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2. AGILE: Approach-based Grasp Inference Learned from Element Decomposition

Author

Koosheshi, MohammadHossein, Hosseini, Hamed, Masouleh, Mehdi Tale, Kalhor, Ahmad, and Yazdi, Mohammad Reza Hairi

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Systems and Control
Abstract

Humans, this species expert in grasp detection, can grasp objects by taking into account hand-object positioning information. This work proposes a method to enable a robot manipulator to learn the same, grasping objects in the most optimal way according to how the gripper has approached the object. Built on deep learning, the proposed method consists of two main stages. In order to generalize the network on unseen objects, the proposed Approach-based Grasping Inference involves an element decomposition stage to split an object into its main parts, each with one or more annotated grasps for a particular approach of the gripper. Subsequently, a grasp detection network utilizes the decomposed elements by Mask R-CNN and the information on the approach of the gripper in order to detect the element the gripper has approached and the most optimal grasp. In order to train the networks, the study introduces a robotic grasping dataset collected in the Coppeliasim simulation environment. The dataset involves 10 different objects with annotated element decomposition masks and grasp rectangles. The proposed method acquires a 90% grasp success rate on seen objects and 78% on unseen objects in the Coppeliasim simulation environment. Lastly, simulation-to-reality domain adaptation is performed by applying transformations on the training set collected in simulation and augmenting the dataset, which results in a 70% physical grasp success performance using a Delta parallel robot and a 2 -fingered gripper., Comment: Conference Paper, ICROM 2023, 8 pages, 8 figures

Published
2024
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22. Safety Causation Models in Complex Sociotechnical Systems

Author

Shirali, Gholam Abbas, Zarei, Esmaeil, Yazdi, Mohammad, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, and Zarei, Esmaeil, editor

Published
2024
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23. Expert Judgment and Uncertainty in Sociotechnical Systems Analysis

Author

Zarei, Esmaeil, Yazdi, Mohammad, Moradi, Rosita, BahooToroody, Ahmad, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, and Zarei, Esmaeil, editor

Published
2024
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24. Systems Thinking in Safety Causation Analysis in Sociotechnical Systems

Author

Ghasemi, Fakhredin, Zarei, Esmaeil, Biglari, Behnam, Yazdi, Mohammad, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, and Zarei, Esmaeil, editor

Published
2024
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26. Safety Causation Analysis in Sociotechnical Systems

Author

Zarei, Esmaeil, Biglari, Behnam, Yazdi, Mohammad, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, and Zarei, Esmaeil, editor

Published
2024
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27. Data-Driven Approaches for Accident Analysis in Sociochemical Systems

Author

Gholamizadeh, Kamran, Zarei, Esmaeil, Yazdi, Mohammad, Amin, Md Tanjin, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, and Zarei, Esmaeil, editor

Published
2024
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28. Applying Bayesian Networks to Safety Causation Analysis and Modeling in Socio-technical Systems: Bridging Theory and Practice

Author

Gholamizadeh, Kamran, Zarei, Esmaeil, Yazdi, Mohammad, Amin, Md. Tanjin, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, and Zarei, Esmaeil, editor

Published
2024
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33. WIDESim: A toolkit for simulating resource management techniques of scientific Workflows In Distributed Environments with graph topology

Author

Rayej, Mohammad Amin, Siar, Hajar, Hamzei, Ahmadreza, Yazdi, Mohammad Sadegh Majidi, Mohammadian, Parsa, and Izadi, Mohammad

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture
Abstract

IoT devices trigger real-time applications by receiving data from their vicinity. Modeling these applications in the form of workflows enables automating their procedure, especially for the business and industry. Depending on the features of the applications, they can be modeled in different forms, including single workflow, multiple workflows, and workflow ensembles. Since the whole data must be sent to the cloud servers for processing and storage, cloud computing has many challenges for executing real-time applications, such as bandwidth limitation, delay, and privacy. Edge paradigms are introduced to address the challenges of cloud computing in executing IoT applications. Executing IoT applications using device-to-device communications in edge paradigms requiring direct communication between devices in a network with a graph topology. While there is no simulator supporting simulating workflow-based applications and device-to-device communication, this paper introduces a toolkit for simulating resource management of scientific workflows in distributed environments with graph topology called WIDESim.The graph topology of WIDESim enables D2D communications in edge paradigms. WIDESim can work with all three different structures of scientific workflows: single, multiple workflows, and workflow ensembles. It has no constraint on the topology of the distributed environment. Also, unlike most existing network simulators, this simulator enables dynamic resource management and scheduling. We have validated the performance of WIDESim in comparison to standard simulators and workflow management tools. Also, we have evaluated its performance in different scenarios of distributed computing systems using different types of workflow-based applications. The results indicate that WIDESim's performance is close to existing standard simulators besides its improvements.

Published
2022

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