Search results for: optimal weighting

Authors: Dzmitry M. Kabanau, Vladimir V. Kabanov, Yahor V. Lebiadok, Denis V. Shabrov, Pavel V. Shpak, Gevork T. Mikaelyan, Alexandr P. Bunichev

Abstract:

This article is deal with the experimental investigations of the laser diode matrixes (LDM) based on the AlGaAs/GaAs heterostructures (lasing wavelength 790-880 nm) to find optimal LDM parameters for active vision systems. In particular, the dependence of LDM radiation pulse power on the pulse duration and LDA active layer heating as well as the LDM radiation divergence are discussed.

Keywords: active vision systems, laser diode matrixes, thermal properties, radiation divergence

Procedia PDF Downloads 611

Authors: Omer Al-Kaabi, Ahmed Nasr, Abdullah Al-Ghafri, Mohammed Abdelfattah

Abstract:

The Falaj system, derived from natural water sources, is a man-made canal system designed to supply communities of farmers with water for domestic and agricultural purposes. For thousands of years, Falaj has served communities by harnessing the force of gravity; it persists as a vital water management system in numerous regions across the Sultanate of Oman. Remarkably, predates the establishment of many fundamental hydraulic principles used today. Al-Khatmain Falaj, with its accessibility and historical significance spanning over 2000 years, was chosen as the focal point of this study. The research aimed to investigate the efficiency of Al-Khatmain Falaj in meeting specific water demands. The HEC-RAS model was utilized to visualize water flow dynamics within the Falaj channels, accompanied by graphical representations of pertinent variables. The application of HEC-RAS helped to measure different water flow scenarios within the channel, enabling a clear comparison with the demand area catchment. The cultivated land of Al-Khatmain is 723,124 m² and consists of 16,873 palm trees representing 91% of the total area and the remaining 9% is mixed types of trees counted 3,920 trees. The study revealed a total demand of 8,244 m³ is required to irrigate the cultivated land. Through rigorous analysis, the study has proven that the Falaj system in Al-Khatmain operates with high efficiency, as the average annual water supply is 9676.8 m3/day. Additionally, the channel designed at 0.6m width x 0.3m height efficiently holds the optimal water supply, with an average flow depth of 0.21m. Also, the system includes an overflow drainage channel to mitigate floods and prevent crop damage based on seasonal requirements. This research holds promise for examining diverse hydrological conditions and devising effective strategies to manage scenarios of both high and low flow rates.

Keywords: Al-Khatmain, sustainability, Falaj, HEC-RAS, water management system

Procedia PDF Downloads 44

Authors: Noor F. Ilias, Mazlifah Omar, Hashbullah Ismail

Abstract:

Exercise training has been shown to improve functional capacity and is recommended as a therapy for management of blood pressure. Our purpose was to establish whether different exercise capacity produces different effect size for Cardiorespiratory Fitness (CRF), systolic (SBP) and diastolic (DBP) blood pressure in patients with hypertension. Exercise characteristic is required in order to have optimal benefit from the training, but optimal exercise capacity is still unwarranted. A MEDLINE search (1985 to 2015) was conducted for exercise based rehabilitation trials in hypertensive patients. Thirty-seven studies met the selection criteria. Of these, 31 (83.7%) were aerobic exercise and 6 (16.3%) aerobic with additional resistance exercise, providing a total of 1318 exercise subjects and 819 control, the total of subjects was 2137. We calculated exercise volume and energy expenditure through the description of exercise characteristics. 4 studies (18.2%) were 451kcal - 900 kcal, 12 (54.5%) were 900 kcal – 1350 kcal and 6 (27.3%) >1351kcal per week. Peak oxygen consumption (peak VO2) increased by mean difference of 1.44 ml/kg/min (95% confidence interval [CI]: 1.08 to 1.79 ml/kg/min; p = 0.00001) with weighted mean 21.2% for aerobic exercise compare to aerobic with additional resistance exercise 4.50 ml/kg/min (95% confidence interval [CI]: 3.57 to 5.42 ml/kg/min; p = 0.00001) with weighted mean 14.5%. SBP was clinically reduce for both aerobic and aerobic with resistance training by mean difference of -4.66 mmHg (95% confidence interval [CI]: -5.68 to -3.63 mmHg; p = 0.00001) weighted mean 6% reduction and -5.06 mmHg (95% confidence interval [CI]: -7.32 to -2.8 mmHg; p = 0.0001) weighted mean 5% reduction respectively. Result for DBP was clinically reduce for aerobic by mean difference of -1.62 mmHg (95% confidence interval [CI]: -2.09 to -1.15 mmHg; p = 0.00001) weighted mean 4% reduction and aerobic with resistance training reduce by mean difference of -3.26 mmHg (95% confidence interval [CI]: -4.87 to -1.65 mmHg; p = 0.0001) weighted mean 6% reduction. Optimum exercise capacity for 451 kcal – 900 kcal showed greater improvement in peak VO2 and SBP by 2.76 ml/kg/min (95% confidence interval [CI]: 1.47 to 4.05 ml/kg/min; p = 0.0001) with weighted mean 40.6% and -16.66 mmHg (95% confidence interval [CI]: -21.72 to -11.60 mmHg; p = 0.00001) weighted mean 9.8% respectively. Our data demonstrated that aerobic exercise with total volume of 451 kcal – 900 kcal/ week energy expenditure may elicit greater changes in cardiorespiratory fitness and blood pressure in hypertensive patients. Higher exercise capacity weekly does not seem better result in management hypertensive patients.

Keywords: blood Pressure, exercise, hypertension, peak VO2

Procedia PDF Downloads 282

Authors: Mohd. Khaidir Abu Talib, Noriyuki Yasufuku, Ryohei Ishikura

Abstract:

It is well recognized that peat can impede the proper hydration of cement because of high organic content, presence of humic acid and less solid particles. That means the large amount of cement is required in order to neutralize the acids or otherwise the process of the peat stabilization remains retarded. Nevertheless, adding a great quantity of cement into the peat is absolutely an unfriendly and uneconomical solution. Sugarcane production is world number one commodities and produced a lot of bagasse. Bagasse is burnt to generate power required for diverse activities in the factory and leave bagasse ash as a waste. Increasing concern of disposal of bagasse residual creates interest to explore the potential application of this material. The objective of this study is to develop alternative binders that are environment friendly and contribute towards sustainable management by utilizing sugarcane bagasse ash (SCBA) in the stabilization of peat soil. Alongside SCBA, Ordinary Portland Cement (OPC), calcium chloride (CaCl2) and silica sand (K7) were used as additives to stabilize the peat that sampled from Hokkaido, Japan. In obtaining the optimal mix design, specimens of stabilized peat were tested in unconfined compression. It was found that stabilized peat comprising 20% and 5% (PCB1-20 and PCB2-5) partial replacement of OPC with SCBA 1 and SCBA 2 attain the maximum unconfined compressive strength (UCS) and discovered greater than untreated soil (P) and UCS of peat-cement (PC) specimen. At the optimal mix design, the UCS of the stabilized peat specimens increased with increasing of curing time, preloading during curing, OPC dosage and K7 dosage. For PCB1-20 mixture, inclusion of a minimum OPC dosage of 300 kg/m3 and K7 dosage of 500 kg/m3 along with curing under 20kPa pressure is recommendable for the peat stabilization to be effective. However for PCB2-5 mixture, it suggested to use more OPC and K7 dosage or alternatively increase the preloading during curing to 40kPa in order to achieve minimum strength target. It can be concluded that SCBA 1 has better quality than SCBA 2 in peat stabilization especially the contribution made by its fine particle size.

Keywords: peat stabilization, sugarcane bagasse ash utilization, partial cement replacement, unconfined strength

Procedia PDF Downloads 535

Authors: Zhenyu Zhang, Hsi-Hsien Wei

Abstract:

Highway network systems play a vital role in disaster response for disaster-damaged areas. Damaged bridges in such network systems can impede disaster response by disrupting transportation of rescue teams or humanitarian supplies. Therefore, emergency inspection and repair of bridges to quickly collect damage information of bridges and recover the functionality of highway networks is of paramount importance to disaster response. A widely used measure of a network’s capability to recover from disasters is resilience. To enhance highway network resilience, plenty of studies have developed various repair scheduling methods for the prioritization of bridge-repair tasks. These methods assume that repair activities are performed after the damage to a highway network is fully understood via inspection, although inspecting all bridges in a regional highway network may take days, leading to the significant delay in repairing bridges. In reality, emergency repair activities can be commenced as soon as the damage data of some bridges that are crucial to emergency response are obtained. Given that emergency bridge inspection and repair (EBIR) activities are executed simultaneously in the response phase, the real-time interactions between these activities can occur – the blockage of highways due to repair activities can affect inspection routes which in turn have an impact on emergency repair scheduling by providing real-time information on bridge damages. However, the impact of such interactions on the optimal emergency inspection routes (EIR) and emergency repair schedules (ERS) has not been discussed in prior studies. To overcome the aforementioned deficiencies, this study develops a routing and scheduling model for EBIR while accounting for real-time inspection-repair interactions to maximize highway network resilience. A stochastic, time-dependent integer program is proposed for the complex and real-time interacting EBIR problem given multiple inspection and repair teams at locations as set post-disaster. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. Computational tests are performed using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that the simultaneous implementation of bridge inspection and repair activities can significantly improve the highway network resilience. Moreover, the deployment of inspection and repair teams should match each other, and the network resilience will not be improved once the unilateral increase in inspection teams or repair teams exceeds a certain level. This study contributes to both knowledge and practice. First, the developed mathematical model makes it possible for capturing the impact of real-time inspection-repair interactions on inspection routing and repair scheduling and efficiently deriving optimal EIR and ERS on a large and complex highway network. Moreover, this study contributes to the organizational dimension of highway network resilience by providing optimal strategies for highway bridge management. With the decision support tool, disaster managers are able to identify the most critical bridges for disaster management and make decisions on proper inspection and repair strategies to improve highway network resilience.

Keywords: disaster management, emergency bridge inspection and repair, highway network, resilience, uncertainty

Procedia PDF Downloads 109

Authors: C. H. Chang, R. Z. Qiu, C. W. Tsao, Y. H. Cheng, C. H. Chen, W. J. Hsueh

Abstract:

Characteristics of photoluminescence (PL) in a resonant quasi-periodic double-period quantum wells (DPQW) are demonstrated. The maximum PL intensity in the DPQW is remarkably greater than that in a traditional periodic QW (PQW) under the Bragg or anti-Bragg conditions. The optimal PL spectrum in the DPQW has an asymmetrical form instead of the symmetrical form in the PQW. Moreover, there are two large values of PL intensity in the DPQW, which also differs from the PQW.

Keywords: Photoluminescence, quantum wells, quasiperiodic structure

Procedia PDF Downloads 719

Authors: Kamyar Tolouei, Ehsan Moosavi

Abstract:

In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

Procedia PDF Downloads 105

Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

Abstract:

The PUSPATI TRIGA Reactor (RTP), Malaysia reached its first criticality on June 28, 1982, with power capacity 1MW thermal. The Feedback Control Algorithm (FCA) which is conventional Proportional-Integral (PI) controller, was used for present power control method to control fission process in RTP. It is important to ensure the core power always stable and follows load tracking within acceptable steady-state error and minimum settling time to reach steady-state power. At this time, the system could be considered not well-posed with power tracking performance. However, there is still potential to improve current performance by developing next generation of a novel design nuclear core power control. In this paper, the dual-mode predictions which are proposed in modelling Optimal Model Predictive Control (OMPC), is presented in a state-space model to control the core power. The model for core power control was based on mathematical models of the reactor core, OMPC, and control rods selection algorithm. The mathematical models of the reactor core were based on neutronic models, thermal hydraulic models, and reactivity models. The dual-mode prediction in OMPC for transient and terminal modes was based on the implementation of a Linear Quadratic Regulator (LQR) in designing the core power control. The combination of dual-mode prediction and Lyapunov which deal with summations in cost function over an infinite horizon is intended to eliminate some of the fundamental weaknesses related to MPC. This paper shows the behaviour of OMPC to deal with tracking, regulation problem, disturbance rejection and caters for parameter uncertainty. The comparison of both tracking and regulating performance is analysed between the conventional controller and OMPC by numerical simulations. In conclusion, the proposed OMPC has shown significant performance in load tracking and regulating core power for nuclear reactor with guarantee stabilising in the closed-loop.

Keywords: core power control, dual-mode prediction, load tracking, optimal model predictive control

Procedia PDF Downloads 162

Authors: Miroslav Hyza, Petr Rulik, Vojtech Bednar, Jan Sury

Abstract:

An early and reliable detection of an increased radioactivity level in the atmosphere is one of the key aspects of atmospheric radiological monitoring. Although the standard laboratory procedures provide detection limits as low as few µBq/m³, their major drawback is the delayed result reporting: typically a few days. This issue is the main objective of the HAMRAD project, which gave rise to a prototype of an autonomous monitoring device. It is based on the idea of sequential aerosol sampling using a carrousel sample changer combined with a gamma-ray spectrometer. In our hardware configuration, the air is drawn through a filter positioned on the carrousel so that it could be rotated into the measuring position after a preset sampling interval. Filter analysis is performed via a 50% HPGe detector inside an 8.5cm lead shielding. The spectrometer output signal is then analyzed using DSP electronics and Gamwin software with preset nuclide libraries and other analysis parameters. After the counting, the filter is placed into a storage bin with a capacity of 250 filters so that the device can run autonomously for several months depending on the preset sampling frequency. The device is connected to a central server via GPRS/GSM where the user can view monitoring data including raw spectra and technological data describing the state of the device. All operating parameters can be remotely adjusted through a simple GUI. The flow rate is continuously adjustable up to 10 m³/h. The main challenge in spectrum analysis is the natural background subtraction. As detection limits are heavily influenced by the deposited activity of radon decay products and the measurement time is fixed, there must exist an optimal sample decay time (delayed spectrum acquisition). To solve this problem, we adopted a simple procedure based on sequential spectrum acquisition and optimal partial spectral sum with respect to the detection limits for a particular radionuclide. The prototyped device proved to be able to detect atmospheric contamination at the level of mBq/m³ per an 8h sampling.

Keywords: aerosols, atmosphere, atmospheric radioactivity monitoring, autonomous sampler

Procedia PDF Downloads 148

Authors: Rachid Ziteuni, Selt Omar

Abstract:

In this paper, we propose a new polynomial metaheuristic elaboration (tabu search) for solving scheduling problems. This method allows us to solve the scheduling problem of n tasks on m identical parallel machines with unavailability periods. This problem is NP-complete in the strong sens and finding an optimal solution appears unlikely. Note that all data in this problem are integer and deterministic. The performance criterion to optimize in this problem which we denote Pm/N-c/summs of (wjCj) is the weighted sum of the end dates of tasks.

Keywords: scheduling, parallel identical machines, unavailability periods, metaheuristic, tabu search

Procedia PDF Downloads 331

Authors: J. Ritonja

Abstract:

Available commercial applications of power system stabilizers assure optimal damping of synchronous generator’s oscillations only in a small part of operating range. Parameters of the power system stabilizer are usually tuned for the selected operating point. Extensive variations of the synchronous generator’s operation result in changed dynamic characteristics. This is the reason that the power system stabilizer tuned for the nominal operating point does not satisfy preferred damping in the overall operation area. The small-signal stability and the transient stability of the synchronous generators have represented an attractive problem for testing different concepts of the modern control theory. Of all the methods, the adaptive control has proved to be the most suitable for the design of the power system stabilizers. The adaptive control has been used in order to assure the optimal damping through the entire synchronous generator’s operating range. The use of the adaptive control is possible because the loading variations and consequently the variations of the synchronous generator’s dynamic characteristics are, in most cases, essentially slower than the adaptation mechanism. The paper shows the development and the application of the self-tuning power system stabilizer based on recursive least square identification method and linear quadratic regulator. Identification method is used to calculate the parameters of the Heffron-Phillips model of the synchronous generator. On the basis of the calculated parameters of the synchronous generator’s mathematical model, the synthesis of the linear quadratic regulator is carried-out. The identification and the synthesis are implemented on-line. In this way, the self-tuning power system stabilizer adapts to the different operating conditions. A purpose of this paper is to contribute to development of the more effective power system stabilizers, which would replace currently used linear stabilizers. The presented self-tuning power system stabilizer makes the tuning of the controller parameters easier and assures damping improvement in the complete operating range. The results of simulations and experiments show essential improvement of the synchronous generator’s damping and power system stability.

Keywords: adaptive control, linear quadratic regulator, power system stabilizer, recursive least square identification

Procedia PDF Downloads 247

Authors: Liping Qiu, Xiaofeng Zhang

Abstract:

The ion chromatography method for the rapid determination of four anions (F⁻、Cl⁻、SO₄²⁻、NO₃⁻) in burial ground poles was optimized. The L₉(₃⁴) orthogonal test was used to determine the optimal parameters of sample pretreatment: accurately weigh 2.000g of sample, add 10mL of ultrapure water, and extract for 40min under the conditions of shaking temperature 40℃ and shaking speed 180 r·min-1. The eluent was 25 mmol/L KOH solution, the analytical column was Ion Pac® AS11-SH (250 mm × 4.0 mm), and the purified filtrate was measured by a conductivity detector. Under this method, the detection limit of each ion is 0.066～0.078mg/kg, the relative standard deviation is 0.86%～2.44% (n=7), and the recovery rate is 94.6～101.9.

Keywords: ion chromatography, tomb, anion (F⁻, Cl⁻, SO₄²⁻, NO₃⁻), environmental protection

Procedia PDF Downloads 102

Authors: M. Derraz, M.Farhaoui

Abstract:

In the water treatment processes, the determination of the optimal dose of the coagulant is an issue of particular concern. Coagulant dosing is correlated to raw water quality which depends on some parameters (turbidity, ph, temperature, conductivity…). The objective of this study is to provide water treatment operators with a tool that enables to predict and replace, sometimes, the manual method (jar testing) used in this plant to predict the optimum coagulant dose. The model is constructed using actual process data for a water treatment plant located in the middle of Morocco (Meknes).

Keywords: coagulation process, aluminum sulfate, model, coagulant dose

Procedia PDF Downloads 277

Authors: Azam Zare

Abstract:

Separation flow control for performance enhancement over airfoils at high incidence angle has become an increasingly important topic. This work details the characteristics of an efficient feedback control of the turbulent subsonic flow over NACA23012 airfoil using forced reduced‐order model based on the proper orthogonal decomposition/Galerkin projection and perturbation method on the compressible Reynolds Averaged Navier‐Stokes equations. The forced reduced‐order model is used in the optimal control of the turbulent separated flow over a NACA23012 airfoil at Mach number of 0.2, Reynolds number of 5×106, and high incidence angle of 24° using blowing/suction controlling jets. The Spallart-Almaras turbulence model is implemented for high Reynolds number calculations. The main shortcoming of the POD/Galerkin projection on flow equations for controlling purposes is that the blowing/suction controlling jet velocity does not show up explicitly in the resulting reduced order model. Combining perturbation method and POD/Galerkin projection on flow equations introduce a forced reduced‐order model that can predict the time-varying influence of the blowing/suction controlling jet velocity. An optimal control theory based on forced reduced‐order system is used to design a control law for a nonlinear reduced‐order model, which attempts to minimize the vorticity content in the turbulent flow field over NACA23012 airfoil. Numerical simulations were performed to help understand the behavior of the controlled suction jet at 12% to 18% chord from leading edge and a pair of blowing/suction jets at 15% to 18% and 24% to 30% chord from leading edge, respectively. Analysis of streamline profiles indicates that the blowing/suction jets are efficient in removing separation bubbles and increasing the lift coefficient up to 22%, while the perturbation method can predict the flow field in an accurate Manner.

Keywords: flow control, POD, Galerkin projection, separation

Procedia PDF Downloads 149

Authors: Mustapha Kamel Mihoubi, Hocine Dahmani

Abstract:

The purpose of work is to study the phenomenon of agitation of storm waves at basin caused by different directions of waves relative to the current provision thrown numerical model based on the equation in shallow water using Boussinesq model MIKE 21 BW. According to the diminishing effect of penetration of a wave optimal solution will be available to be reproduced in reduced model. Another alternative arrangement throws will be proposed to reduce the agitation and the effects of the swell reflection caused by the penetration of waves in the harbor.

Keywords: agitation, Boussinesq equations, combination, harbor

Procedia PDF Downloads 389

Authors: Anna Avramenko, Heikki Haario

Abstract:

This paper reports the development and application of a 2D depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. K-E and 2D LES turbulence models were consider in this article. 2D CFD simulations for one hill was done to check the depth-averaged model in practise.

Keywords: depth-averaged equations, numerical modeling, CFD, wind park model

Procedia PDF Downloads 603

Authors: Mohanad Al-Behadili, Xiang Song, Djamila Ouelhadj, Alex Fraess-Ehrfeld

Abstract:

In off-shore wind farms, turbine blade inspection accessibility under various sea states is very challenging and greatly affects the downtime of wind turbines. Maintenance of any offshore system is not an easy task due to the restricted logistics and accessibility. The multirotor unmanned helicopter is of increasing interest in inspection applications due to its manoeuvrability and payload capacity. These advantages increase when many of them are deployed simultaneously in a swarm. Hence this paper proposes a drone swarm framework for inspecting offshore wind turbine blades and nacelles so as to reduce downtime. One of the big challenges of this task is that when operating a drone swarm, an individual drone may not have enough power to fly and communicate during missions and it has no capability of refueling due to its small size. Once the drone power is drained, there are no signals transmitted and the links become intermittent. Vessels equipped with 5G masts and small power units are utilised as platforms for drones to recharge/swap batteries. The research work aims at designing a smart energy management system, which provides automated vessel and drone routing and recharging plans. To achieve this goal, a novel mathematical optimisation model is developed with the main objective of minimising the number of drones and vessels, which carry the charging stations, and the downtime of the wind turbines. There are a number of constraints to be considered, such as each wind turbine must be inspected once and only once by one drone; each drone can inspect at most one wind turbine after recharging, then fly back to the charging station; collision should be avoided during the drone flying; all wind turbines in the wind farm should be inspected within the given time window. We have developed a real-time Ant Colony Optimisation (ACO) algorithm to generate real-time and near-optimal solutions to the drone swarm routing problem. The schedule will generate efficient and real-time solutions to indicate the inspection tasks, time windows, and the optimal routes of the drones to access the turbines. Experiments are conducted to evaluate the quality of the solutions generated by ACO.

Keywords: drone swarm, routing, scheduling, optimisation model, ant colony optimisation

Procedia PDF Downloads 264

Authors: Juan M. Rodriguez-Diaz

Abstract:

The precision of parameter estimators in the Gaussian linear model is traditionally accounted by the variance-covariance matrix of the asymptotic distribution. However, this measure can underestimate the true variance, specially for small samples. Traditionally, optimal design theory pays attention to this variance through its relationship with the model's information matrix. For this reason it seems convenient, at least in some cases, adapt the optimality criteria in order to get the best designs for the actual variance structure, otherwise the loss in efficiency of the designs obtained with the traditional approach may be very important.

Keywords: correlated observations, information matrix, optimality criteria, variance-covariance matrix

Procedia PDF Downloads 443

Authors: Pejman Hosseiniun, Rose Shayeghi, Alireza Farzaneh, Abolghasem Ghasempour

Abstract:

Using LED lamps as lighting resources with new technology in designing lighting systems has been studied in this article. In this respect a history of LED emergence, its different manufacturing methods and technologies were revised, then their structure, light production line, its application and benefits in lighting industry has been evaluated. Finally, there is a comparison between these lamps and ordinary lamps to assess light parameters as well as energy consumption using DIALux software. Considering the results of analogies LED lamps have lower consumption and more lighting yield, therefore they are more economically feasible. Color variety, longer usage lap (circa 10 years) and compatibility with DC voltages are other LED lamps perquisites.

Keywords: LED, lighting efficiency, lighting intensity, luminance

Procedia PDF Downloads 594

Authors: Robert Lenzie

Abstract:

Transcranial electrical stimulation (tES) is significant in the research literature. However, the effects of tES on brain activity are still poorly understood at the surface level, the Brodmann Area level, and the impact on neural networks. Using a method like electroencephalography (EEG) in conjunction with tES might make it possible to comprehend the brain response and mechanisms behind published observed alterations in more depth. Using a method to directly see the effect of tES on EEG may offer high temporal resolution data on the brain activity changes/modulations brought on by tES that correlate to various processing stages within the brain. This paper provides unpublished information on a cutting-edge methodology that may reveal details about the dynamics of how the human brain works beyond what is now achievable with existing methods.

Keywords: tACS, frequency, EEG, optimal

Procedia PDF Downloads 82

Authors: H. Al-Qassem, L. Cheng, Y. Pan

Abstract:

In this paper, we establish sharp bounds for oscillatory singular integrals with an arbitrary real polynomial phase P. Our kernels are allowed to be rough both on the unit sphere and in the radial direction. We show that the bounds grow no faster than log (deg(P)), which is optimal and was first obtained by Parissis and Papadimitrakis for kernels without any radial roughness. Our results substantially improve many previously known results. Among key ingredients of our methods are an L¹→L² sharp estimate and using extrapolation.

Keywords: oscillatory singular integral, rough kernel, singular integral, orlicz spaces, block spaces, extrapolation, L^{p} boundedness

Procedia PDF Downloads 456

Authors: Alexis Winfield, Carly Sugar, Barbara Fenesi

Abstract:

The COVID-19 pandemic uprooted regular routines forcing many children to learn from home, requiring many adults to work from home, and cutting families off from support outside the home. Public health restrictions associated with the pandemic caused widespread psychological distress, including depression and anxiety, increased fear, panic, and stress. These trends are particularly concerning for families raising neuroatypical children, such as those with Attention-Deficit Hyperactivity Disorder (ADHD), as these children are already more likely than their typically developing peers to experience comorbid mental health issues and to experience greater distress when required to stay indoors. Families with children who have ADHD are also at greater risk for experiencing heightened familial stress due to the challenges associated with managing ADHD behavioural symptoms, greater parental discord and divorce, and greater financial difficulties compared to other families. The current study engaged families comprised of at least one child diagnosed with ADHD to elucidate 1) the unique ways that the COVID-19 pandemic affected their mental health and 2) the specific barriers these families faced to maintaining optimal mental wellbeing. A total of 33 participants (15 parent-child dyads) engaged in virtual interviews. Content analysis revealed that the most frequently identified mental health effects for families were increased child anxiety and disconnectedness, as well as deteriorating parental mental health. The most frequently identified barriers to maintaining optimal mental well-being were lack of routine, lack of social interaction and social support, and uncertainty and fear. Findings underscore areas of need during times of large-scale social isolation, bring voice to the families of children with ADHD, and contribute to our understanding of the pandemic’s impact on the wellbeing of vulnerable families. This work contributes to a growing body of research aimed at creating safeguards to support mental wellbeing for vulnerable families during times of crisis.

Keywords: attention-deficit hyperactivity disorder, COVID-19, mental health, vulnerable families

Procedia PDF Downloads 289

Authors: Jae-Hyun Ro, Yong-Jun Kim, Chang-Bin Ha, Hyoung-Kyu Song

Abstract:

In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection.

Keywords: detection, DFE, MIMO-OFDM, ML

Procedia PDF Downloads 610

Authors: Hao Yan, Xiaobing Zhang

Abstract:

The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

Procedia PDF Downloads 90

Authors: Young-Min Ko, Dong-Hyun Ha, Chang-Bin Ha, Hyoung-Kyu Song

Abstract:

In recent years, a multi-antenna system is actively used to improve the performance of the communication. A MIMO-OFDM system can provide multiplexing gain or diversity gain. These gains are obtained in proportion to the increase of the number of antennas. In order to provide the optimal gain of the MIMO-OFDM system, various transmission and reception schemes are presented. This paper aims to propose a hybrid scheme that base station provides both diversity gain and multiplexing gain at the same time.

Keywords: DFE, diversity gain, hybrid, MIMO, multiplexing gain.

Procedia PDF Downloads 685

Authors: Christian Skowronski, Andrew Shanholtzer, Brent Yelton, Muayad Almahariq, Daniel J. Krauss

Abstract:

Prostate cancer has the third highest incidence rate and is the second leading cause of cancer death for men in the United States. Of the diagnostic tools available for intermediate-risk prostate cancer, magnetic resonance imaging (MRI) provides superior soft tissue delineation serving as a valuable tool for both diagnosis and treatment planning. Currently, there is minimal data regarding the practical utility of MRI for evaluation of intermediate-risk prostate cancer. As such, the National Comprehensive Cancer Network’s guidelines indicate MRI as optional in intermediate-risk prostate cancer evaluation. This project aims to elucidate whether MRI affects radiation treatment decisions for intermediate-risk prostate cancer. This was a retrospective study evaluating 210 patients with intermediate-risk prostate cancer, treated with definitive radiotherapy at our institution between 2019-2020. NCCN risk stratification criteria were used to define intermediate-risk prostate cancer. Patients were divided into two groups: those with pretreatment prostate MRI, and those without pretreatment prostate MRI. We compared the use of external beam radiotherapy, brachytherapy alone, brachytherapy boost, and androgen depravation therapy between the two groups. Inverse probability of treatment weighting was used to match the two groups for age, comorbidity index, American Urologic Association symptoms index, pretreatment PSA, grade group, and percent core involvement on prostate biopsy. Wilcoxon Rank Sum and Chi-squared tests were used to compare continuous and categorical variables. Of the patients who met the study’s eligibility criteria, 133 had a prostate MRI and 77 did not. Following propensity matching, there were no differences between baseline characteristics between the two groups. There were no statistically significant differences in treatments pursued between the two groups: 42% vs 47% were treated with brachytherapy alone, 40% vs 42% were treated with external beam radiotherapy alone, 18% vs 12% were treated with external beam radiotherapy with a brachytherapy boost, and 24% vs 17% received androgen deprivation therapy in the non-MRI and MRI groups, respectively. This analysis suggests that pretreatment MRI does not significantly impact radiation therapy or androgen deprivation therapy decisions in patients with intermediate-risk prostate cancer. Obtaining a pretreatment prostate MRI should be used judiciously and pursued only to answer a specific question, for which the answer is likely to impact treatment decision. Further follow up is needed to correlate MRI findings with their impacts on specific oncologic outcomes.

Keywords: magnetic resonance imaging, prostate cancer, definitive radiotherapy, gleason score 7

Procedia PDF Downloads 89

Authors: Peng Liu

Abstract:

Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework.

Keywords: deep reinforcement learning, deep Q networks, portfolio optimization, multi-period optimization

Procedia PDF Downloads 32

Authors: Iman Farasat, Howard M. Salis

Abstract:

The CRISPR/Cas9 system has revolutionized genome engineering by enabling site-directed and high-throughput genome editing, genome insertion, and gene knockdowns in several species, including bacteria, yeast, flies, worms, and human cell lines. This technology has the potential to enable human gene therapy to treat genetic diseases and cancer at the molecular level; however, the current CRISPR/Cas9 system suffers from seemingly sporadic off-target genome mutagenesis that prevents its use in gene therapy. A comprehensive mechanistic model that explains how the CRISPR/Cas9 functions would enable the rational design of the guide-RNAs responsible for target site selection while minimizing unexpected genome mutagenesis. Here, we present the first quantitative model of the CRISPR/Cas9 genome mutagenesis system that predicts how guide-RNA sequences (crRNAs) control target site selection and cleavage activity. We used statistical thermodynamics and law of mass action to develop a five-step biophysical model of cas9 cleavage, and examined it in vivo and in vitro. To predict a crRNA's binding specificities and cleavage rates, we then compiled a nearest neighbor (NN) energy model that accounts for all possible base pairings and mismatches between the crRNA and the possible genomic DNA sites. These calculations correctly predicted crRNA specificity across 5518 sites. Our analysis reveals that cas9 activity and specificity are anti-correlated, and, the trade-off between them is the determining factor in performing an RNA-mediated cleavage with minimal off-targets. To find an optimal solution, we first created a scheme of safe-design criteria for Cas9 target selection by systematic analysis of available high throughput measurements. We then used our biophysical model to determine the optimal Cas9 expression levels and timing that maximizes on-target cleavage and minimizes off-target activity. We successfully applied this approach in bacterial and mammalian cell lines to reduce off-target activity to near background mutagenesis level while maintaining high on-target cleavage rate.

Keywords: biophysical model, CRISPR, Cas9, genome editing

Procedia PDF Downloads 406

Authors: Jin Liang, James H. Liu, Ti-Jun Xiao

Abstract:

It is known that there exist many physical phenomena where abrupt or impulsive changes occur either in the system dynamics, for example, ad-hoc network, or in the input forces containing impacts, for example, the bombardment of space antenna by micrometeorites. There are many other examples such as ultra high-speed optical signals over communication networks, the collision of particles, inventory control, government decisions, interest changes, changes in stock price, etc. These are impulsive phenomena. Hence, as a combination of the traditional initial value problems and the short-term perturbations whose duration can be negligible in comparison with the duration of the process, the systems with impulsive conditions (i.e., impulsive systems) are more realistic models for describing the impulsive phenomenon. Such a situation is also suitable for the delay systems, which include some of the past states of the system. So far, there have been a lot of research results in the study of impulsive systems with delay both in finite and infinite dimensional spaces. In this paper, we investigate the periodicity of solutions to the nonautonomous impulsive evolution equations with infinite delay in Banach spaces, where the coefficient operators (possibly unbounded) in the linear part depend on the time, which are impulsive systems in infinite dimensional spaces and come from the optimal control theory. It was indicated that the study of periodic solutions for these impulsive evolution equations with infinite delay was challenging because the fixed point theorems requiring some compactness conditions are not applicable to them due to the impulsive condition and the infinite delay. We are happy to report that after detailed analysis, we are able to combine the techniques developed in our previous papers, and some new ideas in this paper, to attack these impulsive evolution equations and derive periodic solutions. More specifically, by virtue of the related transition operator family (evolution family), we present a Poincaré operator given by the nonautonomous impulsive evolution system with infinite delay, and then show that the operator is a condensing operator with respect to Kuratowski's measure of non-compactness in a phase space by using an Amann's lemma. Finally, we derive periodic solutions from bounded solutions in view of the Sadovskii fixed point theorem. We also present a relationship between the boundedness and the periodicity of the solutions of the nonautonomous impulsive evolution system. The new results obtained here extend some earlier results in this area for evolution equations without impulsive conditions or without infinite delay.

Keywords: impulsive, nonautonomous evolution equation, optimal control, periodic solution

Procedia PDF Downloads 252

Authors: Victor Bloch, Eerikki Kaila, Reetta Palva

Abstract:

Field robots are an important tool for enhancing efficiency and decreasing the climatic impact of food production. There exists a number of commercial field robots; however, since this technology is still new, the robot advantages and limitations, as well as methods for optimal using of robots, are still unclear. In this study, the performance of a commercial field robot for seeding and weeding was assessed. A research 2-ha sugar beet field with 0.5m row width was used for testing, which included robotic sowing of sugar beet and weeding five times during the first two months of the growing. About three and five percent of the field were used as untreated and chemically weeded control areas, respectively. The plant detection was based on the exact plant location without image processing. The robot was equipped with six seeding and weeding tools, including passive between-rows harrow hoes and active hoes cutting inside rows between the plants, and it moved with a maximal speed of 0.9 km/h. The robot's performance was assessed by image processing. The field images were collected by an action camera with a height of 2 m and a resolution 27M pixels installed on the robot and by a drone with a 16M pixel camera flying at 4 m height. To detect plants and weeds, the YOLO model was trained with transfer learning from two available datasets. A preliminary analysis of the entire field showed that in the areas treated by the robot, the weed average density varied across the field from 6.8 to 9.1 weeds/m² (compared with 0.8 in the chemically treated area and 24.3 in the untreated area), the weed average density inside rows was 2.0-2.9 weeds / m (compared with 0 on the chemically treated area), and the emergence rate was 90-95%. The information about the robot's performance has high importance for the application of robotics for field tasks. With the help of the developed method, the performance can be assessed several times during the growth according to the robotic weeding frequency. When it’s used by farmers, they can know the field condition and efficiency of the robotic treatment all over the field. Farmers and researchers could develop optimal strategies for using the robot, such as seeding and weeding timing, robot settings, and plant and field parameters and geometry. The robot producers can have quantitative information from an actual working environment and improve the robots accordingly.

Keywords: agricultural robot, field robot, plant detection, robot performance

Procedia PDF Downloads 87