| 1. |
- Aslani, Mohammad, et al.
(författare)
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Developing adaptive traffic signal control by actor-critic and direct exploration methods
- 2019
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Ingår i: Proceedings of the Institution of Civil Engineers. - : Thomas Telford. - 0965-092X .- 1751-7710. ; 172:5, s. 289-298
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Tidskriftsartikel (refereegranskat)abstract
- Designing efficient traffic signal controllers has always been an important concern in traffic engineering. This is owing to the complex and uncertain nature of traffic environments. Within such a context, reinforcement learning has been one of the most successful methods owing to its adaptability and its online learning ability. Reinforcement learning provides traffic signals with the ability automatically to determine the ideal behaviour for achieving their objective (alleviating traffic congestion). In fact, traffic signals based on reinforcement learning are able to learn and react flexibly to different traffic situations without the need of a predefined model of the environment. In this research, the actor-critic method is used for adaptive traffic signal control (ATSC-AC). Actor-critic has the advantages of both actor-only and critic-only methods. One of the most important issues in reinforcement learning is the trade-off between exploration of the traffic environment and exploitation of the knowledge already obtained. In order to tackle this challenge, two direct exploration methods are adapted to traffic signal control and compared with two indirect exploration methods. The results reveal that ATSC-ACs based on direct exploration methods have the best performance and they consistently outperform a fixed-time controller, reducing average travel time by 21%.
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| 2. |
- Aslani, Mohammad, et al.
(författare)
-
Traffic signal optimization through discrete and continuous reinforcement learning with robustness analysis in downtown Tehran
- 2018
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Ingår i: Advanced Engineering Informatics. - : Elsevier BV. - 1474-0346 .- 1873-5320. ; 38, s. 639-655
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Tidskriftsartikel (refereegranskat)abstract
- Traffic signal control plays a pivotal role in reducing traffic congestion. Traffic signals cannot be adequately controlled with conventional methods due to the high variations and complexity in traffic environments. In recent years, reinforcement learning (RL) has shown great potential for traffic signal control because of its high adaptability, flexibility, and scalability. However, designing RL-embedded traffic signal controllers (RLTSCs) for traffic systems with a high degree of realism is faced with several challenges, among others system disturbances and large state-action spaces are considered in this research.The contribution of the present work is founded on three features: (a) evaluating the robustness of different RLTSCs against system disturbances including incidents, jaywalking, and sensor noise, (b) handling a high-dimensional state-action space by both employing different continuous state RL algorithms and reducing the state-action space in order to improve the performance and learning speed of the system, and (c) presenting a detailed empirical study of traffic signals control of downtown Tehran through seven RL algorithms: discrete state Q-learning(λ" role="presentation">), SARSA(λ" role="presentation">), actor-critic(λ" role="presentation">), continuous state Q-learning(λ" role="presentation">), SARSA(λ" role="presentation">), actor-critic(λ" role="presentation">), and residual actor-critic(λ" role="presentation">).In this research, first a real-world microscopic traffic simulation of downtown Tehran is carried out, then four experiments are performed in order to find the best RLTSC with convincing robustness and strong performance. The results reveal that the RLTSC based on continuous state actor-critic(λ" role="presentation">) has the best performance. In addition, it is found that the best RLTSC leads to saving average travel time by 22% (at the presence of high system disturbances) when it is compared with an optimized fixed-time controller.
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| 3. |
- Abad, Shayan, et al.
(författare)
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Classification of Malicious URLs Using Machine Learning
- 2023
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:18
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Tidskriftsartikel (refereegranskat)abstract
- Amid the rapid proliferation of thousands of new websites daily, distinguishing safe ones from potentially harmful ones has become an increasingly complex task. These websites often collect user data, and, without adequate cybersecurity measures such as the efficient detection and classification of malicious URLs, users’ sensitive information could be compromised. This study aims to develop models based on machine learning algorithms for the efficient identification and classification of malicious URLs, contributing to enhanced cybersecurity. Within this context, this study leverages support vector machines (SVMs), random forests (RFs), decision trees (DTs), and k-nearest neighbors (KNNs) in combination with Bayesian optimization to accurately classify URLs. To improve computational efficiency, instance selection methods are employed, including data reduction based on locality-sensitive hashing (DRLSH), border point extraction based on locality-sensitive hashing (BPLSH), and random selection. The results show the effectiveness of RFs in delivering high precision, recall, and F1 scores, with SVMs also providing competitive performance at the expense of increased training time. The results also emphasize the substantial impact of the instance selection method on the performance of these models, indicating its significance in the machine learning pipeline for malicious URL classification
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| 4. |
- Aslani, Mohammad, et al.
(författare)
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A fast instance selection method for support vector machines in building extraction
- 2020
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Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.
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| 5. |
- Aslani, Mohammad, et al.
(författare)
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A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data
- 2022
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Ingår i: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM. - Setúbal : ScitePress. - 9789897585715 ; , s. 56-63
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Konferensbidrag (refereegranskat)abstract
- Rooftop solar energy has long been regarded as a promising solution to cities’ growing energy demand and environmental problems. A reliable estimate of rooftop solar energy facilitates the deployment of photovoltaics and helps formulate renewable-related policies. This reliable estimate underpins the necessity of accurately pinpointing the areas utilizable for mounting photovoltaics. The size, shape, and superstructures of rooftops as well as shadow effects are the important factors that have a considerable impact on utilizable areas. In this study, the utilizable areas and solar energy potential of rooftops are estimated by considering the mentioned factors using a three-step methodology. The first step involves training PointNet++, a deep network for object detection in point clouds, to recognize rooftops in LiDAR data. Second, planar segments of rooftops are extracted using clustering. Finally, areas that receive sufficient solar irradiation, have an appropriate size, and fulfill photovoltaic installation requirements are identified using morphological operations and predefined thresholds. The obtained results show high accuracy for rooftop extraction (93%) and plane segmentation (99%). Moreover, the spatially detailed analysis indicates that 17% of rooftop areas are usable for photovoltaics.
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| 6. |
- Aslani, Mohammad, et al.
(författare)
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Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment
- 2022
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 306
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Tidskriftsartikel (refereegranskat)abstract
- The considerable potential of rooftop photovoltaics (RPVs) for alleviating the high energy demand of cities has made them a proven technology in local energy networks. Identification of rooftop areas suitable for installing RPVs is of importance for energy planning. Having these suitable areas referred to as utilizable areas greatly assists in a reliable estimate of RPVs energy production. Within such a context, this research aims to propose a spatially detailed methodology that involves (a) automatic extraction of buildings footprint, (b) automatic segmentation of roof faces, and (c) automatic identification of utilizable areas of roof faces for solar infrastructure installation. Specifically, the innovations of this work are a new method for roof face segmentation and a new method for the identification of utilizable rooftop areas. The proposed methodology only requires digital surface models (DSMs) as input, and it is independent of other auxiliary spatial data to become more functional. A part of downtown Gothenburg composed of vegetation and high-rise buildings with complex shapes was selected to demonstrate the methodology performance. According to the experimental results, the proposed methodology has a high success rate in building extraction (about 95% correctness and completeness) and roof face segmentation (about 85% completeness and correctness). Additionally, the results suggest that the effects of roof occlusions and roof superstructures are satisfactorily considered in the identification of utilizable rooftop areas. Thus, the methodology is practically effective and relevant for the detailed RPVs assessments in arbitrary urban regions where only DSMs are accessible.
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| 7. |
- Aslani, Mohammad
(författare)
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Computational and spatial analyses of rooftops for urban solar energy planning
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In cities where land availability is limited, rooftop photovoltaic panels (RPVs) offer high potential for satisfying concentrated urban energy demand by using only rooftop areas. However, accurate estimation of RPVs potential in relation to their spatial distribution is indispensable for successful energy planning. Classification, plane segmentation, and spatial analysis are three important aspects in this context. Classification enables extracting rooftops and allows for estimating solar energy potential based on existing training samples. Plane segmentation helps to characterize rooftops by extracting their planar patches. Additionally, spatial analyses enable the identification of rooftop utilizable areas for placing RPVs. This dissertation aims to address some issues associated with these three aspects, particularly (a) training support vector machines (SVMs) in large datasets, (b) plane segmentation of rooftops, and (c) identification of utilizable areas for RPVs. SVMs are among the most potent classifiers and have a solid theoretical foundation. However, they have high time complexity in their training phase, making them inapplicable in large datasets. Two new instance selection methods were proposed to accelerate the training phase of SVMs. The methods are based on locality-sensitive hashing and are capable of handling large datasets. As an application, they were incorporated into a rooftop extraction procedure, followed by plane segmentation. Plane segmentation of rooftops for the purpose of solar energy potential estimation should have a low risk of overlooking superstructures, which play an essential role in the placement of RPVs. Two new methods for plane segmentation in high-resolution digital surface models were thus developed. They have an acceptable level of accuracy and can successfully extract planar segments by considering superstructures. Not all areas of planar segments are utilizable for mounting RPVs, and some factors may further limit their useability. Two spatial methods for identifying RPV-utilizable areas were developed in this realm. They scrutinize extracted planar segments by considering panel installation regulations, solar irradiation, roof geometry, and occlusion, which are necessary for a realistic assessment of RPVs potential. All six proposed methods in this thesis were thoroughly evaluated, and the experimental results show that they can successfully achieve the objectives for which they were designed.
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| 8. |
- Aslani, Mohammad, et al.
(författare)
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Continuous residual reinforcement learning for traffic signal control optimization
- 2018
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Ingår i: Canadian journal of civil engineering (Print). - : NRC Research Press. - 0315-1468 .- 1208-6029. ; 45:8, s. 690-702
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Tidskriftsartikel (refereegranskat)abstract
- Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on continuous reinforcement learning. Although they have been successful in traffic signal control, they may become unstable and fail to converge to near-optimal solutions. We develop adaptive traffic signal controllers based on continuous residual reinforcement learning (CRL-TSC) that is more stable. The effect of three feature functions is empirically investigated in a microscopic traffic simulation. Furthermore, the effects of departing streets, more actions, and the use of the spatial distribution of the vehicles on the performance of CRL-TSCs are assessed. The results show that the best setup of the CRL-TSC leads to saving average travel time by 15% in comparison to an optimized fixed-time controller.
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| 9. |
- Aslani, Mohammad, et al.
(författare)
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Efficient and decision boundary aware instance selection for support vector machines
- 2021
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Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 577, s. 579-598
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Tidskriftsartikel (refereegranskat)abstract
- Support vector machines (SVMs) are powerful classifiers that have high computational complexity in the training phase, which can limit their applicability to large datasets. An effective approach to address this limitation is to select a small subset of the most representative training samples such that desirable results can be obtained. In this study, a novel instance selection method called border point extraction based on locality-sensitive hashing (BPLSH) is designed. BPLSH preserves instances that are near the decision boundaries and eliminates nonessential ones. The performance of BPLSH is benchmarked against four approaches on different classification problems. The experimental results indicate that BPLSH outperforms the other methods in terms of classification accuracy, preservation rate, and execution time. The source code of BPLSH can be found in https://github.com/mohaslani/BPLSH.
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| 10. |
- Aslani, Mohammad, et al.
(författare)
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Rooftop segmentation and optimization of photovoltaic panel layouts in digital surface models
- 2023
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Ingår i: Computers, Environment and Urban Systems. - : Elsevier. - 0198-9715 .- 1873-7587. ; 105
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Tidskriftsartikel (refereegranskat)abstract
- Rooftop photovoltaic panels (RPVs) are being increasingly used in urban areas as a promising means of achieving energy sustainability. Determining proper layouts of RPVs that make the best use of rooftop areas is of importance as they have a considerable impact on the RPVs performance in efficiently producing energy. In this study, a new spatial methodology for automatically determining the proper layouts of RPVs is proposed. It aims to both extract planar rooftop segments and identify feasible layouts with the highest number of RPVs in highly irradiated areas. It leverages digital surface models (DSMs) to consider roof shapes and occlusions in placing RPVs. The innovations of the work are twofold: (a) a new method for plane segmentation, and (b) a new method for optimally placing RPVs based on metaheuristic optimization, which best utilizes the limited rooftop areas. The proposed methodology is evaluated on two test sites that differ in urban morphology, building size, and spatial resolution. The results show that the plane segmentation method can accurately extract planar segments, achieving 88.7% and 99.5% precision in the test sites. In addition, the results indicate that complex rooftops are adequately handled for placing RPVs, and overestimation of solar energy potential is avoided if detailed analysis based on panel placement is employed.
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