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arXiv 2023年

作者： Zhang, Huijie Opipari, Anthony Chen, Xiaotong Zhu, Jiyue Yu, Zeren Jenkins, Odest Chadwicke Electrical Engineering and Computer Science Mechanical Engineering and Robotics departments University of Michigan Ann ArborMI United States Department of Computer Science University of Southern California Los AngelesCA United States

Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even humans find certain transparent surfaces with little specular reflection or refraction, like glass doors, difficult to perceive. A second challenge is that depth sensors typically used for opaque object perception cannot obtain accurate depth measurements on transparent surfaces due to their unique reflective properties. Stemming from these challenges, we observe that transparent object instances within the same category, such as cups, look more similar to each other than to ordinary opaque objects of that same category. Given this observation, the present paper explores the possibility of category-level transparent object pose estimation rather than instance-level pose estimation. We propose TransNet, a two-stage pipeline that estimates category-level transparent object pose using localized depth completion and surface normal estimation. TransNet is evaluated in terms of pose estimation accuracy on a large-scale transparent object dataset and compared to a state-of-the-art category-level pose estimation approach. Results from this comparison demonstrate that TransNet achieves improved pose estimation accuracy on transparent objects. Moreover, we use TransNet to build an autonomous transparent object manipulation system for robotic pick-and-place and pouring tasks. The project webpage is available at https://***/projects/transnet/. © 2023, CC BY.

关键词： Object detection

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An IoT Based Smart Garbage Monitoring and Disposal Support System 5

An IoT Based Smart Garbage Monitoring and Disposal Support S...

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5th International Conference on Computing Methodologies and Communication, ICCMC 2021

作者： Vamsi, T.M.N. Kalyan Chakravarthi, G. Lanka, Pratibha Divakar, B. Dept of Computer Science and Engineering Engineering and Technology Program Visakhapatnam AP India Depts of Computer Science Visakhapatnam AP India Department of Computer Applications Visakhapatnam AP India

ISBN: (纸本)9781665403603

Today globally people designed different systems based on Internet of Things (IoT) principles and made human with smart living. In this article, an important application is proposed for our daily usage and is named as Smart Garbage Monitoring and Disposal Support System (SGMDSS). Since the advent of mobile communication and the spread of the Internet anywhere and anytime, there is a lot of scopes to control the devices from remote places. Solid garbage is one of the prior concerns today globally because improper execution of garbage removal from the villages, towns, and cities affects the people living there and it is the ultimate cause for new diseases. So, proper garbage management by using technology is a major concern of the hour. In the traditional existing system, the monitoring and disposal of garbage are done by humans and it is cumbersome, therefore supporting the traditional system with the flavor of IoT, then monitoring of garbage bins will be easy. This helps the people who are involved in the traditional garbage collection system and it will be the best feasible solution. The SGMDSS is a very innovative information management control system that helps the metros, cities, and villages hygiene and clean with a better garbage disposal. This system uses an advanced approach in which waste monitoring and disposal support are automated. SGMDSS monitors the garbage bins located at different locations and notifies about the level of garbage accumulated in the garbage bins through an android mobile application to the cleaning personnel for disposal and provides the shortest path to the garbage bin location that is almost filled. This information is also sent to the webpage and the entire data is stored and accessed through the cloud. Also, an alert message is sent to the worker. © 2021 IEEE.

关键词： Internet of things

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Cognitive Approach to Hierarchical Task Selection for Human-Robot Interaction in Dynamic Environments

arXiv

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arXiv 2023年

作者： Bukhari, Syed Tanweer Shah Anima, Bashira Akter Feil-Seifer, David Qazi, Wajahat Mahmood Cognitive Robotics Group Faculty of IT and CS University of Central Punjab Lahore Pakistan Intelligent Machines & Robotics Group Department of Computer Science COMSATS University Islamabad Lahore Campus Pakistan Socially Assistive Robotics Group Department of Computer Science & Engineering University of Nevada Reno RenoNV89557 United States

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying "what to do" in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make "tea" and "sandwich". We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says "I am thirsty" or "It is cold outside" the robot will start to perform the tea-making skill. In contrast, if the person says, "I am hungry" or "I need something to eat", the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill. © 2023, CC BY.

关键词： Anthropomorphic robots

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Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models

arXiv

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arXiv 2024年

作者： Yi, Jihun Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of Electrical and Computer Engineering and Interdisciplinary Program in AI Seoul National University Seoul Korea Republic of

The task of image anomaly detection (IAD) aims to identify deviations from normality in image data. These anomalies are patterns that deviate significantly from what the IAD model has learned from the data during training. However, in real-world scenarios, the criteria for what constitutes normality often change, necessitating the reclassification of previously anomalous instances as normal. To address this challenge, we propose a new scenario termed "normality addition," involving the post-training adjustment of decision boundaries to incorporate new normalities. To address this challenge, we propose a method called Normality Addition via Normality Detection (NAND), leveraging a vision-language model. NAND performs normality detection which detect patterns related to the intended normality within images based on textual descriptions. We then modify the results of a pre-trained IAD model to implement this normality addition. Using the benchmark dataset in IAD, MVTec AD, we establish an evaluation protocol for the normality addition task and empirically demonstrate the effectiveness of the NAND method. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance 8

Updating Clinical Risk Stratification Models Using Rank-Base...

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8th Machine Learning for Healthcare Conference, MLHC 2023

作者： Ötles, Erkin Denton, Brian T. Wiens, Jenna Medical Scientist Training Program University of Michigan Ann ArborMI United States Department of Industrial and Operations Engineering University of Michigan Ann ArborMI United States Division of Computer Science and Engineering University of Michigan Ann ArborMI United States

As data shift or new data become available, updating clinical machine learning models may be necessary to maintain or improve performance over time. However, updating a model can introduce compatibility issues when the behavior of the updated model does not align with user expectations, resulting in poor user-model team performance. Existing compatibility measures depend on model decision thresholds, limiting their applicability in settings where models are used to generate rankings based on estimated risk. To address this limitation, we propose a novel rank-based compatibility measure, CR, and a new loss function that aims to optimize discriminative performance while encouraging good compatibility. Applied to a case study in mortality risk stratification leveraging data from MIMIC, our approach yields more compatible models while maintaining discriminative performance compared to existing model selection techniques, with an increase in CR of 0.019 (95% confidence interval: 0.005, 0.035). This work provides new tools to analyze and update risk stratification models used in clinical care. © 2023 E. Ötles., B.T. Denton & J. Wiens.

关键词： Risk assessment

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Purchase intention of Amazon sustainable brands: a hybrid approach of structural equation modeling and Rasch model

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SN Business and Economics 2023年第12期3卷 1-29页

作者： Santos, Luiz Diego Vidal Holanda, Francisco Sandro Rodrigues de Santana Oliveira, Catuxe Varjão Pedrotti, Alceu de Menezes, Pedro Vinícius Bertulino Villwock, Ana Paula Schervinski Graduate Program in Intellectual Property Science Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Agronomy Engineering Department Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Computer Science Department Instituto Federal de Sergipe-UFS Sergipe Lagarto Brazil

This article conducts a comprehensive examination of brand-based equity closely linked to environmental sustainability in the Amazon Rainforest. The research is primarily dedicated to identifying the factors that influence consumers’ willingness to purchase products from sustainable brands within six categories: cosmetics, tourism, clothing, accessories, and food. The central aim of this study is to investigate how brand equity constructs, rooted in environmental sustainability within the Amazon Rainforest, impact consumer loyalty and purchase intention. To accomplish this objective, a mixed-methodology approach was employed, combining structural equation modeling with item response theory. This study involved 535 potential consumers, and data from eight selected brands served as the basis for the analysis. Within the framework of Purchase Intention (PI), the following variables were regarded as formative constructs: Strategic Brand Positioning (SBP), Perceived Brand Quality (PBQ), Retail Innovation (RI), and Brand Authenticity (BA). The results obtained from the structural equation models underscore the statistical significance and substantial impact of all pathways. Notably, SBP emerged as the most significant contributor to the effect on PI, as hypothesized. Brand equity and environmental sustainability were found to indirectly affect behavioral PI. Consumers exhibited a high level of latent trait (θ), with items consistently reflecting elevated values, confirming their acceptance of products, except brand differentiation. Overall, the findings highlight the effectiveness of branding strategies rooted in environmental sustainability in enhancing the brand equity of small forest companies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Amazon Rainforest Brands Entrepreneurship Microenterprises Sustainability performance Sustainable development

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SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization

SDF-Pack: Towards Compact Bin Packing with Signed-Distance-F...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jia-Hui Pan Ka-Hei Hui Xiaojie Gao Shize Zhu Yun-Hui Liu Pheng-Ann Heng Chi-Wing Fu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Centre for Logistics Robotics Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more compact bin packing. Our method adopts a truncated SDF representation to localize the computation, and based on it, we formulate the SDF -minimization heuristic to find optimized placements to compactly pack objects with the existing ones. To further improve space utilization, if the packing sequence is controllable, our method can suggest which object to be packed next. Experimental results on a large variety of everyday objects show that our method can consistently achieve higher packing compactness over 1,000 packing cases, enabling us to pack more objects into the container, compared with the existing heuristics under various packing settings. The code is publicly available at: https://***/kwpoon/SDF-Pack.

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Embodied-AI Wheelchair Framework with Hands-free Interface and Manipulation

Embodied-AI Wheelchair Framework with Hands-free Interface a...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Leaman, Jesse F. Yang, Zongming Elglaly, Yasmine N. La, Hung Li, Bing Automotive Engineering Department GreenvilleSC United States Gryphon Foundation for People with Disadvantages Western Washington University Computer Science Department United States University of Nevada Advanced Robotics and Automation Laboratory Reno United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Assistive robots can be found in hospitals and rehabilitation clinics, where they help patients maintain a positive disposition. Our proposed robotic mobility solution combines state of the art hardware and software to provide a safer, more independent, and more productive lifestyle for people with some of the most severe disabilities. New hardware includes, a retractable roof, manipulator arm, a hard backpack, a number of sensors that collect environmental data and processors that generate 3D maps for a hands-free human-machine *** proposed new system receives input from the user via head tracking or voice command, and displays information through augmented reality into the user's field of view. The software algorithm will use a novel cycle of self-learning artificial intelligence that achieves autonomous navigation while avoiding collisions with stationary and dynamic objects. The prototype will be assembled and tested over the next three years and a publicly available version could be ready two years thereafter. © 2022 IEEE.

关键词： Wheelchairs

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Preprocessing Taste Data for Deep Neural Networks

Preprocessing Taste Data for Deep Neural Networks

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Hyunjong Lee Han Hee Jung Jeongho Kwak Junwoo Yea Jihwan P. Choi Kyung-In Jang Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea Department of Robotics and Mechatronics Engineering DGIST Daegu Republic of Korea Department of Electrical Engineering and Computer Science DGIST Daegu Republic of Korea Department of Aerospace Engineering Korea Advance Institute of Science and Technology (KAIST) Dajeon Republic of Korea

Analyzing wine using taste data is a promising field due to the explosive expansion of online commerce. However, because of the wide variety of wine types with different flavors and aromas, it is difficult for consumers to choose the wine that suits their taste, and also difficult for sellers to recommend appropriate wines to consumers. Therefore, it is necessary to numerically analyze and classify wine, and a deep learning algorithm which mimics the human brain is appropriate for analyzing the wine data [1]. In this paper, we introduce several studies of wine classification using deep learning architectures and propose preprocessing methods for applying the taste data of wine to deep learning networks.

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