.. nunggu-web documentation master file, created by sphinx-quickstart on Sat Apr 2 21:28:31 2011. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. toctree:: :hidden: :maxdepth: 2 Publications ====================================== Books --------------------- :doc:`book_sysiden` .. image:: /images/bookcover2.png :width: 100 :align: left | The book is in Thai and in a draft version. It will be first used in EE531 class (Semester 1, 2021). The book is currently available in hard-copy and upon request for an inspection copy. Some chapters are available for preview. | | .. image:: /images/convexopt_bookchapter.jpg :width: 100 :align: left | | J. Songsiri, J. Dahl, and L. Vandenberghe, `Graphical models of autoregressive processes `_, In: Y. Eldar and D. Palomar (Editors), **Convex Optimization in Signal Processing and Communications**, Cambridge University Press (2010), 89-116 | | Papers --------------------- The followings are the list of my publications and some of them can be found from `my Google Scholar profile `_. My research `social network at Researchgate `_ 1. P. Boonyakitanont, A. Lek-uthai and J. Songsiri, `ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs `_, **IEEE Transactions on Neural Systems and Rehabilitation Engineering (IEEE TNRSE)**, Dec 2021, `[bioRxiv Preprint] `_ #. S. Suksamosorn, N. Hoonchareon and J. Songsiri, `Post-processing of NWP forecasts using Kalman filtering with operational constraints for day-ahead solar power forecasting in Thailand `_, **IEEE Access**, July 2021. `[Codes] `_ #. P. Manomaisaowapak, A. Nartkulpat and J. Songsiri, `Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach `_, **IEEE Transactions on Neural Network and Learning Systems**, July 2021. `[bioRxiv Preprint] `_. `[Codes] `_ #. P. Manomaisaowapak and J. Songsiri, `Joint estimation of multiple Granger causal networks: Inference of group-level brain connectivity `_, arXiv:2105.07196 [cs.LG], May 2021. `[Codes] `_ #. P. Manomaisaowapak and J. Songsiri, `Learning a Common Granger Causality Network using a Non-convex Regularization `_, *Proceedings of The 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)*, Barcelona, Spain, 4-8 May, 2020. This is a virtual conference. `[Codes] `_ #. P. Boonyakitanont, A. Lek-uthai and J. Songsiri, `Automatic Epileptic Seizure Onset-Offset Detection Based on CNN in Scalp EEG `_, *Proceedings of The 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)*, Barcelona, Spain, 4-8 May, 2020. This is a virtual conference. #. P. Boonyakitanont, A. Lek-uthai, K. Chomtho and J. Songsiri, `A Review of Feature Extraction and Performance Evaluation in Epileptic Seizure Detection Using EEG `_, **Biomedical Signal Processing and Control**, Mar 2020, `[ArXiv Preprint] `_ #. A. Pruttiakaravanich and J. Songsiri, `Convex Formulation for Regularized Estimation of Structural Equation Models `_, **Signal Processing**, Vol. 166, Jan, 2020, `[Codes] `_ #. P. Boonyakitanont, A. Lek-uthai, K. Chomtho and J. Songsiri, `A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals `_, bioRxiv Preprint, Jul 2019. #. N. Plub-in and J. Songsiri, `Estimation of Granger Causality of State-Space Models using a Clustering with Gaussian Mixture Models `_ , *Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC)*, Bari, Italy, Oct, 2019. #. N. Plub-in and J. Songsiri, `State-space model estimation of EEG time series for classifying active brain sources `_ , Proceedings of The 2018 Biomedical Engineering International Conference (BMEiCON-2018), Chiangmai, Thailand 2018. #. C. Anuntasethakul, N. Techaphangam, and J. Songsiri, `A State-Space Identification of Building Temperature System `_, Proceedings of ECTI Conference, Thailand 2018. #. S. Suksamosorn, N. Hoonchareon, and J. Songsiri, `Influential Variable Selection for Improving Solar Forecasts from Numerical Weather Prediction `_, Proceedings of ECTI Conference, Thailand 2018. #. V. Layanun, S. Suksamosorn, and J. Songsiri, `Missing-data Imputation for Solar Irradiance Forecasting in Thailand `_, Proceedings of SICE Annual Conference, Kanazawan, Japan, 2017. #. N. Raksasri and J. Songsiri, `Guaranteed Stability of Autoregressive Models with Granger Causality Learned from Wald Tests `_, **Engineering Journal**, vol 21, no 6, 2017. #. F. Van de Steen, L. Faes, E. Karahan, J. Songsiri, P.A. Valdes-Sosa, and D. Marinazzo, `Critical comments on EEG sensor space dynamical connectivity analysis `_, **Brain topography**, 2016. #. N. Raksasri and J. Songsiri, `Exploring Granger Causality for Time series via Wald Tests on Estimated Models with Guaranteed Stability <./pdf/eecon_stableAR_wald.pdf>`_, *Proc. of Electrical Engineering Conference (EECON)*, 2016 (in Thai). #. T. Aranchayanont, J. Songsiri, and K. Srungboonmee, `Spectral Analysis on Vibroartrographic Signal of Total Knee Arthroplasty <./pdf/knee_tencon2016.pdf>`_, *Proc. of IEEE Region 10 Conference (TENCON)*, 2016. #. A. Pruttiakaravanich and J. Songsiri, `A Review on Exploring Brain Networks from fMRI Data `_, **Engineering Journal**, Vol.20, No.3, 2016. #. A. Pruttiakaravanich and J. Songsiri, `A Convex Formulation for Path Analysis in Structural Equation Modeling <./pdf/sem_convex_sice2016.pdf>`_, *Proc. of SICE Annual Conference (SICE 2016)*, Japan, 2016. #. J. Songsiri, `Learning Multiple Granger Graphical Models via Group Fused Lasso <./pdf/ar_fusedlasso.pdf>`_, *Proc. of the 10th Asian Control Conference (ASCC2015)*, Kinabalu, Malaysia, Jun 2015. #. J. Songsiri, `Sparse Optimization Problems in System Identification `_, *Engineering Journal*, vol 5, issue 1, page 51-75, 2014 (in Thai). #. A. Pongrattarakul, P. Lerdkultanon and J. Songsiri, `Sparse System Identification for Discovering Brain Connectivity from fMRI Time Series <./pdf/paper0189_sice.pdf>`_, *Proc. of SICE Annual Conference*, Japan, Sep 2013. #. J. Songsiri, `Sparse autoregressive model estimation for learning Granger causality in time series <./pdf/paper06638248_icassp.pdf>`_, *Proc. of the 38th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)*, Canada, May 2013. #. S. Suwanwimolkul, J. Songsiri, P. Sermwuthisarn and S. Auethavekiat, L1-norm of high frequency components as a regularization term for compressed sensing reconstruction of image signals, *Proc. of IEEE Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)*, Taiwan, 2012. #. J. Songsiri, `Projection onto an l1-norm ball with application to identification of sparse autoregressive models <./pdf/projlp.pdf>`_, *Asean Symposium on Automatic Control*, Vietnam, 2011. #. J. Songsiri and L. Vandenberghe, `Topology selection in graphical models of autoregressive processes `_, **Journal of Machine Learning Research**, 11, 2671-2705, 2010. #. J. Songsiri, J. Dahl, and L. Vandenberghe, `Maximum-likelihood estimation of autoregressive models with conditional independence constraints `_, *Proc. of International Conference on Acoustics, Speech, and Signal Processing (ICASSP)*, Taiwan, 2009. #. J. Songsiri, D. Banjerdpongchai, Dynamic Models of Servo-Driven Conveyor System, Proc. of the 2004 IEEE Region 10 Conference on Analog and Digital Techniques in Electrical Engineering, 2004. #. P. Pakdeepattarakorn, P. Thamvechvitee, J. Songsiri, M. Wongsaisuwan, D.Banjerdpongchai, Dynamic Models of A Rotary Double Inverted Pendulum System, Proc. of the 2004 IEEE Region 10 Conference on Analog and Digital Techniques in Electrical Engineering, 2004. #. J. Songsiri, W. Khovidhungij, Feedback Stabilization of One-Link Flexible Robot Arms: An Infinite-Dimensional System Approach, Proc. of the 42nd SICE Annual Conference, 2003. #. J. Songsiri, W. Khovidhungij, Feedback Control of an Euler-Bernoulli Beam with a Tip Mass: An Infinite-Dimensional Control System Approach, Proc. of the 25th Thailand’s Electrical Engineering Conference, 2002 (In Thai). #. J. Songsiri, N. Phasukvanich, K. Sombatvilailert, W. Apichartwallop, W. Khovidhungij, Adaptive Control for a One-Link Flexible Robot Arms Using Wavelet Networks, Proc. of the 25th Thailand’s Electrical Engineering Conference, 1999 (In Thai). Technical talks ------------------- running from the newest 1. `< NIDA lecture 2020 > Introduction to Solar Forecasting`_ and the `lecture video `_ #. `< IEEE SMC 2019 > Estimation of Granger Causality of State-Space Models using a Clustering with Gaussian Mixture Models`_ #. `< Chula 2018 > Solar Power Forecast for Energy Management Systems in Smart Grid`_ #. `< Nanjing 2017 > Solar Irradiance Forecasting at Chulalongkorn University`_ #. `< TENCON 2016 > Spectral Analysis on Vibroartrographic Signal of Total Knee Arthroplasty`_ #. `< Chengdu 2016 > Sparse Optimization in Exploring Brain Networks`_ #. `< EECON 2016 > Exploring Granger Causality for Time series via Wald Test on Estimated Models with Guaranteed Stability`_ #. `< ASCC 2015 > Learning Multiple Granger Graphical Models via Group Fused Lasso`_ #. `< USM 2013 > Sparse System Identification for Discovering Brain Connectivity from fMRI time series`_ #. `< Joint Seminar 2012 > Learning Granger Graphical Models for Google Flu Trends Data`_ #. `< ASAC 2011 > Projection onto an l1-norm Ball with Application to Identification of Sparse Autoregressive Models`_ .. _< NIDA lecture 2020 > Introduction to Solar Forecasting: ./pdf/solarforecast_intro.pdf .. _< IEEE SMC 2019 > Estimation of Granger Causality of State-Space Models using a Clustering with Gaussian Mixture Models: ./pdf/eegbc_smc2019.pdf .. _< Chula 2018 > Solar Power Forecast for Energy Management Systems in Smart Grid: ./pdf/solar-cu2018.pdf .. _< Nanjing 2017 > Solar Irradiance Forecasting at Chulalongkorn University: ./pdf/solar-cu2017.pdf .. _< TENCON 2016 > Spectral Analysis on Vibroartrographic Signal of Total Knee Arthroplasty: ./pdf/talk-tencon2016.pdf .. _< Chengdu 2016 > Sparse Optimization in Exploring Brain Networks: ./pdf/talk-chengdu-june16.pdf .. _< EECON 2016 > Exploring Granger Causality for Time series via Wald Test on Estimated Models with Guaranteed Stability: ./pdf/talk-eecon2016.pdf .. _< ASCC 2015 > Learning Multiple Granger Graphical Models via Group Fused Lasso: ./pdf/talk-ascc2015.pdf .. _< USM 2013 > Sparse System Identification for Discovering Brain Connectivity from fMRI time series: ./pdf/talk-fmri-usm.pdf .. _< Joint Seminar 2012 > Learning Granger Graphical Models for Google Flu Trends Data: ./pdf/talk-joint-csrl.pdf .. _< ASAC 2011 > Projection onto an l1-norm Ball with Application to Identification of Sparse Autoregressive Models: ./pdf/talk-asac2011.pdf PhD Thesis ------------ `Graphical Models of Time Series: Parameter Estimation and Topology Selection <./pdf/thesis.pdf>`_ Electrical Engineering, UCLA, 2010 **Advisor:** Lieven Vandenberghe