Abstract Process Intelligence refers to the extraction and analysis of valuable knowledge nuggets embedded in business process instances/event logs or enterprise applications, for the purpose of supporting various decision-making processes. Researchers and practitioners mine such event logs using Process Mining and Analytics (PMA) techniques that help analyze business processes across three perspectives:

Abstract Dimensional accuracy in additive manufacturing (AM) is still an issue compared with the tolerances for injection molding. In order to make AM suitable for the medical, aerospace, and automotive industries, geometry variations should be controlled and managed with a tight tolerance range. In the previously published article, the authors used statistical analysis to develop linear models for

In this article, the problem of robust finite‐time H∞ synchronization control is investigated for a class of uncertain discrete‐time master‐slave systems with Markovian switching parameters in the observer‐based case. Parameter uncertainties are assumed to be norm‐bounded, and the polyhedral character is utilized to describe the transition probabilities of nonhomogeneous Markov chain. By using stochastic

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing

Zeroing dynamics (ZD) was originally proposed and investigated for solving time-varying problems. Due to its advantages in convergence and accuracy, ZD has been successfully extended to various areas, including automatic control, robotics and numerical computation. In this paper, we further propose a novel one called event-triggered zeroing dynamics (ETZD) by incorporating the event-triggered strategy

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to

Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance spectroscopy, combined with the partial least squares regression (PLSR) method is considered to be an effective way

Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying

Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing

Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network

Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and

Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors

Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived

The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar outlines but different planting years, and it is, therefore, difficult to effectively determine the actual

Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for

The ionosphere is one of the largest error sources in GNSS (Global Navigation Satellite Systems) applications and can cause up to several meters of error in positioning. Especially for single-frequency users, who cannot correct the ionospheric delay, the information about the state of the ionosphere is mandatory. Dual- and multi-frequency GNSS users, on the other hand, can correct the ionospheric effect

Much attention is being paid to using high-performance convolutional neural networks (CNNs) in the area of ship detection in optical remoting sensing (ORS) images. However, the problem of false negatives (FNs) caused by side-by-side ships cannot be solved, and the number of false positives (FPs) remains high. This paper uses a DLA-34 network with deformable convolution layers as the backbone. The network

Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving

With the development of deep learning technology, an enormous number of convolutional neural network (CNN) models have been proposed to address the challenging building extraction task from very high-resolution (VHR) remote sensing images. However, searching for better CNN architectures is time-consuming, and the robustness of a new CNN model cannot be guaranteed. In this paper, an improved boundary-aware

Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to

Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X

The electromagnetic imaging of multi-layered circular structures can be accomplished by means of different approaches. Among these approaches, the use of synthetic aperture radar (SAR) techniques with the compensation of the Green’s function for multi-layered circular cylinders provides a trade-off between accuracy and computational complexity. Nevertheless, this approach relies on measurement points

Swarm-GAP is a heuristic that combines a swarm intelligence strategy with the generalized assignment problem (GAP) method. This approach is especially appropriate when there are agents engaged in a collaborative task, but in general, heuristics have drawbacks to optimize resource allocation. A previous work proposed the usage of three swarm-GAP variants to solve the task allocation problem among agents

In this paper, congruences, ideals, and prime ideals of an EMV-semiring and of its associated EMV-algebra are studied. Then EMV-semirings are characterized and it is proved that each EMV-semiring can be embedded into a direct product of a family of MV-semirings as an EMV-semiring. Moreover, another representation of EMV-semirings are presented by EMV-semirings of continuous sections in a sheaf of commutative

Numerous situations in decision-making deal with a set of agents who need to work together but they have some a priori bilateral problems among them. This paper introduces cooperative games with a priori incompatibilities using particular coalition systems. In these games, there are some red lines between some players, so that the negotiation has two stages. In the first stage, players can only negotiate

In this paper, we introduce the notions of the linear and generalized convex combination (shortly, g-convex combination) for triangular norms on bounded lattices. We investigate the conditions for the g-convex combination to be a triangular norm again. We introduce a triangular norm defined on a bounded lattice of equivalence classes and investigate some basic properties of the introduced triangular

Homomorphisms between information systems as a kind of tools to study communication between information systems are based on data compression, i.e. a complex massive information system is compressed into a relatively small-scale information system by means of homomorphisms under the premise that some of the same data structures are maintained. This paper is devoted to obtaining some invariant and inverse

L-equivalence relations on L-fuzzy sets, equivalent to L-valued equalities on ordinary sets, are modelling fuzzy similarity of two objects that are partial members of a fuzzily defined set. This paper deals with construction and representation properties of L-equivalence relations on L-fuzzy sets. To this end, we set up a method for constructing them by means of quasi-pseudo-metrics, and then point

Abstract Frequent itemset mining is considered a popular tool to discover knowledge from transactional datasets. It also serves as the basis for association rule mining. Several algorithms have been proposed to find frequent patterns in which the apriori algorithm is considered as the earliest proposed. Apriori has two significant bottlenecks associated with it: first, repeated scanning of input dataset

Synthetic Aperture Radar (SAR) polarimetric datasets are widely used in the detection and classification of urban areas. Most methods used today are based on the decomposition of fully polarimetric SAR data, which allows for the extraction of physical information about the nature of the medium and the application of proper classification methods. According to the theory, the main and predominant backscattering

This work presents an overview of the multiple aperture synthetic aperture radar interferometric (MAI) technique, which is primarily used to measure the along-track components of the Earth’s surface deformation, by investigating its capabilities and potential applications. Such a method is widely used to monitor the time evolution of ground surface changes in areas with large deformations (e.g., due

As an essential climate variable (ECV), land surface albedo plays an important role in the Earth surface radiation budget and regional or global climate change. The Tibetan Plateau (TP) is a sensitive environment to climate change, and understanding its albedo seasonal and inter-annual variations is thus important to help capture the climate change rules. In this paper, we analyzed the large-scale

Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become

Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species

Traditional choropleth maps, created on the basis of administrative units, often fail to accurately represent population distribution due to the high spatial heterogeneity and the temporal dynamics of the population within the units. Furthermore, updating the data of spatial population statistics is time-consuming and costly, which underlies the relative lack of high-resolution and high-quality population

This paper deals with the retrieval of snow depth (SD) and snow water equivalent (SWE) using dual-polarization (HH-VV) synthetic aperture radar (SAR) data. The effect of different snowpack conditions on the SD and SWE inversion accuracy was demonstrated by using three TerraSAR-X acquisitions. The algorithm is based on the relationship between the SD, the co-polar phase difference (CPD), and particle

Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of

An accurate assessment of evapotranspiration (ET) is crucially needed at the basin scale for studying the hydrological processes and water balance especially from upstream to downstream. In the mountains, this term is poorly understood because of various challenges, including the vegetation complexity, plant diversity, lack of available data and because the in situ direct measurement of ET is difficult

Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a highly valuable remote sensing technique for probing the Earth’s atmosphere, due to its global coverage, high accuracy, long-term stability, and essentially all-weather capability. In order to ensure the highest quality of essential climate variables (ECVs), derived from GNSS signal tracking by RO satellites in low Earth orbit (LEO)

This paper proposes three feature extraction (FE) methods based on density estimation for hyperspectral images (HSIs). The methods are a mixture of factor analyzers (MFA), deep MFA (DMFA), and supervised MFA (SMFA). The MFA extends the Gaussian mixture model to allow a low-dimensionality representation of the Gaussians. DMFA is a deep version of MFA and consists of a two-layer MFA, i.e, samples from

In the context of the Copernicus Program, EUMETSAT prioritizes the creation of an ocean color infrastructure for system vicarious calibration (OC-SVC). This work aims to reply to this need by proposing the European Radiometry Buoy and Infrastructure (EURYBIA). EURYBIA is designed as an autonomous European infrastructure operating within the Marine Optical Network (MarONet) established by University

The paper describes the development of predictive equations of windthrow for five tree species based on remote sensing of wind-affected stands in southwestern New Brunswick (NB). The data characterises forest conditions before, during and after the passing of extratropical cyclone Arthur, July 4–5, 2014. The five-variable logistic function developed for balsam fir (bF) was validated against remote-sensing-acquired

Segmentation is a crucial phase in image processing because it simplifies the representation of an image and facilitates its analysis. The multilevel thresholding method is more efficient for segmenting digital mammograms compared to the classic bi-level thresholding since it uses a higher number of intensities to represent different regions in the image. In the literature, there are different techniques

We consider the problem of identifying within a given document all text items which follow a certain pattern to be specified by a user. In particular, we focus on scenarios in which the task is to be completed very quickly and the user is not able to specify the exact pattern of interest. The key use case corresponds to the interactive exploration of documents in search of snippets that do not fit

During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes

The purpose of this paper is to study the dynamics of a quantum coherent feedback network composed of two two-level systems (qubits) driven by two counter-propagating photons, one in each input channel. The coherent feedback network enhances the nonlinear photon–photon interaction inside the feedback loop. By means of quantum stochastic calculus and the input–output framework, the analytic form of

This paper studies adaptive algorithms for simultaneous regulation (i.e., control) and estimation (i.e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems. It proposes practical, easy to implement control policies based on perturbations of input signals. Such policies are shown to achieve a worst-case regret that scales as the square-root of the time horizon, and holds uniformly

We formulate a recursive estimation problem for multiple dynamical systems coupled through a low dimensional stochastic input, and we propose an efficient sub-optimal solution. The suggested approach is an approximation of the Kalman filter that discards the off diagonal entries of the correlation matrix in its “update” step. The time complexity associated with propagating this approximate block-diagonal

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Quality control / assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP)

In the medical ultrasound field, ultrafast imaging has recently become a hot topic. However, the diagnostic reliability of ultrafast high-frame rate plane-wave (PW) imaging is reduced by its low-quality images. The medical ultrasound equipment on the market usually adopts the line-scanning mode, which can obtain high-quality images at a very low frame rate. In addition, many proven data-driven ultrasound

Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-artifacts, e.g., A- and B- line artifacts. While A-lines are a visual pattern which essentially represent a healthy lung surface, B-line artifacts correlate with a wide range of pathological conditions affecting the lung parenchyma. In fact, the appearance

Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the low intrinsic contrast between the thermal lesion and normal tissue. Deep learning, especially convolutional neural network (CNN), has shown significant improvements in medical image analysis. Here, we propose and evaluate an US imaging based on a CNN architecture

Transvaginal ultrasound (TVUS) is widely used in infertility treatment. The size and shape of the ovary and follicles must be measured manually for assessing their physiological status by sonographers. However, this process is extremely time-consuming and operator-dependent. In this study, we propose a novel composite network, namely CR-Unet, to simultaneously segment the ovary and follicles in TVUS

Breast cancer is a high-incidence type of cancer for women. Early diagnosis plays a crucial role in the successful treatment of the disease and the effective reduction of deaths. In this paper, deep learning technology combined with ultrasound imaging diagnosis was used to identify and determine whether the tumors were benign or malignant. First, the tumor regions were segmented from the breast ultrasound

3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential

OBJECTIVE To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia. METHODS We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range

Ultrasonography is one of the main imaging methods for diagnosing thyroid nodules. Automatic differentiation between benign and malignant nodules in ultrasound images can great assist inexperienced clinicians in their diagnosis. The core of problem is the effective utilization of the features of ultrasound images. In this study, we propose a method that is based on the combination of conventional ultrasound