Search results for: memory optimization

Authors: Changqiao Wu, Guoqing Ding, Xin Chen

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In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: dynamic system modeling, neural network, normal equation, second order gradient descent

Procedia PDF Downloads 111

Authors: Dejan Tanikić, Miodrag Manić, Jelena Đoković, Saša Kalinović

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This paper deals with the determination of the optimum machining parameters, according to the measured and modelled data of the cutting temperature and surface roughness, during the turning of the AISI 4140 steel. The high cutting temperatures are unwanted occurences in the metal cutting process. They impact negatively on the quality of the machined part. The machining experiments were performed using different cutting regimes (cutting speed, feed rate and depth of cut), with different values of the workpiece hardness, which causes different values of the measured cutting temperature as well as the measured surface roughness. The temperature and surface roughness data were modelled after that using Response Surface Methodology (RSM). The obtained RSM models are used in the process of optimization of the cutting regimes using the Genetic Algorithms (GA) tool, which enables the metal cutting process in the optimum conditions.

Keywords: genetic algorithms, machining parameters, response surface methodology, turning process

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Authors: Saida Haider, Syeda Madiha

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Parkinson’s disease (PD) is a common neurological disorder characterized by motor deficits and loss of dopaminergic neurons. Oxidative stress is said to play a pivotal role in the pathophysiology of the disease. In the present study, PD was induced by injection of rotenone (1.5 mg/kg/day, s.c.) for eight days. Pistachio (800 mg/kg/day, p.o.) was given for two weeks. At the end of treatment brains were dissected out and striatum was isolated for biochemical and neurochemical analysis. Morris water maze (MWM) test and novel object recognition (NOR) task was used to test the memory function while motor behavior was determined by open field test (OFT), Kondziela inverted screen test (KIST), pole test (PT), beam walking test (BWT), inclined plane test (IPT) and footprint (FP) test. Several dietary components have been evaluated as potential therapeutic compounds in many neurodegenerative diseases. Increasing evidence shows that nuts have protective effects against various diseases by improving the oxidative status and reducing lipid peroxidation. Pistachio is the only nut that contains anthocyanin, a potent antioxidant having neuroprotective properties. Results showed that pistachio supplementation significantly restored the rotenone-induced motor deficits and improved the memory performance. Moreover, rats treated with pistachio also exhibited enhanced oxidative status and increased dopamine (DA) and 5-hydroxytryptamine (5-HT) concentration in striatum. In conclusion, to our best knowledge, we have for the first time shown that pistachio nut possesses neuroprotective effects against rotenone-induced motor and cognitive deficits. These beneficial effects of pistachio may be attributed to its high content of natural antioxidant and phenolic compounds. Hence, consumption of pistachio regularly as part of a daily diet can be beneficial in the prevention and treatment of PD.

Keywords: rotenone, pistachio, oxidative stress, Parkinson’s disease

Procedia PDF Downloads 92

Authors: Ujwal Kishor Zore, Shankar Balajirao Kausley, Aniruddha Bhalchandra Pandit

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There are some important design parameters of activated sludge process (ASP) for wastewater treatment and they must be optimally defined to have the optimized plant working. To know them, developing a mathematical model is a way out as it is nearly commensurate the real world works. In this study, a mathematical model was developed for ASP, solved under activated sludge model no 1 (ASM 1) conditions and MATLAB tool was used to solve the mathematical equations. For its real-life validation, the developed model was tested for the inputs from the municipal wastewater treatment plant and the results were quite promising. Additionally, the most cardinal assumptions required to design the treatment plant are discussed in this paper. With the need for computerization and digitalization surging in every aspect of engineering, this mathematical model developed might prove to be a boon to many biological wastewater treatment plants as now they can in no time know the design parameters which are required for a particular type of wastewater treatment.

Keywords: waste water treatment, activated sludge process, mathematical modeling, optimization

Procedia PDF Downloads 129

Authors: Haruo Okabayashi

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Human biological signals (pulse wave and brain wave, etc.) have a rhythm which shows fluctuations. This study investigates the relationship between fluctuations of biological signals which are shown by a finger plethysmogram (i.e., finger pulse wave) in conversation and anthropophobic tendency, and identifies whether the fluctuation could be an index of mental health. 32 college students participated in the experiment. The finger plethysmogram of each subject was measured in the following conversation situations: Fun memory talking/listening situation and regrettable memory talking/ listening situation for three minutes each. Lyspect 3.5 was used to collect the data of the finger plethysmogram. Since Lyspect calculates the Lyapunov spectrum, it is possible to obtain the largest Lyapunov exponent (LLE). LLE is an indicator of the fluctuation and shows the degree to which a measure is going away from close proximity to the track in a dynamical system. Before the finger plethysmogram experiment, each participant took the psychological test questionnaire “Anthropophobic Scale.” The scale measures the social phobia trend close to the consciousness of social phobia. It is revealed that there is a remarkable relationship between the fluctuation of the finger plethysmography and anthropophobic tendency scale in talking about a regrettable story in conversation: The participants (N=15) who have a low anthropophobic tendency show significantly more fluctuation of finger pulse waves than the participants (N=17) who have a high anthropophobic tendency (F (1, 31) =5.66, p<0.05). That is, the participants who have a low anthropophobic tendency make conversation flexibly using large fluctuation of biological signal; on the other hand, the participants who have a high anthropophobic tendency constrain a conversation because of small fluctuation. Therefore, fluctuation is not an error but an important drive to make better relationships with others and go towards the development of interaction. In considering mental health, the fluctuation of biological signals would be an important indicator.

Keywords: anthropophobic tendency, finger plethymogram, fluctuation of biological signal, LLE

Procedia PDF Downloads 224

Authors: Mohammad Sadeghian, Mohsen Sadeghian

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In this paper, planning catamaran and mono-hull vessels resistance and trim in calm waters were considered. Hydrodynamic analysis of fast mono-hull planning vessel was also investigated. For hull form geometry optimization, numerical methods of different parameters were used for this type of vessels. Hull material was selected as carbon fiber composite. Exact architectural aspects were specified and stability calculations were performed, as well. Hydrodynamic calculations to extract the resistance force using semi-analytical methods and numerical modeling were carried out. Free surface numerical analysis of vessel in designed draft using finite volume method and double phase were evaluated and verified by experimental tests.

Keywords: fast vessel, hydrostatic and hydrodynamic optimization, free surface flow, computational fluid dynamics

Procedia PDF Downloads 267

Authors: Kyoungwoo Park, Gil Dong Kim, Seong Joon Moon, Ho Kil Lee

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The Urea-SCR is one of the most efficient technologies to reduce NOx emissions in diesel engines. In the present work, the computational prediction of internal flow and spray characteristics in the Urea-SCR system was carried out by using 3D-CFD simulation to evaluate NH3 uniformity index (NH3 UI) and its activation time according to the official New European Driving Cycle (NEDC). The number of nozzle and its diameter, two types of injection directions, and penetration length were chosen as the design variables. The optimal solutions were obtained by coupling the CFD analysis with Taguchi method. The L16 orthogonal array and small-the-better characteristics of the Taguchi method were used, and the optimal values were confirmed to be valid with 95% confidence and 5% significance level through analysis of variance (ANOVA). The results show that the optimal solutions for the NH3 UI and activation time (NH3 UI 0.22) are obtained by 0.41 and 0,125 second, respectively, and their values are improved by 85.0% and 10.7%, respectively, compared with those of the base model.

Keywords: computational fluid dynamics, NH3 uniformity index, optimization, Taguchi method, Urea-SCR system, UWS injector

Procedia PDF Downloads 250

Authors: Madia Safdar, G. Amjad Hussain, Mashhood Ahmad

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One of the major challenges of today’s era is peak demand which causes stress on the transmission lines and also raises the cost of energy generation and ultimately higher electricity bills to the end users, and it was used to be managed by the supply side management. However, nowadays this has been withdrawn because of existence of potential in the demand side management (DSM) having its economic and- environmental advantages. DSM in domestic load can play a vital role in reducing the peak load demand on the network provides a significant cost saving. In this paper the potential of demand response (DR) in reducing the peak load demands and electricity bills to the electric users is elaborated. For this purpose the domestic appliances are modeled in MATLAB Simulink and controlled by a module called energy management controller. The devices are categorized into controllable and uncontrollable loads and are operated according to real-time tariff pricing pattern instead of fixed time pricing or variable pricing. Energy management controller decides the switching instants of the controllable appliances based on the results from optimization algorithms. In GAMS software, the MILP (mixed integer linear programming) algorithm is used for optimization. In different cases, different constraints are used for optimization, considering the comforts, needs and priorities of the end users. Results are compared and the savings in electricity bills are discussed in this paper considering real time pricing and fixed tariff pricing, which exhibits the existence of potential to reduce electricity bills and peak loads in demand side management. It is seen that using real time pricing tariff instead of fixed tariff pricing helps to save in the electricity bills. Moreover the simulation results of the proposed energy management system show that the gained power savings lie in high range. It is anticipated that the result of this research will prove to be highly effective to the utility companies as well as in the improvement of domestic DR.

Keywords: controllable and uncontrollable domestic loads, demand response, demand side management, optimization, MILP (mixed integer linear programming)

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Authors: Rakesh Prajapati, Purvik Patel, Hardik Patel

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Productivity and quality are two important aspects that have become great concerns in today’s competitive global market. Every production/manufacturing unit mainly focuses on these areas in relation to the process, as well as the product developed. The electrical discharge machining (EDM) process, even now it is an experience process, wherein the selected parameters are still often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. Material Removal Rate (MRR) during the process has been considered as a productivity estimate with the aim to maximize it, with an intention of minimizing surface roughness taken as most important output parameter. These two opposites in nature requirements have been simultaneously satisfied by selecting an optimal process environment (optimal parameter setting). Objective function is obtained by Regression Analysis and Analysis of Variance. Then objective function is optimized using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness improved using optimized machining parameters.

Keywords: MMR, TWR, OC, DOE, ANOVA, minitab

Procedia PDF Downloads 309

Authors: G. S. Ayyappan, Srinivas Kota, Jaffer R. C. Sheriff, C. Prakash Chandra Joshua

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In the present scenario of energy crises, energy conservation in the electrical machines is very important in the industries. In order to conserve energy, one needs to monitor the performance of an induction motor on-site and in-situ. The instruments available for this purpose are very meager and very expensive. This paper deals with the design and development of induction motor performance analyser on-line, on-site, and in-situ. The system measures only few electrical input parameters like input voltage, line current, power factor, frequency, powers, and motor shaft speed. These measured data are coupled to name plate details and compute the operating efficiency of induction motor. This system employs the method of computing motor losses with the help of equivalent circuit parameters. The equivalent circuit parameters of the concerned motor are estimated using the developed algorithm at any load conditions and stored in the system memory. The developed instrument is a reliable, accurate, compact, rugged, and cost-effective one. This portable instrument could be used as a handy tool to study the performance of both slip ring and cage induction motors. During the analysis, the data can be stored in SD Memory card and one can perform various analyses like load vs. efficiency, torque vs. speed characteristics, etc. With the help of the developed instrument, one can operate the motor around its Best Operating Point (BOP). Continuous monitoring of the motor efficiency could lead to Life Cycle Assessment (LCA) of motors. LCA helps in taking decisions on motor replacement or retaining or refurbishment.

Keywords: energy conservation, equivalent circuit parameters, induction motor efficiency, life cycle assessment, motor performance analysis

Procedia PDF Downloads 365

Authors: Sebastian Kehne, Alexander Epple, Werner Herfs

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A new method for optimal selection of components for multi-axes forward-feed drive systems is proposed in which the choice of motors, gear boxes and ball screw drives is optimized. Essential is here the synchronization of electrical and mechanical frequency behavior of all axes because even advanced controls (like H∞-controls) can only control a small part of the mechanical modes – namely only those of observable and controllable states whose value can be derived from the positions of extern linear length measurement systems and/or rotary encoders on the motor or gear box shafts. Further problems are the unknown processing forces like cutting forces in machine tools during normal operation which make the estimation and control via an observer even more difficult. To start with, the open source Modelica Feed Drive Library which was developed at the Laboratory for Machine Tools, and Production Engineering (WZL) is extended from one axis design to the multi axes design. It is capable to simulate the mechanical, electrical and thermal behavior of permanent magnet synchronous machines with inverters, different gear boxes and ball screw drives in a mechanical system. To keep the calculation time down analytical equations are used for field and torque producing equivalent circuit, heat dissipation and mechanical torque at the shaft. As a first step, a small machine tool with a working area of 635 x 315 x 420 mm is taken apart, and the mechanical transfer behavior is measured with an impulse hammer and acceleration sensors. With the frequency transfer functions, a mechanical finite element model is built up which is reduced with substructure coupling to a mass-damper system which models the most important modes of the axes. The model is modelled with Modelica Feed Drive Library and validated by further relative measurements between machine table and spindle holder with a piezo actor and acceleration sensors. In a next step, the choice of possible components in motor catalogues is limited by derived analytical formulas which are based on well-known metrics to gain effective power and torque of the components. The simulation in Modelica is run with different permanent magnet synchronous motors, gear boxes and ball screw drives from different suppliers. To speed up the optimization different black-box optimization methods (Surrogate-based, gradient-based and evolutionary) are tested on the case. The objective that was chosen is to minimize the integral of the deviations if a step is given on the position controls of the different axes. Small values are good measures for a high dynamic axes. In each iteration (evaluation of one set of components) the control variables are adjusted automatically to have an overshoot less than 1%. It is obtained that the order of the components in optimization problem has a deep impact on the speed of the black-box optimization. An approach to do efficient black-box optimization for multi-axes design is presented in the last part. The authors would like to thank the German Research Foundation DFG for financial support of the project “Optimierung des mechatronischen Entwurfs von mehrachsigen Antriebssystemen (HE 5386/14-1 | 6954/4-1)” (English: Optimization of the Mechatronic Design of Multi-Axes Drive Systems).

Keywords: ball screw drive design, discrete optimization, forward feed drives, gear box design, linear drives, machine tools, motor design, multi-axes design

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Authors: Daniela Tapusi, Adrian Andronic, Naomi Tufan, Ruxandra Erbașu, Ioana Teodorescu

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The proposed theme of this article falls within the policy of reducing carbon emissions imposed by the ‘Green New Deal’ by replacing structural elements made of energy-intensive materials with ecological materials. In this sense, wood has many qualities (high strength/mass and stiffness/mass ratio, low specific gravity, recovery/recycling) that make it competitive with classic building materials. The topological optimization of the linear glulam elements, resulting from different types of analysis (Finite Element Method, simple regression on metamodels), tests on models or by Monte-Carlo simulation, leads to a material reduction of more than 10%. This article proposes a method of obtaining topologically optimized shapes for different types of glued laminated timber beams. The results obtained will constitute the database for AI training.

Keywords: timber, glued laminated timber, artificial-intelligence, environment, carbon emissions

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Authors: Alireza Pirkhaefi

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Background: Multiple sclerosis (MS) is one of the most common diseases of the central nervous system (brain and spinal cord). Given the importance of cognitive disorders in patients with multiple sclerosis, the present study was in order to compare cognitive functions (Working memory, Attention and Centralization, and Visual-spatial perception) in patients with relapsing- remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS). Method: Present study was performed as a retrospective study. This research was conducted with Ex-Post Facto method. The samples of research consisted of 60 patients with multiple sclerosis (30 patients relapsing-retrograde and 30 patients secondary progressive), who were selected from Tehran Community of MS Patients Supported as convenience sampling. 30 normal persons were also selected as a comparison group. Montreal Cognitive Assessment (MOCA) was used to assess cognitive functions. Data were analyzed using multivariate analysis of variance. Results: The results showed that there were significant differences among cognitive functioning in patients with RRMS, SPMS, and normal individuals. There were not significant differences in working memory between two groups of patients with RRMS and SPMS; while significant differences in these variables were seen between the two groups and normal individuals. Also, results showed significant differences in attention and centralization and visual-spatial perception among three groups. Conclusions: Results showed that there are differences between cognitive functions of RRMS and SPMS patients so that the functions of RRMS patients are better than SPMS patients. These results have a critical role in improvement of cognitive functions; reduce the factors causing disability due to cognitive impairment, and especially overall health of society.

Keywords: multiple sclerosis, cognitive function, secondary-progressive, normal subjects

Procedia PDF Downloads 223

Authors: Yang Yi-Fei, Luo Min-Zhou, Zhang Fu-Chun, He Nai-Bao, Xing Shao-Bang

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This paper presents an electromagnetic finite element model of permanent magnet synchronous linear motor and distortion rate of the air gap flux density waveform is analyzed in detail. By designing the sample space of the parameters, nonlinear regression modeling of the orthogonal experimental design is introduced. We put forward for possible air gap flux density waveform sine electromagnetic scheme. Parameters optimization of the permanent magnet synchronous linear motor is also introduced which is based on chaotic search and adaptation function. Simulation results prove that the pole shifting does not affect the motor back electromotive symmetry based on the structural parameters, it provides a novel way for the optimum design of permanent magnet synchronous linear motor and other engineering.

Keywords: permanent magnet synchronous linear motor, finite element analysis, chaotic search, optimization design

Procedia PDF Downloads 399

Authors: N. Beit Saeid

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Cyclones are used in the field of air industrial gases pollution and control the pollution with centrifugal forces that is generated with spatial geometry of the cyclone. Their simple design, low capital and maintenance costs and adaptability to a wide range of operating conditions have made cyclones one of the most widely used industrial dust collectors. Their cost of operation is proportional to the fan energy required to overcome their pressure drop. Optimized geometry of outlet diffuser of the cyclones potentially could reduce exit pressure losses without affecting collection efficiency. Three rectangular outlets and a radial outlet with a variable opening had been analyzed on two cyclones. Pressure drop was investigated for inlet velocities from about 10 to 20 m s−1. The radial outlet reduced cyclone pressure drop by between 8.7 and 11.9 percent when its exit area was equal to the flow area of the cyclone vortex finder or gas exit. A simple payback based on avoided energy costs was estimated to be between 3600 and 5000 h, not including installation cost.

Keywords: cyclone, CFD, optimization, genetic algorithm

Procedia PDF Downloads 369

Authors: Parker Townes, Aisouda Savadlou, Shoshana Weiss, Marina Jarenova, Suzzane Ferris, Dan Devoe, Russel Schachar, Scott Patten, Tomas Lange, Marlena Colasanto, Holly McGinn, Paul Arnold

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Some research suggests obsessive-compulsive disorder (OCD) is associated with impaired executive functioning: higher-level mental processes involved in carrying out tasks and solving problems. Relevant literature was identified systematically through online databases. Meta-analyses were conducted for task performance metrics reported by at least two articles. Results were synthesized by the executive functioning domain measured through each performance metric. Heterogeneous literature was identified, typically involving few studies using consistent measures. From 29 included studies, analyses were conducted on 33 performance metrics from 12 tasks. Results suggest moderate associations of working memory (two out of five tasks presented significant findings), planning (one out of two tasks presented significant findings), and visuospatial abilities (one out of two tasks presented significant findings) with OCD in youth. There was inadequate literature or contradictory findings for other executive functioning domains. These findings suggest working memory, planning, and visuospatial abilities are impaired in pediatric OCD, with mixed results. More work is needed to identify the effect of age and sex on these results. Acknowledgment: This work was supported by the Alberta Innovates Translational Health Chair in Child and Youth Mental Health. The funders had no role in the design, conducting, writing, or decision to submit this article for publication.

Keywords: obsessive-compulsive disorder, neurocognition, executive functioning, adolescents, children

Procedia PDF Downloads 81

Authors: F. Gökgöz, M. E. Atmaca

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Electricity plays an indispensable role in human life and the economy. It is a unique product or service that must be balanced instantaneously, as electricity is not stored, generation and consumption should be proportional. Effective and efficient use of electricity is very important not only for society, but also for the environment. A competitive electricity market is one of the best ways to provide a suitable platform for effective and efficient use of electricity. On the other hand, it carries some risks that should be carefully managed by the market players. Risk management is an essential part in market players’ decision making. In this paper, risk management through diversification is applied with the help of Markowitz’s Mean-variance, Down-side and Semi-variance methods for a case study. Performance of optimal electricity sale solutions are measured and evaluated via Sharpe-Ratio, and the optimal portfolio solutions are improved. Two years of historical weekdays’ price data of the Turkish Day Ahead Market are used to demonstrate the approach.

Keywords: electricity market, portfolio optimization, risk management in electricity market, sharpe ratio

Procedia PDF Downloads 344

Authors: Maatoug Hassine, Mourad Hrizi

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In this paper, we consider a geometric inverse source problem for the heat equation with Dirichlet and Neumann boundary data. We will reconstruct the exact form of the unknown source term from additional boundary conditions. Our motivation is to detect the location, the size and the shape of source support. We present a one-shot algorithm based on the Kohn-Vogelius formulation and the topological gradient method. The geometric inverse source problem is formulated as a topology optimization one. A topological sensitivity analysis is derived from a source function. Then, we present a non-iterative numerical method for the geometric reconstruction of the source term with unknown support using a level curve of the topological gradient. Finally, we give several examples to show the viability of our presented method.

Keywords: geometric inverse source problem, heat equation, topological optimization, topological sensitivity, Kohn-Vogelius formulation

Procedia PDF Downloads 288

Authors: Mohammad Ali Badri, Pouya Molana, Amin Rezvanpour

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In this paper, planning catamaran and mono-hull vessels resistance and trim in calm waters were considered. Hydrodynamic analysis of fast mono-hull planning vessel was also investigated. In order to hull form geometry optimization, numerical methods of different parameters were used for this type of vessels. Hull material was selected in carbon fiber composite. Exact architectural aspects were specified and stability calculations were performed as well. Hydrodynamic calculations to extract the resistance force using semi-analytical methods and numerical modeling were carried out. Free surface numerical analysis of vessel in designed draft using finite volume method and double phase were evaluated and verified by experimental tests.

Keywords: fast vessel, hydrostatic and hydrodynamic optimization, free surface flow, computational fluid dynamics

Procedia PDF Downloads 502

Authors: Mohammad Hossein Bayati Chaleshtari, Hadi Khoramishad

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Minimizing the stress concentration around triangular cutout in infinite perforated plates subjected to a uniform heat flux induces thermal stresses is an important consideration in engineering design. Furthermore, understanding the effective parameters on stress concentration and proper selection of these parameters enables the designer to achieve a reliable design. In the analysis of thermal stress, the effective parameters on stress distribution around cutout include fiber angle, flux angle, bluntness and rotation angle of the cutout for orthotropic materials. This paper was tried to examine effect of these parameters on thermal stress analysis of infinite perforated plates with central triangular cutout. In order to achieve the least amount of thermal stress around a triangular cutout using a novel swarm intelligence optimization technique called dragonfly optimizer that inspired by the life method and hunting behavior of dragonfly in nature. In this study, using the two-dimensional thermoelastic theory and based on the Likhnitskiiʼ complex variable technique, the stress analysis of orthotropic infinite plate with a circular cutout under a uniform heat flux was developed to the plate containing a quasi-triangular cutout in thermal steady state condition. To achieve this goal, a conformal mapping function was used to map an infinite plate containing a quasi- triangular cutout into the outside of a unit circle. The plate is under uniform heat flux at infinity and Neumann boundary conditions and thermal-insulated condition at the edge of the cutout were considered.

Keywords: infinite perforated plate, complex variable method, thermal stress, optimization method

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Ibrahim Isa Ali (Pantami), Abdu Jaafaru Bambale, Abimbola Alale, Danjuma Ibrahim Ndihgihdah, Muhammad Alkali, Adamu Idris Umar, Babadoko Dantala Mohammed, Moshood Kareem Olawole

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This paper dwells on the risk management required for High throughput Satellite (HTS) project, and major subsystems that pertains to the improved performance and reliability of the spacecraft. The paper gives a clear picture of high‐throughput satellites (HTS) and the associated technologies with performances as they align and differ with the traditional geostationary orbit or Geosynchronous Equatorial Orbit (GEO) Communication Satellites. The paper also highlights critical subsystems and processes in project conceptualization and execution. The paper discusses the configuration of the payload. The need for optimization of resources for the HTS project and successful integration of critical subsystems for spacecraft requires implementation of risk analysis and mitigation from the preliminary design stage; Assembly, Integration and Test (AIT); Launch and in-orbit- Management stage.

Keywords: AIT, HTS, in-orbit management, optimization

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Authors: M. Firoz Kalam, Wilfried Schuetz, Jan Hendrik Bredehoeft

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Thiophene sulfur compounds' removal from diesel oil by batch adsorption process using commercial powdered activated carbon was designed and optimized in two-level factorial design method. This design analysis was used to find out the effects of operating parameters directing the adsorption process, such as amount of adsorbent, temperature and stirring time. The desulfurization efficiency was considered the response or output variable. Results showed that the stirring time had the largest effects on sulfur removal efficiency as compared with other operating parameters and their interactions under the experimental ranges studied. A regression model was generated to observe the closeness between predicted and experimental values. The three-dimensional plots and contour plots of main factors were generated according to the regression results to observe the optimal points.

Keywords: activated carbon, adsorptive desulfurization, factorial design, process optimization

Procedia PDF Downloads 153

Authors: Roghieh A. Biroon, Zoleikha Abdollahi

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The recent growth of renewables, e.g., solar panels, batteries, and electric vehicles (EVs) in residential and small commercial sectors, has potential impacts on the stability and operation of power grids. Considering approximately 50 percent share of the residential and the commercial sectors in the electricity demand market, the significance of these impacts, and the necessity of addressing them are more highlighted. Utilities and power system operators should manage the renewable electricity sources integration with power systems in such a way to extract the most possible advantages for the power systems. The most common effect of high penetration level of the renewables is the reverse power flow in the distribution feeders when the customers generate more power than their needs. The reverse power flow causes voltage rise and thermal issues in the power grids. To overcome the voltage rise issues in the distribution system, several techniques have been proposed including reducing transformers short circuit resistance and feeder impedance, installing autotransformers/voltage regulators along the line, absorbing the reactive power by distributed generators (DGs), and limiting the PV and battery sizes. In this study, we consider a medium-scale battery energy storage to manage the power energy and address the aforementioned issues on voltage deviation and power loss increase. We propose an optimization algorithm to find the optimum size and location for the battery. The optimization for the battery location and size is so that the battery maintains the feeder voltage deviation and power loss at a certain desired level. Moreover, the proposed optimization algorithm controls the charging/discharging profile of the battery to absorb the negative power flow from residential and commercial customers in the feeder during the peak time and sell the power back to the system during the off-peak time. The proposed battery regulates the voltage problem in the distribution system while it also can play frequency regulation role in islanded microgrids. This battery can be regulated and controlled by the utilities or a third-party ancillary service provider for the utilities to reduce the power system loss and regulate the distribution feeder voltage and frequency in standard level.

Keywords: ancillary services, battery, distribution system and optimization

Procedia PDF Downloads 116

Authors: Michal Friedrich

Abstract:

Cross-border investment has never been easier than in today’s global economy. Despite recent initiatives tightening the international tax landscape, profit shifting and tax optimization by multinational entities (MNEs) in the context of cross-border M&A remain persistent and complex phenomena that warrant in-depth exploration. By synthesizing the outcomes of existing research, this study aims to first provide a methodological framework for identifying MNEs’ profit-shifting behavior and quantifying its fiscal impacts via various macroeconomic and microeconomic approaches. The study also proposes additional methods and qualitative/quantitative measures for extracting insight into the profit shifting behavior of MNEs in the context of their M&A activities at industry and entity levels. To develop the proposed methods, this study applies the knowledge of international tax laws and known profit shifting conduits (incl. dividends, interest, and royalties) on several model cases/types of cross-border acquisitions and post-acquisition integration activities by MNEs and highlights important factors that encourage or discourage tax optimization. Follow-up research is envisaged to apply the methods outlined in this study on published data on real-world M&A transactions to gain practical country-by-country, industry and entity-level insights. In conclusion, this study seeks to contribute to the ongoing discourse on profit shifting by providing a methodological toolkit for exploring profit shifting tendencies MNEs in connection with their M&A activities and to serve as a backbone for further research. The study is expected to provide valuable insight to policymakers, tax authorities, and tax professionals alike.

Keywords: BEPS, cross-border M&A, international taxation, profit shifting, tax optimization

Procedia PDF Downloads 58

Authors: Ammarah Irum, Muhammad Ali Tahir

Abstract:

Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

Procedia PDF Downloads 51

Authors: Ana C. R. P. Ferreira, Adriano H. P. Pereira

Abstract:

Given the frequent variation of mineral properties throughout the Araxá pyrochlore deposit, even if a good homogenization work has been carried out before feeding the processing plants, an operation with quality and performance’s high variety standard is expected. These results could be improved and standardized if the blend composition parameters that most influence the processing route are determined, and then the types of raw materials are grouped by them, finally presenting a great reference with operational settings for each group. Associating the physical and chemical parameters of a unit operation through benchmarking or even an optimal reference of metallurgical recovery and product quality reflects in the reduction of the production costs, optimization of the mineral resource, and guarantee of greater stability in the subsequent processes of the production chain that uses the mineral of interest. Conducting a comprehensive exploratory data analysis to identify which characteristics of the ore are most relevant to the process route, associated with the use of Machine Learning algorithms for grouping the raw material (ore) and associating these with reference variables in the process’ benchmark is a reasonable alternative for the standardization and improvement of mineral processing units. Clustering methods through Decision Tree and K-Means were employed, associated with algorithms based on the theory of benchmarking, with criteria defined by the process team in order to reference the best adjustments for processing the ore piles of each cluster. A clean user interface was created to obtain the outputs of the created algorithm. The results were measured through the average time of adjustment and stabilization of the process after a new pile of homogenized ore enters the plant, as well as the average time needed to achieve the best processing result. Direct gains from the metallurgical recovery of the process were also measured. The results were promising, with a reduction in the adjustment time and stabilization when starting the processing of a new ore pile, as well as reaching the benchmark. Also noteworthy are the gains in metallurgical recovery, which reflect a significant saving in ore consumption and a consequent reduction in production costs, hence a more rational use of the tailings dams and life optimization of the mineral deposit.

Keywords: mineral clustering, machine learning, process optimization, pyrochlore processing

Procedia PDF Downloads 132

Authors: Anusuya Ghosh, Vishnu Narayanan

Abstract:

The approximation of convex set by semidefinite representable set plays an important role in semidefinite programming, especially in modern convex optimization. To optimize a linear function over a convex set is a hard problem. But optimizing the linear function over the semidefinite representable set which approximates the convex set is easy to solve as there exists numerous efficient algorithms to solve semidefinite programming problems. So, our approximation technique is significant in optimization. We develop a technique to approximate any closed convex set, say K by compactly semidefinite representable set. Further we prove that there exists a sequence of compactly semidefinite representable sets which give tighter approximation of the closed convex set, K gradually. We discuss about the convergence of the sequence of compactly semidefinite representable sets to closed convex set K. The recession cone of K and the recession cone of the compactly semidefinite representable set are equal. So, we say that the sequence of compactly semidefinite representable sets converge strongly to the closed convex set. Thus, this approximation technique is very useful development in semidefinite programming.

Keywords: semidefinite programming, semidefinite representable set, compactly semidefinite representable set, approximation

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Authors: Sevilay Çankaya

Abstract:

The detection of deception in children’s interviews is essential for statement veracity. The widely used method for deception detection is building cognitive load, which is the logic of the cognitive interview (CI), and its effectiveness for adults is approved. This meta-analysis delves into the effectiveness of inducing cognitive load as a means of enhancing veracity detection during interviews with children. Additionally, the effectiveness of cognitive load on children's total number of events recalled is assessed as a second part of the analysis. The current meta-analysis includes ten effect sizes from search using databases. For the effect size calculation, Hedge’s g was used with a random effect model by using CMA version 2. Heterogeneity analysis was conducted to detect potential moderators. The overall result indicated that cognitive load had no significant effect on veracity outcomes (g =0.052, 95% CI [-.006,1.25]). However, a high level of heterogeneity was found (I² = 92%). Age, participants’ characteristics, interview setting, and characteristics of the interviewer were coded as possible moderators to explain variance. Age was significant moderator (β = .021; p = .03, R2 = 75%) but the analysis did not reveal statistically significant effects for other potential moderators: participants’ characteristics (Q = 0.106, df = 1, p = .744), interview setting (Q = 2.04, df = 1, p = .154), and characteristics of interviewer (Q = 2.96, df = 1, p = .086). For the second outcome, the total number of events recalled, the overall effect was significant (g =4.121, 95% CI [2.256,5.985]). The cognitive load was effective in total recalled events when interviewing with children. All in all, while age plays a crucial role in determining the impact of cognitive load on veracity, the surrounding context, interviewer attributes, and inherent participant traits may not significantly alter the relationship. These findings throw light on the need for more focused, age-specific methods when using cognitive load measures. It may be possible to improve the precision and dependability of deceit detection in children's interviews with the help of more studies in this field.

Keywords: deceit detection, cognitive load, memory recall, children interviews, meta-analysis

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Authors: Ritsuko Kawasaki (Aiba), Takeshi Hiromatsu

Abstract:

Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Moreover, risks generally have trends and it also should be considered in risk treatment. Therefore, this paper provides the extension of the model proposed in the previous study. The original model supports the selection of measures by applying a combination of weighted average method and goal programming method for multi-objective analysis to find an optimal solution. The extended model includes the notion of weights to the risks, and the larger weight means the priority of the risk.

Keywords: information security risk treatment, selection of risk measures, risk acceptance, multi-objective optimization

Procedia PDF Downloads 443