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Alfian Ma'arif
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alfian.maarif@te.uad.ac.id
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ijrcs@ascee.org
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Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 10 Documents
 1 
Search results for , issue "Vol 2, No 4 (2022)" : 10 Documents clear
Concept of Operations as a Boundary Object for Knowledge Sharing in the Design of Robotic Swarms Jari Laarni; Hanna Koskinen; Antti Väätänen
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.834

Abstract

Designing a swarm of autonomous robots for commercial, military, or other purposes is a challenging engineering and human factors design effort. The challenges argue in favor of practices and tools for better integration of different engineering disciplines and for the advancement of communication between stakeholders with different interests. The Concept of Operations (ConOps) approach is widely used in Systems Engineering for this purpose. A ConOps is a high-level description of how the elements of a system and entities in its environment interact in order to achieve their stated goals. This paper will present the development of a ConOps for a swarm of autonomous robotic vehicles in the military domain to demonstrate how autonomic robotic swarms can be deployed in different military branches in the future. The proposed ConOps can be considered as a boundary object in the design, validation, or procurement of an autonomous robotic swarm system. We also propose that the ConOps should be maintained throughout the system life-cycle as an overview description and definition of overall goals and policies.
Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms Taraneh Kamyab; Alireza Delrish; Haitham Daealhaq; Ali Mojarrad Ghahfarokhi; Fatemehalsadat Beheshtinejad
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.759

Abstract

The face plays an essential role in identifying people and showing their emotions in society. The human ability to recognize faces is remarkable. But face recognition is a fundamental problem in many computer programs. Due to the inherent complexities of the face and the many changes in its features, different algorithms for face recognition have been introduced in the last 20 years. Face recognition methods that are based on the structure of the face are unsupervised methods that produce good results compared to the linear changes that occur in the image. In this article, the Gabor algorithm, which is the origin of face recognition algorithms, has been described. Over the past decade, most of the research in the area of pattern classification has emphasized the use of the Gabor filter bank for extracting features. Because the Gabor algorithm has shortcomings, researchers have introduced a new method that is a combination of Gabor and PCA. After the introduction of the Gabor method, more complete and accurate algorithms have been introduced, such as Boosting algorithms, which we have briefly explained in this article. Also, here are the results of the comparison made by the researchers between Boosting and Gabor algorithms. The results show that Boosting-based algorithms have performed better compared to Gabor-based algorithms.
A Survey of Control Methods for Quadrotor UAV Muhammad Maaruf; Magdi Sadek Mahmoud; Alfian Ma'arif
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.743

Abstract

Flight control design of unmanned aerial vehicles UAVs is becoming increasingly important due to advances in computational power of computers with lower cost. The control algorithms are mainly employed for the attitude and position control of the UAVs. In the past decades, quadrotors have become the most popular UAVs, their adaptability and small size. They are employed to carry out tasks such as delivery, exploration,  fumigation, mapping, surveillance, rescue mission, traffic monitoring, and so on. While carrying out these tasks, quadrotor UAVs face various challenges, such as environmental disturbances, obstacles, and parametric and non-parametric perturbations. Therefore, they require robust and effective control to stabilize them and enhance their performance. This paper provides a survey of recent developments in control algorithms applied to attitude and position loops of quadrotor UAVs. In addition, the limitations of the previous control approaches are presented. In order to overcome the relative drawbacks of the previous control techniques and enhance the performance of the quadrotor, researchers are combining various control approaches to obtain the hybrid control architecture. In this study, a review of the recent hybrid control schemes is presented.
Numerical Simulation of Non-toxic ZnSe Buffer Layer to Enhance Sb2S3 Solar Cell Efficiency Using SCAPS-1D Software Md. Abdul Halim; Sunirmal Kumar Biswas; Md. Shafiqul Islam; Md. Mostak Ahmed
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.757

Abstract

The use of renewable energy, especially solar photovoltaic, has grown more and more necessary in the context of the diversification of the use of natural resources. Sb2S3 is emerged as an attractive candidate for today's thin-film solar cells due to its band gap of 1.65 eV and high absorption coefficient greater than 105 cm-1. Cadmium Sulfide is the most commonly used buffer layer material in thin film solar cells, but cadmium is a metal that causes severe toxicity in humans and the environment. This article tried to avoid cadmium for solar cell generation. This paper presents the findings of a computer simulation analysis of a thin film solar cell based on a p-type Sb2S3 absorber layer and an n-type ZnSe buffer layer in a structure of (Sb2S3/ZnSe/i-ZnO/ZnO: Al) utilizing simulation software (SCAPS-1D). The simulation included detailed configuration optimization for the thickness of the absorber layer, buffer layer, defect density, temperature, and series-shunt resistance. In this work, the Efficiency (η), Fill Factor (FF), Open-circuit Voltage (Voc), and short-circuit current (Jsc) have been measured by varying thickness of absorber layer in the range of 0.5µm to 4 µm and by varying thickness of buffer layer in the range of 0.05 µm to 0.1µm. The optimized solar cell shows an efficiency of 20.03% when the absorber layer thickness is 4µm and the buffer layer thickness is 0.08µm.
Robotic Motion Planning in Dynamic Environments and its Applications M. G. Mohanan; Ambuja Salgaonkar
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.816

Abstract

The fundamental problem of robot motion planning in a dynamic environment (RMPDE) is to find an optimal collision-free path from the start to the goal in a dynamic environment. Our literature survey of over 100 papers from the last four decades reveals that there are more than 30 models of RMPDE, and there is no benchmarking criterion to select one that is the best in a given situation. In this context, generating a regression-based model with 10 attributes is the first and foremost contribution of our research. Given a highly human-interactive environment like a cafeteria or a bus stand, the gross hidden Markov model has special importance for modeling a robot path. A variant of the growing hidden Markov model for a serving robot in a cafeteria is the second contribution of this paper. We simulated the behavior of GHMM in a cafeteria with static and dynamic obstacles (static obstacles were both convex and concave) and with three different arrangements of the tables and obstacles. Robots have been employed in mushroom harvesting. A novel proposition discussed in this paper is probabilistic road map planning for a robot that finds an optimum path for reaching the ripened mushrooms in a randomly planted mushroom farm and a dexterous hand to pluck the selected mushrooms by employing inverse kinematics. Further, two biologically inspired meta-heuristic algorithms, ant colony optimization, and firefly has been studied for their application to latex collection. The simulation results with this environment show that the firefly algorithm outperforms ant colony optimization in the general case. Finally, we have proposed a few pointers for future research in this domain.  The compilation and comparison of various approaches to robot motion planning in highly dynamic environments, and the simulation of a few models for some typical scenarios, have been the contributions of this paper.
An Examination of the Secure Chaos of 5G Wireless Communication Based on the Intelligent Internet of Things Janan Farag Yonan
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.769

Abstract

The implementation of an intelligent system for network control and monitoring that is built on an Internet of Things (IoT) is a focus of this line of research, with the end objective of improving the level of precision inside the network and its applications. You did indeed read it correctly; the system that is being referred to here is a deep neural network. The manner that it is constructed makes it possible for the layer that cannot be seen to contain more data. The application of element-modified deep learning and network buffer capacity control helps to improve the overall service quality that is provided by each sensor node. One method that can be applied to the process of instructing a machine to pay more attention includes deep learning in its various incarnations. The team was able to do calculations with a precision of 96.68 percent and the quickest execution time, thanks to the usage of wireless sensors. Using a sensor-based technique that has a brief implementation period, this piece has a degree of accuracy of 97.69 % when it comes to detecting and classifying proxies, and it does so using a method that is very efficient. On the other hand, our research represents a significant leap forward in comparison to earlier studies due to the fact that we were able to accurately identify and categorize a wide variety of invasions and real-time proxies.
Spectrum Sensing Utilizing Power Threshold and Artificial Intelligence in Cognitive Radio Zainab Ali Abbood; Noor Alhuda F. Abbas; Basma Makki
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.771

Abstract

The purpose of this paper was to take a preventative approach to predict the transmission status of the primary user (PU) on the white spectrum throughout the duration of the simulation. We intend to reduce the computational simulation budget to eliminate user interference by precise spectrum sensing handoff. We develop PU behaviors to use a time waiting estimate methodology that gives band occupancy time intervals. The proposed strategy may also lessen risks associated with flexible clients and adapt to changing channels. Real-time applications will use ANN range sharing, which reduces transmission delay, while high-throughput applications will use PTHD range sharing. Since noise as well as obscuring effects after each transmission plan, their proximity in the channel can influence range detection. This finding is in line with published investigations. Most range detection systems compare frequency test results with the operating channel's control scope thickness. The paper met design objectives by developing a noise-independent sensing methodology with proactive time estimation. The trial is accomplished with reduced cost and latency.
Design of Hybrid Controller using Qualitative Simulation Internal Modeling for Inverted Pendulum Chunrong Xia; Irfan Qaisar; Muhammad Shamrooz Aslam
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.777

Abstract

Multiple model methods for nonlinear dynamical system control are appealing because local models can be simple and obvious, and global dynamics can be studied in terms of transitions between small operating zones. In this study, we propose that using qualitative models strengthens the multiple model method even more by enabling each local model to explain a huge class of effective nonlinear dynamical systems. Furthermore, reasoning using qualitative models reveals weak necessary conditions sufficient to verify qualitative features like stability analysis. The authors show the method by creating a global controller for the free pendulum. In addition, local controllers are specified and validated by comparing their patterns to basic general qualitative models. Our proposed procedure establishes qualitative limitations on controller designs that are sufficient to ensure the necessary local attributes and to establish feasible transitions between local areas for the existing problems. As a result, the continuous phase picture may be reduced to a simple transitional graph. The degrees of freedom in the system that are not bound by the qualitative description are still accessible to the designer for optimization for any other purpose. An example of a pendulum plant illustrates the effectiveness of the proposed method.
Smart Indoor Plantation System Using Soil Moisture Sensor and Light Dependent Resistor Sensor N. S. Abu; W. M. Bukhari; M. A. Firdaus; N. M. Sukri
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.845

Abstract

Plantation methods, including hydroponics, have been extensively used in agriculture. They also employed a time-based irrigation system for the plant. The goal of this project was to create a self-sustaining indoor plantation system that uses soil moisture sensor data to control the flow of water when the sensor detects that the soil is almost dry. Soil conditions are monitored, and crops are irrigated more efficiently with the help of this new technology. Water is conserved by just watering the plants when they absolutely need it, rather than watering them continually all the time as the traditional method would require. Light-dependent resistors are used to measure the brightness of the surroundings in this project. As a result, the grow light will be activated when the ambient light level drops. With the help of a soil moisture sensor and a light-dependent resistor (LDR), one can create a system that automatically waters and lights plants. Finally, the soil moisture sensor collects data for the sprinkler system and displays it on the LCD screen, and then the appropriate measures are taken. When the soil's humidity level is high, the water that flows will be stopped.
Understanding of Convolutional Neural Network (CNN): A Review Purwono, Purwono; Ma'arif, Alfian; Rahmaniar, Wahyu; Fathurrahman, Haris Imam Karim; Frisky, Aufaclav Zatu Kusuma; Haq, Qazi Mazhar ul
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.888

Abstract

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

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