243 research outputs found

    A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis

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    The vibration signals of a gearbox always contain the dynamic operation information, which are important for the feature extraction and further work. However, the low signal-to-noise ratio and combined multi-mode faults make it difficult to extract discriminable features of gearboxes. In this study, a feature fusion method based on wavelet packet decomposition (WPD), singular value decomposition (SVD) and t-Distributed stochastic neighbor embedding (t-SNE) for gearbox fault diagnosis is proposed. First, time-frequency analysis method of WPT-SVD as well as time-domain analysis methods are utilized to extract robust feature vectors of gearboxes with different conditions. As an effective method for the visualization of high-dimensional datasets, t-SNE is then introduced to realize the dimensionality reduction of feature vectors. Finally, with the fused features, a radial basis function (RBF) neural network is trained to realize the classification of gearbox fault modes. Sufficient experiments have been implemented to validate the effectiveness and superiority of the proposed method by analyzing the vibration signals of gearboxes

    The performance persistence of Chinese open-ended funds

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    1 online resource (3, 66 leaves)Includes abstract and appendices.Includes bibliographical references (leaves 24-25).With the rapid growth of Chinese fund market, as at July 31, 2012, there were 74 fund companies and 800 open-ended funds in the market. The Chinese open-ended fund market is an emerging industry with relatively thin research work done compared to that of developed countries. Therefore, this paper attempts to test persistence in the performance of Chinese open-ended mutual funds. This paper chooses all the data from January, 2004 to December, 2011 of 33 open-ended funds. The empirical study indicated that the performance persistence of Chinese open-ended fund was insignificant in both short-term and long term. Nevertheless, the relative performance persistence as compared with market benchmark is significant in the short term but insignificant in the long term. Therefore, in the short term people can invest in the funds of which short-term performance are better than the market bench mark to receive the excess return. At the same time, investors should take the situation of fund companies, fund managers and other factors into consideration

    On the clustering property of the random intersection graphs

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    A random intersection graph \mtl{\mcal{G}_{V,W,p}} is induced from a random bipartite graph \mtl{\mcal{G}^{*}_{V,W,p}} with vertices classes \mtl{V}, \mtl{W} and the edges incident between \mtl{v \in V} and \mtl{w \in W} with probability \mtl{p}. Two vertices in \mtl{V} are considered to be connected with each other if both of them connect with some common vertices in \mtl{W}. The clustering properties of the random intersection graph are investigated completely in this article. Suppose that the vertices number be \mtl{N = \mabs{V}} and \mtl{M=\mabs{W}} and \mtl{M = N^{\alpha},\ p=N^{-\beta}}, where \mtl{\alpha > 0,\, \beta > 0}, we derive the exact expressions of the clustering coefficient \mtl{C_{v}} of vertex \mtl{v} in \mtl{\mcal{G}_{V,W,p}}. The results show that if \mtl{\alpha < 2\beta} and \mtl{\alpha \neq \beta}, \mtl{C_{v}} decreases with the increasing of the graph size; if \mtl{\alpha = \beta} or \mtl{\alpha \geq 2\beta}, the graph has the constant clustering coefficients, in addition, if \mtl{\alpha > 2\beta}, the graph connecChangshui Zhangts almost completely. Therefore, we illustrate the phase transition for the clustering property in the random intersection graphs and give the condition that \mtl{\riG} being high clustering graph

    Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes

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    Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term nested suggests. These nested structures make traditional sequence labeling methods unable to properly recognize all entities. While recent researches focus on designing better recognition methods for NNER in a variety of languages, the Chinese NNER (CNNER) still lacks attention, where a free-for-access, CNNER-specialized benchmark is absent. In this paper, we aim to solve CNNER problems by providing a Chinese dataset and a learning-based model to tackle the issue. To facilitate the research on this task, we release ChiNesE, a CNNER dataset with 20,000 sentences sampled from online passages of multiple domains, containing 117,284 entities failing in 10 categories, where 43.8 percent of those entities are nested. Based on ChiNesE, we propose Mulco, a novel method that can recognize named entities in nested structures through multiple scopes. Each scope use a designed scope-based sequence labeling method, which predicts an anchor and the length of a named entity to recognize it. Experiment results show that Mulco has outperformed several baseline methods with the different recognizing schemes on ChiNesE. We also conduct extensive experiments on ACE2005 Chinese corpus, where Mulco has achieved the best performance compared with the baseline methods

    Tomographic and Particle Tracking Studies in a Liquid-Solid Riser

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    A Liquid-Solid Circulating Fluidized Bed is a Potential Reactor of Interest in a Variety of Industrial Processes, Such as Petroleum Refining, and in the Synthesis of Fine Chemicals, Petrochemicals, and Foodstuffs. Rapid Deactivation of the Solid Catalyst in These Processes Necessitates Regeneration and Regulation of the Solids into the Riser Section in Which the Principal Reaction is Accomplished. in This Study We Show that Computer-Automated Radioactive Particle Tracking (CARPT) Can Be Used to Obtain Solids Velocity Patterns in the Riser and that Backflow of Solids Exists at the Tested Liquid Velocities. Γ-Ray Computed Tomography (CT) Reveals Slightly Higher Solids Concentrations in the Center of the Column. This is in Contrast to Gas-Solid Riser Reactors in Which the Concentration of Solids is Higher at the Walls

    Meta-analysis of MMP-9 levels in the serum of patients with epilepsy

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    BackgroundEpilepsy’s pathogenesis and progression are significantly influenced by neuroinflammation, blood–brain barrier function, and synaptic remodeling function. Matrix metalloproteinase 9 (MMP-9), as a critical factor, may contribute to the development of epilepsy through one or more of the above-mentioned pathways. This study aims to evaluate and quantify the correlation between MMP-9 levels and epilepsy.MethodsWe conducted a comprehensive search of Embase, Web of Science, PubMed, Cochrane Library, WanFang DATA, VIP, and the CNKI to identify studies that investigate the potential association between MMP-9 and epilepsy. The data were independently extracted by two researchers and assessed for quality using the Cochrane Collaboration tool. The extracted data were analyzed using Stata 15 and Review Manager 5.4. The study protocol was registered prospectively at PROSPERO, ID: CRD42023468493.ResultsThirteen studies with a total of 756 patients and 611 matched controls met the inclusion criteria. Eight of these studies reported total serum MMP-9 levels, and the other five studies were used for a further subgroup analysis. The meta-analysis indicated that the serum MMP-9 level was higher in epilepsy patients (SMD = 4.18, 95% confidence interval = 2.18–6.17, p &lt; 0.00001) compared with that in the control group. Publication bias was not detected according to Begg’s test. The subgroup analysis of country indicated that the epilepsy patients in China, Poland, and Egypt had higher levels of serum MMP-9 than the control group, with the increase being more pronounced in Egypt. The subgroup analysis of the age category demonstrated that the serum MMP-9 levels of the adult patients with epilepsy were significantly higher than those of the matched controls. However, the serum MMP-9 levels did not significantly differ in children with epilepsy. The subgroup analysis of the seizure types demonstrated substantial difference in the MMP-9 levels between patients of seizure-free epilepsy (patients who have been seizure-free for at least 7 days) and the control group. Meanwhile, the serum MMP-9 level in patients with epileptic seizures was significantly higher than that in the control group. The subgroup analysis based on seizure duration in patients showed that the serum MMP-9 levels at 1–3, 24, and 72 h after seizure did not exhibit significant differences between female and male patients with epilepsy when compared with the control group. The serum MMP-9 levels at 1–3 and 24 h were significantly higher than those of the matched controls. Nevertheless, the serum MMP-9 level at 72 h was not significantly different from that in the control group.ConclusionThis meta-analysis presents the first comprehensive summary of the connection between serum MMP-9 level and epilepsy. The MMP-9 levels in epilepsy patients are elevated. Large-scale studies with a high level of evidence are necessary to determine the exact relationship between MMP-9 and epilepsy

    Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering

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    Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by 3.11×3.11\times and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding
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