Search results for: single machine total weighted tardiness

Authors: Purna Singh, Vaibhav Tripathi, Vinayak Kalluri, Sumit Roy

Abstract:

The present experimental work was carried out to investigate performance and emission characteristics of single cylinder diesel engine operating under dual-fuel mode with coconut oil blended with diesel. Coconut oil is one of the edible oil which is abundant in tropical countries and has properties like diesel. To this end, performance and emission parameters of diesel-coconut oil blends were reported in the current study. The results were drawn at different load steps of engine operation with 10% and 20% of coconut oil linearly blended with diesel. From the results, it was evident that coconut oil can be successfully replaced up to 20% of diesel without hampering the performance-emission characteristics of the existing diesel engine.

Keywords: coconut oil, alternative fuel, emissions, dual-fuel

Procedia PDF Downloads 186

Authors: Alireza Rasekh, Peter Sergeant, Jan Vierendeels

Abstract:

This paper copes with the numerical simulation for convective heat transfer in the stator disk of an axial flux permanent magnet (AFPM) electrical machine. Overheating is one of the main issues in the design of AFMPs, which mainly occurs in the stator disk, so that it needs to be prevented. A rotor-stator configuration with 16 magnets at the periphery of the rotor is considered. Air is allowed to flow through openings in the rotor disk and channels being formed between the magnets and in the gap region between the magnets and the stator surface. The rotating channels between the magnets act as a driving force for the air flow. The significant non-dimensional parameters are the rotational Reynolds number, the gap size ratio, the magnet thickness ratio, and the magnet angle ratio. The goal is to find correlations for the Nusselt number on the stator disk according to these non-dimensional numbers. Therefore, CFD simulations have been performed with the multiple reference frame (MRF) technique to model the rotary motion of the rotor and the flow around and inside the machine. A minimization method is introduced by a pattern-search algorithm to find the appropriate values of the reference temperature. It is found that the correlations are fast, robust and is capable of predicting the stator heat transfer with a good accuracy. The results reveal that the magnet angle ratio diminishes the stator heat transfer, whereas the rotational Reynolds number and the magnet thickness ratio improve the convective heat transfer. On the other hand, there a certain gap size ratio at which the stator heat transfer reaches a maximum.

Keywords: AFPM, CFD, magnet parameters, stator heat transfer

Procedia PDF Downloads 240

Authors: Illyas Md Isa, Maryam Musfirah Che Sobry, Mohamad Syahrizal Ahmad, Nurashikin Abd Azis

Abstract:

A single-walled carbon nanotube (SWCNT) that has been modified with magnesium layered hydroxide 3-(4-methoxyphenyl)propionic acid nanocomposite was proposed for the determination of uric acid and bisphenol A by square wave voltammetry. The results obtained denote that MLH-MPP nanocomposites enhance the sensitivity of the voltammetry detection responses. The best performance is shown by the modified carbon nanotube paste electrode (CNTPE) with the composition of single-walled carbon nanotube: magnesium layered hydroxide 3-(4-methoxyphenyl)propionic acid nanocomposite at 100:15 (% w/w). The linear range where the sensor works well is within the concentration 1.0 10-7 – 1.0 10-4 and 3.0 10-7 – 1.0 10-4 for uric acid and bisphenol A respectively with the limit of detection of 1.0 10-7 M for both organics. The interferences of uric acid and bisphenol A with other organic were studied and most of them did not interfere. The results shown for each experimental parameter on the proposed CNTPE showed that it has high sensitivity, good selectivity, repeatability and reproducibility. Therefore, the modified CNTPE can be used for the determination of uric acid and bisphenol A in real samples such as blood, plastic bottles and foods.

Keywords: bisphenol A, magnesium layered hydroxide 3-(4-methoxyphenyl)propionic acid nanocomposite, Nanocomposite, uric acid

Procedia PDF Downloads 201

Authors: Mohammad R. Aghakashkooli, Milutin G. Jovanovic

Abstract:

The brushless doubly-fed reluctance generator (BDFRG) is an emerging, medium-speed alternative to a conventional wound rotor slip-ring doubly-fed induction generator (DFIG) in wind energy conversion systems (WECS). It can provide competitive overall performance and similar low failure rates of a typically 30% rated back-to-back power electronics converter in 2:1 speed ranges but with the following important reliability and cost advantages over DFIG: the maintenance-free operation afforded by its brushless structure, 50% synchronous speed with the same number of rotor poles (allowing the use of a more compact, and more efficient two-stage gearbox instead of a vulnerable three-stage one), and superior grid integration properties including simpler protection for the low voltage ride through compliance of the fractional converter due to the comparatively higher leakage inductances and lower fault currents. Vector controlled pulse-width-modulated converters generally feature a much lower total harmonic distortion relative to hysteresis counterparts with variable switching rates and as such have been a predominant choice for BDFRG (and DFIG) wind turbines. Eliminating a shaft position sensor, which is often required for control implementation in this case, would be desirable to address the associated reliability issues. This fact has largely motivated the recent growing research of sensorless methods and developments of various rotor position and/or speed estimation techniques for this purpose. The main limitation of all the observer-based control approaches for grid-connected wind power applications of the BDFRG reported in the open literature is the requirement for pre-commissioning procedures and prior knowledge of the machine inductances, which are usually difficult to accurately identify by off-line testing. A model reference adaptive system (MRAS) based sensor-less vector control scheme to be presented will overcome this shortcoming. The true machine parameter independence of the proposed field-oriented algorithm, offering robust, inherently decoupled real and reactive power control of the grid-connected winding, is achieved by on-line estimation of the inductance ratio, the underlying rotor angular velocity and position MRAS observer being reliant upon. Such an observer configuration will be more practical to implement and clearly preferable to the existing machine parameter dependent solutions, and especially bearing in mind that with very little modifications it can be adapted for commercial DFIGs with immediately obvious further industrial benefits and prospects of this work. The excellent encoder-less controller performance with maximum power point tracking in the base speed region will be demonstrated by realistic simulation studies using large-scale BDFRG design data and verified by experimental results on a small laboratory prototype of the WECS emulation facility.

Keywords: brushless doubly fed reluctance generator, model reference adaptive system, sensorless vector control, wind energy conversion

Procedia PDF Downloads 55

Authors: Suparna Roy, P. Anantharaman

Abstract:

The aim of this work is to estimate some in-vitro antioxidant activities and total phenols of various extracts such as aqueous, acetone, ethanol, methanol extract of seaweeds and pigments content by Spectrophotometric method. The seaweeds were collected during 2016 from Rameshwaram, southeast coast of India. Among four different extracts, aqueous extracts from all seaweeds had minimum activity than acetone, methanol and ethanol. The Rhodophyta and Phaeophyta had high antioxidant activity in comparing to Chlorophyta. The highest total antioxidant activity was found in acetone extract fromTurbinaria decurrens (98.97±0.00%), followed by its methanol extract (98.81±0.60%) and ethanol extract (98.58±0.53%). The highest reducing power and H2O2 scavenging activity were found in acetone extract of Caulerpa racemosa (383.25±1.04%), and methanol extract from Caulerpa racemosa var. macrophysa (24.91±0.49%). The methanol extract from Caulerpa scalpelliformis contained the highest total phenol (85.23±0.12%). The Chloro-a and Chloro-b contents were the highest in Gracilaria foliifera (13.69±0.38% mg/gm dry wt.) and Caulerpa racemosa var. macrophysa (9.12 ±0.12% mg/gm dry wt.) likewise carotenoid was also the highest in Gracilaria foliifera (0.054±0.0003% mg/gm dry wt.) and Caulerpa racemosa var. macrophysa (0.04 ±0.002% mg/gm dry wt.). It can be concluded from this study that some seaweed extract can be used for natural antioxidant production, after further characterization to negotiate the side effect of synthetic, market available antioxidants.

Keywords: seaweeds, antioxidant, total phenol, pigment, Olaikuda, Vadakkadu, Rameshwaram

Procedia PDF Downloads 261

Authors: Yahia. Kourd, N. Guersi D. Lefebvre

Abstract:

In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor.

Keywords: faults diagnosis, neural networks, multi-models, squirrel-cage induction motor

Procedia PDF Downloads 623

Authors: Muge Unal, Cengiz Uslu, Mehmet Faruk Altunkasa

Abstract:

Walkability relates to the ability of the places to connect people with varied destinations within a reasonable amount of time and effort, and to offer visual interest in journeys throughout the network. So, the good quality of the physical environment and arrangement of walkway and sidewalk appear to be more crucial in influencing the pedestrian route choice. Also, proximity, connectivity, and accessibility are significant factor for walkability in terms of an equal opportunity for using public spaces. As a result, there are two important points for walkability. Firstly, the place should have a well-planned street network for accessible and secondly facilitate the pedestrian need for comfort. In this respect, this study aims to examine the both physical and bioclimatic comfort levels of the current condition of pedestrian route with reference to design criteria of a street to access the urban green spaces. These aspects have been identified as the main indicators for walkable streets such as continuity, materials, slope, bioclimatic condition, walkway width, greenery, and surface. Additionally, the aim was to identify the factors that need to be considered in future guidelines and policies for planning and design in urban spaces especially streets. Adana city was chosen as a study area. Adana is a province of Turkey located in south-central Anatolia. This study workflow can be summarized in four stages: (1) environmental and physical data were collected by referred to literature and used in a weighted criteria method to determine the importance level of these data , (2) environmental characteristics of pedestrian routes gained from survey studies are evaluated to hierarchies these criteria of the collected information, (3) and then each pedestrian routes will have a score that provides comfortable access to the park, (4) finally, the comfortable routes to park will be mapped using GIS. It is hoped that this study will provide an insight into future development planning and design to create a friendly and more comfort street environment for the users.

Keywords: comfort level, geographical information system (GIS), walkability, weighted criteria method

Procedia PDF Downloads 301

Authors: Mohand Yaghi, O. Kehinde

Abstract:

Background: For the diagnosis of prostate cancer Trans-rectal prostate biopsy (TRPB) is used commonly, the procedure is associated with infective complications. There is evidence that antibiotics (ABx) decrease infective events after TRPB, but different regimens are used. Aim: To systematically review different regimens of prophylactic oral antibiotics in TRPB. Design: Medline, Embase, Clinical trials site, and Cochrane library were searched, experts were consulted about relevant studies. Randomized clinical trials (RCT) conducted in the last twenty years, which investigated different oral antibiotic regimens in TRPB, and compared their efficacy to reduce infectious complications were analyzed. Measurements: Primary outcomes were bacteriuria, urinary tract infection (UTI), fever, bacteremia, sepsis. Secondary outcomes were hospitalization rate, and the prevalence of ABx-resistant bacteria. Results: Nine trials were eligible with 3012 patients. Antibiotics prevented bacteriuria (3.5% vs. 9.88%), UTI (4.46% vs. 9.75%), and hospitalization (0.21% vs. 2.13%) significantly in comparison with placebo or no treatment. No significant difference was found in all outcomes of the review between the single dose regimen and the 3 days. The single dose regimen was as effective as the multiple dose except in Bacteriuria (6.75% vs. 3.25%), and the prevalence of ABx-resistant bacteria (1.57% vs. 0.27%). Quinolones reduced only UTI significantly in comparison with other antibiotics. Lastly, Ciprofloxacin is the best Quinolone to prevent UTI, and hospitalization. Conclusion: it is essential to prescribe prophylactic Antibiotics in TRPB. No conclusive evidence could be claimed about the superiority of the multiple or the 3 days regimens to the single dose regimen. Unexpectedly, ABx-resistant bacteria was identified more often in the single dose cohorts.

Keywords: infection, prostate cancer, sepsis, TRPB

Procedia PDF Downloads 363

Authors: Paam Bidaya

Abstract:

The red seaweed Gracilaria fisheri, widely distributed along Thailand's southern coastlines, has been discovered to be edible. Sulfated polysaccharides from G. fisheri were extracted in low-temperature (25 °C) water. Seaweed polysaccharides (SPs) have been shown to have various advantageous biological effects. This study aims to investigate total phenolic content and antioxidant capacity of G. fisheri extract. The total phenolic content of G. fisheri extract was determined using Folin-Cioucalteu method and calculated as gallic acid equivalents (GAE). The antioxidant activity of G. fisheri extract was performed via 2, 2-diphenyl-1- picrylhydrazyl (DPPH) free radical scavenging assay and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacity assays. The findings exhibited a strong correlation between antioxidant activity and the total phenol contents. In addition, DPPH and ABTS assays showed that G. fisheri extract showed antioxidant activities as a concentration-dependent manner. The IC50 values of G. fisheri extract were 902.19 μg/mL ± 0.785 and 727.98 μg/mL ± 0.822 for DPPH and ABTS, respectively. Vitamin C was used as a positive control in DPPH assay, while Trolox was used as a positive control in ABTS assay. To conclude, G. fisheri extract consists of a high amount of total phenolic content, which exhibit a significant antioxidant activity. However, further investigation regarding antioxidant activity should be performed in order to identify the mechanism of Gracilaria fisheri action.

Keywords: ABTS assay, DPPH assay, sulfated polysaccharides, total phenolic content

Procedia PDF Downloads 187

Authors: Forouzan Salehi Fergeni

Abstract:

Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

Procedia PDF Downloads 30

Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

Abstract:

This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

Procedia PDF Downloads 449

Authors: Ammaarah Gamieldien

Abstract:

Depression is a very common and serious mental illness that has a significant impact on both the social and economic aspects of sufferers worldwide. This study aimed to investigate the association between body mass index (BMI), blood pressure, and lipid profiles with mental health symptoms in university students. Secondary objectives included the associations between the variables (BMI, blood pressure, and lipids) with themselves, as they are key factors in cardiometabolic disease. Sixty-three (63) students participated in the study. Thirty-two (32) were assigned to the control group (minimal-mild depressive symptoms), while 31 were assigned to the depressive group (moderate to severe depressive symptoms). Montgomery-Asberg Depression Rating Scale (MADRS) and Beck Depression Inventory (BDI) were used to assess depressive scores. Anthropometric measurements such as weight (kg), height (m), waist circumference (WC), and hip circumference were measured. Body mass index (BMI) and ratios such as waist-to-hip ratio (WHR) and waist-to-height ratio (WtHR) were also calculated. Blood pressure was measured using an automated AfriMedics blood pressure machine, while lipids were measured using a CardioChek plus analyzer machine. Statistics were analyzed via the SPSS statistics program. There were no significant associations between anthropometric measurements and depressive scores (p > 0.05). There were no significant correlations between lipid profiles and depression when running a Spearman’s rho correlation (P > 0.05). However, total cholesterol and LDL-C were negatively associated with depression, and triglycerides were positively associated with depression after running a point-biserial correlation (P < 0.05). Overall, there were no significant associations between blood pressure measurements and depression (P > 0.05). However, there was a significant moderate positive correlation between systolic blood pressure and MADRS scores in males (P < 0.05). Depressive scores positively and strongly correlated to how long it takes participants to fall asleep. There were also significant associations with regard to the secondary objectives. This study indicates the importance of determining the prevalence of depression among university students in South Africa. If the prevalence and factors associated with depression are addressed, depressive symptoms in university students may be improved.

Keywords: depression, blood pressure, body mass index, lipid profiles, mental health symptoms

Procedia PDF Downloads 56

Authors: P. S. Percin, O. Inanli, S. Karakaya

Abstract:

Bitter melon (Momordica charantia) is a medicinal vegetable, which is used traditionally to remedy diabetes. Bitter melon contains several classes of primary and secondary metabolites. In traditional Turkish medicine bitter melon is used for wound healing and treatment of peptic ulcers. Nowadays, bitter melon is used for the treatment of diabetes and ulcerative colitis in many countries. The main constituents of bitter melon, which are responsible for the anti-diabetic effects, are triterpene, protein, steroid, alkaloid and phenolic compounds. In this study total phenolics, total carotenoids and β-carotene contents of mature and immature bitter melons were determined. In addition, in vitro α-amylase and α-glucosidase activities of mature and immature bitter melons were studied. Total phenolic contents of immature and mature bitter melon were 74 and 123 mg CE/g bitter melon respectively. Although total phenolics of mature bitter melon was higher than that of immature bitter melon, this difference was not found statistically significant (p > 0.05). Carotenoids, a diverse group of more than 600 naturally occurring red, orange and yellow pigments, play important roles in many physiological processes both in plants and humans. The total carotenoid content of mature bitter melon was 4.36 fold higher than the total carotenoid content of immature bitter melon. The compounds that have hypoglycaemic effect of bitter melon are steroidal saponins known as charantin, insulin-like peptides and alkaloids. α-Amylase is one of the main enzymes in human that is responsible for the breakdown of starch to more simple sugars. Therefore, the inhibitors of this enzyme can delay the carbohydrate digestion and reduce the rate of glucose absorption. The immature bitter melon extract showed α-amylase and α-glucosidase inhibitory activities in vitro. α-Amylase inhibitory activity was higher than that of α-glucosidase inhibitory activity when IC50 values were compared. In conclusion, the present results provide evidence that aqueous extract of bitter melon may have an inhibitory effect on carbohydrate breakdown enzymes.

Keywords: bitter melon, in vitro antidiabetic activity, total carotenoids, total phenols

Procedia PDF Downloads 232

Authors: Doseong Eom, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.

Keywords: building energy management, machine learning, operation planning, simulation-based optimization

Procedia PDF Downloads 316

Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

Abstract:

Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

Procedia PDF Downloads 121

Authors: Haw-Long Lee, Win-Jin Chang, Yu-Ching Yang

Abstract:

In the paper, the coupled equation of motion for the dynamic displacement of a fullerene moving in a (10,10) single-walled carbon nanotube (SWCNT) is derived using nonlocal Timoshenko beam theory, including the effects of rotary inertia and shear deformation. The effects of confined stiffness between the fullerene and nanotube, foundation stiffness, and nonlocal parameter on the dynamic behavior are analyzed using the Runge-Kutta Method. The numerical solution is in agreement with the analytical result for the special case. The numerical results show that increasing the confined stiffness and foundation stiffness decrease the dynamic displacement of SWCNT. However, the dynamic displacement increases with increasing the nonlocal parameter. In addition, result using the Euler beam theory and the Timoshenko beam theory are compared. It can be found that ignoring the effects of rotary inertia and shear deformation leads to an underestimation of the displacement.

Keywords: single-walled carbon nanotube, nanoparticle delivery, Nonlocal Timoshenko beam theory, Runge-Kutta Method, Van der Waals force

Procedia PDF Downloads 369

Authors: Adamu Abdullahi, On Francisca, Saidu Isah Rambo, G. N. Obunadike, D. T. Chinyio

Abstract:

The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings: The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed.

Keywords: algorithm, CoAP, DoS, IoT, machine learning

Procedia PDF Downloads 63

Authors: Muhanad Al-Jubouri

Abstract:

A local scour is the most important of the several scours impacting bridge performance and security. Even though scour is widespread in bridges, especially during flood seasons, the experimental tests could not be applied to many standard highway bridges. A computational fluid dynamics numerical model was used to solve the problem of calculating local scouring and deposition for non-cohesive silt and clear water conditions near single and double cylindrical piers with the effect of floating debris. When FLOW-3D software is employed with the Rang turbulence model, the Nilsson bed-load transfer equation and fine mesh size are considered. The numerical findings of single cylindrical piers correspond pretty well with the physical model's results. Furthermore, after parameter effectiveness investigates the range of outcomes based on predicted user inputs such as the bed-load equation, mesh cell size, and turbulence model, the final numerical predictions are compared to experimental data. When the findings are compared, the error rate for the deepest point of the scour is equivalent to 3.8% for the single pier example.

Keywords: local scouring, non-cohesive, clear water, computational fluid dynamics, turbulence model, bed-load equation, debris

Procedia PDF Downloads 64

Authors: Shoji Matsumoto

Abstract:

Friction Stir Welding (FSW) has been widely used in the automotive, aerospace, and high-tech industries due to its superior mechanical properties after welding. However, when it becomes a matter to perform a high-quality joint using FSW, it is necessary to secure an advanced tilt angle (usually 1 to 5 degrees) using a dedicated FSW machine and to use a joint structure and a restraining jig that can withstand the tool pressure applied during the jointing process using a highly rigid processing machine. One issue that has become a challenge in this process is ‘productivity and versatility’. To solve this problem, we have conducted research and development of multi-functioning machines and robotics with FSW tools, which combine cutting/milling and FSW functions as one in recent years. However, the narrow process window makes it prone to welding defects and lacks repeatability, which makes a limitation for FSW its use in the fields where precisions required. Another reason why FSW machines are not widely used in the world is because of the matter of very high cost of ownership.

Keywords: synchronized, stir, welding, friction, traveling speed, synchronized stir welding, friction stir welding

Procedia PDF Downloads 34

Authors: Albert Acheampong, Tamer Elshandidy

Abstract:

We investigate the extent to which sustainability disclosure from the narrative section of European banks’ annual reports improves analyst forecast accuracy. We capture sustainability disclosure using a machine learning approach and use forecast error to proxy analyst forecast accuracy. Our results suggest that sustainability disclosure significantly improves analyst forecast accuracy by reducing the forecast error. In a further analysis, we also find that the induction of Directive 2014/95/European Union (EU) is associated with increased disclosure content, which then reduces forecast error. Collectively, our results suggest that sustainability disclosure improves forecast accuracy, and the induction of the new EU directive strengthens this improvement. These results hold after several further and robustness analyses. Our findings have implications for market participants and policymakers.

Keywords: sustainability disclosure, machine learning, analyst forecast accuracy, forecast error, European banks, EU directive

Procedia PDF Downloads 61

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

Procedia PDF Downloads 472

Authors: B. V. Lande, Suhas Kongare

Abstract:

Fuel blend of alcohol and conventional hydrocarbon fuels for a spark ignition engine can increase the fuel octane rating and the power for a given engine displacement and compression ratio. The greatest advantage of ethanol as a fuel in SI Engines is its high octane number. The efficiency of an SI engine that is the ability to convert fuel energy to mechanical energy, mainly depends on the compression ratio. It is, therefore, an advantage to increase this as much as possible. The major restraint is the fuel octane number – high octane fuels can be used with high compression ratios, thus yielding higher energy efficiency. This work investigates to suggest suitable ethanol gasoline blend and compression ratio for single cylinder four strokes SI Engine on the basis of performance and exhaust emissions. A single cylinder four stroke SI Engine was tested with different blend of ethanol – gasoline like E5 (5% ethanol +95% gasoline), E10 (10% ethanol + 90% gasoline) E15 (15% ethanol + 85% petrol) and E20 ( 20% + 80% gasoline) with Variable compression ratio. The performance parameter evaluated BSFC, Brake thermal efficiency and also exhaust emission CO2, Co & HC%. The result showed that higher compression ratio improved engine Performance and reduction in exhaust emission.

Keywords: blend, compression ratio, ethanol, performance, blend

Procedia PDF Downloads 389

Authors: Henny A. Dien, Roike I. Montolalu, Feny Mentang, Jupni Keno, Reynerd S. Burdam, Siegfried Berhimpon

Abstract:

Fish Sausages became popular nowadays, because of high nutritious and low in cholesterol. However, this food is also highly perishable and often contaminated by pathogen bacteria. Edible film was made from myofibril of Black Marlin (Makaira indica) waste, with addition of liquid smoke 0.8%. The aim of this study were to determine the TPC, total coliform and Escherichia coli in fish sausages coated with smoked edible film, and stored in room temperature (26-29oC), and refrigerator (5-10oC). Results shown that TPC in fish sausages coated with smoked edible film were lower than that of without coated, both for storage in room temperature and in refrigerator. Total coliform in coated with smoked edible film and stored in room temperature ranged between 7-120 MPN/g (1-4 days), while stored in refrigerator ranged between 7-93 MPN/g (1-6 days); while fish sausages coated with edible film without liquid smoke were 7-240 MPN/g (1-4 days) in room temperature, and 7-150 MPN/g in refrigerator. Total E. coli of fish sausages coated with smoked edible film and stored in room temperature ranged between 3-4 MPN/g (1-4 days), while stored in refrigerator ranged were 3 MPN/g (1-6 days); while fish sausages coated with edible film without smoked both stored in room temperature and in refrigerator, shown total E. coli 3 MPN/g during 4 days in room temperature, and 6 days in refrigerator. Total E. coli of sausages without coated stored in room temperature ranged between 7-24 MPN/g, and that of stored in refrigerator ranged between 3-4 MPN/g.

Keywords: smoke liquid, edible film, coating, sausages

Procedia PDF Downloads 439

Authors: Mihai I. Sturza, Laura T. Corredor, Kaustuv Manna, Gizem A. Cansever, Tushar Dey, Andrey Maljuk, Olga Kataeva, Sabine Wurmehl, Anja Wolter, Bernd Buchner

Abstract:

Recently, the iridate double perovskite Sr₂YIrO₆ has attracted considerable attention due to the report of unexpected magnetism in this Ir⁵⁺ material, in which according to the Jeff model, a non-magnetic ground state is expected. Structural, magnetic and thermodynamic investigations of Sr₂YIrO₆ and Ba2YIrO6 single crystals, with emphasis on the temperature and magnetic field dependence of the specific heat will be presented. The single crystals were grown by using SrCl₂ and BaCl₂ as flux. Single-crystal X-ray diffraction measurements performed on several crystals from different preparation batches showed a high quality of the crystals, proven by the good internal consistency of the data collected using the full-sphere mode and an extremely low R factor. In agreement with the expected non-magnetic ground state of Ir⁵⁺ (5d4) in these iridates, no magnetic transition is observed down to 430 mK. Moreover, our results suggest that the low-temperature anomaly observed in the specific heat is not related to the onset of long-range magnetic order. Instead, it is identified as a Schottky anomaly caused by paramagnetic impurities present in the sample, of the order of

Keywords: double perovskites, iridates, self-flux grown synthesis, spin-orbit coupling

Procedia PDF Downloads 322

Authors: Said Alshukri, Mazhar Hussain Malik

Abstract:

Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

Procedia PDF Downloads 66

Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

Abstract:

We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

Procedia PDF Downloads 536

Authors: Bhaveek Maini, Sanjay Dhanka, Surita Maini

Abstract:

Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively.

Keywords: epileptic seizure recognition, logistic regression, Naïve Bayes, machine learning

Procedia PDF Downloads 50

Authors: Devendra Kr Tayal, Vrinda Gupta, Aastha Bansal, Khushi Singh, Sristi Sharma, Hunny Gaur

Abstract:

In today's world, suicide is a serious problem. In order to save lives, early suicide attempt detection and prevention should be addressed. A good number of at-risk people utilize social media platforms to talk about their issues or find knowledge on related chores. Twitter and Reddit are two of the most common platforms that are used for expressing oneself. Extensive research has already been done in this field. Through supervised classification techniques like Nave Bayes, Bernoulli Nave Bayes, and Multiple Layer Perceptron on a Reddit dataset, we demonstrate the early recognition of suicidal ideation. We also performed comparative analysis on these approaches and used accuracy, recall score, F1 score, and precision score for analysis.

Keywords: machine learning, suicide ideation detection, supervised classification, natural language processing

Procedia PDF Downloads 85

Authors: Luai Z. Hasoun, Steven W. Bailey, Kitti K. Outlaw, June E. Ayling

Abstract:

Human skin color is thought to have evolved to balance sufficient photochemical synthesis of vitamin D versus the need to protect not only DNA but also folate from degradation by ultraviolet light (UV). Although the risk of DNA damage and subsequent skin cancer is related to light skin color, the effect of UV on skin folate of any species is unknown. Here we show that UVA irradiation at 13 mW/cm2 for a total exposure of 187 J/cm2 (similar to a maximal daily equatorial dose) induced a significant loss of total folate in epidermis of ex vivo white skin. No loss was observed in black skin samples, or in the dermis of either color. Interestingly, while the concentration of 5 methyltetrahydrofolate (5-MTHF) fell in white epidermis, a concomitant increase of tetrahydrofolic acid was found, though not enough to maintain the total pool. These results demonstrate that UVA indeed not only decreases folate in skin, but also rearranges the pool components. This could be due in part to the reported increase of NADPH oxidase activity upon UV irradiation, which in turn depletes the NADPH needed for 5-MTHF biosynthesis by 5,10-methylenetetrahydrofolate reductase. The increased tetrahydrofolic acid might further support production of the nucleotide bases needed for DNA repair. However, total folate was lost at a rate that could, with strong or continuous enough exposure to ultraviolet radiation, substantially deplete light colored skin locally, and also put pressure on total body stores for individuals with low intake of folate.

Keywords: depletion, folate, human skin, ultraviolet

Procedia PDF Downloads 379

Authors: G. Krishna Mohana Rao, P. Ravi Kumar

Abstract:

Milling is the process of removing unwanted material with suitable tool. Even though the milling process is having wider application, the vibration of machine tool and work piece during the process produces chatter on the products. Various methods of preventing the chatter have been incorporated into machine tool systems. Damper is cut into equal number of parts. Each part is called as finger. Multiple fingers were inserted in the hollow portion of the shank to reduce tool vibrations. In the present work, nonlinear static and dynamic analysis of the damper inserted end milling cutter used to reduce the chatter was done. A comparison is made for the milling cutter with multiple dampers. Surface roughness was determined by machining with multiple finger inserted milling cutters.

Keywords: damping inserts, end milling, vibrations, nonlinear dynamic analysis, number of fingers

Procedia PDF Downloads 512