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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images 19

Assessing Parkinson's Disease Severity Using Channel Attenti...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Fadaie, Fatemeh Aghapanah, Hamed Sedighifard, Alireza Shirvan, Hadi Niknam AI and Robotics University of Isfahan Department of Computer Engineering Isfahan Iran School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Department of Bioelectrics and Biomedical Engineering Isfahan Iran Iran University of Science and Technology Department of Artificial Intelligence Isfahan Iran University of Tehran Faculty of New Sciences and Technologies Isfahan Iran

ISBN: (纸本)9798331507565

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

arXiv

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arXiv 2024年

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computer Science University of Nottingham China

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt Generator (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation

arXiv

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arXiv 2025年

作者： Fan, Shichao Yang, Quantao Liu, Yajie Wu, Kun Che, Zhengping Liu, Qingjie Wan, Min School of Mechanical Engineering and Automation BeiHang University China School of Computer Science and Engineering BeiHang University China Beijing Innovation Center of Humanoid Robotics China KTH Royal Institute of Technology Sweden

Recently, Vision-Language-Action models (VLA) have advanced robot imitation learning, but high data collection costs and limited demonstrations hinder generalization and current imitation learning methods struggle in out-of-distribution scenarios, especially for long-horizon tasks. A key challenge is how to mitigate compounding errors in imitation learning, which lead to cascading failures over extended trajectories. To address these challenges, we propose the Diffusion Trajectory-guided Policy (DTP) framework, which generates 2D trajectories through a diffusion model to guide policy learning for long-horizon tasks. By leveraging task-relevant trajectories, DTP provides trajectory-level guidance to reduce error accumulation. Our two-stage approach first trains a generative vision-language model to create diffusion-based trajectories, then refines the imitation policy using them. Experiments on the CALVIN benchmark show that DTP outperforms state-of-the-art baselines by 25% in success rate, starting from scratch without external pretraining. Moreover, DTP significantly improves real-world robot performance. All source code and experiment videos will be released upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Enhanced integral imaging 3D depth map by using SRCNN 13

Enhanced integral imaging 3D depth map by using SRCNN

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13th International Conference on Information and Communication technology Convergence, ICTC 2022

作者： Uchino, Hideaki Lee, Jaehoon Kim, Hyun-Woo Honda, Kazuaki Cho, Myungjin Lee, Min-Chul Kyusyu Institute of Technology Dept. of Computer Science and Networks Fukuoka Iizuka Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Anseong Kyonggi-do Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we propose a method that can generate a three-dimensional (3D) depth map accurately by using an integral imaging technique through low-resolution elemental images. The conventional method may produce an accurate 3D depth map when it utilizes high-resolution images. However, if the elemental image has low resolution, it can cause the shifting pixel error when it reconstructs the 3D image by using volumetric computational reconstruction (VCR). Therefore, it may not provide a high-quality 3D depth map through the reconstructed image. To solve this problem, our proposed method utilizes a machine learning technique that can enhance the resolution of the elemental image. Our proposed method enlarged the low-resolution elemental image. Then, it generates the high-resolution image through the super-resolution convolution neural network (SRCNN). Therefore, it can reduce the shifting pixel error and it can generate various reconstructed images according to the depth. Finally, it can generate an accurate 3D depth map through the error-reduced 3D image. To prove our proposed depth map accuracy, we implement the simulation experiment and evaluate the image through the image quality metric. © 2022 IEEE.

关键词： Errors

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India’s Stake in Emerging technology - A Review

India’s Stake in Emerging Technology - A Review

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sugandha Singh S China Ramu Ruchika Lalit Department of Computer Science and Engineering SGT University Gurugram India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India University School of Automation and Robotics GGSIPU East Campus New Delhi India

Emerging technologies of today are altering the way we think about the built environment. In the meantime, many technologies have undergone revolutionary changes, and India, an expanding nation, offers opportunities for development in several new areas. The goal of the study is to show how organizations need to adapt their perspective and create new strategies for emerging technologies such as artificial intelligence (AI), machine learning (ML), big data, quantum, and space exploration in light of the swift advancements in technology. This paper presents that although India has come across a lot of advancements in this era of emerging technology, certain challenges have been faced in aspects like adapting, feasibility, and approval which brought many changes across the country, which in turn is impacting the pace to transform the country towards Industry 4.0 and Industry 5.0. Therefore a study is presented to understand the impact of emerging technologies for countries coming up as Industry 4.0 and Industry 5.0. This paper emphasizes on for India’s initiatives for emerging technologies to come up as VIKSIT BHARAT @2047.

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Robust STL Control Synthesis under Maximal Disturbance Sets

arXiv

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arXiv 2024年

作者： Verhagen, Joris Lindemann, Lars Tumova, Jana The Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States

This work addresses maximally robust control synthesis under unknown disturbances. We consider a general nonlinear system, subject to a Signal Temporal Logic (STL) specification, and wish to jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances. Yet, to the authors' best knowledge, this is the first work that aims to maximize the permissible disturbance set and find the corresponding controllers that ensure satisfying the STL specification with maximum disturbance robustness. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm to get the maximal disturbance robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV). Copyright © 2024, The Authors. All rights reserved.

关键词： Controllers

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Internet of Things (IoT) Based Continuous Growth Rate Monitoring System of Plant Stem 3

Internet of Things (IoT) Based Continuous Growth Rate Monito...

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3rd IEEE Conference on VLSI Device, Circuit and System, VLSI DCS 2022

作者： Nayak, Shikha Das, Subir Chakraborty, Badal Chakraborty, Tanmoy Roy, Kishor Meghnad Saha Institute of Technology Computer Science Engineering Kolkata India Indian Institute of Engineering Science & Technology School of Mechatronics and Robotics Shibpur India Bidhan Chandra Krishi Viswavidyalaya Faculty of Agricultural Engineering Kalyani India Techno Main Salt Lake Computer Science Engineering Kolkata India Techno India Group Kolkata India

ISBN: (纸本)9781665438018

In the field of precision agriculture, plant growth rate monitoring is an important issue to study the influence of soil, weather, or agronomic practices on its growing condition. A plant's growth rate can be evaluated by measuring the radial/ circumference growth of its body parts like stem, trunk, branch, and fruit. In this paper, the internet of things (IoT) based plant's stem growth rate monitoring system has been developed. Here, the most commonly used PC (Personal computer) optical mouse is used as a sensor for monitoring the radial growth of the sunflower stem. To sense the radial growth of the stem, a paper-based strap is used across the stem. A computer mouse is used for the measurement of strap movement which is occurred due to the radial growth of the stem. Eventually, the mouse sensor output has been calibrated using Wi-Fi enabled processing unit and transmits the same data to the server for real-time monitoring. To store the growth rate information, an open-source IoT platform namely 'ThingSpeak' has been used. In presence of the ambient condition, the performance of the developed system has been tested for a long duration of time over a sunflower stem and provided satisfactory results. © 2022 IEEE.

关键词： Internet of things

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A study on the classification of red blood cells specimen and data analysis method for the diagnosis of blood disease using a digital holographic microscopy 13

A study on the classification of red blood cells specimen an...

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13th International Conference on Information and Communication technology Convergence, ICTC 2022

作者： Kim, Hyun-Woo Lee, Jaehoon Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Dept. of Computer Science and Networks Fukuoka Iizuka Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Recently, there have been studied using Digital Holographic Microscopy (DHM) research and deep learning technology to analyze cells or diagnose diseases. 3D information of microscopic substances can be obtained through DHM, and several diseases can be diagnosed through the analysis of red blood cell (RBC)s in the human body. However, deep learning systems still show diagnostic results with lower accuracy than human judgment and have disadvantages in that more training data and expensive computers are needed to increase the accuracy. To overcome these shortcomings and help human judgment, this study proceeds with a study to automatically display 3D visual information and statistical data of RBC s in a system using 3D imaging technology based on DHM. Through this, depth information and shape data that are difficult to obtain with only two-dimensional information can be obtained. Therefore, using the average and variance values of each parameter as indicators, it is expected to help diagnose the presence or absence of diseases in which shape changes such as expansion or loss of groove height of the RBC occur. © 2022 IEEE.

关键词： Learning systems

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare 5

Exploring Innovations in Autonomous Robotics and Mechatronic...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Shalini, T.Grace Ganeshabu, L. Chemudugunta, Priyanka Yamsani, Nagendar Kumar, Ch.Ashok Athiraja, A. School of Computing Department of Computational Intelligence Chengalpattu Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Mevalurkuppam Rajalakshmi Nagar Tamil Nadu Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India Sr University Department of School of Computer Science and Artificial Engineering Warangal India Malla Reddy Engineering College Department of Mechanical Engineering Main Campus Hyderabad Telangana Maisammaguda India Telangana Secunderabad India

ISBN: (纸本)9798331504403

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics. © 2024 IEEE.

关键词： Deep reinforcement learning

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