This paper explores the concept of δ-labeling in graph theory, introduced in 2021. It addresses the problem of identifying graphs that can be labeled with δ-labeling and presents generalized results for various grap...
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In this research paper we have attempted to elicit Non-Functional Requirements (NFR) which may or not have been explicitly added to the Request for Proposal (RFP) in housing industry but is important for the success o...
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Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly *** limitations can res...
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Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly *** limitations can result in the misjudgment of models,leading to a degradation in overall detection *** paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above *** contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained *** memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration *** two modules together effectively alleviate the problem of ***,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature *** a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly *** proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly *** validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and *** results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,*** findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.
Random deployment and wide surveillance of a geographical area can be carried out in the Wireless Sensor Networks (WSN). A wireless sensor network is called a network with several sensor nodes that operate in wireless...
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Traditional unimodal biometric recognition technologies, wh-ile widely applied across various fields, still face limitations such as environmental interference, spoofing attacks, and individual differences, leading to...
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The outbreak of the unconventional coronavirus or COVID-19 is affecting the whole global in various parts of the arena and has resulted in hundreds of thousands of deaths. This stays an ominous public fitness caution ...
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Class-imbalanced datasets are commonly observed in hyperspectral image classification and deep learning models often tend to demonstrate a bias toward the dominant class, resulting in very low efficiency for minority ...
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To address the problems of complex real voice feature distribution, easy overfitting by learning only one classification boundary, and poor generalization ability of existing voice spoofing detection methods for unkno...
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The accessibility and readability of Generative Artificial Intelligence systems like GPT and Google BARD are crucial factors that require thorough examination. In today’s digitally connected world, where AI-generated...
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With the excavation of underground opening,the roof rock block may fall under external dynamic *** this paper,the roof rock mass was simplified as a rock block system composed of trapezoidal rock blocks.A series of ex...
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With the excavation of underground opening,the roof rock block may fall under external dynamic *** this paper,the roof rock mass was simplified as a rock block system composed of trapezoidal rock blocks.A series of experiments and numerical simulations were performed to study the sliding process of the key block under the horizontal static clamping load and vertical impact *** propagation of stress wave in the block system were captured and analyzed by using high-speed camera and digital image correlation *** results reveal that a pendulum-type wave was generated due to the propagation and superposition of stress waves in the block ***,the governing mechanism of the sliding displacement of the key block was clarified based on the propagation of incident stress waves or pendulum-type ***,it is found that the sliding distance of the key block decreases in a power function with the increasing friction coefficient,or decreases in a parabolic function with the increasing trapezoid internal ***,a case study on the roof block sliding of the roadway at a gold mine was conducted,and it is concluded that the sliding of the key block resulted from the coupled effects of"shear driving"and"low friction"driven by stress wave propagation,regardless of a single or multi-layer rock block *** results may provide technical guideline for preventing rock-falling accidents caused by blasting distur-bances in underground mining.
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