In autonomous driving, accurately interpreting the movements of other road users and leveraging this knowledge to forecast future trajectories is crucial. This is typically achieved through the integration of map data...
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Textual sentiment analysis (TSA) has gained significant attention recently for its wide-ranging applications across various research domains and industries. However, most existing research and sentiment analysis tools...
Textual sentiment analysis (TSA) has gained significant attention recently for its wide-ranging applications across various research domains and industries. However, most existing research and sentiment analysis tools are primarily tailored for English texts. The unique linguistic complexities of the Bengali language, coupled with a paucity of comprehensive resources and tools, pose distinctive challenges for TSA in Bengali. This paper introduces an intelligent approach, leveraging transformer-based learning techniques by harnessing the potent capabilities of self-attention mechanisms for dealing with Bengali sentences containing ungrammatical structures or local dialects. To tackle the downstream TSA task in Bengali, this work explores a range of machine learning (ML), deep learning (DL), and transformer-based baselines. Experimental results reveal that the Bangla BERT model outperforms the other baselines, achieving the highest weighted f 1 -score of 0.69.
Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorpora...
Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.
Demand forecasting is crucial in the business sector. Despite the inherent uncertainty of the future, it is essential for any firm to be able to accurately predict the market for both short- and long-term planning in ...
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Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainabilit...
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Collaboration between humans and robots is becoming increasingly crucial in our daily life. In order to accomplish efficient cooperation, trust recognition is vital, empowering robots to predict human behaviors and ma...
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作者：
Josh LuzierDavid C. ConnerDepartment of Physics
Computer Science and Engineering Capable Humanitarian Robotics and Intelligent Systems Lab (CHRISLab) Christopher Newport University Newport News Virginia
This paper presents synthesis tools for the ROS 2 version of the Flexible Behavior Engine (FlexBE). Synthesis reduces the need for extensive testing and validation of hand crafted controllers by using mathematically p...
This paper presents synthesis tools for the ROS 2 version of the Flexible Behavior Engine (FlexBE). Synthesis reduces the need for extensive testing and validation of hand crafted controllers by using mathematically precise specifications to generate “correct-by-construction” behavior controllers. Our approach builds upon work using the GR(1) (General Reactivity of rank 1) fragment of LTL to synthesize a reactive hierarchical finite state machine that can be directly executed in FlexBE. The presented work expands on previous ROS 1 tools, and extends them to ROS 2 in a way that supports more general specifications that incorporate environmental states. This paper presents an accessible open-source demonstration that serves as a general introduction to these powerful synthesis techniques.
Promoting sustainable water usage is a critical imperative across all sectors of society. Households are no exception since a significant portion of water is wasted daily due to inefficient appliances or improper habi...
Promoting sustainable water usage is a critical imperative across all sectors of society. Households are no exception since a significant portion of water is wasted daily due to inefficient appliances or improper habits. Thus, there is a need for innovative solutions that not only improve water utilization but also raise residents' awareness about this issue. This paper presents a promising solution leveraging the Internet of Things (IoT) and Machine Learning (ML) techniques to detect water wastage stemming from sink usage automatically. We have designed and developed a low-cost prototype equipped with an array of sensors, including a microphone, an ultrasonic sensor, and a PIR, to monitor sink usage. A deep learning model based on Gated Recurrent Units (GRU) has been trained to classify the wastage events. To validate our concept, we have gathered a small dataset relative to nine common daily water usage activities through the IoT prototype. Our preliminary findings demonstrate the feasibility of our solution, with an average accuracy exceeding 90% in detecting wastage events.
In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect ...
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There is a surging interest in developing integrated Optical Coherence Tomography (OCT) system. However, most components are based on silicon which cannot be used for wavelength below 1.2 µm. Here, we discuss the...
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