Anomalous Point Research Articles

Accurate backside boundary recognition of girth weld beads

Visual recognition of weld beads is essential for post-weld robotic grinding. The recognition of thin-walled weld bead boundary, especially the backside boundary, remains challenging due to the diverse features such as debris, misalignment, and deformation. Based on point cloud from a laser scanner, we present a robust and accurate backside boundary recognition method for girth weld beads of thin-walled pipes. A boundary point extraction method is designed based on an adaptive sliding window model. Without prior morphology features, the influence of misalignment and deformation on the accuracy of boundary point recognition is greatly reduced by the local model matching strategy. Leveraging the correlation among overall weld bead features, an anomalous boundary point recognition and correction method based on DBSCAN clustering is proposed to further enhance robustness. A series of validation experiments were conducted by the obtained backside point cloud data inside a girth weld pipe, and our proposed method showed a high accuracy and a high robustness to misalignment, deformation and debris features.

Consumer Perceptions Matter: A Case Study of an Anomaly in English Football

Abstract In 1983 England’s fifth-tier football competition introduced a two-points-for-a-home-win and three-points-for-an-away-win reward system. This system was abolished after three seasons. The anomalous point system may have been introduced to reduce home advantage but the reasons are not fully clear and neither are the reasons for abolishing the system shortly after its introduction. We find that the new point system did not affect match outcomes but it did influence match attendance negatively. We speculate that the alternative point system was perceived as unfair to potential buyers of seasonal tickets or individual match tickets some of whom as a response decided to avoid watching the game in person. Consumer perceptions seem to matter.

Contrasting irradiation behavior of dual phases in Ti-6Al-4V alloy at low-temperature due to ω-phase precursors in β-phase matrix

Aiming to simulate the radiation damage effect on a dual α+β phase Ti-6Al-4V alloy utilized as high-intensity accelerator beam window material, a series of irradiation experiments were conducted with a 2.8 MeV-Fe2+ ion beam in several dpa regions at room temperature. The nano-indentation hardness increased steeply at 1 dpa and unchanged up to 10 dpa, due to the saturation of defect clusters and tangled dislocations in the dominant α-phase matrix with a size of 2–3 nm and a density of about 1×1023 m−3. In contrast in the intergranular β-phase, larger loops of 20–30 nm diameter were observed with much less density of about 5×1020 m−3. The diffraction pattern showed rectilinear diffuse streaks between the β-phase reflections, corresponding to the ω-phase precursor, without dose dependency in its intensity. FFT/I-FFT analysis of the HREM revealed a sub-nanometer-sized lattice disorder with local fluctuations, not discrete but continuous, and homogeneously distributed within the matrix β-phase stably against the irradiation. The significantly low dislocation density and the absence of phase transformation in the β-phase matrix could be attributed either to the strong sink effect expected for this distinctive sub-nanometer-sized homogeneous lattice disorder or to the anomalous point defect recombination induced by the high mobility of vacancies, both of which are originated from the metastable ω-phase precursors specifically formed in the β(BCC) phase of group-4 transition metals.

Direct observation of concentration fluctuations in Au–Si eutectic liquid by small-angle neutron scattering

It is well-known that eutectic gold–silicon (Au–Si) alloys exhibit anomalous melting point depression, which is more than 1000 °C from the melting point of elemental Si (1414 °C). The melting point depression in eutectic alloys is generally explained in terms of a decrease of the free energy by mixing. However, it is difficult to understand the anomalous melting point depression only from the stability of the homogeneous mixing. Some researchers suggest that there are concentration fluctuations in the liquids, where the atoms are inhomogeneously mixed. In this paper, we measure the small-angle neutron scattering (SANS) of Au81.4Si18.6 (eutectic composition) and Au75Si25 (off-eutectic composition) at temperatures from room temperature to 900 °C in both solid and liquid states to observe such concentration fluctuations directly. It is surprising that large SANS signals are observed in the liquids. This indicates that there are concentration fluctuations in the liquids. The concentration fluctuations are characterized by either the correlation lengths in multiple length scales or surface fractals. This finding yields new insight into the mixing state in the eutectic liquids. The mechanism of the anomalous melting point depression is discussed based on the concentration fluctuations.

The Effect of Strain Rate on the Tensile Deformation Behavior of Single Crystal, Ni-Based Superalloys

Single crystal Nickel-based superalloys exhibit an anomalous yield point, the yield stress increasing with temperature to a maximum at around 750 ºC. Here, we demonstrate in the alloy CMSX-4 at 750 ºC that, although there is virtually no effect of strain rate on the initial yield point, at slow strain rates a second mechanism can initiate leading to a considerable softening effect. By examining the microstructures of a series of interrupted tests, this is attributed to the initiation of stacking fault shear after the operation of a secondary slip system. Using high-resolution TEM, the dislocation structures are shown to be identical in both structure and in the segregation of Co, Cr, and W, to those observed during creep deformation of single crystal alloys, although the conformation of the dislocations and faults differs from that observed during creep. This drop in flow stress at low strain rates is not observed in the alloys TMS138A and SRR99, in the former case, the improved creep resistance of this fourth-generation alloy would require a much slower strain rate to match the creep rate achievable at this temperature.

The anomalous annealing hardening behaviors in commercial pure Tantalum foil

Annealing softening phenomenon is usually observed in pure metals. However, in this study, the anomalous annealing hardening behavior is found in commercial pure Tantalum (Ta) foil. When the cold-rolled Ta foils are annealed at a temperature lower than 700 °C, both the yield strength and ductility gradually increase with the rise in annealing temperature, resulting in an anomalous "low-temperature annealing hardening" phenomenon. The yield strength of Ta foils annealed at 700 °C reaches the maximum value of about 750 MPa. This phenomenon in Ta foil is closely related with the shear bands and the pinning behavior between interstitial atoms and dislocations. When annealing temperature is higher than 700 °C, the yield strength decreases. When annealing temperature exceeds 900 °C, recrystallization occurs in Ta foil. Anomalous yield point phenomenon appears in the recrystallization foils during stretching, which is also related with the pinning of dislocations by interstitial atoms. As annealing temperature further increases, the volume fraction of surface grains gets larger. And {112}<111> slip systems are preferred over {110}<111> slip systems in the surface grains.

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Continental-scale distribution of niobium and tantalum in catchment sediments throughout China: Prospecting implications from the China geochemical Baselines project

Niobium (Nb) and tantalum (Ta), which are chemical twins with very similar geochemical properties, are critical mineral resources for emerging global industries. Owing to increasing demands, understanding their concentrations and distributions in the pedosphere is essential for assessing potential Nb and Ta resources in China. To address this, we used geochemical data obtained from the China Geochemical Baselines (CGB) project, which began in 2008 and has since then yielded continental-scale datasets. With a density of approximately-one sampling site per 3,000 km2, the CGB project involved catchment sediments from two horizons at each site: a top sample collected at a depth of 0–25 cm and a deep sample collected at a depth greater than 100 cm. In total, 3,394 samples each of top and deep catchment sediments were collected, and Nb and Ta concentrations were measured using inductively coupled plasma mass spectrometry under strict quality control conditions. The Nb concentrations in the top and deep sediment samples varied in the ranges 0.04–89.86 ppm with a median value of 12.71 ppm and 1.20–104.12 ppm with a median value of 12.41 ppm, respectively. Further, the Ta concentrations in the top samples ranged from 0.01 to 26.41 ppm with a median of 0.97 ppm, while those in the deep samples ranged from 0.14 to 26.41 ppm with a median of 0.96 ppm. Furthermore, both the top and deep samples had similar 2.5th, 25th, 50th, 75th, and 97.5th percentile Nb and Ta contents, indicating that Nb and Ta in the catchment sediments underwent similar and consistent formation processes. Our results also indicated that the concentrations of both elements varied widely across regions, and their distributions in the catchment sediments throughout China were predominantly governed by the parent rock, particularly granites and alkaline rocks. Mineralization and sedimentation properties also played roles at a regional scale. Overall, the spatial distributions of Nb and Ta in the top and deep catchment sediments were relatively consistent, with concentrations being higher in southeastern China and lower in northwestern China. Twelve large-scale geochemical Nb-Ta belts were delineated based on continuous anomalous point distributions wherein many well known Nb-Ta deposits are already located. These nationwide Nb and Ta concentrations and distributions provide foundational large-scale data for further exploration and geological studies.

The Tuning of Exciton‐Phonon Coupling in Colloidal Nanocrystals by a Dielectric Medium

AbstractHere, a thorough study is reported that exposes the influence of surfactants’ dielectric screening on the exciton energy and exciton‐phonon coupling in CdSe and CdSe/CdS colloidal quantum dots and nanoplatelets. The study follows the temperature dependence of the photoluminescence spectrum to monitor thermally activated exciton‐phonon interactions. The dependence of the exciton energy on temperature exhibits an anomalous point at ≈250 K, which coincides with the melting point of surfactant ligands (oleate molecules), an effect that is accompanied by a change in the dielectric constant. Interestingly, this anomalous effect gradually fades away upon increasing the shell thickness in CdSe/CdS derivatives. Furthermore, inspecting the exciton band's full‐width‐at‐half‐maximum (FWHM) dependence on temperature reveals the tuning of the optical phonon frequency when in direct contact with surfactant ligands. However, this effect is replaced by a surplus coupling to the phonon mode of the shell (CdS or CdSeS) when the thickest shell solely isolates the ligand dielectric screening. The study uncovers the impact of the dielectric screening on the optical properties of semiconductor nanocrystals, with an added value in the implementation of those materials in various optoelectronic, quantum, and tagging devices.

A novel machine learning pipeline to detect malicious anomalies for the Internet of Things

Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The anomalies are considered as samples that do not follow a normal pattern and significantly differ from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data generated from IoT devices. There has been wide-scale research done on anomaly detection problems in general, i.e., finding the samples in data that differ significantly from the expected values. However, there has been limited work done to figure out the inherent cause of the anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed, the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data manipulation by an attacker has not been explored in detail.In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning. The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector, the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement.

Anomalous-order exceptional point and non-Markovian Purcell effect at threshold in one-dimensional continuum systems

For a system consisting of a quantum emitter coupled near threshold (band edge) to a one-dimensional continuum with a van Hove singularity in the density of states, we demonstrate general conditions such that a characteristic triple level convergence occurs directly on the threshold as the coupling $g$ is shut off. For small $g$ values the eigenvalue and norm of each of these states can be expanded in a Puiseux expansion in terms of powers of $g^{2/3}$, which suggests the influence of a third-order exceptional point. However, in the actual $g \rightarrow 0$ limit, only two discrete states in fact coalesce as the system can be reduced to a $2 \times 2$ Jordan block; the third state instead merges with the continuum. Moreover, the decay width of the resonance state involved in this convergence is significantly enhanced compared to the usual Fermi golden rule, which is consistent with the Purcell effect. However, non-Markovian dynamics due to the branch-point effect are also enhanced near the threshold. Applying a perturbative analysis in terms of the Puiseux expansion that takes into account the threshold influence, we show that the combination of these effects results in quantum emitter decay of the unusual form $1 - C t^{3/2}$ on the key timescale during which most of the decay occurs. We then present two conditions that must be satisfied at the threshold for the anomalous exceptional point to occur: the density of states must contain an inverse square-root divergence and the potential must be non-singular. We further show that when the energy of the quantum emitter is detuned from threshold, the anomalous exceptional point splits into three ordinary exceptional points, two of which appear in the complex-extended parameter space. These results provide deeper insight into a well-known problem in spontaneous decay at a photonic band edge.

High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations

Abstract. We demonstrate the capability of the Sentinel-2 MultiSpectral Instrument (MSI) to detect and quantify anomalously large methane point sources with fine pixel resolution (20 m) and rapid revisit rates (2–5 d). We present three methane column retrieval methods that use shortwave infrared (SWIR) measurements from MSI spectral bands 11 (∼ 1560–1660 nm) and 12 (∼ 2090–2290 nm) to detect atmospheric methane plumes. The most successful is the multi-band–multi-pass (MBMP) method, which uses a combination of the two bands and a non-plume reference observation to retrieve methane columns. The MBMP method can quantify point sources down to about 3 t h−1 with a precision of ∼ 30 %–90 % (1σ) over favorable (quasi-homogeneous) surfaces. We applied our methods to perform high-frequency monitoring of strong methane point source plumes from a well-pad device in the Hassi Messaoud oil field of Algeria (October 2019 to August 2020, observed every 2.5 d) and from a compressor station in the Korpezhe oil and gas field of Turkmenistan (August 2015 to November 2020, observed every 5 d). The Algerian source was detected in 93 % of cloud-free scenes, with source rates ranging from 2.6 to 51.9 t h−1 (averaging 9.3 t h−1) until it was shut down by a flare lit in August 2020. The Turkmen source was detected in 40 % of cloud-free scenes, with variable intermittency and a 9-month shutdown period in March–December 2019 before it resumed; source rates ranged from 3.5 to 92.9 t h−1 (averaging 20.5 t h−1). Our source-rate retrievals for the Korpezhe point source are in close agreement with GHGSat-D satellite observations for February 2018 to January 2019, but provide much higher observation density. Our methods can be readily applied to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. High-frequency satellite-based detection of anomalous methane point sources as demonstrated here could enable prompt corrective action to help reduce global methane emissions.

Using Exploratory Data Analysis and Big Data Analytics for Detecting Anomalies in Cloud Computing

While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection, which is a necessary part of the management of modern applications. As the record of user behavior, weblogs surely become the research item related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where anomalous behavior needs to be detected in understanding phases prior to modeling a system for such use. Big Data Analytics often ignores anomalous point due to high volume of data. To address this problem, we propose a complemented methodology for Big Data Analytics – the Exploratory Data Analysis, which assists in gaining insight into data relationships without the classical hypothesis modeling. In that way, we can gain better understanding of the patterns and spot anomalies. Results show that Exploratory Data Analysis facilitates anomaly detection and the CRISP-DM Business Understanding phase, making it one of the key steps in the Data Understanding phase.

Anomalous Melting Point of Multicharge Ionic Liquids: Structural, Electrostatic, and Orbital Properties of [Ln(NO3)6]3- (Ln = Ce, Pr) Anions.

Salts composed of multicharged cations/anions usually exhibit a large lattice energy and strong Coulomb force, which results in high melting points. However, an increasing number of highly charged ionic liquids exceed expectations based on conventional experience; even their melting points are much lower than those found for simple ionic liquids composed of monovalent ions. To further study this phenomenon, we studied a group of stable ionic liquids containing tricharged [Ce(NO3)6]3- and [Pr(NO3)6]3- anions. The structures for [C6mim]3[Ce(NO3)6] and [C6mim]3[Pr(NO3)6] were determined by single-crystal X-ray diffraction with triclinic and P1̅ space groups. The electrostatic potential density per unit ion surface and volume was proposed and calculated. Additionally, theoretical analysis based on Hirshfeld surface and charge decomposition was carried out to explore the intermolecular interaction and electronic structure of the lanthanide anions. The electrostatic and orbital properties were found to be more useful for understanding the melting points of highly charged salts compared with the sole use of lattice energy. The electrostatic potential density per unit ion surface and volume showed a linear relationship with the melting point of ionic liquids composed of monovalent to trivalent ions. These structure-melting point relationships will be beneficial for expounding new low-melting-point ionic liquids with a wide liquidus range.

Multifunction switching by a flat structurally tunable acoustic metasurface for transmitted waves

In this paper a flat structurally tunable acoustic metasurface is constructed based on the helical unit cell. The length of the acoustic channel can be tuned by the screw-in depth of the helix. Accordingly, the wave phase for the transmitted acoustic wave can be tuned and the wavefront can be manipulated. Then multifunctions such as anomalous refraction, point focusing, beam focusing and self-bending can be realized and switched just by screwing in or out the helixes. At the same time, the broadband operating frequency is also realized. The experiments for anomalous refraction and point focusing are also performed, and the results show that the designed metasurface is effective. The present studies have important applications in dynamic manipulation of acoustic waves by metasurfaces.

A study on anomalous yield drop behaviour in modified beta titanium alloys

ABSTRACTThe yield drop phenomenon observed in the Ti–15V-3Al–3Sn-3Cr (Ti–15–3) beta-titanium alloy and its anomalous behaviour in the boron and carbon added Ti–15–3 alloys have been studied. While the base and the carbon containing alloys exhibit yield drop, the boron containing alloy with smaller grain size than base alloy does not appear to show this phenomenon. Tensile tests were interrupted at different stress levels followed by analyses of slip lines and sub-structural characteristics using scanning and transmission electron microscopes to understand this anomalous yield point phenomenon. Infrared thermal imaging technique was used to map the strain localisation and the spatiotemporal evolution of deformation along the gauge length of the specimens during the tensile tests. Deformation in these alloys initiates only in a few grains. Pile-up of dislocations in these grains subsequently triggers the formation of dislocations in other grains and their rapid multiplications. The spreading of deformation by the generation of dislocations from pile up dislocations in one grain to neighbouring un-deformed grains and their rapid multiplication to new regions influence the yield drop phenomenon and its characteristics. It is shown in this study that microscopic instability in the grain level is a necessary, but not the sufficient condition for the manifestation of macroscopic instability during tensile deformation in polycrystalline materials. The presence of boride particles at grain boundaries restricts the slip transfer across the grains as well as the spreading of deformation to new regions, which causes the suppression of yield drop in the boron containing alloy.

Analysis and prediction of traffic flow based on Wavelet-BP neural network

Through on-the-spot investigation, collected data of traffic flow from a certain section of 3rd Ring Road in Beijing, the investigation results were analyzed from the perspective of time and space, macro rules of traffic flow and the influence of incidental traffic events on roads was summarized. The MATLAB software was used to decompose traffic flow data by three-layer wavelet, and the variation characteristics of the detail coefficients were obtained by decomposing the parameters of traffic flow by wavelet. Analyzed the abrupt change phenomenon of the traffic flow parameters, so as to judge whether some traffic incident occurred and which kind of it. With the level of road traffic service, the same moment which anomalous traffic flow data produced theoretically and traffic service levels were greater than or equal to 4 levels was chosen, so as to verify whether the anomalous time point analyzed by wavelet-analysis were precise. Used MATLAB software to establish the prediction model by a neural network, and determined the various parameters, functions, training and testing samples. Coefficients of different parameters in the BP neural network were used to predict the changes of traffic flow in the future. Compared the predicted data with the actually observed traffic flow, the starting time of traffic incident and the anomalous mutation starting point of three traffic flow parameters was consistent. This research had great significance to the future intelligent traffic decision-making.

Enabling digital health companionship is better than empowerment

Enabling digital health companionship is better than empowerment

Statistical methods for the geochemical characterisation of surface waters: The case study of the Tiber River basin (Central Italy)

Studying the chemistry of surface waters and their resilience to compositional changes represents a global challenge to preserve them from human- and changing climate-induced modifications. River waters are sentinel parameters for all processes occurring in a river basin. This is particularly true for regional watersheds, which can include both natural- and anthropogenic-sourced solutes such as those present in the Tiber River basin, the biggest one in Central Italy. In this work, innovative applications of statistical methods are proposed, from the perspective of Compositional Data Analysis theory, in order to consider the geochemical riverine system as a whole and detect compositional changes throughout the catchment. Robust compositional biplots highlighted different sources of variability linked to geological (low variability) and anthropogenic origin (high variability) of the main compounds, thus identifying a hierarchy in the variance of the riverine geochemical processes. On a different scale, the innovative use of the robust Mahalanobis distance in an iterative way monitored spatial compositional shifts for single cases. The effectiveness of this method consists in minimising the influence of an individual anomalous point on the compositional centre and the covariance structure of the data highlighting when along the river a significant compositional shift occurs. The study provides powerful compositional tools for detecting potential contamination events or climate-induced modifications both at catchment and river scale.

Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection

High temporal resolution remote sensing satellite data can be used to collect vegetation phenology observations over regional and global scales. Logistic and polynomial functions are the most widely used methods for fitting time series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, the maximum in the curvature of the fitted curve is usually considered as the spring green-up date. However, the existing green-up date calculation methods have low accuracy for sparse vegetation. This paper proposes an improved green-up date calculation method using a coupled model and anomalous point detection (CMAPD). This model is based on a combination of logistic and polynomial functions, which is used to fit time series vegetation index. Anomalous values were identified using the nearest neighbor algorithm, and these values were corrected by the combination of growing degree-days (GDD) and land use type. Then, the trends and spatial patterns of green-up date was analyzed in the Sanjiangyuan area. The results show that the coupled model fit the time series data better than a single logistic or polynomial function. Besides, the anomalous point detection method properly controlled the green-up date within the local threshold, and could reflect green-up date more accurately. In addition, a weak statistically significant advance trend for average vegetation green-up date was observed from 2000 to 2016. However, in 10.4% of the study area, the the green-up date has significant advanced. Regression analysis showed that the green-up date is correlated to elevation: the green-up date is clearly later at higher elevations.