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Chen, Xi, Shaojie Sun, Jun Zhao, and Bin Ai. "Spectral Discrimination of Pumice Rafts in Optical MSI Imagery." Remote Sensing 14, no. 22 (November 18, 2022): 5854. http://dx.doi.org/10.3390/rs14225854.
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Pumice rafts are considered to be a long-range drifting agent that promotes material exchange and the dispersal of marine species. Large ones can also interfere with vessel navigation and have a negative impact on the social economy and marine ecosystems. Synoptic observations from the Multispectral Instrument (MSI) on-board Sentinel-2, with a spatial resolution of up to 10 m, provide an excellent means to monitor and track pumice rafts. In this study, the use of a Spectral-Feature-Based Extraction (SFBE) algorithm to automatically discriminate and extract pumice on the ocean surface from submarine volcano eruptions was proposed. Specifically, a Pumice Raft Index (PRI) was developed based on the spectral signatures of pumice in MSI imagery to identify potential pumice features. After pre-processing, the PRI image was then subjected to a series of per-pixel and object-based processes to rule out false-positive detections, including shallow water, striped edges, mudflats, and cloud edges. The SFBE algorithm showed excellent performance in extracting pumice rafts and was successfully applied to extract pumice rafts near the Fiji Yasawa islands in 2019 and Hunga Tonga island in 2022, with an overall pumice extraction accuracy of 95.5% and a proportion of pixels mis-extracted as pumice of <3%. The robustness of the algorithm has also been tested and proved through applying it to data and comparing its output to results from previous studies. The timely and accurate detection of pumice using the algorithm proposed here is expected to provide important information to aid in response actions and ecological assessments, and will lead to a better understanding of the fate of pumice.
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Shaji, C., and A. Gangopadhyay. "Synoptic Modeling in the Eastern Arabian Sea during the Southwest Monsoon Using Upwelling Feature Models." Journal of Atmospheric and Oceanic Technology 24, no. 5 (May 1, 2007): 877–93. http://dx.doi.org/10.1175/jtech1984.1.
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Abstract Hydrographic observations along the western coast of India during the southwest (SW) monsoon season reveal upwelling in the equatorward surface flow and downwelling below the thermocline with a weak poleward undercurrent. Observations made previously during the peak of the SW monsoon in boreal summer showed that upwelling temperatures are much cooler (<23°C) than compared to the available climatology data in this region. A feature modeling technique is used to describe the temperature distribution associated with the West India Coastal Current (WICC) upwelling. This kind of formulation captures the upwelling and downwelling associated with the WICC reasonably well, though it requires the specification of a few observed offshore and nearshore temperature profiles. The temperature–salinity relationship from the Levitus climatology data is further used to obtain a compatible salinity distribution for the feature model. The efficiency of this feature model is further validated via a dynamical model simulation: here, the temperature–salinity feature model profiles are objectively melded with the Levitus climatology to create the synoptic initial condition. The WICC’s local circulation and simulated upwelling temperatures are more realistic in the dynamical model simulation with the feature model than in a simulation that does not utilize the upwelling feature model. The advantage of the feature modeling technique used herein is that it provides additional or new information that the OGCMs or prognostic ocean models can adapt to improve the latter’s initial condition and for synoptic forecasting. Furthermore, the generalized formulation of the upwelling feature model developed here may be used in other regional coastal oceans as well.
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Ma, Hualin, and Liyan Zhang. "Ocean SAR Image Segmentation and Edge Gradient Feature Extraction." Journal of Coastal Research 94, sp1 (September 9, 2019): 141. http://dx.doi.org/10.2112/si94-028.1.
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Wang, Xiong Liang, and Chun Ling Wang. "Extraction of Ocean Fronts Based on Empirical Mode Decomposition." Applied Mechanics and Materials 701-702 (December 2014): 303–7. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.303.
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Ocean front is a narrow transitional zone that the penetration of sea is obviously different between two or more waters there. It is an important feature of geophysical turbulence which plays an important role in ocean dynamics. Ocean fronts become visible on radar images because they are associated with a variable surface current which modulates the sea surface roughness and thus the backscattered radar power. This paper propose a new integrated method to extract ocean fronts based on two-dimensional Empirical Mode Decomposition (EMD), image edge detection and mathematical morphology processing. Experimental results show that this integrated method can be effective in ocean front feature extraction.
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Ibebuchi, Chibuike Chiedozie, and Itohan-Osa Abu. "Relationship between synoptic circulations and the spatial distributions of rainfall in Zimbabwe." AIMS Geosciences 9, no. 1 (2022): 1–15. http://dx.doi.org/10.3934/geosci.2023001.
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<abstract> <p>This study examines how the atmospheric circulation patterns in Africa south of the equator govern the spatial distribution of precipitation in Zimbabwe. The moisture circulation patterns are designated by an ample set of eight classified circulation types (CTs). Here it is shown that all wet CTs over Zimbabwe features enhanced cyclonic/convective activity in the southwest Indian Ocean. Therefore, enhanced moisture availability in the southwest Indian Ocean is necessary for rainfall formation in parts of Zimbabwe. The wettest CT in Zimbabwe is characterized by a ridging South Atlantic Ocean high-pressure, south of South Africa, driving an abundance of southeast moisture fluxes, from the southwest Indian Ocean into Zimbabwe. Due to the proximity of Zimbabwe to the Agulhas and Mozambique warm current, the activity of the ridging South Atlantic Ocean anticyclone is a dominant synoptic feature that favors above-average rainfall in Zimbabwe. Also, coupled with a weaker state of the Mascarene high, it is shown that a ridging South Atlantic Ocean high-pressure, south of South Africa, can be favorable for the southwest movement of tropical cyclones into the eastern coastal landmasses resulting in above-average rainfall in Zimbabwe. The driest CT is characterized by the northward track of the Southern Hemisphere mid-latitude cyclones leading to enhanced westerly fluxes in the southwest Indian Ocean, limiting moist southeast winds into Zimbabwe.</p> </abstract>
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Dai, Panxi, and Ji Nie. "A Global Quasigeostrophic Diagnosis of Extratropical Extreme Precipitation." Journal of Climate 33, no. 22 (November 15, 2020): 9629–42. http://dx.doi.org/10.1175/jcli-d-20-0146.1.
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AbstractThis paper presents a global picture of the dynamic processes and synoptic characteristics of extratropical extreme precipitation events (EPEs), defined as annual maximum daily precipitation averaged over 7.5° × 7.5° regional boxes. Based on the quasigeostrophic omega equation, extreme precipitation can be decomposed into components forced by large-scale adiabatic disturbances and amplified by diabatic heating feedback. The spatial distribution of the diabatic feedback parameter is largely controlled by atmospheric precipitable water and captured by a simple model. Most spatial heterogeneities of EPEs in the middle and high latitudes are due to the spatial variations of large-scale adiabatic forcing. The adiabatic component includes the processes of vorticity advection, in which the synoptic vorticity advection by background wind dominates; temperature advection, in which the total meridional temperature advection by synoptic wind dominates; and boundary forcing. The synoptic patterns of EPEs in all extratropical regions can be classified into six clusters using the self-organizing map method: two clusters in low latitudes and four clusters in middle and high latitudes. Synoptic disturbances are characterized by strong pressure anomalies throughout the troposphere over the coastal regions and oceans and feature upper-level shortwave disturbances and a large westward tilt with height over land. Synoptic configurations favor moisture transport from ocean to land over coastal regions.
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Jinkerson, Richard A., Stephen L. Abrams, Leonidas Bardis, Chryssostomos Chryssostomidis, Andre Cldment, Nicholas M. Patrikalakis, and Franz-Erich Wolter. "Inspection and Feature Extraction of Marine Propellers." Journal of Ship Production 9, no. 02 (May 1, 1993): 88–106. http://dx.doi.org/10.5957/jsp.1993.9.2.88.
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Localization is the process of determining the rigid-body translations and rotations that must be performed on the set of points measured on a manufactured surface to move those points into closest correspondence with the ideal design surface. In unconstrained localization all points have equal effect on the determination of the rigid-body transformation, while constrained localization allows a subset of the points to have stronger influence on the transformation. The measured points are physical points in space obtained by direct measurement of a manufactured marine propeller blade. The ideal design surface is the surface description of the propeller blade provided by the blade designer. Given that the measured blade is manufactured from the design surface description, the localization determines a Euclidean motion that brings the measured points of the manufactured surface as close as possible to the design surface. An additional option is to determine an offset distance, such that the Euclidean motion brings the measured points as close as possible to the offset of the design surface. For this optimization problem the offset distance is a seventh parameter that must be determined in addition to the six parameters of the Euclidean motion. After localization, the offset of the design surface that was determined can be used to extract the gross geometric features of the manufactured blade. These features have important hydrodynamic functions and include the camber surface, section thickness function, pitch, rake, skew, chord length, maximum thickness, maximum camber, and the leading-edge curve. The approximation of the camber surface, which is the basis of most of the remaining features, is an intricate problem relying on an extension of the concept of a Brooks ribbon. It requires the solution of a system of nonlinear differential equations and a complicated error evaluation scheme.
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Spensberger, Clemens, and Thomas Spengler. "Feature-Based Jet Variability in the Upper Troposphere." Journal of Climate 33, no. 16 (August 15, 2020): 6849–71. http://dx.doi.org/10.1175/jcli-d-19-0715.1.
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AbstractJets in the upper troposphere constitute a cornerstone of both synoptic meteorology and climate dynamics, providing a direct link between weather and midlatitude climate variability. Conventionally, jet variability is often inferred indirectly through the variability of geopotential or sea level pressure. As recent findings pointed to physical discrepancies of this interpretation for the Southern Hemisphere, this study presents a global overview of jet variability based on automated jet detections in the upper troposphere. Consistent with previous studies, most ocean basins are dominated by variability patterns comprising either a latitudinal shift of the jet or a so-called pulsing, a broadening/narrowing of the jet distribution without a change in the mean position. Whereas previous studies generally associate a mode of storm track variability with either shifting or pulsing, jet-based variability patterns frequently represent a transition from shifting to pulsing, or vice versa, across the respective ocean basin. In the Northern Hemisphere, jet variability is consistent with geopotential variability, confirming earlier analyses. In the Southern Hemisphere, however, the variability of geopotential and jets often indicates different modes of variability. Notable exceptions are the consistent dominant modes of jet and geopotential variability in the South Pacific and, to a lesser extent, the south Indian Ocean during winter, as well as the dominant modes in the South Atlantic and south Indian Ocean during summer. Finally, tropical variability is shown to modulate the jet distribution in the Northern Hemisphere, which is in line with previous results. The response in the Southern Hemispheric, however, is shown to be markedly different.
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MATSUOKA, Daisuke, Fumiaki ARAKI, Shinichiro KIDA, Hideharu SASAKI, and Bunmei TAGUCHI. "J013024 Feature Extraction and Visualization of Ocean Currents via Cluster Analysis." Proceedings of Mechanical Engineering Congress, Japan 2013 (2013): _J013024–1—_J013024–3. http://dx.doi.org/10.1299/jsmemecj.2013._j013024-1.
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González-Alemán, Juan J., Francisco Valero, Francisco Martín-León, and Jenni L. Evans. "Classification and Synoptic Analysis of Subtropical Cyclones within the Northeastern Atlantic Ocean*." Journal of Climate 28, no. 8 (April 7, 2015): 3331–52. http://dx.doi.org/10.1175/jcli-d-14-00276.1.
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Abstract Since more research is needed on subtropical cyclones (STCs) formed within the North Atlantic eastern basin, this survey analyzes them from a synoptic point of view, on a climatological basis, with the main aims of studying their common features, complementing other studies of these storms in the North Atlantic, and aiding the forecasting community. Fifteen cases of STCs were identified during the period 1979–2011 by applying a set of criteria from two databases. Composite analysis reveals that an extratropical depression acts as a precursor when it is isolated from the westerlies and then suffers a deepening when becoming subtropical instead of decaying through occlusion. This process is accompanied by an atmospheric circulation, within the North Atlantic, whose main feature is characterized by notable departures from the climatological pattern with a statistically significant anomalous high pressure to the north of the STCs. Three conceptual models of synoptic pattern of subtropical cyclogenesis are derived and show that these departures appeared because the westerly circulation moves poleward and/or the flow has a great meridional component, with the possibility of a blocked flow pattern occurring. Moreover, the identified STCs predominantly formed in a highly sheared (>10 m s−1) environment with low sea surface temperature values (<25°C), which differs from the dominant features of STCs in the North Atlantic, especially within its western region. Finally, a recent (2010) STC, identified by the authors, is synoptically discussed in order to achieve a better interpretation of the general results.
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Li, Xiukun, and Yushuang Wu. "Feature Extraction for Acoustic Scattering from a Buried Target." Journal of Marine Science and Application 18, no. 3 (August 15, 2019): 380–86. http://dx.doi.org/10.1007/s11804-019-00102-9.
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Lin, Haixia, Jianhong Cui, and Xiangwei Bai. "Feature Extraction of Marine Water Pollution Based on Data Mining." Symmetry 13, no. 2 (February 22, 2021): 355. http://dx.doi.org/10.3390/sym13020355.
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The ocean occupies more than two-thirds of the earth’s area, providing a lot of oxygen and materials for human survival and development. However, with human activities, a large number of sewage, plastic bags, and other wastes are discharged into the ocean, and the problem of marine water pollution has become a hot topic in the world. In order to extract the characteristics of marine water pollution, this study proposed K-means clustering technology based on cosine distance and discrimination to study the polluted water. In this study, the polygonal area method combined with six parameters of water quality is used to analyze the marine water body anomalies, so as to realize the rapid and real-time monitoring of marine water body anomalies. At the same time, the cosine distance method and discrimination are used to classify marine water pollutants, so as to improve the classification accuracy. The results show that the detection rate of water quality anomalies is more than 88.2%, and the overall classification accuracy of water pollution is 96.3%, which proves the effectiveness of the method. It is hoped that this study can provide timely and effective data support for the detection of marine water bodies.
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Zhang, Hu, Lei Zhao, Quan Liu, Jingjing Luo, Qin Wei, Zude Zhou, and Yongzhi Qu. "An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE." Polish Maritime Research 25, s2 (August 1, 2018): 98–106. http://dx.doi.org/10.2478/pomr-2018-0080.
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Abstract The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.
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Yutani, Taku, Oak Yono, Tatsu Kuwatani, Daisuke Matsuoka, Junji Kaneko, Mitsuko Hidaka, Takafumi Kasaya, et al. "Super-Resolution and Feature Extraction for Ocean Bathymetric Maps Using Sparse Coding." Sensors 22, no. 9 (April 21, 2022): 3198. http://dx.doi.org/10.3390/s22093198.
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The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world’s seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especially in the rugged part of the terrain. The proposed method, which learns a dictionary to capture topographical features and reconstructs them using a dictionary, produces super-resolution with high interpretability.
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Gabruseva, Tatiana, Sergey Zlobin, and Peter Wang. "Photometric Light Curves Classification with Machine Learning." Journal of Astronomical Instrumentation 09, no. 01 (March 2020): 2050005. http://dx.doi.org/10.1142/s2251171720500051.
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The Large Synoptic Survey Telescope will begin its survey in 2022 and produce terabytes of imaging data each night. To work with this massive onset of data, automated algorithms to classify astronomical light curves are crucial. Here, we present a method for automated classification of photometric light curves for a range of astronomical objects. Our approach is based on the gradient boosting of decision trees, feature extraction and selection, and augmentation. The solution was developed in the context of The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) and achieved one of the top results in the challenge.
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Zare, Mehdi, and Nowrouz Mohammad Nouri. "Novel feature extraction of underwater targets by encoding hydro-acoustic signatures as image." Applied Ocean Research 138 (September 2023): 103627. http://dx.doi.org/10.1016/j.apor.2023.103627.
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Li, Yuxing, Yilan Lou, Lili Liang, and Shuai Zhang. "Research on Feature Extraction of Ship-Radiated Noise Based on Multiscale Fuzzy Dispersion Entropy." Journal of Marine Science and Engineering 11, no. 5 (May 8, 2023): 997. http://dx.doi.org/10.3390/jmse11050997.
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In recent years, fuzzy dispersion entropy (FDE) has been proposed and used in the feature extraction of various types of signals. However, FDE can only analyze a signal from a single time scale during practical application and ignores some important information. In order to overcome this drawback, on the basis of FDE, this paper introduces the concept of multiscale process and proposes multiscale FDE (MFDE), based on which an MFDE-based feature extraction method for ship-radiated noise is proposed. The experimental results of the simulated signals show that MFDE can reflect the changes in signal complexity, frequency, and amplitude, which can be applied in signal feature extraction; in addition, the measured experimental results demonstrate that the MFDE-based feature extraction method has a better feature extraction effect on ship-radiated noise, and the highest recognition rate is 99.5%, which is an improvement of 31.9% compared to the recognition rate of a single time scale. All the results show that MFDE can be better applied to the feature extraction and identification classification of ship-radiated noise.
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Tu, Wanli, Shuncong Zhong, Atilla Incecik, and Xibin Fu. "Defect feature extraction of marine protective coatings by terahertz pulsed imaging." Ocean Engineering 155 (May 2018): 382–91. http://dx.doi.org/10.1016/j.oceaneng.2018.01.033.
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Juliano, Timothy W., and Zachary J. Lebo. "Linking large-scale circulation patterns to low-cloud properties." Atmospheric Chemistry and Physics 20, no. 12 (June 17, 2020): 7125–38. http://dx.doi.org/10.5194/acp-20-7125-2020.
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Abstract. The North Pacific High (NPH) is a fundamental meteorological feature present during the boreal warm season. Marine boundary layer (MBL) clouds, which are persistent in this oceanic region, are influenced directly by the NPH. In this study, we combine 11 years of reanalysis and an unsupervised machine learning technique to examine the gamut of 850 hPa synoptic-scale circulation patterns. This approach reveals two distinguishable regimes – a dominant NPH setup and a land-falling cyclone – and in between a spectrum of large-scale patterns. We then use satellite retrievals to elucidate for the first time the explicit dependence of MBL cloud properties (namely cloud droplet number concentration, liquid water path, and shortwave cloud radiative effect – CRESW) on 850 hPa circulation patterns over the northeast Pacific Ocean. We find that CRESW spans from −146.8 to −115.5 W m−2, indicating that the range of observed MBL cloud properties must be accounted for in global and regional climate models. Our results demonstrate the value of combining reanalysis and satellite retrievals to help clarify the relationship between synoptic-scale dynamics and cloud physics.
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Wu, S. Y., and A. K. Liu. "Towards an automated ocean feature detection, extraction and classification scheme for SAR imagery." International Journal of Remote Sensing 24, no. 5 (January 2003): 935–51. http://dx.doi.org/10.1080/01431160210144606.
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Xiao, Yang, Zhiguo Cao, Wen Zhuo, Liang Ye, and Lei Zhu. "mCLOUD: A Multiview Visual Feature Extraction Mechanism for Ground-Based Cloud Image Categorization." Journal of Atmospheric and Oceanic Technology 33, no. 4 (April 2016): 789–801. http://dx.doi.org/10.1175/jtech-d-15-0015.1.
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AbstractIn this paper, a novel Multiview CLOUD (mCLOUD) visual feature extraction mechanism is proposed for the task of categorizing clouds based on ground-based images. To completely characterize the different types of clouds, mCLOUD first extracts the raw visual descriptors from the views of texture, structure, and color simultaneously, in a densely sampled way—specifically, the scale invariant feature transform (SIFT), the census transform histogram (CENTRIST), and the statistical color features are extracted, respectively. To obtain a more descriptive cloud representation, the feature encoding of the raw descriptors is realized by using the Fisher vector. This is followed by the feature aggregation procedure. A linear support vector machine (SVM) is employed as the classifier to yield the final cloud image categorization result. The experiments on a challenging cloud dataset termed the six-class Huazhong University of Science and Technology (HUST) cloud demonstrate that mCLOUD consistently outperforms the state-of-the-art cloud classification approaches by large margins (at least 6.9%) under all the different experimental settings. It has also been verified that, compared to the single view, the multiview cloud representation generally enhances the performance.
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Kaufman, Zachary S., Nicole Feldl, Wilbert Weijer, and Milena Veneziani. "Causal Interactions between Southern Ocean Polynyas and High-Latitude Atmosphere–Ocean Variability." Journal of Climate 33, no. 11 (June 1, 2020): 4891–905. http://dx.doi.org/10.1175/jcli-d-19-0525.1.
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AbstractWeddell Sea open-ocean polynyas have been observed to occasionally release heat from the deep ocean to the atmosphere, indicating that their sporadic appearances may be an important feature of high-latitude atmosphere–ocean variability. Yet, observations of the phenomenon are sparse and many standard-resolution models represent these features poorly, if at all. We use a fully coupled, synoptic-scale preindustrial control simulation of the Energy Exascale Earth System Model (E3SMv0-HR) to effectively simulate open-ocean polynyas and investigate their role in the climate system. Our approach employs statistical tests of Granger causality to diagnose local and remote drivers of, and responses to, polynya heat loss on interannual to decadal time scales. First, we find that polynya heat loss Granger causes a persistent increase in surface air temperature over the Weddell Sea, strengthening the local cyclonic wind circulation. Along with responding to polynyas, atmospheric conditions also facilitate their development. When the Southern Ocean experiences a rapid poleward shift in the circumpolar westerlies following a prolonged negative phase of the southern annular mode (SAM), Weddell Sea salinity increases, promoting density destratification and convection in the water column. Finally, we find that the reduction of surface heat fluxes during periods of full ice cover is not fully compensated by ocean heat transport into the high latitudes. This imbalance leads to a buildup of ocean heat content that supplies polynya heat loss. These results disentangle the complex, coupled climate processes that both enable the polynya’s existence and respond to it, providing insights to improve the representation of these highly episodic sea ice features in climate models.
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Calbó, Josep, and Jeff Sabburg. "Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition." Journal of Atmospheric and Oceanic Technology 25, no. 1 (January 1, 2008): 3–14. http://dx.doi.org/10.1175/2007jtecha959.1.
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Abstract Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques.
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Yang, Hong, Xiaodie Yang, and Guohui Li. "Dual feature extraction system for ship-radiated noise and its application extension." Ocean Engineering 285 (October 2023): 115352. http://dx.doi.org/10.1016/j.oceaneng.2023.115352.
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Li, Yuxing, Xiao Chen, and Jing Yu. "A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy." Processes 7, no. 2 (February 1, 2019): 69. http://dx.doi.org/10.3390/pr7020069.
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Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise (S-RN) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with energy difference (ED) and energy entropy (EE). This approach, named CEEMDAN-ED-EE, has two main advantages: (i) compared with empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN has better decomposition performance by overcoming mode mixing, and the intrinsic mode function (IMF) obtained by CEEMDAN is beneficial to feature extraction; (ii) the classification performance of the single energy feature has some limitations, nevertheless, the proposed hybrid energy feature extraction approach has a better classification performance. In this paper, we first decompose three types of S-RN into sub-signals, named intrinsic mode functions (IMFs). Then, we obtain the features of energy difference and energy entropy based on IMFs, named CEEMDAN-ED and CEEMDAN-EE, respectively. Finally, we compare the recognition rate for three sorts of S-RN by using the following three energy feature extraction approaches, which are CEEMDAN-ED, CEEMDAN-EE and CEEMDAN-ED-EE. The experimental results prove the effectivity and the high recognition rate of the proposed approach.
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Patrikalakis, Nicholas M., and Leonidas Bardis. "Feature Extraction from B-Spline Marine Propeller Representations." Journal of Ship Research 36, no. 03 (September 1, 1992): 233–47. http://dx.doi.org/10.5957/jsr.1992.36.3.233.
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This paper presents accurate algorithms for the extraction of gross geometrical features of marine propeller blades represented in terms of B-spline surfaces. These features have important hydrodynamic function and include specifically the camber surface, the section thickness function, pitch, rake, skew, chord length, maximum thickness, maximum camber of the section and leading edge. The approximation of the camber surface, on which most of the remaining features are based, is an intricate problem relying on an extension of the concept of a Brooks ribbon and requiring the solution of a complex system of nonlinear differential equations, developed for the problem at hand, and a sophisticated error evaluation scheme. The above features provide an alternate abstract representation of propeller blades useful in geometric design through lofting and fairing processes, in idealization for hydrodynamic and structural dynamic analyses and in verification of the quality of a manufactured blade. Furthermore, the derivation of these design features provides an alternate shape interrogation method for propeller blades, which is more intuitive for propeller designers in comparison with existing surface interrogation techniques.
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Lomazzi, Marco, Dara Entekhabi, Joaquim G. Pinto, Giorgio Roth, and Roberto Rudari. "Synoptic Preconditions for Extreme Flooding during the Summer Asian Monsoon in the Mumbai Area." Journal of Hydrometeorology 15, no. 1 (February 1, 2014): 229–42. http://dx.doi.org/10.1175/jhm-d-13-039.1.
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Abstract The summer monsoon season is an important hydrometeorological feature of the Indian subcontinent and it has significant socioeconomic impacts. This study is aimed at understanding the processes associated with the occurrence of catastrophic flood events. The study has two novel features that add to the existing body of knowledge about the South Asian monsoon: 1) it combines traditional hydrometeorological observations (rain gauge measurements) with unconventional data (media and state historical records of reported flooding) to produce value-added century-long time series of potential flood events and 2) it identifies the larger regional synoptic conditions leading to days with flood potential in the time series. The promise of mining unconventional data to extend hydrometeorological records is demonstrated in this study. The synoptic evolution of flooding events in the western-central coast of India and the densely populated Mumbai area are shown to correspond to active monsoon periods with embedded low pressure centers and have far-upstream influences from the western edge of the Indian Ocean basin. The coastal processes along the Arabian Peninsula where the currents interact with the continental shelf are found to be key features of extremes during the South Asian monsoon.
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Situmorang, Boldson Herdianto. "Identification of Biometrics Using Fingerprint Minutiae Extraction Based on Crossing Number Method." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 20, no. 1 (December 2, 2022): 71–80. http://dx.doi.org/10.33751/komputasi.v20i1.6814.
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Biometrics based on fingerprint images is a self-recognition technique using fingerprint to represent a person's identity. Fingerprint is characteristic of someone's identity precisely and safely because there are no similarities and cannot be falsified. The purpose of this research is to develop a biometrics identification system based on fingerprint images by utilizing a cell phone camera for the acquisition of fingerprint images. This is based on its simplicity because almost everyone has a cell phone so that a person's identification system based on fingerprint can be used anytime and anywhere. The research was conducted using images generated from cell phone cameras with camera specifications of 2, 5 and 8 mega pixels. The method used in image processing consists of the minutiae crossing number method for the feature extraction process and the minutiae based matching method for the similarity measurement process. The results of the research concluded that cell phone cameras with specifications of 5 and 8 mega pixels can be used for the process of image acquisition in biometrics systems based on fingerprint. The feature extraction process of image results using the minutiae crossing number method and the match measurement process using the minutiae based matching method resulted in an accuracy value of 92.8% on a 5 mega pixel camera and 95.3% on an 8 mega pixel camera. The accuracy value depends on the results of the image acquisition stage, pre-processing, the threshold value in the identification process, and the number of images used in the training data in the database.
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Ye, Jing, Zhaoyu Yuan, Cheng Qian, and Xiaoqiong Li. "CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection." Sensors 22, no. 10 (May 16, 2022): 3782. http://dx.doi.org/10.3390/s22103782.
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Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.
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Ioana, Cornel, Andr Quinquis, and Yann Stephan. "Feature Extraction From Underwater Signals Using Time-Frequency Warping Operators." IEEE Journal of Oceanic Engineering 31, no. 3 (July 2006): 628–45. http://dx.doi.org/10.1109/joe.2006.875275.
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Ashcroft, Linden Claire, Alexandre Bernardes Pezza, and Ian Simmonds. "Cold Events over Southern Australia: Synoptic Climatology and Hemispheric Structure." Journal of Climate 22, no. 24 (December 15, 2009): 6679–98. http://dx.doi.org/10.1175/2009jcli2997.1.
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Abstract Cold events (CEs) are an important feature of southern Australian weather. Unseasonably cold conditions can have a significant impact on Australia’s agricultural industry and other aspects of society. In this study the bottom 0.4% of maximum temperatures in Melbourne and Perth from the 1958–2006 period are defined as CEs, representing the large-scale patterns affecting most of extratropical Australia. Compiling 6-hourly progressions of the tracks of the cyclones and anticyclones that are geostrophically associated with CEs gives for the first time a detailed synoptic climatology over the area. The anticyclone tracks display a “cloud” of high density across the Indian Ocean, which is linked, in the mean, to weak but significant negative SST anomalies in the region. The cyclone tracks display much variability, with system origins ranging from subpolar to tropical. Several CEs are found to involve tropical and extratropical interaction or extratropical transition of originally tropical cyclones (hurricanes). CE-associated systems travel farther and exhibit longer life spans than similar, non-CE systems. Upper-level analyses indicate the presence of a wave train originating more than 120° west of the CE. This pattern greatly intensifies over the affected area in conjunction with a merging of the subpolar and subtropical jets. The upper-level wave train is present up to five days before the CE. The absence of large orographic features in Australia highlights the importance of wave amplification in CE occurrence. No consistent trend in CE intensity over the period is found, but a significant negative trend in event frequency is identified for both Melbourne and Perth.
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Kubicek, Bernice, Ananya Sen Gupta, and Ivars Kirsteins. "Statistical-based feature extraction and classification of active sonar data." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A267—A268. http://dx.doi.org/10.1121/10.0011297.
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Sonar target recognition is difficult due to the potential nonlinear overlap within an acoustic color response due to various backscatter and clutter within the ocean. This talk presents initial results from using a statistical model of feature vectors in conjunction with machine learning classifiers. Canonical correlation analysis (CCA) seeks to find two linear combinations of data by maximizing the correlation between the linear combinations while maintaining unit variance. In this application, CCA is used as a feature extraction method before target classification of active sonar data experimentally collected during the Shallow Water Active Classification (SWAC)-1 and SWAC-2 sea trials in the Malta Channel. The database consists of 20 targets; three were analyzed using this method. The data are generated by taking windows of consecutive pings from the ping-vs-time domain and performing CCA. The intuition behind using CCA is that there are persistent features within the data that morph over time due to changing target aspect angles and platform positions which can be represented by the maximally correlated linear combinations of data among consecutive pings. The resulting linear combinations are feature vectors used to train a single hidden-layer neural network classifier. Results are reported as overall classification accuracy and confusion matrices.
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Spondylidis, Spyros, Konstantinos Topouzelis, Dimitris Kavroudakis, and Michail Vaitis. "Mesoscale Ocean Feature Identification in the North Aegean Sea with the Use of Sentinel-3 Data." Journal of Marine Science and Engineering 8, no. 10 (September 25, 2020): 740. http://dx.doi.org/10.3390/jmse8100740.
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The identification of oceanographic circulation related features is a valuable tool for environmental and fishery management authorities, commercial use and institutional research. Remote sensing techniques are suitable for detection, as in situ measurements are prohibitively costly, spatially sparse and infrequent. Still, these imagery applications require a certain level of technical and theoretical skill making them practically unreachable to the immediate beneficiaries. In this paper a new geospatial web service is proposed for providing daily data on mesoscale oceanic feature identification in the North Aegean Sea, produced by Sentinel-3 SLSTR Sea Surface Temperature (SST) imagery, to end users. The service encompasses an automated process for: raw data acquisition, interpolation, oceanic feature extraction and publishing through a webGIS application. Level-2 SST data are interpolated through a Co-Kriging algorithm, involving information from short term historical data, in order to retain as much information as possible. A modified gradient edge detection methodology is then applied to the interpolated products for the mesoscale feature extraction. The resulting datasets are served according to the Open Geospatial Consortium (OGC) standards and are available for visualization, processing and download though a dedicated web portal.
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Oliveira, António José, Bruno Miguel Ferreira, and Nuno Alexandre Cruz. "A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation." Journal of Marine Science and Engineering 9, no. 4 (March 28, 2021): 361. http://dx.doi.org/10.3390/jmse9040361.
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In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.
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Song, Xiangzhou, Chunlin Ning, Yongliang Duan, Huiwu Wang, Chao Li, Yang Yang, Jianjun Liu, and Weidong Yu. "Observed Extreme Air–Sea Heat Flux Variations during Three Tropical Cyclones in the Tropical Southeastern Indian Ocean." Journal of Climate 34, no. 9 (May 2021): 3683–705. http://dx.doi.org/10.1175/jcli-d-20-0170.1.
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AbstractSix-month buoy-based heat flux observations from the poorly sampled tropical southeastern Indian Ocean are examined to document the extremes during three tropical cyclones (TCs) from December 2018 to May 2019. The most striking feature at the mooring site (16.9°S, 115.2°E) during the TCs is the extensively suppressed diurnal cycle of the net surface flux (Qnet), with a mean daytime (nighttime) reduction of 470 (131) W m−2, a peak decrease at approximately noon of 695 W m−2 and an extreme drop during TC Riley of 800 W m−2. The mean surface cooling in the daytime is primarily contributed by the 370 W m−2 decrease in shortwave radiation associated with the increased cloudiness. The air–sea turbulent heat fluxes increase by approximately 151 W m−2 in response to the enhanced wind speed under near-neutral boundary conditions. The daily mean rainfall-induced cooling is 8 W m−2, with a maximum magnitude of 90 W m−2. The mean values, seasonal variation, and synoptic variability of the characteristic heat fluxes are used to assess the new reanalysis data from ERA5 and MERRA2 and the analyzed OAFlux. The overall performance of the high-frequency net heat flux estimates at the synoptic scale is satisfactory, but the four flux components exhibit different quality levels. A serious error is that ERA5 and MERRA2 poorly represent TCs, and they show significant daily mean Qnet biases with opposite directions, −59 W m−2 (largely due to the overestimated latent heat with a bias of −76 W m−2) and 50 W m−2 (largely due to the overestimated shortwave radiation with a bias of 41 W m−2), respectively.
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Zhang, Pengfei, Shun Liu, and Qin Xu. "Identifying Doppler Velocity Contamination Caused by Migrating Birds. Part I: Feature Extraction and Quantification." Journal of Atmospheric and Oceanic Technology 22, no. 8 (August 1, 2005): 1105–13. http://dx.doi.org/10.1175/jtech1757.1.
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Abstract Radar echoes from migrating birds can severely contaminate Doppler velocity measurements. For meteorological applications, especially quantitative applications in radar data assimilation, it is necessary to remove bird-contaminated velocity scans by using an automated identification technique. Such a technique should be also useful for ornithologists in selecting bird echoes automatically from radar scans. This technique can be developed in two steps: (i) extract the main features of migrating-bird echoes from reflectivity and Doppler velocity images and find proper parameters to quantify these features; (ii) utilize these parameters to develop an automated quality control procedure to identify and flag migrating-bird-contaminated Doppler velocity scans (sweeps). The first step is accomplished in this study (Part I) by identifying possible migrating-bird echoes in the level II data collected from the Oklahoma KTLX radar during the 2003 spring migrating season. The identifications are further verified by polarimetric radar measurements from the National Severe Storms Laboratory (NSSL) KOUN radar, Geostationary Operational Environmental Satellite (GOES) IR images, and rawinsonde measurements. Three proper parameters are found, and their histograms are prepared for the second step of development (reported in Part II).
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Wu, Ji, Peng Li, Yongxian Wang, Qiang Lan, Wenbin Xiao, and Zhenghua Wang. "VFR: The Underwater Acoustic Target Recognition Using Cross-Domain Pre-Training with FBank Fusion Features." Journal of Marine Science and Engineering 11, no. 2 (January 23, 2023): 263. http://dx.doi.org/10.3390/jmse11020263.
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Underwater acoustic target recognition is a hot research area in acoustic signal processing. With the development of deep learning, feature extraction and neural network computation have become two major steps of recognition. Due to the complexity of the marine environment, traditional feature extraction cannot express the characteristics of the targets well. In this paper, we propose an underwater acoustic target recognition approach named VFR. VFR adopts a novel feature extraction method by fusing three-dimensional FBank features, and inputs the extracted features into a residual network, instead of the classical CNN network, plus cross-domain pre-training to perform target recognition. The experimental results show that VFR achieves 98.5% recognition accuracy on the randomly divided ShipsEar dataset and 93.8% on the time-divided dataset, respectively, which are better than state-of-the-art results.
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Zhao, Di, Hongyan Xing, Haifeng Wang, Huaizhou Zhang, Xinyi Liang, and Haoqi Li. "Sea-Surface Small Target Detection Based on Four Features Extracted by FAST Algorithm." Journal of Marine Science and Engineering 11, no. 2 (February 3, 2023): 339. http://dx.doi.org/10.3390/jmse11020339.
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On account of current algorithm and parameter design difficulties and low detection accuracy in feature extractions of small target detections in sea clutter environment, this paper proposes a correspondingly improved four feature extraction method by FAST. After the short-time Fourier transform is applied, a time–frequency distribution spectrogram of original data is generated. Candidate feature points (CFP) are first extracted by FAST algorithm, and then a four-feature extraction is implemented with FAST and DBSCAN combined. The feature distinction is enhanced through a feature optimization. Upon the construction of the four-dimensional feature vectors, XGBoost classifier algorithm classifies and detects these feature vectors. The genetic algorithm optimizes the hyperparameters in XGBoost and updates the decision threshold in real time to control the detection method’s false alarm rate. The IPIX dataset is employed for experimental verification. Verification results confirm that this proposed detection method has better performance than several other currently used detection methods. The detection performance is improved by 7% and 13.8% when observation time is set at 0.512 s and 1.024 s, respectively.
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Sonawane, Jitendra, Mukesh Patil, and Gajanan Birajdar. "A novel feature extraction and mapping using convolutional autoencoder for enhancement of Underwater image/video." ITM Web of Conferences 44 (2022): 03066. http://dx.doi.org/10.1051/itmconf/20224403066.
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Marine resources known to human are very limited and as 71% world is surrounded by ocean, we are yet to discover the many of the species and the enriched resources. Often the Underwater scenery collected are poorly illuminated, degraded, and distorted due to light propagation model underwater, water molecules and impurities as well. Counting on to these factors images/videos collected in underwater environment are in need of enhancement. We propose a method of utilizing convolution autoencoder, which can be able to collect the features of underwater images and enhanced image and then the feature mapping of this can be used in testing of the other underwater images/videos. The method utilizes the technique, which combines benefits of unsupervised convolution autoencoder to extract non-trivial features and utilized them for the enhancement of the underwater images. In order to evaluate the performance, we have used both subjective as well as objective evaluation method. Evaluation parameters used represent the results of the proposed method are significant for enhancement of underwater imagery. With the proposed network, we expect to advance underwater image enhancement research and its applications in many areas like in study of marine organism, their behaviour according to the environment, ocean exploration and Autonomous underwater vehicle.
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Ferreira, David, John Marshall, and Brian Rose. "Climate Determinism Revisited: Multiple Equilibria in a Complex Climate Model." Journal of Climate 24, no. 4 (February 15, 2011): 992–1012. http://dx.doi.org/10.1175/2010jcli3580.1.
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Abstract Multiple equilibria in a coupled ocean–atmosphere–sea ice general circulation model (GCM) of an aquaplanet with many degrees of freedom are studied. Three different stable states are found for exactly the same set of parameters and external forcings: a cold state in which a polar sea ice cap extends into the midlatitudes; a warm state, which is ice free; and a completely sea ice–covered “snowball” state. Although low-order energy balance models of the climate are known to exhibit intransitivity (i.e., more than one climate state for a given set of governing equations), the results reported here are the first to demonstrate that this is a property of a complex coupled climate model with a consistent set of equations representing the 3D dynamics of the ocean and atmosphere. The coupled model notably includes atmospheric synoptic systems, large-scale circulation of the ocean, a fully active hydrological cycle, sea ice, and a seasonal cycle. There are no flux adjustments, with the system being solely forced by incoming solar radiation at the top of the atmosphere. It is demonstrated that the multiple equilibria owe their existence to the presence of meridional structure in ocean heat transport: namely, a large heat transport out of the tropics and a relatively weak high-latitude transport. The associated large midlatitude convergence of ocean heat transport leads to a preferred latitude at which the sea ice edge can rest. The mechanism operates in two very different ocean circulation regimes, suggesting that the stabilization of the large ice cap could be a robust feature of the climate system. Finally, the role of ocean heat convergence in permitting multiple equilibria is further explored in simpler models: an atmospheric GCM coupled to a slab mixed layer ocean and an energy balance model.
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Liu, Kaiyue, Qi Sun, Daming Sun, Lin Peng, Mengduo Yang, and Nizhuan Wang. "Underwater Target Detection Based on Improved YOLOv7." Journal of Marine Science and Engineering 11, no. 3 (March 22, 2023): 677. http://dx.doi.org/10.3390/jmse11030677.
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Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed, and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3 × 3 convolution block in the E-ELAN structure, and incorporates jump connections and 1 × 1 convolution architecture between ACmixBlock modules to improve feature extraction and network reasoning speed. Additionally, a ResNet-ACmix module is designed to avoid feature information loss and reduce computation, while a Global Attention Mechanism (GAM) is inserted in the backbone and head parts of the model to improve feature extraction. Furthermore, the K-means++ algorithm is used instead of K-means to obtain anchor boxes and enhance model accuracy. Experimental results show that the improved YOLOv7 network outperforms the original YOLOv7 model and other popular underwater target detection methods. The proposed network achieved a mean average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and Brackish dataset, respectively, and demonstrated a higher frame per second (FPS) compared to the original YOLOv7 model. In conclusion, the improved YOLOv7 network proposed in this study represents a promising solution for underwater target detection and holds great potential for practical applications in various underwater tasks.
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Zare, Mehdi, and Nowrouz Mohammad Nouri. "A new analysis of flow noise outside the time-frequency representation using graph-based feature extraction." Ocean Engineering 266 (December 2022): 112700. http://dx.doi.org/10.1016/j.oceaneng.2022.112700.
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Lv, Tu, Zeyu Chen, Feng Yao, and Mingjun Zhang. "Fault feature extraction method based on optimized sparse decomposition algorithm for AUV with weak thruster fault." Ocean Engineering 233 (August 2021): 109013. http://dx.doi.org/10.1016/j.oceaneng.2021.109013.
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Chen, Xiaoying, Aiguo Song, Jianqing Li, Yimin Zhu, Xuejin Sun, and Hong Zeng. "Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition." Journal of Atmospheric and Oceanic Technology 31, no. 9 (September 1, 2014): 1982–94. http://dx.doi.org/10.1175/jtech-d-13-00238.1.
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Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.
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Li, Yuxing, Bingzhao Tang, and Shangbin Jiao. "SO-slope entropy coupled with SVMD: A novel adaptive feature extraction method for ship-radiated noise." Ocean Engineering 280 (July 2023): 114677. http://dx.doi.org/10.1016/j.oceaneng.2023.114677.
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Deng, Tianmin, Xuhui Liu, and Li Wang. "Occluded Vehicle Detection via Multi-Scale Hybrid Attention Mechanism in the Road Scene." Electronics 11, no. 17 (August 29, 2022): 2709. http://dx.doi.org/10.3390/electronics11172709.
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The obstruction of vehicles by surrounding vehicles, obstacles, etc. is a common phenomenon in the practical application of automatic driving. In view of the problem that the vehicle’s vision is affected by the occlusion, the vehicle feature information is incomplete, resulting in the low detection accuracy of the occlusion vehicle, and the occlusion vehicle detection method based on the multi-scale hybrid attention mechanism is proposed. The paper aims to fully excavate the advantages of multi-scale feature extraction, channel/space attention and other modules, and to design a multi-scale hybrid attention module suitable for occlusion vehicle detection to improve the detection accuracy of occlusion vehicles. Multi-scale features are enriched by the grouping convolution of different sizes of multi-scale feature extraction networks, and the parallel connection channels and spatial attention modules form different scale hybrid domain attention modules, which enhance the local feature information of the occluded vehicles and realize the reinforcement learning of multi-scale features and the suppression of occlusion interference information. Experimental results show that in the self-made occlusion vehicle dataset and the BDD100K occlusion vehicle dataset, the average mean accuracy of this method is 95.2% and 59.3%, respectively, which is 1.5% and 2.9% higher than that of the baseline network YOLOv5, respectively.
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Li, Yuxing, Bo Geng, and Shangbin Jiao. "Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise." Entropy 23, no. 4 (April 17, 2021): 476. http://dx.doi.org/10.3390/e23040476.
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Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.
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Li, Weijia, Xiaohong Shen, and Yaan Li. "A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise." Entropy 21, no. 8 (August 14, 2019): 793. http://dx.doi.org/10.3390/e21080793.
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The presence of marine ambient noise makes it difficult to extract effective features from ship-radiated noise. Traditional feature extraction methods based on the Fourier transform or wavelets are limited in such a complex ocean environment. Recently, entropy-based methods have been proven to have many advantages compared with traditional methods. In this paper, we propose a novel feature extraction method for ship-radiated noise based on hierarchical entropy (HE). Compared with the traditional entropy, namely multiscale sample entropy (MSE), which only considers information carried in the lower frequency components, HE takes into account both lower and higher frequency components of signals. We illustrate the different properties of HE and MSE by testing them on simulation signals. The results show that HE has better performance than MSE, especially when the difference in signals is mainly focused on higher frequency components. Furthermore, experiments on real-world data of five types of ship-radiated noise are conducted. A probabilistic neural network is employed to evaluate the performance of the obtained features. Results show that HE has a higher classification accuracy for the five types of ship-radiated noise compared with MSE. This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing.
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Randolph Ford, W., and Ingrid G. Farreras. "Using Human Intelligence to Test the Impact of Popular Preprocessing Steps and Feature Extraction in the Analysis of Human Language." International Journal of Data Science and Analysis 8, no. 1 (2022): 18. http://dx.doi.org/10.11648/j.ijdsa.20220801.13.
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Zhang, Dehuan, Wei Cao, Jingchun Zhou, Yan-Tsung Peng, Weishi Zhang, and Zifan Lin. "Two-Branch Underwater Image Enhancement and Original Resolution Information Optimization Strategy in Ocean Observation." Journal of Marine Science and Engineering 11, no. 7 (June 25, 2023): 1285. http://dx.doi.org/10.3390/jmse11071285.
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In complex marine environments, underwater images often suffer from color distortion, blur, and poor visibility. Existing underwater image enhancement methods predominantly rely on the U-net structure, which assigns the same weight to different resolution information. However, this approach lacks the ability to extract sufficient detailed information, resulting in problems such as blurred details and color distortion. We propose a two-branch underwater image enhancement method with an optimized original resolution information strategy to address this limitation. Our method comprises a feature enhancement subnetwork (FEnet) and an original resolution subnetwork (ORSnet). FEnet extracts multi-resolution information and utilizes an adaptive feature selection module to enhance global features in different dimensions. The enhanced features are then fed into ORSnet as complementary features, which extract local enhancement features at the original image scale to achieve semantically consistent and visually superior enhancement effects. Experimental results on the UIEB dataset demonstrate that our method achieves the best performance compared to the state-of-the-art methods. Furthermore, through comprehensive application testing, we have validated the superiority of our proposed method in feature extraction and enhancement compared to other end-to-end underwater image enhancement methods.