Privacy-Preserving Tampering Detection in Automotive Systems

Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data.

Hard Backreaming Due to Hole Shrinkage Through Carbonate Reservoir in Offshore Abu Dhabi

The 3,000 ft long lateral holes drilled through water-injected area in the carbonate reservoir in the offshore Abu Dhabi have been forced to implement hard backreaming. The abnormal extra operational time has been taken due to poor performance in the operation to pull out a bottomhole assembly after drilling to the total depth. The study aims to analyze root-causes of the hard backreaming through the carbonate reservoir in the studied field. The speed of tripping-out was analyzed every stand of drill pipe by using time domain data of movement of traveling block. The correlations between the speed of tripping-out and rock characteristics such as porosity and constituent minerals in rocks were investigated. Hole shape was analyzed in the representative intervals of low trip-out speed using 16-sector caliper derived from azimuthal density logging. Stress concentration around the borehole wall was also analyzed using geomechanical model. The investigation revealed that hole shrinkage due to plastic deformation of the borehole wall was the most possible root-cause of the hard backreaming in the carbonate reservoir. Namely, BHA had to ream up deformed borehole wall in tripping-out. From the viewpoint of rock characteristics, the speed of tripping-out was found to be lower in the specific geologic layers with higher content of dolomite. This is because dolomite rocks cause larger resistance in reaming it in tripping-out since the strength of dolomite rocks is larger than that of limestone. Based on our findings, use of reamers on bit is found to be the better solution to improve the tripping-out performance in the problematic geologic layers instead of conventional operational attempts such as spotting of acid and use of high viscous fluids in hole cleaning. In addition, optimization of the design and position of reamers and stabilizers is essential to succeed in the future 10,000 ft long extended-reach wells in the studied oil field.

Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.

Heterodox transients in time-domain induced polarization

Negative induced polarization (IP) time-domain transients, sign-changing or non-monotonically decaying transients are currently often considered as measurement errors and removed in the data processing. These transients, here named heterodox in the sense of other than generally accepted signals, might originate from measurement errors, inductive effects, or poor signal processing, but synthetic modelling and field measurements show that these transients are physically possible. A simple theoretical explanation of the basic mechanism for their origin can be found through the superposition of contributions from regions with different sensitivities and such heterodox transients can be identified through the processing of full-waveform IP data. A mathematical classification of orthodox and heterodox IP transients into six different types is introduced based on the temporal development of the sign of their amplitude and derivative. The basic mechanism for IP transients with heterodox shapes is further investigated by considering the subsurface Cole-Cole parameter sensitivities and time-varying IP potential for 2D synthetic models. The time-domain forward response and sensitivities are computed through a time transformation that accounts for the current waveform. This approach allows for quantitative unbiased estimates of the time-domain transients and sensitivities, different from the estimates obtained when using multiple direct-current forward computations, as often done in the inversion of time-domain IP data. Time-domain IP transients may differ from the traditionally expected decaying-like transients when the electrode geometry has IP potential sensitivities with different signs for areas with different IP parameters. Hence, previously disregarded IP transients containing valuable information of the subsurface can be kept for inversion and contribute to the final parameter distribution. Increased understanding of theoretically possible IP transients makes way for more accurate processing of data in the future, reducing the time and resources needed for spectral inversion of time-domain data.

Adding colour to mass spectra: Charge Determination Analysis (CHARDA) assigns charge state to every ion peak

Traditionally, mass spectrometry (MS) output is the ion abundance plotted versus ionic mass-to-charge ratio m/z. While employing only commercially available equipment, Charge Determination Analysis (CHARDA) adds a third dimension to MS, estimating for individual peaks their charge states z, starting from z=1, and colour-coding z in m/z spectra. CHARDA combines the analysis of ion signal decay rates in the time-domain data (transients) in Fourier transform (FT) MS with the interrogation of mass defects of biopolymers. Being applied to individual isotopic peaks in a complex protein tandem (MS/MS) dataset, CHARDA facilitates charge state deconvolution of large ionic species in crowded regions, estimating z even in the absence of isotopic distribution (e.g., for monoisotopic mass spectra). CHARDA is fast, robust and consistent with conventional FT MS and FT MS/MS data acquisition procedures. An effective charge state resolution Rz≥6 is obtained, with potential for further improvements.

Seismic Attributes and Acoustic Inversion for Shallow Marine Slope Stratigraphy Analysis

Geophysical seismic surveys have been used in marine site characterization for subsea engineering and the design of offshore structures. Signal processing plays a key role in obtaining seismic attributes from observed seismic data to identify subsurface geological features within complex shallow sediments. Instantaneous amplitude, phase, and frequency are the most widely used seismic attributes to indicate geological features, but those time-domain data are too limited to define an accurate subsurface model in depth. Therefore, seismic inversion is also required to generate additional geospatial subsurface model information to aid in shallow stratigraphy interpretation. In this paper, we applied both geophysical signal processing and stochastic seismic inversion to a high-resolution multichannel seismic dataset from the Eastern North American Margin (ENAM). Seismic attributes from the Hilbert transform and inversion modeling results (acoustic impedance and modeling uncertainty) were integrated to define better geological horizons and discontinuities. The results show the integrated geophysical subsurface models can support seismic interpretation and improve shallow marine site characterization.

Terahertz Waveguide-Based Pulse-Shaper System Analysis and Modeling

In this paper, the transfer function of a passive waveguide-based terahertz pulse shaper is achieved using the time domain data provided by the full-wave simulation of the structure. The fractional order of the transfer function is determined based on the time response resulting from an arbitrary excitation of the proposed pulse shaper. The full-wave electromagnetic numerical analyses are applied to attain the time-domain output data of the helical gold-ribbon dielectric-lined waveguide as the terahertz pulse shaper. In order to verify the simulation results, the proposed device has been examined using two different numerical methods which are the Finite Element Method (FEM) and the Finite Integral Technique (FIT). A good agreement was found between the results of FIT and FEM methods. The use of the system transfer function to analyze the structure is preferable to the full-wave simulation because of saving the execution time. Once the transfer function is determined, one could apply it for the subsequent time-domain analysis of the pulse shaper with various inputs.