This article primarily introduces a design and algorithm system of a one-click measuring platform, which can quickly and accurately measure the size and dimensions of parts. Accomplishing by combining a camera and an ...
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ISBN:
(纸本)9798400708268
This article primarily introduces a design and algorithm system of a one-click measuring platform, which can quickly and accurately measure the size and dimensions of parts. Accomplishing by combining a camera and an algorithm, the one-click detection function effectively lowers the measurement time and labor costs in industrial production, increasing production efficiency. The platform's algorithm system is based on the Halcon platform and may employ various algorithms depending on the form, size, and backdrop of the gathered photos to extract models and highlight data. This essay concludes by summarizing the various operators utilized in the algorithm design and implementation process of the platform, including threshold segmentation techniques, binary image processing, grayscale processing, and contrast-increasing techniques.
For the application of probabilistic linguistic term sets (PLTSs) in multicriteria group decision making (MCGDM), this paper aims to develop an approach to solve the preference problem. First, a belief interval interp...
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For the application of probabilistic linguistic term sets (PLTSs) in multicriteria group decision making (MCGDM), this paper aims to develop an approach to solve the preference problem. First, a belief interval interpretation of PLTSs is presented, which makes it possible to represent the mathematical operations on PLTSs as the operations on belief intervals. This can reduce the uncertainty degree of information caused by the differentiated knowledge and cognitions of decision makers. Then, two methods of belief interval measure are proposed. One is the distance measure, which is utilized to obtain the criteria weights of each decision maker, so as to recognize the preferences for criteria. The other is the probability degree, which is used to get the partial order relation for alternatives of each decision maker, and to recognize the preferences for alternatives. Next, employing Dempster's rule of combination and graph theory, a visual algorithm is constructed to solve the MCGDM preference problem in the application of PLTSs. Finally, an illustrative example for the selection of emergency materials deployment scheme and the comparative analyses are shown to demonstrate the effectiveness of the proposed method.
The prevalence of cheating in first-person shooter (FPS) games poses a formidable challenge, undermining user experience and the integrity of competitive play. In response to this issue, a visual-based anti-cheating d...
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The prevalence of cheating in first-person shooter (FPS) games poses a formidable challenge, undermining user experience and the integrity of competitive play. In response to this issue, a visual-based anti-cheating detection network, termed VADNet, has been developed, harnessing the capabilities of deep learning and computer vision techniques. VADNet incorporates a focus module designed to segment high-resolution images, alongside a Feature Pyramid Network (FPN) for the fusion of multi-scale features, culminating in a classifier module tasked with the quantification of cheating behaviors. Rigorous experimentation on a dataset derived from a real online FPS game substantiates VADNet's efficacy in identifying players who resort to cheating, as evidenced by high precision, recall, and F1 scores. This investigation advances the field of anti-cheating mechanisms for FPS games, offering a robust and reliable system to preserve the fairness and integrity of online gaming environments.
In this study, we developed an automatic algorithm for sleep-wake detection based on Electrooculography (EOG) in healthy and non-healthy patients. Several features were extracted in time and frequency domains from the...
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ISBN:
(纸本)9781479940844
In this study, we developed an automatic algorithm for sleep-wake detection based on Electrooculography (EOG) in healthy and non-healthy patients. Several features were extracted in time and frequency domains from the EOG signal. The artificial neural network (ANN) was used as a classifier. This pilot study consisted of three aims;the first aim was to utilise only the EOG signal for automatic sleep-wake stage detection. The second objective was to investigate which features were the most effective in detecting the sleep-wake phases in healthy and non-healthy individuals. The third important aim is to investigate which suitable and effective channel can be utilized for detecting the sleep-wake stages. The database was built up using 7 healthy subjects and 9 patients with mixed sleep apnoea, sleep apnoea hypopnea syndrome (SAHS), dyssomnia and periodic limb movements of sleep (PLMS). The inter-rater reliability was 91.3%. The sensitivity and specificity were 84.5% and 91.5%, respectively. Cohen's kappa between visual and automatic algorithm in detection of the sleep-wake stages was 0.74.
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