Routing in congested baggage handling systems using deep reinforcement learning

Sørensen, René Arendt; Nielsen, Michael; Karstoft, Henrik

doi:10.3233/ica-190613

Cited by 24 publications

(15 citation statements)

References 11 publications

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“…Furthermore, this study combines the machine learning with GA, in which machine learning adopts RL. In the follow‐up research, the effects of combining different learning methods (Ahmadlou & Adeli, 2010; Pereira et al., 2020; Rafiei & Adeli, 2017; Rokibul Alam et al., 2020; Sørensen et al., 2020) with GA will be investigated. In future extensions, effects of other distribution types for random variables may also be addressed.…”

Section: Discussionmentioning

confidence: 99%

Optimal route design of electric transit networks considering travel reliability

Tong

Yan

Wang

et al. 2021

Computer aided Civil Eng

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Travel reliability is the most essential determinant for operating the transit system and improving its service level. In this study, an optimization model for the electric transit route network design problem is proposed, under the precondition that the locations of charging depots are predetermined. Objectives are to pursue maximum travel reliability and meanwhile control the total cost within a certain range. Constraints about the bus route and operation are also considered. A Reinforcement Learning Genetic Algorithm is developed to solve the proposed model. Two case studies including the classic Mandl's road network and a large road network in the context of Zhengzhou city are conducted to demonstrate the effectiveness of the proposed model and the solution algorithm. Results suggest that the proposed methodology is helpful for improving the travel reliability of the transit network with minimal cost increase.

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Section: Discussionmentioning

confidence: 99%

Optimal route design of electric transit networks considering travel reliability

Tong

Yan

Wang

et al. 2021

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Model-free frameworks are mainly based on deep learning approaches. They have achieved excellent applications in image object detection, image retrieval, video, and other fields with their strong learning ability (Benito-Picazo et al, 2020;García-González et al, 2020;Hamreras et al, 2020;Lara-Benítez et al, 2020;Simões et al, 2020;Sørensen et al, 2020;Thurnhofer-Hemsi et al, 2020;Vera-Olmos et al, 2019;Yang et al, 2019). In many fields of civil engineering, deep learning has achieved promising results, such as concrete compressive strength estimation (Rafiei et al, 2017), earthquake early warning (Rafiei & Adeli, 2017a), construction cost estimation (Rafiei & Adeli, 2018), sale prices estimation (Rafiei & Adeli, 2016), pavement roughness assessment (Jeong et al, 2020), and so on.…”

Section: F I G U R E 1 Different Image Sources For Pavement Distress Detectionmentioning

confidence: 99%

Cross‐scene pavement distress detection by a novel transfer learning framework

Che

Liu

et al. 2021

Computer aided Civil Eng

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Deep learning has achieved promising results in pavement distress detection. However, the training model's effectiveness varies according to the data and scenarios acquired by different camera types and their installation positions. It is time consuming and labor intensive to recollect labeled data and retrain a new model every time the scene changes. In this paper, we propose a transfer learning pipeline to address this problem, which enables a distress detection model to be applied to other untrained scenarios. The framework consists of two main components: data transfer and model transfer. The former trains a generative adversarial network to transfer existing image data into a new scene style. Then, attentive CutMix and image melding are applied to insert distress annotations to synthesize the new scene's labeled data. After data expansion, the latter step transfers the feature extracted by the existing model to the detection application of the new scene through domain adaptation. The effects of varying degrees of knowledge transfer are also discussed. The proposed method is evaluated on two data sets from two different scenes with more than 40,000 images totally. This method can reduce the demand for training data by at least 25% when the model is applied in a new scene. With the same number of training images, the proposed method can improve the model accuracy by 26.55%.

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“…Due to industrial demand, the pattern analysis by DNNs has been introduced for contentbased image retrieval [10], noisy image recognition [11], 3D medical image super-resolution [12], video surveillance [13], foreground detection [14], multi-object tracking [15], explosive device detection [16], pupil detection [17], online data streaming [18], airport baggage handling [19], and many other objectives. Computational photography [20] and purpose-specific machine learning [21] have been also employed to solve industrial problems.…”

Section: Pattern Analysis For Fabric Imagesmentioning

confidence: 99%

Image-based textile decoding

Chen

Toyoura

Terada

et al. 2021

ICA

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A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning viau the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

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Routing in congested baggage handling systems using deep reinforcement learning

Cited by 24 publications

References 11 publications

Optimal route design of electric transit networks considering travel reliability

Optimal route design of electric transit networks considering travel reliability

Cross‐scene pavement distress detection by a novel transfer learning framework

Image-based textile decoding

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