Search results for: laundry algorithm
813 Study on Optimization Design of Pressure Hull for Underwater Vehicle
Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran
Abstract:
In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.Keywords: parameterization, response surface, structure optimization, pressure hull
Procedia PDF Downloads 222812 Inversion of Electrical Resistivity Data: A Review
Authors: Shrey Sharma, Gunjan Kumar Verma
Abstract:
High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.Keywords: inversion, limitations, optimization, resistivity
Procedia PDF Downloads 355811 System for the Detecting of Fake Profiles on Online Social Networks Using Machine Learning and the Bio-Inspired Algorithms
Authors: Sekkal Nawel, Mahammed Nadir
Abstract:
The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network
Procedia PDF Downloads 60810 The Trigger-DAQ System in the Mu2e Experiment
Authors: Antonio Gioiosa, Simone Doanti, Eric Flumerfelt, Luca Morescalchi, Elena Pedreschi, Gianantonio Pezzullo, Ryan A. Rivera, Franco Spinella
Abstract:
The Mu2e experiment at Fermilab aims to measure the charged-lepton flavour violating neutrino-less conversion of a negative muon into an electron in the field of an aluminum nucleus. With the expected experimental sensitivity, Mu2e will improve the previous limit of four orders of magnitude. The Mu2e data acquisition (DAQ) system provides hardware and software to collect digitized data from the tracker, calorimeter, cosmic ray veto, and beam monitoring systems. Mu2e’s trigger and data acquisition system (TDAQ) uses otsdaq as its solution. developed at Fermilab, otsdaq uses the artdaq DAQ framework and art analysis framework, under-the-hood, for event transfer, filtering, and processing. Otsdaq is an online DAQ software suite with a focus on flexibility and scalability while providing a multi-user, web-based interface accessible through the Chrome or Firefox web browser. The detector read out controller (ROC) from the tracker and calorimeter stream out zero-suppressed data continuously to the data transfer controller (DTC). Data is then read over the PCIe bus to a software filter algorithm that selects events which are finally combined with the data flux that comes from a cosmic ray veto system (CRV).Keywords: trigger, daq, mu2e, Fermilab
Procedia PDF Downloads 145809 Grain Size Characteristics and Sediments Distribution in the Eastern Part of Lekki Lagoon
Authors: Mayowa Philips Ibitola, Abe Oluwaseun Banji, Olorunfemi Akinade-Solomon
Abstract:
A total of 20 bottom sediment samples were collected from the Lekki Lagoon during the wet and dry season. The study was carried out to determine the textural characteristics, sediment distribution pattern and energy of transportation within the lagoon system. The sediment grain sizes and depth profiling was analyzed using dry sieving method and MATLAB algorithm for processing. The granulometric reveals fine grained sand both for the wet and dry season with an average mean value of 2.03 ϕ and -2.88 ϕ, respectively. Sediments were moderately sorted with an average inclusive standard deviation of 0.77 ϕ and -0.82 ϕ. Skewness varied from strongly coarse and near symmetrical 0.34- ϕ and 0.09 ϕ. The kurtosis average value was 0.87 ϕ and -1.4 ϕ (platykurtic and leptokurtic). Entirely, the bathymetry shows an average depth of 4.0 m. The deepest and shallowest area has a depth of 11.2 m and 0.5 m, respectively. High concentration of fine sand was observed at deep areas compared to the shallow areas during wet and dry season. Statistical parameter results show that the overall sediments are sorted, and deposited under low energy condition over a long distance. However, sediment distribution and sediment transport pattern of Lekki Lagoon is controlled by a low energy current and the down slope configuration of the bathymetry enhances the sorting and the deposition rate in the Lekki Lagoon.Keywords: Lekki Lagoon, Marine sediment, bathymetry, grain size distribution
Procedia PDF Downloads 227808 Effect of Atmospheric Turbulence on Hybrid FSO/RF Link Availability under Qatar's Harsh Climate
Authors: Abir Touati, Syed Jawad Hussain, Farid Touati, Ammar Bouallegue
Abstract:
Although there has been a growing interest in the hybrid free-space optical link and radio frequency FSO/RF communication system, the current literature is limited to results obtained in moderate or cold environment. In this paper, using a soft switching approach, we investigate the effect of weather inhomogeneities on the strength of turbulence hence the channel refractive index under Qatar harsh environment and their influence on the hybrid FSO/RF availability. In this approach, either FSO/RF or simultaneous or none of them can be active. Based on soft switching approach and a finite state Markov Chain (FSMC) process, we model the channel fading for the two links and derive a mathematical expression for the outage probability of the hybrid system. Then, we evaluate the behavior of the hybrid FSO/RF under hazy and harsh weather. Results show that the FSO/RF soft switching renders the system outage probability less than that of each link individually. A soft switching algorithm is being implemented on FPGAs using Raptor code interfaced to the two terminals of a 1Gbps/100 Mbps FSO/RF hybrid system, the first being implemented in the region. Experimental results are compared to the above simulation results.Keywords: atmospheric turbulence, haze, hybrid FSO/RF, outage probability, refractive index
Procedia PDF Downloads 410807 Modeling Anisotropic Damage Algorithms of Metallic Structures
Authors: Bahar Ayhan
Abstract:
The present paper is concerned with the numerical modeling of the inelastic behavior of the anisotropically damaged ductile materials, which are based on a generalized macroscopic theory within the framework of continuum damage mechanics. Kinematic decomposition of the strain rates into elastic, plastic and damage parts is basis for accomplishing the structure of continuum theory. The evolution of the damage strain rate tensor is detailed with the consideration of anisotropic effects. Helmholtz free energy functions are constructed separately for the elastic and inelastic behaviors in order to be able to address the plastic and damage process. Additionally, the constitutive structure, which is based on the standard dissipative material approach, is elaborated with stress tensor, a yield criterion for plasticity and a fracture criterion for damage besides the potential functions of each inelastic phenomenon. The finite element method is used to approximate the linearized variational problem. Stress and strain outcomes are solved by using the numerical integration algorithm based on operator split methodology with a plastic and damage (multiplicator) variable separately. Numerical simulations are proposed in order to demonstrate the efficiency of the formulation by comparing the examples in the literature.Keywords: anisotropic damage, finite element method, plasticity, coupling
Procedia PDF Downloads 195806 Integrated Process Modelling of a Thermophilic Biogas Plant
Authors: Obiora E. Anisiji, Jeremiah L. Chukwuneke, Chinonso H. Achebe, Paul C. Okolie
Abstract:
This work developed a mathematical model of a biogas plant from a mechanistic point of view, for urban area clean energy requirement. It aimed at integrating thermodynamics; which deals with the direction in which a process occurs and Biochemical kinetics; which gives the understanding of the rates of biochemical reaction. The mathematical formulation of the proposed gas plant follows the fundamental principles of thermodynamics, and further analysis were accomplished to develop an algorithm for evaluating the plant performance preferably in terms of daily production capacity. In addition, the capacity of the plant is equally estimated for a given cycle of operation and presented in time histories. A nominal 1500m3 biogas plant was studied characteristically and its performance efficiency evaluated. It was observed that the rate of biogas production is essentially a function of enthalpy ratio, the reactor temperature, pH, substrate concentration, rate of degradation of the biomass, and the accumulation of matter in the system due to bacteria growth. The results of this study conform to a very large extent with reported empirical data of some existing plant and further model validations were conducted in line with classical records found in literature.Keywords: anaerobic digestion, biogas plant, biogas production, bio-reactor, energy, fermentation, rate of production, temperature, therm
Procedia PDF Downloads 426805 Interaction of Non-Gray-Gas Radiation with Opposed Mixed Convection in a Lid-Driven Square Cavity
Authors: Mohammed Cherifi, Abderrahmane Benbrik, Siham Laouar-Meftah, Denis Lemonnier
Abstract:
The present study was conducted to numerically investigate the interaction of non-gray-gas radiation with opposed mixed convection in a vertical two-sided lid-driven square cavity. The opposing flows are simultaneously generated by the vertical boundary walls which slide at a constant speed and the natural convection due to the gradient temperature of differentially heated cavity. The horizontal walls are thermally insulated and perfectly reflective. The enclosure is filled with air-H2O-CO2 gas mixture, which is considered as a non-gray, absorbing, emitting and not scattering medium. The governing differential equations are solved by a finite-volume method, by adopting the SIMPLER algorithm for pressure–velocity coupling. The radiative transfer equation (RTE) is solved by the discrete ordinates method (DOM). The spectral line weighted sum of gray gases model (SLW) is used to account for non-gray radiation properties. Three cases of the effects of radiation (transparent, gray and non-gray medium) are studied. Comparison is also made with the parametric studies of the effect of the mixed convection parameter, Ri (0.1, 1, 10), on the fluid flow and heat transfer have been performed.Keywords: opposed mixed convection, non-gray-gas radiation, two-sided lid-driven cavity, discrete ordinate method, SLW model
Procedia PDF Downloads 310804 Improving Seat Comfort by Semi-Active Control of Magnetorheological Damper
Authors: Karel Šebesta, Jiří Žáček, Matuš Salva, Mohammad Housam
Abstract:
Drivers of agricultural vehicles are exposed to continuous vibration caused by driving over rough terrain. The long-term effects of these vibrations could start with a decreased level of vigilance at work and could reach the level of several health problems. Therefore, eliminating the vibration to maximize the comfort of the driver is essential for better/longer performance. One of the modern damping systems, which can deal with this problem is the Semi-active (S/A) suspension system featuring a Magnetorheological (MR) damper. With this damper, the damping level can be adjusted using varying currents through the coil. Adjustments of the damping force can be carried out continuously based on the evaluated data (position and acceleration of seat) by the control algorithm. The advantage of this system is the wide dynamic range and the high speed of force response time. Compared to other S/A or active systems, the MR damper does not need as much electrical power, and the system is much simpler. This paper aims to prove the effectiveness of this damping system used in the tractor seat. The vibration testing stand was designed and manufactured specifically for this type of research, which is used to simulate vibrations with constant amplitude at variable frequency.Keywords: magnetorheological damper, semi-active suspension, seat scissor mechanism, sky-hook
Procedia PDF Downloads 90803 Comparative Study of Deep Reinforcement Learning Algorithm Against Evolutionary Algorithms for Finding the Optimal Values in a Simulated Environment Space
Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt
Abstract:
Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO
Procedia PDF Downloads 104802 Attribute Analysis of Quick Response Code Payment Users Using Discriminant Non-negative Matrix Factorization
Authors: Hironori Karachi, Haruka Yamashita
Abstract:
Recently, the system of quick response (QR) code is getting popular. Many companies introduce new QR code payment services and the services are competing with each other to increase the number of users. For increasing the number of users, we should grasp the difference of feature of the demographic information, usage information, and value of users between services. In this study, we conduct an analysis of real-world data provided by Nomura Research Institute including the demographic data of users and information of users’ usages of two services; LINE Pay, and PayPay. For analyzing such data and interpret the feature of them, Nonnegative Matrix Factorization (NMF) is widely used; however, in case of the target data, there is a problem of the missing data. EM-algorithm NMF (EMNMF) to complete unknown values for understanding the feature of the given data presented by matrix shape. Moreover, for comparing the result of the NMF analysis of two matrices, there is Discriminant NMF (DNMF) shows the difference of users features between two matrices. In this study, we combine EMNMF and DNMF and also analyze the target data. As the interpretation, we show the difference of the features of users between LINE Pay and Paypay.Keywords: data science, non-negative matrix factorization, missing data, quality of services
Procedia PDF Downloads 122801 An Effective and Efficient Web Platform for Monitoring, Control, and Management of Drones Supported by a Microservices Approach
Authors: Jorge R. Santos, Pedro Sebastiao
Abstract:
In recent years there has been a great growth in the use of drones, being used in several areas such as security, agriculture, or research. The existence of some systems that allow the remote control of drones is a reality; however, these systems are quite simple and directed to specific functionality. This paper proposes the development of a web platform made in Vue.js and Node.js to control, manage, and monitor drones in real time. Using a microservice architecture, the proposed project will be able to integrate algorithms that allow the optimization of processes. Communication with remote devices is suggested via HTTP through 3G, 4G, and 5G networks and can be done in real time or by scheduling routes. This paper addresses the case of forest fires as one of the services that could be included in a system similar to the one presented. The results obtained with the elaboration of this project were a success. The communication between the web platform and drones allowed its remote control and monitoring. The incorporation of the fire detection algorithm in the platform proved possible a real time analysis of the images captured by the drone without human intervention. The proposed system has proved to be an asset to the use of drones in fire detection. The architecture of the application developed allows other algorithms to be implemented, obtaining a more complex application with clear expansion.Keywords: drone control, microservices, node.js, unmanned aerial vehicles, vue.js
Procedia PDF Downloads 132800 Lab Bench for Synthetic Aperture Radar Imaging System
Authors: Karthiyayini Nagarajan, P. V. Ramakrishna
Abstract:
Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar (SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System (Lab Bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.Keywords: synthetic aperture radar, radio reflection model, lab bench, imaging engineering
Procedia PDF Downloads 483799 Numerical Simulation of Natural Gas Dispersion from Low Pressure Pipelines
Authors: Omid Adibi, Nategheh Najafpour, Bijan Farhanieh, Hossein Afshin
Abstract:
Gas release from the pipelines is one of the main factors in the gas industry accidents. Released gas ejects from the pipeline as a free jet and in the growth process, the fuel gets mixed with the ambient air. Accordingly, an accidental spark will release the chemical energy of the mixture with an explosion. Gas explosion damages the equipment and endangers the life of staffs. So due to importance of safety in gas industries, prevision of accident can reduce the number of the casualties. In this paper, natural gas leakages from the low pressure pipelines are studied in two steps: 1) the simulation of mixing process and identification of flammable zones and 2) the simulation of wind effects on the mixing process. The numerical simulations were performed by using the finite volume method and the pressure-based algorithm. Also, for the grid generation the structured method was used. The results show that, in just 6.4 s after accident, released natural gas could penetrate to 40 m in vertical and 20 m in horizontal direction. Moreover, the results show that the wind speed is a key factor in dispersion process. In fact, the wind transports the flammable zones into the downstream. Hence, to improve the safety of the people and human property, it is preferable to construct gas facilities and buildings in the opposite side of prevailing wind direction.Keywords: flammable zones, gas pipelines, numerical simulation, wind effects
Procedia PDF Downloads 159798 Real-Time Path Planning for Unmanned Air Vehicles Using Improved Rapidly-Exploring Random Tree and Iterative Trajectory Optimization
Authors: A. Ramalho, L. Romeiro, R. Ventura, A. Suleman
Abstract:
A real-time path planning framework for Unmanned Air Vehicles, and in particular multi-rotors is proposed. The framework is designed to provide feasible trajectories from the current UAV position to a goal state, taking into account constraints such as obstacle avoidance, problem kinematics, and vehicle limitations such as maximum speed and maximum acceleration. The framework computes feasible paths online, allowing to avoid new, unknown, dynamic obstacles without fully re-computing the trajectory. These features are achieved using an iterative process in which the robot computes and optimizes the trajectory while performing the mission objectives. A first trajectory is computed using a modified Rapidly-Exploring Random Tree (RRT) algorithm, that provides trajectories that respect a maximum curvature constraint. The trajectory optimization is accomplished using the Interior Point Optimizer (IPOPT) as a solver. The framework has proven to be able to compute a trajectory and optimize to a locally optimal with computational efficiency making it feasible for real-time operations.Keywords: interior point optimization, multi-rotors, online path planning, rapidly exploring random trees, trajectory optimization
Procedia PDF Downloads 131797 Development of a Shape Based Estimation Technology Using Terrestrial Laser Scanning
Authors: Gichun Cha, Byoungjoon Yu, Jihwan Park, Minsoo Park, Junghyun Im, Sehwan Park, Sujung Sin, Seunghee Park
Abstract:
The goal of this research is to estimate a structural shape change using terrestrial laser scanning. This study proceeds with development of data reduction and shape change estimation algorithm for large-capacity scan data. The point cloud of scan data was converted to voxel and sampled. Technique of shape estimation is studied to detect changes in structure patterns, such as skyscrapers, bridges, and tunnels based on large point cloud data. The point cloud analysis applies the octree data structure to speed up the post-processing process for change detection. The point cloud data is the relative representative value of shape information, and it used as a model for detecting point cloud changes in a data structure. Shape estimation model is to develop a technology that can detect not only normal but also immediate structural changes in the event of disasters such as earthquakes, typhoons, and fires, thereby preventing major accidents caused by aging and disasters. The study will be expected to improve the efficiency of structural health monitoring and maintenance.Keywords: terrestrial laser scanning, point cloud, shape information model, displacement measurement
Procedia PDF Downloads 227796 Offline Signature Verification Using Minutiae and Curvature Orientation
Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee
Abstract:
A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.Keywords: signature, ridge breaks, minutiae, orientation
Procedia PDF Downloads 138795 Analysis of Brain Signals Using Neural Networks Optimized by Co-Evolution Algorithms
Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,
Abstract:
Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model
Procedia PDF Downloads 268794 Design and Implementation of a Lab Bench for Synthetic Aperture Radar Imaging System
Authors: Karthiyayini Nagarajan, P. V. RamaKrishna
Abstract:
Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar(SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System(lab bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.Keywords: synthetic aperture radar, radio reflection model, lab bench
Procedia PDF Downloads 458793 Stock Market Prediction Using Convolutional Neural Network That Learns from a Graph
Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn
Abstract:
Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction
Procedia PDF Downloads 421792 A Fuzzy Analytic Hierarchy Process Approach for the Decision of Maintenance Priorities of Building Entities: A Case Study in a Facilities Management Company
Authors: Wai Ho Darrell Kwok
Abstract:
Building entities are valuable assets of a society, however, all of them are suffered from the ravages of weather and time. Facilitating onerous maintenance activities is the only way to either maintain or enhance the value and contemporary standard of the premises. By the way, maintenance budget is always bounded by the corresponding threshold limit. In order to optimize the limited resources allocation in carrying out maintenance, there is a substantial need to prioritize maintenance work. This paper reveals the application of Fuzzy AHP in a Facilities Management Company determining the maintenance priorities on the basis of predetermined criteria, viz., Building Status (BS), Effects on Fabrics (EF), Effects on Sustainability (ES), Effects on Users (EU), Importance of Usage (IU) and Physical Condition (PC) in dealing with categorized 8 predominant building components maintenance aspects for building premises. From the case study, it is found that ‘building exterior repainting or re-tiling’, ‘spalling concrete repair works among exterior area’ and ‘lobby renovation’ are the top three maintenance priorities from facilities manager and maintenance expertise personnel. Through the application of the Fuzzy AHP for maintenance priorities decision algorithm, a more systemic and easier comparing scalar linearity factors being explored even in considering other multiple criteria decision scenarios of building maintenance issue.Keywords: building maintenance, fuzzy AHP, maintenance priority, multi-criteria decision making
Procedia PDF Downloads 234791 Using Closed Frequent Itemsets for Hierarchical Document Clustering
Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu
Abstract:
Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.Keywords: FIHC, documents clustering, ontology, closed frequent itemset
Procedia PDF Downloads 386790 Controller Design for Highly Maneuverable Aircraft Technology Using Structured Singular Value and Direct Search Method
Authors: Marek Dlapa
Abstract:
The algebraic approach is applied to the control of the HiMAT (Highly Maneuverable Aircraft Technology). The objective is to find a robust controller which guarantees robust stability and decoupled control of longitudinal model of a scaled remotely controlled vehicle version of the advanced fighter HiMAT. Control design is performed by decoupling the nominal MIMO (multi-input multi-output) system into two identical SISO (single-input single-output) plants which are approximated by a 4th order transfer function. The algebraic approach is then used for pole placement design, and the nominal closed-loop poles are tuned so that the peak of the µ-function is minimal. As an optimization tool, evolutionary algorithm Differential Migration is used in order to overcome the multimodality of the cost function yielding simple controller with decoupling for nominal plant which is compared with the D-K iteration through simulations of standard longitudinal manoeuvres documenting decoupled control obtained from algebraic approach for nominal plant as well as worst case perturbation.Keywords: algebraic approach, evolutionary computation, genetic algorithms, HiMAT, robust control, structured singular value
Procedia PDF Downloads 132789 Real-Time Classification of Hemodynamic Response by Functional Near-Infrared Spectroscopy Using an Adaptive Estimation of General Linear Model Coefficients
Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan
Abstract:
Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter
Procedia PDF Downloads 145788 Obstacle Avoidance Using Image-Based Visual Servoing Based on Deep Reinforcement Learning
Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie
Abstract:
This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network
Procedia PDF Downloads 134787 Effect of pH-Dependent Surface Charge on the Electroosmotic Flow through Nanochannel
Authors: Partha P. Gopmandal, Somnath Bhattacharyya, Naren Bag
Abstract:
In this article, we have studied the effect of pH-regulated surface charge on the electroosmotic flow (EOF) through nanochannel filled with binary symmetric electrolyte solution. The channel wall possesses either an acidic or a basic functional group. Going beyond the widely employed Debye-Huckel linearization, we develop a mathematical model based on Nernst-Planck equation for the charged species, Poisson equation for the induced potential, Stokes equation for fluid flow. A finite volume based numerical algorithm is adopted to study the effect of key parameters on the EOF. We have computed the coupled governing equations through the finite volume method and our results found to be in good agreement with the analytical solution obtained from the corresponding linear model based on low surface charge condition or strong electrolyte solution. The influence of the surface charge density, reaction constant of the functional groups, bulk pH, and concentration of the electrolyte solution on the overall flow rate is studied extensively. We find the effect of surface charge diminishes with the increase in electrolyte concentration. In addition for strong electrolyte, the surface charge becomes independent of pH due to complete dissociation of the functional groups.Keywords: electroosmosis, finite volume method, functional group, surface charge
Procedia PDF Downloads 414786 Design and Analysis of Adaptive Type-I Progressive Hybrid Censoring Plan under Step Stress Partially Accelerated Life Testing Using Competing Risk
Authors: Ariful Islam, Showkat Ahmad Lone
Abstract:
Statistical distributions have long been employed in the assessment of semiconductor devices and product reliability. The power function-distribution is one of the most important distributions in the modern reliability practice and can be frequently preferred over mathematically more complex distributions, such as the Weibull and the lognormal, because of its simplicity. Moreover, it may exhibit a better fit for failure data and provide more appropriate information about reliability and hazard rates in some circumstances. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests for competing risk based on adoptive type-I progressive hybrid censoring criteria. The life data of the units under test is assumed to follow Mukherjee-Islam distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameters and tampering coefficient. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm.Keywords: adoptive progressive hybrid censoring, competing risk, mukherjee-islam distribution, partially accelerated life testing, simulation study
Procedia PDF Downloads 340785 Stackelberg Security Game for Optimizing Security of Federated Internet of Things Platform Instances
Authors: Violeta Damjanovic-Behrendt
Abstract:
This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning
Procedia PDF Downloads 345784 Optimization of Lubricant Distribution with Alternative Coordinates and Number of Warehouses Considering Truck Capacity and Time Windows
Authors: Taufik Rizkiandi, Teuku Yuri M. Zagloel, Andri Dwi Setiawan
Abstract:
Distribution and growth in the transportation and warehousing business sector decreased by 15,04%. There was a decrease in Gross Domestic Product (GDP) contribution level from rank 7 of 4,41% in 2019 to 3,81% in rank 8 in 2020. A decline in the transportation and warehousing business sector contributes to GDP, resulting in oil and gas companies implementing an efficient supply chain strategy to ensure the availability of goods, especially lubricants. Fluctuating demand for lubricants and warehouse service time limits are essential things that are taken into account in determining an efficient route. Add depots points as a solution so that demand for lubricants is fulfilled (not stock out). However, adding a depot will increase operating costs and storage costs. Therefore, it is necessary to optimize the addition of depots using the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). This research case study was conducted at an oil and gas company that produces lubricants from 2019 to 2021. The study results obtained the optimal route and the addition of a depot with a minimum additional cost. The total cost remains efficient with the addition of a depot when compared to one depot from Jakarta.Keywords: CVRPTW, optimal route, depot, tabu search algorithm
Procedia PDF Downloads 128