Andrzej Skoczen

@agh.edu.pl

Faculty of Physics and Applied Computer Science
AGH University of Cracow

Andrzej Skoczen


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https://researchid.co/skoczen

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Nuclear and High Energy Physics
26

Scopus Publications

580

Scholar Citations

11

Scholar h-index

11

Scholar i10-index

Scopus Publications

  • Quench Detection System Consolidation for the HL-LHC Era
    Jelena Spasic, Reiner Denz, Guzman Martin Garcia, Tomasz Podzorny, Tetiana Pridii, Jens Steckert, Andrzej Skoczen
    IEEE Transactions on Applied Superconductivity, 2024
    The High Luminosity LHC project (HL-LHC) will lead to increased radiation loads in some of the existing areas of LHC. To anticipate that and to replace aging equipment based on obsolete technologies, a consolidation program for the quench detection systems (QDS) of LHC's superconducting magnet circuits was launched. The existing technology based on digital signal processors and microcontrollers is replaced by radiation tolerant field programmable gate arrays (FPGA). These components provide a significantly increased radiation tolerance and act as a replacement of meanwhile obsolescent components. Two versatile quench detection boards, two acquisition controller boards and one radiation tolerant power supply form the basic building blocks of this consolidation campaign. This contribution gives an overview about the new FPGA-based quench detectors as well as the acquisition controllers. The versatile design and the software-defined algorithms of these building blocks allow to upgrade several classes of quench detectors on LHC's main and corrector magnets. Dependent on the use case, the original functionality of the protection devices is replaced by a functionally improved device or new features are added to enhance diagnostics and protection of the superconducting circuits.
  • Roadmap on artificial intelligence and big data techniques for superconductivity
    Mohammad Yazdani-Asrami, Wenjuan Song, Antonio Morandi, Giovanni De Carne, Joao Murta-Pina, Anabela Pronto, Roberto Oliveira, Francesco Grilli, Enric Pardo, Michael Parizh, Boyang Shen, Tim Coombs, Tiina Salmi, Di Wu, Eric Coatanea, Dominic A Moseley, Rodney A Badcock, Mengjie Zhang, Vittorio Marinozzi, Nhan Tran, Maciej Wielgosz, Andrzej Skoczeń, Dimitrios Tzelepis, Sakis Meliopoulos, Nuno Vilhena, Guilherme Sotelo, Zhenan Jiang, Veit Große, Tommaso Bagni, Diego Mauro, Carmine Senatore, Alexey Mankevich, Vadim Amelichev, Sergey Samoilenkov, Tiem Leong Yoon, Yao Wang, Renato P Camata, Cheng-Chien Chen, Ana Maria Madureira, Ajith Abraham
    Superconductor Science and Technology, 2023
    This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10–20 yr time-frame.
  • Towards Analog Implementation of Spiking Neural Networks for Audio Signals
    Maciej Wielgosz, Andrzej Skoczeń, Jerzy Dąbrowski, Aleksandra Dąbrowska, Waldemar Tabaczynski
    Lecture Notes in Networks and Systems, 2023
  • The upgraded quench protection system for main quadrupoles in the LHC
    Andrzej Skoczeń, Jens Steckert, Jelena Spasic, Daniel Blasco Serrano, Surbhi Mundra, Tetiana Pridii
    Journal of Instrumentation, 2023
    The protection of superconducting magnets is a very important issue and demanding challenge in the LHC and other superconducting accelerating facilities. The quench phenomenon can destroy components of the accelerator, and therefore this digital system was designed, implemented, tested, and installed near each superconducting magnet in the LHC tunnel. The quench detection principle relies on the extraction of resistive voltage by compensation of the inductive part of the voltage. This article presents briefly the architecture applied to the design and the validation of the FPGA-based quench detector for the main quadrupoles of the LHC. The article focusses on digital design with the use of FPGA by VHDL coding and on the verification by simulation. The design is a replacement for the old detection system.
  • Modular data acquisition system for recording activity and electrical stimulation of brain tissue using dedicated electronics
    Paweł Jurgielewicz, Tomasz Fiutowski, Ewa Kublik, Andrzej Skoczeń, Małgorzata Szypulska, Piotr Wiącek, Paweł Hottowy, Bartosz Mindur
    Sensors, 2021
    In this paper, we present a modular Data Acquisition (DAQ) system for simultaneous electrical stimulation and recording of brain activity. The DAQ system is designed to work with custom-designed Application Specific Integrated Circuit (ASIC) called Neurostim-3 and a variety of commercially available Multi-Electrode Arrays (MEAs). The system can control simultaneously up to 512 independent bidirectional i.e., input-output channels. We present in-depth insight into both hardware and software architectures and discuss relationships between cooperating parts of that system. The particular focus of this study was the exploration of efficient software design so that it could perform all its tasks in real-time using a standard Personal Computer (PC) without the need for data precomputation even for the most demanding experiment scenarios. Not only do we show bare performance metrics, but we also used this software to characterise signal processing capabilities of Neurostim-3 (e.g., gain linearity, transmission band) so that to obtain information on how well it can handle neural signals in real-world applications. The results indicate that each Neurostim-3 channel exhibits signal gain linearity in a wide range of input signal amplitudes. Moreover, their high-pass cut-off frequency gets close to 0.6Hz making it suitable for recording both Local Field Potential (LFP) and spiking brain activity signals. Additionally, the current stimulation circuitry was checked in terms of the ability to reproduce complex patterns. Finally, we present data acquired using our system from the experiments on a living rat’s brain, which proved we obtained physiological data from non-stimulated and stimulated tissue. The presented results lead us to conclude that our hardware and software can work efficiently and effectively in tandem giving valuable insights into how information is being processed by the brain.
  • Recurrent neural networks with grid data quantization for modeling LHC superconducting magnets behavior
    Maciej Wielgosz, Andrzej Skoczeń
    Advances in Intelligent Systems and Computing, 2020
  • Using Neural Networks with data Quantization for time Series Analysis in LHC Superconducting Magnets
    Maciej Wielgosz, Andrzej Skoczeń
    International Journal of Applied Mathematics and Computer Science, 2019
    The aim of this paper is to present a model based on the recurrent neural network (RNN) architecture, the long short-term memory (LSTM) in particular, for modeling the work parameters of Large Hadron Collider (LHC) super-conducting magnets. High-resolution data available in the post mortem database were used to train a set of models and compare their performance for various hyper-parameters such as input data quantization and the number of cells. A novel approach to signal level quantization allowed reducing the size of the model, simplifying the tuning of the magnet monitoring system and making the process scalable. The paper shows that an RNN such as the LSTM or a gated recurrent unit (GRU) can be used for modeling high-resolution signals with the accuracy of over 0.95 and a small number of parameters, ranging from 800 to 1200. This makes the solution suitable for hardware implementation, which is essential in the case of monitoring the performance critical and high-speed signal of LHC superconducting magnets.
  • Looking for a Correct Solution of Anomaly Detection in the LHC Machine Protection System
    Maciej Wielgosz, Andrzej Skoczen, Kazimierz Wiatr
    2018 International Conference on Signals and Electronic Systems Icses 2018 Proceedings, 2018
    Demand for high availability and performance of the LHC accelerator imposes stringent constraints on safety-critical systems handled by Machine Protection and Electrical Integrity Group at CERN. Consequently, unique means and equipment were introduced to monitor and protect superconducting magnets of the LHC. This work intends to ameliorate available algorithms by using RNN (Recurrent Neural Network) architectures. The proposed solution is equipped with a series of architecture modifications and optimizations which enable efficient deployment of the RNN model on embedded systems. A set of experiments were conducted which shows that the proposed system outperforms Isolation Forest and OC-SVM (One-Class SVM) algorithms reaching the accuracy of 0.93 for the two-layer LSTM (Long Short-Term Memory) model. Furthermore, the proposed approach allows for significant reduction of input data stream to few bits which enable low latency response of the module.
  • The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
    Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń, Ernesto De Matteis
    Engineering Applications of Artificial Intelligence, 2018
    This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom Gated Recurrent Unit-based detector and developed a method for the detector parameters selection.\n\nThree different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the dataset intended for real-life experiments and model training was composed of the signals acquired from a new type of magnet, to be used during High-Luminosity Large Hadron Collider project. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art One Class Support Vector Machine (OC-SVM) reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties.\n\nIt was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the setup with the lowest maximum false anomaly length of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.
  • Protection of superconducting industrial machinery using RNN-based anomaly detection for implementation in smart sensor
    Maciej Wielgosz, Andrzej Skoczeń, Ernesto De Matteis
    Sensors Switzerland, 2018
    Sensing the voltage developed over a superconducting object is very important in order to make superconducting installation safe. An increase in the resistive part of this voltage (quench) can lead to significant deterioration or even to the destruction of the superconducting device. Therefore, detection of anomalies in time series of this voltage is mandatory for reliable operation of superconducting machines. The largest superconducting installation in the world is the main subsystem of the Large Hadron Collider (LHC) accelerator. Therefore a protection system was built around superconducting magnets. Currently, the solutions used in protection equipment at the LHC are based on a set of hand-crafted custom rules. They were proved to work effectively in a range of applications such as quench detection. However, these approaches lack scalability and require laborious manual adjustment of working parameters. The presented work explores the possibility of using the embedded Recurrent Neural Network as a part of a protection device. Such an approach can scale with the number of devices and signals in the system, and potentially can be automatically configured to given superconducting magnet working conditions and available data. In the course of the experiments, it was shown that the model using Gated Recurrent Units (GRU) comprising of two layers with 64 and 32 cells achieves 0.93 accuracy for anomaly/non-anomaly classification, when employing custom data compression scheme. Furthermore, the compression of proposed module was tested, and showed that the memory footprint can be reduced four times with almost no performance loss, making it suitable for hardware implementation.
  • Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
    Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik
    Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 2017
  • Design of FPGA-based radiation tolerant quench detectors for LHC
    J. Steckert, A. Skoczen
    Journal of Instrumentation, 2017
  • Modular ASIC-based system for large-scale electrical stimulation and recording of brain activity in behaving animals
    Malgorzata Szypulska, Michal Dwuznik, Piotr Wiacek, Andrzej Skoczen, Tomasz Fiutowski, Mariusz Jedraczka, Joanna Dusik, Mohammed Imran Ahmed, Wladyslaw Dabrowski, Pawel Hottowy, Ewa Kublik
    Proceedings of the 23rd International Conference Mixed Design of Integrated Circuits and Systems Mixdes 2016, 2016
  • Properties and application of a multichannel integrated circuit for low-artifact, patterned electrical stimulation of neural tissue
    Paweł Hottowy, Andrzej Skoczeń, Deborah E Gunning, Sergei Kachiguine, Keith Mathieson, Alexander Sher, Piotr Wiącek, Alan M Litke, Władysław Dąbrowski
    Journal of Neural Engineering, 2012
  • Commissioning of quench protection instruments in the LHC superconduction circuits
    Przeglad Elektrotechniczny, 2009
  • Protections of LHC superconducting elements against reslts of disappearance of regard to the influences of short-circult currents effects and overcurrents
    Przeglad Elektrotechniczny, 2009
  • Analysis of power transformers reliability with regard to the influences of short-circuit currents effects and overcurrents
    Przeglad Elektrotechniczny, 2009
  • An integrated multichannel waveform generator for large-scale spatio-temporal stimulation of neural tissue
    Pawel Hottowy, Władysław Dąbrowski, Andrzej Skoczeń, Piotr Wiącek
    Analog Integrated Circuits and Signal Processing, 2008
  • Design of a multichannel ASIC for large scale spatio-temporal distributed stimulation of neural tissue
    Proceedings of the International Conference on Mixed Design of Integrated Circuits and Systems Mixdes 2006, 2006
  • Design of low noise charge amplifier in sub-micron technology for fast shaping time
    Paweł Gryboś, Marek Idzik, Andrzej Skoczeń
    Analog Integrated Circuits and Signal Processing, 2006
  • Development of front-end ASICs for imaging neuronal activity in live tissue
    W. Dabrowski, P. Grybos, P. Hottowy, A. Skoczen, K. Swientek, A.A. Grillo, S. Kachiguine, A.M. Litke, A. Sher
    Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 2005
  • The HADES Pre-Shower detector
    A. Bałanda, M. Jaskuła, M. Kajetanowicz, L. Kidoń, K. Korcyl, W. Kühn, R. Kulessa, A. Malarz, J. Otwinowski, M. Petri, J. Pietraszko, W. Prokopowicz, W. Przygoda, P. Salabura, A. Skoczeń, M. Szczybura, E. Wajda, W. Waluś, M. Wiśniowski, T. Wójcik
    Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 2004
  • Development of integrated circuits for readout of microelectrode arrays to image neuronal activity in live retinal tissue
    W. Dabrowski, P. Grybos, P. Hottowy, A. Skoczen, K. Swientek, N. Bezayiff, A.A. Grillo, S. Kachiguine, A.M. Litke, A. Sher
    IEEE Nuclear Science Symposium Conference Record, 2003
  • Development of a fast pad readout system for the HADES shower detector
    A Bałanda, M Dębowski, M Jaskuła, L Kidoń, R Kulessa, J Foryciarz, E Lubkiewicz, A Malarz, J Otwinowski, J Pietraszko, W Prokopowicz, A Ratajczak, P Salabura, E Wajda, W Waluś, M Kajetanowicz, K Korcyl, A Skoczeń, W Kühn, M Petri
    Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 1998
  • Radiation damage studies of field plate and p-stop n-side silicon microstrip detectors
    J Matheson, H.-G Moser, S Roe, P Weilhammer, S Moszczynski, W Dabrowski, P Grybos, M Idzik, A Skoczen, K Gill, G Hall, B MacEvoy, D Vitè, R Wheadon, P Allport, C Green, J Richardson, R Apsimon, L Evensen, B Avset, P Giubellino, L Ramello
    Nuclear Inst and Methods in Physics Research A, 1995
  • Fast neutron damage of silicon PIN photodiodes
    Władysław Dąbrowski, Kazimierz Korbel, Andrzej Skoczeń
    Nuclear Inst and Methods in Physics Research A, 1991

RECENT SCHOLAR PUBLICATIONS

  • FLAME—a readout ASIC for a compact electromagnetic calorimeter
    M Firlej, T Fiutowski, M Idzik, J Moroń, D Pietruch, A Skoczeń, K Świentek
    Journal of Instrumentation 21 (03), P03005 , 2026
    2026
  • Quench detection system consolidation for the HL-LHC era
    J Spasic, R Denz, GM Garcia, T Podzorny, T Pridii, J Steckert, A Skoczen
    IEEE Transactions on Applied Superconductivity 34 (5), 1-5 , 2024
    2024
    Citations: 1
  • Towards Analog Implementation of Spiking Neural Networks for Audio Signals
    M Wielgosz, A Skoczeń, J Dąbrowski, A Dąbrowska, W Tabaczynski
    Science and Information Conference, 905-922 , 2023
    2023
  • Roadmap on artificial intelligence and big data techniques for superconductivity
    M Yazdani-Asrami, W Song, A Morandi, G De Carne, J Murta-Pina, ...
    Superconductor Science and Technology 36 (4), 043501 , 2023
    2023
    Citations: 88
  • The upgraded quench protection system for main quadrupoles in the LHC
    A Skoczeń, J Steckert, J Spasic, D Blasco Serrano, S Mundra, T Pridii
    Journal of Instrumentation 18 (01), T01004 , 2023
    2023
    Citations: 1
  • Modular data acquisition system for recording activity and electrical stimulation of brain tissue using dedicated electronics
    P Jurgielewicz, T Fiutowski, E Kublik, A Skoczeń, M Szypulska, P Wiącek, ...
    Sensors 21 (13), 4423 , 2021
    2021
    Citations: 7
  • Using neural networks with data quantization for time series analysis in LHC superconducting magnets
    M Wielgosz, A Skoczeń
    International Journal of Applied Mathematics and Computer Science 29 (3) , 2019
    2019
    Citations: 4
  • Protection of superconducting industrial machinery using RNN-based anomaly detection for implementation in smart sensor
    M Wielgosz, A Skoczeń, E De Matteis
    Sensors 18 (11), 3933 , 2018
    2018
    Citations: 19
  • Looking for a correct solution of anomaly detection in the LHC machine protection system
    M Wielgosz, A Skoczen, K Wiatr
    2018 International Conference on Signals and Electronic Systems (ICSES), 257-262 , 2018
    2018
    Citations: 7
  • The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
    M Wielgosz, M Mertik, A Skoczeń, E De Matteis
    Engineering Applications of Artificial Intelligence 74, 166-185 , 2018
    2018
    Citations: 29
  • Recurrent neural networks with grid data quantization for modeling LHC superconducting magnets behavior
    M Wielgosz, A Skoczeń
    Conference on Information Technology, Systems Research and Computational … , 2018
    2018
    Citations: 2
  • The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
    A Skoczeń
    2017
  • Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
    M Wielgosz, A Skoczeń, M Mertik
    Nuclear Instruments and Methods in Physics Research Section A: Accelerators … , 2017
    2017
    Citations: 120
  • Design of FPGA-based radiation tolerant quench detectors for LHC
    J Steckert, A Skoczen
    Journal of Instrumentation 12 (04), T04005-T04005 , 2017
    2017
    Citations: 27
  • arXiv: Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
    M Wielgosz, A Skoczeń, M Mertik
    2017
  • Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
    M Wielgosz, M Mertik
    arXiv preprint arXiv:1702.00833 , 2017
    2017
    Citations: 22
  • A conceptual development of quench prediction app build on LSTM and ELQA framework
    M Mertik, M Wielgosz
    arXiv preprint arXiv:1610.09201 , 2016
    2016
    Citations: 3
  • Modular ASIC-based system for large-scale electrical stimulation and recording of brain activity in behaving animals
    M Szypulska, M Dwużnik, P Wiącek, A Skoczeń, T Fiutowski, M Jędraczka, ...
    2016 MIXDES-23rd International Conference Mixed Design of Integrated … , 2016
    2016
    Citations: 3
  • Using LSTM recurrent neural networks for detecting anomalous behavior of LHC superconducting magnets.[J]
    M Wielgosz, A Skoczen, M Mertik
    CoRR , 2016
    2016
    Citations: 1
  • Properties and application of a multichannel integrated circuit for low-artifact, patterned electrical stimulation of neural tissue
    P Hottowy, A Skoczeń, DE Gunning, S Kachiguine, K Mathieson, A Sher, ...
    Journal of neural engineering 9 (6), 066005 , 2012
    2012
    Citations: 95

MOST CITED SCHOLAR PUBLICATIONS

  • Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
    M Wielgosz, A Skoczeń, M Mertik
    Nuclear Instruments and Methods in Physics Research Section A: Accelerators … , 2017
    2017
    Citations: 120
  • Properties and application of a multichannel integrated circuit for low-artifact, patterned electrical stimulation of neural tissue
    P Hottowy, A Skoczeń, DE Gunning, S Kachiguine, K Mathieson, A Sher, ...
    Journal of neural engineering 9 (6), 066005 , 2012
    2012
    Citations: 95
  • Roadmap on artificial intelligence and big data techniques for superconductivity
    M Yazdani-Asrami, W Song, A Morandi, G De Carne, J Murta-Pina, ...
    Superconductor Science and Technology 36 (4), 043501 , 2023
    2023
    Citations: 88
  • The HADES pre-shower detector
    A Bałanda, M Jaskuła, M Kajetanowicz, L Kidoń, K Korcyl, W Kühn, ...
    Nuclear Instruments and Methods in Physics Research Section A: Accelerators … , 2004
    2004
    Citations: 54
  • An integrated multichannel waveform generator for large-scale spatio-temporal stimulation of neural tissue
    P Hottowy, W Dąbrowski, A Skoczeń, P Wiącek
    Analog Integrated Circuits and Signal Processing 55 (3), 239-248 , 2008
    2008
    Citations: 46
  • The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
    M Wielgosz, M Mertik, A Skoczeń, E De Matteis
    Engineering Applications of Artificial Intelligence 74, 166-185 , 2018
    2018
    Citations: 29
  • Design of FPGA-based radiation tolerant quench detectors for LHC
    J Steckert, A Skoczen
    Journal of Instrumentation 12 (04), T04005-T04005 , 2017
    2017
    Citations: 27
  • Development of a fast pad readout system for the HADES shower detector
    A Bałanda, M Dębowski, M Jaskuła, L Kidoń, R Kulessa, J Foryciarz, ...
    Nuclear Instruments and Methods in Physics Research Section A: Accelerators … , 1998
    1998
    Citations: 27
  • Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
    M Wielgosz, M Mertik
    arXiv preprint arXiv:1702.00833 , 2017
    2017
    Citations: 22
  • Protection of superconducting industrial machinery using RNN-based anomaly detection for implementation in smart sensor
    M Wielgosz, A Skoczeń, E De Matteis
    Sensors 18 (11), 3933 , 2018
    2018
    Citations: 19
  • Design of low noise charge amplifier in sub-micron technology for fast shaping time
    P Gryboś, M Idzik, A Skoczeń
    Analog Integrated Circuits and Signal Processing 49 (2), 107-114 , 2006
    2006
    Citations: 16
  • Modular data acquisition system for recording activity and electrical stimulation of brain tissue using dedicated electronics
    P Jurgielewicz, T Fiutowski, E Kublik, A Skoczeń, M Szypulska, P Wiącek, ...
    Sensors 21 (13), 4423 , 2021
    2021
    Citations: 7
  • Looking for a correct solution of anomaly detection in the LHC machine protection system
    M Wielgosz, A Skoczen, K Wiatr
    2018 International Conference on Signals and Electronic Systems (ICSES), 257-262 , 2018
    2018
    Citations: 7
  • Fast neutron damage of silicon PIN photodiodes
    W Dąbrowski, K Korbel, A Skoczeń
    Nuclear Instruments and Methods in Physics Research Section A: Accelerators … , 1991
    1991
    Citations: 7
  • Using neural networks with data quantization for time series analysis in LHC superconducting magnets
    M Wielgosz, A Skoczeń
    International Journal of Applied Mathematics and Computer Science 29 (3) , 2019
    2019
    Citations: 4
  • A conceptual development of quench prediction app build on LSTM and ELQA framework
    M Mertik, M Wielgosz
    arXiv preprint arXiv:1610.09201 , 2016
    2016
    Citations: 3
  • Modular ASIC-based system for large-scale electrical stimulation and recording of brain activity in behaving animals
    M Szypulska, M Dwużnik, P Wiącek, A Skoczeń, T Fiutowski, M Jędraczka, ...
    2016 MIXDES-23rd International Conference Mixed Design of Integrated … , 2016
    2016
    Citations: 3
  • Recurrent neural networks with grid data quantization for modeling LHC superconducting magnets behavior
    M Wielgosz, A Skoczeń
    Conference on Information Technology, Systems Research and Computational … , 2018
    2018
    Citations: 2
  • Quench detection system consolidation for the HL-LHC era
    J Spasic, R Denz, GM Garcia, T Podzorny, T Pridii, J Steckert, A Skoczen
    IEEE Transactions on Applied Superconductivity 34 (5), 1-5 , 2024
    2024
    Citations: 1
  • The upgraded quench protection system for main quadrupoles in the LHC
    A Skoczeń, J Steckert, J Spasic, D Blasco Serrano, S Mundra, T Pridii
    Journal of Instrumentation 18 (01), T01004 , 2023
    2023
    Citations: 1