Search results for: growth signals

Authors: Alain Villemeur

Abstract:

The relationships between the profit share in income on the one hand and the growth rates of output and employment on the other hand have been studied for 17 advanced economies since 1961. The vast majority (98%) of annual values for the profit share fall between 20% and 40%, with an average value of 33.9%. For the 17 advanced economies, Gross Domestic Product and productivity growth rates tend to fall as the profit share in income rises. For the employment growth rates, the relationships are complex; nevertheless, over long periods (1961-2000), it appears that the more job-creating economies are Australia, Canada, and the United States; they have experienced a profit share close to 1/3. This raises a number of questions, not least the value of 1/3 for the profit share and its role in macroeconomic fundamentals. To explain these facts, an endogenous growth model is developed. This growth and distribution model reconciles the great ideas of Kaldor (economic growth as a chain reaction), of Keynes (effective demand and marginal efficiency of capital) and of Ricardo (importance of the wage-profit distribution) in an economy facing creative destruction. A production function is obtained, depending mainly on the growth of employment, the rate of net investment and the profit share in income. In theory, we show the existence of incentives: an incentive for job creation when the profit share is less than 1/3 and another incentive for job destruction in the opposite case. Thus, increasing the profit share can boost the employment growth rate until it reaches the value of 1/3; otherwise lowers the employment growth rate. Three key findings can be drawn from these considerations. The first reveals that the best GDP and productivity growth rates are obtained with a profit share of less than 1/3. The second is that maximum job growth is associated with a 1/3 profit share, given the existence of incentives to create more jobs when the profit share is less than 1/3 or to destroy more jobs otherwise. The third is the decline in performance (GDP growth rate and productivity growth rate) when the profit share increases. In conclusion, increasing the profit share in income weakens GDP growth or productivity growth as a long-term trend, contrary to the trickle-down hypothesis. The employment growth rate is maximum for a profit share in income of 1/3. All these lessons suggest macroeconomic policies considering the profit share in income.

Keywords: advanced countries, GDP growth, employment growth, profit share, economic policies

Procedia PDF Downloads 35

Authors: Ya-Chi Yu, Pei-Syuan Ruan, Shao-Wen Chen, Yu-Hsien Chang, Jin-Der Lee, Jong-Rong Wang, Chunkuan Shih

Abstract:

This study analyzed the transient signals of central region void fraction of air-water two-phase flow in a 3x3 rod bundle. Experimental tests were carried out utilizing a vertical rod bundle test section along with a set of air-water supply/flow control system, and the transient signals of the central region void fraction were collected through the electrical conductivity sensors as well as visualized via high speed photography. By converting the electric signals, transient void fraction can be obtained through the voltage ratios. With a fixed superficial water velocity (J_f=0.094 m/s), two different superficial air velocities (J_g=0.094 m/s and 0.236 m/s) were tested and presented, which were corresponding to the flow conditions of bubbly flows and cap/slug flows, respectively. The time averaged central region void fraction was obtained as 0.109-0.122 with 0.028 standard deviation for the selected bubbly flow and 0.188-0.221with 0.101 standard deviation for the selected cap/slug flow, respectively. Through Fast Fourier Transform (FFT) analysis, no clear frequency peak was found in bubbly flow, while two dominant frequencies were identified around 1.6 Hz and 2.5 Hz in the present cap/slug flow.

Keywords: central region, rod bundles, transient void fraction, two-phase flow

Procedia PDF Downloads 164

Authors: Maimuna Nontji

Abstract:

The life cycle of rice plant has three phases of growth; they are the vegetative, reproductive and maturation phase. They greatly affect the life of dynamics metanotrof bacterial as reducer methane emissions in the rice field, both of population and on the rate of growth. The aim of this study was to analyze the population and growth rate of methanotrof isolates which has been isolated in previous studies. Isolates were taken at all the life cycle of rice plant. Population of analysis was conducted by standard plate count method and growth rate was analysed by logarithmic calculation. The results showed that each isolate varied in population and growth rate. The highest population was obtained in the isolates Gowa Methanotrof Reproductive (GMR 8) about 7.06 x 10 11 cfu / ml on 3 days of incubation and the lowest population was obtained in the Gowa Methanotrof Maturation (GMP 5) about 0.27 x 10 11 cfu / ml on 7 day of incubation. Some isolate were demonstrated in long growth rate about 5 days of incubation and another are 3 days.

Keywords: emission, methanotrof, methane, population

Procedia PDF Downloads 423

Authors: Osman Kök, İlhami̇ Tüzün, Yaşar Aluç

Abstract:

This study was conducted to explore the effect of direct current (DC) applications on the growth of microalgae Chlorella vulgaris KKU71, isolated from highly saline freshwater. Experiments were implemented based upon the cross-combinations of both the intensity and duration of electric applications, generating a full factorial design of 10V, 20V, 30V, and 5s, 30s, 60s, respectively. Growth parameters of cultures were monitored on Optical Density (OD), Cell Count (CC), Chlorophyll-a, b (Chl-a, b), and Total Carotenoids (TCar). All DC-assisted treatments stimulated the growth and thus led to higher values of growth parameters such as OD, CC, Chl-a, and TCar. Monotonically increasing with the intensity and duration of DC applications, wet and dry biomass yields of the harvested algae reached their highest level at 30V-60s in all sets of treatments. In addition, this increase between DC applications was listed as C(control)<10V<20V<30V and C<5s<30s<60s. As a result, direct current applications increased the biomass.

Keywords: Chlorella Vulgaris, direct current, growth, biomass

Procedia PDF Downloads 112

Authors: Chia-Ju Peng, Shih-Jui Chen

Abstract:

This paper proposes a method to discriminate electroencephalogram (EEG) signals between different concentration states using empirical mode decomposition (EMD). Brain-computer interface (BCI), also called brain-machine interface, is a direct communication pathway between the brain and an external device without the inherent pathway such as the peripheral nervous system or skeletal muscles. Attention level is a common index as a control signal of BCI systems. The EEG signals acquired from people paying attention or in relaxation, respectively, are decomposed into a set of intrinsic mode functions (IMF) by EMD. Fast Fourier transform (FFT) analysis is then applied to each IMF to obtain the frequency spectrums. By observing power spectrums of IMFs, the proposed method has the better identification of EEG attention level than the original EEG signals between different concentration states. The band power of IMF3 is the most obvious especially in β wave, which corresponds to fully awake and generally alert. The signal processing method and results of this experiment paves a new way for BCI robotic system using the attention-level control strategy. The integrated signal processing method reveals appropriate information for discrimination of the attention and relaxation, contributing to a more enhanced BCI performance.

Keywords: biomedical engineering, brain computer interface, electroencephalography, rehabilitation

Procedia PDF Downloads 371

Authors: Laszlo Szerb, Zsofia Voros

Abstract:

In this article, we seek to answer why many attempts to empirically link entrepreneurial self-efficacy to growth expectations have failed. While doing so, we reconcile the literature on entrepreneurial self-efficacy and overconfidence. By analyzing GEM APS (Global Entrepreneurship Monitor Adult Population Survey) data, we show that early-stage entrepreneurs’ self-efficacy statements are systematically inflated. Our results also indicate that entrepreneurial overconfidence is fading and its form changes as business owners learn and gather experience. In addition, by using Ajzen’s Theory of Planned Behavior (2006) as a modeling framework, we illustrate that early stage business owners’ overconfidence results in overly high firm growth expectations. However, the changes in the form of overconfidence and the adjustments of expectations on market conditions as a venture ages alter the relationship between overconfidence and growth expectations across the business life-cycle stages. Overall, our study empirically links young entrepreneurs’ overconfidence to their growth expectations at the firm level. This link is important to establish as expected growth was linked to realized growth both on micro and macro levels. Moreover, we detected several moderators of this relationship providing a potential answer to why many studies failed to link entrepreneurial self-efficacy to growth expectations.

Keywords: self-efficacy, overconfidence, entrepreneurship, expected growth

Procedia PDF Downloads 248

Authors: Thitima Chaiyakul

Abstract:

The number of previous literature regarding to the effect of asset growth and equity returns is small. Furthermore, those literature are mainly focus in the developed markets. According to my knowledge, there is no published paper examining the effect of asset growth and equity returns in the Stock Exchange of Thailand in different industry groups. The main objective in this research is the testing the effect of asset growth to equity returns in different industry groups. This study employs the data of the listed companies in the Stock Exchange of Thailand during January 1996 and December 2014. The data of financial industry are exclude from this study due to the different meaning of accounting terms. The results show the supported evidence that the asset growth positively affects the equity returns at a statistically significance level of at least 5% in Agro& Food Industry, Industrials, and Services Industry Groups. These results are inconsistent with the previous research testing in developed markets. Nevertheless, the statistically significances of the effect of asset growth to equity returns appear in some cases. In summary, the asset growth is a non-systematic risk and it is a mispricing factor.

Keywords: asset growth, asset pricing, equity returns, Thailand

Procedia PDF Downloads 324

Authors: Ping Tan, Xiaomeng Su, Yi Shen

Abstract:

The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

Procedia PDF Downloads 87

Authors: Temitope L. A. Leshoro

Abstract:

This study explored several variables that enhance economic growth in South Africa, based on different growth theories while using the vector error correction model (VECM) technique. The impacts and contributions of each of these variables on GDP in South Africa were investigated. The motivation for this study was as a result of the weak economic growth that the country has been experiencing lately, as well as the continuous increase in unemployment rate and deteriorating health care system. Annual data spanning over the period 1974 to 2013 was employed. The results showed that the major determinants of GDP are trade openness, government spending, and health indicator; as these variables are not only economically significant but also statistically significant in explaining the changes in GDP in South Africa. Policy recommendations for economic growth enhancement are suggested based on the findings of this study.

Keywords: economic growth, GDP, investment, health indicator, VECM

Procedia PDF Downloads 252

Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

Abstract:

This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

Procedia PDF Downloads 42

Authors: Mrinalini Ranjan, Sudheesh Chethil

Abstract:

Epilepsy is a common neurological disorder characterised by the recurrence of seizures. Electroencephalogram (EEG) signals are complex biomedical signals which exhibit nonlinear and nonstationary behavior. We use two methods 1) Detrended Fluctuation Analysis (DFA) and 2) Recurrence Plots (RP) to capture this complex behavior of EEG signals. DFA considers fluctuation from local linear trends. Scale invariance of these signals is well captured in the multifractal characterisation using detrended fluctuation analysis (DFA). Analysis of long-range correlations is vital for understanding the dynamics of EEG signals. Correlation properties in the EEG signal are quantified by the calculation of a scaling exponent. We report the existence of two scaling behaviours in the epileptic EEG signals which quantify short and long-range correlations. To illustrate this, we perform DFA on extant ictal (seizure) and interictal (seizure free) datasets of different patients in different channels. We compute the short term and long scaling exponents and report a decrease in short range scaling exponent during seizure as compared to pre-seizure and a subsequent increase during post-seizure period, while the long-term scaling exponent shows an increase during seizure activity. Our calculation of long-term scaling exponent yields a value between 0.5 and 1, thus pointing to power law behaviour of long-range temporal correlations (LRTC). We perform this analysis for multiple channels and report similar behaviour. We find an increase in the long-term scaling exponent during seizure in all channels, which we attribute to an increase in persistent LRTC during seizure. The magnitude of the scaling exponent and its distribution in different channels can help in better identification of areas in brain most affected during seizure activity. The nature of epileptic seizures varies from patient-to-patient. To illustrate this, we report an increase in long-term scaling exponent for some patients which is also complemented by the recurrence plots (RP). RP is a graph that shows the time index of recurrence of a dynamical state. We perform Recurrence Quantitative analysis (RQA) and calculate RQA parameters like diagonal length, entropy, recurrence, determinism, etc. for ictal and interictal datasets. We find that the RQA parameters increase during seizure activity, indicating a transition. We observe that RQA parameters are higher during seizure period as compared to post seizure values, whereas for some patients post seizure values exceeded those during seizure. We attribute this to varying nature of seizure in different patients indicating a different route or mechanism during the transition. Our results can help in better understanding of the characterisation of epileptic EEG signals from a nonlinear analysis.

Keywords: detrended fluctuation, epilepsy, long range correlations, recurrence plots

Procedia PDF Downloads 155

Authors: Endri Rama, Genci Capi, Shigenori Kawahara

Abstract:

The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment.

Keywords: brain-machine interface, decision-making, mobile robot, neural network

Procedia PDF Downloads 277

Authors: Zubda Zia, Zainab Masood

Abstract:

This study has investigated the relationship between renewable energy, urbanization, industrialization, and economic growth in Pakistan, through the years 1990-2016. All the three explanatory variables play a pivotal role in their contribution to growth in any economy, especially a developing one such as Pakistan. Auto-regressive distributive lag (ARDL) model has been used to determine the co-integration and relationship between the variables. The empirical results indicate that there exists a positive and significant relationship between all the three variables and economic growth and that there is a stable, long-run relationship among them. Policy suggestions that incorporate the results include having a larger share of renewable energy in the energy sector, using urbanization as a means to remove the big city trend and move towards, smaller sustainable cities, etc.

Keywords: economic growth, energy crisis, industrialization, renewable energy, SGDs, urbanization

Procedia PDF Downloads 160

Authors: Godwin Chigozie Okpara, William Chimee Nwaoha

Abstract:

The paper set out to stress on how financial inclusion index could be calculated and also investigated the impact of inclusive finance on inclusive growth in an emerging economy. In the light of these objectives, chi-wins method was used to calculate indexes of financial inclusion while co-integration and error correction model were used for evaluation of the impact of financial inclusion on inclusive growth. The result of the analysis revealed that financial inclusion while having a long-run relationship with GDP growth is an insignificant function of the growth of the economy. The speed of adjustment is correctly signed and significant. On the basis of these results, the researchers called for tireless efforts of government and banking sector in promoting financial inclusion in developing countries.

Keywords: chi-wins index, co-integration, error correction model, financial inclusion

Procedia PDF Downloads 628

Authors: Loïc Warscotte, Jehan Boreux

Abstract:

The scientific literature of changepoint detection is vast. Today, a lot of methods are available to detect abrupt changes or slight drift in a signal, based on CUSUM or EWMA charts, for example. However, these methods rely on strong assumptions, such as the stationarity of the stochastic underlying process, or even the independence and Gaussian distributed noise at each time. Recently, the breakthrough research on locally stationary processes widens the class of studied stochastic processes with almost no assumptions on the signals and the nature of the changepoint. Despite the accurate description of the mathematical aspects, this methodology quickly suffers from impractical time and space complexity concerning the signals with high-rate data collection, if the characteristics of the process are completely unknown. In this paper, we then addressed the problem of making this theory usable to our purpose, which is monitoring a high-speed weigh-in-motion system (HS-WIM) towards direct enforcement without supervision. To this end, we first compute bounded approximations of the initial detection theory. Secondly, these approximating bounds are empirically validated by generating many independent long-run stochastic processes. The abrupt changes and the drift are both tested. Finally, this relaxed methodology is tested on real signals coming from a HS-WIM device in Belgium, collected over several months.

Keywords: changepoint, weigh-in-motion, process, non-parametric

Procedia PDF Downloads 42

Authors: Naser Safdarian, Nader Jafarnia Dabanloo

Abstract:

In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

Procedia PDF Downloads 432

Authors: Hamid Khachab, Yamani Abdelkafi, Mouna Barhmi

Abstract:

The epitaxial growth has great important in the fabricate of the new semi-conductors devices and upgrading many factors, such as the quality of crystallization and efficiency with their deferent types and the most effective epitaxial technique is the molecular beam epitaxial. The MBE growth modeling allows to confirm the experiments results out by atomic beam and to analyze the microscopic phenomena. In of our work, we determined the growth processes specially the ZnSe epitaxial technique by Kinetic Monte Carlo method and we also give observations that are made in real time at the growth temperature using reflection high energy electron diffraction (RHEED) and photoemission current.

Keywords: molecular beam epitaxy, II-VI, morpholy, photoemission, RHEED, simulation, kinetic Monte Carlo, ZnSe

Procedia PDF Downloads 465

Authors: Darrol Stanley, Levan Efremidze, Jannie Rossouw

Abstract:

We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets.

Keywords: currency trading, entropy, market timing, risk factor model

Procedia PDF Downloads 246

Authors: Priyadarsini Samal, Rajesh Singla

Abstract:

Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

Procedia PDF Downloads 165

Authors: Mutaju Marobhe, Pastory Dickson

Abstract:

This study explores the role of Foreign Direct Investment (FDI) in stimulating economic growth of emerging economies. FDIs have been associated with higher economic growth rates in developed countries due to the presence of conducive business conditions e.g. advanced financial markets which may accelerate the rate at which FDI boosts economic growth. So this study sets out to evaluate this macroeconomic phenomenon in emerging economies using the case study of Southern Africa Development Community (SADC) countries. The study uses Ease of Doing Business Index as a variable that moderates the relationship between FDI and economic growth. Panel data ranging from 2010 to 2019 from all SADC members are used and due to the unbalanced nature of the data, fixed effects regression analysis with moderation effect is used to assess this phenomenon. The conclusions and recommendations generated by this study will enable emerging economies to depict how they can be able to significantly improve FDI’s role in accelerating economic growth similarly to developed economies.

Keywords: ease of doing business, economic growth, emerging economies, foreign direct investment

Procedia PDF Downloads 124

Authors: Henrique Alpalhão

Abstract:

This paper attempts to model the widely studied relationship between a country's economic growth and its level of democracy, with an emphasis on possible non-linearities. We adopt the concept of 'political capital' as a measure of democracy, which is extremely uncommon in the literature and brings considerable advantages both in terms of dynamic considerations and plausibility. While the literature is not consensual on this matter, we obtain, via panel Arellano-Bond regression analysis on a database of more than 60 countries over 50 years, significant and robust results that indicate that the impact of democratization on economic growth varies according to the stage of democratic development each country is in.

Keywords: democracy, economic growth, political capital, political economy

Procedia PDF Downloads 289

Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

Abstract:

This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

Procedia PDF Downloads 109

Authors: Esmaeil Kouhgardi, Saeedeh Dashti, Hakimeh Fekrandish

Abstract:

This research has been conducted to study the method of culture and comparing growth and biomass of Gracilariacorticata cultured on rope and net for 50 days through two treatments (first treatment: culture of alga on net and the second treatment: culture of alga on rope and each treatment was repeated by four cases). During culture period, the water of aquariums was replaced once every two days for 40-50%. Also, 0.3-0.5 grams of Urea fertilizer was added to the culture environment for fertilization. Moreover, some of the environmental factors such as pH, salinity and temperature of the environment were measured on a daily basis. During the culture period, extent of longitudinal growth of the species of both treatments was equal. The said length was reached from 8-10 cm to 10.5-13 cm accordingly. The resulted weight in repetitions of the first treatment was higher than that of the second treatment in such a way as in the first treatment, its weight reached from 10 grams to 21.119 grams and in the second treatment, its weight reached from 10 grams to 17.663 grams. On a whole, it may be stated that that kind of alga being studied has a considerable growth with respect to its volume. The results have revealed that the percentage of daily growth and wet weight at the end of the first treatment was higher than that of the second treatment and it was registered as 0.934, 6.072 and 811.432 in the first treatment and 0.797, 4.990 and 758.071 in the second treatment respectively. This difference is significant (P < 0.05). Growth and biomass of G. corticata through culture on net was more emphasizing on numerous branches due to wider bed. Moreover, higher level of the species in this method was exposed to sunlight and this increased biosynthesis and eventually increases of growth and biomass.

Keywords: red alga, growth, biomass, culture, net, rope

Procedia PDF Downloads 408

Authors: Takuro Kida, Yuichi Kida

Abstract:

In this paper, it is shown a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detail algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output-signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory, and it is indicated that introducing conversations with feedback do not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: matrix filterbank, optimum signal approximation, category theory, simultaneous minimization

Procedia PDF Downloads 105

Authors: Burcu Guvenek, Volkan Alptekin

Abstract:

Turkey is a country in the process of development and its economy has undergone structural reforms in order to realize a sustainable development and energy has vital role as a basic input for this aim. Turkey has been in the process of economic growth and development and, because of this, has an increasing energy need. This paper investigates relationship between economic growth and electricity consumption using annual data for Turkey between 1970-2008 by using bounds test. As economic growth and energy consumption variables used in empirical analysis was different order of integration I(0) and I(1), we employed bounds test approach. We have not found co-integration relationship between the variables.

Keywords: bounds test, economic growth, energy consumption, Turkey

Procedia PDF Downloads 340

Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

Abstract:

This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

Procedia PDF Downloads 367

Authors: S. O. Oyamakin, A. U. Chukwu,

Abstract:

We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

Procedia PDF Downloads 416

Authors: Arkaprabha Bhattacharyya, Makarand Hastak

Abstract:

The COVID-19 pandemic is unprecedented in terms of its global reach and economic impacts. Historically, investment in infrastructure development projects has been touted to boost the economic growth of a nation. The State and Local governments responsible for delivering infrastructure assets work under tight budgets. Therefore, it is important to understand which infrastructure projects have the highest potential of boosting economic growth in the post-pandemic era. This paper presents relationships between infrastructure projects and economic growth. Statistical relationships between investment in different types of infrastructure projects (transit, water and wastewater, highways, power, manufacturing etc.) and indicators of economic growth are presented using historic data between 2002 and 2020 from the U.S. Census Bureau and U.S. Bureau of Economic Analysis (BEA). The outcome of the paper is the comparison of statistical correlations between investment in different types of infrastructure projects and indicators of economic growth. The comparison of the statistical correlations is useful in ranking the types of infrastructure projects based on their ability to influence economic prosperity. Therefore, investment in the infrastructures with the higher rank will have a better chance of boosting the economic growth. Once, the ranks are derived, they can be used by the decision-makers in infrastructure investment related decision-making process.

Keywords: economic growth, infrastructure development, infrastructure projects, strategic investment

Procedia PDF Downloads 146

Authors: Sanjay Kumar, Arumugam Sankaran, Arjun K., Mousumi Das

Abstract:

The amount of domestic consumption and population growth is having a positive impact on economic growth and development as observed by the Harrod-Domar and endogenous growth models. The paper negates the Solow growth model which argues the population growth has a detrimental impact on per capita and steady-state growth. Unlike the Solow model, the paper observes, the per capita income growth never falls zero, and it sustains as positive. Hence, our goal here is to investigate the relationship among population, domestic consumption and economic growth of India. For this estimation, annual data from 1980-2016 has been collected from World Development Indicator and Reserve Bank of India. To know the long run as well as short-run dynamics among the variables, we have employed the ARDL bounds testing approach of cointegration followed by modified Wald causality test to know the direction of causality. The conclusion from cointegration and ARDL estimates reveal that there is a long run positive and statistically significant relationship among the variables under study. At the same time, the causality test shows that there is a causal relationship that exists among the variables. Hence, this calls for policies which have a long run perspective in strengthening the capabilities and entitlements of people and stabilizing domestic demand so as to serve long run and short run growth and stability of the economy.

Keywords: cointegration, consumption, economic development, population growth

Procedia PDF Downloads 126

Authors: Dejene Gizaw Kidane

Abstract:

This study examines the impact of foreign direct investment on economic growth of Ethiopia using yearly time-series data for 1974 through 2013. Economic growth is proxies by real per capita gross domestic product and foreign direct investment proxies by the inflow of foreign direct investment. Other control variables such as gross domestic saving, trade, government consumption and inflation has been incorporated. In order to fully account for feedbacks, a vector autoregressive model is utilized. The results show that there is a stable, long-run relationship between foreign direct investment and economic growth. The variance decomposition results show that the main sources of Ethiopia economic growth variations are due largely own shocks. The pairwise Granger causality results show that there is a unidirectional causality that runs from FDI to economic growth of Ethiopia. Hence, the researcher therefore recommends that, FDI facilitate economic growth, so the government has to exert much effort in order to attract more FDI into the country.

Keywords: real per capita GDP, FDI, co-integration, VECM, Granger causality

Procedia PDF Downloads 399