Recent advancements in wearable and Internet of Things (IoT) technologies have yet to be fully realized in combination with Mixed Reality (MR) for comprehensive real-time health monitoring systems. This paper introduc...
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Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system m...
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Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions ...
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Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks ofte...
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Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.
As the demand for reliable and accurate electricity metering escalates in the digital era, traditional systems increasingly need to improve due to their centralized nature, prone to inaccuracies, privacy breaches, and...
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In healthcare, remote sensing technologies are popular for smart patient health monitoring. Real-time health assessment and early intervention are possible using remote sensing data from wearable sensors and imaging e...
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Automatic timetable generation is a complex optimization problem with practical applications in various domains such as education, healthcare, and event management. The challenge lies in efficiently scheduling activit...
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ISBN:
(纸本)9798350318609
Automatic timetable generation is a complex optimization problem with practical applications in various domains such as education, healthcare, and event management. The challenge lies in efficiently scheduling activities while satisfying numerous constraints and objectives. In this study, we propose an OptiSchedule algorithm for automatic timetable generation. The algorithm employs a combination of heuristic search techniques and metaheuristic optimization methods to iteratively improve timetable solutions. It starts with initializing a timetable grid and iteratively refines the solution by generating neighbouring solutions and selecting the most promising ones based on an evaluation function. Through extensive testing and validation, our OptiSchedule algorithm demonstrates significant improvements in timetable quality and efficiency compared to existing approaches. The algorithm effectively minimizes conflicts, optimizes resource utilization, and balances workload distribution. Furthermore, it provides flexibility for users to input constraints and preferences, allowing customization to specific scheduling requirements. The OptiSchedule algorithm represents a significant advancement in the field of automatic timetable generation. Its ability to produce high-quality schedules while considering complex constraints makes it a valuable tool for educational institutions, healthcare facilities, and businesses alike. By streamlining scheduling processes and optimizing resource allocation, OptiSchedule contributes to improved operational efficiency and overall organizational performance. Through rigorous experimentation and evaluation, our study demonstrates the effectiveness of the OptiSchedule algorithm in improving timetable quality and reducing scheduling overhead. Compared to traditional methods, OptiSchedule generates timetables with fewer conflicts and better resource utilization, leading to enhanced productivity and satisfaction among stakeholders. Moreover, its fl
Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend...
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Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadv...
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The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered ...
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