278 research outputs found
Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN
Data class imbalance is a common problem in classification problems, where
minority class samples are often more important and more costly to misclassify
in a classification task. Therefore, it is very important to solve the data
class imbalance classification problem. The SMART dataset exhibits an evident
class imbalance, comprising a substantial quantity of healthy samples and a
comparatively limited number of defective samples. This dataset serves as a
reliable indicator of the disc's health status. In this paper, we obtain the
best balanced disk SMART dataset for a specific classification model by mixing
and integrating the data synthesised by multivariate generative adversarial
networks (GAN) to balance the disk SMART dataset at the data level; and combine
it with genetic algorithms to obtain higher disk fault classification
prediction accuracy on a specific classification model
A Survey of Methods for Handling Disk Data Imbalance
Class imbalance exists in many classification problems, and since the data is
designed for accuracy, imbalance in data classes can lead to classification
challenges with a few classes having higher misclassification costs. The
Backblaze dataset, a widely used dataset related to hard discs, has a small
amount of failure data and a large amount of health data, which exhibits a
serious class imbalance. This paper provides a comprehensive overview of
research in the field of imbalanced data classification. The discussion is
organized into three main aspects: data-level methods, algorithmic-level
methods, and hybrid methods. For each type of method, we summarize and analyze
the existing problems, algorithmic ideas, strengths, and weaknesses.
Additionally, the challenges of unbalanced data classification are discussed,
along with strategies to address them. It is convenient for researchers to
choose the appropriate method according to their needs
LSTM Deep Neural Network Based Power Data Credit Tagging Technology
The value of power data credit reporting in the social credit system continues to increase, and the government, users and the whole society have deep expectations and support for power data credit reporting. This paper will combine the data labeling theory as the support, define the power data label and explain its labeling implementation. Based on the construction of knowledge graph, the method of labeling power data is introduced in detail: demand analysis method, index selection method, data cleaning method and data desensitization method. Use the sorted data labels to establish a label system for power data, and through its system, visualize the comprehensive situation of enterprise power data credit information to meet the development of power data credit business. This paper takes shell enterprises as the main representatives of credit risk enterprises, analyzes the power data in the three stages before and after loans, and builds a value mining model for power credit data. In the future, the data labeling technology and value mining model of the power data credit business will be comprehensively applied, and the power data label library and credit model library will be established and continuously improved, so as to facilitate the evaluation of the operation of the enterprise at different stages
A distributed hybrid index for processing continuous range queries over moving objects
Central to many location-based services is the problem of processing concurrent continuous range queries over a large scale of moving objects. Most relevant works to this problem mainly investigate the centralized search algorithms based on a single server for handling range queries. However, due to the limited resources of a single server, these algorithms hardly can deal with an ocean of objects and extensive concurrent queries. Moreover, these approaches usually suppose either objects or queries are static but seldom consider the scenario that objects and queries are both moving simultaneously, restricting the practicability of these approaches. To resolve the above issues, we propose a distributed hybrid index (DHI) that consists of a global grid index and extensive local VR-tree indexes. DHI is apt to be deployed on a cluster of servers, and owns a good scalability to maintain numerous moving objects and concurrent range queries. Based on DHI, we further design a distributed incremental search approach, which organizes multiple servers with a publish/subscribe mechanism to calculate and monitor the results for continuous range queries in a distributed pattern. Finally, we conduct extensive experiments to fully evaluate the performance of our paper.Peer ReviewedPostprint (author's final draft
The molecular clouds in a section of the third Galactic quadrant: observational properties and chemical abundance ratio between CO and its isotopologues
We compare the observational properties between CO, CO, and
CO and summarize the observational parameters based on 7069 clouds
sample from the Milky Way Imaging Scroll Painting (MWISP) CO survey in a
section of the third Galactic quadrant. We find that the CO angular area
() generally increases with that of CO (), and the ratio of to is 0.38 by
linear fitting. We find that the CO and CO flux are tightly
correlated as with both fluxes
calculated within the CO-bright region. This indicates that the
abundance is a constant to be 6.5 for all samples under assumption of local thermodynamic equilibrium
(LTE). Additionally, we observed that the X-factor is approximately constant in
large sample molecular clouds. Similarly, we find with both fluxes calculated within
CO-bright region, which indicates that the abundance ratios stays the same value 9.7 across the
molecular clouds under LTE assumption. The linear relationships of vs. and vs.
hold not only for the CO-bright region or CO-bright region, but
also for the entire molecular cloud scale with lower flux ratio. The abundance
ratio inside clouds shows a strong
correlation with column density and temperature. This indicates that the
is dominated by a combination of chemical
fractionation, selectively dissociation, and self-shielding effect inside
clouds.Comment: 11 pages, 16 figures, 1 table, accepted by A
Advances in Renal Denervation in the Treatment of Hypertension
Hypertension significantly increases the risk of cardiovascular events and it is associated with high rates of disability and mortality. Hypertension is a common cause of cardiovascular and cerebrovascular accidents, which severely affect patients’ quality of life and lifespan. Current treatment strategies for hypertension are based primarily on medication and lifestyle interventions. The renal sympathetic nervous system plays an important role in the pathogenesis of hypertension, and catheter-based renal denervation (RDN) has provided a new concept for the treatment of hypertension. In recent years, studies on RDN have been performed worldwide. This article reviews the latest preclinical research and clinical evidence for RDN
Scalable colored sub-ambient radiative coolers based on a polymer-Tamm photonic structure
Daytime radiative coolers cool objects below the air temperature without any
electricity input, while most of them are limited by a silvery or whitish
appearance. Colored daytime radiative coolers (CDRCs) with diverse colors,
scalable manufacture, and sub-ambient cooling have not been achieved. We
introduce a polymer-Tamm photonic structure to enable a high infrared emittance
and an engineered absorbed solar irradiance, governed by the quality factor
(Q-factor). We theoretically determine the theoretical thresholds for
sub-ambient cooling through yellow, magenta, and cyan CDRCs. We experimentally
fabricate and observe a temperature drop of 2.6-8.8 degrees Celsius on average
during daytime and 4.0-4.4degrees Celsius during nighttime. Furthermore, we
demonstrate a scalable-manufactured magenta CDRC with a width of 60 cm and a
length of 500 cm by a roll-to-roll deposition technique. This work provides
guidelines for large-scale CDRCs and offers unprecedented opportunities for
potential applications with energy-saving, aesthetic, and visual comfort
demands
Microbial characteristics and potential mechanisms of souring control for a hypersaline oil reservoir
The production of hydrogen sulfide (H2S) had caused huge economic losses and security risks to oil fields, which had attracted the attention of many researchers. Studies focused on hypersaline oil reservoirs were scarce. Here, we simulated H2S inhibition by adding NO3- and NO2- in anaerobic bottles and up-flow sand-packed bioreactor in the laboratory using water samples from injection wells in the Jianghan reservoir. The microbial characteristics, pH, salinity, sulfate, sulfide, NO3- and NO2- were measured at every stage. The content of H2S and elemental sulfur (S0) gradually reduced to 0 mg/L with injected 400 mg/L of NO3- and 300 mg/L of NO2-. Halophilic nitrate-reducing bacteria (NRBs) related to Halomonas and Arcobacter could suppress the activity of sulfate-reducing bacteria (SRBs) through the accumulation of nitrite toxicity and the competition of organic substrates with SRBs. When SRBs activity could not be suppressed, the sulfur-oxidizing bacteria related to Sulfurimonas, Thiomicrospira and Acrobacter were accumulated for souring control through microbial sulfur cycle of H2S production and oxidation. Salinity could be a key parameter affecting microbial development and interactions. This study provides a fundamental data to souring control in the hypersaline oil reservoir by reinjection produced water amended with NO3- and NO2-.<br/
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