Update Interval Research Articles

Site-specific real-time GPS multipath mitigation based on coordinate time series window matching

Multipath effect is one of the main errors in precise global navigation satellite system (GNSS) positioning. Although there are many GNSS multipath mitigation models available, each model has some limitations. We discuss the problem of mitigating low-frequency multipath in a specific site from the coordinate domain. At present, sidereal filtering is one of the most commonly used methods, in which the repetition period of multipath has to be estimated before multipath mitigation. However, in such a method, the maneuver of the satellite orbit and the long ephemeris update interval will cause inaccurate estimation of the repetition period. We propose a variation of the sidereal filtering method based on window matching. In that method, the window of coordinate time series formed by the current epoch and several previous epochs is used as the query window, and the matched window is searched in the time series of the previous day. The degree of similarity between windows is evaluated by similarity measures. Based on the coordinate point pairs in the matching window, multipath mitigation of the current epoch is performed by an affine transformation. The effects of different similarity measures in window matching are explored, and an early and late matching algorithm is proposed to solve the ambiguous problem of multiple matching sequences in integer-valued similarity metrics. Two sets of short baseline data were collected by different types of receivers in harsh environments to validate the method. The results show the feasibility and effectiveness of the proposed method for site-specific real-time multipath mitigation.

DSDV Extension to Enhance the Performance of Ad Hoc Networks in High Diverse-Velocity Environments

<p class="0abstractCxSpFirst">Mobile Ad-Hoc Networks (MANETs) are characterized as decentralized control networks. The mobile nodes route and forward data based on their routing information without the need for routing devices. In this type of networks, nodes move in an unstructured environment where some nodes are still fixed, others are moving in a constant velocity, and others move with diverse velocities; and thus, they need special protocols to keep track of network changes and velocity changes among the nodes. Destination Sequenced Distance-Vector (DSDV) routing protocol is one of the most popular proactive routing protocols for wireless networks. This protocol has a good performance in general, but with high speed nodes and congested networks its performance degrades quickly.</p><p class="0abstractCxSpLast">In this paper we propose an extension to the DSDV (we call it Diverse-Velocity DSDV) to address this problem. The main idea is to modify the protocol to include node speed, determine update intervals and the duration of settling time. To evaluate the performance of the new protocol, we have carried a number of simulation scenarios using the Network Simulator tool (NS-3) and measured relevant parameters such as: packet delivery ratio, throughput, end-to-end delay, and routing overhead. We have compared our results with the original DSDV and some of its new variants. The new protocol has demonstrated a noticeable improvement of performance in all scenarios, and the measured performance metrics outperform the others except the average delay where the performance of the new protocol was modest.</p>

Adaptive Double-Exploration Tradeoff for Outlier Detection

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold. Distinct from the traditional TBP, the threshold is defined as a function of the rewards of all the arms, which is motivated by the criterion for identifying outliers. The learner needs to explore the rewards of the arms as well as the threshold. We refer to this problem as "double exploration for outlier detection". We construct an adaptively updated confidence interval for the threshold, based on the estimated value of the threshold in the previous rounds. Furthermore, by automatically trading off exploring the individual arms and exploring the outlier threshold, we provide an efficient algorithm in terms of the sample complexity. Experimental results on both synthetic datasets and real-world datasets demonstrate the efficiency of our algorithm.

Ancillary services from wind turbines: automatic generation control (AGC) from a single Type 4 turbine

Abstract. Wind turbines possess the technical ability to provide various ancillary services to the electrical grid. Despite this, renewable generators such as wind and solar have traditionally not been allowed to provide significant amounts of ancillary services, in part due to the variable and uncertain nature of their electricity generation. Increasing levels of renewable generation, however, continue to displace existing synchronous generation and thus necessitate new sources of ancillary or system services. This work is part of an ongoing project that seeks to provide empirical evidence and an examination of how ancillary services can be provided from commercially available wind turbines. We focus specifically on providing secondary frequency response (automatic generation control or AGC) and demonstrate that wind turbines have the technical capability to provide this service. The algorithms used are intentionally simple so as to evaluate the capabilities and limitations of the turbine technology. This work presents results from a single, 800 kW, International Electrotechnical Commission (IEC) Type 4 wind turbine. A total of 10 % of rated power is offered on the regulation market. We do not separate up- and downregulation into individual services. Upregulation is offered through a 5 % constant power curtailment. The AGC update interval is 4 s, to mimic real-world conditions. We use performance scoring methods from the Pennsylvania–Jersey–Maryland (PJM) operator and the National Research Council (NRC) of Canada to quantify the wind turbine's response. We use the calculated performance scores, annual site wind data, and 2017 PJM market price data to estimate income from providing secondary frequency regulation. In all cases presented, income from the regulation market is greater than the energy income lost due to curtailment.

An Improved Method with High Anti-interference Ability for R Peak Detection in Wearable Devices

The rapid development of the wearable electrocardiogram monitoring equipment increases the requirements for R peak detection in wearable devices. An improved method called ISC algorithm is proposed with high anti-interference ability for R peak detection in wearable devices based on a simple basic algorithm called SC algorithm. The proposed method is characterized by using the updated amplitude selection threshold, updated slope comparison threshold and RR interval judgement to reduce false positives and false negatives. For data from MIT-BIH Arrhythmia Database, the positive predictivity P+ of ISC algorithm can reach 99.12%, and the sensitivity Se of ISC algorithm is more than 95%. For MIT-BIH Noise Stress Test Database, the accuracy of ISC algorithm for both sensitivity Se and positive predictivity P+ can exceed 94% under three common noise, baseline wander, muscle artifact, and electrode motion artifact, where the positive predictivity P+ of ISC algorithm is 44.46% higher than that of SC algorithm on average. For wearable devices in exercise, even under the exercise intensity of 7 km per hour, the average positive predictivity P+ of ISC algorithm is 99.32%, which is 60.93% higher than that of SC algorithm. The high anti-interference ability shows that ISC algorithm is suitable for R peak detection in wearable devices.

Real-Time Stochastic Optimization of Energy Storage Management Using Deep Learning-Based Forecasts for Residential PV Applications

A computationally proficient real-time energy management method with stochastic optimization is presented for a residential photovoltaic (PV)-storage hybrid system comprised of a solar PV generation and a battery energy storage (BES). Existing offline energy management approaches for day-ahead scheduling of BES suffer from energy loss in real time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome these limitations, we propose an integrated energy management framework consisting of an offline optimization model concurrent with a real-time rule-based controller. The optimization is performed in receding horizon with load and solar generation forecast profiles using deep learning-based long short term memory method in rolling horizon to reduce the daily electricity purchase costs. The optimization model is formulated as a multistage stochastic program where we use the stochastic dual dynamic programming algorithm in the receding horizon to update the optimal set point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update intervals, we introduce a rule-based controller underneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a real-time controller-hardware-in-the-loop test platform in an OPAL-RT simulator. The proposed real-time method is effective in reducing the net electricity purchase cost compared to other existing energy management methods.

Velocity propagation with a time-varying slew rate vector for strapdown INS

The traditional velocity integration algorithms in a strapdown inertial navigation system are to approximate the rotational vector which related to the transformation matrix and then integrate the transformed specific force vector over the velocity update interval. In order to eliminate approximate integration error existed in the traditional algorithms, a new velocity integration algorithm with a time-varying slew rate vector is developed in this paper. An additional frame, the slewing frame which slews along with the angular rate and specific force vectors, is constructed to design and form the optimized algorithms of velocity. The relative position among the angular rate vector, the specific force vector and the slewing frame is fixed. So it is convenient to get a precise rotation vector. In addition, a more general testing circumstance called the time-varying slewing motion developed in this paper for evaluation of the velocity integration algorithms. It is more complex and severer than the classic sculling motion. Simulation test results under the classic sculling motion and the time-varying slewing motion demonstrate that the proposed velocity integration algorithm has better performance than the traditional one.

Topology control adopting optimal topology over update interval in mobile ad hoc networks

Topology control adopting optimal topology over update interval in mobile ad hoc networks

Ionospheric Anomaly Detection to Support the BDSBAS

Corrections and integrity bounds of the ionospheric delay generated at the Satellite-Based Augmentation System (SBAS) for aviation users may confront a wide range of ionospheric conditions over its update interval. Ionospheric irregularities affecting Single Frequency (SF) SBAS can cause the risk of service integrity and a loss of service continuity. The aim of the Ionospheric Anomaly Detection (IAD) scheme is to detect the effect of ionospheric irregularities on the SBAS SF service operational capability over China. It will help in establishing statistical anomaly models and then provides a high level of service for aviation users. The IAD scheme uses a fusion protocol to reach consensus among subdetectors, which monitor different ionospheric features. Moreover, the threshold of every sub-detector is derived based on system availability. We analyze the IAD scheme by practical ionosphere observations recorded at the crustal movement observation network of China in 2015. It successfully identifies ionospheric anomalies affecting SF SBAS and conforming with physical characteristics. Furthermore, it provides sufficient detection reliability. The statistical results illustrate that the difference of 95% residual error for post-ionosphere correction between normal and anomalous zones is between 1.4 m and 3.2 m. The average value is 2.1723 m. Additionally, there exist two types of ionospheric anomalies affecting SBAS SF service over China. The range is mainly at latitude 15°-35°N. The occurrence time is mainly at GPS time 4-15 h and 12-19 h, respectively.

Bisection and Exact Algorithms Based on the Lagrangian Dual for a Single-Constrained Shortest Path Problem

We propose two new algorithms called BiLAD and ExactBiLAD for the well-known Single-Constrained Shortest Path (SCSP) problem. It is a fundamental problem in quality-of-service (QoS) routing, where one seeks a source-destination path with the least cost satisfying a delay QoS constraint in a network. As pointed out by Juttner et al. , there is no widely accepted algorithm with polynomial time to the SCSP problem because the SCSP problem is NP-hard. The remarkable feature of BiLAD is that it ensures that the length of iteratively updated angle interval is shrunk at least at a constant ratio. With the help of this feature, we prove its polynomial time complexity. To the best of our knowledge, this is the first time that the polynomial time complexity is proved in details. The numerical results show that, in most QoS routing test instances, the performance of BiLAD is close to their primal optimal solutions. The proposed modified Dijkstra procedure, whose complexity is the same as that of the Dijkstra algorithm, also accelerates BiLAD. In the second part of the paper, based on the information obtained by BiLAD, we design an exact algorithm–ExactBiLAD, in which an optimal solution to the SCSP problem is finally obtained by scanning the steadily reduced optimal-path-candidate triangle area. The simulation results indicate that ExactBiLAD needs only a dozen times of executing the modified Dijkstra algorithm regardless of the network size or the average node degree. Distinguished from many other exact algorithms, ExactBiLAD has a satisfactory performance in the practical computation.

Hyper-Scalable JSQ with Sparse Feedback

Load balancing algorithms play a vital role in enhancing performance in data centers and cloud networks. Due to the massive size of these systems, scalability challenges, and especially the communication overhead associated with load balancing mechanisms, have emerged as major concerns. Motivated by these issues, we introduce and analyze a novel class of load balancing schemes where the various servers provide occasional queue updates to guide the load assignment. We show that the proposed schemes strongly outperform JSQ(d) strategies with comparable communication overhead per job, and can achieve a vanishing waiting time in the many-server limit with just one message per job, just like the popular JIQ scheme. The proposed schemes are particularly geared however towards the sparse feedback regime with less than one message per job, where they outperform corresponding sparsified JIQ versions. We investigate fluid limits for synchronous updates as well as asynchronous exponential update intervals. The fixed point of the fluid limit is identified in the latter case, and used to derive the queue length distribution. We also demonstrate that in the ultralow feedback regime the mean stationary waiting time tends to a constant in the synchronous case, but grows without bound in the asynchronous case.

Energy Harvesting Wireless Sensor Networks With Channel Estimation: Delay and Packet Loss Performance Analysis

This paper considers a single-source status monitoring system with energy harvesting in the context of applications where keeping information up-to-date is of primary interest. Specifically, a sensor node, relying on harvested energy and operating according to a harvest-then-use protocol, estimates the channel state to decide whether to perform or defer data delivery to a sink. The sink keeps track of the system status through the successfully delivered updates. To comprehensively evaluate the performance of the proposed scheme, we analytically derive the exact-closed form expressions of the packet loss probability, the age of information and the update interval metric statistics considering both constant-rate and random energy arrival processes and taking into account both the time and energy costs of sensing, transmitting and estimating the channel state. We asymptotically obtain the necessary conditions under which estimating the channel state before transmitting, despite the associated time and energy costs, allow to efficiently manage the harvested energy by avoiding erroneous transmissions and performs strictly better than transmitting without estimating the channel state. Numerical results demonstrate that in most cases, estimating the channel state before transmitting significantly reduces the packet loss probability, the age of information and the update interval of node-sink transmission.

Development and Calibration of a Low-Cost Radar Testbed Based on the Universal Software Radio Peripheral

For a cognitive radar algorithm to be useful, it must run on radar hardware that is flexible. Typically, this has been viewed as requiring a software-defined radio. With the emergence of cheap, digital transceiver systems, such as the Universal Software Radio Peripheral (USRP) from Ettus, these systems are cheaper than ever to produce. This article reports on the development of a USRP-based radar system. Calibration testing for radar cross section is undertaken. The phase noise is characterized as having a standard deviation of 0.02 radians. Detections of commercial aircraft at ranges up to 5.5 km are demonstrated. An adaptive update interval tracking experiment, using the fully adaptive radar framework for cognitive radar, is reported on. In this experiment, a small unmanned aerial vehicle is detected at ranges of up to 300 m. Using the radar equation with a signal loss proportional to R−4, the interpolated maximum detection range is approximately 600 m.

Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees

Closing feedback loops fast and over long distances is key to emerging cyber-physical applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless communication technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. Thus far, however, feedback control over multi-hop low-power wireless networks has only been demonstrated for update intervals on the order of seconds. To fill this gap, this article presents a wireless embedded system that supports dynamic mode changes and tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics in the presence of mode changes. Using experiments on a cyber-physical testbed with 20 wireless devices and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination with mode changes over multi-hop networks for update intervals of 20 to 50 milliseconds.

Numerical stability of co-simulation approaches to evaluate wheel profile evolution due to wear

ABSTRACTWear simulation for railway wheel profiles is a complex problem which requires dynamics simulation of the vehicle on a long track and detailed analyses of the contact forces, which have to adopt non-linear methods to simulate the material behaviour. Since the dynamics of the vehicle and the behaviour of the material evolve differently, it is possible to use different codes to solve the problems. Furthermore, when thousands of kilometres are needed to produce wear, the profile wear is usually virtually amplified using wear multipliers to reduce computing time. This process can produce numerical instability if the multipliers and profile update intervals are not chosen appropriately. In this paper, the Simpack multibody code is used to simulate vehicle dynamics and to calculate the wear removed on a reference track, then the MATLAB code is used to smooth the profile and define the update intervals.

Enhancing Security of MME Handover via Fractional Programming and Firefly Algorithm

Key update and residence management have been investigated as an effective solution to cope with desynchronization attacks in mobility management entity (MME) handovers. In this paper, we first analyze the impacts of the key update interval (KUI) and MME residence interval (MRI) on handover processes and their secrecy performance in terms of the number of exposed packets (NEP), signaling overhead rate (SOR), and outage probability of vulnerability (OPV). Specifically, the bounds of the derived NEP and SOR not only capture their behaviors at the boundary of the KUI and MRI, but also show the tradeoff between the NEP and SOR. Additionally, through the analysis of the OPV, it is shown that the handover security can be enhanced by shortening the KUI and the desynchronization attacks can be avoided with high-mobility users. The above facts accordingly motivate us to propose a multi-objective optimization (MO) problem to find the optimal KUI and MRI that minimize both the NEP and SOR subject to the constraint on the OPV. To this end, two scalarization techniques are adapted to transform the proposed MO problem into single-objective optimization problems, i.e., an achievement-function method via fractional programming (FP) and a weighted-sum method. Based on the derived bounds on NEP and SOR, the FP approach can be optimally solved via a simple numerical method. For the weighted-sum method, the firefly algorithm (FA) is utilized to find the optimal solution. The results show that both techniques can solve the proposed MO problem with a significantly reduced searching complexity compared to the conventional heuristic iterative search technique.

Developing a Hydrology Coupled 2D Cellular Automata Model for Efficient Urban Flood Simulation

To achieve fast and efficient flood modelling for urban area, a two-dimensional cellular automata based model was developed and further coupled with a hydrological model. The model was implemented in parallel environments and tested by an analytical solution and a real word case. The analytical case results showed that the model is capable of simulating water-depth under a reasonable slope threshold and grid resolution condition using only a short computational time. Slope threshold and grid resolution are the two key factors affecting the computational efficiency and model accurcay. The introduction of the state update interval significantly improved the computational efficiency. In the case of the real world example, the results were also in reasonable range.In general, this model can be applied to rapid flood analysis for large urban areas.

Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China

AbstractWe use the Weather Research and Forecasting Model with the chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART) chemical weather forecasting/data assimilation system with multiconstituent data assimilation to investigate the improvement of air quality forecasts over eastern China. We assimilate surface in situ observations of sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), particulate matter with diameters less than 2.5 μm (PM2.5) and 10 μm (PM10), and satellite aerosol optical depth to adjust the related anthropogenic emissions as well as the chemical initial conditions. We validate our forecast results out to 72 hr by comparison with the in situ observations. Results show that updated emissions improve the model performance between 10% and 65% root mean square error reduction for the assimilated species except particulate matter with a diameter between 2.5 and 10 μm (PM2.5‐10), which is slightly improved due to the limited anthropogenic contribution to it. In a sensitivity experiment with a different update interval, the CO improvement is found to be sensitive to the cycling time used to update the CO emissions. In another sensitivity experiment when NO2 observations are not assimilated and nitrogen oxides (NOx) emission are adjusted by only O3, NO2 forecasts show similar root mean square error improvement but have lower spatial correlation, indicating the value and limitation of the O3‐NOx cross‐variable relationship.

Managing Taxon Data in FinBIF

The Finnish Biodiversity Information Facility, FinBIF (https://species.fi), has developed its own taxon database. This allows FinBIF taxon specialists to maintain their own, expert-validated view of Finnish species. The database covers national needs and can be rapidly expanded by our own development team. Furthermore, in the database each taxon is given a globally unique persistent URI identifier (https://www.w3.org/TR/uri-clarification), which refers to the taxon concept, not just to the name. The identifier doesn’t change if the taxon concept doesn’t change. We aim to ensure compatibility with checklists from other countries by linking taxon concepts as Linked Data (https://www.w3.org/wiki/LinkedData) — a work started as a part of the Nordic e-Infrastructure Collaboration (NeIC) DeepDive project (https://neic.no/deepdive). The database is used as a basis for observation/specimen searches, e-Learning and identification tools, and it is browsable by users of the FinBIF portal. The data is accessible to everyone under CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0) in machine readable formats. The taxon specialists maintain the taxon data using a web application. Currently, there are 60 specialists. All changes made to the data go live every night. The nightly update interval allows the specialists a grace period to make their changes. Allowing the taxon specialists to modify the taxonomy database themselves leads to some challenges. To maintain the integrity of critical data, such as lists of protected species, we have had to limit what the specialists can do. Changes to critical data is carried out by an administrator. The database has special features for linking observations to the taxonomy. These include hidden species aggregates and tools to override how a certain name used in observations is linked to the taxonomy. Misapplied names remain an unresolved problem. The most precise way to record an observation is to use a taxon concept: Most observations are still recorded using plain names, but it is possible for the observer to pick a concept. Also, when data is published in FinBIF from other information systems, the data providers can link their observations to the concepts using the identifiers of concepts. The ability to use taxon concepts as basis of observations means we have to maintain the concepts over time — a task that may become arduous in the future (Fig. 1). As it stands now, the FinBIF taxon data model — including adjacent classes such as publication, person, image, and endangerment assessments — consists of 260 properties. If the data model were stored in a normalized relational database, there would be approximately 56 tables, which could be difficult to maintain. Keeping track of a complete history of data is difficult in relational databases. Alternatively, we could use document storage to store taxon data. However, there are some difficulties associated with document storages: (1) much work is required to implement a system that does small atomic update operations; (2) batch updates modifying multiple documents usually require writing a script; and (3) they are not ideal for doing searches. We use a document storage for observation data, however, because they are well suited for storing large quantities of complex records. In FinBIF, we have decided to use a triplestore for all small datasets, such as taxon data. More specifically, the data is stored according to the RDF specification (https://www.w3.org/RDF). An RDF Schema defines the allowed properties for each class. Our triplestore implementation is an Oracle relational database with two tables (resource and statement), which gives us the ability to do SQL queries and updates. Doing small atomic updates is easy as only a small subset of the triplets can be updated instead of the entire data entity. Maintaining a complete record of history comes without much effort, as it can be done on an individual triplet level. For performance-critical queries, the taxon data is loaded into an Elasticsearch (https://www.elastic.co) search engine.

ORIENTATION OF UAV IMAGE BLOCKS BY SURFACE MATCHING

Abstract. Topographic maps are produced in Portugal by the Army Geospatial Data Centre, based on a geographical database collected from aerial photography in resolutions from 0.30 to 0.50 m. Each map sheet is revised with an update interval of 10 years or more. Many changes, such as new roads or power lines, would be possible to update with much higher frequency using UAV imagery. Although the nominal scale of the published paper map sheets is 1 : 25,000, the actual positional accuracy requirement is of 1 meter, which is compatible with larger scales. Exterior orientation parameters obtained by UAV navigation equipment does not have enough accuracy for that. This paper deals with a method to automatically improve the UAV exterior orientation, intended to be compatible with the positional accuracy standards of the geographic database. The method is based on matching a point cloud derived from UAV imagery, without GCPs, with a reference DSM obtained from conventional aerial photographs, which were oriented by standard aerial triangulation. Results allowed to improve the UAV data to a positional accuracy of 1 meter, making it compatible with the requirements established for the geographic database.