review of previous articles and research in the field of information security was conducted. Then, using the Delphi method, interviews with 30 information security experts were conducted to gather their insights on security challenges and solutions. Based on the results of these interviews, a comprehensive model for information security management was developed. The proposed model includes advanced encryption techniques, machine learning-based intrusion detection systems, and network security protocols. AES and RSA encryption algorithms were used for data protection, and machine learning models such as Random Forest and Neural Networks were utilized for intrusion detection. Statistical analyses were performed using SPSS software. To evaluate the effectiveness of the proposed model, T-Test and ANOVA statistical tests were employed, and results were measured using accuracy, sensitivity, and specificity indicators of the models. Additionally, multiple regression analysis was conducted to examine the impact of various variables on information security. The findings of this study indicate that the comprehensive proposed model reduced cyber-attacks by an average of 85%. Statistical analysis showed that the combined use of encryption techniques and intrusion detection systems significantly improves information security. Based on the obtained results, it is recommended that organizations continuously update their information security systems and use a combination of multiple security methods to protect their data. Additionally, educating employees and raising public awareness about information security can serve as an effective tool in reducing security risks. This research demonstrates that effective and up-to-date information security management requires a comprehensive and coordinated approach, including the development and implementation of advanced techniques and continuous training of human resources.

Keywords: data protection, digital technologies, information security, modern management

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Authors: Mohammad Hashemi, John Herbert

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Quantifying and measuring Quality of Experience (QoE) are important and difficult concerns in Human Computer Interaction (HCI). Quality of Service (QoS) and the actual User Interface (UI) of the application are both important contributors to the QoE of a user. This paper describes a framework that measures accurately the way a user uses the UI in order to model users' behaviours and profiles. It monitors the use of the mouse and use of UI elements with accurate time measurement. It does this in real-time and does so unobtrusively and efficiently allowing the user to work as normal with the application. This real-time accurate measurement of the user's interaction provides valuable data and insight into the use of the UI, and is also the basis for analysis of the user's QoE.

Keywords: user modelling, user interface experience, quality of experience, user experience, human and computer interaction

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Authors: Neil Bar, Andrew Heweston

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Probability of failure (PF) often appears alongside factor of safety (FS) in design acceptance criteria for rock slope, underground excavation and open pit mine designs. However, the design acceptance criteria generally provide no guidance relating to how PF should be calculated for homogeneous and heterogeneous rock masses, or what qualifies a ‘reasonable’ PF assessment for a given slope design. Observational and kinematic methods were widely used in the 1990s until advances in computing permitted the routine use of numerical modelling. In the 2000s and early 2010s, PF in numerical models was generally calculated using the point estimate method. More recently, some limit equilibrium analysis software offer statistical parameter inputs along with Monte-Carlo or Latin-Hypercube sampling methods to automatically calculate PF. Factors including rock type and density, weathering and alteration, intact rock strength, rock mass quality and shear strength, the location and orientation of geologic structure, shear strength of geologic structure and groundwater pore pressure influence the stability of rock slopes. Significant engineering and geological judgment, interpretation and data interpolation is usually applied in determining these factors and amalgamating them into a geotechnical model which can then be analysed. Most factors are estimated ‘approximately’ or with allowances for some variability rather than ‘exactly’. When it comes to numerical modelling, some of these factors are then treated deterministically (i.e. as exact values), while others have probabilistic inputs based on the user’s discretion and understanding of the problem being analysed. This paper discusses the importance of understanding the key aspects of slope design for homogeneous and heterogeneous rock masses and how they can be translated into reasonable PF assessments where the data permits. A case study from a large open pit gold mine in a complex geological setting in Western Australia is presented to illustrate how PF can be calculated using different methods and obtain markedly different results. Ultimately sound engineering judgement and logic is often required to decipher the true meaning and significance (if any) of some PF results.

Keywords: probability of failure, point estimate method, Monte-Carlo simulations, sensitivity analysis, slope stability

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Authors: Ahmed O. Babaleye, Rafet E. Kurt

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The global oil and gas industry is beginning to witness an increase in the number of installations moving towards decommissioning. Decommissioning of offshore installations is a complex, costly and hazardous activity, making safety one of the major concerns. Among existing removal options, complete and partial removal options pose the highest risks. Therefore, a dynamic risk model of the accidents from the two options is important to assess the risks on an overall basis. In this study, a risk-based safety model is developed to conduct quantitative risk analysis (QRA) for jacket structure systems failure. Firstly, bow-tie (BT) technique is utilised to model the causal relationship between the system failure and potential accident scenarios. Subsequently, to relax the shortcomings of BT, Bayesian Belief Networks (BBNs) were established to dynamically assess associated uncertainties and conditional dependencies. The BBN is developed through a similitude mapping of the developed bow-tie. The BBN is used to update the failure probabilities of the contributing elements through diagnostic analysis, thus, providing a case-specific and realistic safety analysis method when compared to a bow-tie. This paper presents the application of dynamic safety analysis to guide the allocation of risk control measures and consequently, drive down the avoidable cost of remediation.

Keywords: Bayesian belief network, offshore decommissioning, dynamic safety model, quantitative risk analysis

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Authors: Tahseen Saad, Halim Ceylan, Jonathan Weaver, Osman Nuri Çelik, Onur Gungor Sahin

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A new concept of uniform delay offset dependent mathematical optimization problem is derived as the main objective for this study using a differential evolution algorithm. To control the coordination problem, which depends on offset selection and to estimate uniform delay based on the offset choice in a traffic signal network. The assumption is the periodic sinusoidal function for arrival and departure patterns. The cycle time is optimized at the entry links and the optimized value is used in the non-entry links as a common cycle time. The offset optimization algorithm is used to calculate the uniform delay at each link. The results are illustrated by using a case study and are compared with the canonical uniform delay model derived by Webster and the highway capacity manual’s model. The findings show new model minimizes the total uniform delay to almost half compared to conventional models. The mathematical objective function is robust. The algorithm convergence time is fast.

Keywords: area traffic control, traffic flow, differential evolution, sinusoidal periodic function, uniform delay, offset variable

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Authors: Md Murshed Bhuyan, Hirotaka Okabe, Yoshiki Hidaka, Kazuhiro Hara

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Pectin-Acrylamide- (Vinyl Phosphonic Acid) Hydrogels were prepared from their blend by using gamma radiation of various doses. It was found that the gel fraction of hydrogel increases with increasing the radiation dose reaches a maximum and then started decreasing with increasing the dose. The optimum radiation dose and the composition of raw materials were determined on the basis of equilibrium swelling which resulted in 20 kGy absorbed dose and 1:2:4 (Pectin:AAm:VPA) composition. Differential scanning calorimetry reveals the gel strength for using them as the adsorbent. The FTIR-spectrum confirmed the grafting/ crosslinking of the monomer on the backbone of pectin chain. The hydrogels were applied in adsorption of Al, Ga, and In from multielement solution where the adsorption capacity order for those three elements was found as – In>Ga>Al. SEM images of hydrogels and metal adsorbed hydrogels indicate the gel network and adherence of the metal ions in the interpenetrating network of the hydrogel which were supported by EDS spectra. The adsorption isotherm models were studied and found that the Langmuir adsorption isotherm model was well fitted with the data. Adsorption data were also fitted to different adsorption kinetic and diffusion models. Desorption of metal adsorbed hydrogels was performed in 5% nitric acid where desorption efficiency was found around 90%.

Keywords: hydrogel, gamma radiation, vinyl phosphonic acid, metal adsorption

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Authors: Laggoun Chouki

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In this paper we present the design and mechanisms of the physics process and discuss the performances of continuous gas laser dynamics, based on molecules N2(v=1)→C02(001)(v=3). The main objectives of work in this area are, obtaining the high laser energies in short time durations needed for the feasibility studies the physical principles that can be used to make laser sources capable of delivering high average powers. We note that, in order to reach both objectives, one has to convert electrical or chemical energy into laser energy, using gaseous media. The process generating the wave excited, on the basis of the excited level vibration, Theoretical predictions are compared with experimental results. The feasibility and effectiveness of the proposed method is demonstrated by computer simulation.

Keywords: modelling, lasers, gas, numerical, nozzle

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Authors: Aitana Alonso-Nogueira, Helia Estévez-Fernández, Isaías García

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This paper presents an approach to reduce some of its current flaws in the requirements phase inside the software development process. It takes the software requirements of an application, makes a conceptual modeling about it and formalizes it within JSON documents. This formal model is lodged in a NoSQL database which is document-oriented, that is, MongoDB, because of its advantages in flexibility and efficiency. In addition, this paper underlines the contributions of the detailed approach and shows some applications and benefits for the future work in the field of automatic code generation using model-driven engineering tools.

Keywords: conceptual modelling, JSON, NoSQL databases, requirements engineering, software development

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Authors: Izabela L. Ciesielska-Wrόbel

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This paper presents finite element models of the fingertip skin which have been created to simulate the contact of textile objects with the skin to gain a better understanding of the perception of textiles through the skin, so-called Hand of Textiles (HoT). Many objective and subjective techniques have been developed to analyze HoT, however none of them provide exact overall information concerning the sensation of textiles through the skin. As the human skin is a complex heterogeneous hyperelastic body composed of many particles, some simplifications had to be made at the stage of building the models. The same concerns models of woven structures, however their utilitarian value was maintained. The models reflect only friction between skin and woven textiles, deformation of the skin and fabrics when “touching” textiles and heat transfer from the surface of the skin into direction of textiles.

Keywords: fingertip skin models, finite element models, modelling of textiles, sensation of textiles through the skin

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Authors: Nicolas Delcey, Philippe Baucour, Didier Chamagne, Geneviève Wimmer, Auditeau Gérard, Bausseron Thomas, Bouger Odile, Blanvillain Gérard

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This paper proposes a thermal study of the catenary/pantograph interface for a train in motion. A 2.5D complex model of the pantograph strip has been defined and created by a coupling between a 1D and a 2D model. Experimental and simulation results are presented and with a comparison allow validating the 2.5D model. Some physical phenomena are described and presented with the help of the model such as the stagger motion thermal effect, particular heats and the effect of the material characteristics. Finally it is possible to predict the critical thermal configuration during a train trip.

Keywords: electro-thermal studies, mathematical optimizations, multi-physical approach, numerical model, pantograph strip wear

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Authors: Priyanka Agrawal, Janou Koskamp, Amir Raoof, Mariette Wolthers

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Calcite is the main mineral found in carbonate rocks, which form significant hydrocarbon reservoirs and subsurface repositories for CO2 sequestration. The injected CO2 mixes with the reservoir fluid and disturbs the geochemical equilibrium, triggering calcite dissolution. Different combinations of fluid chemistry and injection rate may therefore result in different evolution of porosity, permeability and dissolution patterns. To model the changes in porosity and permeability Kozeny-Carman equation K∝〖(∅)〗^n is used, where K is permeability and ∅ is porosity. The value of n is mostly based on experimental data or pore network models. In pore network models, this derivation is based on accuracy of relation used for conductivity and pore volume change. In fact, at a single pore scale, this relationship is the result of the pore shape development due to dissolution. We have prepared a new reactive transport model for a single pore which simulates the complex chemical reaction of carbonic-acid induced calcite dissolution and subsequent pore-geometry evolution at a single pore scale. We use COMSOL Multiphysics package 5.3 for the simulation. COMSOL utilizes the arbitary-Lagrangian Eulerian (ALE) method for the free-moving domain boundary. We examined the effect of flow rate on the evolution of single pore shape profiles due to calcite dissolution. We used three flow rates to cover diffusion dominated and advection-dominated transport regimes. The fluid in diffusion dominated flow (Pe number 0.037 and 0.37) becomes less reactive along the pore length and thus produced non-uniform pore shapes. However, for the advection-dominated flow (Pe number 3.75), the fast velocity of the fluid keeps the fluid relatively more reactive towards the end of the pore length, thus yielding uniform pore shape. Different pore shapes in terms of inlet opening vs overall pore opening will have an impact on the relation between changing volumes and conductivity. We have related the shape of pore with the Pe number which controls the transport regimes. For every Pe number, we have derived the relation between conductivity and porosity. These relations will be used in the pore network model to get the porosity and permeability variation.

Keywords: single pore, reactive transport, calcite system, moving boundary

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Motalleb Byzedi, Seyedeh Chaman Naderi Korvandan

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Regarding annual statistics of groundwater level resources about current piezometers at Qorveh plains, both ARMA & PARMA modeling methods were applied in this study by the using of SAMS software. Upon performing required tests, a model was used with minimum amount of Akaike information criteria and suitable model was selected for piezometers. Then it was possible to make necessary estimations by using these models for future fluctuations in each piezometer. According to the results, ARMA model had more facilities for modeling of aquifer. Also it was cleared that eastern parts of aquifer had more failures than other parts. Therefore it is necessary to prohibit critical parts along with more supervision on taking rates of wells.

Keywords: qorveh plain, groundwater level, ARMA, PARMA

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Authors: Purnank Bhatt, Mit Shah, Pawan Nagda, Vimal Jasoliya

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The influence of parameters like velocity and depth of cut on cutting forces is investigated for the empirical relation of the coefficient of friction derived for CRS 1018 for different materials like AISI 4340 and Ti6Al4V. For this purpose, turning tests were carried out on the above materials using coated cemented carbide tool inserts for steel grade and uncoated cemented carbide cutting tool inserts for Titanium with different chip breaker geometries. The cutting forces were measured using a Kistler dynamometer where the multiplication factor taken is 200.The effect of cutting force variation was analyzed experimentally and are compared with the analytical results.

Keywords: cutting forces, coefficient of friction, carbide tool inserts, titanium

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Authors: A. Khan, H. Mahmood

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In Underwater Wireless Sensor Networks (UWSNs), sensor nodes close to water surface (final destination) are often preferred for selection as forwarders. However, their frequent selection makes them depleted of their limited battery power. In consequence, these nodes die during early stage of network operation and create energy holes where forwarders are not available for packets forwarding. These holes severely affect network throughput. As a result, system performance significantly degrades. In this paper, a routing protocol is proposed to avoid energy holes during packets forwarding. The proposed protocol does not require the conventional position information (localization) of holes to avoid them. Localization is cumbersome; energy is inefficient and difficult to achieve in underwater environment where sensor nodes change their positions with water currents. Forwarders with the lowest water pressure level and the maximum number of neighbors are preferred to forward packets. These two parameters together minimize packet drop by following the paths where maximum forwarders are available. To avoid interference along the paths with the maximum forwarders, a packet holding time is defined for each forwarder. Simulation results reveal superior performance of the proposed scheme than the counterpart technique.

Keywords: energy holes, interference, routing, underwater

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Authors: Andrii Shalaginov, Katrin Franke, Xiongwei Huang

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One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities.

Keywords: malware detection, network security, targeted attack, computational intelligence

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Authors: L. Torchane

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This study is devoted to defining the optimal conditions for the nitriding of pure iron at atmospheric pressure by using NH3-Ar-C3H8 gas mixtures. After studying the mechanisms of phase formation and mass transfer at the gas-solid interface, a mathematical model is developed in order to predict the nitrogen transfer rate in the solid, the ε-carbonitride layer growth rate and the nitrogen and carbon concentration profiles. In order to validate the model and to show its possibilities, it is compared with thermogravimetric experiments, analyses and metallurgical observations (X-ray diffraction, optical microscopy and electron microprobe analysis). Results obtained allow us to demonstrate the sound correlation between the experimental results and the theoretical predictions.

Keywords: gaseous nitrocarburizing, kinetic model, diffusion, layer growth kinetic

Procedia PDF Downloads 523

Authors: Mahbub C. Mishu, Venktesh N. Dubey, Tamas Hickish, Jonathan Cole

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Pressure ulcer is a common problem for today's healthcare industry. It occurs due to external load applied to the skin. Also when the subject is immobile for a longer period of time and there is continuous load applied to a particular area of human body,blood flow gets reduced and as a result pressure ulcer develops. Body support surface has a significant role in preventing ulceration so it is important to know the characteristics of support surface under loading conditions. In this paper we have presented mathematical models of different types of viscoelastic materials and also we have shown the validation of our simulation results with experiments.

Keywords: pressure ulcer, viscoelastic material, mathematical model, experimental validation

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Authors: Cristian Patrascioiu, Loredana Negoita

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This paper is focused on modeling and simulation of the tubular heaters. The paper is structured in four parts: the structure of the tubular convection section, the heat transfer model, the adaptation of the mathematical model and the solving model. The main hypothesis of the heat transfer modeling is that the heat exchanger of the convective tubular heater is a lumped system. In the same time, the model uses the heat balance relations, Newton’s law and criteria relations. The numerical program achieved allows for the estimation of the burn gases outlet temperature and the heated flow outlet temperature.

Keywords: heat exchanger, mathematical modelling, nonlinear equation system, Newton-Raphson algorithm

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowdsourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowdsourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is the first to develop safety performance functions using crowdsourced data by adopting a negative binomial structure model and Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Tao Peng

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This paper presents a holistic model of political economy based on Daoism – the foundational philosophy of classical Chinese epistemology. Daoism is both comprehensive and subtle in its manifestations and applications in all aspects of nature and society. Based on Daoist creation theory of the universe, life theory and five element functioning theory, a holistic model in economics with minimal assumptions and independent of ideology are constructed. Under this framework, different schools of economics, such as neo-liberal, Marxism, and Austrian school, are explored and shed new light on. Economic and financial predictions can be realized in applications to Qi Men Dun Jia. This framework can provide guidelines and inspirations to economic modelling, economic policies formulation and strategy development and guide society towards a more sustainable future.

Keywords: daoism, economics, holistic, philosophy

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Authors: Fouzia Amireche-Ziar, Ilham Mokbel, Jacques Jose

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The vapour pressures of the three binary mixtures: 1, 2- dichloroethane + 1,3-dioxolane, + 1,4-dioxane or + tetrahydropyrane, are carried out at ten temperatures ranging from 273 to 353.15 K. An accurate static device was employed for these measurements. The VLE data were reduced using the Redlich-Kister equation by taking into consideration the vapour pressure non-ideality in terms of the second molar virial coefficient. The experimental data were compared to the results predicted with the DISQUAC and Dortmund UNIFAC group contribution models for the total pressures P and the excess molar Gibbs energies GE.

Keywords: disquac model, dortmund UNIFAC model, excess molar Gibbs energies GE, VLE

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Authors: Rahisham Abd Rahman

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In this paper, a nonlinear pollution model has been proposed to compute electric field distribution over the polymeric insulator surface under wet contaminated conditions. A 2D axial-symmetric insulator geometry, energized with 11kV was developed and analysed using Finite Element Method (FEM). A field-dependent conductivity with simplified assumptions was established to characterize the electrical properties of the pollution layer. Comparative field studies showed that simulation of dynamic pollution model results in a more realistic field profile, offering better understanding on how the electric field behaves under wet polluted conditions.

Keywords: electric field distributions, pollution layer, dynamic model, polymeric outdoor insulators, finite element method (FEM)

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Authors: Pui Shan Wong, Kosuke Tashiro, Satoru Kuhara, Sachiyo Aburatani

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Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords: Escherichia coli, gene regulation, network, time-series

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Tarek Dandashi

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WSNs may provide a fast and reliable solution for the early detection of environment events like forest fires. This is crucial for alerting and calling for fire brigade intervention. Sensor nodes communicate sensor data to a host station, which enables a global analysis and the generation of a reliable decision on a potential fire and its location. A WSN with TinyOS and nesC for the capturing and transmission of a variety of sensor information with controlled source, data rates, duration, and the records/displaying activity traces is presented. We propose a similarity distance (SD) between the distribution of currently sensed data and that of a reference. At any given time, a fire causes diverging opinions in the reported data, which alters the usual data distribution. Basically, SD consists of a metric on the Cumulative Distribution Function (CDF). SD is designed to be invariant versus day-to-day changes of temperature, changes due to the surrounding environment, and normal changes in weather, which preserve the data locality. Evaluation shows that SD sensitivity is quadratic versus an increase in sensor node temperature for a group of sensors of different sizes and neighborhood. Simulation of fire spreading when ignition is placed at random locations with some wind speed shows that SD takes a few minutes to reliably detect fires and locate them. We also discuss the case of false negative and false positive and their impact on the decision reliability.

Keywords: forest fire, WSN, wireless sensor network, algortihm

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Authors: Iris Vogeler, Rogerio Cichota, Armin Werner

Abstract:

Dairy farms are under pressure to increase productivity while reducing environmental impacts. Effective fertiliser management practices are critical to achieve this. Determination of optimum nitrogen (N) fertilisation rates which maximise pasture growth and minimise N losses is challenging due to variability in plant requirements and likely near-future supply of N by the soil. Remote sensing can be used for mapping N nutrition status of plants and to rapidly assess the spatial variability within a field. An algorithm is, however, lacking which relates the N status of the plants to the expected yield response to additions of N. The aim of this simulation study was to develop a multi-variate model for determining N fertilisation rate for a target percentage of the maximum achievable yield based on the pasture N concentration (ii) use of an algorithm for guiding fertilisation rates, and (iii) evaluation of the model regarding pasture yield and N losses, including N leaching, denitrification and volatilisation. A simulation study was carried out using the Agricultural Production Systems Simulator (APSIM). The simulations were done for an irrigated ryegrass pasture in the Canterbury region of New Zealand. A multi-variate model was developed and used to determine monthly required N fertilisation rates based on pasture N content prior to fertilisation and targets of 50, 75, 90 and 100% of the potential monthly yield. These monthly optimised fertilisation rules were evaluated by running APSIM for a ten-year period to provide yield and N loss estimates from both nonurine and urine affected areas. Comparison with typical fertilisation rates of 150 and 400 kg N/ha/year was also done. Assessment of pasture yield and leaching from fertiliser and urine patches indicated a large reduction in N losses when N fertilisation rates were controlled by the multi-variate model. However, the reduction in leaching losses was much smaller when taking into account the effects of urine patches. The proposed approach based on biophysical modelling to develop a multi-variate model for determining optimum N fertilisation rates dependent on pasture N content is very promising. Further analysis, under different environmental conditions and validation is required before the approach can be used to help adjust fertiliser management practices to temporal and spatial N demand based on the nitrogen status of the pasture.

Keywords: APSIM modelling, optimum N fertilization rate, pasture N content, ryegrass pasture, three dimensional surface response function.

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Authors: Salim Belouettar, Kodjo Attipou

Abstract:

The thermal wrinkling behavior of thin membranes is investigated. The Fourier double scale series are used to deduce the macroscopic membrane wrinkling equations. The obtained equations account for the global and local wrinkling modes. Numerical examples are conducted to assess the validity of the approach developed. Compared to the finite element full model, the present model needs only few degrees of freedom to recover accurately the bifurcation curves and wrinkling paths. Different parameters such as membrane’s aspect ratio, wave number, pre-stressed membranes are discussed from a numerical point of view and the properties of the wrinkles (critical load, wavelength, size and location) are presented.

Keywords: wrinkling, thermal stresses, Fourier series, thin membranes

Procedia PDF Downloads 377

Authors: Ghodhbani Salah, Jemili Farah

Abstract:

With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

Procedia PDF Downloads 395