Search results for: recursive least square algorithm

Authors: Ghada El-Husseiny, Dina Yousri, Christian Richter

Abstract:

Social services are often characterized by market failures, which justifies government intervention in the provision of these services. It is widely acknowledged that government intervention breeds corruption since resources are being transferred from one party to another. However, what is still being extensively studied is the magnitude of the negative impact of corruption on publicly provided services and development outcomes. Corruption has the power to hinder development and cripple our march towards the Sustainable Development Goals. Corruption diminishes the efficiency and effectiveness of public health and education spending and directly impacts the outcomes of these sectors. This paper empirically examines the impact of Institutional Failure on public sector services provision, with the sole purpose of studying the impact of corruption on SDG3 and 4; Good health and wellbeing and Quality education, respectively. The paper explores the effect of corruption on these goals from various perspectives and extends the analysis by examining if the impact of corruption on these goals differed when it accounted for the current corruption state. Using Pooled OLS(Ordinary Least Square) and Fixed effects panel estimation on 22 corrupt and 22 clean countries between 2000 and 2017. Results show that corruption in both corrupt and clean countries has a more severe impact on Health than the Education sector. In almost all specifications, corruption has an insignificant effect on School Enrollment rates but a significant effect on Infant Mortality rates. Results further indicate that, on average, a 1 point increase in the CPI(Consumer Price Index) can increase health expenditures by 0.116% in corrupt and clean countries. However, the fixed effects model indicates that the way Health and Education expenditures are determined in clean and corrupt countries are completely country-specific, in which corruption plays a minimal role. Moreover, the findings show that School Enrollment rates and Infant Mortality rates depend, to a large extent, on public spending. The most astounding results-driven is that corrupt countries, on average, have more effective and efficient healthcare expenditures. While some insights are provided as to why these results prevail, they should be further researched. All in all, corruption impedes development outcomes, and any Anti-corrupt policies taken will bring forth immense improvements and speed up the march towards sustainability.

Keywords: corruption, education, health, public spending, sustainable development

Procedia PDF Downloads 161

Authors: Christian Angerer, Markus Lienkamp

Abstract:

Electric vehicles offer a high variety of possible drivetrain topologies with up to 4 motors. Multi-motor-designs can have several advantages regarding traction, vehicle dynamics, safety and even efficiency. With a rising number of motors, the whole drivetrain becomes more complex. All permutations of gearings, drivetrain-layouts, motor-types and –sizes lead up in a very large solution space. Single elements of this solution space can be analyzed by simulation methods. In addition to longitudinal vehicle behavior, which most optimization-approaches are restricted to, also lateral dynamics are important for vehicle dynamics, stability and efficiency. In order to compete large solution spaces and to find an optimal result, genetic algorithm based optimization is state-of-the-art. As lateral dynamics simulation is way more CPU-intensive, optimization takes much more time than in case of longitudinal-only simulation. Therefore, this paper shows an approach how to create meta-models from a 14-degree of freedom vehicle model in order to enable a numerically efficient drivetrain-layout optimization process under consideration of lateral dynamics. Different meta-modelling approaches such as neural networks or DoE are implemented and comparatively discussed.

Keywords: driving dynamics, drivetrain layout, genetic optimization, meta-modeling, lateral dynamicx

Procedia PDF Downloads 403

Authors: Mdluli Fezile, Schmidt Stefan, Thamaga-Chitja Joyce

Abstract:

In view of the frequently reported foodborne disease outbreaks caused by contaminated fresh produce, consumers have a preference for foods that meet requisite hygiene standards to reduce the risk of foodborne illnesses. Producing good quality fresh produce then becomes critical in improving market access and food security, especially for small-scale farmers. Questions of hygiene and subsequent microbiological quality in the rural small-scale farming sector of South Africa are even more crucial, given the policy drive to develop small-scale farming as a measure for reinforcement of household food security and reduction of poverty. Farming practices and methods, throughout the fresh produce value chain, influence the quality of the final product, which in turn determines its success in the market. This study’s aim was to therefore determine the extent to which training on organic farming methods, including modules such as Importance of Production Hygiene, influenced the hygienic farming practices employed by eTholeni small-scale organic farmers in uMbumbulu, KwaZulu-Natal- South Africa. Questionnaires were administered to 73 uncertified organic farmers and analysis showed that a total of 33 farmers were trained and supplied the local Agri-Hub while 40 had not received training. The questionnaire probed respondents’ attitudes, knowledge of hygiene and composting practices. Data analysis included descriptive statistics such as the Chi-square test and a logistic regression model. Descriptive analysis indicated that a majority of the farmers (60%) were female, most of which (73%) were above the age of 40. The logistic regression indicated that factors such as farmer training and prior experience in the farming sector had a significant influence on hygiene practices both at 5% significance levels. These results emphasize the importance of training, education and farming experience in implementing good hygiene practices in small-scale farming. It is therefore recommended that South African policies should advocate for small-scale farmer training, not only for subsistence purposes, but also with an aim of supplying produce markets with high fresh produce.

Keywords: small-scale farmers, leafy salad vegetables, organic produce, food safety, hygienic practices, food security

Procedia PDF Downloads 416

Authors: Manpreet Kaur, Amita Rani, Sanjay Kumar

Abstract:

The Internet of Things (IoT), a cyber-physical paradigm, allows a large number of devices to connect and send the sensory data in the network simultaneously. This tremendous amount of data generated leads to very high network load consequently resulting in network congestion. It further amounts to frequent loss of useful information and depletion of significant amount of nodes’ energy. Therefore, there is a need to control congestion in IoT so as to prolong network lifetime and improve the quality of service (QoS). Hence, we propose a two-level clustering based routing algorithm considering congestion score and packet priority metrics that focus on minimizing the network congestion. In the proposed Priority based Congestion Control (PBCC) protocol the sensor nodes in IoT network form clusters that reduces the amount of traffic and the nodes are prioritized to emphasize important data. Simultaneously, a congestion score determines the occurrence of congestion at a particular node. The proposed protocol outperforms the existing Packet Discard Network Clustering (PDNC) protocol in terms of buffer size, packet transmission range, network region and number of nodes, under various simulation scenarios.

Keywords: internet of things, cyber-physical systems, congestion control, priority, transmission rate

Procedia PDF Downloads 298

Authors: Mohammad Besharatloo

Abstract:

Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

Procedia PDF Downloads 76

Authors: Zahra Abdolkarimi, Naser Zouri

Abstract:

Nowadays, there is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Additionally, robotics system recommended RoboCup factor as a provider of some standardization and testing method in case of computer discussion widely. The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. In addition, decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidently. Consequences, shows method of our discussion is the best way for Particle Swarm Optimization and Fuzzy system compare to other decision of robotics algorithmic.

Keywords: PSO algorithm, inference fuzzy system, chaos theory, soccer robot league

Procedia PDF Downloads 394

Authors: Shireen Bhatti, M. Asif Shaikh, Atif Khan

Abstract:

Sexually abusive behavior is seen as a social phenomenon across different societies and different territories. An institution of sport has its own uniqueness. It is different from other workplaces and academia. The challenges in sports raised are different, which require a call to action for specific sexual harassment policies and practices. Many sportswomen who are members of team games or individual games experience sexually abusive behavior from their male counterparts, including trainers, coaches, and lower staff. The power of the coach over the athlete is massive due to the coach’s position. The power can be disguised for possible abuse, whether physical or emotional. Female athletes are victims in most offensive situations that occur in collegiate settings by male coaches. The objective of the study is to identify the relationship between the sexually abusive behavior of male coaches and the motivation of female athletes at public sector universities in Sindh, Pakistan. The descriptive approach was used, whereas The cross-sectional survey design was used to support the study. Intercollegiate, intervarsity, provincial, and national level female athletes of public sector universities of Sindh province were the subject of this study. The tool of research was a self-developed scale that encompassed the relationship between the sexually abusive behaviors of coaches and the motivation of female athletes. Frequency, percentage, and mean and Pearson Correlation, chi square, and ANOVA were used. The results indicate that there is a strong negative relationship between the sexually abusive behavior of male coaches and female athletes’ sports motivation. The Pearson correlation shows that there is a strong negative relation between the sexually abusive behavior of male athletes and female athletes’ sports motivation. The significant level is (r = -.741); however, The findings confirmed that the coach’s power, authority, decision-making position, the threat of rejection on the refusal of sexual cooperation, the ready availability of inexperienced female athletes, and lack of implication of policies regarding sexual misconducts in public sector universities decline motivation of female athletes witnessed. Based on the findings, the study recommended that the family background, career history, and participation record of coaches should be investigated to ensure that they have ever been involved in any criminal activity or sexual misconduct during their career or participation.

Keywords: abusive, athlete, coach, motivation

Procedia PDF Downloads 322

Authors: Sukhleen Kaur

Abstract:

In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

Procedia PDF Downloads 407

Authors: Flora Paganelli

Abstract:

The radiometric performance of remotely sensed images is important for volcanic monitoring. The Thermal Infrared Sensor (TIRS) on-board Landsat 8 was designed with specific requirements in regard to the noise-equivalent change in temperature (NEΔT) at ≤ 0.4 K at 300 K for the two thermal infrared bands B10 and B11. This study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over the volcanic activity of Kīlauea Volcano and the active East Rift Zone (Hawaii), in order to optimize the use of TIRS data. Results showed that the NEΔTs of the two bands exceeded the design specification by an order of magnitude at 300 K. Both separate bands and split window algorithm were examined to estimate the effect of NEΔT on the land surface temperature (LST) retrieval, and NEΔT contribution to the final LST error. These results were also useful in the current efforts to assess the requirements for volcanology research campaign using the Hyperspectral Infrared Imager (HyspIRI) whose airborne prototype MODIS/ASTER instruments is plan to be flown by NASA as a single campaign to the Hawaiian Islands in support of volcanology and coastal area monitoring in 2016.

Keywords: landsat 8, radiometric performance, thermal infrared sensor (TIRS), volcanology

Procedia PDF Downloads 229

Authors: Bruk Gebregziabher

Abstract:

The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multiobject tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

Keywords: autonomous mobile robots, multi-object tracking, predictive collision avoidance, ensemble Kalman filter, lidar sensors

Procedia PDF Downloads 72

Authors: Gozde Inal Kiziltepe, Ozgun Uyanik

Abstract:

There is growing evidence that socioeconomic (SES)-related differences in mathematical knowledge primarily start in early childhood period. Preschoolers from low-income families are likely to perform substantially worse in mathematical knowledge than their counterparts from middle and higher income families. The differences are seen on a wide range of recognizing written numerals, counting, adding and subtracting, and comparing numerical magnitudes. Early differences in numerical knowledge have a permanent effect childrens’ mathematical knowledge in other grades. In this respect, analyzing the effect of number board game on the number knowledge of 48-60 month-old children from disadvantaged low-income families constitutes the main objective of the study. Participants were the 71 preschoolers from a childcare center which served low-income urban families. Children were randomly assigned to the number board condition or to the color board condition. The number board condition included 35 children and the color board game condition included 36 children. Both board games were 50 cm long and 30 cm high; had ‘The Great Race’ written across the top; and included 11 horizontally arranged, different colored squares of equal sizes with the leftmost square labeled ‘Start’. The numerical board had the numbers 1–10 in the rightmost 10 squares; the color board had different colors in those squares. A rabbit or a bear token were presented to children for selecting, and on each trial spun a spinner to determine whether the token would move one or two spaces. The number condition spinner had a ‘1’ half and a ‘2’ half; the color condition spinner had colors that matched the colors of the squares on the board. Children met one-on-one with an experimenter for four 15- to 20-min sessions within a 2-week period. In the first and fourth sessions, children were administered identical pretest and posttest measures of numerical knowledge. All children were presented three numerical tasks and one subtest presented in the following order: counting, numerical magnitude comparison, numerical identification and Count Objects – Circle Number Probe subtest of Early Numeracy Assessment. In addition, same numerical tasks and subtest were given as a follow-up test four weeks after the post-test administration. Findings obtained from the study; showed that there was a meaningful difference between scores of children who played a color board game in favor of children who played number board game.

Keywords: low income, numerical board game, numerical knowledge, preschool education

Procedia PDF Downloads 342

Authors: Lalit Kumar, Rashid Al Shidi

Abstract:

Phoenix dactylifera (date palm) is a major crop in many middle-eastern countries, including Oman. The Dubas bug Ommatissus lybicus is the main pest that affects date palm crops. However not all plantations are infested. It is still uncertain why some plantations get infested while others are not. This research investigated whether tree density and the system of planting (random versus systematic) had any relationship with infestation and levels of infestation. Remote Sensing and Geographic Information Systems were used to determine the density of trees (number of trees per unit area) while infestation levels were determined by manual counting of insects on 40 leaflets from two fronds on each tree, with a total of 20-60 trees in each village. The infestation was recorded as the average number of insects per leaflet. For tree density estimation, WorldView-3 scenes, with eight bands and 2m spatial resolution, were used. The Local maxima method, which depends on locating of the pixel of highest brightness inside a certain exploration window, was used to identify the trees in the image and delineating individual trees. This information was then used to determine whether the plantation was random or systematic. The ordinary least square regression (OLS) was used to test the global correlation between tree density and infestation level and the Geographic Weight Regression (GWR) was used to find the local spatial relationship. The accuracy of detecting trees varied from 83–99% in agricultural lands with systematic planting patterns to 50–70% in natural forest areas. Results revealed that the density of the trees in most of the villages was higher than the recommended planting number (120–125 trees/hectare). For infestation correlations, the GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R² = 0.60 and medium positive significant relationship in the autumn season, with R² = 0.30. In contrast, the OLS model results showed a weaker positive significant relationship in the spring season with R² = 0.02, p < 0.05 and insignificant relationship in the autumn season with R² = 0.01, p > 0.05. The results showed a positive correlation between infestation and tree density, which suggests the infestation severity increased as the density of date palm trees increased. The correlation result showed that the density alone was responsible for about 60% of the increase in the infestation. This information can be used by the relevant authorities to better control infestations as well as to manage their pesticide spraying programs.

Keywords: dubas bug, date palm, tree density, infestation levels

Procedia PDF Downloads 178

Authors: Iqbal Pramukti, Mamat Lukman, Hasniatisari Harun, Kusman Ibrahim

Abstract:

Introduction: People living with HIV had a higher risk of osteoporotic fracture than the general population. The purpose of this study was to predict the 10-year risk of fracture among people living with HIV (PLWH) using FRAX™ and to identify characteristics related to the fracture risk. Methodology: This study consisted of 75 subjects. The ten-year probability of major osteoporotic fractures (MOF) and hip fractures was assessed using the FRAX™ algorithm. A cross-tabulation was used to identify the participant’s characteristics related to fracture risk. Results: The overall mean 10-year probability of fracture was 2.4% (1.7) for MOF and 0.4% (0.3) for hip fractures. For MOF score, participants with parents’ hip fracture history, smoking behavior and glucocorticoid use showed a higher MOF score than those who were not (3.1 vs. 2.5; 4.6 vs 2.5; and 3.4 vs 2.5, respectively). For HF score, participants with parents’ hip fracture history, smoking behavior and glucocorticoid use also showed a higher HF score than those who were not (0.5 vs. 0.3; 0.8 vs. 0.3; and 0.5 vs. 0.3, respectively). Conclusions: The 10-year risk of fracture was higher among PLWH with several factors, including the parent’s hip. Fracture history, smoking behavior and glucocorticoid used. Further analysis on determining factors using multivariate regression analysis with a larger sample size is required to confirm the factors associated with the high fracture risk.

Keywords: HIV, PLWH, osteoporotic fractures, hip fractures, 10-year risk of fracture, FRAX

Procedia PDF Downloads 35

Authors: Prama Mukhopadhyay

Abstract:

Starting from transport to food, today’s world platform economy and digital markets have taken over almost every sphere of consumers’ lives. Sellers and buyers are getting connected through platforms, which is acting as an intermediary. It has made consumer’s life easier in terms of time, price, choice and other factors. Having said that, there are several concerns regarding platforms. There are competition law concerns like unfair pricing, deep discounting by the platforms which affect the consumer welfare. Apart from that, the biggest problem is lack of transparency with respect to the business models, how it operates, price calculation, etc. In most of the cases, consumers are unaware of how their personal data are being used. In most of the cases, they are unaware of how algorithm uses their personal data to determine the price of the product or even to show the relevant products using their previous searches. Using personal or non-personal data without consumer’s consent is a huge legal concern. In addition to this, another major issue lies with the question of liability. If a dispute arises, who will be responsible? The seller or the platform? For example, if someone ordered food through a food delivery app and the food was bad, in this situation who will be liable: the restaurant or the food delivery platform? In this paper, the researcher tries to examine the legal concern related to platform economy from the consumer protection and consumer welfare perspectives. The paper analyses the cases from different jurisdictions and approach taken by the judiciaries. The author compares the existing legislation of EU, US and other Asian Countries and tries to highlight the best practices.

Keywords: competition, consumer, data, platform

Procedia PDF Downloads 131

Authors: Alija Uzunovic, Sasa Pilipovic, Aida Sapcanin, Zahida Ademovic, Berina Pilipović

Abstract:

Capsaicin is a naturally occurring alkaloid extracted from capsicum fruit extracts of different of Capsicum species. It has been employed topically to treat many diseases such as rheumatoid arthritis, osteoarthritis, cancer pain and nerve pain in diabetes. The high degree of pre-systemic metabolism of intragastrical capsaicin and the short half-life of capsaicin by intravenous administration made topical application of capsaicin advantageous. In this study, we have evaluated differences in the dissolution characteristics of capsaicin patch 11 mg (purchased from market) at different dissolution rotation speed. The proposed patch area is 308 cm2 (22 cm x 14 cm; it contains 36 µg of capsaicin per square centimeter of adhesive). USP Apparatus 5 (Paddle Over Disc) is used for transdermal patch testing. The dissolution study was conducted using USP apparatus 5 (n=6), ERWEKA DT800 dissolution tester (paddle-type) with addition of a disc. The fabricated patch of 308 cm2 is to be cut into 9 cm2 was placed against a disc (delivery side up) retained with the stainless-steel screen and exposed to 500 mL of phosphate buffer solution pH 7.4. All dissolution studies were carried out at 32 ± 0.5 °C and different rotation speed (50± 5; 100± 5 and 150± 5 rpm). 5 ml aliquots of samples were withdrawn at various time intervals (1, 4, 8 and 12 hours) and replaced with 5 ml of dissolution medium. Withdrawn were appropriately diluted and analyzed by reversed-phase liquid chromatography (RP-LC). A Reversed Phase Liquid Chromatography (RP-LC) method has been developed, optimized and validated for the separation and quantitation of capsaicin in a transdermal patch. The method uses a ProntoSIL 120-3-C18 AQ 125 x 4,0 mm (3 μm) column maintained at 600C. The mobile phase consisted of acetonitrile: water (50:50 v/v), the flow rate of 0.9 mL/min, the injection volume 10 μL and the detection wavelength 222 nm. The used RP-LC method is simple, sensitive and accurate and can be applied for fast (total chromatographic run time was 4.0 minutes) and simultaneous analysis of capsaicin and dihydrocapsaicin in a transdermal patch. According to the results obtained in this study, we can conclude that the relative difference of dissolution rate of capsaicin after 12 hours was elevated by increase of dissolution rotation speed (100 rpm vs 50 rpm: 84.9± 11.3% and 150 rpm vs 100 rpm: 39.8± 8.3%). Although several apparatus and procedures (USP apparatus 5, 6, 7 and a paddle over extraction cell method) have been used to study in vitro release characteristics of transdermal patches, USP Apparatus 5 (Paddle Over Disc) could be considered as a discriminatory test. would be able to point out the differences in the dissolution rate of capsaicin at different rotation speed.

Keywords: capsaicin, in vitro, patch, RP-LC, transdermal

Procedia PDF Downloads 218

Authors: Buruk Kitachew Wossenyeleh

Abstract:

Groundwater is the largest freshwater reservoir in the world. Like the other reservoirs of the hydrologic cycle, it is a finite resource. This study focused on the groundwater modeling of the Ter Kamerenbos well field to understand the groundwater flow system and the impact of different scenarios. The study area covers 68.9Km2 in the Brussels Capital Region and is situated in two river catchments, i.e., Zenne River and Woluwe Stream. The aquifer system has three layers, but in the modeling, they are considered as one layer due to their hydrogeological properties. The catchment aquifer system is replenished by direct recharge from rainfall. The groundwater recharge of the catchment is determined using the spatially distributed water balance model called WetSpass, and it varies annually from zero to 340mm. This groundwater recharge is used as the top boundary condition for the groundwater modeling of the study area. During the groundwater modeling using Processing MODFLOW, constant head boundary conditions are used in the north and south boundaries of the study area. For the east and west boundaries of the study area, head-dependent flow boundary conditions are used. The groundwater model is calibrated manually and automatically using observed hydraulic heads in 12 observation wells. The model performance evaluation showed that the root means the square error is 1.89m and that the NSE is 0.98. The head contour map of the simulated hydraulic heads indicates the flow direction in the catchment, mainly from the Woluwe to Zenne catchment. The simulated head in the study area varies from 13m to 78m. The higher hydraulic heads are found in the southwest of the study area, which has the forest as a land-use type. This calibrated model was run for the climate change scenario and well operation scenario. Climate change may cause the groundwater recharge to increase by 43% and decrease by 30% in 2100 from current conditions for the high and low climate change scenario, respectively. The groundwater head varies for a high climate change scenario from 13m to 82m, whereas for a low climate change scenario, it varies from 13m to 76m. If doubling of the pumping discharge assumed, the groundwater head varies from 13m to 76.5m. However, if the shutdown of the pumps is assumed, the head varies in the range of 13m to 79m. It is concluded that the groundwater model is done in a satisfactory way with some limitations, and the model output can be used to understand the aquifer system under steady-state conditions. Finally, some recommendations are made for the future use and improvement of the model.

Keywords: Ter Kamerenbos, groundwater modelling, WetSpass, climate change, well operation

Procedia PDF Downloads 145

Authors: Dana El-Mughayyar, Neil Manson, Erin Bigney, Eden Richardson, Dean Tripp, Edward Abraham

Abstract:

Use of narcotics to treat pain has increased over the past two decades and is a contributing factor to the current public health crisis. Understanding the pre-operative risks of chronic narcotic use may be aided through investigation of psychological measures. The objective of the reported study is to determine predictors of narcotic use two years post-surgery in a thoracolumbar spine surgery population, including an array of psychological factors. A prospective observational study of 191 consecutively enrolled adult patients having undergone thoracolumbar spine surgery is presented. Baseline measures of interest included the Pain Catastrophizing Scale (PCS), Tampa Scale for Kinesiophobia, Multidimensional Scale for Perceived Social Support (MSPSS), Chronic Pain Acceptance Questionnaire (CPAQ-8), Oswestry Disability Index (ODI), Numeric Rating Scales for back and leg pain (NRS-B/L), SF-12’s Mental Component Summary (MCS), narcotic use and demographic variables. The post-operative measure of interest is narcotic use at 2-year follow-up. Narcotic use is collapsed into binary categories of use and no use. Descriptive statistics are run. Chi Square analysis is used for categorical variables and an ANOVA for continuous variables. Significant variables are built into a hierarchical logistic regression to determine predictors of post-operative narcotic use. Significance is set at α < 0.05. Results: A total of 27.23% of the sample were using narcotics two years after surgery. The regression model included ODI, NRS-Leg, time with condition, chief complaint, pre-operative drug use, gender, MCS, PCS subscale helplessness, and CPAQ subscale pain willingness and was significant χ² (13, N=191)= 54.99; p = .000. The model accounted for 39.6% of the variance in narcotic use and correctly predicted in 79.7% of cases. Psychological variables accounted for 9.6% of the variance over and above the other predictors. Conclusions: Managing chronic narcotic usage is central to the patient’s overall health and quality of life. Psychological factors in the preoperative period are significant predictors of narcotic use 2 years post-operatively. The psychological variables are malleable, potentially allowing surgeons to direct their patients to preventative resources prior to surgery.

Keywords: narcotics, psychological factors, quality of life, spine surgery

Procedia PDF Downloads 131

Authors: Handie Pramana Putra, Ani Dijah Rahajoe

Abstract:

The proliferation of smart devices and advancements in mobile communication technologies have permeated various facets of life with the widespread influence of e-commerce. Detecting abnormal transactions holds paramount significance in this realm due to the potential for substantial financial losses. Moreover, the fusion of data flow and control flow assumes a critical role in the exploration of process modeling and data analysis, contributing significantly to the accuracy and security of business processes. This paper introduces an alternative approach to identify abnormal transactions through a model that integrates both data and control flows. Referred to as the Extended Data Petri net (DPNE), our model encapsulates the entire process, encompassing user login to the e-commerce platform and concluding with the payment stage, including the mobile transaction process. We scrutinize the model's structure, formulate an algorithm for detecting anomalies in pertinent data, and elucidate the rationale and efficacy of the comprehensive system model. A case study validates the responsive performance of each system component, demonstrating the system's adeptness in evaluating every activity within mobile transactions. Ultimately, the results of anomaly detection are derived through a thorough and comprehensive analysis.

Keywords: database, data analysis, DPNE, extended data flow, e-commerce

Procedia PDF Downloads 44

Authors: Seungwoo Kim, Yeongcheol Cho

Abstract:

In this paper, a holonomic mobile robot is designed in omnidirectional wheels and an adaptive fuzzy controller is presented for its precise trajectories. A kind of adaptive controller based on fuzzy multi-layered algorithm is used to solve the big parametric uncertainty of motor-controlled dynamic system of 3-wheels omnidirectional mobile robot. The system parameters such as a tracking force are so time-varying due to the kinematic structure of omnidirectional wheels. The fuzzy adaptive control method is able to solve the problems of classical adaptive controller and conventional fuzzy adaptive controllers. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a fuzzy multi-layered architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. This paper proposes a design procedure which can be carried out mathematically and systematically from the model of a controlled system. Finally, the good performance of a holonomic mobile robot is confirmed through live tests of the tracking control task.

Keywords: fuzzy adaptive control, fuzzy multi-layered controller, holonomic mobile robot, omnidirectional wheels, robustness and stability.

Procedia PDF Downloads 347

Authors: Salman Hameed

Abstract:

In this paper, a self-tuning fuzzy PI controller (STFPIC) is proposed for thyristor controlled series capacitor (TCSC) to improve power system dynamic performance. In a STFPIC controller, the output scaling factor is adjusted on-line by an updating factor (α). The value of α is determined from a fuzzy rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The comparative performances of the proposed STFPIC and the standard fuzzy PI controller (FPIC) have been investigated on a multi-machine power system (namely, 4 machine two area system) through detailed non-linear simulation studies using MATLAB/SIMULINK. From the simulation studies it has been found out that for damping oscillations, the performance of the proposed STFPIC is better than that obtained by the standard FPIC. Moreover, the proposed STFPIC as well as the FPIC have been found to be quite effective in damping oscillations over a wide range of operating conditions and are quite effective in enhancing the power carrying capability of the power system significantly.

Keywords: genetic algorithm, power system stability, self-tuning fuzzy controller, thyristor controlled series capacitor

Procedia PDF Downloads 415

Authors: Urooj Saeed, Sajid Rashid Ahmad, Iqra Khalid, Sahar Mirza, Imtiaz Younas

Abstract:

Pakistan, a flood-prone country, has experienced worst floods in the recent past which have caused extensive damage to the urban and rural areas by loss of lives, damage to infrastructure and agricultural fields. Poor flood management system in the country has projected the risks of damages as the increasing frequency and magnitude of floods are felt as a consequence of climate change; affecting national economy directly or indirectly. To combat the needs of flood emergency, this paper focuses on remotely sensed data based approach for rapid mapping and monitoring of flood extent and its damages so that fast dissemination of information can be done, from local to national level. In this research study, spatial extent of the flooding caused by heavy rains of 2014 has been mapped by using space borne data to assess the crop damages and affected population in sixteen districts of Punjab. For this purpose, moderate resolution imaging spectroradiometer (MODIS) was used to daily mark the flood extent by using Normalised Difference Water Index (NDWI). The highest flood value data was integrated with the LandScan 2014, 1km x 1km grid based population, to calculate the affected population in flood hazard zone. It was estimated that the floods covered an area of 16,870 square kilometers, with 3.0 million population affected. Moreover, to assess the flood damages, Object Based Image Analysis (OBIA) aided with spectral signatures was applied on Landsat image to attain the thematic layers of healthy (0.54 million acre) and damaged crops (0.43 million acre). The study yields that the population of Jhang district (28% of 2.5 million population) was affected the most. Whereas, in terms of crops, Jhang and Muzzafargarh are the ‘highest damaged’ ranked district of floods 2014 in Punjab. This study was completed within 24 hours of the peak flood time, and proves to be an effective methodology for rapid assessment of damages due to flood hazard

Keywords: flood hazard, space borne data, object based image analysis, rapid damage assessment

Procedia PDF Downloads 314

Authors: Shan Jia, Jinbao Chen, Jinhua Zhou, Jiacheng Qian

Abstract:

In response to the functional limitations of the fixed landers, those are to expand the detection range by the use of wheeled rovers with unavoidable path-repeatability in deep space exploration currently, a movable lander based on the leg-foot walking mechanism is presented. Firstly, a quadruped landing mechanism based on pushrod-damping is proposed. The configuration is of the bionic characteristics such as hip, knee and ankle joints, and the multi-function main/auxiliary buffers based on the crumple-energy absorption and screw-nut mechanism. Secondly, the workspace of the end of the leg-foot mechanism is solved by Monte Carlo method, and the key points on the desired trajectory of the end of the leg-foot mechanism are fitted by cubic spline curve. Finally, an optimal time-jerk trajectory based on weight coefficient is planned and analyzed by an adaptive genetic algorithm (AGA). The simulation results prove the rationality and stability of walking motion of the movable leg-foot lander in the star catalogue. In addition, this research can also provide a technical solution integrating of soft-landing, large-scale inspection and material transfer for future star catalogue exploration, and can even serve as the technical basis for developing the reusable landers.

Keywords: motion performance, trajectory planning, movable, leg-foot lander

Procedia PDF Downloads 128

Authors: Yao-Hong Tsai

Abstract:

Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

Procedia PDF Downloads 143

Authors: Shreyansh Bharadwaj, Raghavendra Kollipara, Sijoy C. D., R. K. Mittal

Abstract:

Utilizing the Lorentz force to propel an electrically conductive plunger through a solenoid represents a fundamental application in electromagnetism. The parameters of the solenoid significantly influence the force exerted on the plunger, impacting its response. A parametric study has been done to understand the effect of these parameters on the force acting on the plunger. This study is done to determine the most optimal combination of parameters to obtain the fast response. Analysis has been carried out using an algorithm capable of simulating the scenario of a plunger undergoing acceleration within a solenoid. Authors have conducted an analysis focusing on several key configuration parameters of the solenoid. These parameters include the inter-layer gap (in the case of a multi-turn solenoid), different conductor diameters, varying numbers of turns, and diverse numbers of layers. Primary objective of this paper is to discern how alterations in these parameters affect the force applied to the plunger. Through extensive numerical simulations, a dataset has been generated and utilized to construct informative plots. These plots provide visual representations of the relationships between the solenoid configuration parameters and the resulting force exerted on the plunger, which can further be used to deduce scaling laws. This research endeavors to offer valuable insights into optimizing solenoid configurations for enhanced electromagnetic acceleration, thereby contributing to advancements in electromagnetic propulsion technology.

Keywords: Lorentz force, solenoid configuration, electromagnetic acceleration, parametric analysis, simulation

Procedia PDF Downloads 32

Authors: Alena Zemanová, Jan Zeman, Michal Šejnoha

Abstract:

The polymer foil used for manufacturing of laminated glass members behaves in a viscoelastic manner with temperature dependence. This contribution aims at incorporating the time/temperature-dependent behavior of interlayer to our earlier elastic finite element model for laminated glass beams. The model is based on a refined beam theory: each layer behaves according to the finite-strain shear deformable formulation by Reissner and the adjacent layers are connected via the Lagrange multipliers ensuring the inter-layer compatibility of a laminated unit. The time/temperature-dependent behavior of the interlayer is accounted for by the generalized Maxwell model and by the time-temperature superposition principle due to the Williams, Landel, and Ferry. The resulting system is solved by the Newton method with consistent linearization and the viscoelastic response is determined incrementally by the exponential algorithm. By comparing the model predictions against available experimental data, we demonstrate that the proposed formulation is reliable and accurately reproduces the behavior of the laminated glass units.

Keywords: finite element method, finite-strain Reissner model, Lagrange multipliers, generalized Maxwell model, laminated glass, Newton method, Williams-Landel-Ferry equation

Procedia PDF Downloads 419

Authors: M. Draman, S. Z. Muhamad Yassin, M. S. Alias, Z. Lambak, M. I. Zulkifli, S. N. Padhi, K. N. Baharim, F. Maskuriy, A. I. A. Rahim

Abstract:

The performance of Malay automatic speech recognition (ASR) system for the call centre environment is presented. The system utilizes Kaldi toolkit as the platform to the entire library and algorithm used in performing the ASR task. The acoustic model implemented in this system uses a deep neural network (DNN) method to model the acoustic signal and the standard (n-gram) model for language modelling. With 80 hours of training data from the call centre recordings, the ASR system can achieve 72% of accuracy that corresponds to 28% of word error rate (WER). The testing was done using 20 hours of audio data. Despite the implementation of DNN, the system shows a low accuracy owing to the varieties of noises, accent and dialect that typically occurs in Malaysian call centre environment. This significant variation of speakers is reflected by the large standard deviation of the average word error rate (WERav) (i.e., ~ 10%). It is observed that the lowest WER (13.8%) was obtained from recording sample with a standard Malay dialect (central Malaysia) of native speaker as compared to 49% of the sample with the highest WER that contains conversation of the speaker that uses non-standard Malay dialect.

Keywords: conversational speech recognition, deep neural network, Malay language, speech recognition

Procedia PDF Downloads 314

Authors: L. Diogo, A. Legrand, J. Nguyen-Cao, J. Rogeau, S. Bornhofen

Abstract:

Lenia is a system of cellular automata with continuous states, space and time, which surprises not only with the emergence of interesting life-like structures but also with its beauty. This paper reports ongoing research on a GPU implementation of Lenia using CUDA C++ and OpenGL Interoperability. We demonstrate how CUDA as a low-level GPU programming paradigm allows optimizing performance and memory usage of the Lenia algorithm. A comparative analysis through experimental runs with existing implementations shows that the CUDA implementation outperforms the others by one order of magnitude or more. Cellular automata hold significant interest due to their ability to model complex phenomena in systems with simple rules and structures. They allow exploring emergent behavior such as self-organization and adaptation, and find applications in various fields, including computer science, physics, biology, and sociology. Unlike classic cellular automata which rely on discrete cells and values, Lenia generalizes the concept of cellular automata to continuous space, time and states, thus providing additional fluidity and richness in emerging phenomena. In the current literature, there are many implementations of Lenia utilizing various programming languages and visualization libraries. However, each implementation also presents certain drawbacks, which serve as motivation for further research and development. In particular, speed is a critical factor when studying Lenia, for several reasons. Rapid simulation allows researchers to observe the emergence of patterns and behaviors in more configurations, on bigger grids and over longer periods without annoying waiting times. Thereby, they enable the exploration and discovery of new species within the Lenia ecosystem more efficiently. Moreover, faster simulations are beneficial when we include additional time-consuming algorithms such as computer vision or machine learning to evolve and optimize specific Lenia configurations. We developed a Lenia implementation for GPU using the C++ and CUDA programming languages, and CUDA/OpenGL Interoperability for immediate rendering. The goal of our experiment is to benchmark this implementation compared to the existing ones in terms of speed, memory usage, configurability and scalability. In our comparison we focus on the most important Lenia implementations, selected for their prominence, accessibility and widespread use in the scientific community. The implementations include MATLAB, JavaScript, ShaderToy GLSL, Jupyter, Rust and R. The list is not exhaustive but provides a broad view of the principal current approaches and their respective strengths and weaknesses. Our comparison primarily considers computational performance and memory efficiency, as these factors are critical for large-scale simulations, but we also investigate the ease of use and configurability. The experimental runs conducted so far demonstrate that the CUDA C++ implementation outperforms the other implementations by one order of magnitude or more. The benefits of using the GPU become apparent especially with larger grids and convolution kernels. However, our research is still ongoing. We are currently exploring the impact of several software design choices and optimization techniques, such as convolution with Fast Fourier Transforms (FFT), various GPU memory management scenarios, and the trade-off between speed and accuracy using single versus double precision floating point arithmetic. The results will give valuable insights into the practice of parallel programming of the Lenia algorithm, and all conclusions will be thoroughly presented in the conference paper. The final version of our CUDA C++ implementation will be published on github and made freely accessible to the Alife community for further development.

Keywords: artificial life, cellular automaton, GPU optimization, Lenia, comparative analysis.

Procedia PDF Downloads 20

Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

Abstract:

With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

Procedia PDF Downloads 344

Authors: Bulcha Belay Etana, Benny Malengier, Debelo Oljira, Janarthanan Krishnamoorthy, Lieva Vanlangenhove

Abstract:

Electromyography (EMG) is a technique used to measure the electrical activity of muscles. EMG can be used to assess muscle function in a variety of settings, including clinical, research, and sports medicine. The aim of this study was to develop a wearable textile sensor for EMG monitoring. The sensor was designed to be soft, stretchable, and washable, making it suitable for long-term use. The sensor was fabricated using a conductive thread material that was embroidered onto a fabric substrate. The sensor was then connected to a microcontroller unit (MCU) and a Wi-Fi-enabled module. The MCU was programmed to acquire the EMG signal and transmit it wirelessly to the Wi-Fi-enabled module. The Wi-Fi-enabled module then sent the signal to a server, where it could be accessed by a computer or smartphone. The sensor was able to successfully acquire and transmit EMG signals from a variety of muscles. The signal quality was comparable to that of commercial EMG sensors. The development of this sensor has the potential to improve the way EMG is used in a variety of settings. The sensor is soft, stretchable, and washable, making it suitable for long-term use. This makes it ideal for use in clinical settings, where patients may need to wear the sensor for extended periods of time. The sensor is also small and lightweight, making it ideal for use in sports medicine and research settings. The data for this study was collected from a group of healthy volunteers. The volunteers were asked to perform a series of muscle contractions while the EMG signal was recorded. The data was then analyzed to assess the performance of the sensor. The EMG signals were analyzed using a variety of methods, including time-domain analysis and frequency-domain analysis. The time-domain analysis was used to extract features such as the root mean square (RMS) and average rectified value (ARV). The frequency-domain analysis was used to extract features such as the power spectrum. The question addressed by this study was whether a wearable textile sensor could be developed that is soft, stretchable, and washable and that can successfully acquire and transmit EMG signals. The results of this study demonstrate that a wearable textile sensor can be developed that meets the requirements of being soft, stretchable, washable, and capable of acquiring and transmitting EMG signals. This sensor has the potential to improve the way EMG is used in a variety of settings.

Keywords: EMG, electrode position, smart wearable, textile sensor, IoT, IoT-integrated textile sensor

Procedia PDF Downloads 62

Authors: Showkat Ahmad Lone, Ahmadur Rahman, Ariful Islam

Abstract:

The major assumption in accelerated life tests (ALT) is that the mathematical model relating the lifetime of a test unit and the stress are known or can be assumed. In some cases, such life–stress relationships are not known and cannot be assumed, i.e. ALT data cannot be extrapolated to use condition. So, in such cases, partially accelerated life test (PALT) is a more suitable test to be performed for which tested units are subjected to both normal and accelerated conditions. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests using progressive failure-censored hybrid data with random removals. The life data of the units under test is considered to follow exponential life distribution. The removals from the test are assumed to have binomial distributions. The point and interval maximum likelihood estimations are obtained for unknown distribution parameters and tampering coefficient. An optimum test plan is developed using the D-optimality criterion. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm.

Keywords: binomial distribution, d-optimality, multiple censoring, optimal design, partially accelerated life testing, simulation study

Procedia PDF Downloads 311