The tracking control problem is investigated for a class of nonlinear networked discrete systems with random data-dropout and sensor saturation, and a novel data-driven iterative learning event-triggered control scheme is proposed. First, a new model is established to describe random data-dropout processes. Then, the fixed threshold iterative learning control scheme based on randomly received saturated

At present, the hierarchical control strategy which includes cyber and physical layers is one of the most widely used control strategies in networked direct current microgrid. In general, through the controller and actuator, the communication data in cyber layer can be used to accomplish the control missions in physical layer, e.g. achieving stable operation or economic operation etc. However, when

A class of computationally efficient DOA estimators under the Partial Relaxation (PR) framework has recently been proposed. Conceptually different from conventional DOA estimation methods in the literature, the estimators under the PR framework rely on the non-complete relaxation of the array manifold while performing a spectral-search in the field of view. This particular type of relaxation essentially

Wireless communication systems are inherently vulnerable to intentional jamming. In this paper, two classes of such jammers are considered: those with partial and full knowledge. While the first class accounts for those jammers that know the encoding and decoding function, the latter accounts for those that are further aware of the actual transmitted message. Of particular interest are so-called denial-of-service

In the context of supervised learning, meta learning uses features, metadata and other information to learn about the difficulty, behavior, or composition of the problem. Using this knowledge can be useful to contextualize classifier results or allow for targeted decisions about future data sampling. In this paper, we are specifically interested in learning the Bayes error rate (BER) based on a labeled

Compared with traditional learning criteria, such as minimum mean square error (MMSE), the minimum error entropy (MEE) criterion has received increasing attention in the domains of nonlinear and non-Gaussian signal processing and machine learning. Since the MEE criterion is shift-invariant, one has to add a bias to achieve zero-mean error over training datasets. Thus, a modification of the MEE called

In this paper we study the matrix completion problem: Suppose $X \in {\mathbb {R}}^{n_r \times n_c}$ is unknown except for a known upper bound $r$ on its rank. By measuring a small number $m \ll n_r n_c$ of elements of $X$ , is it possible to recover $X$ exactly with noise-free measurements, or approximately with noisy measurements? Existing solutions to these problems involve sampling the elements

We derive robust linear filtering and experimental design for systems governed by stochastic differential equations (SDEs) under model uncertainty. Given a model of signal and observation processes, an optimal linear filter is found by solving the Wiener-Hopf equation; with model uncertainty, it is desirable to derive a corresponding robust filter. This article assumes that the physical process is

Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a specific noise kernel. This is insufficient to deal with complex noise in real scenarios. In this paper, we present a hierarchical Dirichlet process

Graphs have become a popular toolbox for capturing similarity relationships among data vectors in a variety of networks. Diffusion processes are used to model the data using the graph topology. A novel approach is put forth that exploits data similarity and diffusion models, in a graph, to improve upon the reconstruction mean-square error (MSE) performance of principal component analysis. The tasks

This article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in large-scale distributed graph processing. In particular, we propose a topology-aware coded computing (TACC) algorithm that has two novel salient features: (i) a topology-aware graph allocation strategy, and (ii) a coded aggregation scheme that combines the intermediate computations for

The papers in this special section focus on new methodologies and applications in artificial intelligence (AI) and machine learning (ML). With the recent development of machine learning (ML), artificial intelligence (AI) and cyber technologies in the field of industrial informatics, it is important to migrate the traditional businesses and services in the physical world to the digital cyber-enabled

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

We are very pleased to present this Special Section of these Transactions, including six extended articles selected from the technical program of the 2017 International Conference on Automation Science and Engineering (CASE 2017), held in Xi’an, China, August 20–23, 2017. CASE is an offspring of the Transactions on Automation Science and Engineering and is the flagship automation conference of the

The 14th Annual IEEE International Conference on Automation Science and Engineering (CASE 2018) sponsored by the IEEE Robotics and Automation Society (RAS) was held on August 20–24, 2018, in Munich, Germany. IEEE CASE is the flagship automation conference of the IEEE RAS and constitutes the primary forum for cross-industry and multidisciplinary research in automation. Its goal was to provide a broad

Cyber–physical systems (CPSs) in the manufacturing domain can be deployed to support monitoring and analysis of production systems of a factory in order to improve, support, or automate processes, such as maintenance or scheduling. When a network of CPS is subject to frequent changes, the semantic interoperability between the CPSs is of special interest in order to avoid manual, tedious, and error-prone

Presents a call for nominations for editors and associate editors for this publication.

Advertisement.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Advertisement.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Dissertations can be important sources of information about the future of a research field. These publications capture the ideas, theories, methods, and populations that emerging researchers deem important for study. Dissertation research often exhibits great rigor and innovation. This study of dissertations focuses on one specific field, which has importance in a large variety of academic disciplines:

We investigate the applicability of offshore geoelectrical profiling in the littoral zone, e.g., for archaeological prospection, sediment classification and investigations on coastal ground water upwelling. We performed field measurements with a 20 m long multi-electrode streamer in inverse Schlumberger configuration, which we used to statistically evaluate measurement uncertainty and the reproducibility

Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to

Rain attenuation can hinder the implementation of quantitative precipitation estimations using X-band weather radar. Numerous studies have been conducted on correcting the attenuation of radar reflectivity by utilizing a dual-polarimetric radar and an arbitrary-oriented microwave link; however, there is a need to optimize the required number of microwave links and their locations. In this study, we

Underground mining can produce subsidence phenomena, especially if orebodies are surficial or occur in soft rocks. In some countries, illegal mining is a big problem for environmental, social and economic reasons. However, when unauthorized excavation is conducted underground, it is even more dangerous because it can produce unexpected surficial collapses in areas not adequately monitored. For this

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over

Monitoring uncontained built-up area expansion remains a complex challenge for the development and implementation of a sustainable planning system. In this regard, proper planning requires accurate monitoring tools and up-to-date information on rapid territorial transformations. The purpose of the study was to assess built-up area expansion, comparing two freely available and widely used datasets,

Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval

Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed

In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study

In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent

The change in water storage driven by the Three Gorges Project directly affects the terrestrial water migration and redistribution in the Yangtze River Basin (YRB). As a result, a new water balance is established and regional evapotranspiration (ET) fluctuates in the process. In this paper, data from multiple-sources including from the Gravity Recovery and Climate Experiment (GRACE) satellite, land

The distribution of hydrometeors in thunderstorms is still under investigation as well as the process of electrification in thunderclouds leading to lightning discharges. One indicator of cloud electrification might be high values of the Linear Depolarization Ratio (LDR) at higher vertical levels. This study focuses on LDR values derived from vertically pointing cloud radars and the distribution of

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric

The problem of automatically measuring the degree of semantic similarity between textual expressions is a challenge that consists of calculating the degree of likeness between two text fragments that have none or few features in common according to human judgment. In recent times, several machine learning methods have been able to establish a new state-of-the-art regarding the accuracy, but none or

A major challenge in automating the production of a large number of forecasts, as often required in many business applications, is the need for robust and reliable predictions. Increased noise, outliers and structural changes in the series, all too common in practice, can severely affect the quality of forecasting. We investigate ways to increase the reliability of exponential smoothing forecasts,

Stock returns are continuously generated by different data sources and depend on various factors such as financial policies and national economic growths. Stock returns prediction, unlike traditional regression, requires consideration of both the sequential and interdependent nature of financial time-series. This work uses a two-stage approach, using kernel adaptive filtering (KAF) within a stock market

Non-invasive load monitoring (NILM) is a vital step to realize the smart grid. Although the existing various NILM algorithms have made significant progress in energy consumption feedback, there are still some problems need to further addressed, such as the exponential growth of state space with the increase of the number of multi-state devices, which leads to the dimension disaster; and it is difficult

To address the repair equipment allocation problem for a support-and-repair ship on a deep sea, a hybrid multi-criteria decision making and optimization approach is designed. Evidence reasoning approach is used to aggregate the evaluation information of quantitative criteria (i.e., weight and economics) and qualitative criteria (i.e., repair ability, reliability and convenience). Then, a mathematical

Polymer electrolyte membrane fuel cells (PEMFC) are a promising technology for economic and environmentally friendly energy production. However, they haven’t reached their full potential in the market yet as only few reliable PEMFC systems have successfully passed the prototyping face. A drawback of the current diagnostic tools is that only a select few are of high genericity, reliability and can perform

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed

Switched beam antennas provide an efficient and cost effective alternative to complex phased array and digital beam forming techniques. In this paper, we present the design and characterization of an electrically thin Huygens metasurface lens operating at 83GHz with high transmission efficiency and we demonstrate its applicability to switched beam antenna applications. We accurately characterize the

Highlight videos accompanied by inspiring music can help in enhancing an athlete’s motivational state and self-efficacy (SE). The addition of verbal priming techniques could provide a further boost, but this combination of audiovisual stimuli has yet to be examined in a sport context. A repeated-measures, crossover design was used. The study entailed a pretraining intervention administered to American

Presents the front cover for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context

The papers in this special section were presented at the 2019 IEEE-EMBS International Conferences on Biomedical and Health Informatics (BHI’19) and Wearable and Implantable Body Sensor Networks (BSN’19). Topics of integrative sensor networks, informatics and modeling bring together the tightly coupled and rapidly developing fields of biomedical and health informatics and body sensor networks. Biomedical