Low Sampling Frequency Research Articles

Multi-Sampling Rate Finite Control Set Model Predictive Control and Adaptive Method of Single-Phase Inverter

With the development of power switches and processor performance in recent years, the control frequency of inverters has been significantly improved. However, limited by technology and price, the sensor sampling frequency in large-scale industrial applications is much lower than the inverter control frequency that can be realized. This frequency mismatch limits the performance improvement of the inverter. In this article, the current and voltage at the non-sampling time are reconstructed using the current prediction control principle and the input observer theory, allowing a single-phase inverter to implement multi-sampling rate control with a low sampling frequency and high control frequency. In addition, an improved adaptive controller is designed to solve the effect of incorrect model parameters, which realizes adaptive control when the sampling frequency and control frequency are mismatched. Finally, the effectiveness of the method is verified through a simulation and experiments. The proposed method can solve the problem of high-speed switching for inverters under low-sampling-frequency conditions, improving the inverter’s adaptive performance and robustness.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Duty-Cycle Correction-Based Model Predictive Current Control for PMSM Drives Fed by a Three-Level Inverter With Low Switching Frequency

In this paper, a duty-cycle correction-based model predictive current control (DC-MPCC) is proposed for permanent magnet synchronous motor (PMSM) supplied by a neutral-point clamped three-level voltage source inverter (NPC-3LVSI). Unlike the conventional MPCC, which evaluates the impact of basic voltage vectors on the concerned state variables, the proposed DC-MPCC modifies the output voltage levels with optimized duty-cycle corrections. Firstly, the last three-phase voltage levels are assumed to be kept during the next control period. Then, the current tracking error and neutral-point potential are predicted. After that, the voltage levels are modified with zero, one, or two state changes, which formulate seven candidate solutions. Subsequently, the duty-cycle corrections of the modified voltage levels are computed based on the principle of minimizing current tracking error and neutral-point voltage drift. Finally, the optimal switch sequence is generated by evaluating and sorting a cost function with a penalty on switch actions. The proposed DC-MPCC features variable switching instants, relatively lower sampling frequency, and satisfactory performance under low switching frequency. Experimental tests carried out on a NPC-3LVSI fed PMSM drive, with 100 Hz fundamental frequency and 400 Hz switching frequency, accompanied by a video demonstration, validate the effectiveness of the proposed method.

A Simplified Virtual-Vector-Based Model Predictive Control Technique With a Control Factor for Three-Phase SPMSM Drives

Compared with the single vector-based model predictive control (MPC) technique, the multiple vector-based MPC technique gains interests in improving the total harmonic distortion (THD) of the stator currents and reducing electromagnetic torque and stator flux ripples for a three-phase surface permanent magnet synchronous motor (SPMSM). However, the complicated enumeration and operation in vector selection and duty cycle calculation increase the computational burden and limit its peneration. Hence, in this paper, a simplified virtual vector-based MPC with a control factor for a three-phase SPMSM is proposed. Without sector identification and enumeration operations, the proposed technique utilizes the control factor to automatically generate a large number of virtual vectors to achieve equivalent performance effectiveness with multiple vector-based MPC technique with lower sampling frequency. Subsequently, regardless of the number of virtual vectors, the candidate virtual vectors are directly identified in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> coordinate system to greatly reduce the computational burden. In addition, the duration of the selected optimal virtual vector and the resultant switching sequences are easily obtained and arranged to even the switching losses among the switching devices and further optimize the THD of the stator current. The proposed technique is simplified and flexible. Its control effectiveness is verified by experimental results.

Development of Non-Intrusive Load Monitoring of Electricity Load Classification with Low-Frequency Sampling Based on Support Vector Machine

Non-intrusive load monitoring (NILM) is a promising approach to provide energy consumption monitoring of electrical appliances and analysis of current and voltage data with less instrumentation. This paper proposes an electrical load classification model using support vector machine (SVM). SVM was chosen to keep the computational cost low and be able to implement an embedded system. The SVM model was utilized to classify the on/off state of air conditioners, light bulbs, other uncategorized electronics, and their combinations. It utilizes low-frequency sampling data captured every minute, or at a 0.0167 Hz rate. Utilization change in active and reactive power was used as a feature in the model training. The optimal kernel for the model was the radial basis function (RBF) kernel with C and gamma values of 88.587 and 2.336 as hyperparameters, producing a highly accurate model. In testing with real-time conditions, the model classified the on/off state of the electrical loads with 0.93 precision, 0.91 recall, and 0.91 f-score. The results of testing proved that the model can be applied in real time with high accuracy and with an acceptable performance in field implementation using an embedded system.

Detecting the novel appliance in non-intrusive load monitoring

Results from Non-Intrusive Load Monitoring serve for energy decomposition and load identification, which would facilitate effective energy consumption management. Existing studies have focused on settings with a fixed number of electrical appliances. This differs significantly from real-world scenarios, thus largely limiting the practical application of related research. We study the pattern variations of the aggregated power sequences and separately analyze two different scenarios for new appliance introduction. Based on this, we propose a novel appliance detection method that can be implemented for low-frequency sampling data. The proposed method can determine whether new appliances are introduced without the prior information at the appliance level, thus establishing a basis for load monitoring in scenarios with dynamic changes in load topology. The experimental results show that the proposed method achieves at least a 35.93 % improvement in the metric of F1 compared to the event-based approach in the presence of a different number of unknown appliances.

Limitations of farm management data in analyses of decadal changes in SOC stocks in the Danish soil‐monitoring network

AbstractChanges in soil organic carbon (SOC) storage in agricultural land are an important part of the Land Use, Land‐Use Change and Forestry component of national greenhouse gas emission inventories. Furthermore, as climate mitigation strategies and incentives for carbon farming are being developed, accurate estimates of SOC stocks are essential to verify any management‐induced changes in SOC. Based on agricultural mineral soils in the Danish soil‐monitoring network, we analysed management effects on SOC stocks using data from the two most recent surveys (2009 and 2019). Between 2009 and 2019, the average increase in SOC stock was 1.2 Mg C ha−1 for 0–50 cm despite a loss of 1.2 Mg C ha−1 from the topsoil (0–25 cm), stressing the importance of including deeper soil layers in soil‐monitoring networks. Comparing all four national surveys (1986, 1997, 2009, 2019), the mean SOC stock of mineral soils in Denmark appears stable. The change in SOC stock between 2009 and 2019 was analysed in detail in relation to management practices as reported by farmers. We found that the effects of single management factors were difficult to isolate from co‐varying factors including soil parameters and that the use of farm management data to explain changes in SOC stocks observed in soil‐monitoring networks appears limited. Uncertainty in SOC stock estimates also arises from low sampling frequency and statistical challenges related to regression to the mean. However, repeated stock measurements at decadal intervals still represent a benchmark for the overall development in regional and national SOC storage, as affected by actual farm management.

CONCURRENT VALIDITY OF LINEAR ACCELERATIONS MEASURED BY A LOW SAMPLING FREQUENCY ACCELEROMETER DURING OVERGROUND WALKING IN ELDERLY PATIENTS WITH KNEE OSTEOARTHRITIS

The tendency towards using inertial sensors for remote monitoring of the patients at home is increasing. One of the most important characteristics of the sensors is sampling rate. Higher sampling rate results in higher resolution of the sampled signal and lower amount of noise. However, higher sampling frequency comes with a cost. The main aim of our study was to determine the validity of measurements performed by low sampling frequency (12.5 Hz) accelerometers (SENS) in patients with knee osteoarthritis compared to standard sensor-based motion capture system (Xsens). We also determined the test-retest reliability of SENS accelerometers.Participants were patients with unilateral knee osteoarthritis. Gait analysis was performed simultaneously by using Xsens and SENS sensors during two repetitions of over-ground walking at a self-selected speed. Gait data from Xsens were used as an input for AnyBody musculoskeletal modeling software to measure the accelerations at the exact location of two defined virtual sensors in the model (VirtualSENS). After preprocessing, the signals from SENS and VirtualSENS were compared in different coordinate axes in time and frequency domains. ICC for SENS data from first and second trials were calculated to assess the repeatability of the measurements.We included 32 patients (18 females) with median age 70.1[48.1 – 85.4]. Mean height and weight of the patients were 173.2 ± 9.6 cm and 84.2 ± 14.7 kg respectively. The correlation between accelerations in time domain measured by SENS and VirtualSENS in different axes was r = 0.94 in y-axis (anteroposterior), r = 0.91 in x-axis (vertical), r = 0.83 in z-axis (mediolateral), and r = 0.89 for the magnitude vector. In frequency domain, the value and the power of fundamental frequencies (F0) of SENS and VirtualSENS signals demonstrated strong correlation (r = 0.98 and r = 0.99 respectively). The result of test-retest evaluation showed excellent repeatability for acceleration measurement by SENS sensors. ICC was between 0.89 to 0.94 for different coordinate axes.Low sampling frequency accelerometers can provide valid and reliable measurements especially for home monitoring of the patients, in which handling big data and sensors cost and battery lifetime are among important issues.

Improving accuracy of quantifying nitrate removal performance and enhancing understanding of processes in woodchip bioreactors using high-frequency data

Woodchip bioreactors have gained popularity in many countries as a conservation practice for reducing nitrate load to freshwater. However, current methods for assessing their performance may be inadequate when nitrate removal rates (RR) are determined from low-frequency (e.g., weekly) concurrent sampling at the inlet and outlet. We hypothesised that high-frequency monitoring data at multiple locations can help improve the accuracy of quantifying nitrate removal performance, enhance the understanding of processes occurring within a bioreactor, and therefore improve the design practice for bioreactors. Accordingly, the objectives of this study were to compare RRs calculated using high- and low-frequency sampling and assess the spatiotemporal variability of the nitrate removal within a bioreactor to unravel the processes occurring within a bioreactor. For two drainage seasons, we monitored nitrate concentrations at 21 locations on an hourly or two-hourly basis within a pilot-scale woodchip bioreactor in Tatuanui, New Zealand. A novel method was developed to account for the variable lag time between entry and exit of a parcel of sampled drainage water. Our results showed that this method not only enabled lag time to be accounted for but also helped quantify volumetric inefficiencies (e.g., dead zone) within the bioreactor. The average RR calculated using this method was significantly higher than the average RR calculated using conventional low-frequency methods. The average RRs of each of the quarter sections within the bioreactor were found to be different. 1-D transport modelling confirmed the effect of nitrate loading on the removal process as nitrate reduction followed Michaelis-Menten (MM) kinetics. These results demonstrate that high-frequency temporal and spatial monitoring of nitrate concentrations in the field allows improved description of bioreactor performance and better understanding of processes occurring within woodchip bioreactors. Thus, insights gained from this study can be used to optimise the design of future field bioreactors.

High-Damped Complex Vector Current Regulator for PMSM Based on Active Damping Function

The complex vector current regulator (CVCR) has been widely used in high-performance PMSM drives because of its simple structure and excellent decoupling property. However, the CVCR's disturbance rejection ability is insufficient due to the plant's low-dynamic and poorly damped pole. To address such problem, an active resistance compensation is requisite. This article proposes a high-damped complex vector (HDCV) current regulator. By reconstructing the damping structure, it improves the characteristic of the conventional active resistance complex vector (ARCV) regulator. The eigenvalues of disturbance rejection function of HDCV achieve the higher natural angular frequency and damping ratio. The frequency response demonstrates that HDCV outperforms ARCV in disturbance rejection under similar noise suppression conditions. It also achieves the extended damping coefficient range without compromising system stability. To broaden HDCV's applications, an improved current predictor based on extended state observer is introduced in the damping function, ensuring superior regulation performance even at low sampling frequencies. The experimental results validate the correctness of damping compensation method and demonstrate the superiority of the proposed scheme over conventional methods.

Global Sensitivity Analysis for Impedance Spectrum Identification of Lithium-Ion Batteries Using Time-Domain Response

Accurate modeling is of great importance for describing battery dynamics under different working conditions. To achieve high accuracy and obtain insights into battery operations, it is critical to bridge the time-domain equivalent circuit model and frequency-domain electrochemical impedance spectroscopy. However, the practical battery running data are recorded at a very low sampling frequency, it is thus challenging to identify the frequency-domain impedance spectrum using time-domain response data. To solve this problem, this article proposes a systematic battery time-domain modeling approach considering impedance spectrum identification. First, a probabilistic approach for global sensitivity analysis is proposed to evaluate the impact of different parameters on model performance. The parameter sensitivity of both integer-order and fractional-order models are evaluated using the Sobol indices obtained by Monte Carlo simulations. Then, a Bayesian optimization algorithm is proposed to identify model parameters to reduce the evaluation times of the battery model. Finally, the experimental data conducted on LiFePO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{4}$</tex-math></inline-formula> cells are employed to verify the proposed method. The results indicate that the total Sobol indices show a miscellaneous sensitivity of parameters on impedance spectra, which consolidates the understanding of impedance characteristics, and the higher order integer-order models with well-initialized low sensitive parameters are more suitable for predicting frequency-domain characteristics.

Feature Cloning and Feature Fusion Based Transportation Mode Detection Using Convolutional Neural Network

The smartphone-based sensors (including accelero-meter, proximity, and gyroscope sensors) are ubiquitous and emerging mobility data sources that could be used for transportation modes (i.e. bus, train, car, walking, and stationary) detection. One of the important challenges in transportation modes detection is to build an appropriate model that can extract useful data from the sensor outputs and that can reduce misclassifications. Several factors make the feature modeling difficult including inappropriate sampling frequency of input signals, wavering behavior of devices (e.g. the changing orientation of a device relative to the human body), and continuous base vibration causing similar sensor outputs for both stationary and non-stationary states and related threshold values of velocity. This paper proposes novel approaches to address these challenges by developing a robust transportation mode detector based on a convolution neural network (CNN). The proposed robust detector develops a feature modeling technique by novel feature fusion and cloning techniques. Pre-trained features are constructed using a separate vanilla neural network (VNN) framework to extract the distinguishing components from the original features that are combined with the original and cloned features. The proposed feature fusion technique is successfully able to overcome the noise from the base vibration and the minimal informative outputs from the lower sampling frequency. This enables the CNN to be trained with more efficient and discriminative features that result in a better classification model. The proposed approaches have been validated using a large volume of mobile sensor data based on the movements of travelers. Different types of mobile sensors have been used to collect data including accelerometer, proximity, and gyroscope. Experimental results demonstrate that the proposed approaches can improve the performance of the detection engine significantly over conventional techniques and reduces the misclassification rate.

Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based on instance-batch normalization network is proposed. This method uses an encoder-decoder structure with attention mechanism, in which skip connections are introduced at the corresponding layers of the encoder and decoder. In this way, the decoder can reconstruct a more accurate power sequence of the target. The proposed model was tested on two public datasets, REDD and UKDALE, and the performance was compared with mainstream algorithms. The results show that the F1 score was higher by an average of 18.4 when compared with mainstream algorithms. Additionally, the mean absolute error reduced by an average of 25%, and the root mean square error was reduced by an average of 22%.

IntelliSleepScorer, a software package with a graphic user interface for automated sleep stage scoring in mice based on a light gradient boosting machine algorithm

Machine learning has been applied in recent years to categorize sleep stages (NREM, REM, and wake) using electroencephalogram (EEG) recordings; however, a well-validated sleep scoring automatic pipeline in rodent research is still not publicly available. Here, we present IntelliSleepScorer, a software package with a graphic user interface to score sleep stages automatically in mice. IntelliSleepScorer uses the light gradient boosting machine (LightGBM) to score sleep stages for each epoch of recordings. We developed LightGBM models using a large cohort of data, which consisted of 5776 h of sleep EEG and electromyogram (EMG) signals across 519 unique recordings from 124 mice. The LightGBM model achieved an overall accuracy of 95.2% and a Cohen’s kappa of 0.91, which outperforms the baseline models such as the logistic regression model (accuracy = 93.3%, kappa = 0.88) and the random forest model (accuracy = 94.3%, kappa = 0.89). The overall performance of the LightGBM model as well as the performance across different sleep stages are on par with that of the human experts. Most importantly, we validated the generalizability of the LightGBM models: (1) The LightGBM model performed well on two publicly available, independent datasets (kappa > = 0.80), which have different sampling frequency and epoch lengths; (2) The LightGBM model performed well on data recorded at a lower sampling frequency (kappa = 0.90); (3) The performance of the LightGBM model is not affected by the light/dark cycle; and (4) A modified LightGBM model performed well on data containing only one EEG and one EMG electrode (kappa > = 0.89). Taken together, the LightGBM models offer state-of-the-art performance for automatic sleep stage scoring in mice. Last, we implemented the IntelliSleepScorer software package based on the validated model to provide an out-of-box solution to sleep researchers (available for download at https://sites.broadinstitute.org/pan-lab/resources).

Accelerations Recorded by Simple Inertial Measurement Units with Low Sampling Frequency Can Differentiate between Individuals with and without Knee Osteoarthritis: Implications for Remote Health Care.

Determining the presence and severity of knee osteoarthritis (OA) is a valuable application of inertial measurement units (IMUs) in the remote monitoring of patients. This study aimed to employ the Fourier representation of IMU signals to differentiate between individuals with and without knee OA. We included 27 patients with unilateral knee osteoarthritis (15 females) and 18 healthy controls (11 females). Gait acceleration signals were recorded during overground walking. We obtained the frequency features of the signals using the Fourier transform. The logistic LASSO regression was employed on the frequency domain features as well as the participant's age, sex, and BMI to distinguish between the acceleration data from individuals with and without knee OA. The model's accuracy was estimated by 10-fold cross-validation. The frequency contents of the signals were different between the two groups. The average accuracy of the classification model using the frequency features was 0.91 ± 0.01. The distribution of the selected features in the final model differed between patients with different severity of knee OA. In this study, we demonstrated that using logistic LASSO regression on the Fourier representation of acceleration signals can accurately determine the presence of knee OA.

A novel combined FFOC-DPC control for wind turbine based on the permanent magnet synchronous generator

The quality of the electrical energy produced from renewable energies is a preponderant need sought by electricity producers. It is in this vision that this present article tries to design a control that combines two control algorithms in order to improve the operation of a variable speed wind energy conversion system (VSWECS). Interestingly, combined control brings together fuzzy field oriented control (FFOC) with direct power control (DPC). The complete conversion chain includes the 1.5 MW permanent magnet synchronous generator (PMSG) and two full-scale mounted back-to-back power converters. Compared to FOC method based on the classical PI regulator, the proposed FFOC scheme is employed to guarantee a better performance between the effect of artificial intelligence (AI) and the simplicity of the standard regulator. This is because the FFOC smart control algorithm is used to control the machine-side-converter (MSC). The DPC control algorithm which uses a relatively low sampling frequency of around 5 kHz is proposed to estimate the active and reactive powers injected into the grid through the grid-side converter (GSC). In addition to the simplicity of implementation and the reduced simulation time, the combination of the FFOC-DPC control algorithm is very reassuring in terms of stability, robustness and tracking of set-points. The overall control scheme is simple to implement and the simulation results clearly show that the proposed control system offers very good performance in terms of total harmonic distortion (THD), which does not exceed 1.49%, a wave of well-balanced injected electric current, and a power factor almost equal to unity. Therefore, the proposed solution is finally compared with other alternatives to validate the results developed by the MATLAB/Simulink platform and consequently guarantee the improvement of the variable speed direct drive WECS.

Impact of assimilating repeated subsurface temperature transects on state estimates of a western boundary current

Western Boundary Currents and the eddies they shed are high priorities for numerical estimation and forecasting due to their economic, ecological and dynamical importance. However, the rapid evolution, complex dynamics and baroclinic structure that is typical of eddies and the relatively sparse sampling in western boundary currents leads to significant challenges in understanding the 3-dimensional structure of these boundary currents and mesoscale eddies. Here, we use Observing System Simulation Experiments (OSSEs) to explore the impact of assimilating synthetic subsurface temperature observations at a range of temporal resolutions, to emulate expendable bathythermograph transects with different repeat frequencies (weekly to quarterly). We explore the improvement in the representation of mesoscale eddies and subsurface conditions in a dynamic western boundary current system, the East Australian Current, with a data-assimilating regional ocean model. A characterisation of the spatial and temporal ocean variability spectrum demonstrates the potential for undersampling and aliasing by a lower sampling frequency. We find that assimilating subsurface temperature data with at least a weekly repeat time best improves subsurface representation of this dynamic, eddy-rich region. However, systemic biases introduced by the data assimilation system hinder the ability of the model to produce more accurate subsurface representation with fortnightly or monthly sampling. Removal of this bias may improve subsurface representation in eddy-rich regions with fortnightly or even less frequent observations. These results highlight the value of both increased subsurface observation density in regions of dynamic oceanography as well as continued development of data assimilation techniques in order to optimise the impact of existing observations.

A Power Routing-Based Fault Detection Strategy for Multi-Terminal VSC-HVDC Grids

Fault detection is one of the most important research topics till today in the field of VSC-HVDC grids protection. In this subject, fast and reliable identification of faults is the most demanding issue, which still requires further study. In this respect, this paper proposes an index for fault detection, which is based on correlation between the powers of DC-link capacitor and the protected line. It is proved by an analytical time-domain transient expression that there is a direct correlation between these two powers under fault conditions. To implement this scheme, the routing of the discharged power of DC-link capacitor into the protected line is the key technique. To do so, the Pearson correlation factor is used in this paper, which efficiently captures the level and direction of the correlation between these two powers. The proposed method benefits from different salient features of very fast fault detection, accurate discrimination of faulted line from healthy lines, covering high-resistance faults, noise immunity, robustness against severe power reversal conditions, robustness against low sampling frequencies, not requiring extra sensor and communication infrastructure, etc. To evaluate the performance of the proposed approach, a complete set of fault scenarios are simulated on a meshed multi-terminal MMC-based VSC-HVDC network.

Tuning and implementation variants of discrete-time ADRC

Practical implementations of active disturbance rejection control (ADRC) will almost always take place in discretized form. Since applications may have quite different needs regarding their discrete-time controllers, this article summarizes and extends the available set of ADRC implementations to provide a suitable variant for as many as possible use cases. In doing so, the gap between quasi-continuous and discrete-time controller tuning is being closed for applications with low sampling frequencies. The main contribution of this article is the derivation of three different discrete-time implementations of error-based ADRC. It is shown that these are almost one-to-one counterparts of existing output-based implementations, to the point where transfer functions and coefficients can be reused in unaltered form. In this way, error-based implementations become firmly rooted in the established landscape of discrete-time ADRC. Furthermore, it becomes possible to equip error-based variants with windup protection abilities known from output-based ADRC.

Novel insights gained from tagging walleye (Sander vitreus) with pop-off data storage tags and acoustic transmitters in Lake Ontario

Improvements in electronic tagging techniques provide new opportunities to gain insights into fish habitat selection and behaviours that have been difficult to capture using traditional assessment methods. However, data from acoustic telemetry studies in large freshwater systems may bias our understanding of fish habitat use and behaviour because of the typically low sampling frequency of transmitters as well as the limited spatial coverage and distribution of receivers in a waterbody. This study combined acoustic transmitters and pop-off data storage tags (pDSTs) on individual walleye in Lake Ontario to gain a better understanding of the feasibility and utility of double tagging a large nearshore freshwater fish. High frequency pDST data (every 2 s) revealed a novel diving behaviour by walleye which made repeated rapid dives beyond their standard daily depth range. Comparison of data from the two tag types showed that in this large freshwater system with low overall receiver coverage (∼4%), mean monthly and daily depth and temperature occupancy of walleye were similar, although many of the extreme values observed in the pDST data were not observed in the acoustic data. The accuracy of daily vertical distance travelled by walleye and summertime diving parameters was dependent on sampling frequency and only the pDST logging on a 2 s interval was able to provide reliable results. The results of this study show that novel insights can be gained, for fish large enough to handle the burden of multiples tags, ranging from spatial ecology to diving behaviours.