Optimizing computational efficiency while maintaining accuracy in electrical machine fault detection is a critical challenge. To address this, the Frequency-Scaled Convolutional Neural Network is proposed as a lightwe...
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In this paper, a four unit cell micron-sized magnetic particles embedded composite right/left-handed (CRLH) transmission line metamaterial phase shifter is proposed. The variable phases are achieved by activating the ...
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Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, ...
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Question generation (QG), the task of generating questions from text or other forms of data, a significant and challenging subject, has recently attracted more attention in natural language processing (NLP) due to its...
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作者：
Hyeon, SoojeongASRI
Department of Electrical and Computer Engineering Seoul National University Korea Republic of
This paper presents a distributed estimation scheme for determining the geometric center of multiple targets by a group of agents in 2-dimensional space. These agents have the knowledge of bearing measurements to a pa...
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In Natural Language Processing (NLP), textual data is foundational, yet it presents substantial challenges, especially for under-resourced languages like Bengali. The complexity and volume of Bengali textual data requ...
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Depression is a common and serious mental illness that affects millions of people worldwide. Early diagnosis and treatment of depression are essential to improve the quality of life of those affected. Traditional meth...
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Due to increasing congestion in the radar frequencies due to reallocations, the pressure upon radar systems to avoid interference through dynamically changing operating frequency has intensified. Many modern radar sys...
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The substantial rise in Oman's load in recent years prompted researchers to investigate ways to reduce the construction of new power plants. Reducing the electricity consumption during the peak hours will provide ...
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With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,...
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With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim
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