Autonomous Industrial Management via Reinforcement Learning

Espinosa-Leal, Leonardo; Chapman, Anthony; Westerlund, Magnus

doi:10.3233/jifs-189161

Cited by 14 publications

(9 citation statements)

References 26 publications

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“…In the field of embodied agents and reinforcement learning, the improved speed of YOLO could be beneficial when performing studies investigating reinforcement learning and object navigation. This is because reinforcement learning is already computationally expensive, and using a relatively “simple” object detection framework could be beneficial [ 34 , 35 ]. Additionally, we are aware of the current controversy revolving around YOLOv4 [ 36 ] and YOLOv5 [ 32 ] and have no reason to select one over the other.…”

Section: Methodsmentioning

confidence: 99%

Indoor Scene Recognition via Object Detection and TF-IDF

Heikel

Espinosa-Leal

2022

J. Imaging

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Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision and natural language processing (YOLO and TF-IDF, respectively). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

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Section: Methodsmentioning

confidence: 99%

Indoor Scene Recognition via Object Detection and TF-IDF

Heikel

Espinosa-Leal

2022

J. Imaging

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“…Machine learning methods have become a fundamental part of all industries [20], and it is expected to continue improving processes and decision-making [21]. One fundamental part is sustainability, which is becoming more relevant in our society to ensure a stable quality of life and preserve natural resources for future generations.…”

Section: Fundamentals and State-of-the-artmentioning

confidence: 99%

Natural Language Processing Methods for Scoring Sustainability Reports – A Study of Nordic Listed Companies

Gutierrez-Bustamante¹,

Espinosa-Leal²

2022

Preprint

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This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

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“…We trained a recent implementation of YOLO [22,34], YOLOv5L This is because reinforcement learning is already computationally expensive, and using 173 a relatively "simple" object detection framework could be beneficial [35,36]. Additionally,…”

Section: Object Detection Modules 165mentioning

confidence: 99%

Indoor Scene Recognition via Object Detection and TF-IDF

Heikel¹,

Espinosa-Leal²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

show abstract

Autonomous Industrial Management via Reinforcement Learning

Cited by 14 publications

References 26 publications

Indoor Scene Recognition via Object Detection and TF-IDF

Indoor Scene Recognition via Object Detection and TF-IDF

Natural Language Processing Methods for Scoring Sustainability Reports – A Study of Nordic Listed Companies

Indoor Scene Recognition via Object Detection and TF-IDF

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