Search results for: interval estimation

Authors: Wirat Sonchan, Pratoom Moungmee, Anek Sootmongkol

Abstract:

This study aimed to examine the effects of a circuit training program on muscle strength, agility, anaerobic performance and cardiovascular endurance. The study involved 24 freshmen (age 18.87+0.68 yr.) male students of the Faculty of Sport Science, Burapha University. They sample study were randomly divided into two groups: Circuit Training group (CT; n=12) and a Control group (C; n=12). Baseline data on height, weight, muscle strength (hand grip dynamometer and leg strength dynamometer), agility (agility T-Test), and anaerobic performance (Running-based Anaerobic Sprint Test) and cardiovascular endurance (20 m Endurance Shuttle Run Test) were collected. The circuit training program included one circuit of eight stations of 30/60 seconds of work/rest interval with two cycles in Week 1-4, and 60/90 seconds of work/rest interval with three cycles in Week 5-8, performed three times per week. Data were analyzed using paired t-tests and independent sample t-test. Statistically significance level was set at 0.05. The results show that after 8 weeks of a training program, muscle strength, agility, anaerobic capacity and cardiovascular endurance increased significantly in the CT Group (p < 0.05), while significant increase was not observed in the C Group (p < 0.05). The results of this study suggest that the circuit training program improved muscle strength, agility, anaerobic capacity and cardiovascular endurance of the study subjects. This program may be used as a guideline for selecting a set of exercise to improve physical fitness.

Keywords: circuit training, physical fitness, cardiovascular endurance, anaerobic performance

Procedia PDF Downloads 493

Authors: Falana Yetunde Olaitan, Ijah U. J. J, Solebo Shakirat O.

Abstract:

Genetically modified crops are already phenomenally successful and are grown worldwide in more than eighteen countries on more than 67 million hectares. Nigeria, in October 2018, approved Bacillus thuringiensis (Bt) cotton and maize; therefore, the need to carry out environmental risk assessment studies. A total of 15 4L octagonal ceramic pots were filled with 4kg of soil and placed on the bench in 2 rows of 10 pots each and the 3rd row of 5 pots, 1st-row pots were used to plant GM cotton seeds, while the 2nd-row pots were used for non-GM cotton seeds and the 3rd row of 5 pots served as control, all in the screen house. Soil samples for metagenomic DNA extraction were collected at random and at the monthly interval after planting at a distance of 2mm from the plant’s root and at a depth of 10cm using a sterile spatula. Soil samples for physicochemical analysis were collected before planting and after harvesting the GM and non-GM crops as well as from the control soil. The DNA was extracted, quantified and sequenced; Sample 1A (DNA from GM cotton Soil at 1st interval) gave the lowest sequence read with 0.853M while sample 2B (DNA from GM cotton Soil at 2nd interval) gave the highest with 5.785M, others gave between 1.8M and 4.7M. The samples treatment were grouped into four, Group 1 (GM cotton soil from 1 to 3 intervals) had between 800,000 and 5,700,000 strains of microbes (SOM), Group 2 (non GM cotton soil from 1 to 3 intervals) had between 1,400,600 and 4,200,000 SOM, Group 3 (control soil) had between 900,000 and 3,600,000 SOM and Group 4 (initial soil) had between 3,700,000 and 4,000,000 SOM. The microbes observed were predominantly bacteria (including archaea), fungi, dark matter alongside protists and phages. The predominant bacterial groups were the Terrabacteria (Bacillus funiculus, Bacillus sp.), the Proteobacteria (Microvirga massiliensis, sphingomonas sp.) and the Archaea (Nitrososphaera sp.), while the fungi were Aspergillus fischeri and Fusarium falciforme. The comparative analysis between groups was done using JACCARD PERMANOVA beta diversity analysis at P-value not more than 0.76 and there was no significant pair found. The pH for initial, GM cotton, non-GM cotton and control soil were 6.28, 6.26, 7.25, 8.26 and the percentage moisture was 0.63, 0.78, 0.89 and 0.82, respectively, while the percentage Nitrogen was observed to be 17.79, 1.14, 1.10 and 0.56 respectively. Other parameters include, varying concentrations of Potassium (0.46, 1,284.47, 1,785.48, 1,252.83 mg/kg) and Phosphorus (18.76, 17.76, 16.87, 15.23 mg/kg) were recorded for the four treatments respectively. The soil consisted mainly of silt (32.09 to 34.66%) and clay (58.89 to 60.23%), reflecting the soil texture as silty – clay. The results were then tested with ANOVA at less than 0.05 P-value and no pair was found to be significant as well. The results suggest that the GM crops have no significant effect on microbial ecology and physicochemical properties of the soil and, in turn, no direct or indirect effects on human health.

Keywords: genetically modified crop, microbial ecology, physicochemical properties, metagenomics, DNA, soil

Procedia PDF Downloads 145

Authors: Xin Yuan, Yuji Ryu

Abstract:

The indoor environment has a significant impact on occupants and a suitable indoor thermal environment can improve the children’s physical health and study efficiency during school hours. In this study, we explored the thermal environment in infant facilities classrooms for infants and children aged 1-5 and evaluated their thermal comfort. An infant facility in Fukuoka, Japan was selected for a case study to capture the infant and children’s thermal comfort characteristics in summer and winter from August 2019 to February 2020. Previous studies have pointed out using PMV indices to evaluate the thermal comfort for children could create errors that may lead to misleading results. Thus, to grasp the actual thermal environment and thermal comfort characteristics of infants and children, we retrieved the operative temperature of each child through the thermal model, based on the sensible heat transfer from the skin to the environment, and the measured classroom indoor temperature, relative humidity, and pocket temperature of children’s shorts. The statistical and comparative analysis of the results shows that (1) the operative temperature showed a large individual difference among children, with the maximum reached 6.25 °C. (2) The children might feel slightly cold in the classrooms in summer, with the frequencies of operative temperature within the interval of 26-28 ºC were only 5.33% and 16.6% for children respectively. (3) The thermal environment around children is more complicated in winter the operative temperature could exceed or fail to reach the thermal comfort temperature zone (20-23 ºC interval). (4) The environmental conditions surrounding the children may account for the reduction of their thermal comfort. The findings contribute to improving the understanding of the infant and children’s thermal comfort and provide valuable information for designers and governments to develop effective strategies for the indoor thermal environment considering the perspective of children.

Keywords: infant and children, thermal environment, thermal model, operative temperature.

Procedia PDF Downloads 119

Authors: Ashish James, Anusha James

Abstract:

The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

Procedia PDF Downloads 135

Authors: Jesika A. Walker, Mohammed Aswad, Guy Lacroix, Denis Cousineau

Abstract:

There is a fundamental disconnect between the experience of time passing and the chronometric units by which time is quantified. Specifically, there appears to be no relationship between the passage of time judgments (PoTJs) and verbal duration estimates at short durations (e.g., < 2000 milliseconds). When a duration is longer than several minutes, however, evidence suggests that a slower feeling of time passing is predictive of overestimation. Might the length of a task moderate the relation between PoTJs and duration estimates? Similarly, the estimation paradigm (prospective vs. retrospective) and the mental effort demanded of a task (task demand) have both been found to influence duration estimates. However, only a handful of experiments have investigated these effects for tasks of long durations, and the results have been mixed. Thus, might the length of a task also moderate the effects of the estimation paradigm and task demand on duration estimates? To investigate these questions, 273 participants performed either an easy or difficult visual and memory search task for either eight or 58 minutes, under prospective or retrospective instructions. Afterward, participants provided a duration estimate in minutes, followed by a PoTJ on a Likert scale (1 = very slow, 7 = very fast). A 2 (prospective vs. retrospective) × 2 (eight minutes vs. 58 minutes) × 2 (high vs. low difficulty) between-subjects ANOVA revealed a two-way interaction between task demand and task duration on PoTJs, p = .02. Specifically, time felt faster in the more challenging task, but only in the eight-minute condition, p < .01. Duration estimates were transformed into RATIOs (estimate/actual duration) to standardize estimates across durations. An ANOVA revealed a two-way interaction between estimation paradigm and task duration, p = .03. Specifically, participants overestimated the task more if they were given prospective instructions, but only in the eight-minute task. Surprisingly, there was no effect of task difficulty on duration estimates. Thus, the demands of a task may influence ‘feeling of time’ and ‘estimation time’ differently, contributing to the existing theory that these two forms of time judgement rely on separate underlying cognitive mechanisms. Finally, a significant main effect of task duration was found for both PoTJs and duration estimates (ps < .001). Participants underestimated the 58-minute task (m = 42.5 minutes) and overestimated the eight-minute task (m = 10.7 minutes). Yet, they reported the 58-minute task as passing significantly slower on a Likert scale (m = 2.5) compared to the eight-minute task (m = 4.1). In fact, a significant correlation was found between PoTJ and duration estimation (r = .27, p <.001). This experiment thus provides evidence for a compensatory effect at longer durations, in which people underestimate a ‘slow feeling condition and overestimate a ‘fast feeling condition. The results are discussed in relation to heuristics that might alter the relationship between these two variables when conditions range from several minutes up to almost an hour.

Keywords: duration estimates, long durations, passage of time judgements, task demands

Procedia PDF Downloads 130

Authors: Rashid Ahmed , John N. Avaritsiotis

Abstract:

Many signal subspace-based approaches have already been proposed for determining the fixed Direction of Arrival (DOA) of plane waves impinging on an array of sensors. Two procedures for DOA estimation based neural networks are presented. First, Principal Component Analysis (PCA) is employed to extract the maximum eigenvalue and eigenvector from signal subspace to estimate DOA. Second, minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will modify a Minor Component Analysis (MCA(R)) learning algorithm to enhance the convergence, where a convergence is essential for MCA algorithm towards practical applications. The learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Preliminary results will be furnished to illustrate the convergences results achieved.

Keywords: Direction of Arrival, neural networks, Principle Component Analysis, Minor Component Analysis

Procedia PDF Downloads 451

Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

Abstract:

In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

Procedia PDF Downloads 252

Authors: Chhabi Nigam, S. Ramakrishnan

Abstract:

This paper brings out the analysis of the airborne Synthetic Aperture Radar (SAR) data to study the impact of Doppler centroid on Image quality and geocoding accuracy from the perspective of Stripmap mode of data acquisition. Although in Stripmap mode of data acquisition radar beam points at 90 degrees broad side (side looking), shift in the Doppler centroid is invariable due to platform motion. In-accurate estimation of Doppler centroid leads to poor image quality and image miss-registration. The effect of Doppler centroid is analyzed in this paper using multiple sets of data collected from airborne platform. Occurrences of ghost (ambiguous) targets and their power levels have been analyzed that impacts appropriate choice of PRF. Effect of aircraft attitudes (roll, pitch and yaw) on the Doppler centroid is also analyzed with the collected data sets. Various stages of the RDA (Range Doppler Algorithm) algorithm used for image formation in Stripmap mode, range compression, Doppler centroid estimation, azimuth compression, range cell migration correction are analyzed to find the performance limits and the dependence of the imaging geometry on the final image. The ability of Doppler centroid estimation to enhance the imaging accuracy for registration are also illustrated in this paper. The paper also tries to bring out the processing of low squint SAR data, the challenges and the performance limits imposed by the imaging geometry and the platform dynamics on the final image quality metrics. Finally, the effect on various terrain types, including land, water and bright scatters is also presented.

Keywords: ambiguous target, Doppler Centroid, image registration, Airborne SAR

Procedia PDF Downloads 218

Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Aleksandra Tepic-Horecki, Zdravko Sumic

Abstract:

In this paper, the influence of six different phytochemical content (phenols, carotenoids, chlorophyll a, chlorophyll b, chlorophyll a + b and vitamin C) on antioxidant potential of Murai and Levistro lettuce varieties was evaluated. Variable selection was made by generalized pair correlation method (GPCM) as a novel ranking method. This method is used for the discrimination between two variables that almost equal correlate to a dependent variable. Fisher’s conditional exact and McNemar’s test were carried out. Established multiple linear (MLR) models were statistically evaluated. As the best phytochemicals for the antioxidant potential prediction, chlorophyll a, chlorophyll a + b and total carotenoids content stand out. This was confirmed through both GPCM and MLR, predictive ability of obtained MLR can be used for antioxidant potential estimation for similar lettuce samples. This article is based upon work from the project of the Provincial Secretariat for Science and Technological Development of Vojvodina (No. 114-451-347/2015-02).

Keywords: antioxidant activity, generalized pair correlation method, lettuce, regression analysis

Procedia PDF Downloads 387

Authors: Rania Jradi, Christophe Marvillet, Mohamed Razak Jeday

Abstract:

One of the most frequently encountered problems in industrial heat exchangers is fouling, which degrades the thermal and hydraulic performances of these types of equipment, leading thus to failure if undetected. And it occurs due to the accumulation of undesired material on the heat transfer surface. So, it is necessary to know about the heat exchanger fouling dynamics to plan mitigation strategies, ensuring a sustainable and safe operation. This paper proposes an Artificial Neural Network (ANN) approach to estimate the fouling resistance in a cross-flow heat exchanger by the collection of the operating data of the phosphoric acid concentration loop. The operating data of 361 was used to validate the proposed model. The ANN attains AARD= 0.048%, MSE= 1.811x10⁻¹¹, RMSE= 4.256x 10⁻⁶ and r²=99.5 % of accuracy which confirms that it is a credible and valuable approach for industrialists and technologists who are faced with the drawbacks of fouling in heat exchangers.

Keywords: cross-flow heat exchanger, fouling, estimation, phosphoric acid concentration loop, artificial neural network approach

Procedia PDF Downloads 198

Authors: Ke Zhang, Yongli Zhu

Abstract:

Due to rapid load growths in today’s highly electrified societies and the requirement for green energy sources, large-scale wind farm power transmission system is constantly developing. This system is a typical multi-terminal power supply system, whose structure of the network topology of transmission lines is complex. What’s more, it locates in the complex terrain of mountains and grasslands, thus increasing the possibility of transmission line faults and finding the fault location with difficulty after the faults and resulting in an extremely serious phenomenon of abandoning the wind. In order to solve these problems, a fault location method for multi-terminal transmission line based on wind farm characteristics and improved single-ended traveling wave positioning method is proposed. Through studying the zero sequence current characteristics by using the characteristics of the grounding transformer(GT) in the existing large-scale wind farms, it is obtained that the criterion for judging the fault interval of the multi-terminal transmission line. When a ground short-circuit fault occurs, there is only zero sequence current on the path between GT and the fault point. Therefore, the interval where the fault point exists is obtained by determining the path of the zero sequence current. After determining the fault interval, The location of the short-circuit fault point is calculated by the traveling wave method. However, this article uses an improved traveling wave method. It makes the positioning accuracy more accurate by combining the single-ended traveling wave method with double-ended electrical data. What’s more, a method of calculating the traveling wave velocity is deduced according to the above improvements (it is the actual wave velocity in theory). The improvement of the traveling wave velocity calculation method further improves the positioning accuracy. Compared with the traditional positioning method, the average positioning error of this method is reduced by 30%.This method overcomes the shortcomings of the traditional method in poor fault location of wind farm transmission lines. In addition, it is more accurate than the traditional fixed wave velocity method in the calculation of the traveling wave velocity. It can calculate the wave velocity in real time according to the scene and solve the traveling wave velocity can’t be updated with the environment and real-time update. The method is verified in PSCAD/EMTDC.

Keywords: grounding transformer, multi-terminal transmission line, short circuit fault location, traveling wave velocity, wind farm

Procedia PDF Downloads 263

Authors: Hasan Farahneh, Xavier Fernando

Abstract:

Leaky Feeder (LF) has been a proven technology for many decades and its promises broadband wireless access in short range but being overlooked until now. The LF is a natural MIMO transceiver ideal for micro and pico cells. In this work, the LF is considered as a linear antenna array MultiInput-Single-Output (MISO) and derive the average bit error rate (BER) in Rayleigh fading channel considering ideal and independent paths (iid) which consider there is no correlation and mutual coupling between transmit antennas (slots) or receiver antenna considering QPSK modulation with imperfect phase estimation. We consider maximal ratio transmission (MRT) at the transmit end and maximal ratio combining (MRC) at the receiving end. Analytical expressions are derived for the BER with radiating cable transmitters. The effects of slot spacing and carrier frequency on the BER are also studied. Numerical evaluations show the radiating cable transmitter offer much lower BER than a single antenna transmitter with same SNR.

Keywords: leaky feeder, BER, QPSK, rayleigh fading, channel gain, phase mismatch

Procedia PDF Downloads 381

Authors: Jefferson Camacho Mejia, Jenny Paola Forero Pachon, Luis Carlos Gomez Florez

Abstract:

The innumerable possibilities offered by the use of Information Technology (IT) in the development of different socio-economic activities has made a change in the social paradigm and the emergence of the so-called information and knowledge society. The Colombian government, aware of this reality, has been promoting the use of IT as part of the E-government strategy adopted in the country. However, it is well known that the use of IT implies the existence of certain threats that put the security of information in the digital environment at risk. One of the priorities of the Colombian government is to improve access to alternative justice through IT, in particular, access to Alternative Dispute Resolution (ADR): conciliation, arbitration and friendly composition; by means of which it is sought that the citizens directly resolve their differences. To this end, a trend has been identified in the use of Online Dispute Resolution (ODR) systems, which extend the benefits of ADR to the digital environment through the use of IT. This article presents a process for the analysis of information security risks associated with the incorporation of ODR systems in the context of conciliation in Colombia, based on four fundamental stages identified in the literature: (I) Identification of assets, (II) Identification of threats and vulnerabilities (III) Estimation of the impact and 4) Estimation of risk levels. The methodological design adopted for this research was the grounded theory, since it involves interactions that are applied to a specific context and from the perspective of diverse participants. As a result of this investigation, the activities to be followed are defined to carry out an analysis of information security risks, in the context of the conciliation in Colombia supported by ODR systems, thus contributing to the estimation of the risks to make possible its subsequent treatment.

Keywords: alternative dispute resolution, conciliation, information security, online dispute resolution systems, process, risk analysis

Procedia PDF Downloads 239

Authors: Alexander N. Savostyanov, Tatiana A. Dolgorukova, Elena A. Esipenko, Mikhail S. Zaleshin, Margherita Malanchini, Anna V. Budakova, Alexander E. Saprygin, Tatiana A. Golovko, Yulia V. Kovas

Abstract:

EEG correlates of mathematical and trait anxiety level were studied in 52 healthy Russian-speakers during execution of error-recognition tasks with lexical, arithmetic and algebraic conditions. Event-related spectral perturbations were used as a measure of brain activity. The ERSP plots revealed alpha/beta desynchronizations within a 500-3000 ms interval after task onset and slow-wave synchronization within an interval of 150-350 ms. Amplitudes of these intervals reflected the accuracy of error recognition, and were differently associated with the three conditions. The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16-20 Hz) frequency bands. In theta band the effects of mathematical anxiety were stronger expressed in lexical, than in arithmetic and algebraic condition. The mathematical anxiety effects in theta band were associated with differences between anterior and posterior cortical areas, whereas the effects of trait anxiety were associated with inter-hemispherical differences. In beta1 and beta2 bands effects of trait and mathematical anxiety were directed oppositely. The trait anxiety was associated with increase of amplitude of desynchronization, whereas the mathematical anxiety was associated with decrease of this amplitude. The effect of mathematical anxiety in beta2 band was insignificant for lexical condition but was the strongest in algebraic condition. EEG correlates of anxiety in theta band could be interpreted as indexes of task emotionality, whereas the reaction in beta2 band is related to tension of intellectual resources.

Keywords: EEG, brain activity, lexical and numerical error-recognition tasks, mathematical and trait anxiety

Procedia PDF Downloads 561

Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

Abstract:

In this paper, we briefly introduce the concept of Poisson attacks in the case of defense/attack strategies where attacks are assumed to be continuous. We suggest a game model in which the attacker will combine both criteria of a sufficient confidence level of a successful attack and a reasonably small size of the estimation error in order to launch an attack. Here, estimation error arises from assessing the system failure upon attack using aggregate data at the system level. The corresponding error is referred to as aggregation error. On the other hand, the defender will attempt to deter attack by making one or both criteria inapplicable. The defender will build his/her strategy by both strengthening the targeted system and increasing the size of error. We will formulate the defender problem based on appropriate optimization models. The attacker will opt for a Bayesian updating in assessing the impact on the improvement made by the defender. Then, the attacker will evaluate the feasibility of the attack before making the decision of whether or not to launch it. We will provide illustrations to better explain the process.

Keywords: attacker, defender, game theory, information

Procedia PDF Downloads 468

Authors: Jyoti Sahu, Vinay A. Juvekar

Abstract:

Electrolytes are often used to bring about salting-in and salting-out of organic molecules and polymers (e.g. polyethylene glycols/proteins) from the aqueous solutions. For quantification of these phenomena, a thermodynamic model which can accurately predict activity coefficient of electrolyte as a function of temperature is needed. The thermodynamics models available in the literature contain a large number of empirical parameters. These parameters are estimated using lower/upper critical solution temperature of the solution in the electrolyte/organic molecule at different temperatures. Since the number of parameters is large, inaccuracy can bethe creep in during their estimation, which can affect the reliability of prediction beyond the range in which these parameters are estimated. Cloud point of solution is related to its free energy through temperature and composition derivative. Hence, the Cloud point measurement can be used for accurate estimation of the temperature and composition dependence of parameters in the model for free energy. Hence, if we use a two pronged procedure in which we first use cloud point of solution to estimate some of the parameters of the thermodynamic model and determine the rest using osmotic coefficient data, we gain on two counts. First, since the parameters, estimated in each of the two steps, are fewer, we achieve higher accuracy of estimation. The second and more important gain is that the resulting model parameters are more sensitive to temperature. This is crucial when we wish to use the model outside temperatures window within which the parameter estimation is sought. The focus of the present work is to prove this proposition. We have used electrolyte (NaCl/Na2CO3)-water-organic molecule (Iso-propanol/ethanol) as the model system. The model of Robinson-Stokes-Glukauf is modified by incorporating the temperature dependent Flory-Huggins interaction parameters. The Helmholtz free energy expression contains, in addition to electrostatic and translational entropic contributions, three Flory-Huggins pairwise interaction contributions viz., and (w-water, p-polymer, s-salt). These parameters depend both on temperature and concentrations. The concentration dependence is expressed in the form of a quadratic expression involving the volume fractions of the interacting species. The temperature dependence is expressed in the form .To obtain the temperature-dependent interaction parameters for organic molecule-water and electrolyte-water systems, Critical solution temperature of electrolyte -water-organic molecules is measured using cloud point measuring apparatus The temperature and composition dependent interaction parameters for electrolyte-water-organic molecule are estimated through measurement of cloud point of solution. The model is used to estimate critical solution temperature (CST) of electrolyte water-organic molecules solution. We have experimentally determined the critical solution temperature of different compositions of electrolyte-water-organic molecule solution and compared the results with the estimates based on our model. The two sets of values show good agreement. On the other hand when only osmotic coefficients are used for estimation of the free energy model, CST predicted using the resulting model show poor agreement with the experiments. Thus, the importance of the CST data in the estimation of parameters of the thermodynamic model is confirmed through this work.

Keywords: concentrated electrolytes, Debye-Hückel theory, interaction parameters, Robinson-Stokes-Glueckauf model, Flory-Huggins model, critical solution temperature

Procedia PDF Downloads 391

Authors: Yang Fu, Jaime Rodrigo Navarro, Jose F. Monserrat, Faiza Bouchmal, Oscar Carrasco Quilis

Abstract:

Cellular Vehicle to Everything (C-V2X) is considered a promising solution for future autonomous driving. From Release 16 to Release 17, the Third Generation Partnership Project (3GPP) has introduced the definitions and services for 5G New Radio (NR) V2X. Experience from previous generations has shown that establishing a simulator for C-V2X communications is an essential preliminary step to achieve reliable and stable communication links. This paper proposes a complete framework of a link-level simulator based on the 3GPP specifications for the Physical Sidelink Share Channel (PSSCH) of the 5G NR Physical Layer (PHY). In this framework, several algorithms in the receiver part, i.e., sliding window in channel estimation and Minimum Mean Square Error (MMSE)-based equalization, are developed. Finally, the performance of the developed PSSCH receiver is validated through extensive simulations under different assumptions.

Keywords: C-V2X, channel estimation, link-level simulator, sidelink, 3GPP

Procedia PDF Downloads 130

Authors: Saoussen Dhib, Chris M. Mannaerts, Zoubeida Bargaoui, Ben H. P. Maathuis, Petra Budde

Abstract:

In this study, a new method to delineate rain areas in northern Tunisia is presented. The proposed approach is based on the blending of the geostationary Meteosat Second Generation (MSG) infrared channel (IR) with the low-earth orbiting passive Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). To blend this two products, we need to apply two main steps. Firstly, we have to identify the rainy pixels. This step is achieved based on a classification using MSG channel IR 10.8 and the water vapor WV 0.62, applying a threshold on the temperature difference of less than 11 Kelvin which is an approximation of the clouds that have a high likelihood of precipitation. The second step consists on fitting the relation between IR cloud top temperature with the TMI rain rates. The correlation coefficient of these two variables has a negative tendency, meaning that with decreasing temperature there is an increase in rainfall intensity. The fitting equation will be applied for the whole day of MSG 15 minutes interval images which will be summed. To validate this combined product, daily extreme rainfall events occurred during the period 2007-2009 were selected, using a threshold criterion for large rainfall depth (> 50 mm/day) occurring at least at one rainfall station. Inverse distance interpolation method was applied to generate rainfall maps for the drier summer season (from May to October) and the wet winter season (from November to April). The evaluation results of the estimated rainfall combining MSG and TMI was very encouraging where all the events were detected rainy and the correlation coefficients were much better than previous evaluated products over the study area such as MSGMPE and PERSIANN products. The combined product showed a better performance during wet season. We notice also an overestimation of the maximal estimated rain for many events.

Keywords: combination, extreme, rainfall, TMI-MSG, Tunisia

Procedia PDF Downloads 175

Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

Abstract:

In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

Procedia PDF Downloads 242

Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

Abstract:

Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

Procedia PDF Downloads 97

Authors: Yi Huang, Clemens Guehmann

Abstract:

In order to monitor the thermal behavior of an asynchronous machine with squirrel cage rotor, a 9th-order extended Kalman filter (EKF) algorithm is implemented to estimate the temperatures of the stator windings, the rotor cage and the stator core. The state-space equations of EKF are established based on the electrical, mechanical and the simplified thermal models of an asynchronous machine. The asynchronous machine with simplified thermal model in Dymola is compiled as DymolaBlock, a physical model in MATLAB/Simulink. The coolant air temperature, three-phase voltages and currents are exported from the physical model and are processed by EKF estimator as inputs. Compared to the temperatures exported from the physical model of the machine, three parts of temperatures can be estimated quite accurately by the EKF estimator. The online EKF estimator is independent from the machine control algorithm and can work under any speed and load condition if the stator current is nonzero current system.

Keywords: asynchronous machine, extended Kalman filter, resistance, simulation, temperature estimation, thermal model

Procedia PDF Downloads 285

Authors: Tasir Khan, Ishfaq Ahmad, Yejuan Wang, Muhammad Salam

Abstract:

Distribution models of the wind speed data are essential to assess the potential wind speed energy because it decreases the uncertainty to estimate wind energy output. Therefore, before performing a detailed potential energy analysis, the precise distribution model for data relating to wind speed must be found. In this research, material from numerous criteria goodness-of-fits, such as Kolmogorov Simonov, Anderson Darling statistics, Chi-Square, root mean square error (RMSE), AIC and BIC were combined finally to determine the wind speed of the best-fitted distribution. The suggested method collectively makes each criterion. This method was useful in a circumstance to fitting 14 distribution models statistically with the data of wind speed together at four sites in Pakistan. The consequences show that this method provides the best source for selecting the most suitable wind speed statistical distribution. Also, the graphical representation is consistent with the analytical results. This research presents three estimation methods that can be used to calculate the different distributions used to estimate the wind. In the suggested MLM, MOM, and MLE the third-order moment used in the wind energy formula is a key function because it makes an important contribution to the precise estimate of wind energy. In order to prove the presence of the suggested MOM, it was compared with well-known estimation methods, such as the method of linear moment, and maximum likelihood estimate. In the relative analysis, given to several goodness-of-fit, the presentation of the considered techniques is estimated on the actual wind speed evaluated in different time periods. The results obtained show that MOM certainly provides a more precise estimation than other familiar approaches in terms of estimating wind energy based on the fourteen distributions. Therefore, MOM can be used as a better technique for assessing wind energy.

Keywords: wind-speed modeling, goodness of fit, maximum likelihood method, linear moment

Procedia PDF Downloads 84

Authors: Nada R. Abuknesha, John P. Morgan, Andrew J. Searle

Abstract:

Information regarding post-mortem interval (PMI) in criminal investigations is vital to establish a time frame when reconstructing events. PMI is defined as the time period that has elapsed between the occurrence of death and the discovery of the corpse. Adipocere, commonly referred to as ‘grave-wax’, is formed when post-mortem adipose tissue is converted into a solid material that is heavily comprised of fatty acids. Adipocere is of interest to forensic anthropologists, as its formation is able to slow down the decomposition process. Therefore, analysing the changes in the patterns of fatty acids during the early decomposition process may be able to estimate the period of burial, and hence the PMI. The current study concerned the investigation of the fatty acid composition and patterns in buried pig fat tissue. This was in an attempt to determine whether particular patterns of fatty acid composition can be shown to be associated with the duration of the burial, and hence may be used to estimate PMI. The use of adipose tissue from the abdominal region of domestic pigs (Sus-scrofa), was used to model the human decomposition process. 17 x 20cm piece of pork belly was buried in a shallow artificial grave, and weekly samples (n=3) from the buried pig fat tissue were collected over an 11-week period. Marker fatty acids: palmitic (C16:0), oleic (C18:1n-9) and linoleic (C18:2n-6) acid were extracted from the buried pig fat tissue and analysed as fatty acid methyl esters using the gas chromatography system. Levels of the marker fatty acids were quantified from their respective standards. The concentrations of C16:0 (69.2 mg/mL) and C18:1n-9 (44.3 mg/mL) from time zero exhibited significant fluctuations during the burial period. Levels rose (116 and 60.2 mg/mL, respectively) and fell starting from the second week to reach 19.3 and 18.3 mg/mL, respectively at week 6. Levels showed another increase at week 9 (66.3 and 44.1 mg/mL, respectively) followed by gradual decrease at week 10 (20.4 and 18.5 mg/mL, respectively). A sharp increase was observed in the final week (131.2 and 61.1 mg/mL, respectively). Conversely, the levels of C18:2n-6 remained more or less constant throughout the study. In addition to fluctuations in the concentrations, several new fatty acids appeared in the latter weeks. Other fatty acids which were detectable in the time zero sample, were lost in the latter weeks. There are several probable opportunities to utilise fatty acid analysis as a basic technique for approximating PMI: the quantification of marker fatty acids and the detection of selected fatty acids that either disappear or appear during the burial period. This pilot study indicates that this may be a potential semi-quantitative methodology for determining the PMI. Ideally, the analysis of particular fatty acid patterns in the early stages of decomposition could be an additional tool to the already available techniques or methods in improving the overall processes in estimating PMI of a corpse.

Keywords: adipocere, fatty acids, gas chromatography, post-mortem interval

Procedia PDF Downloads 131

Authors: Saowaluck Ukrisdawithid

Abstract:

The thermal conductivity of thermal insulation materials are measured by Heat Flow Meter (HFM) apparatus. The components of uncertainty are complex and difficult on routine measurement by modelling approach. In this study, uncertainty of thermal conductivity measurement was estimated by single laboratory validation approach. The within-laboratory reproducibility was 1.1%. The standard uncertainty of method and laboratory bias by using SRM1453 expanded polystyrene board was dominant at 1.4%. However, it was assessed that there was no significant bias. For sample measurement, the sources of uncertainty were repeatability, density of sample and thermal conductivity resolution of HFM. From this approach to sample measurements, the combined uncertainty was calculated. In summary, the thermal conductivity of sample, polystyrene foam, was reported as 0.03367 W/m·K ± 3.5% (k = 2) at mean temperature 23.5 °C. The single laboratory validation approach is simple key of routine testing laboratory for estimation uncertainty of thermal conductivity measurement by using HFM, according to ISO/IEC 17025-2017 requirements. These are meaningful for laboratory competent improvement, quality control on products, and conformity assessment.

Keywords: single laboratory validation approach, within-laboratory reproducibility, method and laboratory bias, certified reference material

Procedia PDF Downloads 153

Authors: Nsimbe Allan

Abstract:

The study examines the impact of Building Information Modeling (BIM) on the cost management of engineering projects, focusing specifically on the Mombasa Port Area Development Project. The objective of this research venture is to determine the mechanisms through which Building Information Modeling (BIM) facilitates stakeholder collaboration, reduces construction-related expenses, and enhances the precision of cost estimation. Furthermore, the study investigates barriers to execution, assesses the impact on the project's transparency, and suggests approaches to maximize resource utilization. The study, selected for its practical significance and intricate nature, conducted a Systematic Literature Review (SLR) using credible databases, including ScienceDirect and IEEE Xplore. To constitute the diverse sample, 69 individuals, including project managers, cost estimators, and BIM administrators, were selected via stratified random sampling. The data were obtained using a mixed-methods approach, which prioritized ethical considerations. SPSS and Microsoft Excel were applied to the analysis. The research emphasizes the crucial role that project managers, architects, and engineers play in the decision-making process (47% of respondents). Furthermore, a significant improvement in cost estimation accuracy was reported by 70% of the participants. It was found that the implementation of BIM resulted in enhanced project visibility, which in turn optimized resource allocation and facilitated the process of budgeting. In brief, the study highlights the positive impacts of Building Information Modeling (BIM) on collaborative decision-making and cost estimation, addresses challenges related to implementation, and provides solutions for the efficient assimilation and understanding of BIM principles.

Keywords: cost management, resource utilization, stakeholder collaboration, project transparency

Procedia PDF Downloads 67

Authors: Farhana Tarannum

Abstract:

Ambient (outdoor) air pollution is now recognized as an important problem, both nationally and worldwide. The concentrations of gaseous pollutants (SOx, NOx, CO and O3) have been determined from samples collected at Kallyanpur along Shamoli corridor in Dhaka city. Pollutants were determined in a sample collected at ground level and a roof of a 7-storied building. These pollutants are emitted largely from stationary sources like fossil fuel fired power plants, industrial plants, and manufacturing facilities as well as mobile sources. The incomplete combustion of fuel, wood and the Sulphur containing fuel used in the vehicles are one of the main causes of CO and SOx respectively in our natural environment. When the temperature of combustion in high enough and some of that nitrogen reacts with oxygen in the air, various nitrogen oxides (NOx) are then formed. The VOCs react with NOx in the presence of sunlight to form O3. UV Visible spectrophotometric method has been used for the determination of SOx, NOx and O3. The sensor type device was used for the estimation of CO. It was found that the air pollutants (CO, SOx, NOx and O3) of a sample collected at the roof of a building were lower compared to the ground level; it indicated that ground level people are mostly affected by the gaseous pollutants.

Keywords: gaseous pollutants, UV-visible spectrophotometry, ambient air quality, Dhaka city

Procedia PDF Downloads 347

Authors: Jungyeol Hong, Dongjoo Park

Abstract:

The primary purpose of this study is to develop a methodological framework to predict daily subway ridership at a station-level and to examine the association between subway ridership and bus demand incorporating commercial business facility in the vicinity of each subway station. The socio-economic characteristics, land-use, and built environment as factors may have an impact on subway ridership. However, it should be considered not only the endogenous relationship between bus and subway demand but also the characteristics of commercial business within a subway station’s sphere of influence, and integrated transit network topology. Regarding a statistical approach to estimate subway ridership at a station level, therefore it should be considered endogeneity and heteroscedastic issues which might have in the subway ridership prediction model. This study focused on both discovering the impacts of bus demand, commercial business characteristics, and network topology on subway ridership and developing more precise subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers entire Seoul city in South Korea and includes 243 stations with the temporal scope set at twenty-four hours with one-hour interval time panels each. The data for subway and bus ridership was collected Seoul Smart Card data from 2015 and 2016. Three-Stage Least Square(3SLS) approach was applied to develop daily subway ridership model as capturing the endogeneity and heteroscedasticity between bus and subway demand. Independent variables incorporating in the modeling process were commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. As a result, it was found that bus ridership and subway ridership were endogenous each other and they had a significantly positive sign of coefficients which means one transit mode could increase another transportation mode’s ridership. In other words, two transit modes of subway and bus have a mutual relationship instead of the competitive relationship. The commercial business characteristics are the most critical dimension among the independent variables. The variables of commercial business facility rate in the paper containing six types; medical, educational, recreational, financial, food service, and shopping. From the model result, a higher rate in medical, financial buildings, shopping, and food service facility lead to increment of subway ridership at a station, while recreational and educational facility shows lower subway ridership. The complex network theory was applied for estimating integrated network topology measures that cover the entire Seoul transit network system, and a framework for seeking an impact on subway ridership. The centrality measures were found to be significant and showed a positive sign indicating higher centrality led to more subway ridership at a station level. The results of model accuracy tests by out of samples provided that 3SLS model has less mean square error rather than OLS and showed the methodological approach for the 3SLS model was plausible to estimate more accurate subway ridership. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017R1C1B2010175).

Keywords: subway ridership, bus ridership, commercial business characteristic, endogeneity, network topology

Procedia PDF Downloads 144

Authors: Suparman Suparman

Abstract:

A white noise in autoregressive (AR) model is often assumed to be normally distributed. In application, the white noise usually do not follows a normal distribution. This paper aims to estimate a parameter of AR model that has a exponential white noise. A Bayesian method is adopted. A prior distribution of the parameter of AR model is selected and then this prior distribution is combined with a likelihood function of data to get a posterior distribution. Based on this posterior distribution, a Bayesian estimator for the parameter of AR model is estimated. Because the order of AR model is considered a parameter, this Bayesian estimator cannot be explicitly calculated. To resolve this problem, a method of reversible jump Markov Chain Monte Carlo (MCMC) is adopted. A result is a estimation of the parameter AR model can be simultaneously calculated.

Keywords: autoregressive (AR) model, exponential white Noise, bayesian, reversible jump Markov Chain Monte Carlo (MCMC)

Procedia PDF Downloads 355

Authors: Che Hsiu Chen, Su Yun Chen, Hon Wen Cheng

Abstract:

Greater core stability may benefit sports performance by providing a foundation for greater force production in the upper and lower extremities. Core stability exercises on instability device (such as the TRX suspension systems) were found to be able to induce higher core muscle activity than performing on a stable surface. However, high intensity interval TRX suspension exercises training on sport performances remain unclear. The purpose of this study was to examine whether high intensity TRX suspension training could improve sport performance. Twenty-four healthy university female students (age 19.0 years, height 157.9 cm, body mass 51.3 kg, fat mass 25.2 %) were voluntarily participated in this study. After a familiarization session, each participant underwent five suspension exercises (e.g., hip abduction in plank alternative, hamstring curl, 45-degree row, lunge and oblique crunch). Each type of exercise was performed for 30 seconds, followed by 30 seconds break, two times per week for eight weeks while each exercise session was increased by 10 seconds every week. The results showed that the fat mass (about 12.92%) decreased significantly, sit and reach test (9%), 1 minute sit-up test (17.5%), standing broad jump (4.8%), physical fitness index (10.3%) increased significantly after 8-week high intensity TRX suspension training. Hence, eight weeks of high intensity interval TRX suspension exercises training can improve hamstring flexibility, trunk endurance, jump ability, aerobic fitness and fat mass percentage decreased substantially.

Keywords: core endurance, jump, flexibility, cardiovascular fitness

Procedia PDF Downloads 408

Authors: Baghdasaryan Marinka

Abstract:

The particular significance of comprehensive estimation of the increase in the operation efficiency of the mill motor electromechanical system, providing the main technological process for obtaining a metallic concentrate, as well as the technical state of the system are substantiated. The works carried out in the sphere of investigating, creating, and improving the operation modes of electric drive motors and ore-grinding mills have been studied. Analytic dependences for estimating the operation modes of the ore-grinding mills aimed at improving the ore-crashing process maintenance and technical service efficiencies have been obtained. The obtained analytic dependencies establish a link between the technological and power parameters of the electromechanical system, and allow to estimate the state of the system and reveal the controlled parameters required for the efficient management in case of changing the technological parameters. It has been substantiated that the changes in the technological factors affecting the consumption power of the drive motor do not cause an instability in the electromechanical system.

Keywords: electromechanical system, estimation, operation mode, productivity, technological process, the mill filling degree

Procedia PDF Downloads 270