scispace - formally typeset
Search or ask a question

Showing papers in "International journal of engineering and technology in 2021"


Journal ArticleDOI
TL;DR: HealthTracker serves as a model for both a recording and a recommending system that will serve as a bridge for future personal health systems to build from when running on mobile platforms.
Abstract: This paper describes HealthTracker, a mobile health application to record, store, display, and analyze personal health data. This application allows an individual to log several types of data encompassing their personal health. HealthTracker serves as a model for both a recording and a recommending system. Its goal is to serve as a bridge for future personal health systems to build from. A person’s health information is displayed in an easy-to-understand manner but is also practical for medical professionals. Users should find the system useful and effective no matter if they use it simply or extensively. Currently, the system serves as a prototype for determining the practical applications for smart health systems running on mobile platforms.

8 citations


Journal ArticleDOI
TL;DR: In this article, a feasibility analysis was carried out for a hybrid energy system using solar and wind energy sources to supply to uninterrupted electricity demand of a region with 100 villas in Izmir, Turkey.
Abstract: Nowadays, off-grid systems, which do not require grid connection investment instead of grid connected systems, have become quite feasible. In this study, a feasibility analysis was carried out for a hybrid energy system using solar and wind energy sources to supply to uninterrupted electricity demand of a region with 100 villas in Izmir, Turkey. It has been shown that how changes cost of the hybrid energy system sizing according to the control strategies by using the HOMER software. In the paper, two different control strategies are determined as Cycle Charging (CC) and Load Following (LF), and then the control strategies are compared. According to the results obtained as a result of the simulations, it has been revealed that the research region to operate with CC can supply to the electrical energy demand with lower capacity system architecture. The CC was found to be more suitable for the research region than LF in terms of both Cost of Energy (COE) and Net Preset Cost (NPC).

6 citations


Journal ArticleDOI
TL;DR: A learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree, was proposed and the results showed that the En ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model.
Abstract: Learning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model.

6 citations


Journal ArticleDOI
TL;DR: It is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1, CA, precision & recall.
Abstract: Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983.

5 citations


Journal ArticleDOI
TL;DR: A student behavior analysis feature on self-developed Virtual Learning Environment (VLE) called Enterprise Hybrid Online Learning (ETHOL) is developed and students’ data collected includes data on online activities, personal data, and survey data on student learning styles.
Abstract: Students have different learning styles when studying online. Meanwhile, lecturers use the same method for all students who take their online lectures. These different learning styles can affect the level of understanding and the results obtained by students. By knowing student learning styles, lecturers are expected to be able to use the right way in delivering material. In this research, we developed a student behavior analysis feature on self-developed Virtual Learning Environment (VLE) called Enterprise Hybrid Online Learning (ETHOL). Students’ data collected includes data on online activities, personal data, and survey data on student learning styles. User behavior analysis was carried out by dividing into three clusters: average scores, time to collect assignments, and student learning styles. The clustering method used is the Hierarchical KMeans. The results obtained are students who have the habit of collecting assignments on time have higher scores than others. In addition, the lecturer is able to see the results of the analysis of the behavior and learning styles of each student. These results can be used as information in delivering lecture material.

4 citations


Journal ArticleDOI
TL;DR: The purpose of this game is to increase the interest and insight of children on Scout activities and it can be seen that after playing the game, 95,7% of children thought it is exciting, and 87% of them became enthusiastic join scouting activity.
Abstract: Scouts is the scouting level after the cub scout aged 11-15 years old. In their age range, they can use logical thinking in the form of physical objects to solve a problem. The development of the Scout Movement has had ups and downs, and recently the number of children interest in scouting activities decreases. The impact is the scouting insight they get isn't optimal. One strategy to solve this problem is by developed forms, tools, and learning media of scouting. Game is one of the learning media that can be used to create effective learning. The educational game is a popular learning media and widely developed by experts, as well as in Indonesia. Unfortunately, in the field of scouting, educational games are less developed. In this research, the author will build an educational scouting game for scouts. In the scouts level, they began to be introduced about communication code, skills, natural recognition, and others. Games created using Embodied Interaction technology. This technology allows users to control the game using body movement. The purpose of this game is to increase the interest and insight of children on Scout activities. From the results of research that has been done, it can be seen that after playing the game, 95,7% of children thought it is exciting, and 87% of them became enthusiastic join scouting activity. Based on the results of the pre-test and post-test, scouting insight increased after playing the game with an average percentage of increased insight being 18.7%.

3 citations


Journal ArticleDOI
TL;DR: In this article, the authors described concrete damage plasticity theory under experiment on concrete cylinder considering uni-axial compression loading and interpreted with analytical data calculated using CEB-FIP model code equation.
Abstract: Concrete is a quasi-brittle material and shows different behavior in compression and tension. It shows elastic behavior at initial stage and damage-plasticity behavior beyond elastic limit. Therefore, development of material behavior model of concrete is a complex phenomenon. In this study, concrete damage plasticity theory has been described under experiment on concrete cylinder considering uni-axial compression loading and interpreted with analytical data calculated using CEB-FIP model code equation. The code has divided the stress-strain curve for concrete compression into three sections according to concrete’s elastic and non-elastic behaviors. Those three sections have been considered to calculate analytical data. In experiment, concrete behavior has been observed in two phases. The damage value for different stresses at the various points on the stress strain curve has been calculated. According to analytical data, the concrete shows elastic behavior up to 8.3MPa stress point and no damage occur in the concrete within the limit. However, in experimental data, concrete shows elastic behavior up to only 2.28MPa and damage occurred beyond the stress. Finally, the percentage of damage of concrete due to compression obtained from analysis and experiment has been assessed and compared. Above 32 percent of concrete damage is found for 22.5 MPa in both cases.

3 citations


Journal ArticleDOI
TL;DR: In this article, a method for selecting the modulation index (ma) and frequency ratio (mf) using Cubic Spline Interpolation to get minimum harmonic of SPWM inverter that generated is presented.
Abstract: Harmonic content is an important parameter in relation to the power generated by inverter. In power conversion technology of inverter, sinusoidal pulse width modulation (SPWM) is the most popular used by many researchers. The advantages of SPWM inverter operation as a conversion technique compared to other inverter types can be seen from the low harmonic distortion in the output voltage of inverter. Therefore, the SPWM signal generation process becomes a determining factor for the performance of the overall system. This paper present the method for selecting the modulation index (ma) and frequency ratio (mf) using Cubic Spline Interpolation to get minimum harmonic of SPWM inverter that generated. Both parameters controlled with varied values digitally using microcontroller to generate SPWM, then the output of inverter with and without LC filter was investigated. The results show that the use of Cubic Spline Interpolation method in the selection of ma and mf precisely managed to produce SPWM inverter with minimum harmonic content. At the inverter output, the use of LC filter is not only useful for converting SPWM signals to sinusoidal waveforms but can also reduce harmonic content significantly less than 3 %.

3 citations


Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the thermal transport characteristics of Casson nanofluid through a porous microtube under the effect of streaming potential and constant pressure gradient with electrokinetic effect associated with applied magnetic field.
Abstract: Thermal transport characteristics of Casson nanofluid through a porous microtube is analyzed under the effect of streaming potential and constant pressure gradient with electrokinetic effect associated with applied magnetic field. An analytical solution of the velocity and temperature distribution of Casson-nano fluid through the porous microtube related to combining effects of electromagnetohydrodynamics forces under the effect of streaming potential have been obtained. The significant influences of various non-dimensional parameters on velocity and temperature profiles are discussed in this study. Also, it is revealed the impact of nano particles on flow transport and heat transfer phenomenon. Furthermore, the Nusselt number is calculated analytically. The variations of pertinent parameters such as Hartmann number, Darcy number,Casson parameter, volume friction parameter of nanoparticles, joule heating parameter are delineated graphically and discussed in details.

3 citations


Journal ArticleDOI
TL;DR: This paper aims to provide a solution of the dimensionality problem by proposing a new mixed model for heart disease prediction based on the Naive Bayes algorithm and several machine learning techniques including Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest.
Abstract: These days, heart disease comes to be one of the major health problems which have affected the lives of people in the whole world. Moreover, death due to heart disease is increasing day by day. So the heart disease prediction systems play an important role in the prevention of heart problems. Where these prediction systems assist doctors in making the right decision to diagnose heart disease easily. The existing prediction systems suffering from the high dimensionality problem of selected features that increase the prediction time and decrease the performance accuracy of the prediction due to many redundant or irrelevant features. Therefore, this paper aims to provide a solution of the dimensionality problem by proposing a new mixed model for heart disease prediction based on (Naive Bayes method, and machine learning classifiers). In this study, we proposed a new heart disease prediction model (NB-SKDR) based on the Naive Bayes algorithm (NB) and several machine learning techniques including Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest. This prediction model consists of three main phases which include: preprocessing, feature selection, and classification. The main objective of this proposed model is to improve the performance of the prediction system and finding the best subset of features. This proposed approach uses the Naive Bayes technique based on the Bayes theorem to select the best subset of features for the next classification phase, also to handle the high dimensionality problem by avoiding unnecessary features and select only the important ones in an attempt to improve the efficiency and accuracy of classifiers. This method is able to reduce the number of features from 13 to 6 which are (age, gender, blood pressure, fasting blood sugar, cholesterol, exercise induce engine) by determining the dependency between a set of attributes. The dependent attributes are the attributes in which an attribute depends on the other attribute in deciding the value of the class attribute. The dependency between attributes is measured by the conditional probability, which can be easily computed by Bayes theorem. Moreover, in the classification phase, the proposed system uses different classification algorithms such as (DT Decision Tree, RF Random Forest, SVM Support Vector machine, KNN Nearest Neighbors) as a classifiers for predicting whether a patient has heart disease or not. The model is trained and evaluated using the Cleveland Heart Disease database, which contains 13 features and 303 samples. Different algorithms use different rules for producing different representations of knowledge. So, the selection of algorithms to build our model is based on their performance. In this work, we applied and compared several classification algorithms which are (DT, SVM, RF, and KNN) to identify the best-suited algorithm to achieve high accuracy in the prediction of heart disease. After combining the Naive Bayes method with each one of these previous classifiers the performance of these combines algorithms is evaluated by different performance metrics such as (Specificity, Sensitivity, and Accuracy). Where the experimental results show that out of these four classification models, the combination between the Naive Bayes feature selection approach and the SVM RBF classifier can predict heart disease with the highest accuracy of 98%. Finally, the proposed approach is compared with another two systems which developed based on two different approaches in the feature selection step. The first system, based on the Genetic Algorithm (GA) technique, and the second uses the Principal Component Analysis (PCA) technique. Consequently, the comparison proved that the Naive Bayes selection approach of the proposed system is better than the GA and PCA approach in terms of prediction accuracy.

2 citations


Journal ArticleDOI
TL;DR: In this paper, a tool for HR work, in context of talent management and how to utilize people skills and productivity analytics to improve team performance and related KPIs is proposed, where a project data-based case study is illustrated, in which a set of developer and content marketers were analyzed as core team members.
Abstract: In most companies and organizations, performance is related to talent management and skills to analyze what and why people are working on. However, many companies do fail to implement a long-term strategy for the performance enhancement activities, considering the talents they have recruited. In this article, we propose a tool for HR work, in context of talent management and how to utilize people skills and productivity analytics to improve team performance and related KPIs. A project data-based case study is illustrated, in which a set of devel-oper and content marketers were analyzed as core team members. In practice, the presented framework makes an important contribution to decision-making activities, where people analytics and proper software tools are used to build new novel knowledge into talent pool of the team. With the framework-based analysis, it is possible to analytically compare team members’ performance and enhance the team’s skill and structural development which means that we can employ analytics to find best performers and set their roles for more optimally working teams. Our research supports the concept of using the right framework can make a big positive difference in team analytics.

Journal ArticleDOI
TL;DR: In this article, a finite difference model using Crank Nicholson implicit method was developed based on the two dimensional unsteady state heat conduction to predict the temperature profile of the mass concrete at early ages of cement hydration.
Abstract: Thermally induced cracks due to temperature gradient in mass concrete have adverse effects on its durability and service life. Heat released during the hydration of Portland cement in early age mass concrete can be quite excessive depending on the ambient temperature, cement content of the concrete mix and the size. Finite difference model using Crank Nicholson implicit method was developed based on the two dimensional unsteady state heat conduction. Optimized MATLAB based software was developed for simulation and data visualization. A mass concrete block cast with standard mix ratio and water cement ratio was used to verify the efficacy of the model. Type-K thermocouple and digital thermometer were used to monitor the temperature at time intervals. The temperature profile showed a hotter core and cooler surface except for the initial placement temperature, which exhibited a uniform temperature for all thermocouple locations. Peak temperature values were recorded within the first day of concrete placement. The model successfully predicted the temperature profile of the mass concrete at early ages of cement hydration. With the knowledge of the ambient temperature and the configuration of the mass concrete, the model can reliably predict the temperature profile from which potential for thermal cracks occurrence can be determined to enable suitable proactive preventive and control measures.

Journal ArticleDOI
TL;DR: In this paper, a system for screening method to detect mastitis in dairy cow milk using Electrical Conductivity (EC) and Power of Hydrogen (pH) sensor is built.
Abstract: This study build a system for screening method to detect mastitis in dairy cow milk using Electrical Conductivity (EC) and Power of Hydrogen (pH) sensor. The value of EC and pH sensor is analyze using fuzzy logic to clarify the truth value between it. Mastitis in cows can cause loss and decrease milk production and quality in the dairy farmer industry. Currently, detecting mastitis in cow’s milk still done manually by looking at the color change of the milk and analyzing the cow behavior. This paper has designed a mastitis detection system using the Mamdani type fuzzy inference system and the final result will be displayed on an Android-based smartphone. From the test result, it was found that the system has 79.2% detection accuracy value. This system is suitable for alternative screening method that used to detect mastitis in dairy cow milk.

Journal ArticleDOI
TL;DR: The real-time Indian sign language (ISL) recognition system is developed using the hybrid CNN-RNN architecture to break the hurdles present in the communication between hearing-impaired people and normal people.
Abstract: Dynamic hand gesture recognition is a challenging task of Human-Computer Interaction (HCI) and Computer Vision. The potential application areas of gesture recognition include sign language translation, video gaming, video surveillance, robotics, and gesture-controlled home appliances. In the proposed research, gesture recognition is applied to recognize sign language words from real-time videos. Classifying the actions from video sequences requires both spatial and temporal features. The proposed system handles the former by the Convolutional Neural Network (CNN), which is the core of several computer vision solutions and the latter by the Recurrent Neural Network (RNN), which is more efficient in handling the sequences of movements. Thus, the real-time Indian sign language (ISL) recognition system is developed using the hybrid CNN-RNN architecture. The system is trained with the proposed CasTalk-ISL dataset. The ultimate purpose of the presented research is to deploy a real-time sign language translator to break the hurdles present in the communication between hearing-impaired people and normal people. The developed system achieves 95.99% top-1 accuracy and 99.46% top-3 accuracy on the test dataset. The obtained results outperform the existing approaches using various deep models on different datasets.

Journal ArticleDOI
TL;DR: In this article, a single slope solar still has been constructed from materials available on the local market, and series of experiments were carried out on solar still using brackish water, where the average analysis results obtained showed a removal efficiency of 98.16%, 98.42%, 97.43% and 95.39% for TDS, electrical conductivity, hardness water and chloride, respectively.
Abstract: A single sloped solar still were designed and fabricated to operate under Djibouti city weather condition during the period April-May 2019. In this study, a single slope solar still has been constructed from materials available on the local market. Series of experiments were carried out on solar still using brackish water. Ambient temperature, water temperature in basin, absorber plate temperature, glass cover temperature and vapor temperature were measured along with the hourly water production. This study has revelead that the vapor temperature is always above that the others temperatures for all the four days of experiments. Then, the effect of water amount in the basin on productivity of solar still was investigated using different amount of water 6 L, 8 L, 10 L and 12 L. The experimental results show that the total accumulated distillate output for the single slope solar still is 2490 mL, 2390 mL, 2240 mL and 2015 mL, respectively. However, it is observed that with increase in basin water amount distillate water production decreases. On the other hand, the effect of wind speed on the daily productivity of solar still is evaluated. Experimental investigations show that the cumulative productivity increases when the wind speed average increase. Finally, water quality analyses were conducted before and after the experiments. The average analysis results obtained showed a removal efficiency of 98.16%, 98.42%, 97.43% and 95.39% for TDS, electrical conductivity, hardness water and chloride, respectively. It was also observed that data obtained of the product water were within the normal range prescribed by World Health Organization (WHO) standards.

Journal ArticleDOI
TL;DR: In this article, the effect of the percentage of tricalcium silicate (C 3 S) on the quality of composite cement with free lime 2% with variations of C 3 S in clinkers, namely 55, 57, 59, 61, 63, 65, and 67.
Abstract: Composite cement products produced by national cement factories in Indonesia should follow the required quality standards. The quality standard of composite cement refers to the SNI 7064:2014. Some physical parameters of the quality standards set are mortar compressive strength and autoclave expansion. Compressive strength is influenced by C 3 S and C 2 S in the clinker. The reaction of the formation of mineralogical compounds occurs when clinkers formed. Whereas the expansion by autoclave is influenced by the levels of free lime in the cement. This research was conducted to determine the effect of the percentage of tricalcium silicate (C 3 S) on the quality of cement with free lime 2% with variations of C 3 S in clinkers, namely 55%, 57%, 59%, 61%, 63%, 65%, and 67%. Physical parameters tested in this study are compressive strength of mortar, blaine, and autoclave expansion. While the chemical parameters tested in this study are free lime in cement and SO 3 . Based on the research, it was found that if the same percentage of C 3 S quality of cement having FCaO 2%, the ideal condition of the development of compressive strength for FcaO > 2%, 3 to 7 days was at the percentage of C 3 S clinker of 63,48%. Whereas the development of ideal compressive strength for 7 to 28 days is at the clinker C 3 S percentage of 64,85%. For FCaO 2%.

Journal ArticleDOI
TL;DR: From the experimental analysis, it is observed that the proposed E-CLPS scheme yields better Attack Detection Rate, True positive rate, True negative rate and Minimum False Positives and False Negatives than the existing schemes.
Abstract: Certificateless Public Key Cryptography (CL-PKC) scheme is a new standard that combines Identity (ID)-based cryptography and tradi- tional PKC. It yields better security than the ID-based cryptography scheme without requiring digital certificates. In the CL-PKC scheme, as the Key Generation Center (KGC) generates a public key using a partial secret key, the need for authenticating the public key by a trusted third party is avoided. Due to the lack of authentication, the public key associated with the private key of a user may be replaced by anyone. Therefore, the ciphertext cannot be decrypted accurately. To mitigate this issue, an Enhanced Certificateless Proxy Signature (E-CLPS) is proposed to offer high security guarantee and requires minimum computational cost. In this work, the Hackman tool is used for detecting the dictionary attacks in the cloud. From the experimental analysis, it is observed that the proposed E-CLPS scheme yields better Attack Detection Rate, True Positive Rate, True Negative Rate and Minimum False Positives and False Negatives than the existing schemes.

Journal ArticleDOI
TL;DR: The benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress contributes to reducing both data dimension and computation time and simultaneously retains the speech information.
Abstract: Initially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acousticbased features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time.

Journal ArticleDOI
TL;DR: This work revisits the already designed traditional energy efficient methods with cluster head selection protocols and optimal path transformation in wireless body area networks and encourages researchers to insert WBANs with existing methods to improve performance.
Abstract: Wireless body area networks (WBANs) a special type of wireless sensor networks (WSNs) in which sensor nodes to actualize continuous wearable wellbeing observing of patients are able to provide improved healthcare services in a distributed infrastructure less environments. However, the mobile node, due to less battery power, can easily suffer from the problem of energy level when control packets are transfer among nodes—a problem that can occurs by the fact that some sensor nodes may select wrong cluster head with inappropriate path and waste the resources. Although many energy efficient methods have been designed for the traditional sensor networks, there has been limited focus on incorporating WBANs into energy efficient schemes. Therefore, in order to incorporate above issue we revisit the already designed traditional energy efficient methods with cluster head selection protocols and optimal path transformation. Therefore, we encourage researchers to insert WBANs with existing methods to improve performance. However, some work has been done in WBANs that uses energy efficient methods to manage the routing issue, this research domain requires further research attention. Therefore, we discuss the current research work and purpose many future directions of research.

Journal ArticleDOI
TL;DR: In this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM), which is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called GBDTI2HO method.
Abstract: In this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM). The proposed method is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called the GBDTI2HO method. Here, additional operators are included in this paper to improve HHO’s search behaviour namely crossover and mutation. Distorted waveforms are generated by different frequency patterns to indicate the time domain frequency as an assessment of failure. For this signal representation, the discrete wavelet transformation (DWT) is suggested. It extracts the characteristics and forwards them to IHHO technique to form the possible data sets. After the generation of the data set, GBDT classifies the ways of failure reached as winding of stator in multi-phase IM. The implementation of the proposed system is compared with existing systems, such as ANN, STransform and GBDT. The proposed method is executed on MATLAB/Simulink work platform to demonstrate the successfulness of proposed system, statistical measures are determined, as precision, sensitivity and specificity, mean median and standard deviation. For demonstrating the successfulness of proposed system, statistical measures are determined as precision, sensitivity, specificity, mean median as well as standard deviation. In 50 trails the proposed method, 0.98 for accuracy, 0.96 for specificity, 1.60 for recall as well as 0.97 for precision. In 100 trail the proposed method, 0.96 for accuracy, 0.93 for specificity, 0.87 for recall as well as 0.99 for precision.

Journal ArticleDOI
TL;DR: In this paper, the authors evaluated the seismic behavior of steel momment frames by nonlinear static analysis and incremental dynamic analysis and found that the ratio of the collapse capacity of the frames to each other varies between 1% to 6%.
Abstract: In this paper, the aim is to evaluate the seismic behavior of steel momment frames by nonlinear static analysis and incremental dynamic analysis. In this regard, 5 and 10 story frames in both intermediate and special ductility have been used. Since the type of sections and elements used in modeling are among the parameters that affect the behavior of the structure, in this study, which was performed using Opensees software, fiber sections were used for two types of beam elements. Non-linear column (distributed plasticity) and articulated beam element (concentrated plasticity) are used. The results of the analysis show that the ratio of the collapse capacity of the frames to each other varies between 1% to 6%. On the other hand, by deepening the research on one of the frames, it was shown that the stiffness ratio between the end springs and the middle member will affect the difference between the collapse capacity shown in the analysis.

Journal ArticleDOI
TL;DR: In this paper, a sample pipe geometry for studies that require a low pressure zone in hydraulic pipelines is presented, where the low pressure region is provided with a pipe and flow arrangement without consuming energy.
Abstract: Feeding the material to be transported in the hydraulic pipelines to the system is a subject open to research. The shape, size and density of the material gain importance in the selection of feeding systems. Finding the pressure drops that occur in the flow of spherical ice capsules with water is the basis of the research. However, before the measurements were made, preliminary research was carried out on feeding the capsules to the system during the installation of the experimental set-up. In the experimental study with solid particles with the diameter ratios (0.8) and densities (960 kg/m3) with smaller dimensions (d=0.014m), a pipe construction was obtained in which the solid particles are easily fed into the hydraulic pipeline. Experimental study revealed that lower than predicted pressures occur at the point where solid particles are fed into the pipe. This result means a greater pressure drop than the pressure drops obtained in the venturimeter zone with the same diameter ratio. In this article includes a step-by-step method and a sample pipe geometry for studies that require a low pressure zone in hydraulic pipelines. The pipe geometry designed in this study will form a model for the supply systems in the pipelines. The low pressure region is provided with a pipe and flow arrangement without consuming energy.