High Sampling Frequency Research Articles

Assessing the impact of borehole field data on AI-based deep learning models for heating and cooling prediction

Sensor readings play a critical role in the prediction performance of AI-based data-driven fault-detection and diagnostics for borehole heat exchangers in heating and cooling systems. The accuracy and algorithm selection of individual deep neural networks are well-investigated research topics but the impact of quality of sensors data on algorithm selection and its further impact on prediction performance has not been investigated and quantified for geothermal heating/cooling systems. The objective of the current work is to investigate the impact of data quality, available as measured time-series data from the borehole field, on the prediction accuracy of the deep neural networks-based models. In the current work four different data sets (D-1, D-2, D-3, D-4) are used for data-driven modeling using four different deep machine learning approaches, i.e., LSTM, BD-LSTM, GRU and CNN. The developed AI models have been trained, validated, and tested using the real-field data sets with same number of independent input parameters i.e., inlet temperature and mass flow rate to predict the fluid outlet temperature. The validated AI models are used to predict the performance of borehole heat exchanger to meet the heating and cooling loads. The prediction results show that the BD-LSTM outperforms other deep neural networks in terms of accuracy. With the data sets D-1, D-2, D-3 and D-4 it was observed that BD-LSTM had lowest MAPE of 2.99 %, 0.22 %, 6.39 % and 2.21 % respectively. Several experiments show that the BD-LSTM and LSTM models are better than the other used approaches in terms of prediction accuracy, but larger number of trainable parameters results in more computation time than CNN and GRU respectively. More data points with higher sampling frequency, results in more accurate prediction as depicted by the results from D-2 and D-4. Although, the results showed that the data collection interval is crucial, but different test runs indicate that avoiding the loss of time-series data during the cleaning process is equally important to accurately capture the Seasonality and Trend of the heating/cooling data. This is realized in case of D-1 where almost 58 % of the values were removed as outliers and these missing values were needed to be approximated which also affected the model prediction accuracy. The presented analysis highlights that the LSTM based DNN model has the capability to predict borehole heat exchanger performance accurately and the predictions are highly dependent on data frequency, the quality as well as data quantity from geothermal installations used to train the model.

Groundwater recharge sources and processes in northwest India: Evidence from high frequency water isotope observations

Study regionBist-Doab, Punjab, northwest India. Study focusWater isotopes and specific electrical conductivity (SEC) are used in this paper to investigate groundwater recharge sources and processes in a catchment with a history of high groundwater abstraction for irrigation and canal irrigation. High frequency (every 10 days) samples for water stable isotope analysis (δ18O and δ2H) were sampled from a dense network of shallow (<50 m) and deep (>100 m) hand pumps and tube wells in Bist-Doab, northern Punjab between 2009 and 2014. New hydrological insights for this regionThe study indicates that groundwater recharge is dominated by meteoric sources from high intensity events. However, in a small proportion of sites located in close proximity to some canals and rivers have significant surface water inputs. Isotope observations indicate rapid changes in groundwater recharge sources linked to post-monsoon pumping and seasonal connectivity to surface water inputs, even at some deep sites. Rapid changes are likely linked to poor well integrity, highlighting the risk to groundwater sources from surface water ingress. Shallow groundwaters had significantly higher SEC compared to deeper groundwater observations (p = 0.0002). Overall groundwater SEC is still relatively low (<2000 μS/cm). However, based on previously published mean residence time of (<50 years) in shallow groundwater the high rate of change in salinity at some sites (2.5–10% per year) is of potential concern.

Ultrasonic Thickness Measurement Method and System Implementation Based on Sampling Reconstruction and Phase Feature Extraction.

The existing ultrasonic thickness measurement systems require high sampling frequencies for echo signal acquisition, leading to complex circuit designs and high costs. Moreover, extracting the characteristics of ultrasonic echo signals for accurate thickness measurement poses significant challenges. To address these issues, this paper proposes a method that utilizes conventional sampling frequencies to acquire high-frequency ultrasonic echo signals, overcoming the limitations of high-frequency data acquisition imposed by the Nyquist-Shannon sampling theorem. By employing an improved sampling reconstruction technique, the multi-cycle sampling signals are reconstructed and rearranged within a single cycle, effectively increasing the equivalent sampling frequency. Additionally, a combination of coarse estimation using fast Fourier transform (FFT) and precise phase extraction using the moving sine fitting algorithm is proposed for accurate thickness measurement, resolving the limitations of common thickness measurement methods such as peak detection, envelope detection, and Hilbert autocorrelation in terms of low measurement accuracy. Experimental results obtained from thickness measurements on 45 steel ultrasonic test blocks within the range of 3 mm to 20 mm indicate a measurement error of ±0.01 mm, while for thicknesses ranging from 1 mm to 50 mm, the measurement error is ±0.05 mm.

OFIDS : Online Learning-Enabled and Fingerprint-Based Intrusion Detection System in Controller Area Networks

As a widely used industrial field bus, the controller area network (CAN) lacks security mechanisms (e.g., encryption and authentication). With the development of network connectivity and the increase in external communication, CAN networks are vulnerable to security attacks (e.g., masquerade). Attackers can intrude on CAN networks to control CAN devices, causing security threats. A fingerprint-based intrusion detection system (IDS) in CAN networks can detect masquerade attacks by scanning the unique clock signals of CAN devices. However, most state-of-the-art fingerprint-based IDSs commonly use an analog-to-digital converter module with a low frequency of 60 MHz to sample CAN signals, lowering the accuracy of the trained fingerprint-based IDS model. In addition, almost all fingerprint-based IDSs are trained offline and then detected online in CAN networks, ignoring that system clock signals of hardware change over time, resulting in degraded detection performance. An intrusion detection system based on physical fingerprints can achieve effective identification of masquerade attacks. This paper proposes an online learning-enabled and fingerprint-based IDS (OFIDS) in CAN networks to increase the sampling frequency, simplify the sampling circuit, shorten the delay time, improve the detection response time, and increase the detection accuracy. OFIDS uses a high-speed comparator (i.e., TLV3501) and field-programmable gate arrays (FPGA; i.e., Xilinx ZYNQ-7010) to sample the CAN_High signal, achieving a low sampling delay time of 4.5 ns and a high sampling frequency of 1 GHz. The self-adaptability of the backpropagation neural network is taken advantage of and used to train the OFIDS model with a detection accuracy of 99.9992%. The OFIDS model is deployed to a CAN network prototype with five CAN devices (i.e., two Arduino UNO boards and three STM32 microcontrollers), where online learning is conducted with a low training time of less than 2 min to update the OFIDS model. Experimental results show that OFIDS can achieve at least 99.99% detection accuracy within 0.18 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s in a CAN network prototype and can achieve 98% detection accuracy in a real vehicle.

Multi-Signal Multifractal Detrended Fluctuation Analysis for Uncertain Systems —Application to the Energy Consumption of Software Programs in Microcontrollers

Uncertain systems are those wherein some variability is observed, meaning that different observations of the system will produce different measurements. Studying such systems demands the use of statistical methods over multiple measurements, which allows overcoming the uncertainty, based on the premise that a single measurement is not representative of the system’s behavior. In such cases, the current multifractal detrended fluctuation analysis (MFDFA) method cannot offer confident conclusions. This work presents multi-signal MFDFA (MS-MFDFA), a novel methodology for accurately characterizing uncertain systems using the MFDFA algorithm, which enables overcoming the uncertainty of the system by simultaneously considering a large set of signals. As a case study, we consider the problem of characterizing software (Sw) consumption. The difficulty of the problem mainly comes from the complexity of the interactions between Sw and hardware (Hw), as well as from the high uncertainty level of the consumption measurements, which are affected by concurrent Sw services, the Hw, and external factors such as ambient temperature. We apply MS-MFDFA to generate a signature of the Sw consumption profile, regardless of the execution time, the consumption levels, and uncertainty. Multiple consumption signals (or time series) are built from different Sw runs, obtaining a high frequency sampling of the instant input current for each of them while running the Sw. A benchmark of eight Sw programs for analysis is also proposed. Moreover, a fully functional application to automatically perform MS-MFDFA analysis has been made freely available. The results showed that the proposed methodology is a suitable approximation for the multifractal analysis of a large number of time series obtained from uncertain systems. Moreover, analysis of the multifractal properties showed that this approach was able to differentiate between the eight Sw programs studied, showing differences in the temporal scaling range where multifractal behavior is found.

Inadequate Sampling Frequency and Imprecise Taxonomic Identification Mask Results in Studies of Migratory Freshwater Fish Ichthyoplankton

In South America, knowledge of major spawning sites is crucial for maintaining migratory fish populations. In this study, we aimed to understand the spatio-temporal distribution of fish eggs in the upper São Francisco River using high sampling frequency and DNA metabarcoding identification. We evaluated the possible effects of the non-molecular identification of eggs and decreased sampling frequency on the determination of spawning sites and major breeding periods. Collections were carried out every three days from November 2019 to February 2020. We found that, if we had assumed that all of the free and non-adhesive sampled eggs belonged to migratory species, as is usual in the literature, this assumption would have been wrong for both the spawning sites and the breeding periods. Moreover, any decrease in the frequency of sampling could dramatically affect the determination of the major spawning rivers, and the spawning events of some of the migratory species may not have been detected. Therefore, without the proper identification and adequate sampling frequency of eggs, important spawning sites may be overlooked, leading to ineffective or inappropriate conservation measures.

High-Power-Efficiency Readout Circuit Employing Average Capacitance-to-Voltage Converter for Micro-Electro-Mechanical System Capacitive Accelerometers.

A capacitance-to-voltage converter (CVC) is proposed in this paper and applied to a readout circuit for a micro-electro-mechanical system (MEMS) accelerometer to improve the power efficiency. In a traditional readout circuit, the front-end CVC has to operate at a high sampling frequency to resist thermal noise deterioration due to the large parasitic capacitance introduced by the mechanical sensing element. Thus, the back-end analog-to-digital converter (ADC) also has to operate at a high sampling frequency to avoid noise aliasing when sampling the output signal of the CVC, which leads to high power consumption. The average CVC technique is proposed in this paper to reduce the sampling frequency requirement of the back-end ADC and thus reduce the power consumption. Both the traditional readout circuit and the proposed readout circuit are simulated with a commercial 0.18 μm BCD process. The simulation results show that noise aliasing occurs, and the noise power spectral density (PSD) of the traditional readout circuit increases by 12 dB when the sampling frequency of back-end ADC is reduced by 24 dB. However, in the proposed readout circuit, a noise aliasing effect does not occur. Moreover, the proposed readout circuit reduces the power consumption by 53% without thermal noise deterioration. In addition, the proposed CVC circuits are fabricated in an 0.18 μm BCD process, and the test results show that the presented readout circuit based on the average CVC technique can obtain better performance than the traditional CVC-based readout circuit.

AugerPrime surface detector electronics

Operating since 2004, the Pierre Auger Observatory has led to major advances in our understanding of the ultra-high-energy cosmic rays. The latest findings have revealed new insights that led to the upgrade of the Observatory, with the primary goal of obtaining information on the primary mass of the most energetic cosmic rays on a shower-by-shower basis. In the framework of the upgrade, called AugerPrime, the 1660 water-Cherenkov detectors of the surface array are equipped with plastic scintillators and radio antennas, allowing us to enhance the composition sensitivity. To accommodate new detectors and to increase experimental capabilities, the electronics is also upgraded. This includes better timing with up-to-date GPS receivers, higher sampling frequency, increased dynamic range, and more powerful local processing of the data. In this paper, the design characteristics of the new electronics and the enhanced dynamic range will be described. The manufacturing and test processes will be outlined and the test results will be discussed. The calibration of the SD detector and various performance parameters obtained from the analysis of the first commissioning data will also be presented.

Spot Invalid Point Repair Algorithm of Detector Array Measurement System Based on Image Correlation Coefficient

The detector array method has been widely used in the field of high-energy laser far-field spot parameter measurement due to its ability to directly measure the far-field spot of high-energy lasers, wide dynamic range of the detectors, high system sampling frequency, good real-time performance, and suitability for various testing environment requirements. However, during the measurement process, the irradiation of strong lasers or damage to other hardware systems can result in invalid points in the acquired spot images, thereby reducing the measurement accuracy of the system. In order to achieve accurate measurement of laser far-field spot parameters, this paper establishes an experimental model based on the analysis of the sampling spacing of the detector array target and proposes a laser far-field spot invalid point repair algorithm based on image correlation coefficient. Experimental results demonstrate that the algorithm proposed in this paper effectively reduces the impact of invalid points in the measurement system on the measurement accuracy, and achieves accurate measurement of high-energy laser measurement systems.

Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights

With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.

Laboratory studies of the impact of non-breaking waves on a horizontal emerged deck

The horizontal elements of maritime structures are susceptible to both wave impact and resulting vibrations. These vibrations can lead to a partial or complete failure of structures and require detailed investigations. To address this issue, laboratory experiments were conducted in a wave flume. The aim of this study was to investigate the problem of standing wave impact underneath a horizontal deck. The main focus was on the effect of the wave-induced vertical forces on the global response of the structure. The physical model of the deck was installed above a still water level in front of a vertical barrier. The model was supported by a system of strings and springs. The mechanical system had one degree of freedom corresponding to a small rotational displacement around rear deck edge. Among other physical parameters, the impact pressure generated under the structure due to wave impact was measured. The high sampling frequency of the data acquisition system allowed to capture momentary slamming pressure. The systems of accelerometers and displacement gauges were installed to measure induced vibrations. Thorough analysis of the influence of wave parameters and the deck clearance on the structure response was performed based on the measurements. The rapid slamming force increase, recorded at the moment of impact is not revealed in the dynamic response of the plate.

A Fast Interface Circuit for the Measurement of 10 Ω to 1 GΩ Resistance

In this work, an interface circuit applied to resistive gas or chemical sensors is proposed. The interface circuit includes a detection front-end, a single-end to differential circuit, a successive approximation analog-to-digital converter (SAR ADC), and some reference auxiliary circuits. In detection front-end circuits, mirrored currents in a current mirror usually differ by several orders of magnitude. In order to ensure that the current mirror can be copied accurately, this work uses a negative feedback structure consisting of an operational amplifier and an NMOS tube to ensure that the VDS of the current mirroring tube remains consistent. Simulation results show that the replication error of the current mirror is 0.015%. The proposed interface circuit has a detection range of 10 Ω to 1 GΩ with a relative error of 0.55%. The current multiplication or divided technique allows the interface circuit to have a high sampling frequency of up to 10 kHz. The proposed circuit is based on a 180 nm CMOS process with a chip area of 0.308 mm2 (723 μm ∗ 426 μm). The power consumption of the whole interface circuit is 3.66 mW when the power supply voltage is 1.8 V.

Frequency and seasonality of viable Helicobacter pylori in drinking water in Dhamar Governorate, Yemen.

Helicobacter pylori is an important and common bacterial pathogen in humans. The accumulated evidence of the existence of H. pylori in water from different environmental sources suggests a water-borne transmission route. This study aimed to investigate the occurrence of H. pylori in different water sources used by human populations in Dhamar Governorate, Yemen. 250 samples were randomly collected from the municipal water supply network, wells, and springs. The samples were processed, plated onto modified campy-blood agar, and incubated under microaerobic conditions for 4-10 days. Bacterial identification was based on morphological properties and biochemical tests. Bacteriological analysis showed that 9.6% and 13.2% of tap and surface water samples were contaminated with H. pylori, respectively. Despite a higher frequency in samples from rural areas, these were not significantly (p=0.068) more contaminated than the samples from urban areas. Regarding the seasonal variations of H. pylori detection, 85.71% of positive samples were detected in the late winter and spring seasons (February to May). To conclude, H. pylori transmission through water is likely to occur in Dhamar Governorate. Further prospective studies are highly recommended to provide further evidence and a clearer picture of H. pylori transmission.

Upsampling wave temporal resolution: Investigating wave parameters and the influence on WEC power performance

Power production of wave energy converters (WEC) predicted in the time domain utilize wave resource parameters and time-domain hydrodynamic model simulations of the WEC. While the hydrodynamic model provides high temporal resolution of power production (10’s of Hz), the wave resource parameters are often based on frequency-domain calculations with temporal resolution of 30 minutes to an hour. However, real ocean wave conditions vary on much shorter time scales. Relying on frequency-domain calculations will not be sufficient to capture the short-term variability and accurately predict WEC power production for a standardized methodology that follows power system requirements. These requirements need forecasted data with high sampling frequency for accurate energy predictions. Looking specifically at resource characterization, high temporal resolution datasets are not publicly available or do not exist for many coastal locations. Due to data availability, low temporal resolution datasets are being used in a majority of studies to generate representative wave conditions as inputs to numerical simulations. Representative wave conditions are used to generate wave spectrums. The issue with this practice is spectrums are then used to predict the efficiency of systems that will not accurately capture the variability of waves in short timeframes. Creating a standardized methodology to increase the temporal resolution of metaocean conditions to inform model development can provide better forecasting of power production. Random amplitude, Fourier coefficient methods have been suggested for WEC simulations of finite durations to improve the observed variability in wave heights and power production. Variability using this method does increase for finite durations compared to the commonly used deterministic amplitude method. In this paper we will investigate the influence of wave parameters (significant wave height, maximum wave height, and energy period) on the prediction of WEC power production. A better understanding of the influence of these parameters will provide a path towards future standardization methodology for resource inputs for time-domain modeling.

AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini

Abstract. Ice sheet marine margins via outlet glaciers are susceptible to climate change and are expected to respond through retreat, steepening, and acceleration, although with significant spatial heterogeneity. However, research on ice–ocean interactions has continued to rely on decentralized, manual mapping of features at the ice–ocean interface, impeding progress in understanding the response of glaciers and ice sheets to climate change. The proliferation of remote-sensing images lays the foundation for a better understanding of ice–ocean interactions and also necessitates the automation of terminus delineation. While deep learning (DL) techniques have already been applied to automate the terminus delineation, none involve sufficient quality control and automation to enable DL applications to “big data” problems in glaciology. Here, we build on established methods to create a fully automated pipeline for terminus delineation that makes several advances over prior studies. First, we leverage existing manually picked terminus traces (16 440) as training data to significantly improve the generalization of the DL algorithm. Second, we employ a rigorous automated screening module to enhance the data product quality. Third, we perform a thoroughly automated uncertainty quantification on the resulting data. Finally, we automate several steps in the pipeline allowing data to be regularly delivered to public databases with increased frequency. The automation level of our method ensures the sustainability of terminus data production. Altogether, these improvements produce the most complete and high-quality record of terminus data that exists for the Greenland Ice Sheet (GrIS). Our pipeline has successfully picked 278 239 termini for 295 glaciers in Greenland from Landsat 5, 7, 8 and Sentinel-1 and Sentinel-2 images, spanning the period from 1984 to 2021. The pipeline has been tested on glaciers in Greenland with an error of 79 m. The high sampling frequency and the controlled quality of our terminus data will enable better quantification of ice sheet change and model-based parameterizations of ice–ocean interactions.

Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers

Conventional forest inventories are labour-intensive. This limits the spatial extent and temporal frequency at which woody vegetation is usually monitored. Remote sensing provides cost-effective solutions that enable extensive spatial coverage and high sampling frequency. Recent studies indicate that convolutional neural networks (CNNs) can classify woody forests, plantations, and urban vegetation at the species level using consumer-grade unmanned aerial vehicle (UAV) imagery. However, whether such an approach is feasible in species-rich savanna ecosystems remains unclear. Here, we tested whether small data sets of high-resolution RGB orthomosaics suffice to train U-Net, FC-DenseNet, and DeepLabv3 + in semantic segmentation of savanna tree species. We trained these models on an 18-ha training area and explored whether models could be transferred across space and time. These models could recognise trees in adjacent (mean F1-Score = 0.68) and distant areas (mean F1-Score = 0.61) alike. Over time, a change in plant morphology resulted in a decrease of model accuracy. Our results show that CNN-based tree mapping using consumer-grade UAV imagery is possible in savanna ecosystems. Still, larger and more heterogeneous data sets can further improve model robustness to capture variation in plant morphology across time and space.

A non-unit line protection method for MMC-HVDC grids based on the curvatures of backward traveling waves

The existing protection techniques for high-voltage direct-current (HVDC) grids suffer from several shortcomings such as high sampling frequency, poor robustness, and reliance on simulation for threshold setting. To solve these problems, this paper proposes a non-unit protection method for modular multilevel converter (MMC)-based HVDC grids using the curvatures of backward traveling waves. To this end, the propagation characteristics of traveling waves and the boundary characteristics of DC lines are first studied, then the analytical expressions of backward traveling waves are derived. Moreover, the curvatures of backward traveling waves are analyzed. On this basis, a non-unit protection method is proposed, including zone selection, disturbance identification, and pole selection. At last, with a protection platform and a real-time digital simulator (RTDS) platform of the MMC-HVDC grid, the accuracy and the robustness of the proposed protection method are verified. The results show that the protection method can correctly identify faults with different distances and resistance in 1 ms and has strong robustness against transition resistance, sampling frequency, boundary value, noise, system topology, and line parameters.

One-terminal traveling wave-based transmission line protection for LCC-HVDC systems

This paper proposes a one-terminal traveling wave (TW)-based transmission line protection for line commutated converter (LCC) of high-voltage direct current (HVDC) systems. The method requires the first and second wavefronts to reach the local bus and considers the boundary conditions of the system. A detailed mathematical analysis of the sampling frequency effects, as the basis for a number of innovations of practical interest, is presented here. Firstly, a definition of a minimum sampling frequency is formulated. This is crucial when dealing with the high sampling frequencies traditionally needed by TW-based methods. Secondly, mathematical expressions of protected, unprotected, and uncertainty zones on the transmission line are defined. Thanks to these calculations, the method is also capable of distinguishing faults at the line terminals from faults on the protected transmission line. Thirdly, the non-requirement for the TW propagation speed estimation is proven. Some TW-based protection elements require knowledge of the TW propagation speed, which is a source of errors. The proposed function presented good dependability and was able to operate below 15 ms for a transmission line of 2900 km in length.

Terrestrial dominance of organic carbon in an Early Cretaceous syn-rift lake and its correlation with depositional sequences and paleoclimate

Organic carbon (OC) burial in lakes has been identified as an efficient sink in the global carbon cycle. Abundant input of terrestrial-derived OC leads to high variability in OC origin and type, but its role in determining organic-rich sediments has been overlooked in prior studies. Here, we investigated the OC source and concentration of the Lower Cretaceous (Middle Aptian to Lower Albian) Shahezi Formation (Songliao Basin, NE Asia) to reveal the burial of terrestrial OC in relation to syn-rift lake evolution and paleoclimate change. The sequence stratigraphic framework of fan-deltaic and lacustrine successions was established by identifying depositional facies and sequence boundaries. The lacustrine-dominated interval was further subdivided into four facies associations (i.e., lake shore to littoral siltstones, shallow-littoral mudstones, sublittoral, and profundal) and a few cyclic, parasequence-order packages, using 90 m of continuous cores and high sampling frequency of RoqSCAN SEM-EDS. Multiple independent proxies (macerals identified using correlative light and electron microscopy, pyrolysis indices, Ro, TOC/TN, and δ13Corg) suggest that the organic fraction of the highly mature mudstones was predominantly contributed by terrestrial-derived OC (gas-prone type III/IV kerogen). A direct correlation between depositional facies, chemical weathering proxies (CIA, CIAcorr, and Ln(Al2O3/Na2O)), and OC burial (TOC and HI) has been established. A steep syn-rift slope, a warm–humid climate, abundant vegetation, and the subaqueous transport of OC-bearing sediments (e.g., massive mud-rich conglomerates and sand- to pebble-bearing mudstones), may have jointly promoted the high input of terrestrial OC. A comparison between two Lower Cretaceous terrestrial records from high and low paleolatitudes suggests that the shift from the syn-rift to post-rift phase was accompanied by an increase in TOC concentration and a change toward Type I kerogen of aquatic origin. The tectonically-controlled evolution of rift basins might be an important forcing function for the change of OC sources and concentrations, which is responsible for long-term OC burial in hinterland environments.

Experimental study of mutual effects of high carrier frequency, dead-time and control sample time on IPMSM core loss under SiC inverter excitation

This paper experimentally analyzes the core loss characteristics of an interior permanent magnet synchronous motor (IPMSM) excited by a SiC inverter with the sinusoidal pulse-width modulation (PWM) and high modulation index of 1.1 considering the mutual effects of the high carrier frequencies of up to 200 kHz, different dead-times of 250 and 1000 ns, control sample times of 100–1000 μs, and stator core temperature. The experimental IPMSM drive system is operated in load condition with a torque of 1.05 Nm and rotational velocity of 1500 rpm. Furthermore, the ringing phenomenon and rise time in the motor voltage are measured and analyzed using a high-resolution oscilloscope that has a superior sampling rate of up to 5 giga-samples per second (GS/s), which helps to thoroughly examine the impact of the SiC inverter excitation on the motor core loss. The relations of the total harmonic distortion (THD) of the measured motor voltage and current and the distortions in the magnetic flux density through the stator core to the IPMSM core loss properties are also evaluated. Lastly, explanations and insights based on physics of the obtained results are presented.