Team performance modeling for HRA in dynamic situations

This paper proposes a team behavior network model that can simulate and analyze response of an operator team to an incident in a dynamic and context-sensitive situation. The model is composed of four sub-models, which describe the context of team performance. They are task model, event model, team model and human–machine interface model. Each operator demonstrates aspects of his/her specific cognitive behavior and interacts with other operators and the environment in order to deal with an incident. Individual human factors, which determine the basis of communication and interaction between individuals, and cognitive process of an operator, such as information acquisition, state-recognition, decision-making and action execution during development of an event scenario are modeled. A case of feed and bleed operation in pressurized water reactor under an emergency situation was studied and the result was compared with an experiment to check the validity of the proposed model.     

Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations

Wireless sensor networks are increasingly used in sensitive event monitoring. However, various abnormal data generated by sensors greatly decrease the accuracy of the event detection. Although many methods have been proposed to deal with the abnormal data, they generally detect and/or repair all abnormal data without further differentiate. Actually, besides the abnormal data caused by events, it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources. Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events. In this paper, we propose a data cleaning approach based on Stacked Denoising Autoencoders (SDAE) and multisensor collaborations. We detect all abnormal data by SDAE, then differentiate the abnormal data by multi-sensor collaborations. The abnormal data caused by events are unchanged, while the abnormal data caused by other factors are repaired. Real data based simulations show the efficiency of the proposed approach.     

Event storm detection and identification in communication systems

Event storms are the manifestation of an important class of abnormal behaviors in communication systems. They occur when a large number of nodes throughout the system generate a set of events within a small period of time. It is essential for network management systems to detect every event storm and identify its cause, in order to prevent and repair potential system faults.This paper presents a set of techniques for the effective detection and identification of event storms in communication systems. First, we introduce a new algorithm to synchronize events to a single node in the system. Second, the system's event log is modeled as a normally distributed random process. This is achieved by using data analysis techniques to explore and then model the statistical behavior of the event log. Third, event storm detection is proposed using a simple test statistic combined with an exponential smoothing technique to overcome the non-stationary behavior of event logs. Fourth, the system is divided into non-overlapping regions to locate the main contributing regions of a storm. We show that this technique provides us with a method for event storm identification. Finally, experimental results from a commercially deployed multimedia communication system that uses these techniques demonstrate their effectiveness.     

Finite-element calculations of remote field eddy current interactions with slit defects

Remote field eddy current (RFEC) excitation is a promising approach for detection of the very fine axial cracks typical of stress corrosion cracking (SCC) in pipelines. Interactions between adjacent cracks or slits can enhance responses in some cases. Detailed finite-element modeling was undertaken to establish the behavior and interactions of multiple slits such as those occurring in SCC. Three different field/slit configurations are considered, with anomalous source models used to aid interpretation of the results. The study noted that magnetic perturbations generated by ferromagnetic material tend to be vanishingly small, and that the interactions between multiple cracks give minimal enhancement, indicating that eddy current rather than magnetic field excitation is best for the detection of SCC. With eddy current excitation, field perturbations are generated by even very fine slits, and are larger in non-ferromagnetic material. For nonferromagnetic pipes, the perturbations tend to merge as a circumferential separation between parallel axial cracks decreases, resulting in significant interaction and signal enhancement.     

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e., fill hole inside objects algorithm. Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm. The classification task is achieved using binary and multi class support vector machines. The proposed technique is validated through accuracy, precision, recall and F-measure metrics.     

Finite-Element Calculations of Remote Field Eddy Current Interactions with Slit Defects

Abstract

Remote field eddy current (RFEC) excitation is a promising approach for detection of the very fine axial cracks typical of stress corrosion cracking (SCC) in pipelines. Interactions between adjacent cracks or slits can enhance responses in some cases. Detailed finite-element modeling was undertaken to establish the behavior and interactions of multiple slits such as those occurring in SCC. Three different field/slit configurations are considered, with anomalous source models used to aid interpretation of the results. The study noted that magnetic perturbations generated by ferromagnetic material tend to be vanishingly small, and that the interactions between multiple cracks give minimal enhancement, indicating that eddy current rather than magnetic field excitation is best for the detection of SCC. With eddy current excitation, field perturbations are generated by even very fine slits, and are larger in non-ferromagnetic material. For non-ferromagnetic pipes, the perturbations tend to merge as a circumferential separation between parallel axial cracks decreases, resulting in significant interaction and signal enhancement.     

Optimum dispersion map profile for a stable DM soliton system

This paper investigates the optimum dispersion map profile for stable DM soliton formation and propagation. Results of numerical analysis suggest that anomalous–normal–normal–anomalous (A-N-N-A) profile is the most suitable one for a dispersion map with perfect dispersion compensation. DM soliton stability and propagation behavior has been further explored for an undercompensated A-N-N-A profile which increases the distance for stable propagation but requires a positive initial chirp. The stability region has been identified for such DM links in the parametric space of residual dispersion and initial chirp. The study of DM soliton interaction and collision dynamics indicates that a DM soliton formed in such stable parametric space offers improved spectral efficiency as compared to the fundamental solitons.     

基于模糊概率赋值的新型贝叶斯异常检测模型

提出了一种结合模糊决策与贝叶斯方法的异常检测模型，该模型将系统中与安全相关的事件进行分类，并以模糊隶属度函数的形式给出各类事件发生异常的实时置信度。异常检测系统综合某时刻所有实时概率取值，做出贝叶斯决策。同简单使用阈值方法的贝叶斯入侵检测模型相比，采用了模糊概率赋值的贝叶斯异常检测模型，在提高对问题描述的精确性同时，由于它对多种类型安全相关事件提供支持而具有更好的适应性，可以更全面地对更复杂的系统行为进行建模。     

Anomalous behavior of electron temperature in silane glow discharge plasmas

The electron temperature measured by an optical emission intensity ratio of Si* to SiH* in a silane (SiH₄) glow-discharge plasma shows an anomalous behavior against film preparation conditions such as gas pressure and substrate temperature. When increasing the gas pressure, the electron temperature decreases first, takes a minimum value at a certain pressure range and then it increases. The electron temperature decreases with increasing substrate temperature, which is quite the opposite trend to a conventional non-reactive hydrogen plasma. These anomalous behaviors of electron temperature in silane plasmas have been explained in terms of feed-back phenomenon in the plasma, starting from an electron-attachment event to higher silane molecules produced in the plasma, causing an increase of electron temperature due to an increase of electron-loss rate, followed by an enhanced production of higher silane molecules. It has also been suggested that the responsible higher silane molecules for the above mentioned feedback phenomenon is penta-silane, Si₅H₁₂.     

Anomaly detection in heterogeneous bibliographic information networks using co-evolution pattern mining

Detecting evolution-based anomalies have emerged as an effective research topic in many domains, such as social and information networks, bioinformatics, and diverse security applications. However, the majority of research has focused on detecting anomalies using evolutionary behavior among objects in a network. The real-world networks are omnipresent, and heterogeneous in nature, while, in these networks, multiple types of objects co-evolve together with their attributes. To understand the anomalous co-evolution of multi-typed objects in a heterogeneous information network (HIN), we need an effective technique that can capture abnormal co-evolution of multi-typed objects. For example, detecting co-evolution-based anomalies in the heterogeneous bibliographic information network (HBIN) can depict better the object-oriented semantics than just scrutinizing the co-author or citation network alone. In this paper, we introduce the novel notion of a co-evolutionary anomaly in the HBIN, detect anomalies using co-evolution pattern mining (CPM), and study how multi-typed objects influence each other in their anomalous declaration by following a special type of HIN called star networks. The influence of three pre-defined attributes namely paper-count, co-author, and venue over target objects is measured to detect co-evolutionary anomalies in HBIN. The anomaly scores are calculated for each 510 target objects and individual influence of attributes is measured for two top target objects in case-studies. It is observed that venue has the most influence on the target objects discussed as case studies, however, about the rest of anomalies in the list, the most anomalous influential attribute could be rather different than the venue. Indeed, the CABIN algorithm constructs the way to find out the most influential attributes in co-evolutionary anomaly detection. Experiments on bibliographic dataset validate the effectiveness of the model and dominance of the algorithm. The proposed technique can be applied on various HINs such as Facebook, Twitter, Delicious to detect co-evolutionary anomalies.     

Quantitative Investigation of Tomographic Effects in Abnormal Regions of Complex Structures

The detection of abnormal regions in complex structures is one of the most challenging targets for underground space engineering. Natural or artificial geologic variations reduce the effectiveness of conventional exploration methods. With the emergence of real-time monitoring, seismic wave velocity tomography allows the detection and imaging of abnormal regions to be accurate, intuitive, and quantitative. Since tomographic results are affected by multiple factors in practical small-scale applications, it is necessary to quantitatively investigate those influences. We adopted an improved three-dimensional (3D) tomography method combining passive acoustic emission acquisition and active ultrasonic measurements. By varying individual parameters (i.e., prior model, sensor configuration, ray coverage, event distributions, and event location errors), 37 comparative tests were conducted. The quantitative impact of different factors was obtained. Synthetic experiments showed that the method could effectively adapt to complex structures. The optimal input parameters based on quantization results can significantly improve the detection reliability in abnormal regions.     

具身与离身认知在多模态交互环境下的交互语法研究

目的 针对目前具身交互设计领域中具身认知和离身认知割裂的现状,将心理学第一代认知科学的离身认知与第二代认知科学的具身认知相结合,构建在多模态交互环境下的交互语法,指导智能体与人的具身交互和离身交互。方法 以智能体为多模态交互环境的媒介,通过把人类的具身性逐步转化为智能体的离身性,运用隐喻、转喻、隐转喻的方式,使智能体的离身性激发人类交互主体,产生新的具身认知。结论 在多模态交互环境中,把具身交互中的示能性、交互行为、交互前馈与离身交互中的符号、语义、交互反馈进行组建,形成完整交互语法体系。将交互事件中的3个要素进行映射,匹配到人与智能体的多模态交互关系中。     

Some experimental observations of anomalous response of fully clamped beams

We describe tests made in an attempt to observe anomalous behavior that had been predicted in computations for the elastic-plastic response of fixed pin ended beams to short pulse loading ( and , ASME J. Appl. Mech., 52, 517–522, 1985). By ‘anomalous’ here is meant permanent deflections in the direction opposite that of the applied impulse, and abnormal sensitivity to parameters. Direct impact tests were unsuccessful. A simpler test was devised, in which the impact loading was simulated by pulling the specimen to an initial deflection and then releasing it abruptly. Two out of 36 such tests led to permanent deflections in the direction opposite to that of the imposed initial deflection. A further series of tests is described in which the loading reversals of the dynamic tests were simulated by quasi-static loading, accompanied by concurrent energy calculations. These help to understand the more complex dynamic phenomena, and provide approximate criteria for the possible occurrence of anomalous behavior.     

A database for fast access to Particle-Gated Event data

In nuclear physics experiments involving in-flight fragmentation of ions, usually a large number of different nuclei is produced and various detection systems are employed to identify the species event by event, e.g., by measuring their specific energy loss and time of flight. For such cases – not necessarily limited to nuclear physics – where subsets of a large data set can be identified using a small number of measured signals, a software for fast access to varying subsets of such a data set has been developed. The software has been used successfully in the analysis of a one neutron knock-out experiment at GANIL.     

First-Order Phase Transition and Anomalous Hysteresis of Binary Bose Mixtures in an Optical Lattice

We study a first-order phase transition between superfluid and Mott insulator phases in binary Bose mixtures loaded into a hypercubic optical lattice. The system is described by a two-component Bose-Hubbard model. Considering the difference between the two kinds of bosons in the intra-component interaction strength, we discuss the metastability of the system and the hysteresis associated with the first-order superfluid-Mott insulator transition. It is found that the sweeping of hopping amplitude induces a conventional hysteresis-loop behavior. We also find an anomalous hysteresis behavior when the chemical potential is varied. In the anomalous hysteresis, the phase transition occurs in a unidirectional way and a hysteresis loop does not form.     

Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model

The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately. The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture, so as to solve the problem of recognizing them. In response to this difficulty, this paper introduces an adjustable jump link coefficients model based on the residual network. The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior. A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper. In order to reduce the noise of the data edge, and at the same time, improve the accuracy of the data and speed up the training, a BN (Batch Normalization) layer is added before the activation function in this network. This paper trains this network model on the public ImageNet dataset, and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network. Under the same experimental conditions, compared with the original ResNet-50 model, the improved model in this paper has a 2.8% higher accuracy in recognition of abnormal behaviors on the public UTI dataset.     

Identification and Classification of Crowd Activities

The identification and classification of collective people's activities are gaining momentum as significant themes in machine learning, with many potential applications emerging. The need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd congestion. This paper investigates the capability of deep neural network (DNN) algorithms to achieve our carefully engineered pipeline for crowd analysis. It includes three principal stages that cover crowd analysis challenges. First, individual's detection is represented using the You Only Look Once (YOLO) model for human detection and Kalman filter for multiple human tracking; Second, the density map and crowd counting of a certain location are generated using bounding boxes from a human detector; and Finally, in order to classify normal or abnormal crowds, individual activities are identified with pose estimation. The proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities change. Experimental results on MOT20 and SDHA datasets demonstrate that the proposed system is robust and efficient. The framework achieves an improved performance of recognition and detection people with a mean average precision of 99.0%, a real-time speed of 0.6 ms non-maximum suppression (NMS) per image for the SDHA dataset, and 95.3% mean average precision for MOT20 with 1.5 ms NMS per image.     

Deformation behavior during nanoindentation of epitaxial ZnO thin films on sapphire substrate

Nanoindentation studies were carried out on epitaxial ZnO thin films on (0001) sapphire substrates grown by radio frequency magnetron sputtering. A single discontinuity (‘pop-in’) in the load–displacement curve was observed at a specific depth (13−16 nm) irrespective of the film thickness. The physical mechanism responsible for the ‘pop-in’ event in these epitaxial films may be due to the nucleation, propagation and interaction behavior of the glissile threading dislocations during mechanical deformation. Indentation well below the critical depth was found to be plastic deformation behavior (residual impression of 4 nm).     

Fluorescence correlation spectroscopy in semiadhesive wall proximity

With examination of diffusion in heterogeneous media through fluorescence correlation spectroscopy, the temporal correlation of the intensity signal shows a long correlation tail and the characteristic diffusion time results are no longer easy to determine. Excluded volume and sticking effects have been proposed to justify such deviations from the standard behavior since all contribute and lead to anomalous diffusion mechanisms . Usually, the anomalous coefficient embodies all the effects of environmental heterogeneity providing too general explanations for the exotic diffusion recorded. Here, we investigated whether the reason of anomalies could be related to a lack of an adequate interpretative model for heterogeneous systems and how the presence of obstacles on the detection volume length scale could affect fluorescence correlation spectroscopy experiments. We report an original modeling of the autocorrelation function where fluorophores experience reflection or adsorption at a wall placed at distances comparable with the detection volume size. We successfully discriminate between steric and adhesion effects through the analysis of long time correlations and evaluate the adhesion strength through the evaluation of probability of being adsorbed and persistence time at the wall on reference data. The proposed model can be readily adopted to gain a better understanding of intracellular and nanoconfined diffusion opening the way for a more rational analysis of the diffusion mechanism in heterogeneous systems and further developing biological and biomedical applications.     

Exponential random graph modeling for diagnosis of out-of-control signals in social network surveillance: Signal interpretation in social networks with ERG modeling

Monitoring of the social networks for detecting anomalous behavior could be vital for the system's survival. This anomalous behavior could raise from any changes in behavior or attributes of a particular individual or groups of individuals in the network and causes structural changes. Multivariate statistical process control charts are effective tools for this purpose while Exponential Random Graph Models are used to model highly interdependent data of the network. So after selecting a model for specific network, T² control charts are used for monitoring the network data to detect any anomalous behavior. Then the Mason, Tracy, and Young method is utilized for interpreting an out-of-control condition. Finally, some real-world examples are used to evaluate the performance of the proposed diagnosis approach. Since complicated dependency in a social network makes different choices in model selection for Exponential Random Graph Models and this causes various results in the evaluation study, if the impact of diagnosis result is not seen in model selection, the appropriate model will not be necessarily selected and this will affect the effectiveness of the whole system. So, in this paper for improving the performance of diagnosis, two indices are introduced and added to model selection criteria and then the appropriate model could be selected based on the decision-maker's preferences.