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1
Sodagudi, Suhasini. "Identification of Anomalous Activities in Using Digital Technology." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (2020): 530–37. http://dx.doi.org/10.5373/jardcs/v12sp8/20202552.
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Shi, Jian, and Weihong Qian. "Connection between Anomalous Zonal Activities of the South Asian High and Eurasian Summer Climate Anomalies." Journal of Climate 29, no. 22 (2016): 8249–67. http://dx.doi.org/10.1175/jcli-d-15-0823.1.
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Abstract Using the daily mean anomalies of atmospheric variables from the NCEP Reanalysis-1 (NCEP R1), this study reveals the connection between anomalous zonal activities of the South Asian high (SAH) and Eurasian climate anomalies in boreal summer. An analysis of variance identifies two major domains with larger geopotential height variability located in the eastern and western flanks of the SAH at around 100 and 150 hPa, respectively. For both eastern and western domains, extreme events are selected during 1981–2014 when normalized height anomalies are greater than 1.0 (less than −1.0) standard deviation for at least 10 consecutive days. Based on these events, four SAH modes that include strong and weak Tibetan modes (STM and WTM, respectively) and strong and weak Iranian modes (SIM and WIM, respectively) are defined to depict the zonal SAH features. The positive composite in the eastern (western) domain indicates the STM (SIM) manifests a robust wavelike pattern with an anomalous low at 150 hPa, and surface cold and wet anomalies over Mongolia and northern China (Kazakhstan and western Siberia) are surrounded by three anomalous highs at 150 hPa and surface warm and dry anomalies over Eurasia. Opposite distributions are also evident in the negative composites of the two domains (WTM and WIM). The surface air temperature anomalies are the downward extension of an anomalous air column aloft while the precipitation anomalies are directly associated with the height anomalies above the air column.
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N.T, Vasanth Kumar, and Geetha Kiran A. "An Overview of Various Techniques Involved in Detection of Anomalies from Surveillance Cameras." International Journal of Computer Science, Engineering and Information Technology 13, no. 4 (2023): 15–22. http://dx.doi.org/10.5121/ijcseit.2023.13402.
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In recent years, the use of surveillance cameras is rapidly increasing in both public and private areas to enhance the security measures. Many companies are recruiting people to monitor the activities captured by surveillance cameras and due to human error they may failed to monitor the abnormal events. So, an automated system to detect the anomalous events acts as a significant approach in surveillance applications. Due to sparse occurrence of anomalous activities, the detection of anomalies is remaining as a challenging task. To overcome these drawbacks, many researchers have worked to develop an effective anomaly detection methods using different approaches. This study prioritized some existing approaches to detect anomalies takes place in surveillance videos. The existing researches utilized University of Central Florida (UCF) Crime video dataset to collect the data about the anomalous activities, UCF crime video dataset consist of 13 categories of anomalies which consist of 1900 surveillance videos. The key parameters such as accuracy, recall, F1 score and Area Under Curve (AUC) are evaluated to analyse the efficiency of the existing anomaly detection methods. This survey acts as a tool for future researchers to overcome the drawbacks in the existing methods and create a novel anomaly detection approach.
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Rattanavipanon, Norrathep, Donlapark Ponnoprat, Hideya Ochiai, Kuljaree Tantayakul, Touchai Angchuan, and Sinchai Kamolphiwong. "Detecting Anomalous LAN Activities under Differential Privacy." Security and Communication Networks 2022 (April 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/1403200.
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Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to protect privacy of LAN users; at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present four approaches, namely, naïve, histogram-based, naïve- δ , and histogram-based- δ and show that they satisfy different levels of differential privacy—a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, all of our approaches preserve more than 75% utility of detecting anomalies in the released data.
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Chen, Dong, Ya Gao, and Huijun Wang. "Why Was the August Rainfall Pattern in the East Asia–Pacific Ocean Region in 2016 Different from That in 1998 under a Similar Preceding El Niño Background?" Journal of Climate 32, no. 18 (2019): 5785–97. http://dx.doi.org/10.1175/jcli-d-18-0589.1.
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AbstractPrevious studies have noted that a strong El Niño event occurring in the preceding winter will result in westward stretching of the western North Pacific subtropical high (WPSH) in the following summer, causing anomalously high precipitation in the Yangtze–Huaihe River basin and anomalously low precipitation in southern China. The winters preceding the summers of 1998 and 2016 featured strong El Niño events, which, along with the El Niño event of 1982, represented the strongest El Niño events since 1950. Under these similar El Niño event backgrounds, the July precipitation anomaly in 2016 was similar to that in 1998, but the August precipitation anomalies in the two years featured opposite distributions. According to the atmospheric circulation analysis, we found that an anomalous ascending motion appeared over the Indian Ocean, while an anomalous descending motion appeared over the Pacific Ocean in August 1998. In addition, the WPSH stretched westward over southern China. However, the atmospheric circulation distribution in August 2016 was the opposite of that in 1998, and the WPSH was divided into eastern and western parts by the anomalous western Pacific cyclone. Further analysis showed that the number of tropical cyclones and typhoons over the western Pacific Ocean increased significantly in August 2016, and their activities were concentrated in the South China Sea (SCS)–southern China region and the western Pacific Ocean, resulting in the division of the WPSH. Therefore, the numbers, tracks, and strengths of tropical cyclones and typhoons were responsible for the differences in the anomalous precipitation distributions over the East Asia–Pacific Ocean region between August 2016 and August 1998.
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Akhoondzadeh, M. "Anomalous TEC variations associated with the powerful Tohoku earthquake of 11 March 2011." Natural Hazards and Earth System Sciences 12, no. 5 (2012): 1453–62. http://dx.doi.org/10.5194/nhess-12-1453-2012.
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Abstract. On 11 March 2011 at 14:46:23 LT, the 4th largest earthquake ever recorded with a magnitude of 9.0 occurred near the northeast coast of Honshu in Japan (38.322° N, 142.369° E, Focal depth 29.0 km). In order to acknowledge the capabilities of Total Electron Content (TEC) ionospheric precursor, in this study four methods including mean, median, wavelet transform, and Kalman filter have been applied to detect anomalous TEC variations concerning the Tohoku earthquake. The duration of the TEC time series dataset is 49 days at a time resolution of 2 h. All four methods detected a considerable number of anomalous occurrences during 1 to 10 days prior to the earthquake in a period of high geomagnetic activities. In this study, geomagnetic indices (i.e. Dst, Kp, Ap and F10.7) were used to distinguish pre-earthquake anomalies from the other anomalies related to the geomagnetic and solar activities. A good agreement in results was found between the different applied anomaly detection methods on TEC data.
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Akhoondzadeh, M., M. Parrot, and M. R. Saradjian. "Electron and ion density variations before strong earthquakes (<i>M</i>>6.0) using DEMETER and GPS data." Natural Hazards and Earth System Sciences 10, no. 1 (2010): 7–18. http://dx.doi.org/10.5194/nhess-10-7-2010.
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Abstract. Using IAP (plasma analyzer) and ISL (Langmuir probe) experiments onboard DEMETER (Detection of Electromagnetic Emissions Transmitted from Earthquake Regions) satellite and GPS (Global Positioning System) measurements, we have statistically analyzed the variations of the electron and ion densities to search for disturbances in the vicinity of four large earthquakes prior to events. The indices Dst and Kp were used to distinguish pre-earthquake anomalies from the other anomalies related to the geomagnetic activities. For each studied case, a very good agreement was found between the different parameters estimated by DEMETER and GPS data in the detection of pre-seismic anomalies. Our statistics results show that the anomalous deviations prior to earthquakes have different sign from case to case, and that their amplitude depends on the magnitude of the earthquake. It has also been found that the electron density measured by the ISL experiment at night detects anomalous variations significantly before the earthquakes. The appearance of positive and negative anomalies in both of DEMETER and TEC (Total Electron Content) data during 1 to 5 days before all studied earthquakes during quiet geomagnetic conditions indicates that these anomalous behaviors are highly regarded as seismo-ionospheric precursors.
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Tao, Dan, Jinbin Cao, Roberto Battiston, et al. "Seismo-ionospheric anomalies in ionospheric TEC and plasma density before the 17 July 2006 <i>M</i>7.7 south of Java earthquake." Annales Geophysicae 35, no. 3 (2017): 589–98. http://dx.doi.org/10.5194/angeo-35-589-2017.
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Abstract. In this paper, we report significant evidence for preseismic ionospheric anomalies in total electron content (TEC) of the global ionosphere map (GIM) and plasma density appearing on day 2 before the 17 July 2006 M7.7 south of Java earthquake. After distinguishing other anomalies related to the geomagnetic activities, we found a temporal precursor around the epicenter on day 2 before the earthquake (15 July 2006), which agrees well with the spatial variations in latitude–longitude–time (LLT) maps. Meanwhile, the sequences of latitude–time–TEC (LTT) plots reveal that the TECs on epicenter side anomalously decrease and lead to an anomalous asymmetric structure with respect to the magnetic equator in the daytime from day 2 before the earthquake. This anomalous asymmetric structure disappears after the earthquake. To further confirm these anomalies, we studied the plasma data from DEMETER satellite in the earthquake preparation zone (2046.4 km in radius) during the period from day 45 before to day 10 after the earthquake, and also found that the densities of both electron and total ion in the daytime significantly increase on day 2 before the earthquake. Very interestingly, O+ density increases significantly and H+ density decreases, while He+ remains relatively stable. These results indicate that there exists a distinct preseismic signal (preseismic ionospheric anomaly) over the epicenter.
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Yu, B., H. Lin, V. V. Kharin, and X. L. Wang. "Interannual Variability of North American Winter Temperature Extremes and Its Associated Circulation Anomalies in Observations and CMIP5 Simulations." Journal of Climate 33, no. 3 (2020): 847–65. http://dx.doi.org/10.1175/jcli-d-19-0404.1.
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AbstractThe interannual variability of wintertime North American surface temperature extremes and its generation and maintenance are analyzed in this study. The leading mode of the temperature extreme anomalies, revealed by empirical orthogonal function (EOF) analyses of December–February mean temperature extreme indices over North America, is characterized by an anomalous center of action over western-central Canada. In association with the leading mode of temperature extreme variability, the large-scale atmospheric circulation features an anomalous Pacific–North American (PNA)-like pattern from the preceding fall to winter, which has important implications for seasonal prediction of North American temperature extremes. A positive PNA pattern leads to more warm and fewer cold extremes over western-central Canada. The anomalous circulation over the PNA sector drives thermal advection that contributes to temperature anomalies over North America, as well as a Pacific decadal oscillation (PDO)-like sea surface temperature (SST) anomaly pattern in the midlatitude North Pacific. The PNA-like circulation anomaly tends to be supported by SST warming in the tropical central-eastern Pacific and a positive synoptic-scale eddy vorticity forcing feedback on the large-scale circulation over the PNA sector. The leading extreme mode–associated atmospheric circulation patterns obtained from the observational and reanalysis data, together with the anomalous SST and synoptic eddy activities, are reasonably well simulated in most CMIP5 models and in the multimodel mean. For most models considered, the simulated patterns of atmospheric circulation, SST, and synoptic eddy activities have lower spatial variances than the corresponding observational and reanalysis patterns over the PNA sector, especially over the North Pacific.
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Song, Jie, and Chongyin Li. "Contrasting Relationship between Tropical Western North Pacific Convection and Rainfall over East Asia during Indian Ocean Warm and Cold Summers." Journal of Climate 27, no. 7 (2014): 2562–76. http://dx.doi.org/10.1175/jcli-d-13-00207.1.
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Abstract Using daily data, this study compares the subseasonal seesaw relationship between anomalous tropical western North Pacific (WNP) convection and anomalous rainfall over subtropical East Asia during boreal summers (June–August) in which the Indian Ocean (IO) sea surface temperature is either warmer or colder than normal. It is found that the precipitation anomalies over central-eastern China (25°–35°N, 110°–120°E) associated with the anomalous tropical WNP convection activities during the IO cold summers are weaker and less evident compared to that in the IO warm summers, indicating the seesaw relationship in the IO cold summers becomes obscure. This contrasting seesaw relationship between the IO warm and cold summers is attributed to different patterns of anomalous moisture transportation and vertical motion over central-eastern China. The anomalous circulations associated with the anomalous tropical WNP convection [the Pacific–Japan (PJ) pattern] during the IO warm and cold summers show that, relative to the IO warm summers, the Japan action center of the PJ pattern has an evident northwestward displacement in the IO cold summers. It is argued that this northwestward displacement of the Japan action center plays a key role in the formation of the distinct seesaw relationship through modifying the anomalous moisture transportation and vertical motion.
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Xu, Falei, Shuang Wang, Yan Li, and Juan Feng. "Synergistic effects of the winter North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) on dust activities in North China during the following spring." Atmospheric Chemistry and Physics 24, no. 18 (2024): 10689–705. http://dx.doi.org/10.5194/acp-24-10689-2024.
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Abstract. Dust significantly influences global weather and climate by impacting the Earth's radiative balance. Based on reanalysis datasets, this study explores how the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) during winter impact dust activities in North China in the following spring. It is found that both the NAO and the ENSO significantly affect dust activities in North China, especially during their negative phases. When both are in their negative phases, their combined impact on dust activities exceeds that of each factor individually. The previous winter's NAO notably affects sea surface temperatures (SSTs) in the North Atlantic, associated with an anomalous tripole SST pattern. These SST anomalies persist into the following spring due to their inherent persistence, inducing an anomalous atmospheric teleconnection wave train that influences dust activities in North China. The ENSO, on the one hand, directly impacts dust activities in North China by modulating circulation over the western North Pacific. Moreover, the ENSO enhances the NAO's effect on North Atlantic SST, which explains the synergistic effects of the ENSO and NAO on dust activities in North China. This study elucidates the combined role of the NAO and ENSO in influencing dust activities in North China, providing one-season-ahead signals for predicting spring dust activities in North China.
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Patel, Jinish, Joseph Reiner, Brenden Stilwell, Abdullah Wahbeh, and Raed Seetan. "Leveraging K-Means Clustering and Z-Score for Anomaly Detection in Bitcoin Transactions." Informatics 12, no. 2 (2025): 43. https://doi.org/10.3390/informatics12020043.
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With the growing popularity of cryptocurrencies, detecting potential market manipulation and fraudulent activities has become crucial for maintaining market integrity. In this study, we aim to detect anomalous Bitcoin transactions using an integrated approach by combining clustering techniques with statistical outlier detection. More specifically, anomalies were detected using three approaches: a distance-based method, flagging points with distances greater than the 95th percentile from their cluster centers; a statistical method, identifying transactions with any feature having an absolute Z-score greater than 3; and a hybrid approach, where transactions flagged by either method were considered anomalous. Using sample subset Bitcoin transaction data from 2015, our results showed that the combined approach was able to achieve the best performance with a total of 6492 (6.61%) detected anomalous transactions out of a total of 98,151 transactions.
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Sun, Ruizao, Anmin Duan, Lilan Chen, Yanjie Li, Zhiang Xie, and Yu Zhao. "Interannual Variability of the North Pacific Mixed Layer Associated with the Spring Tibetan Plateau Thermal Forcing." Journal of Climate 32, no. 11 (2019): 3109–30. http://dx.doi.org/10.1175/jcli-d-18-0577.1.
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Abstract By using multisourced data and two sets of sensitivity runs from the coupled general circulation model CESM1.2.0, we investigated the effects of the spring [March, April, and May (MAM)] surface sensible heating over the Tibetan Plateau (SHTP) on the interannual variability of the North Pacific Ocean sea surface temperature (SST) and mixed layer. The results indicated that an above-normal MAM SHTP can generate a Rossby wave downstream and form an anomalous equivalent barotropic anticyclone over the North Pacific, inducing anticyclonic wind stress anomalies. As a result of Ekman transport and Ekman pumping, sea currents converge near 40°N, accompanied by weak downwelling motion. The mixed layer heat budget diagnosis indicates that the net heat fluxes, together with meridional advection anomalies, contributed significantly to changes in the mixed layer temperature (MLT). As a result, the SST anomalies (SSTAs) and MLT anomalies both present a horseshoelike pattern. In addition, the significant warm SSTAs show a maximum in the late spring, but the significant warm MLT anomalies centered under the sea surface (25-m depth) could be sustained until summer, acting like a signal storage for the anomalous spring SHTP. Moreover, the midlatitude ocean–atmosphere interaction provides a positive feedback on the development of the anomalous anticyclone over the North Pacific, since the SSTA pattern could strengthen the oceanic front and induce more active transient eddy activities. The eddy vorticity forcing that is dominant among the total atmospheric forcings tends to produce an equivalent barotropic atmospheric high pressure, which in turn intensifies the initial anomalous anticyclone.
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Subbaraman, Ram, N. Danilovic, P. P. Lopes, et al. "Origin of Anomalous Activities for Electrocatalysts in Alkaline Electrolytes." Journal of Physical Chemistry C 116, no. 42 (2012): 22231–37. http://dx.doi.org/10.1021/jp3075783.
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Deepak, K., Mohamed Yacin Sikkandar, S. Siddharth, and S. Chandrakala. "A Similarity Based Representation for Identifying Healthcare Anomalous Activities." Journal of Medical Imaging and Health Informatics 10, no. 4 (2020): 787–94. http://dx.doi.org/10.1166/jmihi.2020.2903.
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A Vision Based Patient Monitoring system focuses on detecting abnormal activities of a patient. In real-world, factors like occlusion and view point variations make the activity recognition task challenging. This work proposes a similarity-based representation for healthcare activities including abnormal patient activities such as coughing, sneezing, vomiting, falling, etc. Global and depth-based representations such as histogram of optical flow, displacement between skeletal sequences and relative position of skeletal joints are used to represent the spatio-temporal dynamics of activities. A benchmark data namely "NTU RGB + D Action Recognition dataset" is used for testing the performance of the proposed approach. A comparison of the proposed methodology against other state-of-the-art approaches has proved the discrimination of the proposed approach.
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Dewi, CN, F. Febriani, T. Anggono, et al. "Anomalous geomagnetic activities before the Karangasem - Bali, Indonesia earthquakes on December 13, 2022." IOP Conference Series: Earth and Environmental Science 1373, no. 1 (2024): 012010. http://dx.doi.org/10.1088/1755-1315/1373/1/012010.
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Abstract A series of earthquakes occurred on Bali Island, Indonesia, on December 13, 2022. The United States Geological Survey (USGS) recorded four shallow earthquakes around Karangasem - Bali with magnitude (M) > 4 at that time. The largest was the M 5.2 earthquake, which occurred at 10:38:21.67 UTC with 10 km of depth. We analyzed the anomalous geomagnetic activities during these earthquakes by utilizing the geomagnetic data from the Bayan geomagnetic station located on Lombok Island, less than 100 km from the earthquake’s epicenters. We conducted the polarization ratio analysis by applying the Fast Fourier Transform (FFT) on the five hours of night geomagnetic data (16:00 - 21:00 UTC). The spectral power values of X, Y, and Z geomagnetic data at frequencies 0.04 - 0.06 Hz were calculated and compared with the disturbance storm time (Dst) to find their correlation. The Pearson correlation analysis indicates that they are significantly uncorrelated. Finally, we calculated the Sz/Sg to analyze the geomagnetic anomalies and found them 6 - 11 days before the earthquakes at frequencies 0.04 - 0.06 Hz. We consider that these anomalies are possibly caused by the M 5.2 earthquake since it had the highest magnitude, Es, and Kls values.
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GUEVARA, MALDONADO CESAR BYRON, Peñas Matilde Santos, and López Victoria. "Negative selection and Knuth Morris Pratt algorithm for anomaly detection." IEEE Latin America Transactions 14 (March 1, 2016): 1473–79. https://doi.org/10.1109/TLA.2016.7459637.
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n this paper an algorithm for detecting anomalous behavior on computer systems is proposed. The work is based on information from the behavior of authorized users who have performed various tasks on a computer system over two years. The study uses a dynamic data structure that can encode the current activities of users and their behaviors. The identification of the most and least frequent tasks, based on the historical database of each user, provides a simple way of creating a single profile of behavior. With this profile, we apply negative selection techniques to obtain a reasonable computational size set of anomalous detectors. We then apply the Knuth-Morris-Pratt algorithm for locating detectors of anomalies as indicators of fraudulent behavior. This procedure for detecting anomalous behavior has been tested on real data and the results prove the effectiveness of the proposal and motivate further research to improve the existing detection system.
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Feng, Wenying, Yu Cao, Yilu Chen, et al. "Multi-Granularity User Anomalous Behavior Detection." Applied Sciences 15, no. 1 (2024): 128. https://doi.org/10.3390/app15010128.
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Insider threats pose significant risks to organizational security, often going undetected due to their familiarity with the systems. Detection of insider threats faces challenges of imbalanced data distributions and difficulties in fine-grained detection. Specifically, anomalous users and anomalous behaviors take up a very small fraction of all insider behavior data, making precise detection of anomalous users challenging. Moreover, not all behaviors of anomalous users are anomalous, so it is difficult to detect their behaviors by standardizing with single rules or models. To address these challenges, this paper presents a novel approach for insider threat detection, leveraging machine learning techniques to conduct multi-granularity anomaly detection. We introduce the Multi-Granularity User Anomalous Behavior Detection (MG-UABD) system, which combines coarse-grained and fine-grained anomaly detection to improve the accuracy and effectiveness of detecting anomalous behaviors. The coarse-grained module screens all of the user activities to identify potential anomalies, while the fine-grained module focuses on specific anomalous users to refine the detection process. Besides, MG-UABD employs a combination of oversampling and undersampling techniques to address the imbalance in the datasets, ensuring robust model performance. Through extensive experimentation on the commonly used dataset CERT R4.2, we demonstrate that the MG-UABD system achieves superior detection rate and precision. Compared to the suboptimal model, the accuracy has increased by 3.1% and the detection rate has increased by 4.1%. Our findings suggest that a multi-granularity approach for anomaly detection, combined with tailored sampling strategies, is highly effective in addressing insider threats.
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Lu, Ke, Xianwen Fang, and Na Fang. "PN-BBN: A Petri Net-Based Bayesian Network for Anomalous Behavior Detection." Mathematics 10, no. 20 (2022): 3790. http://dx.doi.org/10.3390/math10203790.
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Business process anomalous behavior detection reveals unexpected cases from event logs to ensure the trusted operation of information systems. Anomaly behavior is mainly identified through a log-to-model alignment analysis or numerical outlier detection. However, both approaches ignore the influence of probability distributions or activity relationships in process activities. Based on this concern, this paper incorporates the behavioral relationships characterized by the process model and the joint probability distribution of nodes related to suspected anomalous behaviors. Moreover, a Petri Net-Based Bayesian Network (PN-BBN) is proposed to detect anomalous behaviors based on the probabilistic inference of behavioral contexts. First, the process model is filtered based on the process structure of the process activities to identify the key regions where the suspected anomalous behaviors are located. Then, the behavioral profile of the activity is used to prune it to position the ineluctable paths that trigger these activities. Further, the model is used as the architecture for parameter learning to construct the PN-BBN. Based on this, anomaly scores are inferred based on the joint probabilities of activities related to suspected anomalous behaviors for anomaly detection under the constraints of control flow and probability distributions. Finally, PN-BBN is implemented based on the open-source frameworks PM4PY and PMGPY and evaluated from multiple metrics with synthetic and real process data. The experimental results demonstrate that PN-BBN effectively identifies anomalous process behaviors and improves the reliability of information systems.
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Chen, Shengjie, Duanyang Liu, Zhiming Kang, Yan Shi, and Mei Liu. "Anomalous Atmospheric Circulation Associated with the Extremely Persistent Dense Fog Events over Eastern China in the Late Autumn of 2018." Atmosphere 12, no. 1 (2021): 111. http://dx.doi.org/10.3390/atmos12010111.
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Under a declining trend of fog days in China, the duration of fog events since the 1990s reached a significant peak in the late autumn of 2018 over Eastern China. The average anomalous fog days were 4.74 d in November 2018 over Jiangsu Province in Eastern China, with a 1.73 standard deviation departure from climatology. Those fogs can thus be identified as a significantly abnormal climatic event with long duration, strong intensity, and extensive coverage. Based on the daily evolutions and correlations of atmospheric parameters, the dense fogs are revealed to be well configured by favorable metrological conditions such as weak dynamic progress, strong inversion in the lower troposphere and saturated air near the surface. If not disturbed, the intensification or duration of these conditions will further promote and maintain the development of fogs. The anomalous atmospheric background associated with those favorable meteorological conditions is revealed by composing the standardized anomalies of circulation fields during the fog days. Over the fog areas, vortex activities or cold air invasion is effectively hampered and the atmosphere inclines to be stable, due to the anomalous circulation pattern composed of the broadened jet stream, weakened jet core over Eastern China, undermined East Asian trough, declined East Asian winter monsoon, and enhanced anomalous southerly flows that transport abnormal warm and wet air to Eastern China. The vapor supplement is intensified by both sustained anomalous northward wind at the lower troposphere and anomalous westward wind in the near-surface. Overall, the numbers of standardized anomalies of 1000–200-hPa height, temperature, wind, and moisture fields during these fog days all significantly depart from climatology for that locale and time of the season, further demonstrating that the persistent dense fogs over Eastern China in the late autumn of 2018 is an unusual weather event with extreme synoptic-scale departures from normal.
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Didwania, Prerna, and Vandana Jagtap. "Anomalous Activity Detection in Videos Using Increment Learning." European Journal of Engineering Research and Science 5, no. 3 (2020): 297–300. http://dx.doi.org/10.24018/ejers.2020.5.3.1803.
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Nowadays, there is a rapid growth in the number of video cameras at public and private sector because of the monitoring and security purposes. As video surveillance using Closed Circuit Television (CCTV) is in boom nowadays, it has got more research attention due to increased global security concerns. This rapidly growing data can be used to automatically detect the anomalous activities which are going around in our surrounding. Anomalous activity is something that deviates from its normal nature or something that opposes the normal events. This research mainly focuses on detecting anomalous activities in crowded scenes by using video data. Automatically detecting the anomalous activity without using the handcrafted feature has become the need of the hour. This paper contains a survey of different approaches used for anomaly detection in the past. Different incremental and transfer learning approaches are discussed in this paper and it was found that incremental learning has not been used for video-based anomalous activity detection.
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Didwania, Prerna, and Vandana Jagtap. "Anomalous Activity Detection in Videos Using Increment Learning." European Journal of Engineering and Technology Research 5, no. 3 (2020): 297–300. http://dx.doi.org/10.24018/ejeng.2020.5.3.1803.
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Nowadays, there is a rapid growth in the number of video cameras at public and private sector because of the monitoring and security purposes. As video surveillance using Closed Circuit Television (CCTV) is in boom nowadays, it has got more research attention due to increased global security concerns. This rapidly growing data can be used to automatically detect the anomalous activities which are going around in our surrounding. Anomalous activity is something that deviates from its normal nature or something that opposes the normal events. This research mainly focuses on detecting anomalous activities in crowded scenes by using video data. Automatically detecting the anomalous activity without using the handcrafted feature has become the need of the hour. This paper contains a survey of different approaches used for anomaly detection in the past. Different incremental and transfer learning approaches are discussed in this paper and it was found that incremental learning has not been used for video-based anomalous activity detection.
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Abreu, Fernando H. O., Amilcar Soares, Fernando V. Paulovich, and Stan Matwin. "A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics." ISPRS International Journal of Geo-Information 10, no. 6 (2021): 412. http://dx.doi.org/10.3390/ijgi10060412.
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With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable.
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Chakrabarti, S. K., S. Sasmal, and S. Chakrabarti. "Ionospheric anomaly due to seismic activities – Part 2: Evidence from D-layer preparation and disappearance times." Natural Hazards and Earth System Sciences 10, no. 8 (2010): 1751–57. http://dx.doi.org/10.5194/nhess-10-1751-2010.
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Abstract. We show evidences for anomalous ionospheric behaviour in the signal of Indian navy VLF transmitting station named VTX due to earthquakes in the South Asian region. We concentrate on the variation of the D-layer preparation time (DLPT) and D-layer disappearance time (DLDT) in a period of sixteen months and study their average behaviors. We identify those days in which DLPT and DLDT exhibit significant deviations. Separately, we compute the energy release by earthquakes during this period and show that "anomalous VLF" days are associated with anomalous energy release. We find that the anomaly and the deviation of DLPT and DLDTs from the mean are linearly correlated. We discuss the predictability in this approach and compare with the terminator shift approach using the same set of data.
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Kawamura, M., Y. H. Wu, T. Kudo, and C. C. Chen. "A statistical feature of anomalous seismic activities prior to large shallow earthquakes in Japan revealed by the Pattern Informatics method." Natural Hazards and Earth System Sciences Discussions 1, no. 2 (2013): 721–45. http://dx.doi.org/10.5194/nhessd-1-721-2013.
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Abstract. For revealing the preparatory processes of large inland earthquakes, we systematically applied the Pattern Informatics method (PI method) to the earthquake data of Japan. We focused on 12 large earthquakes with magnitudes larger than M = 6.4 (an official magnitude of the Japan Meteorological Agency) that occurred at depths shallower than 30 km between 2000 and 2010. We examined the relation between the spatiotemporal locations of such large shallow earthquakes and those of PI hotspots, which correspond to the grid cells of anomalous seismic activities in a designated time span. Based on a statistical test using Molchan's error diagram, we inquired into the existence of precursory anomalous seismic activities of the large earthquakes and, if any, their characteristic time span. The test indicated that the Japanese M &amp;geqq; 6.4 inland earthquakes tend to be preceded by anomalous seismic activities of 8-to-10-yr time scales.
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Ananthakrishnan, Balasundaram, V. Padmaja, Sruthi Nayagi, and Vijay M. "Deep Neural Network based Anomaly Detection for Real Time Video Surveillance." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 54–64. http://dx.doi.org/10.17762/ijritcc.v10i4.5534.
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One of the main concerns across all kinds of domains has always been security. With the crime rates increasing every year the need to control has become crucial. Among the various methods present to monitor crime or any anomalous behavior is through video surveillance. Nowadays security cameras capture incidents in almost all public and private place if desired. Even though we have abundance of data in the form of videos they need to be analyzed manually. This results in long hours of manual labour and even small human discrepancies may have huge consequences negatively. For this purpose, a Convolution Neural Network (CNN) based model is built to detect any form of abnormal activities or anomalies in the video footages. This model converts the input video into frames and detects the anomalous frames. To increase the efficiency of the model, the data is de-noised with Gaussian blur feature. The avenue dataset is used in this work to detect and predict various kinds of anomalies. The performance of the model is measured using classification accuracy and the results are reported.
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Rezapour, N., M. Fattahi, and A. A. Bidokhti. "Possible soil thermal response to seismic activities in Alborz region (Iran)." Natural Hazards and Earth System Sciences 10, no. 3 (2010): 459–64. http://dx.doi.org/10.5194/nhess-10-459-2010.
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Abstract. In this investigation, relations between the ground's thermal properties and 70 earthquakes with a magnitude >4 Richter in the Alborz region during a period of 12 years (1992 to 2004) were studied. Typical changes of ground temperature, 0.4 °C; thermal diffusivity, 0.028 m2 s−1×10−6 and ground heat flux take place a few hours prior to the earthquakes. The values of thermal diffusivity depend on the ground moisture content, which may change during seismic activities. The analysis of ground heat flux from the epicentre and it's surrounding regions show some anomalous behavior before the earthquakes but with different signs in the areas close to the sea and far away from the sea. The changes of the ground's thermal properties prior to the earthquakes in the Alborz region are attributed to the increase in seismic activities in the epicentre and it's surrounding regions. The anomalous behavior in the ground thermal properties shows great potential in providing early warning of imminent earthquake.
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Sattler, Klaus. "STM OF ANOMALOUS CARBON STRUCTURES." International Journal of Modern Physics B 06, no. 23n24 (1992): 3603–12. http://dx.doi.org/10.1142/s0217979292001675.
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Understanding carbon in its various forms became recently the focus of many research activities due to the production of C 60 and other fullerenes. Besides the fullerenes, other novel forms of carbon might be realized in the laboratory. In this respect, the physics of low-dimensional carbon configurations is of special interest. We will discuss the effect of defects on a graphitic sheet, ‘rotated’ stacking of graphitic sheets, and the adsorption characteristics of submonolayer carbon on graphite. Using a scanning tunneling microscope, we find that the local density of states can strongly vary due to defects, or layer misorientations, leading to periodic modulations of the graphitic electron state density. A variety of observed anomalous structures are explained by lateral electron state density variations rather than by atomic reconstructions.
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Phapale, Anuja, and Sukhada Bhingarkar. "Deep Context-Aware Feature Extraction for Anomaly Detection in Surveillance Videos." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21633–38. https://doi.org/10.48084/etasr.9810.
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Surveillance video analysis plays a crucial role in ensuring public safety and security. Developing a context-aware framework for anomaly detection in surveillance videos is motivated by the need for enhanced security, safety, and efficiency in various domains. Context-aware anomaly detection depends on spatiotemporal features that help the model understand the context of anomalies in surveillance videos. This study aimed to provide a novel deep learning-based context-aware approach to feature extraction to detect anomalies in surveillance videos. The proposed method integrates ResNet50 for spatial feature extraction and 3D Convolutional Neural Network (CNN) for temporal feature extraction. This method identifies six anomalous activities, namely abuse, arrest, fighting, robbery, shooting, and road accidents, using the UCF-Crime dataset. The proposed integrated ResNet50 and 3D CNN model achieves promising accuracy for the six classes, such as 95% for abuse, 93% for arrest, 95.22% for fighting, 94.44% for robbery, 93% for shooting, and 94.22% for road accidents. By combining spatiotemporal features, the proposed model detects anomalies in behavior and unexpected movements, which makes it useful for security monitoring where deviations from normal behavior indicate anomalous events. This research contributes to advancing the capabilities of surveillance systems, enhancing public safety, and enabling proactive security measures in diverse urban environments.
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Liu, Jinjuan, Liang Zhao, Jingsong Wang, and Ziniu Xiao. "Detecting Relationship between the North–South Difference in Extreme Precipitation and Solar Cycle in China." Atmosphere 15, no. 2 (2024): 175. http://dx.doi.org/10.3390/atmos15020175.
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The sun plays a crucial role as the primary source of energy for the Earth’s climate system and the issue of the influence of solar activity on the climate has been actively discussed recently. However, the precise impact of solar activity on extreme precipitation on the decadal timescale remains insufficiently confirmed. In this study, we investigate the relationship between summer extreme precipitation events exceeding 20 mm (R20mm) in China and the 11-year sunspot number (SSN) cycle from 1951 to 2018. Results showed that the first mode of June–July R20mm, a “south-drought and north-flooding (SDNF)” distribution, exhibited a significant correlation with the SSN cycle (p = 0.02). The fundamental driver is likely the pronounced periodic response of stratospheric ozone to solar forcing. During summer of the high-solar-activity years (HSY), there is a notable increase in ozone concentration and high temperatures in the stratosphere, particularly in the Southern Hemisphere. This phenomenon leads to a layer of anomalous temperature inversion, suppressing convection at the subtropics. This induced downward anomalous airflow toward the north stimulates convective activity in the equatorial region and generates northward wave activities. These wave activities produce rising and sinking anomalies at different latitudes in the Northern Hemisphere troposphere, finally causing the “SDNF” pattern in China.
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Ding, Yihui, and Ying Sun. "A Study on Anomalous Activities of East Asian Summer Monsoon during 1999." Journal of the Meteorological Society of Japan 79, no. 6 (2001): 1119–37. http://dx.doi.org/10.2151/jmsj.79.1119.
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王, 立宜. "Analysis of Anomalous Typhoon Activities over the Western North Pacific in 2017." Open Journal of Nature Science 06, no. 02 (2018): 139–46. http://dx.doi.org/10.12677/ojns.2018.62021.
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Perkins, Kedar M., Chetali Gupta, Emily N. Charleson, and Newell R. Washburn. "Surfactant properties of PEGylated lignins: Anomalous interfacial activities at low grafting density." Colloids and Surfaces A: Physicochemical and Engineering Aspects 530 (October 2017): 200–208. http://dx.doi.org/10.1016/j.colsurfa.2017.07.061.
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Tong, Zhaomin, Ziyi Zhang, Rui An, et al. "Detecting anomalous commuting patterns: Mismatch between urban land attractiveness and commuting activities." Journal of Transport Geography 116 (April 2024): 103867. http://dx.doi.org/10.1016/j.jtrangeo.2024.103867.
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Akhoondzadeh, M., M. Parrot, and M. R. Saradjian. "Investigation of VLF and HF waves showing seismo-ionospheric anomalies induced by the 29 September 2009 Samoa earthquake (<i>M</i><sub>w</sub>=8.1)." Natural Hazards and Earth System Sciences 10, no. 5 (2010): 1061–67. http://dx.doi.org/10.5194/nhess-10-1061-2010.
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Abstract. In Samoa Islands, a powerful earthquake took place at 17:48:10.99 UTC (06:48:10.99 LT) on 29 September 2009 with a magnitude Mw=8.1. Using ICE (Instrument Champ Electrique) and IMSC (Instrument Magnetic Search Coil) experiments onboard the DEMETER (Detection of Electromagnetic Emissions Transmitted from Earthquake Regions) satellite we have surveyed possible variations in electromagnetic signals transmitted by the ground-based VLF transmitter NPM in Hawaii and in HF plasma waves close to the Samoa earthquake during the seismic activity. The indices Dst and Kp were used to distinguish pre-earthquake anomalies from the other anomalies related to the geomagnetic activities. In a previous study we have shown that anomalies in IAP (plasma analyzer) and ISL (Langmuir probe) experiments onboard the DEMETER and also TEC (Total Electron Content) data appear 1 to 5 days before the Samoa earthquake. In this paper we show that the anomalies in the VLF transmitter signal and in the HF range appear with the same time scale. The lack of significant geomagnetic activities indicates that these anomalous behaviors could be regarded as seismo-ionospheric precursors. It is also shown that comparative analysis is more effective in seismo-ionospheric studies.
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Rahman, Md Motiur, Deepti Gupta, Smriti Bhatt, Shiva Shokouhmand, and Miad Faezipour. "A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions." Future Internet 16, no. 4 (2024): 139. http://dx.doi.org/10.3390/fi16040139.
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Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.
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Jiang, Ding De, Cheng Yao, Zheng Zheng Xu, Peng Zhang, Zhen Yuan, and Wen Da Qin. "An Continuous Wavelet Transform-Based Detection Approach to Traffic Anomalies." Applied Mechanics and Materials 130-134 (October 2011): 2098–102. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2098.
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Anomalous traffic often has a significant impact on network activities and lead to the severe damage to our networks because they usually are involved with network faults and network attacks. How to detect effectively network traffic anomalies is a challenge for network operators and researchers. This paper proposes a novel method for detecting traffic anomalies in a network, based on continuous wavelet transform. Firstly, continuous wavelet transforms are performed for network traffic in several scales. We then use multi-scale analysis theory to extract traffic characteristics. And these characteristics in different scales are further analyzed and an appropriate detection threshold can be obtained. Consequently, we can make the exact anomaly detection. Simulation results show that our approach is effective and feasible.
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Nelson, Roger D. "FieldREG Measurements in Egypt: Resonant Consciousness at Sacred Sites." Journal of Scientific Exploration 38, no. 4 (2024): 686–97. https://doi.org/10.31275/20243393.
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Over a two-week period, various “sacred sites” in Egypt were visited by a group interested in the spiritual qualities of the ancient temples, pyramids, and tombs. The group engaged in informal ceremonies, including chanting and meditation, to pay respect to the sacred sites of the ancient Egyptians. A portable random event generator and palmtop computer were used to generate and record ongoing random sequences accompanied by a time-stamped computer index and onsite notes of relevant observations and activities. Pre-planned hypotheses predicted anomalous deviations of the sequences during visits to the sacred sites, including the inner sanctum or Holy of Holies in each temple and all the interior chambers of the pyramids. A further prediction was made that resonance- or coherence-building activities of the group, including chanting and meditation in these special locations, would also correlate with anomalous deviations. Both formal hypotheses were confirmed with a combined associated probability of 2.7x10^-6. Other categories of data provided context and helped to distinguish the sources of the anomalous effects.
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Guo, Dong, Zhuoqi Liang, Qiang Gui, Qian Lu, Qiong Zheng, and Shuyang Yu. "Simulation of Northern Winter Stratospheric Polar Vortex Regimes in CESM2-WACCM." Atmosphere 14, no. 2 (2023): 243. http://dx.doi.org/10.3390/atmos14020243.
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The possible impact of various Arctic polar vortex regimes for the stratosphere on the Northern Hemisphere extratropics has not been fully understood. Previous study has classified the stratospheric Arctic vortex to six regimes using the k-mean clustering algorithm based on the ERA5 reanalysis. The stability and robustness of the classification is further verified with a much longer model dataset and historical integrations from CESM2-WACCM. Consistent with the reanalysis, we clustered the Arctic stratospheric polar vortex forms into six patterns, named as homogeneously-intensified and -weakened regimes (HI, HW), North America-intensified and -weakened regimes (NAI, HAW), and Eurasia-intensified and -weakened regimes (EUI, EUW). A zonally uniform positive (negative) Northern Annular Mode (NAM) pattern develops during the HI (HW) regime from the stratosphere to troposphere. The NAM-like pattern shifts toward the western hemisphere with the largest negative (positive) anomalous height center shifting to North America during the NAI (NAW) regime. In contrast, the maximum polar anomaly center moves towards polar Eurasia during the EUI (EUW) regime. The HI, NAI, and EUW regimes are accompanied with weakened wave activities, while the HW, NAW, and EUI regimes are preceded by enhanced planetary waves. Accordingly, persistent anomalies of warmth (coldness) exist over midlatitude Eurasia and North America during the HI (HW). Anomalous warmth (coldness) centers exist in northern Eurasia, while anomalous coldness (warmth) centers exist around the Mediterranean Sea during the NAI (NAW) regime. Anomalous warmth (coldness) centers develop in East Asia in the EUI (EUW) periods. The rainfall anomaly distributions also vary with the stratospheric polar vortex regime. The frequency for stratospheric regimes during SSWs and strong vortex events is also assessed and consistent with previous findings.
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Baiden-Amissah, Josephine, Blestmond A. Brako, Gordon Foli, Jonathan Quaye-Ballard, and Simon K. Y. Gawu. "USING GIS AS A SPATIAL SUPPORT TOOL TO DISCRIMINATE BETWEEN TRUE AND FALSE GEOCHEMICAL ANOMALIES AT THE NORTHERN MARGIN OF THE ASANKRAGWA GOLD BELT IN THE PALEOPROTEROZOIC KUMASI BASIN, GHANA." Earth Science Malaysia 8, no. 1 (2023): 61–69. https://doi.org/10.26480/esmy.01.2024.61.69.
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This study uses Geographical Information Systems (GIS) as a support tool for gold exploration to distinguish between true and false soil geochemical anomalies at the northern segment of the Asankragwa gold belt in the Paleoproterozoic Kumasi Basin, Ghana. The main objective of this study is to identify potentially mineralized zones within the northern segment of the Asankragwa gold belt by integrating GIS, structural and soil geochemical datasets. To reduce the probability of delineating false anomalies as true anomalies, diverse graphical threshold determination methods, namely histogram, box plot, QQ plot, mean+2SD, Jenks Natural Break and Probability plot, as well as advanced threshold determination methods like the Mean Absolute Deviation (MAD) and double MAD were employed. The threshold values established from the graphical methods are 175 ppb, 96 ppb, 335ppb, 384 ppb and 100 ppb respectively. However, the MAD and double MAD methods produced threshold values of 74.5ppb and 130ppb respectively. Based on the high variability in the threshold values, anomalous areas were delineated using thresholds values of 100ppb and 130ppb respectively established from the Jenks Natural Break and Probability plot and double MAD method. About 40%, 35% and 20% of the selected anomalous areas are located within soils overlying volcanoclastic, clastic sedimentary and marine volcanoclastic rocks respectively. These anomalies are not lithologically controlled since they are not confined to a particular rock type. Superimposing the selected aanomalies over geological structures and Landsat imagery, 90% of the anomalies can be linked to the NE-SW geological structures. Upon integrating the anomalies with structural data and illegal mining activities and using the Booleon analysis, not all anomalies may be true anomalies. True gold anomalies within the Asankragwa gold belt are consistent with the central> northern> southern portions. Hence, the discovery of gold in the Asankragwa gold belt has been enhanced using GIS as a spatial support tool.
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Kamble, Kunal, Pranit Jadhav, Atharva Shanware, and Pallavi Chitte. "Smart Surveillance System for Anomaly Recognition." ITM Web of Conferences 44 (2022): 02003. http://dx.doi.org/10.1051/itmconf/20224402003.
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Situation awareness is the key to security. Surveillance systems are installed in all places where security is very important. Manually observing all the surveillance footage captured is a monotonous and time consuming task. Security can be defined in different terms in different conditions like violence detection, theft identification, detecting harmful activities etc. In crowded public places the term security covers almost all type of unusual events. To eliminate the tedious manual surveillance we have developed a smart surveillance which will detect an anomaly and alert the user and authority without any human interference. It is a very critical issue in a smart surveillance system to instantly detect an anomalous behaviour in video surveillance system. In this project, a unified framework based on deep neural network framework is proposed to detect anomalous activities. This neural network framework consists of (a) an object detection module, (b) an object discriminator and tracking module, (c) an anomalous activity detection module based on recurrent neural network. The system is a web application where user can apply for three different security services namely motion detection, fall detection and anomaly detection which is applicable for monitoring different environment like homes, roads, offices, schools, shops, etc. On detection of anomalous activity the system will notify the user and responsible authority regarding the anomaly through mail with an anomaly detected frame attachment.
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Muir, Graham K. P., Gordon T. Cook, Angus B. MacKenzie, Gillian MacKinnon, and Pauline Gulliver. "Anomalous 14C Enrichments in the Eastern UK Coastal Environment." Radiocarbon 57, no. 3 (2015): 337–45. http://dx.doi.org/10.2458/azu_rc.57.18395.
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During the period from 1995 to 2011, radiocarbon measurements from the coast around Hartlepool in NE England have revealed anomalous enrichments in seawater, sediment, and marine biota. These cannot be explained on the basis of atomic weapons testing or authorized nuclear industry discharges, including those from the nearby advanced gas-cooled reactor. Enhanced 14C-specific activities have also been observed since 2005 in biota during routine monitoring at Hartlepool by the Food Standards Agency, but are reported as “likely” originating from a “nearby non-nuclear source.” Studies undertaken in Hartlepool and Teesmouth during 2005 and 2011 suggest that the 14C discharges are in the vicinity of Greatham Creek, with activity levels in biota analogous to those measured at Sellafield, which discharges TBq activities of 14C per annum. However, if the discharges are into Greatham Creek or even the River Tees, it is proposed that they would be much smaller than those at Sellafield and the high specific activities would be due to much smaller dilution factors. The discharge form of the 14C remains unclear. The activity patterns in biota are similar to those at Sellafield, suggesting that initial inputs are dissolved inorganic carbon (DI14C). However, the mussel/seaweed ratios are more akin to those found around Amersham International, Cardiff, which is known to discharge 14C in an organic form. 14C analysis of a sediment core from Seal Sands demonstrated excess 14C to the base of the core (43–44 cm). 210Pb dating of the core (0–32 cm) produced an accumulation rate of 0.7 g cm−2 yr−1, implying that 14C discharges have occurred from the 1960s until the present day.
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Ha, Yao, and Zhong Zhong. "Decadal Change in Tropical Cyclone Activity over the South China Sea around 2002/03." Journal of Climate 28, no. 15 (2015): 5935–51. http://dx.doi.org/10.1175/jcli-d-14-00769.1.
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Abstract This study investigates the decadal change in tropical cyclone (TC) activity over the South China Sea (SCS) in the boreal summer (June–August) since the early 1990s and explores possible causes behind it. Results show that the SCS TC activity experienced an abrupt decadal decrease at around 2003/03. Compared to the TC activities from the early 1990s to 2002, the number of TCs formed in the SCS markedly decreased from 2003 through the early 2010s. Moreover, most of the TCs were primarily confined within the SCS basin during this period. The TCs that formed during the period of 2003–11 usually moved west-northwestward and rapidly weakened after making landfall. It is found that a significant decadal-scale sea surface temperature (SST) warming occurred in the northern Indian Ocean and the western Pacific Ocean after 2002 while convection intensified over the tropical regions between 60° and 80°E and around 150°E, respectively. The warm SST anomalies induced an anomalous subsiding flow over the SCS basin via the Walker-like (zonal) circulation. Meanwhile, anomalously dry, sinking air around 5°–20°N derived from local Hadley (meridional) circulation reinforced the subsiding flow of the zonal circulation. The above circulation patterns suppressed TC genesis over the northern SCS, leading to the decadal decrease in TC activity that occurred around 2002/03. In addition, in conjunction with the local anomalous easterly flow, the intraseasonal atmospheric variability over the SCS has decreased since the early 2000s. This is unfavorable for the development of synoptic-scale disturbances and may also contribute to the decadal decrease in TC activity.
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Luo, Ankun, Shuning Dong, Hao Wang, et al. "Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China." Water 16, no. 11 (2024): 1611. http://dx.doi.org/10.3390/w16111611.
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As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities.
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Xie, Xiang, Qiuchen Lu, David Rodenas-Herraiz, Ajith Kumar Parlikad, and Jennifer Mary Schooling. "Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance." Engineering, Construction and Architectural Management 27, no. 8 (2020): 1835–52. http://dx.doi.org/10.1108/ecam-11-2019-0640.
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PurposeVisual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.Design/methodology/approachThe developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.FindingsTaking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.Originality/valueThe originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.
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Liu, Shihua, Sihua Huang, Yanke Tan, Zhiping Wen, Xiaodan Chen, and Yuanyuan Guo. "Characteristics and Mechanisms of the Interannual Variability of the Northwest–Southeast Shift of the Tropical Easterly Jet’s Core in July." Journal of Climate 37, no. 11 (2024): 3219–35. http://dx.doi.org/10.1175/jcli-d-23-0291.1.
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Abstract Previous studies have pointed out that the tropical easterly jet (TEJ) core varies longitudinally or latitudinally. Whether there is a linkage between longitudinal and latitudinal variations of the TEJ core remains unclear. We found that, on the interannual time scale, the northward (southward) movement of the TEJ core is typically accompanied by a westward (eastward) shift, characterized by a noticeable northwest–southeast (NW–SE) displacement. This NW–SE shift is most evident in July. A locational index is defined to capture this shift by the difference of area-averaged 200-hPa zonal winds between the western Arabian Sea (AS) and the southern tip of the Indian Peninsula. Observations and numerical simulations demonstrated that the northwestward-shifted (southeastward-shifted) TEJ core is caused by the joint and individual influences from the enhanced (suppressed) convective activities over the eastern AS and suppressed (enhanced) convective activities over the northern Bay of Bengal–South China Sea (BOB–SCS). Enhanced (suppressed) convective activities over the eastern AS can induce upper-tropospheric divergence (convergence) and anticyclonic (cyclonic) circulations to the northwest of the convection, leading to anomalous easterly (westerly) over the western AS. The suppressed (enhanced) convective activities over the northern BOB–SCS can further facilitate the northwestward (southeastward) shift through inducing anomalous cyclonic (anticyclonic) circulation centering at the BOB and the associated anomalous westerly (easterly) over the southern tip of the Indian Peninsula. The NW–SE shift of the TEJ core may have an implication for the change in the area of the intense rainfall in South Asia. Significance Statement The purpose of this study is to explore the linkage between the zonal and meridional variations of the core of the tropical easterly jet (TEJ) and its underlying mechanisms. We found that the TEJ core features a pronounced northwest–southeast shift and this phenomenon only occurs in July. Thus, we defined a locational index to depict this unique characteristic and reveal its relationship with the anomalous convective activities over the eastern Arabian Sea and the northern Bay of Bengal–South China Sea. These results may help improve our understanding of the characteristics and mechanisms of the variations of the TEJ core.
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Aarya Gadar. "Intelligent Surveillance System for Real-Time Detection of Anomalous Activities in Video Streams." Journal of Information Systems Engineering and Management 10, no. 7s (2025): 130–37. https://doi.org/10.52783/jisem.v10i7s.803.
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The increasing complexity of surveillance structures necessitates advanced techniques for monitoring big volumes of video statistics. This record opinions the utility of convolutional neural networks (CNN) and deep getting to know techniques for detecting suspicious pastime in video streams. The technique involves preliminary video statistics processing to extract key features, observed by training a CNN version to distinguish between normal and abnormal behaviours through recognizing spatial and temporal patterns within the video frames. techniques consisting of transfer gaining knowledge of and statistics augmentation are employed to enhance the model's generality and robustness. The effectiveness of this approach was validated via various checks, including experiments on datasets like u.s.a. Pedestrian and road, where the CNN-primarily based technique established high accuracy and go back quotes in figuring out suspicious activities. The scalability and actual-time processing abilities of the version make it adaptable to various tracking environments. These findings are great for the advancement of the surveillance era, offering a dependable technique for real-time detection of suspicious sports in video streams. The proposed CNN-based approach is promising for bolstering safety in public spaces, transportation structures, and important infrastructure, thus contributing to better safety features.
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Takla, E. M., A. Khashaba, M. Abdel Zaher, A. Yoshikawa, and T. Uozumi. "Anomalous ultra low frequency signals possibly linked with seismic activities in Sumatra, Indonesia." NRIAG Journal of Astronomy and Geophysics 7, no. 2 (2018): 247–52. http://dx.doi.org/10.1016/j.nrjag.2018.04.004.
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Pawar, Karishma, and Vahida Attar. "Deep learning-based intelligent surveillance model for detection of anomalous activities from videos." International Journal of Computational Vision and Robotics 10, no. 4 (2020): 289. http://dx.doi.org/10.1504/ijcvr.2020.10029168.
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