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1
Ilahude, Delyuzar. "MAGNETIC ANOMALY PATTERNS USING TREND SURFACE ANALYSIS APPLICATION (TSA) ON MARINE GEOLOGY MAPPING IN THE BALIKPAPAN WATERS." BULLETIN OF THE MARINE GEOLOGY 27, no. 1 (February 15, 2016): 19. http://dx.doi.org/10.32693/bomg.27.1.2012.42.
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The application of Trend Surface Analysis (TSA) method an geological and geophysical research in map sheets 1813-1814, Balikpapan Waters and its surrounding, shows the significant value of residual anomaly. The magnetic disseverance of regional and total anomaly value obtained the negative anomaly between -50 nT and -350 nT and positive anomaly between +50 nT and +400 nT. The contour of total and regional anomaly shows the magnetic properties of rocks which characterizes the geological arrangements of the research areas. Residual anomaly yielded from the 2nd order value of regional anomaly might be correlated with the formation of basin structures in the central and northern parts of research area, which is interpreted as a part of Kutai Basin.
Keywords : TSA method, magnetic anomaly, geology and geophisics, Balikpapan Waters.
Penerapan metode TSA dalam penelitian geologi dan geofisika di Lembar Peta 1813-1814, Perairan Balikpapan dan sekitarnya menunjukkan nilai anomali sisa yang cukup signifikan. Hasil pemisahan nilai anomali magnet regional dan anomaly total diperoleh nilai anomali yaitu antara -50 nT dan –350 nT dan anomali positif antara +50 nT dan +400 nT. Kontur anomali total dan anomali regional memperlihatkan sifat kemagnitan batuan yang mencirikan tatanan geologi daerah penelitian. Anomali sisa dihasilkan dari nilai anomali regional orde ke 2, kemungkinan berkaitan dengan pembentukan struktur cekungan di bagian tengah dan utara daerah penelitian yang ditafsirkan sebagai bagian dari Cekungan Kutai.
Kata kunci : metode TSA, anomali magnet, geologi dan geofisika, Perairan Balikpapan.
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Subagio, Subagio, and Tatang Patmawidjaya. "POLA ANOMALI BOUGUER DAN ANOMALI MAGNET DAN KAITANNYA DENGAN PROSPEK SUMBER DAYA MINERAL DAN ENERGI DI PULAU LAUT, PULAU SEBUKU DAN SELAT SEBUKU, KALIMANTAN SELATAN." JURNAL GEOLOGI KELAUTAN 11, no. 3 (February 16, 2016): 115. http://dx.doi.org/10.32693/jgk.11.3.2013.236.
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Anomali Bouguer Pulau Laut, Pulau Sebuku, dan Selat Sebuku dapat dikelompokkan menjadi dua wilayah anomali meliputi anomali berpola melingkar dengan kisaran nilai dari 40 hingga 64 mGal, dan anomali berpola lurus dengan kisaran nilai 40 hingga 50 mGal. Anomali magnet di daerah ini bervariasi dari -700 hingga 1600 nT, membentuk pola tinggian dan rendahan. Anomali Bouguer berpola melingkar dengan kisaran nilai 45-64 mGal mencerminkan batuan ultrabasa yang relatif mendekati permukaan. Batuan ultrabasa yang tersingkap di permukaan dicirikan oleh anomali magnet tinggi. Anomali Bouguer berpola kontur lurus sejajar menunjukkan sesar naik maupun sesar turun yang terdapat di daerah tersebut. Sesar naik yang berkembang di daerah penelitian umumnya terdapat di Pegunungan Meratus yang mempunyai mendala geologi sama. Anomali Bouguer dan anomali magnet rendah mencerminkan cekungan sedimen. yang diakibatkan oleh adanya gaya tarikan yang pernah ada. Batuan terobosan yang dijumpai, diduga terbentuk bersamaan dengan periode gaya tarikan ini. Serangkaian proses tektonik yang hasilnya terekam pada anomali Bouguer, anomali magnet, dan singkapan batuan memberi implikasi kemungkinan terdapatnya sumber daya energi dan mineral di daerah penelitian. Mineralisasi logam diperkirakan dapat dijumpai di sekitar daerah terobosan. Bijih besi, nikel, dan kromit kemungkinan terdapat di daerah ultra-mafik, sedangkan batubara di daerah cekungan sedimen.
Kata kunci : Anomali Bouguer, anomali magnet, sumber daya energi dan mineral, sesar naik dan sesar turun.
Bouguer anomaly of the Laut Island, Sebuku Island, and The Sebuku Strait can be grouped into two anomaly groups covering the circular pattern anomaly with range from 40 to 64 mGals, and the straight pattern with range of values from 40 to 50 mGals. The range of magnetic anomalies in the study area area from -700 to 1600 nT, forming high and low anomay patterns. The circular pattern of the Bouguer anomalies with range from 45 to 64 mGals reflects that the ultramafic rocks relatively close to the surface, while exposed ultrabasic rocks are indicated by high magnetic anomalies. Paralled pattern contour of Bouguer anomaly show a thrust faults and normal faults in this area. Thrust faults of commonly develop in Meratus Mountaint that has the same geological setting. The low Bouguer and magnetic anomalies reflect a sedimentary basin caused by previous tensional force. The intrusion rocks found in the study area suggest to be formed together with this tensional force period. A series of tectonic events recorded in Bougue anomaly, magnetic anomaly, and out crops gave the implication the possibility the present of energy and mineral resources in the study area. Metal mineralization suggests to be found in the intrusion area. Irons, nickels and chromites supposed can be found in the ulta-mafic area, while coal can be found in the sedimentary basin.
Keywords : Bouguer anomalies, magnetic anomalies, energy and mineral resources, thrust and normal faults.
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Peck, Sheldon. "Dental Anomaly Patterns (DAP)." Angle Orthodontist 79, no. 5 (September 1, 2009): 1015–16. http://dx.doi.org/10.2319/0003-3219-079.005.1015.
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Czeizel, Andrew, John M. Optiz, and James F. Reynolds. "Additive congenital anomaly patterns." American Journal of Medical Genetics 29, no. 4 (April 1988): 727–38. http://dx.doi.org/10.1002/ajmg.1320290402.
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Fyfe, John C., and David J. Lorenz. "Characterizing Midlatitude Jet Variability: Lessons from a Simple GCM." Journal of Climate 18, no. 16 (August 15, 2005): 3400–3404. http://dx.doi.org/10.1175/jcli3486.1.
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Abstract Fluctuations in the tropospheric zonal jet are often characterized using anomaly patterns, or empirical orthogonal functions, representing deviations of the zonal-mean flow from climatology. In previous studies the leading anomaly pattern has been interpreted as representing north–south jet movements, while the second anomaly pattern has been interpreted as representing independent fluctuations in jet strength and width. Here it is shown that these leading anomaly patterns are in fact dependent and together represent north–south movements of the jet. Fluctuations in jet strength, which are approximately inversely proportional to jet width, superimpose upon these dominant north–south meanderings. The distinction between the usual anomaly pattern perspective and this new perspective may have important implications in the interpretation of tropospheric zonal jet variability.
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Marpaung, Sartono, and Wawan K. Harsanugraha. "ANALYSIS OF SEA SURFACE HEIGHT ANOMALY CHARACTERISTICS BASED ON SATELLITE ALTIMETRY DATA (CASE STUDY: SEAS SURROUNDING JAVA ISLAND)." International Journal of Remote Sensing and Earth Sciences (IJReSES) 11, no. 2 (April 12, 2017): 137. http://dx.doi.org/10.30536/j.ijreses.2014.v11.a2611.
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Sea surface height anomaly is a oceanographic parameter that has spatial and temporal variability. This paper aims to determine the characters of sea surface height anomaly in the south and north seas of Java Island. To find these characters, a descriptive analysis of monthly anomaly data is performed spatially, zonally and temporally. Based on satellite altimetry data from 1993 to 2010, the analysis shows that the average of sea surface height anomaly varies, ranging from -15 cm to 15 cm. Spatially and zonally, there are three patterns that can be concidered as sea surface height anomaly characteristics: anomaly is higher in coastal areas than in open seas, anomaly is lower in coastal areas than in open seas and anomaly in coastal area is almost the same as in open seas. The first and second patterns occur in the south and north seas of Java Island. The third pattern occurs simultaneously in south and north seas of Java Island. Characteristics of temporal anomaly have a sinusoidal pattern in south and north seas of Java Island.
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Sabeti, Elyas, Sehong Oh, Peter Song, and Alfred Hero. "A Pattern Dictionary Method for Anomaly Detection." Entropy 24, no. 8 (August 9, 2022): 1095. http://dx.doi.org/10.3390/e24081095.
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In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel–Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.
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Plastun, Alex, Inna Makarenko, Lyudmila Khomutenko, Svitlana Shcherbak, and Olha Tryfonova. "Exploring price gap anomaly in the Ukrainian stock market." Investment Management and Financial Innovations 16, no. 2 (June 5, 2019): 150–58. http://dx.doi.org/10.21511/imfi.16(2).2019.13.
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This paper analyzes price gaps in the Ukrainian stock market for the case of UX index over the period 2009–2018. Using different statistical tests (Student’s t-tests, ANOVA, Mann-Whitney test) and regression analysis with dummy variables, as well as modified cumulative approach and trading simulation, the authors test a number of hypotheses searching for price patterns and abnormal market behavior related to price gaps: there is seasonality in price gaps (H1); price gaps generate statistical anomalies in the Ukrainian stock market (H2); upward gaps generate price patterns in the Ukrainian stock market (H3) and downward gaps generate price patterns in the Ukrainian stock market (H4). Overall results are consistent with the Efficient Market Hypothesis: there is no seasonality in price gaps and in most cases there is no evidences of price patterns or abnormal price behavior after the gaps in the Ukrainian stock market. Nevertheless, the authors find very strong and convincing evidences in favor of momentum effect on the days of negative gaps. These observations are confirmed by trading simulations: trading strategy based on detected price pattern generates profits and demonstrates overall efficiency, which is against the market efficiency. These results can be interesting both for academicians (further evidences against market efficiency) and practitioners (real and effective trading strategy to generate profits in the Ukrainian market market).
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Kang, Na Ra, and Wonsun Paek. "Accruals Anomaly and Earnings Announcement Patterns." korean management review 45, no. 2 (April 30, 2016): 503. http://dx.doi.org/10.17287/kmr.2016.45.2.503.
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Fauzi, Arwin Happy Nur, and Masduki Masduki. "Student’s Anomaly Reasoning in Solving Number Pattern in terms of Gender." Jurnal Didaktik Matematika 9, no. 2 (October 31, 2022): 328–42. http://dx.doi.org/10.24815/jdm.v9i2.27146.
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Relational reasoning plays an important role in helping students to understand mathematical concepts. The student's ability to distinguish patterns or objects is one of the understandings of mathematical concept indicators. The anomaly dimension is part of the relational reasoning that students need to be able to determine a pattern or object in mathematics. This study aims to reveal the student's relational reasoning ability of anomaly dimension in solving number pattern problems in terms of gender differences. The subjects of this study are 52 grade-8 students in one of Muhammadiyah Junior High Schools in Kartasura. We used two similar problems on number patterns to disclose the student's ability to identify the pattern deviation in solving problems. The two selected students had relatively similar in their mathematical abilities. The finding showed that female subject met the three anomaly dimension indicators: identification, interpretation, and adaptation. Conversely, male student cannot fulfill the anomaly indicators. He cannot recognize pattern deviation in the formed mathematical model. He also failed to identify a pattern different from the two problems. Although the subjects interviewed were limited, the finding provided the insightful into the differences in anomaly reasoning abilities in male and female students
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Liu, Yonggang, Robert H. Weisberg, and Ruoying He. "Sea Surface Temperature Patterns on the West Florida Shelf Using Growing Hierarchical Self-Organizing Maps." Journal of Atmospheric and Oceanic Technology 23, no. 2 (February 1, 2006): 325–38. http://dx.doi.org/10.1175/jtech1848.1.
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Abstract Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.
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Zhao, Tongtiegang, Wei Zhang, Yongyong Zhang, Zhiyong Liu, and Xiaohong Chen. "Significant spatial patterns from the GCM seasonal forecasts of global precipitation." Hydrology and Earth System Sciences 24, no. 1 (January 3, 2020): 1–16. http://dx.doi.org/10.5194/hess-24-1-2020.
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Abstract. Fully coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations to derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the 10 sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlations at neighbouring grid cells with one another. The global Moran's I associates anomaly correlation at the global scale and indicates that anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells. The local Moran's I links anomaly correlation at one grid cell with its spatial lag and reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate centre exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least 1 of the 10 sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.
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Khan, Hamid Masood, Fazal Masud Khan, Aurangzeb Khan, Muhammad Zubair Asghar, and Daniyal M. Alghazzawi. "Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique." Computational and Mathematical Methods in Medicine 2021 (March 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/5585238.
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Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.
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Prajit Limsaiprom, and Panjai Tantatsanawong Ph D. "Social Networks Anomaly and Attack Patterns Analysis." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 9 (October 31, 2011): 199–206. http://dx.doi.org/10.4156/aiss.vol3.issue9.27.
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Choi, Su Ji, Je Woo Lee, and Ji Hyun Song. "Dental anomaly patterns associated with tooth agenesis." Acta Odontologica Scandinavica 75, no. 3 (January 25, 2017): 161–65. http://dx.doi.org/10.1080/00016357.2016.1273385.
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Zidikheri, Meelis J., and Jorgen S. Frederiksen. "Methods for Estimating Climate Anomaly Forcing Patterns." Journal of the Atmospheric Sciences 70, no. 8 (August 1, 2013): 2655–79. http://dx.doi.org/10.1175/jas-d-12-0304.1.
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Abstract Inverse methods for determining the anomalous mean forcing functions responsible for climate change are investigated. First, an iterative method is considered, and it is shown to successfully reproduce forcing functions for various idealized and observed climate states using quasigeostrophic simulations. Second, a new inverse method that is more computationally efficient is presented. This method closes the mean-field equations by representing the second-order statistical moments, the transient eddy heat and momentum (or potential vorticity) fluxes, as linear functions of the mean field. The coefficients of the linear parameterization are determined by least squares regression. It is shown that the new method also successfully reproduces the anomalous forcing functions responsible for climatic changes in quasigeostrophic simulations.
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Nicholaus, Isack Thomas, Jun Ryeol Park, Kyuil Jung, Jun Seoung Lee, and Dae-Ki Kang. "Anomaly Detection of Water Level Using Deep Autoencoder." Sensors 21, no. 19 (October 8, 2021): 6679. http://dx.doi.org/10.3390/s21196679.
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Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of the water levels and report the analysis and time/point(s) of abnormality. This research’s motivation is the level difficulty and time-consuming managing facilities responsible for controlling water levels due to the rare occurrence of abnormal patterns. Consequently, we employed deep autoencoder, one of the types of artificial neural network architectures, to learn different patterns from the given sequences of data points and reconstruct them. Then we use the reconstructed patterns from the deep autoencoder together with a threshold to report which patterns are abnormal from the normal ones. We used a stream of time-series data collected from sensors to train the model and then evaluate it, ready for deployment as the anomaly detection system framework. We run extensive experiments on sensor data from water tanks. Our analysis shows why we conclude vanilla deep autoencoder as the most effective solution in this scenario.
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Park, W., and M. Latif. "Ocean Dynamics and the Nature of Air–Sea Interactions over the North Atlantic at Decadal Time Scales." Journal of Climate 18, no. 7 (April 1, 2005): 982–95. http://dx.doi.org/10.1175/jcli-3307.1.
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Abstract The dependence of the air–sea interactions over the North Atlantic on the ocean dynamics is explored by analyzing multicentury integrations with two different coupled ocean–atmosphere models. One is a coupled general circulation model (CGCM), in which both the atmospheric and the oceanic components are represented by general circulation models (GCMs). The second coupled model employs the same atmospheric GCM, but the oceanic GCM is replaced by a fixed-depth mixed layer model, so that variations of the ocean dynamics are excluded. The coupled model including active ocean dynamics simulates strong multidecadal variability in the sea surface temperature (SST) of the North Atlantic, with a monopolar spatial structure. In contrast, the coupled model that employs an oceanic mixed layer model and thus does not carry active ocean dynamics simulates a tripolar SST anomaly pattern at decadal time scales. The tripolar SST anomaly pattern is characterized by strong horizontal gradients and is by definition the result of the action of surface heat flux anomalies on the oceanic mixed layer. The differences in the spatial structures of the dominant decadal SST anomaly patterns yield rather different atmospheric responses. While the response to the monopolar SST anomaly pattern is shallow and thermal, the response to the tripolar SST anomaly pattern involves changes in the transient eddy statistics. The latter can be explained by the strong horizontal SST gradients that affect the surface baroclinicity, which in turn affects the growth rate of the transient eddies. The differences in the atmospheric response characteristics yield completely different response patterns. In the coupled run with active ocean dynamics, the sea level pressure (SLP) anomalies exhibit a rather homogeneous pattern that resembles somewhat the East Atlantic Pattern (EAP), while a dipolar (North Atlantic Oscillation) NAO-like SLP anomaly pattern is simulated in the coupled run without active ocean dynamics.
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Zhao, Ying, Junjun Chen, Di Wu, Jian Teng, Nabin Sharma, Atul Sajjanhar, and Michael Blumenstein. "Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern." Information 10, no. 8 (August 17, 2019): 262. http://dx.doi.org/10.3390/info10080262.
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Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
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Jewson, Stephen. "An Alternative to PCA for Estimating Dominant Patterns of Climate Variability and Extremes, with Application to U.S. and China Seasonal Rainfall." Atmosphere 11, no. 4 (April 7, 2020): 354. http://dx.doi.org/10.3390/atmos11040354.
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Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the rapid identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for the rapid identification of patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive floods, droughts or heatwaves, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as “sum of all points in the spatial field”. We compare the first PCA and DCA spatial patterns for U.S. and China winter and summer rainfall anomalies, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain. In three out of four of the examples the first DCA spatial pattern is more uniform over a wide area than the first PCA spatial pattern and as a result is more obviously relevant to large-scale flooding or drought. Also, in all cases the definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for a given likelihood. The first DCA spatial pattern is arguably the best answer to the question: what single spatial pattern is most likely to drive large total rainfall anomalies in the future? It is also simpler to calculate than PCA. In combination PCA and DCA patterns yield more insight into rainfall variability and extremes than either pattern on its own.
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Hu, W., and B. C. Si. "Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis." Hydrology and Earth System Sciences Discussions 12, no. 7 (July 3, 2015): 6467–503. http://dx.doi.org/10.5194/hessd-12-6467-2015.
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Abstract. Soil water content (SWC) at watershed scales is crucial to rainfall–runoff response. A model was used to decompose spatiotemporal SWC into time-stable pattern (i.e., temporal mean), space-invariant temporal anomaly, and space-variant temporal anomaly. This model was compared with a previous model that decomposes spatiotemporal SWC into spatial mean and spatial anomaly. The space-variant temporal anomaly or spatial anomaly was further decomposed using the empirical orthogonal function for estimating spatially distributed SWC. These two models are termed temporal anomaly (TA) model and spatial anomaly (SA) model, respectively. We aimed to test the hypothesis that underlying (i.e., time-invariant) spatial patterns exist in the space-variant temporal anomaly at the small watershed scale, and to examine the advantages of the TA model over the SA model in terms of estimation of spatially distributed SWC. For this purpose, a SWC dataset of near surface (0–0.2 m) and root zone (0–1.0 m) from a small watershed scale in the Canadian prairies was analyzed. Results showed that underlying spatial patterns exist in the space-variant temporal anomaly because of the permanent controls of "static" factors such as depth to the CaCO3 layer and organic carbon content. Combined with time stability analysis, the TA model improved estimation of spatially distributed SWC over the SA model because the latter failed to capture the space-variant temporal anomaly which accounted for non-negligible amounts of spatial variance in SWC. The outperformance was greater when SWC deviated from intermediate conditions, especially for dry conditions. Therefore, the TA model has potential to construct a spatially distributed SWC at watershed scales from remote sensed SWC.
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Chen, Yu H., Se Un Park, Dennis Wei, Greg Newstadt, Michael A. Jackson, Jeff P. Simmons, Marc De Graef, and Alfred O. Hero. "A Dictionary Approach to Electron Backscatter Diffraction Indexing." Microscopy and Microanalysis 21, no. 3 (June 2015): 739–52. http://dx.doi.org/10.1017/s1431927615000756.
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AbstractWe propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel’s neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises–Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.
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Goldberg, Caroline J., Miriam C. Fallon, David P. Moore, Esmond E. Fogarty, and Frank E. Dowling. "Growth Patterns in Children with Congenital Vertebral Anomaly." Spine 27, no. 11 (June 2002): 1191–201. http://dx.doi.org/10.1097/00007632-200206010-00011.
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Yu, Bin, Hai Lin, and Nicholas Soulard. "A Comparison of North American Surface Temperature and Temperature Extreme Anomalies in Association with Various Atmospheric Teleconnection Patterns." Atmosphere 10, no. 4 (April 1, 2019): 172. http://dx.doi.org/10.3390/atmos10040172.
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The atmospheric teleconnection pattern reflects large-scale variations in the atmospheric wave and jet stream, and has pronounced impacts on climate mean and extremes over various regions. This study compares those patterns that have significant circulation anomalies over the North Pacific–North American–North Atlantic sector, which directly influence surface temperature and temperature extremes over North America. We analyze the pattern associated anomalies of surface temperature and warm and cold extremes over North America, during the northern winter and summer seasons. In particular, we assess the robustness of the regional temperature and temperature extreme anomaly patterns by evaluating the field significance of these anomalies over North America, and quantify the percentages of North American temperature and temperature extreme variances explained by these patterns. The surface temperature anomalies in association with the Pacific–North American pattern (PNA), Tropical–Northern Hemisphere pattern (TNH), North Pacific pattern (NP), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Western Pacific pattern (WP), circumglobal teleconnection (CGT), and Asian–Bering–North American (ABNA) patterns are similar to those reported in previous studies based on various datasets, indicating the robustness of the results. During winter, the temperature anomaly patterns considered are field significant at the 5% level over North America, except the WP-related one. These pattern associated anomalies explained about 5–15% of the total interannual temperature variance over North America, with relatively high percentages for the ABNA and PNA patterns, and low for the WP pattern. The pattern associated warm and cold extreme anomalies resemble the corresponding surface mean temperature anomaly patterns, with differences mainly in magnitude of the anomalies. Most of the anomalous extreme patterns are field significant at the 5% level, except the WP-related patterns. These extreme anomalies explain about 5–20% of the total interannual variance over North America. During summer, the pattern-related circulation and surface temperature anomalies are weaker than those in winter. Nevertheless, all of the pattern associated temperature anomalies are of field significance at the 5% level over North America, except the PNA-related one, and explain about 5–10% of the interannual variance. In addition, the temperature extreme anomalies, in association with the circulation patterns, are comparable in summer and winter. Over North America, the NP-, WP-, ABNA-, and CGT-associated anomalies of warm extremes are field significant at the 5% level and explain about 5–15% of the interannual variance. Most of the pattern associated cold extreme anomalies are field significant at the 5% level, except the PNA and NAO related anomalies, and also explain about 5–15% of the interannual variance over North America.
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Zhao, Haifeng, Dibo Hou, Pingjie Huang, and Guangxin Zhang. "Periodic pattern extraction and anomaly detection for free chlorine in drinking water network." Water Supply 15, no. 3 (January 20, 2015): 541–51. http://dx.doi.org/10.2166/ws.2015.003.
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Water contamination events are threatening the safety of drinking water. Free chlorine is widely used as the disinfectant in drinking water, which can be used as a surrogate parameter to provide indications of potential contaminants. In this article, the periodic fluctuation of free chlorine is studied and the fluctuation pattern is extracted by the singular vector decomposition method, and an anomaly detection scheme for free chlorine is proposed and tested. Firstly, the normal periodic pattern and current pattern of free chlorine are both extracted from the historical and online data, and then the difference between the current data pattern and the normal data pattern are compared with thresholds for anomaly declaration. The single point detection and data series detection are investigated for the purpose of short-term and long-term inspection. Further, the anomaly data treatment and the detection method using sub-patterns are discussed. Performance tests show that the proposed method is sensitive to the anomaly data, and is effective to detect anomalous condition in typical contamination scenes.
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Guo, Yuanyuan, Mingfang Ting, Zhiping Wen, and Dong Eun Lee. "Distinct Patterns of Tropical Pacific SST Anomaly and Their Impacts on North American Climate." Journal of Climate 30, no. 14 (July 2017): 5221–41. http://dx.doi.org/10.1175/jcli-d-16-0488.1.
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A neural-network-based cluster technique, the so-called self-organizing map (SOM), was performed to extract distinct sea surface temperature (SST) anomaly patterns during boreal winter. The SOM technique has advantages in nonlinear feature extraction compared to the commonly used empirical orthogonal function analysis and is widely used in meteorology. The eight distinguishable SOM patterns so identified represent three La Niña–like patterns, two near-normal patterns, and three El Niño–like patterns. These patterns show the varied amplitude and location of the SST anomalies associated with El Niño and La Niña, such as the central Pacific (CP) and eastern Pacific (EP) El Niño. The impact of each distinctive SOM pattern on winter-mean surface temperature and precipitation changes over North America was examined. Based on composite maps with observational data, each SOM pattern corresponds to a distinguishable spatial structure of temperature and precipitation anomaly over North America, which seems to result from differing wave train patterns, extending from the tropics to mid–high latitudes induced by longitudinally shifted tropical heating. The corresponding teleconnection as represented by the National Center for Atmospheric Research Community Atmospheric Model, version 4 (CAM4), was compared with the observational results. It was found that the 16-member ensemble average of the CAM4 experiments with prescribed SST can reproduce the observed atmospheric circulation responses to the different SST SOM patterns, which suggests that the circulation differences are largely SST driven rather than due to internal atmospheric variability.
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Choi, Seunghyun, Sekyoung Youm, and Yong-Shin Kang. "Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes." Applied Sciences 9, no. 21 (October 24, 2019): 4502. http://dx.doi.org/10.3390/app9214502.
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Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.
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Marimon, Xavier, Sara Traserra, Marcel Jiménez, Andrés Ospina, and Raúl Benítez. "Detection of Abnormal Cardiac Response Patterns in Cardiac Tissue Using Deep Learning." Mathematics 10, no. 15 (August 5, 2022): 2786. http://dx.doi.org/10.3390/math10152786.
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This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria.
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JIN, SHUYUAN, DANIEL S. YEUNG, and XIZHAO WANG. "INTERNET ANOMALY DETECTION BASED ON STATISTICAL COVARIANCE MATRIX." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 03 (May 2007): 591–606. http://dx.doi.org/10.1142/s0218001407005557.
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Intrusion detection is an important part of assuring the reliability of computer systems. Different intrusion detection approaches vary with different patterns used and different intrusions addressed. However, what patterns are effective in constructing a detection system is still a challenge. This paper attempts to apply the traditional covariance matrix concept to the detection of multiple known and unknown network anomalies. With respect to the initiation of typical flood-based network intrusions, the proposed approach takes the measure of covariance matrix to reflect the changes of sequential correlativity of the network traffic when flood-based attacks happen. The differences among covariance matrices of network samples collected in temporal sequences of fixed and equal length are directly evaluated to detect multiple network anomalies. Extensive experiments on the subset of KDDCUP 1999 dataset show that the covariance matrix, as a new pattern, can be directly utilized to construct an effective detection system for flood-based attacks. It also points out that utilizing the covariance matrix in the detection of flood-based attacks can achieve higher performance over traditional approaches.
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Clark, Joseph P., and Steven B. Feldstein. "What Drives the North Atlantic Oscillation’s Temperature Anomaly Pattern? Part I: The Growth and Decay of the Surface Air Temperature Anomalies." Journal of the Atmospheric Sciences 77, no. 1 (December 16, 2019): 185–98. http://dx.doi.org/10.1175/jas-d-19-0027.1.
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Abstract Composite analysis is used to examine the physical processes that drive the growth and decay of the surface air temperature anomaly pattern associated with the North Atlantic Oscillation (NAO). Using the thermodynamic energy equation that the European Centre for Medium-Range Weather Forecasts implements in their reanalysis model, we show that advection of the climatological temperature field by the anomalous wind drives the surface air temperature anomaly pattern for both NAO phases. Diabatic processes exist in strong opposition to this temperature advection and eventually cause the surface air temperature anomalies to return to their climatological values. Specifically, over Greenland, Europe, and the United States, longwave heating/cooling opposes horizontal temperature advection while over northern Africa vertical mixing opposes horizontal temperature advection. Despite the pronounced spatial correspondence between the skin temperature and surface air temperature anomaly patterns, the physical processes that drive these two temperature anomalies associated with the NAO are found to be distinct. The skin temperature anomaly pattern is driven by downward longwave radiation whereas stated above, the surface air temperature anomaly pattern is driven by horizontal temperature advection. This implies that the surface energy budget, although a useful diagnostic tool for understanding skin temperature changes, should not be used to understand surface air temperature changes.
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Hu, W., and B. C. Si. "Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis." Hydrology and Earth System Sciences 20, no. 1 (February 2, 2016): 571–87. http://dx.doi.org/10.5194/hess-20-571-2016.
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Abstract. Soil water content (SWC) is crucial to rainfall-runoff response at the watershed scale. A model was used to decompose the spatiotemporal SWC into a time-stable pattern (i.e., temporal mean), a space-invariant temporal anomaly, and a space-variant temporal anomaly. The space-variant temporal anomaly was further decomposed using the empirical orthogonal function (EOF) for estimating spatially distributed SWC. This model was compared to a previous model that decomposes the spatiotemporal SWC into a spatial mean and a spatial anomaly, with the latter being further decomposed using the EOF. These two models are termed the temporal anomaly (TA) model and spatial anomaly (SA) model, respectively. We aimed to test the hypothesis that underlying (i.e., time-invariant) spatial patterns exist in the space-variant temporal anomaly at the small watershed scale, and to examine the advantages of the TA model over the SA model in terms of the estimation of spatially distributed SWC. For this purpose, a data set of near surface (0–0.2 m) and root zone (0–1.0 m) SWC, at a small watershed scale in the Canadian Prairies, was analyzed. Results showed that underlying spatial patterns exist in the space-variant temporal anomaly because of the permanent controls of static factors such as depth to the CaCO3 layer and organic carbon content. Combined with time stability analysis, the TA model improved the estimation of spatially distributed SWC over the SA model, especially for dry conditions. Further application of these two models demonstrated that the TA model outperformed the SA model at a hillslope in the Chinese Loess Plateau, but the performance of these two models in the GENCAI network (∼ 250 km2) in Italy was equivalent. The TA model can be used to construct a high-resolution distribution of SWC at small watershed scales from coarse-resolution remotely sensed SWC products.
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Findell, Kirsten L., and Thomas L. Delworth. "Impact of Common Sea Surface Temperature Anomalies on Global Drought and Pluvial Frequency." Journal of Climate 23, no. 3 (February 1, 2010): 485–503. http://dx.doi.org/10.1175/2009jcli3153.1.
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Abstract Climate model simulations run as part of the Climate Variability and Predictability (CLIVAR) Drought Working Group initiative were analyzed to determine the impact of three patterns of sea surface temperature (SST) anomalies on drought and pluvial frequency and intensity around the world. The three SST forcing patterns include a global pattern similar to the background warming trend, a pattern in the Pacific, and a pattern in the Atlantic. Five different global atmospheric models were forced by fixed SSTs to test the impact of these SST anomalies on droughts and pluvials relative to a climatologically forced control run. The five models generally yield similar results in the locations of drought and pluvial frequency changes throughout the annual cycle in response to each given SST pattern. In all of the simulations, areas with an increase in the mean drought (pluvial) conditions tend to also show an increase in the frequency of drought (pluvial) events. Additionally, areas with more frequent extreme events also tend to show higher intensity extremes. The cold Pacific anomaly increases drought occurrence in the United States and southern South America and increases pluvials in Central America and northern and central South America. The cold Atlantic anomaly increases drought occurrence in southern Central America, northern South America, and central Africa and increases pluvials in central South America. The warm Pacific and Atlantic anomalies generally lead to reversals of the drought and pluvial increases described with the corresponding cold anomalies. More modest impacts are seen in other parts of the world. The impact of the trend pattern is generally more modest than that of the two other anomaly patterns.
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Junker, Norman W., Richard H. Grumm, Robert Hart, Lance F. Bosart, Katherine M. Bell, and Frank J. Pereira. "Use of Normalized Anomaly Fields to Anticipate Extreme Rainfall in the Mountains of Northern California." Weather and Forecasting 23, no. 3 (June 1, 2008): 336–56. http://dx.doi.org/10.1175/2007waf2007013.1.
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Abstract Extreme rainfall events contribute a large portion of wintertime precipitation to northern California. The motivations of this paper were to study the observed differences in the patterns between extreme and more commonly occurring lighter rainfall events, and to study whether anomaly fields might be used to discriminate between them. Daily (1200–1200 UTC) precipitation amounts were binned into three progressively heavier categories (12.5–50.0 mm, light; 50–100 mm, moderate; and >100 mm, heavy) in order to help identify the physical processes responsible for extreme precipitation in the Sierra Nevada range between 37.5° and 41.0°N. The composite fields revealed marked differences between the synoptic patterns associated with the three different groups. The heavy composites showed a much stronger, larger-scale, and slower-moving negative geopotential height anomaly off the Pacific coast of Oregon and Washington than was revealed in either of the other two composites. The heavy rainfall events were also typically associated with an atmospheric river with anomalously high precipitable water (PW) and 850-hPa moisture flux (MF) within it. The standardized PW and MF anomalies associated with the heavy grouping were higher and were slower moving than in either of the lighter bins. Three multiday heavy rainfall events were closely examined in order to ascertain whether anomaly patterns could provide forecast utility. Each of the multiday extreme rainfall events investigated was associated with atmospheric rivers that contained highly anomalous 850-hPa MF and PW within it. Each case was also associated with an unusually intense negative geopotential height anomaly that was similarly located off of the west coast of the United States. The similarities in the anomaly pattern among the three multiday extreme events suggest that standardized anomalies might be useful in predicting extreme multiday rainfall events in the northern Sierra range.
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Agnew, Tom. "Simultaneous winter sea‐ice and atmospheric circulation anomaly patterns." Atmosphere-Ocean 31, no. 2 (June 1993): 259–80. http://dx.doi.org/10.1080/07055900.1993.9649471.
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Zhang, Zengxin, Qiu Jin, Xi Chen, Chong-Yu Xu, and Shanshan Jiang. "On the Linkage between the Extreme Drought and Pluvial Patterns in China and the Large-Scale Atmospheric Circulation." Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8010638.
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China is a nation that is affected by a multitude of natural disasters, including droughts and floods. In this paper, the variations of extreme drought and pluvial patterns and their relations to the large-scale atmospheric circulation have been analyzed based on monthly precipitation data from 483 stations during the period 1958–2010 in China. The results show the following:(1)the extreme drought and pluvial events in China increase significantly during that period. During 1959–1966 timeframe, more droughts occur in South China and more pluvial events are found in North China (DSC-PNC pattern); as for the period 1997–2003 (PSC-DNC pattern), the situation is the opposite.(2)There are good relationships among the extreme drought and pluvial events and the Western Pacific Subtropical High, meridional atmospheric moisture flux, atmospheric moisture content, and summer precipitation.(3)A cyclone atmospheric circulation anomaly occurs in North China, followed by an obvious negative height anomaly and a southern wind anomaly at 850 hPa and 500 hPa for the DSC-PNC pattern during the summer, and a massive ascending airflow from South China extends to North China at ~50∘N. As for the PSC-DNC pattern, the situation contrasts sharply with the DSC-PNC pattern.
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Payandeh, Shahram, and Eddie Chiu. "Application of Modified PageRank Algorithm for Anomaly Detection in Movements of Older Adults." International Journal of Telemedicine and Applications 2019 (March 11, 2019): 1–9. http://dx.doi.org/10.1155/2019/8612021.
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It is a well-known statistic that the percentage of our older adult population will globally surpass the other age groups. A majority of the elderly would still prefer to keep an active life style. In support of this life style, various monitoring systems are being designed and deployed to have a seamless integration with the daily living activities of the older adults while preserving various levels of their privacy. Motion tracking is one of these health monitoring systems. When properly designed, deployed, integrated, and analyzed, they can be used to assist in determining some onsets of anomalies in the health of elderly at various levels of their Movements and Activities of Daily Living (MADL). This paper explores how the framework of the PageRank algorithm can be extended for monitoring the global movement patterns of older adults at their place of residence. Through utilization of an existing dataset, the paper shows how the movement patterns between various rooms can be represented as a directed graph with weighted edges. To demonstrate how PageRank can be utilized, a base graph representing a normal pattern can be defined as what can be used for further anomaly detection (e.g., at some instances of observation the measured movement pattern deviates from what is previously defined as a normal pattern). It is shown how the PageRank algorithm can detect simulated change in the pattern of motion when compared with the base-line normal pattern. This feature can offer a practical approach for detecting anomalies in movement patterns associated with older adults in their own place of residence and in support of aging in place paradigm.
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Greenslade, Diana J. M., and Ian R. Young. "The Impact of Altimeter Sampling Patterns on Estimates of Background Errors in a Global Wave Model." Journal of Atmospheric and Oceanic Technology 22, no. 12 (December 1, 2005): 1895–917. http://dx.doi.org/10.1175/jtech1811.1.
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Abstract One of the main limitations to current wave data assimilation systems is the lack of an accurate representation of the structure of the background errors. One method that may be used to determine background errors is the observational method of Hollingsworth and Lönnberg. The observational method considers correlations of the differences between observations and the background. For the case of significant wave height (SWH), potential observations come from satellite altimeters. In this work, the effect of the irregular sampling pattern of the satellite on estimates of background errors is examined. This is achieved by using anomalies from a 3-month mean as a proxy for model errors. A set of anomaly correlations is constructed from modeled wave fields. The isotropic length scales of the anomaly correlations are found to vary considerably over the globe. In addition, the anomaly correlations are found to be significantly anisotropic. The modeled wave fields are then sampled at simulated altimeter observation locations, and the anomaly correlations are recalculated from the simulated altimeter data. The results are compared to the original anomaly correlations. It is found that, in general, the simulated altimeter data can capture most of the geographic and seasonal variability in the isotropic anomaly correlation length scale. The best estimates of the isotropic length scales come from a method in which correlations are calculated between pairs of observations from prior and subsequent ground tracks, in addition to along-track pairs of observations. This method was found to underestimate the isotropic anomaly correlation length scale by approximately 10%. The simulated altimeter data were not so successful in producing realistic anisotropic correlation functions. This is because of the lack of information in the zonal direction in the simulated altimeter data. However, examination of correlations along ascending and descending ground tracks separately can provide some indication of the areas on the globe for which the anomaly correlations are more anisotropic than others.
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Shinker, Jacqueline J., and Patrick J. Bartlein. "Visualizing the Large-Scale Patterns of ENSO-Related Climate Anomalies in North America." Earth Interactions 13, no. 3 (April 1, 2009): 1–50. http://dx.doi.org/10.1175/2008ei244.1.
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Abstract The variations of large-scale climatic controls and surface responses through the annual cycle during strong positive (El Niño) and negative (La Niña) phase ENSO events are analyzed to assess the within-year and among-year variations of climate anomalies. Data from the NCEP–NCAR reanalysis project are presented as small-multiple maps to illustrate the spatial and temporal variability in North American climate associated with extreme phases of ENSO. Temperature, mean sea level pressure, 500-mb geopotential heights, and 850-mb specific humidity have composite-anomaly patterns that exhibit the greatest degree of spatial and temporal coherence. In general, the composite-anomaly patterns for El Niño and La Niña events are of opposite sign, with stronger, more spatially coherent anomalies occurring during El Niño events than during La Niña events. However, the strength and coherency of the precipitation anomaly patterns are reduced in the interior intermountain west during both positive and negative phase of ENSO. The variations in precipitation anomalies are compared to the 500-mb omega and 850-mb specific humidity composite-anomaly patterns, which provide information on the controls of precipitation by large-scale vertical motions and moisture availability thus providing information on the specific mechanisms associated with precipitation variability during ENSO events.
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Kim, Donghyun, Sangbong Lee, and Jihwan Lee. "An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis." Sensors 20, no. 24 (December 18, 2020): 7285. http://dx.doi.org/10.3390/s20247285.
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This study proposes an unsupervised anomaly detection method using sensor streams from the marine engine to detect the anomalous system behavior, which may be a possible sign of system failure. Previous works on marine engine anomaly detection proposed a clustering-based or statistical control chart-based approach that is unstable according to the choice of hyperparameters, or cannot fit well to the high-dimensional dataset. As a remedy to this limitation, this study adopts an ensemble-based approach to anomaly detection. The idea is to train several anomaly detectors with varying hyperparameters in parallel and then combine its result in the anomaly detection phase. Because the anomaly is detected by the combination of different detectors, it is robust to the choice of hyperparameters without loss of accuracy. To demonstrate our methodology, an actual dataset obtained from a 200,000-ton cargo vessel from a Korean shipping company that uses two-stroke diesel engine is analyzed. As a result, anomalies were successfully detected from the high-dimensional and large-scale dataset. After detecting the anomaly, clustering analysis was conducted to the anomalous observation to examine anomaly patterns. By investigating each cluster’s feature distribution, several common patterns of abnormal behavior were successfully visualized. Although we analyzed the data from two-stroke diesel engine, our method can be applied to various types of marine engine.
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Huang, Shaonian, Dongjun Huang, and Xinmin Zhou. "Learning Multimodal Deep Representations for Crowd Anomaly Event Detection." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/6323942.
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Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.
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Meng, Yongwei, Tao Qin, Shancang Li, and Pinghui Wang. "Behavior Pattern Mining from Traffic and Its Application to Network Anomaly Detection." Security and Communication Networks 2022 (June 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/9139321.
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Accurately detecting and identifying abnormal behaviors on the Internet are a challenging task. In this work, an anomaly detection scheme is proposed that employs the behavior attribute matrix and adjacency matrix to characterize user behavior patterns. Then, anomaly detection is conducted by analyzing the residual matrix. By analyzing network traffic and anomaly characteristics, we construct the behavior attribute matrix, which incorporates seven features that characterize user behavior patterns. To include the effects of network environment, we employ the similarity between IP addresses to form the adjacency matrix. Further, we employ CUR matrix decomposition to mine the changing trends of the matrices and obtain the residual pattern characteristics that are used to detect anomalies. To validate the effectiveness and accuracy of the proposed scheme, two datasets are used: (1) the public MAWI dataset, collected from the WIDE backbone network, which is used to validate accuracy; (2) the campus network dataset, collected from the northwest center of Chinese Education and Research Network (CERNET), which is used to verify practicability. The experimental results demonstrate that the proposed scheme can not only accurately detect and identify abnormal behaviors but also trace the source of anomalies.
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Qin, Kun, Qixin Wang, Binbin Lu, Huabo Sun, and Ping Shu. "Flight Anomaly Detection via a Deep Hybrid Model." Aerospace 9, no. 6 (June 19, 2022): 329. http://dx.doi.org/10.3390/aerospace9060329.
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In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.
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Iskhakova, Anastasia, Maxim Alekhin, and Alexey Bogomolov. "Time-frequency transforms in analysis of non-stationary quasi-periodic biomedical signal patterns for acoustic anomaly detection." Information and Control Systems, no. 1 (February 20, 2020): 15–23. http://dx.doi.org/10.31799/1684-8853-2020-1-15-23.
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Introduction: New approaches to efficient compression and digital processing of audio signals are relevant today. Thereis a lot of interest to new pattern recognition methods which can improve the quality of acoustic anomaly detection. Purpose:Comparative analysis of methods for time-frequency transformation of audio signal patterns, including non-stationary quasiperiodicbiomedical signals in the problem of acoustic anomaly detection. Results: The study compared different time-frequencytransforms (such as windowed Fourier, Gabor, Wigner, pseudo Wigner, smoothed pseudo Wigner, Choi — Williams, Bertrand, pseudoBertrand, smoothed pseudo Bertrand, and wavelet transforms) based on systematization of their functional characteristics(such as the existence and limitedness of basis functions, presence of zero moments and biorthogonal form, opportunity oftwo-dimensional representation and inverse transformation, real time processing, time-frequency transform quality, controlof time-frequency definition, time and frequency interference suppression, relative computational complexity, fast algorithmimplementation) for the problem of biomedial signal pattern recognition. A comparative table is presented with estimates ofinformation capacity for the considered time-frequency transforms. Practical relevance: The proposed approach can solve someacoustic anomaly detection algorithm implementation problems common in non-stationary quasi-periodic processes, in order tostudy disruptive effects causing a change in the functional state of ergatic system operators.
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Schmidl, Sebastian, Phillip Wenig, and Thorsten Papenbrock. "Anomaly detection in time series." Proceedings of the VLDB Endowment 15, no. 9 (May 2022): 1779–97. http://dx.doi.org/10.14778/3538598.3538602.
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Detecting anomalous subsequences in time series data is an important task in areas ranging from manufacturing processes over finance applications to health care monitoring. An anomaly can indicate important events, such as production faults, delivery bottlenecks, system defects, or heart flicker, and is therefore of central interest. Because time series are often large and exhibit complex patterns, data scientists have developed various specialized algorithms for the automatic detection of such anomalous patterns. The number and variety of anomaly detection algorithms has grown significantly in the past and, because many of these solutions have been developed independently and by different research communities, there is no comprehensive study that systematically evaluates and compares the different approaches. For this reason, choosing the best detection technique for a given anomaly detection task is a difficult challenge. This comprehensive, scientific study carefully evaluates most state-of-the-art anomaly detection algorithms. We collected and re-implemented 71 anomaly detection algorithms from different domains and evaluated them on 976 time series datasets. The algorithms have been selected from different algorithm families and detection approaches to represent the entire spectrum of anomaly detection techniques. In the paper, we provide a concise overview of the techniques and their commonalities; we evaluate their individual strengths and weaknesses and, thereby, consider factors, such as effectiveness, efficiency, and robustness. Our experimental results should ease the algorithm selection problem and open up new research directions.
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Obuseh, Marian, Denny Yu, and Poching DeLaurentis. "Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms." Biomedical Instrumentation & Technology 56, no. 2 (April 1, 2022): 58–70. http://dx.doi.org/10.2345/1943-5967-56.2.58.
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Abstract Objective To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications. Materials and Methods We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and ML algorithms were compared. Results The propofol data had 3,300 infusion alerts, 92% of which were generated during the day shift and seven of which had a times-limit greater than 10. The mr-chart identified 15 alert pattern anomalies. Different thresholds were set to include the top 15 anomalies from each ML algorithm. A total of 31 unique ML anomalies were grouped and ranked by agreeability. All algorithms agreed on 10% of the anomalies, and at least two algorithms agreed on 36%. Each algorithm detected one specific anomaly that the mr-chart did not detect. The anomaly represented a day with 71 propofol alerts (half of which were overridden) generated at an average rate of 1.06 per infusion, whereas the moving alert rate for the week was 0.35 per infusion. Discussion These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. However, we recommend using a combination of algorithms, as multiple algorithms serve a benchmarking function and allow researchers to focus on data points with the highest algorithm agreeability. Conclusion Unsupervised ML algorithms can assist clinicians in identifying unusual alert patterns as a first step toward achieving safer infusion practices.
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Li, Nannan, Xinyu Wu, Huiwen Guo, Dan Xu, Yongsheng Ou, and Yen-Lun Chen. "Anomaly Detection in Video Surveillance via Gaussian Process." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (August 12, 2015): 1555011. http://dx.doi.org/10.1142/s0218001415550113.
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In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.
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Rashid, A. N. M. Bazlur, Mohiuddin Ahmed, and Al-Sakib Khan Pathan. "Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation." Sensors 21, no. 9 (April 25, 2021): 3005. http://dx.doi.org/10.3390/s21093005.
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While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS.
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Wilson, Ian R. G., and Nikolay S. Sidorenkov. "Long-Term Lunar Atmospheric Tides in the Southern Hemisphere." Open Atmospheric Science Journal 7, no. 1 (May 17, 2013): 51–76. http://dx.doi.org/10.2174/1874282320130415001.
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The longitudinal shift-and-add method is used to show that there are N=4 standing wave-like patterns in the summer (DJF) mean sea level pressure (MSLP) and sea-surface temperature (SST) anomaly maps of the Southern Hemisphere between 1947 and 1994. The patterns in the MSLP anomaly maps circumnavigate the Earth in 36, 18, and 9 years. This indicates that they are associated with the long-term lunar atmospheric tides that are either being driven by the 18.0 year Saros cycle or the 18.6 year lunar Draconic cycle. In contrast, the N=4 standing wave-like patterns in the SST anomaly maps circumnavigate the Earth once every 36, 18 and 9 years between 1947 and 1970 but then start circumnavigating the Earth once every 20.6 or 10.3 years between 1971 and 1994. The latter circumnavigation times indicate that they are being driven by the lunar Perigee-Syzygy tidal cycle. It is proposed that the different drift rates for the patterns seen in the MSLP and SST anomaly maps between 1971 and 1994 are the result of a reinforcement of the lunar Draconic cycle by the lunar Perigee-Syzygy cycle at the time of Perihelion. It is claimed that this reinforcement is part of a 31/62/93/186 year lunar tidal cycle that produces variations on time scales of 9.3 and 93 years. Finally, an N=4 standing wave-like pattern in the MSLP that circumnavigates the Southern Hemisphere every 18.6 years will naturally produce large extended regions of abnormal atmospheric pressure passing over the semi-permanent South Pacific subtropical high roughly once every ~ 4.5 years. These moving regions of higher/lower than normal atmospheric pressure will increase/decrease the MSLP of this semi-permanent high pressure system, temporarily increasing/reducing the strength of the East-Pacific trade winds. This may led to conditions that preferentially favor the onset of La Nina/El Nino events.
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Gutflaish, Eyal, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, and Oded Sofer. "Temporal Anomaly Detection: Calibrating the Surprise." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3755–62. http://dx.doi.org/10.1609/aaai.v33i01.33013755.
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We propose a hybrid approach to temporal anomaly detection in access data of users to databases — or more generally, any kind of subject-object co-occurrence data. We consider a high-dimensional setting that also requires fast computation at test time. Our methodology identifies anomalies based on a single stationary model, instead of requiring a full temporal one, which would be prohibitive in this setting. We learn a low-rank stationary model from the training data, and then fit a regression model for predicting the expected likelihood score of normal access patterns in the future. The disparity between the predicted likelihood score and the observed one is used to assess the “surprise” at test time. This approach enables calibration of the anomaly score, so that time-varying normal behavior patterns are not considered anomalous. We provide a detailed description of the algorithm, including a convergence analysis, and report encouraging empirical results. One of the data sets that we tested is new for the public domain. It consists of two months’ worth of database access records from a live system. This data set and our code are publicly available at https://github.com/eyalgut/TLR anomaly detection.git.
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Chen, Lirong, Dingxiong Wang, Hui Wang, Xiaodong Wu, Landing Gu, Fen Ma, Fei Xie, et al. "Research on an Oil Pipeline Anomaly Identification Method for Distinguishing True and False Anomalies." Mobile Information Systems 2022 (August 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/9366897.
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Anomaly identification is important to ensure the safe and stable operation of oil pipelines and prevent leaks. Leak identification is performed to divide abnormal samples from normal oil transfer samples in monitoring data, and it is a dichotomous problem. However, the traditional machine learning binary classification method is no longer suitable for identifying leak anomalies in complex production environments. The main problem is that leaks in production environments are very rare, and traditional methods cannot directly identify the leaking pattern with their generalizability. The recognized normal pattern lacks the ability to adapt to dynamic environmental changes and an artificial adjustment of the pump frequency, instrument calibration, and other monitoring data mutations. These are known as false anomalies, and they are difficult to distinguish from true anomalies. This results in a lower recall rate for leak anomaly identification and a higher rate of false positives. To solve this problem, this study proposes a leak anomaly recognition method based on the distinction between true and false anomalies. A one-class SVM is used to learn the normal working mode of oil pipelines, and the model is used to screen out suspected pipeline anomalies, namely, true and false anomalies. It increases the morphological difference between true and false anomaly curves by superimposing multisource data and uses similarity clustering to discover anomaly patterns that indicate leak events. The results show that the leakage anomaly recall rate is 100%, and the false anomaly exclusion rate is 83.49%. This method achieves real-time and efficient monitoring of pipeline leaking events in complex production environments, and it is practical for the application of machine learning methods in production environments.