The rapid growth of the internet and the increasing reliance on digital infrastructures have posed significant challenges to cybersecurity. Among the other variants of attacks, Distributed Denial of Service (DDoS) attacks have emerged as one of the most destructive and common threats. These attacks disrupt or slow down network services by overwhelming the network infrastructure with a massive volume of malicious traffic. To effectively identify and mitigate DDoS attacks, machine learning techniques have been extensively employed in intrusion detection systems. Machine learning approaches offer the advantage of automating the detection process by learning patterns and characteristics of DDoS attacks from historical data. Researchers have explored various machine learning algorithms such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes to classify and detect DDoS attacks. These algorithms leverage features extracted from network traffic data, including packet size, packet delay patterns, and traffic behaviour, to differentiate between normal and malicious traffic.
{"title":"A Comparative Review on DDoS Attack Detection Using Machine Learning Techniques","authors":"Zerin Hasan Sahosh, Azraf Faheem, Marzana Bintay Tuba, Syeda Anika Tasnim, Syeda Anika, Tasnim","doi":"10.56532/mjsat.v4i2.208","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.208","url":null,"abstract":"The rapid growth of the internet and the increasing reliance on digital infrastructures have posed significant challenges to cybersecurity. Among the other variants of attacks, Distributed Denial of Service (DDoS) attacks have emerged as one of the most destructive and common threats. These attacks disrupt or slow down network services by overwhelming the network infrastructure with a massive volume of malicious traffic. To effectively identify and mitigate DDoS attacks, machine learning techniques have been extensively employed in intrusion detection systems. Machine learning approaches offer the advantage of automating the detection process by learning patterns and characteristics of DDoS attacks from historical data. Researchers have explored various machine learning algorithms such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes to classify and detect DDoS attacks. These algorithms leverage features extracted from network traffic data, including packet size, packet delay patterns, and traffic behaviour, to differentiate between normal and malicious traffic.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fluid flow in a venturi tube (VT) is a dynamic fluid phenomenon that plays an important role in human life. Flow safety control is necessary by installing several manometers of open (MoO) containing static fluid. The study's purpose was to know the change impacts of the number of installed MoOs against flow speed characters within VTs from conditions of 1 to 2 MoOs. The research uses experimental methods. The main research equipment includes 1 blower-rheostat system, 1 VT with 8 different diameters, and 2 MoOs containing pure water. Data analysis was done using SPSS. Within measurement intervals of 4 Ω ≤ R ≤ 281 Ω and 4.58 cm ≤ d ≤ 9.45 cm, the study showed the comparison of the mean of wind speed, wind speed gradient to R (d fixed), convergent wind speed gradient to d (R fixed), and divergent wind speed gradient to d (R fixed) between 1-MoO and 2-MoO type, respectively, were1. 21E+01 ms-1 vs 1.55E+01 ms-1, -2.73E-02 ms-1Ω-1 vs -4.11E-02 ms-1Ω-1, -3.13E+02 s-1 vs -4.25E+02 s-1, and -4.31E+02 s-1 vs -4.84E+02 s-1. While its impact differences are insignificant, insignificant, significant, and insignificant, respectively.
文丘里管（VT）中的流体流动是一种动态流体现象，在人类生活中发挥着重要作用。有必要通过安装多个包含静态流体的开放式压力计（MoO）来进行流量安全控制。本研究的目的是了解从 1 个到 2 个 MoO 的条件下，安装 MoO 的数量对 VT 内流速特征的变化影响。研究采用了实验方法。主要研究设备包括 1 个鼓风机-恒温器系统、1 个 8 种不同直径的 VT 和 2 个含有纯水的 MoO。数据分析采用 SPSS。研究表明，在 4 Ω ≤ R ≤ 281 Ω 和 4.58 cm ≤ d ≤ 9.45 cm 的测量区间内，1-MoO 型和 2-MoO 型的风速平均值、风速对 R 的梯度（d 固定）、风速对 d 的收敛梯度（R 固定）和风速对 d 的发散梯度（R 固定）的比较分别为 1.21E+01 ms-1 vs 1.55E+01 ms-1，-2.73E-02 ms-1Ω-1 vs -4.11E-02 ms-1Ω-1，-3.13E+02 s-1 vs -4.25E+02 s-1，以及-4.31E+02 s-1 vs -4.84E+02 s-1。而其影响差异分别为不显著、不显著、显著和不显著。
{"title":"Does Number Changes of Open Manometers Filled with Pure Water Impact Wind Speeds in Venturi Tube Systems?","authors":"Gede Rasagama","doi":"10.56532/mjsat.v4i2.283","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.283","url":null,"abstract":"Fluid flow in a venturi tube (VT) is a dynamic fluid phenomenon that plays an important role in human life. Flow safety control is necessary by installing several manometers of open (MoO) containing static fluid. The study's purpose was to know the change impacts of the number of installed MoOs against flow speed characters within VTs from conditions of 1 to 2 MoOs. The research uses experimental methods. The main research equipment includes 1 blower-rheostat system, 1 VT with 8 different diameters, and 2 MoOs containing pure water. Data analysis was done using SPSS. Within measurement intervals of 4 Ω ≤ R ≤ 281 Ω and 4.58 cm ≤ d ≤ 9.45 cm, the study showed the comparison of the mean of wind speed, wind speed gradient to R (d fixed), convergent wind speed gradient to d (R fixed), and divergent wind speed gradient to d (R fixed) between 1-MoO and 2-MoO type, respectively, were1. 21E+01 ms-1 vs 1.55E+01 ms-1, -2.73E-02 ms-1Ω-1 vs -4.11E-02 ms-1Ω-1, -3.13E+02 s-1 vs -4.25E+02 s-1, and -4.31E+02 s-1 vs -4.84E+02 s-1. While its impact differences are insignificant, insignificant, significant, and insignificant, respectively.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nurul Muthmainnah, Mohd Noor, Zarith Sofia Ahmad, Razali, Rohidatun Mahmod, Salmiah Ahmad
Currently, extensive research has been done on improving self-controlling mobile robots. This robot is called autonomous mobile robots (AMRs) and it was designed for various applications like moving around in places such as libraries, restaurants, and healthcare. This paper focuses on designing a six-wheeled mobile robot using Fusion 360 software, based on Rocker-Bogie structure. The main idea from the Rocker-Bogie concept is that it does not use springs or separate stub axles for each wheel, allowing the robot’s body to overcome obstacles like rocks, ditches, and sand. The actuator of the DC motor for each wheel was used to study the robot’s stability and its movement. Meanwhile, the movement of the robot was controlled by implementing a user graphical interface (GUI) using Blynk software. There are four main directions for the movement of the robot: moving forward, backward, and turning left and right position. Importantly, the Rocker-Bogie can be a starting point for developing an independent mobile robot that can navigate different terrains like sand, grass, and roads. This capability comes by using various sensors that help the robot navigate its surroundings effectively.
{"title":"Designing an Autonomous Mobile Robot based on Rocker-Bogie Concept for Terrain Purposes","authors":"Nurul Muthmainnah, Mohd Noor, Zarith Sofia Ahmad, Razali, Rohidatun Mahmod, Salmiah Ahmad","doi":"10.56532/mjsat.v4i2.263","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.263","url":null,"abstract":"Currently, extensive research has been done on improving self-controlling mobile robots. This robot is called autonomous mobile robots (AMRs) and it was designed for various applications like moving around in places such as libraries, restaurants, and healthcare. This paper focuses on designing a six-wheeled mobile robot using Fusion 360 software, based on Rocker-Bogie structure. The main idea from the Rocker-Bogie concept is that it does not use springs or separate stub axles for each wheel, allowing the robot’s body to overcome obstacles like rocks, ditches, and sand. The actuator of the DC motor for each wheel was used to study the robot’s stability and its movement. Meanwhile, the movement of the robot was controlled by implementing a user graphical interface (GUI) using Blynk software. There are four main directions for the movement of the robot: moving forward, backward, and turning left and right position. Importantly, the Rocker-Bogie can be a starting point for developing an independent mobile robot that can navigate different terrains like sand, grass, and roads. This capability comes by using various sensors that help the robot navigate its surroundings effectively.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over 40% percent of the accidents encountered at Unki Mines are related to ground failure and other geotechnical complications. This research sought to address the monetary losses incurred in revenue and productivity through absenteeism and injury. With the development of mine production in Zimbabwe, the depth of mines gradually increased, and the ecological environment developed complex conditions. Deep mining unlike shallow mining is characterized by extra ground pressure, more gas, and faster deformation rates. These factors affect the safety of mining production. Therefore, as the mining depth and breadth increase, the difficulty of mine rock engineering is also increasing. The deepening of mining depth and the improvement of the mechanization level have brought increasing difficulties regarding the stability of surrounding rock hence risk issues arise. The ultimate objective of this study was to ensure a robust design of support systems at Unki Mine that would eventually reduce the risks associated with rock engineering excavations. Findings from the study and its analysis established the following conclusions. On the fore is the fact that hanging wall instability at Unki Mine are predominantly governed by geological and span attributes. The computed k value is 7% less the k value of 57.33 MPa used for existing pillars. An analysis of FOS and its relationship with recovery and Pillar W/H ratio shows that over-break has a huge impact on the span stability hence the effect of the ANFO need to be reviewed for an alternative explosive that ensures recovery within 80% range to ensure that the FOS is maintained above 1.6. A decrease in FoS increases the probability of failure, hence it is also important to device a pillar support system that is less prone to effects of over-break since the ground conditions are poor.
{"title":"Assessment of Rock Engineering Risk Management Frameworks Used in Underground Mines","authors":"None Desire Runganga, None Simbarashe Geja, None Maidei Meck","doi":"10.56532/mjsat.v3i3.161","DOIUrl":"https://doi.org/10.56532/mjsat.v3i3.161","url":null,"abstract":"Over 40% percent of the accidents encountered at Unki Mines are related to ground failure and other geotechnical complications. This research sought to address the monetary losses incurred in revenue and productivity through absenteeism and injury. With the development of mine production in Zimbabwe, the depth of mines gradually increased, and the ecological environment developed complex conditions. Deep mining unlike shallow mining is characterized by extra ground pressure, more gas, and faster deformation rates. These factors affect the safety of mining production. Therefore, as the mining depth and breadth increase, the difficulty of mine rock engineering is also increasing. The deepening of mining depth and the improvement of the mechanization level have brought increasing difficulties regarding the stability of surrounding rock hence risk issues arise. The ultimate objective of this study was to ensure a robust design of support systems at Unki Mine that would eventually reduce the risks associated with rock engineering excavations. Findings from the study and its analysis established the following conclusions. On the fore is the fact that hanging wall instability at Unki Mine are predominantly governed by geological and span attributes. The computed k value is 7% less the k value of 57.33 MPa used for existing pillars. An analysis of FOS and its relationship with recovery and Pillar W/H ratio shows that over-break has a huge impact on the span stability hence the effect of the ANFO need to be reviewed for an alternative explosive that ensures recovery within 80% range to ensure that the FOS is maintained above 1.6. A decrease in FoS increases the probability of failure, hence it is also important to device a pillar support system that is less prone to effects of over-break since the ground conditions are poor.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When an explosive detonates in a blasthole, approximately 20 to 30% of the energy is only utilized for fragmenting the rock mass whilst the bulk of the energy is lost in the form of ground vibrations, flyrocks and airblasts. Employees’ residences, mine offices, processing plants and engineering workshops built close to the mining area and are in danger of being damaged by blast induced hazards. In addition, due to flyrocks there was a high level of ore losses, ore dilution, equipment damages, mine roads and powerlines. To assess and reduce these negative impacts, monitoring of flyrocks, ground vibrations and airblast was carried out and generate a prediction model. A three-layer, feed-forward back-propagation of a 9-10-3 network architecture was trained, validated, and tested using the Bayesian regularization algorithm. A total of 100 monitored blast records obtained from the mine were used as input parameters for the ANN prediction model. Subsequently a Multivariate Regression analysis prediction model was run and used to compare with the results obtained from ANN model. Based on the study's findings, an ANN model proved to be the best for field predictions.To determine the relative impact of each input parameter on flyrocks, gound vibration, and Airblast, a sensitivity analysis was also carried out and lastly blast optimization which managed to reduce blast induced impacts by over 30%.
{"title":"Prediction of Flyrocks, Airblasts and Ground Vibrations Using Neural Computing and Applications at ZCDC Mine","authors":"Charles Chewu, Tonderai Chikwere, Desire Runganga, None Elia Chipfupi, Tatenda Nyamagudza","doi":"10.56532/mjsat.v3i3.135","DOIUrl":"https://doi.org/10.56532/mjsat.v3i3.135","url":null,"abstract":"When an explosive detonates in a blasthole, approximately 20 to 30% of the energy is only utilized for fragmenting the rock mass whilst the bulk of the energy is lost in the form of ground vibrations, flyrocks and airblasts. Employees’ residences, mine offices, processing plants and engineering workshops built close to the mining area and are in danger of being damaged by blast induced hazards. In addition, due to flyrocks there was a high level of ore losses, ore dilution, equipment damages, mine roads and powerlines. To assess and reduce these negative impacts, monitoring of flyrocks, ground vibrations and airblast was carried out and generate a prediction model. A three-layer, feed-forward back-propagation of a 9-10-3 network architecture was trained, validated, and tested using the Bayesian regularization algorithm. A total of 100 monitored blast records obtained from the mine were used as input parameters for the ANN prediction model. Subsequently a Multivariate Regression analysis prediction model was run and used to compare with the results obtained from ANN model. Based on the study's findings, an ANN model proved to be the best for field predictions.To determine the relative impact of each input parameter on flyrocks, gound vibration, and Airblast, a sensitivity analysis was also carried out and lastly blast optimization which managed to reduce blast induced impacts by over 30%.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kingsley Igwe, None Nurudeen Olawale Adeyemi, None Lukman Folorunso Onadiran
In this paper, computation of very high frequency (VHF) signal strength for point to area network was carried out using machine learning modeling techniques. Seven different machine learning models were adopted: Decision Tree, Random Forest, AdaBoost, k-Nearest Neighbor, Support Vector Machine, Artificial Neural Network and Linear Regression. A total of 120 data points was used in computing the signal strength. 72 data points (60%) was used to train the model, while the remaining 48 data points (40%) were used as test data to determine the accuracy of the computation for all the models. From the results, it was observed that the accuracy of the computations was greatly influenced by the amount of training data that was used. Also, from the results, in highest order of accuracy, AdaBoost was adjudged the best model. This was followed by the Artificial Neural Network model. Generally, the error margin of computation obtained for these two models were low, hence indicating that the models can be effectively relied on for computation of signal strength in the study area.
{"title":"Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques","authors":"Kingsley Igwe, None Nurudeen Olawale Adeyemi, None Lukman Folorunso Onadiran","doi":"10.56532/mjsat.v3i3.163","DOIUrl":"https://doi.org/10.56532/mjsat.v3i3.163","url":null,"abstract":"In this paper, computation of very high frequency (VHF) signal strength for point to area network was carried out using machine learning modeling techniques. Seven different machine learning models were adopted: Decision Tree, Random Forest, AdaBoost, k-Nearest Neighbor, Support Vector Machine, Artificial Neural Network and Linear Regression. A total of 120 data points was used in computing the signal strength. 72 data points (60%) was used to train the model, while the remaining 48 data points (40%) were used as test data to determine the accuracy of the computation for all the models. From the results, it was observed that the accuracy of the computations was greatly influenced by the amount of training data that was used. Also, from the results, in highest order of accuracy, AdaBoost was adjudged the best model. This was followed by the Artificial Neural Network model. Generally, the error margin of computation obtained for these two models were low, hence indicating that the models can be effectively relied on for computation of signal strength in the study area.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmad Anwar Zainuddin, None Shaun Tatenda Njazi, None Asmarani Ahmad Puzi, None Nur Athirah Mohd Abu Bakar, None Aly Mennatallah Khaled Mohammad Ramada, None Hasbullah Hamizan, None Rohilah Sahak, None Aiman Najmi Mat Rosani, None Nasyitah Ghazalli, None Siti Husna Abdul Rahman, None Saidatul Izyanie Kamarudin
Monitoring plant growth is a crucial agricultural duty. In addition, the prevention of plant diseases is an essential component of the agricultural infrastructure. This technique must be automated to keep up with the rising food demand caused by increasing population expansion. This work evaluates this business, specifically the production of maize, which is a significant source of food worldwide. Ensure that Mazie's yields are not damaged is a crucial endeavour. Diseases affecting maize plants, such as Common Rust and Blight, are a significant production deterrent. To reduce waste and boost production and disease detection efficiencies, the automation of disease detection is a crucial strategy for the agricultural sector. The optimal solution is a self-diagnosing system that employs machine learning and computer vision to distinguish between damaged and healthy plants. The workflow for machine learning consists of data collection, data preprocessing, model selection, model training and testing, and evaluation.
{"title":"Evaluating the Effectiveness of Machine Learning and Computer Vision Techniques for the Early Detection of Maize Plant Disease","authors":"Ahmad Anwar Zainuddin, None Shaun Tatenda Njazi, None Asmarani Ahmad Puzi, None Nur Athirah Mohd Abu Bakar, None Aly Mennatallah Khaled Mohammad Ramada, None Hasbullah Hamizan, None Rohilah Sahak, None Aiman Najmi Mat Rosani, None Nasyitah Ghazalli, None Siti Husna Abdul Rahman, None Saidatul Izyanie Kamarudin","doi":"10.56532/mjsat.v3i3.180","DOIUrl":"https://doi.org/10.56532/mjsat.v3i3.180","url":null,"abstract":"Monitoring plant growth is a crucial agricultural duty. In addition, the prevention of plant diseases is an essential component of the agricultural infrastructure. This technique must be automated to keep up with the rising food demand caused by increasing population expansion. This work evaluates this business, specifically the production of maize, which is a significant source of food worldwide. Ensure that Mazie's yields are not damaged is a crucial endeavour. Diseases affecting maize plants, such as Common Rust and Blight, are a significant production deterrent. To reduce waste and boost production and disease detection efficiencies, the automation of disease detection is a crucial strategy for the agricultural sector. The optimal solution is a self-diagnosing system that employs machine learning and computer vision to distinguish between damaged and healthy plants. The workflow for machine learning consists of data collection, data preprocessing, model selection, model training and testing, and evaluation.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nurul Muthmainnah Mohd Noor, Muhamad Safwan Sabin, Rohidatun Mahmod@Wahab
Today, the topic of performance in industrial robotics is being addressed by researchers at a much faster pace than a few years ago. In 2023, there are a variety of applications for industrial robotics, such as agriculture, education, and the film industry. In this paper, the implementation of Graphical User Interface (GUI) as a controller input between an Android smartphone and the Arduino was discussed. This GUI was developed to help the user in controlling the robotic arm. For the movement of 5-degree motion of a robot, the concept of inverse kinematics (IK) was applied to the system. To test the performance of the robot, a simple test had done by drawing the basic shape by putting the pencil into the gripper. Then, these drawings were compared with the original by calculating the percentage error of the dimensions from the center to the outside. The result shows that the robotic arm is capable of performing all the tasks assigned to it and can also be controlled through the graphical user interface of a smartphone. Therefore, implementing Bluetooth Wi-Fi as a communication between smartphone and robot is a good way to study the performance of robots, especially for small and lightweight robotic arms.
{"title":"Performance Analysis of a 5-DOF Robotic Arm by Implementing a Graphical User Interface","authors":"Nurul Muthmainnah Mohd Noor, Muhamad Safwan Sabin, Rohidatun Mahmod@Wahab","doi":"10.56532/mjsat.v3i3.170","DOIUrl":"https://doi.org/10.56532/mjsat.v3i3.170","url":null,"abstract":"Today, the topic of performance in industrial robotics is being addressed by researchers at a much faster pace than a few years ago. In 2023, there are a variety of applications for industrial robotics, such as agriculture, education, and the film industry. In this paper, the implementation of Graphical User Interface (GUI) as a controller input between an Android smartphone and the Arduino was discussed. This GUI was developed to help the user in controlling the robotic arm. For the movement of 5-degree motion of a robot, the concept of inverse kinematics (IK) was applied to the system. To test the performance of the robot, a simple test had done by drawing the basic shape by putting the pencil into the gripper. Then, these drawings were compared with the original by calculating the percentage error of the dimensions from the center to the outside. The result shows that the robotic arm is capable of performing all the tasks assigned to it and can also be controlled through the graphical user interface of a smartphone. Therefore, implementing Bluetooth Wi-Fi as a communication between smartphone and robot is a good way to study the performance of robots, especially for small and lightweight robotic arms.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces an extended type-2 fuzzy relational database model (ET-2FRDB) for aggregate and grouping operations that can represent and query uncertain and imprecise information in the real world applications. In ET-2FRDB, each fuzzy relation is represented by a type-2 fuzzy set whose membership degree of each tuple is a fuzzy number on [0, 1], the fuzzy aggregate functions, the fuzzy relational algebraic, aggregate and grouping operations are defined as extensions of those in the classical relational database model thereby the membership degree of tuples associated by using the minimum and maximum of fuzzy numbers. Also, some properties of the fuzzy relational algebraic, aggregate and grouping operations in ET-2FRDB are formulated and proven.
{"title":"An Extended Type-2 Fuzzy Relational Database Model for Aggregate and Grouping Operations","authors":"Hoa Nguyen","doi":"10.56532/mjsat.v3i3.169","DOIUrl":"https://doi.org/10.56532/mjsat.v3i3.169","url":null,"abstract":"This paper introduces an extended type-2 fuzzy relational database model (ET-2FRDB) for aggregate and grouping operations that can represent and query uncertain and imprecise information in the real world applications. In ET-2FRDB, each fuzzy relation is represented by a type-2 fuzzy set whose membership degree of each tuple is a fuzzy number on [0, 1], the fuzzy aggregate functions, the fuzzy relational algebraic, aggregate and grouping operations are defined as extensions of those in the classical relational database model thereby the membership degree of tuples associated by using the minimum and maximum of fuzzy numbers. Also, some properties of the fuzzy relational algebraic, aggregate and grouping operations in ET-2FRDB are formulated and proven.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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