ALGABRI REDHWAN
@skku.edu
Hanyang University
Redhwan Algabri received the B.Eng., the M.Eng., and Ph.D. degrees in mechanical engineering in 2011, 2015, and 2022 from Al-Baath University, Syria, Cairo University, Egypt, and Sungkyunkwan University, South Korea, respectively. He is currently working as a Research Assistant Professor at Hanyang University in South Korea. His research interests in machine & deep learning and intelligent robotics include person tracking and identification for mobile robots.
Scopus Publications
Scholar Citations
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Scopus Publications
Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Sungon Lee, Mohammed A. Al-masni, Yeong Hyeon Gu, and Redhwan Algabri
Elsevier BV
Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Redhwan Algabri, Mohammed A. Al-masni, Mannan Saeed Muhammad, and Yeong Hyeon Gu
Springer Science and Business Media LLC
AbstractSoftware defect prediction aims to find a reliable method for predicting defects in a particular software project and assisting software engineers in allocating limited resources to release high-quality software products. While most earlier research has concentrated on employing traditional features, current methodologies are increasingly directed toward extracting semantic features from source code. Traditional features often fall short in identifying semantic differences within programs, differences that are essential for the development of reliable and effective prediction models. In contrast, semantic features cannot present statistical metrics about the source code, such as the code size and complexity. Thus, using only one kind of feature negatively affects prediction performance. To bridge the gap between the traditional and semantic features, we propose a novel defect prediction model that integrates traditional and semantic features using a hybrid deep learning approach to address this limitation. Specifically, our model employs a hybrid CNN-MLP classifier: the convolutional neural network (CNN) processes semantic features extracted from projects’ abstract syntax trees (ASTs) using Word2vec. In contrast, the traditional features extracted from the dataset repository are processed by a multilayer perceptron (MLP). Outputs of CNN and MLP are then integrated and fed into a fully connected layer for defect prediction. Extensive experiments are conducted on various open-source projects to validate CNN-MLP’s effectiveness. Experimental results indicate that CNN-MLP can significantly enhance defect prediction performance. Furthermore, CNN-MLP’s improvements outperform existing methods in non-effort-aware and effort-aware cases.
Redhwan Algabri, Ahmed Abdu, and Sungon Lee
Springer Science and Business Media LLC
Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, and Redhwan Algabri
Institute of Electrical and Electronics Engineers (IEEE)
Redhwan Algabri, Hyunsoo Shin, and Sungon Lee
Elsevier BV
Riyadh Nazar Ali Algburi, Hakim S. Sultan Aljibori, Zaid Al-Huda, Khalid M. Sowoud, Redhwan Algabri, and Mugahed A. Al-Antari
IEEE
In this study, temperature control of the cooling tower (CT) system is developed and implemented using fuzzy-programmable logic controller (PLC) control technology to improve electric static precipitator (ESP) performance, output, and quality while minimizing losses within the CT in the cement plant. This method makes use of the fuzzy logic controller and integrates it into the PLC. The valve system is programmed to avoid the “wet bottom” phenomenon in the CT. A fuzzy valve controller is intended to solve the problem of opening and closing the valve in the return line at the appropriate time in order to regulate the temperature of the CT. A SIMULINK PLC coder converts the program from MATLAB to structured text language (STL). STL is a functional expression that improves the efficiency of the PLC-1200 work system. The amount of dust and flue gas emitted by the particulate control system is measured using continuous emission monitoring systems. The results show a significant improvement in ESP quality following the implementation of fuzzy control temperatures for the CT, which reduced dust emissions from the cement plant by 10% when compared to the traditional operator. This improved monitoring system detects sudden temperature changes and reduces dust emissions to improve cement production.
Riyadh Nazar Ali Algburi, Hakim S. Sultan Aljibori, Zaid Al-Huda, Khalid M. Sowoud, Redhwan Algabri, and Mugahed A. Al-Antari
IEEE
This paper introduces a novel methodology for fault diagnosis in industrial robots, utilizing a combination of Single Spectrum Analysis (SSA) and Sparse Maximum Harmonics-Noise-Ratio Deconvolution (SMHD). The SSA technique is employed to decompose rotary encoder signals into their fundamental components: residual signals, periodic oscillations, and trends. Given that trends and oscillations represent monotonous waves, they are excluded from further analysis. Instead, the residual signal is processed using SMHD, which is an innovation inspired by the Minimum Entropy Deconvolution (MED) approach. This technique employs a new indicator as the objective function, iteratively choosing a Finite Impulse Response (FIR) filter to optimize the Harmonics-Noise Ratio (HNR) of the filtered signal. The proposed approach is designed to detect the flaw duration by computing the HNR of the signal’s envelope. During the iterative process, the fault period can be dynamically refined based on the HNR measurements of the envelope of the continuously updated filtered signal, particularly when the flaw duration is uncertain or challenging to estimate accurately. If an exact fault period is known, it simplifies the process by eliminating the need for estimation and refinement, repeatedly applying the known period in each iteration. A sparse parameter is also implemented to reduce chaos and enhance signal-to-noise ratio (SNR) after each filtering stage. Consequently, the method delineated herein advances as a feature-enhancement technique that does not require prior knowledge, offering substantial benefits and wider applicability compared to traditional method such as the Maximum Correlated Kurtosis Deconvolution (MCKD).
Riyadh Nazar Ali Algburi, Hakim S. Sultan Aljibori, Zaid Al-Huda, Khalid M. Sowoud, Redhwan Algabri, and Mugahed A. Al-Antari
IEEE
In industrial applications, the reliability of robots is paramount, necessitating efficient fault diagnosis systems. This research presents a novel fault diagnosis approach combining hierarchical hyper-Laplacian prior (HHLP) with singular spectrum analysis (SSA) for analyzing rotary encoder signals in industrial robots. The SSA method decomposes the encoder signals into residual, periodic oscillations, and trend components. The HHLP algorithm identifies harmonic interference, periodic impulse disturbances, and noise, optimized to maximize posterior probability for accurate detection. The proposed method demonstrates superior performance compared to traditional Laplacian prior models, emphasizing the effectiveness of HHLP in fault feature extraction. Experimental applications validate the SSAHHLP method’s efficacy. The study compares the results with spectral kurtosis and minimax concave regularization, confirming the SSA-HHLP method’s robustness and accuracy.
Redhwan Algabri and Sungon Lee
IEEE
Head pose estimation (HPE) is a crucial problem in computer vision, as it significantly enhances the performance of face-related tasks involving a frontal view. However, recent applications demand head analysis across the entire 360° range, which poses significant challenges. This paper introduces an end-to-end method for an HPE task. We propose a continuous 5D rotation representation to address the challenge of discontinuous rotation called 5DResNet, which enables robust and efficient direct regression of the head pose. The proposed method adopted the inverse of the stereographic projection (ISP) with the Gram-Schmidt mapping to orthogonalization procedure in the network. This approach allows our model to learn the full-range angles, exceeding the abilities of most previous techniques that confine pose estimation to a limited angle range to achieve acceptable results. Furthermore, we present an ablation study to gain a deeper understanding of the factors influencing the performance of our method. Our proposed approach demonstrates notable competition over other state-of-the-art methods in comprehensive experiments conducted on the publicly available Carnegie Mellon University (CMU) dataset, which achieved error rates of 5.97° and 6.64° for narrow and full-range angles, respectively.
Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Redhwan Algabri, and Sungon Lee
Computers, Materials and Continua (Tech Science Press)
Redhwan Algabri and Mun-Taek Choi
MDPI AG
It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and the last frame is proposed as a feature to distinguish people, in addition to color, height, and location. To improve the performance of target identification in a dynamic environment, an online boosting method was adapted by continuously updating the features in every frame. Through extensive real-life experiments, the effectiveness of the proposed method was demonstrated by showing experimental results that it outperformed the previous methods.
Ahmed Abdu, Zhengjun Zhai, Redhwan Algabri, Hakim A. Abdo, Kotiba Hamad, and Mugahed A. Al-antari
MDPI AG
Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently. Several research techniques have been proposed over time to predict source code defects. However, most of the previous studies focus on conventional feature extraction and modeling. Such traditional methodologies often fail to find the contextual information of the source code files, which is necessary for building reliable prediction deep learning models. Alternatively, the semantic feature strategies of defect prediction have recently evolved and developed. Such strategies could automatically extract the contextual information from the source code files and use them to directly predict the suspicious defects. In this study, a comprehensive survey is conducted to systematically show recent software defect prediction techniques based on the source code’s key features. The most recent studies on this topic are critically reviewed through analyzing the semantic feature methods based on the source codes, the domain’s critical problems and challenges are described, and the recent and current progress in this domain are discussed. Such a comprehensive survey could enable research communities to identify the current challenges and future research directions. An in-depth literature review of 283 articles on software defect prediction and related work was performed, of which 90 are referenced.
Redhwan Algabri and Mun-Taek Choi
MDPI AG
The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using the single-shot multibox detector deep neural network. It then estimates the real-world positions of the persons by using a point cloud and identifies the target person to be followed by extracting the clothes color using the hue-saturation-value model. The framework allows the robot to learn online the target trajectory prediction according to the historical path of the target person. The global and local path planners create robot trajectories that follow the target while avoiding static and dynamic obstacles, all of which are elaborately designed in the state machine control. We conducted intensive experiments in a realistic environment with multiple people and sharp corners behind which the target person may quickly disappear. The experimental results demonstrated the effectiveness and practicability of the proposed framework in the given environment.
Redhwan Algabri and Mun-Taek Choi
IEEE
Tracking a specific person in environments with non-uniform illumination is a difficult task for mobile robots. Image information such as color is essential to identify a target person. However, the information is not reliable under severe illumination changes unless the system can accommodate these changes over time. In this paper, we propose a robust identifier that has been combined with a deep learning technique to accommodate varying illumination in the ambient lighting of a scene. Moreover, an enhanced online update strategy for the person identification model is used to deal with the challenge of drifting the target person's appearance changes during tracking. Using the proposed method, the system achieves a successfully tracked rate above 90% on real-world video sequences in which variations in illumination are dominant. We confirmed the effectiveness of the proposed method through target-following experiments using five different clothing colors in a real indoor environment where the lighting conditions change extremely.
Redhwan Algabri and Mun-Taek Choi
MDPI AG
Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.