Karl-Philipp Kortmann

11 papers C 3Journal 4Unranked 4
YearRankTypeTitle / Venue / Authors
2024 C conf
Model-Based Maximum Friction Coefficient Estimation for Road Surfaces with Gradient or Cross-Slope.
IV
Nicolas Lampe, Simon F. G. Ehlers, Karl-Philipp Kortmann, Clemens Westerkamp, Thomas Seel
2024 J jnl
Predictive Energy Management for Recuperation Axles in Refrigerated Trailers.
CoRR
Dennis Bank, Simon F. G. Ehlers, Karl-Philipp Kortmann, Zeller Tobias, Cujic Patrick, Thomas Seel
2023 C conf
A Recursive Gaussian Process based Online Driving Style Analysis.
ACC
Daniel Fink, Tobias Dues, Karl-Philipp Kortmann, Pascal Blum, Christoph Schweers, Ahmed Trabelsi
2023 C conf
Active Excitations for Maximum Friction Coefficient Estimation.
IV
Nicolas Lampe, Karl-Philipp Kortmann, Clemens Westerkamp, Hans-Georg Jacob
2023 — conf
Comparative Study of Excitation Signals for Active Fault Diagnosis of Belt Drives.
ISIE
Moritz Fehsenfeld, Johannes Kühn, Karl-Philipp Kortmann
2023 — conf
Friction and Road Condition Estimation using Dynamic Bayesian Networks
SDF-MFI
Björn Volkmann, Karl-Philipp Kortmann, Thomas Seel
2023 — conf
Transformer Neural Networks for Maximum Friction Coefficient Estimation of Tire-Road Contact using Onboard Vehicle Sensors.
CDC
Hendrik Schäfke, Nicolas Lampe, Karl-Philipp Kortmann
2021 J jnl
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems.
CoRR
Karl-Philipp Kortmann, Moritz Fehsenfeld, Mark Wielitzka
2021 — conf
Estimation of Stochastic Time Lags between Data Sources in Distributed Production Facilities Based on Cross-Correlated Signals.
ICIT
Johannes Zumsande, Karl-Philipp Kortmann, Tobias Ortmaier, Mark Wielitzka
2020 J jnl
Calibration of Model Uncertainty for Dropout Variational Inference.
CoRR
Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann, Tobias Ortmaier
2019 J jnl
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference.
CoRR
Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann, Tobias Ortmaier
redb/extractors/enum.py
← Index redb/extractors/enum.py python 
from enum import Enum


class Tag(Enum):
    BASIC_PROPERTIES = "basic_properties"
    DIEC = "detect_it_easy"
    CAPA = "capa"
    MALCONTENT = "malcontent"
    DECOMPILED = "decompiled"
    HASH = "hash"
    STRINGS = "strings"
    RELATED = "relatedness" # unused
    PE_FEATURES = "pe_features"
    PE_IMPORT = "pe_import"
    PE_OVERLAY = "pe_overlay"
    PE_SECTION = "pe_section"
    PE_RESOURCE = "pe_resource"
    PE_SIGNATURE = "pe_signature"
    PE_CERTIFICATE = "pe_certificate"
    PE_SIGNER = "pe_signer"
    PE_DOTNET = "pe_dotnet"
    PE_EMBEDDED_EXTRAS = "pe_embedded_extras"
    PE_INCONSISTENCY_TESTS = "pe_inconsistency_tests"
    DOTNET_INCONSISTENCY_TESTS = "dotnet_inconsistency_tests"
    YARA = "yara"
    # Mach-O
    MACHO_FEATURES = "macho_features"
    MACHO_IMPORT = "macho_import"
    MACHO_EXPORT = "macho_export"
    MACHO_DYLIB = "macho_dylib"
    MACHO_SEGMENT = "macho_segment"
    MACHO_SIGNATURE = "macho_signature"
    MACHO_CERTIFICATE = "macho_certificate"
    MACHO_ENTITLEMENT = "macho_entitlement"
    MACHO_UNIVERSAL = "macho_universal"
    MACHO_HASHES = "macho_hashes"
    # ELF
    ELF_FEATURES = "elf_features"
    ELF_IMPORT = "elf_import"
    ELF_EXPORT = "elf_export"
    ELF_SECTION = "elf_section"
    ELF_SEGMENT = "elf_segment"
    ELF_SYMBOL = "elf_symbol"
    ELF_RELOCATION = "elf_relocation"
    ELF_NOTE = "elf_note"
    ELF_DEPENDENCY = "elf_dependency"
    # APK
    APK_FEATURES = "apk_features"
    APK_MANIFEST = "apk_manifest"
    APK_PERMISSIONS = "apk_permissions"
    APK_SIGNATURE = "apk_signature"
    APK_DEX = "apk_dex"
    APK_RESOURCES = "apk_resources"
    APK_NATIVE_LIBS = "apk_native_libs"
    APK_INCONSISTENCY_TESTS = "apk_inconsistency_tests"
    APK_DECOMPILED = "apk_decompiled"
    # JavaScript
    JS_FEATURES = "js_features"
    JS_SUSPICIOUS_APIS = "js_suspicious_apis"
    JS_STRINGS = "js_strings"
    JS_DEOBFUSCATION = "js_deobfuscation"
    JS_CONTENT = "js_content"