Xiang Xie ITIV

Dr.-Ing. Xiang Xie

  • Engesserstr. 5

    76131 Karlsruhe

Dr.-Ing. Xiang Xie

Curriculum Vitae

  • Bachelor degree in mechatronics from Tongji University, Shanghai
    • Bachelor thesis: Design and implementation of a stand-alone software for automated analysis and display of measurement data at VW AG in Wolfsburg, Germany
  • Master studies in electrical engineering and information technology at KIT
    • Master thesis: Evaluation and optimization of the filter approach for feature selection based on evaluation data sets
  • Research associate at ITIV since July 2019
  • PhD finished in January 2023: "Artificial Intelligence for Spectral Analysis: a Comprehensive Framework"

Supervised completed student theses (selection)

  • MA: "Conception and Optimization of an X-Ray Flouresence based Thickness and Concentration Measurement Method of Coating using Neural Networks and Meta Learning Techniques"
  • MA: "Implementation, Optimization and Evaluation of a Feature Selection Method with Evolutionary Algorithm".
  • MA: "Application of Reinforcement Learning Algorithms in Spectral Analysis".
  • MA: "Implementation and Evaluation of Deep Reinforcement Learning Algorithms and their Ability to Generalize in a Virtual Environment".
  • MA: "Implementation and Evaluation of a Recommender System based on Multi-Label Classication Algorithms for the Identication of Composition of Unknown Alloys"
  • MA: "Feature-Selection-Based Iterative Pruning for Neural Network Compression".
  • MA: "Automatic Calibration of Neural-Network-Based X-Ray fluorescence Analysis Using Meta-Learning Techniques"
  • MA: "Adaptation and Extension of Deep Reinforcement Learning Algorithms for Applicability to a TurtleBot3"

 

Publications


2023
PhD Theses
Artificial Intelligence for Spectral Analysis: a Comprehensive Framework. PhD dissertation
Xie, X.
2023, April 19. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000157893
Conference Papers
Towards Predictive Lifetime-Oriented Temperature Control of Power Electronics in E-vehicles via Reinforcement Learning
Chu, A.; Xie, X.; Hermann, C. M.; Stork, W.; Roth-Stielow, J.
2023. IEEE International Conference on Big Data (BigData), Sorrento, Italy, 15th-18th December 2023, 1667–1676, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData59044.2023.10386292
Artificial Intelligence for Spectral Analysis: Challenges and Opportunities
Chu, A.; Xie, X.; Stork, W.
2023. IEEE International Conference on Big Data (BigData), Sorrento, Italy, 15th-18th December 2023, 5176–5180, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData59044.2023.10386853
LETSCOPE: Lifecycle Extensions Through Software-Defined Predictive Control of Power Electronics
Chu, A.; Hermann, C. M.; Silz, J.; Pfau, J.; Barón, K. M.; Anantharajaiah, N.; Schmidt, P.; Hotfilter, T.; Xie, X.; Becker, J.; Kallfass, I.; Roth-Stielow, J.; Stork, W.
2023. IEEE EUROCON 2023 - 20th International Conference on Smart Technologies, 665–670, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/EUROCON56442.2023.10199076
2022
Journal Articles
RGB Image- and Lidar-Based 3D Object Detection Under Multiple Lighting Scenarios
Chen, W.; Tian, W.; Xie, X.; Stork, W.
2022. Automotive Innovation, 5 (3), 251–259. doi:10.1007/s42154-022-00176-2
Conference Papers
PA-DCGAN: Efficient Spectrum Generation using Physics-Aware Deep Convolutional Generative Adversarial Network with Latent Physical Characteristics and Constraints
Xie, X.; Gao, Y.; Stork, W.
2022. 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 04-07 December 2022, 1164–1171, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SSCI51031.2022.10022246
Efficient Comprehensive Element Identification in Large Scale Spectral Analysis with Interpretable Dimension Reduction
Xie, X.; Stork, W.
2022. IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17th-20th December 2022, 5623–5631, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData55660.2022.10020993
Enabling Real-Time Low-Cost Spectral Analysis on Edge Devices with Deep Neural Networks: a Robust Hybrid Approach
Xie, X.; Chen, T.; Stork, W.
2022. IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17th-20th December 2022, 2431–2436, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData55660.2022.10020541
Minimal Cost Device Calibration in Spectral Analysis via Meta Learning: Towards Efficient Deployment of Deep Neural Networks in Industry
Xie, X.; Jin, M.; Chu, A.; Stork, W.
2022. IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17th-20th December 2022, 2123–2132, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData55660.2022.10020366
Efficient Network Pruning via Feature Selection
Xie, X.; Chen, T.; Chu, A.; Stork, W.
2022. 2022 26th International Conference on Pattern Recognition (ICPR), 1843–1850, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICPR56361.2022.9956190
Large-Scale Spectral Analysis for Element Quantification using Deep Neural Networks
Xie, X.; Stork, W.
2022. 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 18-23 July 2022, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IJCNN55064.2022.9891970
2020
Conference Papers
Watermelon: a Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy
Xie, X.; Stork, W.
2020. Proceedings of ICPR 2020 25th International Conference on Pattern Recognition, Milan, 10 – 15 January 2021, 1360–1367, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICPR48806.2021.9413262