• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2852-2858
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• 2022

A proton therapy facility is under development at Huazhong University of Science and Technology (HUST). To meet the need for fast energy changes during treatments, a beamline control system (BCS) has been designed and implemented. The BCS coordinates and controls various beamline devices by adopting a distributed architecture divided into three layers: the client, server, and device layers. Among these, the design of the server layer is the key to realize fast energy changes. The server layer adopts the submodule programming paradigm and optimizes the data interface among modules, allowing the main workflow to be separated from the device workflow and data. Furthermore, this layer uses asynchronous, multithreaded, and thread-locking methods to improve the system's ability to operation efficiently and securely. Notably, to evaluate the changing energy status over time, a dynamic node update method is adopted, which can dynamically adjust the update frequency of variable nodes. This method not only meets the demand for fast updates on energy changes but also reduces the server's communication load in the steady state. This method is tested on a virtual platform, and the results are as expected.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.174-180
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• 2022

Emotion recognition is an essential component of complete interaction between human and machine. The issues related to emotion recognition are a result of the different types of emotions expressed in several forms such as visual, sound, and physiological signal. Recent advancements in the field show that combined modalities, such as visual, voice and electroencephalography signals, lead to better result compared to the use of single modalities separately. Previous studies have explored the use of multiple modalities for accurate predictions of emotion; however the number of studies regarding real-time implementation is limited because of the difficulty in simultaneously implementing multiple modalities of emotion recognition. In this study, we proposed an emotion recognition system for real-time emotion recognition implementation. Our model was built with a multithreading block that enables the implementation of each modality using separate threads for continuous synchronization. First, we separately achieved emotion recognition for each modality before enabling the use of the multithreaded system. To verify the correctness of the results, we compared the performance accuracy of unimodal and multimodal emotion recognitions in real-time. The experimental results showed real-time user emotion recognition of the proposed model. In addition, the effectiveness of the multimodalities for emotion recognition was observed. Our multimodal model was able to obtain an accuracy of 80.1% as compared to the unimodality, which obtained accuracies of 70.9, 54.3, and 63.1%.