Realistic Anomalies Research Articles

Deep Surveillance System

This study has been undertaken to investigate and implement multiple detection systems into a single surveillance system and check whether the input videos may comprise and capture a variety of realistic anomalies or not. In this paper, we propose to learn various anomalies by exploiting both normal and anomalous videos and implemented it to new model. Real time object detection is a vast, vibrant and sophisticated area of computer vision aimed towards object identification and recognition. Object detection detects the semantic objects of a class objects using Open source Computer Vision, which is a library of programming functions mainly trained towards real time computer vision in digital images and videos. The main aim behind this real time object detection is to help the peoples to overcome their difficulty. Real time object detection finds its uses in the areas like tracking objects, video surveillance, pedestrian detection, people counting, self-driving cars, face detection, tracking in sports and many more. This is achieved using Convolution, Probabilistic Neural Networks, etc. which are a representative tool of Deep learning. This project acts as an aiding tool for peoples who wants to take care of everything inside, outside, and around their house just for their full security expectations. Surveillance is a must for small houses to large-scale industries as they fulfil our safety aspects because theft and burglary have always been a problem. By combining this Surveillance idea to IoT and some Machine Learning stuff this will be a major product. The proposed project is a single autonomous surveillance system, based on analysis and detection technology. The proposed system is capable of monitoring all actions at once and alerts the concerned officials immediately and precisely.

Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning.

Attention-based anomaly detection in multi-view surveillance videos

Anomaly detection is one of the most challenging tasks in visual understanding because anomalous events are diverse and complicated. In this paper, we propose a future frame prediction framework and a Multiple Instance Learning (MIL) framework by leveraging attention schemes to learn anomalies. In both frameworks, we utilize the attention-based module to better localize anomalies. Further, we introduce a memory addressing module for the future frame prediction framework, and a novel loss function for the MIL framework, respectively. We also introduce a new multi-view dataset of 170 videos with 10 realistic anomalies that pose a serious threat to security such as intrusion, crowd, accident, weapon, arson, etc., as well as normal activities. The experimental results demonstrate the effectiveness of our methods on the proposed dataset and multiple benchmark datasets. The proposed dataset brings more opportunities and challenges to future work on anomaly detection.

Mid-Holocene monsoons in South and Southeast Asia: dynamically downscaled simulations and the influence of the Green Sahara

Abstract. Proxy records suggest that the Northern Hemisphere during the mid-Holocene (MH), to be assumed herein to correspond to 6000 years ago, was generally warmer than today during summer and colder in the winter due to the enhanced seasonal contrast in the amount of solar radiation reaching the top of the atmosphere. The complex orography of both South and Southeast Asia (SA and SEA), which includes the Himalayas and the Tibetan Plateau (TP) in the north and the Western Ghats mountains along the west coast of India in the south, renders the regional climate complex and the simulation of the intensity and spatial variability of the MH summer monsoon technically challenging. In order to more accurately capture important regional features of the monsoon system in these regions, we have completed a series of regional climate simulations using a coupled modeling system to dynamically downscale MH global simulations. This regional coupled modeling system consists of the University of Toronto version of the Community Climate System Model version 4 (UofT-CCSM4), the Weather Research and Forecasting (WRF) regional climate model, and the 3D Coastal and Regional Ocean Community model (CROCO). In the global model, we have taken care to incorporate Green Sahara (GS) boundary conditions in order to compare with standard MH simulations and to capture interactions between the GS and the monsoon circulations in India and SEA. Comparison of simulated and reconstructed climates suggest that the dynamically downscaled simulations produce significantly more realistic anomalies in the Asian monsoon than the global climate model, although they both continue to underestimate the inferred changes in precipitation based upon reconstructions using climate proxy information. Monsoon precipitation over SA and SEA is also greatly influenced by the inclusion of a GS, with a large increase particularly being predicted over northern SA and SEA, and a lengthening of the monsoon season. Data–model comparisons with downscaled simulations outperform those with the coarser global model, highlighting the crucial role of downscaling in paleo data–model comparison.

The Effect of Downwelling Longwave and Shortwave Radiation on Arctic Summer Sea Ice

Abstract The Arctic summer sea ice has diminished fast in recent decades. A strong year-to-year variability on top of this trend indicates that sea ice is sensitive to short-term climate fluctuations. Previous studies show that anomalous atmospheric conditions over the Arctic during spring and summer affect ice melt and the September sea ice extent (SIE). These conditions are characterized by clouds, humidity, and heat anomalies that all affect downwelling shortwave (SWD) and longwave (LWD) radiation to the surface. In general, positive LWD anomalies are associated with cloudy and humid conditions, whereas positive anomalies of SWD appear under clear-sky conditions. Here the effect of realistic anomalies of LWD and SWD on summer sea ice is investigated by performing experiments with the Community Earth System Model. The SWD and LWD anomalies are studied separately and in combination for different seasons. It is found that positive LWD anomalies in spring and early summer have significant impact on the September SIE, whereas winter anomalies show only little effect. Positive anomalies in spring and early summer initiate an earlier melt onset, hereby triggering several feedback mechanisms that amplify melt during the succeeding months. Realistic positive SWD anomalies appear only important if they occur after the melt has started and the albedo is significantly reduced relative to winter conditions. Simulations where both positive LWD and negative SWD anomalies are implemented simultaneously, mimicking cloudy conditions, reveal that clouds during spring have a significant impact on summer sea ice while summer clouds have almost no effect.

El Niño teleconnections in CMIP5 models

Teleconnections associated with warm El Nino/southern oscillation (ENSO) events in 20 climate model intercomparison project 5 (CMIP5) models have been compared with reanalysis observations. Focus has been placed on compact time and space indices, which can be assigned a specific statistical confidence. Nearly all of the models have surface temperature, precipitation and 250 hPa geopotential height departures in the Tropics that are in good agreement with the observations. Most of the models also have realistic anomalies of Northern Hemisphere near-surface temperature, precipitation and 500 hPa geopotential height. Model skill for these variables is significantly related to the ability of a model to accurately simulate Tropical 250 hPa height departures. Additionally, most models have realistic temperature and precipitation anomalies over North America, which are linked to a model’s ability to simulate Tropical 250 hPa and Northern Hemisphere 500 hPa height departures. The skills of temperature and precipitation departures over the Northern Hemisphere and North America are associated with the ability to realistically simulate realistic ENSO frequency and length. Neither horizontal nor vertical resolution differences for either the model atmosphere or ocean are significantly related at the 95 % level to variations in El Nino simulation quality. Overall, recent versions of earlier models have improved in their ability to simulate El Nino teleconnections. For instance, the average model skills of temperature and precipitation for the Tropics, Northern Hemisphere and North America for 11 CMIP5 models are all larger than those for prior versions.

A Theoretical Framework for Hyperspectral Anomaly Detection Using Spectral and Spatial A Priori Information

This letter presents a new theoretical approach for anomaly detection using a priori information about targets. This a priori knowledge deals with the general spectral behavior and the spatial distribution of targets. In this letter, we consider subpixel and isolated targets that are spectrally anomalous in one region of the spectrum but not in another. This method is totally different from matched filters that suffer from a relative sensitivity to low errors in the target spectral signature. We incorporate the spectral a priori knowledge in a new detection distance, and we propose a Bayesian approach with a Markovian regularization to suppress the potential targets that do not respect the spatial a priori. The interest of the method is illustrated on simulated data consisting in realistic anomalies that are superimposed on a real HyMap hyperspectral image.

Realistic anomaly mediation with bulk gauge fields

We present a simple general framework for realistic models of supersymmetry breaking driven by anomaly mediation. We consider a 5-dimensional "brane universe" where the visible and hidden sectors are localized on different branes, and the standard model gauge bosons propagate in the bulk. In this framework there can be charged scalar messengers that have contact interactions with the hidden sector, either localized in the hidden sector or in the bulk. These scalars obtain soft masses that feed into visible sector scalar masses at two loop order via bulk gauge interactions. This contribution is automatically flavor-blind, and can be naturally positive. If the messengers are in the bulk this contribution is automatically the same order of magnitude as the anomaly mediated contribution, independent of the brane spacing. If the messengers are localized to a brane the two effects are of the same order for relatively small brane spacings. The gaugino masses and A terms are determined completely by anomaly mediation. In order for anomaly mediation to dominate over radion mediation the radion must be is stabilized in a manner that preserves supersymmetry, with supergravity effects included. We show that this occurs in simple models. We also show that the mu problem can be solved by the vacuum expectation value of a singlet in this framework.