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1,620 results on '"Khosravi, Mohammad"'

1. Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin

Author

Nobar, Mahdi, Keller, Jürg, Rupenyan, Alisa, Khosravi, Mohammad, and Lygeros, John

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggered digital twin of the system based on available closed-loop data. We define a controller tuning framework independent of the controller or the plant structure. Our proposed methodology is model-free, making it suitable for nonlinear and unmodelled plants with measurement noise. The objective function consists of performance metrics modeled by Gaussian processes. We utilize the available information in the closed-loop system to identify and progressively maintain a digital twin that guides the optimizer, improving the data efficiency of our method. Switching the digital twin on and off is triggered by data-driven criteria related to the digital twin's uncertainty estimations in the BO tuning framework. Effectively, it replaces much of the exploration of the real system with exploration performed on the digital twin. We analyze the properties of our method in simulation and demonstrate its performance on two real closed-loop systems with different plant and controller structures. The experimental results show that our method requires fewer experiments on the physical plant than Bayesian optimization to find the optimal controller parameters., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2024

6. Generalized Power Iteration with Application to Distributed Connectivity Estimation of Asymmetric Networks

Author

Asadi, M. Mehdi, Khosravi, Mohammad, Mosalli, Hesam, Blouin, Stephane, and Aghdam, Amir G.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article. A power iteration algorithm in a centralized implementation is developed first to compute the generalized algebraic connectivity of asymmetric networks. After properly transforming the Laplacian matrix of the network, two sequences of one-dimensional and two-dimensional subspaces are generated iteratively, one of which converges to the desired subspace spanned by the eigenvector(s) associated with the eigenvalue(s) representing the network's generalized algebraic connectivity. A distributed implementation of the proposed power iteration algorithm is then developed to compute the generalized algebraic connectivity from the viewpoint of each node, which is scalable to any asymmetric network of any size with a fixed message length per node. The convergence analysis of these algorithms is subsequently provided under some weak assumptions. The efficiency of the developed algorithms in computing the network connectivity is then demonstrated by simulations., Comment: 19 pages, 7 figures

Published
2023

9. Robust Combinatorial Optimization Problems Under Budgeted Interdiction Uncertainty

Author

Goerigk, Marc and Khosravi, Mohammad

Subjects
Mathematics - Optimization and Control
Abstract

In robust combinatorial optimization, we would like to find a solution that performs well under all realizations of an uncertainty set of possible parameter values. How we model this uncertainty set has a decisive influence on the complexity of the corresponding robust problem. For this reason, budgeted uncertainty sets are often studied, as they enable us to decompose the robust problem into easier subproblems. We propose a variant of discrete budgeted uncertainty for cardinality-based constraints or objectives, where a weight vector is applied to the budget constraint. We show that while the adversarial problem can be solved in linear time, the robust problem becomes NP-hard and not approximable. We discuss different possibilities to model the robust problem and show experimentally that despite the hardness result, some models scale relatively well in the problem size.

Published
2023

10. Diagonally Square Root Integrable Kernels in System Identification

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

In recent years, the reproducing kernel Hilbert space (RKHS) theory has played a crucial role in linear system identification. The core of a RKHS is the associated kernel characterizing its properties. Accordingly, this work studies the class of diagonally square root integrable (DSRI) kernels. We demonstrate that various well-known stable kernels introduced in system identification belong to this category. Moreover, it is shown that any DSRI kernel is also stable and integrable. We look into certain topological features of the RKHSs associated with DSRI kernels, particularly the continuity of linear operators defined on the respective RKHSs. For the stability of a Gaussian process centered at a stable impulse response, we show that the necessary and sufficient condition is the diagonally square root integrability of the corresponding kernel. Furthermore, we elaborate on this result by providing proper interpretations.

Published
2023

13. Optimal Scenario Reduction for One- and Two-Stage Robust Optimization

Author

Goerigk, Marc and Khosravi, Mohammad

Subjects
Mathematics - Optimization and Control
Abstract

Robust optimization typically follows a worst-case perspective, where a single scenario may determine the objective value of a given solution. Accordingly, it is a challenging task to reduce the size of an uncertainty set without changing the resulting objective value too much. On the other hand, robust optimization problems with many scenarios tend to be hard to solve, in particular for two-stage problems. Hence, a reduced uncertainty set may be central to find solutions in reasonable time. We propose scenario reduction methods that give guarantees on the performance of the resulting robust solution. Scenario reduction problems for one- and two-stage robust optimization are framed as optimization problems that only depend on the uncertainty set and not on the underlying decision making problem. Experimental results indicate that objective values for the reduced uncertainty sets are closely correlated to original objective values, resulting in better solutions than when using general-purpose clustering methods such as K-means.

Published
2022

14. Representer Theorem for Learning Koopman Operators

Author

Khosravi, Mohammad

Subjects
Electrical Engineering and Systems Science - Systems and Control, Electrical Engineering and Systems Science - Signal Processing
Abstract

In this work, the problem of learning Koopman operator of a discrete-time autonomous system is considered. The learning problem is formulated as a constrained regularized empirical loss minimization in the infinite-dimensional space of linear operators. We show that under certain but general conditions, a representer theorem holds for the learning problem. This allows reformulating the problem in a finite-dimensional space without any approximation and loss of precision. Following this, we consider various cases of regularization and constraints in the learning problem, including the operator norm, the Frobenius norm, rank, nuclear norm, and stability. Subsequently, we derive the corresponding finite-dimensional problem. Furthermore, we discuss the connection between the proposed formulation and the extended dynamic mode decomposition. Finally, we provide an illustrative numerical example.

Published
2022

16. The Existence and Uniqueness of Solutions for Kernel-Based System Identification

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Electrical Engineering and Systems Science - Signal Processing, Mathematics - Optimization and Control
Abstract

The notion of reproducing kernel Hilbert space (RKHS) has emerged in system identification during the past decade. In the resulting framework, the impulse response estimation problem is formulated as a regularized optimization defined on an infinite-dimensional RKHS consisting of stable impulse responses. The consequent estimation problem is well-defined under the central assumption that the convolution operators restricted to the RKHS are continuous linear functionals. Moreover, according to this assumption, the representer theorem hold, and therefore, the impulse response can be estimated by solving a finite-dimensional program. Thus, the continuity feature plays a significant role in kernel-based system identification. This paper shows that this central assumption is guaranteed to be satisfied in considerably general situations, namely when the kernel is an integrable function and the input signal is bounded. Furthermore, the strong convexity of the optimization problem and the continuity property of the convolution operators imply that the kernel-based system identification admits a unique solution. Consequently, it follows that kernel-based system identification is a well-defined approach.

Published
2022

21. Benchmarking Problems for Robust Discrete Optimization

Author

Goerigk, Marc and Khosravi, Mohammad

Subjects
Mathematics - Optimization and Control, Computer Science - Data Structures and Algorithms
Abstract

Robust discrete optimization is a highly active field of research where a plenitude of combinations between decision criteria, uncertainty sets and underlying nominal problems are considered. Usually, a robust problem becomes harder to solve than its nominal counterpart, even if it remains in the same complexity class. For this reason, specialized solution algorithms have been developed. To further drive the development of stronger solution algorithms and to facilitate the comparison between methods, a set of benchmark instances is necessary but so far missing. In this paper we propose a further step towards this goal by proposing several instance generation procedures for combinations of min-max, min-max regret, two-stage and recoverable robustness with interval, discrete or budgeted uncertainty sets. Besides sampling methods that go beyond the simple uniform sampling method that is the de-facto standard to produce instances, also optimization models to construct hard instances are considered. Using a selection problem for the nominal ground problem, we are able to generate instances that are several orders of magnitudes harder to solve than uniformly sampled instances when solving them with a general mixed-integer programming solver. All instances and generator codes are made available online.

Published
2022

24. Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Author

Bhattacharjee, Natalia V, Schumacher, Austin E, Aali, Amirali, Abate, Yohannes Habtegiorgis, Abbasgholizadeh, Rouzbeh, Abbasian, Mohammadreza, Abbasi-Kangevari, Mohsen, Abbastabar, Hedayat, Abd ElHafeez, Samar, Abd-Elsalam, Sherief, Abdollahi, Mohammad, Abdollahifar, Mohammad-Amin, Abdoun, Meriem, Abdullahi, Auwal, Abebe, Mesfin, Abebe, Samrawit Shawel, Abiodun, Olumide, Abolhassani, Hassan, Abolmaali, Meysam, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abrigo, Michael R M, Abtahi, Dariush, Abualruz, Hasan, Abubakar, Bilyaminu, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Adal, Tadele Girum Girum, Adane, Mesafint Molla, Adeagbo, Oluwafemi Atanda Adeagbo, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Aden, Bashir, Adepoju, Abiola Victor, Adetokunboh, Olatunji O, Adetunji, Juliana Bunmi, Adeyinka, Daniel Adedayo, Adeyomoye, Olorunsola Israel, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afolabi, Rotimi Felix, Afyouni, Shadi, Afzal, Muhammad Sohail, Afzal, Saira, Aghamiri, Shahin, Agodi, Antonella, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahlstrom, Austin J, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Tauseef, Ahmed, Ali, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Meqdad Saleh, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Akalu, Gizachew Taddesse, Akbarialiabad, Hossein, Akinyemi, Rufus Olusola, Akkaif, Mohammed Ahmed, Akkala, Sreelatha, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Qadire, Mohammad, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albakri, Almaza, Albashtawy, Mohammed, AlBataineh, Mohammad T, Alemi, Hediyeh, Alemi, Sharifullah, Alemu, Yihun Mulugeta, Al-Eyadhy, Ayman, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhajri, Noora, Alhalaiqa, Fadwa Alhalaiqa Naji, Alhassan, Robert Kaba, Ali, Abid, Ali, Beriwan Abdulqadir, Ali, Liaqat, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Alif, Sheikh Mohammad, Aligol, Mohammad, Alijanzadeh, Mehran, Aljasir, Mohammad A M, Aljunid, Syed Mohamed, Al-Marwani, Sabah, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Omar, Alomari, Mahmoud A, Al-Omari, Basem, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Raddadi, Rajaa M, Al-Sabah, Salman Khalifah, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvi, Farrukh Jawad, Alvis-Guzman, Nelson, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Aly, Safwat, Alzoubi, Karem H, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Amini-Rarani, Mostafa, Amiri, Sohrab, Ampomah, Irene Gyamfuah, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderlini, Deanna, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Anil, Abhishek, Anil, Sneha, Ansar, Adnan, Ansari-Moghaddam, Alireza, Antony, Catherine M, Antriyandarti, Ernoiz, Anvari, Saeid, ANWAR, SALEHA, Anwer, Razique, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Arabzadeh Bahri, Razman, Arafa, Elshaimaa A, Arafat, Mosab, Araújo, Ana Margarida, Aravkin, Aleksandr Y, Aremu, Abdulfatai, Aripov, Timur, Arkew, Mesay, Armocida, Benedetta, Ärnlöv, Johan, Arooj, Mahwish, Artamonov, Anton A, Arulappan, Judie, Aruleba, Raphael Taiwo, Arumugam, Ashokan, Asadi-Lari, Mohsen, Asemi, Zatollah, Asgary, Saeed, Asghariahmadabad, Mona, Asghari-Jafarabadi, Mohammad, Ashemo, Mubarek Yesse, Ashraf, Muhammad, Ashraf, Tahira, Asika, Marvellous O, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Atreya, Alok, Aujayeb, Avinash, Ausloos, Marcel, Avan, Abolfazl, Aweke, Amlaku Mulat, Ayele, Getnet Melaku, Ayyoubzadeh, Seyed Mohammad, Azadnajafabad, Sina, Azevedo, Rui M S, Azzam, Ahmed Y, Badar, Muhammad, Badiye, Ashish D, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bahmanziari, Najmeh, Bai, Ruhai, Baig, Atif Amin, Baker, Jennifer L, Bako, Abdulaziz T, Bakshi, Ravleen Kaur, Balasubramanian, Madhan, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Bandyopadhyay, Soham, Banik, Biswajit, Banik, Palash Chandra, Bansal, Hansi, Baran, Mehmet Firat, Barchitta, Martina, Bardhan, Mainak, Bardideh, Erfan, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barrow, Amadou, Barteit, Sandra, Basharat, Zarrin, Bashir, Asma'u I J, Bashiru, Hameed Akande, Basiru, Afisu, Basso, João Diogo, Basu, Sanjay, Batiha, Abdul-Monim Mohammad, Batra, Kavita, Baune, Bernhard T, Bayati, Mohsen, Begum, Tahmina, Behboudi, Emad, Behnoush, Amir Hossein, Beiranvand, Maryam, Bejarano Ramirez, Diana Fernanda, Bekele, Alehegn, Belay, Sefealem Assefa, Belgaumi, Uzma Iqbal, Bell, Michelle L, Bello, Olorunjuwon Omolaja, Beloukas, Apostolos, Bensenor, Isabela M, Berezvai, Zombor, Berhie, Alemshet Yirga, Bermudez, Amiel Nazer C, Bettencourt, Paulo J G, Bhagavathula, Akshaya Srikanth, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhardwaj, Prarthna V, Bhaskar, Sonu, Bhat, Vivek, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhatti, Rajbir, Biondi, Antonio, Bisignano, Catherine, Biswas, Atanu, Biswas, Raaj Kishore, Bitra, Veera R, Bjørge, Tone, Bliss, Elye, Boachie, Micheal Kofi, Bobirca, Anca Vasilica, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bogale, Eyob Ketema, Bogale, Kassawmar Angaw, Bonakdar Hashemi, Milad, Bora Basara, Berrak, Bouaoud, Souad, Braithwaite, Dejana, Brauer, Michael, Breitborde, Nicholas J K, Bryazka, Dana, Bulamu, Norma B, Buonsenso, Danilo, Burkart, Katrin, Burns, Richard A, Bustanji, Yasser, Butt, Nadeem Shafique, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Calina, Daniela, Campos-Nonato, Ismael R, Cao, Fan, Cao, Shujin, Capodici, Angelo, Carreras, Giulia, Carugno, Andrea, Castañeda-Orjuela, Carlos A, Castelpietra, Giulio, Cattaruzza, Maria Sofia, Caye, Arthur, Cegolon, Luca, Cembranel, Francieli, Cerin, Ester, Chadwick, Joshua, Chahine, Yaacoub, Chakraborty, Chiranjib, Chalek, Julian, Chan, Jeffrey Shi Kai, Charalampous, Periklis, Chattu, Vijay Kumar, Chaturvedi, Sarika, Chavula, Malizgani Paul, Chen, An-Tian, Chen, Haowei, Chen, Simiao, Chi, Gerald, Chichagi, Fatemeh, Chien, Ju-Huei, Ching, Patrick R, Cho, William C S, Choi, Sungchul, Chong, Bryan, Chopra, Hitesh, Choudhari, Sonali Gajanan, Christopher, Devasahayam J, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Chung, Eric, Chung, Sheng-Chia, Cindi, Zinhle, Cioffi, Iolanda, Ciuffreda, Raffaela, Claro, Rafael M, Coberly, Kaleb, Columbus, Alyssa, Comfort, Haley, Conde, Joao, Criqui, Michael H, Cruz-Martins, Natália, Cuadra-Hernández, Silvia Magali, Dadana, Sriharsha, Dadras, Omid, Dahiru, Tukur, Dai, Zhaoli, Dalton, Bronte, Damiani, Giovanni, Darwesh, Aso Mohammad, Das, Jai K, Das, Saswati, Dashti, Mohsen, Dastiridou, Anna, Dávila-Cervantes, Claudio Alberto, Davletov, Kairat, Debele, Aklilu Tamire, Debopadhaya, Shayom, Delavari, Somayeh, Delgado-Enciso, Ivan, Demeke, Dessalegn, Demessa, Berecha Hundessa, Deng, Xinlei, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Desai, Hardik Dineshbhai, Desai, Rupak, Devanbu, Vinoth Gnana Chellaiyan, Dhali, Arkadeep, Dhama, Kuldeep, Dhimal, Meghnath, Dhulipala, Vishal R, Dias da Silva, Diana, Diaz, Daniel, Diaz, Michael J, Dima, Adriana, Ding, Delaney D, Dirac, M Ashworth, Do, Thanh Chi, Do, Thao Huynh Phuong, do Prado, Camila Bruneli, Dohare, Sushil, Dong, Wanyue, D'Oria, Mario, dos Santos, Wendel Mombaque, Doshmangir, Leila, Dowou, Robert Kokou, Dsouza, Ashel Chelsea, Dsouza, Haneil Larson, Dsouza, Viola, Dube, John, Duprey, Joe, Duraes, Andre Rodrigues, Duraisamy, Senbagam, Durojaiye, Oyewole Christopher, Dutta, Sulagna, Dwyer-Lindgren, Laura, Dzianach, Paulina Agnieszka, Dziedzic, Arkadiusz Marian, Ebrahimi, Alireza, Edinur, Hisham Atan, Edvardsson, Kristina, Efendi, Ferry, Eikemo, Terje Andreas, Ekholuenetale, Michael, El Tantawi, Maha, Elemam, Noha Mousaad, ElGohary, Ghada Metwally Tawfik, Elhadi, Muhammed, Elilo, Legesse Tesfaye, Elmeligy, Omar Abdelsadek Abdou, Elmonem, Mohamed A, Elshaer, Mohammed, Elsohaby, Ibrahim, Emami Zeydi, Amir, Engelbert Bain, Luchuo, Eskandarieh, Sharareh, Esposito, Francesco, Estep, Kara, Etaee, Farshid, Fabin, Natalia, Fagbamigbe, Adeniyi Francis, Fahimi, Saman, Fakhri-Demeshghieh, Aliasghar, Falzone, Luca, Faramarzi, Ali, Faris, MoezAlIslam Ezzat Mahmoud, Farmer, Sam, Faro, Andre, Fasanmi, Abidemi Omolara, Fatehizadeh, Ali, Fauk, Nelsensius Klau, Fazeli, Pooria, Feigin, Valery L, Fereshtehnejad, Seyed-Mohammad, Feroze, Abdullah Hamid, Ferrara, Pietro, Ferreira, Nuno, Fetensa, Getahun, Filip, Irina, Fischer, Florian, Flavel, Joanne, Foigt, Nataliya A, Folayan, Morenike Oluwatoyin, Fomenkov, Artem Alekseevich, Foroutan, Behzad, Foschi, Matteo, Fowobaje, Kayode Raphael, Francis, Kate Louise, Freitas, Alberto, Fukumoto, Takeshi, Fuller, John E, Fux, Blima, Gaal, Peter Andras, Gadanya, Muktar A, Gaidhane, Abhay Motiramji, Galali, Yaseen, Gallus, Silvano, Gandhi, Aravind P, Ganesan, Balasankar, Ganiyani, Mohammad Arfat, Garcia-Gordillo, M.A., Garg, Naval, Gautam, Rupesh K, Gazzelloni, Federica, Gbadamosi, Semiu Olatunde, Gebregergis, Miglas W, Gebrehiwot, Mesfin, Gebremariam, Tesfay Brhane, Gebremariam, Tesfay B B, Gebremeskel, Teferi Gebru, Geda, Yohannes Fikadu, Georgescu, Simona Roxana, Gerema, Urge, Geremew, Habtamu, Getachew, Motuma Erena, Gething, Peter W, Ghasemi, MohammadReza, Ghasempour Dabaghi, Ghazal, Ghasemzadeh, Afsaneh, Ghassemi, Fariba, Ghazy, Ramy Mohamed, Ghimire, Sailaja, Gholamian, Asadollah, Gholamrezanezhad, Ali, Ghorbani, Mahsa, Ghoshal, Aloke Gopal, Ghuge, Arun Digambarrao, Gil, Artyom Urievich, Gill, Tiffany K, Giorgi, Matteo, Girmay, Alem, Glasbey, James C, Göbölös, Laszlo, Goel, Amit, Golchin, Ali, Golechha, Mahaveer, Goleij, Pouya, Gopalani, Sameer Vali, Goudarzi, Houman, Goulart, Alessandra C, Goyal, Anmol, Graham, Simon Matthew, Grivna, Michal, Guan, Shi-Yang, Guarducci, Giovanni, Gubari, Mohammed Ibrahim Mohialdeen, Gudeta, Mesay Dechasa, Guicciardi, Stefano, Gulati, Snigdha, Gulisashvili, David, Gunawardane, Damitha Asanga, Guo, Cui, Gupta, Anish Kumar, Gupta, Bhawna, Gupta, Manoj Kumar, Gupta, Mohak, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vijai Kumar, Gupta, Vivek Kumar, Haakenstad, Annie, Habibzadeh, Farrokh, Hadi, Najah R, Haep, Nils, Hajibeygi, Ramtin, Haller, Sebastian, Halwani, Rabih, Hamadeh, Randah R, Hamdy, Nadia M, Hameed, Sajid, Hamidi, Samer, Han, Qiuxia, Handal, Alexis J, Hankey, Graeme J, Haque, Md Nuruzzaman, Haro, Josep Maria, Hasaballah, Ahmed I, Hasan, Ikramul, Hasan, Mohammad Jahid, Hasan, S.M. Mahmudul, Hasani, Hamidreza, Hasnain, Md Saquib, Hassan, Amr, Hassan, Ikrama, Hassanipour, Soheil, Hassankhani, Hadi, Hay, Simon I, Hebert, Jeffrey J, Hegazi, Omar E, Heidari, Mohammad, Helfer, Bartosz, Hemmati, Mehdi, Herrera-Serna, Brenda Yuliana, Herteliu, Claudiu, Hessami, Kamran, Hezam, Kamal, Hiraike, Yuta, Hoan, Nguyen Quoc, Holla, Ramesh, Horita, Nobuyuki, Hossain, Md Mahbub, Hossain, Mohammad Bellal Hossain, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hsairi, Mohamed, Hsieh, Vivian Chia-rong, Hu, Chengxi, Huang, Junjie, Huda, M Mamun, Humayun, Ayesha, Hussain, Javid, Hussein, Nawfal R, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Iftikhar, Pulwasha Maria, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Immurana, Mustapha, Inbaraj, Leeberk Raja, Iqbal, Afrin, Islam, Md. Rabiul, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Isola, Gaetano, Iwagami, Masao, Iyer, Mahalaxmi, J, Linda Merin, Jaafari, Jalil, Jacob, Louis, Jadidi-Niaragh, Farhad, Jaggi, Khushleen, Jahankhani, Kasra, Jahanmehr, Nader, Jahrami, Haitham, Jain, Akhil, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Jakovljevic, Mihajlo, Jamshidi, Elham, Javadov, Sabzali, Javaheri, Tahereh, Jayapal, Sathish Kumar, Jayaram, Shubha, Jee, Sun Ha, Jeganathan, Jayakumar, Jha, Anil K, Jha, Ravi Prakash, Jiang, Heng, Jokar, Mohammad, Jonas, Jost B, Joo, Tamas, Joseph, Nitin, Joshua, Charity Ehimwenma, Joukar, Farahnaz, Jozwiak, Jacek Jerzy, Jürisson, Mikk, K, Vaishali, Kaambwa, Billingsley, Kabir, Abdulkareem, Kabir, Ali, Kabir, Hannaneh, Kabir, Zubair, Kalani, Rizwan, Kalankesh, Leila R, Kaliyadan, Feroze, Kalra, Sanjay, Kamath, Rajesh, Kamath, Sagarika, Kanchan, Tanuj, Kanmiki, Edmund Wedam, Kanmodi, Kehinde Kazeem, Kannan S, Suthanthira, Kansal, Sushil Kumar, Kantar, Rami S, Kapoor, Neeti, Karajizadeh, Mehrdad, Karami, Manoochehr, Karaye, Ibraheem M, Kashoo, Faizan Zaffar, Kasraei, Hengameh, Kassebaum, Nicholas J, Kassel, Molly B, Kauppila, Joonas H, Kazemi, Foad, Kazeminia, sara, Kempen, John H, Kendal, Evie Shoshannah, Keshtkar, Kamyab, Keykhaei, Mohammad, Khajuria, Himanshu, Khalaji, Amirmohammad, Khalid, Nauman, Khalil, Anees Ahmed, Khalilian, Alireza, Khamesipour, Faham, Khan, Ajmal, Khan, Asaduzzaman, Khan, Ikramullah, Khan, M Nuruzzaman, Khan, Maseer, Khan, Mohammad Jobair, Khan, Moien AB, Khang, Young-Ho, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khavandegar, Armin, Khayat Kashani, Hamid Reza, Khidri, Feriha Fatima, Khormali, Moein, Khosravi, Mohammad Ali, Khosrowjerdi, Mahmood, Kidane, Wondwosen Teklesilasie, Kifle, Zemene Demelash, Kim, Julie Sojin, Kim, Min Seo, Kimokoti, Ruth W, Kinzel, Kasey E, Kiross, Girmay Tsegay, Kisa, Adnan, Kisa, Sezer, Kolahi, Ali-Asghar, Kompani, Farzad, Koren, Gerbrand, Korzh, Oleksii, Kosen, Soewarta, Koulmane Laxminarayana, Sindhura Lakshmi, 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Sergeevich, Zhang, Haijun, Zhang, Meixin, Zhang, Yunquan, Zhong, Chenwen, Zhou, Juexiao, Zhu, Bin, Zhu, Lei, Zielińska, Magdalena, Zou, Zhiyong, Zyoud, Samer H, Murray, Christopher J L, Smith, Amanda E, and Vollset, Stein Emil

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2024
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25. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

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Naghavi, Mohsen, Ong, Kanyin Liane, Aali, Amirali, Ababneh, Hazim S, Abate, Yohannes Habtegiorgis, Abbafati, Cristiana, Abbasgholizadeh, Rouzbeh, Abbasian, Mohammadreza, Abbasi-Kangevari, Mohsen, Abbastabar, Hedayat, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed, Abdollahi, Mohammad, Abdollahifar, Mohammad-Amin, Abdoun, Meriem, Abdulah, Deldar Morad, Abdullahi, Auwal, Abebe, Mesfin, Abebe, Samrawit Shawel, Abedi, Aidin, Abegaz, Kedir Hussein, Abhilash, E S, Abidi, Hassan, Abiodun, Olumide, Aboagye, Richard Gyan, Abolhassani, Hassan, Abolmaali, Meysam, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abtahi, Dariush, Abu Rumeileh, Samir, Abualruz, Hasan, Abubakar, Bilyaminu, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Accrombessi, Manfred Mario Kokou, Adal, Tadele Girum, Adamu, Abdu A, Addo, Isaac Yeboah, Addolorato, Giovanni, Adebiyi, Akindele Olupelumi, Adekanmbi, Victor, Adepoju, Abiola Victor, Adetunji, Charles Oluwaseun, Adetunji, Juliana Bunmi, Adeyeoluwa, Temitayo Esther, Adeyinka, Daniel Adedayo, Adeyomoye, Olorunsola Israel, Admass, Biruk Adie Adie, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afolabi, Aanuoluwapo Adeyimika, Afzal, Muhammad Sohail, Afzal, Saira, Agampodi, Suneth Buddhika, Agasthi, Pradyumna, Aggarwal, Manik, Aghamiri, Shahin, Agide, Feleke Doyore, Agodi, Antonella, Agrawal, Anurag, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Shahzaib, Ahmad, Tauseef, Ahmadi, Keivan, Ahmadzade, Amir Mahmoud, Ahmed, Ali, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Mehrunnisha Sharif, Ahmed, Meqdad Saleh, Ahmed, Muktar Beshir, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Akara, Essona Matatom, Akbarialiabad, Hossein, Akinosoglou, Karolina, Akinyemiju, Tomi, Akkaif, Mohammed Ahmed, Akyirem, Samuel, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Alahdab, Fares, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Alam, Manjurul, Alam, Noore, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Al-Azzam, Sayer, Albakri, Almaza, Albashtawy, Mohammed, AlBataineh, Mohammad T, Alcalde-Rabanal, Jacqueline Elizabeth, Aldawsari, Khalifah A, Aldhaleei, Wafa A, Aldridge, Robert W, Alema, Haileselasie Berhane, Alemayohu, Mulubirhan Assefa, Alemi, Sharifullah, Alemu, Yihun Mulugeta, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhalaiqa, Fadwa Alhalaiqa Naji, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Amjad, Ali, Liaqat, Ali, Mohammed Usman, Ali, Rafat, Ali, Shahid, Ali, Syed Shujait Shujait, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Alikhani, Reyhaneh, Alimohamadi, Yousef, Aliyi, Ahmednur Adem, Aljasir, Mohammad A M, Aljunid, Syed Mohamed, Alla, François, Allebeck, Peter, Al-Marwani, Sabah, Al-Maweri, Sadeq Ali Ali, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Louay, Almidani, Omar, Alomari, Mahmoud 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Ales, Ticoalu, Jansje Henny Vera, Tillawi, Tala, Tiruye, Tenaw Yimer, Titova, Mariya Vladimirovna, Tonelli, Marcello, Topor-Madry, Roman, Toriola, Adetunji T, Torre, Anna E, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Tran, Jasmine T, Tran, Nghia Minh, Trico, Domenico, Tromans, Samuel Joseph, Truyen, Thien Tan Tri Tai, Tsatsakis, Aristidis, Tsegay, Guesh Mebrahtom, Tsermpini, Evangelia Eirini, Tumurkhuu, Munkhtuya, Tung, Kang, Tyrovolas, Stefanos, Uddin, Sayed Mohammad Nazim, Udoakang, Aniefiok John, Udoh, Arit, Ullah, Atta, Ullah, Irfan, Ullah, Saeed, Ullah, Sana, Umakanthan, Srikanth, Umeokonkwo, Chukwuma David, Unim, Brigid, Unnikrishnan, Bhaskaran, Unsworth, Carolyn Anne, Upadhyay, Era, Urso, Daniele, Usman, Jibrin Sammani, Vahabi, Seyed Mohammad, Vaithinathan, Asokan Govindaraj, Valizadeh, Rohollah, Van de Velde, Sarah M, Van den Eynde, Jef, Varga, Orsolya, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Vasic, Milena, Vaziri, Siavash, Vellingiri, Balachandar, Venketasubramanian, Narayanaswamy, Verghese, Nicholas Alexander, Verma, Madhur, Veroux, Massimiliano, Verras, Georgios-Ioannis, Vervoort, Dominique, Villafañe, Jorge Hugo, Villanueva, Gabriela Ines, Vinayak, Manish, Violante, Francesco S, Viskadourou, Maria, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vo, Bay, Vollset, Stein Emil, Vongpradith, Avina, Vos, Theo, Vujcic, Isidora S, Vukovic, Rade, Wafa, Hatem A, Waheed, Yasir, Wamai, Richard G, Wang, Cong, Wang, Ning, Wang, Shu, Wang, Song, Wang, Yanzhong, Wang, Yuan-Pang, Waqas, Muhammad, Ward, Paul, Wassie, Emebet Gashaw, Watson, Stefanie, Watson, Stephanie Louise Watson, Weerakoon, Kosala Gayan, Wei, Melissa Y, Weintraub, Robert G, Weiss, Daniel J, Westerman, Ronny, Whisnant, Joanna L, Wiangkham, Taweewat, Wickramasinghe, Dakshitha Praneeth, Wickramasinghe, Nuwan Darshana, Wilandika, Angga, Wilkerson, Caroline, Willeit, Peter, Wilson, Shadrach, Wojewodzic, Marcin W, Woldegebreal, Demewoz H, Wolf, Axel Walter, Wolfe, Charles D A, Wondimagegene, Yohannes Addisu, Wong, Yen Jun, Wongsin, Utoomporn, Wu, Ai-Min, Wu, Chenkai, Wu, Felicia, Wu, Xinsheng, Wu, Zenghong, Xia, Juan, Xiao, Hong, Xie, Yang, Xu, Suowen, Xu, Wang-Dong, Xu, Xiaoyue, Xu, Yvonne Yiru, Yadollahpour, Ali, Yamagishi, Kazumasa, Yang, Danting, Yang, Lin, Yano, Yuichiro, Yao, Yao, Yaribeygi, Habib, Ye, Pengpeng, Yehualashet, Sisay Shewasinad, Yesiltepe, Metin, Yesuf, Subah Abderehim, Yezli, Saber, Yi, Siyan, Yigezu, Amanuel, Yiğit, Arzu, Yiğit, Vahit, Yip, Paul, Yismaw, Malede Berihun, Yismaw, Yazachew, Yon, Dong Keon, Yonemoto, Naohiro, Yoon, Seok-Jun, You, Yuyi, Younis, Mustafa Z, Yousefi, Zabihollah, Yu, Chuanhua, Yu, Yong, Yuh, Faith H, Zadey, Siddhesh, Zadnik, Vesna, Zafari, Nima, Zakham, Fathiah, Zaki, Nazar, Zaman, Sojib Bin, Zamora, Nelson, Zand, Ramin, Zangiabadian, Moein, Zar, Heather J, Zare, Iman, Zarrintan, Armin, Zeariya, Mohammed G M, Zeinali, Zahra, Zhang, Haijun, Zhang, Jianrong, Zhang, Jingya, Zhang, Liqun, Zhang, Yunquan, Zhang, Zhi-Jiang, Zhao, Hanqing, Zhong, Chenwen, Zhou, Juexiao, Zhu, Bin, Zhu, Lei, Ziafati, Makan, Zielińska, Magdalena, Zitoun, Osama A, Zoladl, Mohammad, Zou, Zhiyong, Zuhlke, Liesl J, Zumla, Alimuddin, Zweck, Elric, Zyoud, Samer H, Wool, Eve E, and Murray, Christopher J L

Published
2024
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26. Kernel-Based Identification with Frequency Domain Side-Information

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

In this paper, we discuss the problem of system identification when frequency domain side information is available on the system. Initially, we consider the case where the prior knowledge is provided as being the $\Hcal_{\infty}$-norm of the system bounded by a given scalar. This framework provides the opportunity of considering various forms of side information such as the dissipativity of the system as well as other forms of frequency domain prior knowledge. We propose a nonparametric identification method for estimating the impulse response of the system under the given side information. The estimation problem is formulated as an optimization in a reproducing kernel Hilbert space (RKHS) endowed with a stable kernel. The corresponding objective function consists of a term for minimizing the fitting error, and a regularization term defined based on the norm of the impulse response in the employed RKHS. To guarantee the desired frequency domain features defined based on the prior knowledge, suitable constraints are imposed on the estimation problem. The resulting optimization has an infinite-dimensional feasible set with an infinite number of constraints. We show that this problem is a well-defined convex program with a unique solution. We propose a heuristic that tightly approximates this unique solution. The proposed approach is equivalent to solving a finite-dimensional convex quadratically constrained quadratic program. The efficiency of the discussed method is verified by several numerical examples.

Published
2021

27. Kernel-based Impulse Response Identification with Side-Information on Steady-State Gain

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

In this paper, we consider the problem of system identification when side-information is available on the steady-state (or DC) gain of the system. We formulate a general nonparametric identification method as an infinite-dimensional constrained convex program over the reproducing kernel Hilbert space (RKHS) of stable impulse responses. The objective function of this optimization problem is the empirical loss regularized with the norm of RKHS, and the constraint is considered for enforcing the integration of the steady-state gain side-information. The proposed formulation addresses both the discrete-time and continuous-time cases. We show that this program has a unique solution obtained by solving an equivalent finite-dimensional convex optimization. This solution has a closed-form when the empirical loss and regularization functions are quadratic and exact side-information is considered. We perform extensive numerical comparisons to verify the efficiency of the proposed identification methodology.

Published
2021

28. Regularized Identification with Internal Positivity Side-Information

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

In this paper, we present an impulse response identification scheme that incorporates the internal positivity side-information of the system. The realization theory of positive systems establishes specific criteria for the existence of a positive realization for a given transfer function. These transfer function criteria are translated to a set of suitable conditions on the shape and structure of the impulse responses of positive systems. Utilizing these conditions, the impulse response estimation problem is formulated as a constrained optimization in a reproducing kernel Hilbert space equipped with a stable kernel, and suitable constraints are imposed to encode the internal positivity side-information. The optimization problem is infinite-dimensional with an infinite number of constraints. An equivalent finite-dimensional convex optimization in the form of a convex quadratic program is derived. The resulting equivalent reformulation makes the proposed approach suitable for numerical simulation and practical implementation. A Monte Carlo numerical experiment evaluates the impact of incorporating the internal positivity side-information in the proposed identification scheme. The effectiveness of the proposed method is demonstrated using data from a heating system experiment.

Published
2021

29. Extracting Major Topics of COVID-19 Related Tweets

Author

Azizi, Faezeh, Vahdat-Nejad, Hamed, Hajiabadi, Hamideh, and Khosravi, Mohammad Hossein

Subjects
Computer Science - Social and Information Networks, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period; however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We implement the Latent Dirichlet Allocation (LDA) algorithm to extract the topics and then name them with the "reopening", "death cases", "telecommuting", "protests", "anger expression", "masking", "medication", "social distance", "second wave", and "peak of the disease" titles. We additionally analyze temporal trends of the topics for the whole world and four countries. By analyzing the graphs, fascinating results are obtained from altering users' focus on topics over time.

Published
2021

30. Distributed Model Predictive Control of Buildings and Energy Hubs

Author

Lefebure, Nicolas, Khosravi, Mohammad, de Badyn, Mathias Hudoba, Bünning, Felix, Lygeros, John, Jones, Colin, and Smith, Roy S.

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized due to the fact that energy demands are often assumed to be fixed quantities rather than controlled dynamic variables. The joint optimization of energy hubs and buildings' energy management systems can result in higher energy savings. This paper investigates how different MPC strategies perform on energy management systems in buildings and energy hubs. We first discuss two MPC approaches; centralized and decentralized. While the centralized control strategy offers optimal performance, its implementation is computationally prohibitive and raises privacy concerns. On the other hand, the decentralized control approach, which offers ease of implementation, displays significantly lower performance. We propose a third strategy, distributed control based on dual decomposition, which has the advantages of both approaches. Numerical case studies and comparisons demonstrate that the performance of distributed control is close to the performance of the centralized case, while maintaining a significantly lower computational burden, especially in large-scale scenarios with many agents. Finally, we validate and verify the reliability of the proposed method through an experiment on a full-scale energy hub system in the NEST demonstrator in D\"{u}bendorf, Switzerland., Comment: 16 pages, 8 figures

Published
2021

32. Diversified and Compatible Web APIs Recommendation in IoT

Author

Gong, Wenwen, Wu, Huiping, Wang, Xiaokang, Zhang, Xuyun, Wang, Yawei, Chen, Yifei, and Khosravi, Mohammad R.

Subjects
Computer Science - Social and Information Networks
Abstract

With the ever-increasing popularity of Service-oriented Architecture (SoA) and Internet of Things (IoT), a considerable number of enterprises or organizations are attempting to encapsulate their provided complex business services into various lightweight and accessible web APIs (application programming interfaces) with diverse functions. In this situation, a software developer can select a group of preferred web APIs from a massive number of candidates to create a complex mashup economically and quickly based on the keywords typed by the developer. However, traditional keyword-based web API search approaches often suffer from the following difficulties and challenges. First, they often focus more on the functional matching between the candidate web APIs and the mashup to be developed while neglecting the compatibility among different APIs, which probably returns a group of incompatible web APIs and further leads to a mashup development failure. Second, existing approaches often return a web API composition solution to the mashup developer for reference, which narrows the developer's API selection scope considerably and may reduce developer satisfaction heavily. In view of the above challenges and successful application of game theory in the IoT, based on the idea of game theory, we propose a compatible and diverse web APIs recommendation approach for mashup creations, named MCCOMP+DIV, to return multiple sets of diverse and compatible web APIs with higher success rate. Finally, we validate the effectiveness and efficiency of MCCOMP+DIV through a set of experiments based on a real-world web API dataset, i.e., the PW dataset crawled from ProgrammableWeb.com., Comment: 15 pages, 11 figures

Published
2021

33. On Robustness of Kernel-Based Regularized System Identification

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

This paper presents a novel feature of the kernel-based system identification method. We prove that the regularized kernel-based approach for the estimation of a finite impulse response is equivalent to a robust least-squares problem with a particular uncertainty set defined in terms of the kernel matrix, and thus, it is called kernel-based uncertainty set. We provide a theoretical foundation for the robustness of the kernel-based approach to input disturbances. Based on robust and regularized least-squares methods, different formulations of system identification are considered, where the kernel-based uncertainty set is employed in some of them. We apply these methods to a case where the input measurements are subject to disturbances. Subsequently, we perform extensive numerical experiments and compare the results to examine the impact of utilizing kernel-based uncertainty sets in the identification procedure. The numerical experiments confirm that the robust least square identification approach with the kernel-based uncertainty set improves the robustness of the estimation to the input disturbances.

Published
2021

36. SARS‐CoV‐2 infection and venous thromboembolism after surgery: an international prospective cohort study

Author

Nepogodiev, Dmitri, Simoes, Joana FF, Li, Elizabeth, Picciochi, Maria, Glasbey, James C, Baiocchi, Glauco, Blanco‐Colino, Ruth, Chaudhry, Daoud, AlAmeer, Ehab, El‐Boghdadly, Kariem, Wuraola, Funmilola, Ghosh, Dhruva, Gujjuri, Rohan R, Harrison, Ewen M, Lule, Herman, Kaafarani, Haytham, Khosravi, Mohammad, Kronberger, Irmgard, Leventoğlu, Sezai, Mann, Harvinder, Mclean, Kenneth A, Mengesha, Mengistu Gebreyohanes, Modolo, Maria Marta, Ntirenganya, Faustin, Norman, Lisa, Outani, Oumaima, Pius, Riinu, Pockney, Peter, Qureshi, Ahmad Uzair, Roslani, April Camilla, Satoi, Sohei, Shaw, Catherine, Bhangu, Aneel, Omar, Omar M, Ahmed, Waheed‐Ul‐Rahman, Argus, Leah, Ball, Alasdair, Bywater, Edward P, Brar, Amanpreet, Dawson, Brett E, Duran, Irani, Elhadi, Muhammed, Jones, Conor S, Kamarajah, Sivesh K, Keatley, James M, Lawday, Samuel, Marson, Ella J, Ots, Riinu, Santos, Irène, Taylor, Elliott H, Trout, Isobel M, Varghese, Chris, Venn, Mary L, Xu, William, Dajti, Irida, Gjata, Arben, Kacimi, Salah Eddine Oussama, Boccalatte, Luis, Cox, Daniel, Townend, Philip, Aigner, Felix, Kronberger, Irmgard Elisabeth, Samadov, Elgun, Alderazi, Amer, Hossain, Kamral, Padmore, Greg, van Ramshorst, Gabrielle, Lawani, Ismaïl, Cerovac, Anis, Delibegovic, Samir, Gomes, Gustavo Mendonça Ataíde, Buarque, Igor Lima, Gohar, Muhammad, Slavchev, Mihail, Nwegbu, Chukwuemeka, Agarwal, Arnav, Martin, Janet, and Ng‐Kamstra, Joshua

Subjects
Lung, Patient Safety, Prevention, Clinical Research, Hematology, Pneumonia, Cardiovascular, Good Health and Well Being, Adolescent, Adult, Age Distribution, Aged, COVID-19, Cohort Studies, Female, Humans, Internationality, Male, Middle Aged, Postoperative Complications, Prospective Studies, SARS-CoV-2, Sex Distribution, Venous Thromboembolism, Young Adult, COVIDSurg Collaborative, GlobalSurg Collaborative, deep vein thrombosis, pulmonary embolism, venous thromboembolism, Clinical Sciences, Neurosciences, Anesthesiology
Abstract

SARS-CoV-2 has been associated with an increased rate of venous thromboembolism in critically ill patients. Since surgical patients are already at higher risk of venous thromboembolism than general populations, this study aimed to determine if patients with peri-operative or prior SARS-CoV-2 were at further increased risk of venous thromboembolism. We conducted a planned sub-study and analysis from an international, multicentre, prospective cohort study of elective and emergency patients undergoing surgery during October 2020. Patients from all surgical specialties were included. The primary outcome measure was venous thromboembolism (pulmonary embolism or deep vein thrombosis) within 30 days of surgery. SARS-CoV-2 diagnosis was defined as peri-operative (7 days before to 30 days after surgery); recent (1-6 weeks before surgery); previous (≥7 weeks before surgery); or none. Information on prophylaxis regimens or pre-operative anti-coagulation for baseline comorbidities was not available. Postoperative venous thromboembolism rate was 0.5% (666/123,591) in patients without SARS-CoV-2; 2.2% (50/2317) in patients with peri-operative SARS-CoV-2; 1.6% (15/953) in patients with recent SARS-CoV-2; and 1.0% (11/1148) in patients with previous SARS-CoV-2. After adjustment for confounding factors, patients with peri-operative (adjusted odds ratio 1.5 (95%CI 1.1-2.0)) and recent SARS-CoV-2 (1.9 (95%CI 1.2-3.3)) remained at higher risk of venous thromboembolism, with a borderline finding in previous SARS-CoV-2 (1.7 (95%CI 0.9-3.0)). Overall, venous thromboembolism was independently associated with 30-day mortality (5.4 (95%CI 4.3-6.7)). In patients with SARS-CoV-2, mortality without venous thromboembolism was 7.4% (319/4342) and with venous thromboembolism was 40.8% (31/76). Patients undergoing surgery with peri-operative or recent SARS-CoV-2 appear to be at increased risk of postoperative venous thromboembolism compared with patients with no history of SARS-CoV-2 infection. Optimal venous thromboembolism prophylaxis and treatment are unknown in this cohort of patients, and these data should be interpreted accordingly.

Published
2022

37. Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization

Author

Rupenyan, Alisa, Khosravi, Mohammad, and Lygeros, John

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Accurate positioning and fast traversal times determine the productivity in machining applications. This paper demonstrates a hierarchical contour control implementation for the increase of productivity in positioning systems. The high-level controller pre-optimizes the input to a low-level cascade controller, using a contouring predictive control approach. This control structure requires tuning of multiple parameters. We propose a sample-efficient tuning algorithm, where the performance metrics associated with the full geometry traversal are modelled as Gaussian processes and used to form the global cost and the constraints in a constrained Bayesian optimization algorithm. This approach enables the trade-off between fast traversal, high tracking accuracy, and suppression of vibrations in the system. The performance improvement is evaluated numerically when tuning different combinations of parameters. We demonstrate that tuning the parameters of the MPC contour-controller achieves the best performance in terms of time, tracking accuracy, and minimization of the vibrations in the system., Comment: 7 pages, paper accepted at 2021 60th IEEE Conference on Decision and Control (CDC)

Published
2021
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40. Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach

Author

Stürz, Yvonne R., Khosravi, Mohammad, and Smith, Roy S.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning
Abstract

Tensioned cable nets can be used as supporting structures for the efficient construction of lightweight building elements, such as thin concrete shell structures. To guarantee important mechanical properties of the latter, the tolerances on deviations of the tensioned cable net geometry from the desired target form are very tight. Therefore, the form needs to be readjusted on the construction site. In order to employ model-based optimization techniques, the precise identification of important uncertain model parameters of the cable net system is required. This paper proposes the use of Gaussian process regression to learn the function that maps the cable net geometry to the uncertain parameters. In contrast to previously proposed methods, this approach requires only a single form measurement for the identification of the cable net model parameters. This is beneficial since measurements of the cable net form on the construction site are very expensive. For the training of the Gaussian processes, simulated data is efficiently computed via convex programming. The effectiveness of the proposed method and the impact of the precise identification of the parameters on the form of the cable net are demonstrated in numerical experiments on a quarter-scale prototype of a roof structure.

Published
2020

43. Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization

Author

König, Christopher, Khosravi, Mohammad, Maier, Markus, Smith, Roy S., Rupenyan, Alisa, and Lygeros, John

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian processes. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method in simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization, Comment: 9 pages

Published
2020

44. Timing of surgery following SARS‐CoV‐2 infection: an international prospective cohort study

Author

Nepogodiev, Dmitri, Simoes, Joana FF, Li, Elizabeth, Picciochi, Maria, Glasbey, James C, Baiocchi, Glauco, Blanco‐Colino, Ruth, Chaudhry, Daoud, AlAmeer, Ehab, El‐Boghdadly, Kariem, Wuraola, Funmilola, Ghosh, Dhruva, Gujjuri, Rohan R, Harrison, Ewen M, Lule, Herman, Kaafarani, Haytham, Khosravi, Mohammad, Kronberger, Irmgard, Leventoğlu, Sezai, Mann, Harvinder, Mclean, Kenneth A, Mengesha, Mengistu Gebreyohanes, Modolo, Maria Marta, Ntirenganya, Faustin, Norman, Lisa, Outani, Oumaima, Pius, Riinu, Pockney, Peter, Qureshi, Ahmad Uzair, Roslani, April Camilla, Satoi, Sohei, Shaw, Catherine, Bhangu, Aneel, Omar, Omar M, Ahmed, Waheed‐Ul‐Rahman, Argus, Leah, Ball, Alasdair, Bywater, Edward P, Brar, Amanpreet, Dawson, Brett E, Duran, Irani, Elhadi, Muhammed, Jones, Conor S, Kamarajah, Sivesh K, Keatley, James M, Lawday, Samuel, Marson, Ella J, Ots, Riinu, Santos, Irène, Taylor, Elliott H, Trout, Isobel M, Varghese, Chris, Venn, Mary L, Xu, William, Dajti, Irida, Gjata, Arben, Kacimi, Salah Eddine Oussama, Boccalatte, Luis, Cox, Daniel, Townend, Philip, Aigner, Felix, Kronberger, Irmgard Elisabeth, Samadov, Elgun, Alderazi, Amer, Hossain, Kamral, Padmore, Greg, van Ramshorst, Gabrielle, Lawani, Ismaïl, Cerovac, Anis, Delibegovic, Samir, Gomes, Gustavo Mendonça Ataíde, Buarque, Igor Lima, Gohar, Muhammad, Slavchev, Mihail, Nwegbu, Chukwuemeka, Agarwal, Arnav, Martin, Janet, and Ng‐Kamstra, Joshua

Subjects
Infectious Diseases, Clinical Research, Patient Safety, Lung, Vaccine Related, Pneumonia & Influenza, Biodefense, Pneumonia, Prevention, Emerging Infectious Diseases, 6.4 Surgery, Evaluation of treatments and therapeutic interventions, Infection, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, COVID-19, Child, Child, Preschool, Cohort Studies, Female, Humans, Infant, Internationality, Male, Middle Aged, Practice Guidelines as Topic, Prospective Studies, SARS-CoV-2, Surgical Procedures, Operative, Time, Young Adult, delay, surgery, timing, COVIDSurg Collaborative, GlobalSurg Collaborative, Clinical Sciences, Neurosciences, Anesthesiology
Abstract

Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4-1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0-2 weeks, 3-4 weeks and 5-6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3-4.8), 3.9 (2.6-5.1) and 3.6 (2.0-5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9-2.1)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2-8.7) vs. 2.4% (95%CI 1.4-3.4) vs. 1.3% (95%CI 0.6-2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay.

Published
2021

46. Performance-Driven Cascade Controller Tuning with Bayesian Optimization

Author

Khosravi, Mohammad, Behrunani, Varsha, Myszkorowski, Piotr, Smith, Roy S., Rupenyan, Alisa, and Lygeros, John

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

We propose a performance-based autotuning method for cascade control systems, where the parameters of a linear axis drive motion controller from two control loops are tuned jointly. Using Bayesian optimization as all parameters are tuned simultaneously, the method is guaranteed to converge asymptotically to the global optimum of the cost. The data-efficiency and performance of the method are studied numerically for several training configurations and compared numerically to those achieved with classical tuning methods and to the exhaustive evaluation of the cost. On the real system, the tracking performance and robustness against disturbances are compared experimentally to nominal tuning. The numerical study and the experimental data both demonstrate that the proposed automated tuning method is efficient in terms of required tuning iterations, robust to disturbances, and results in improved tracking., Comment: 9 pages, 9 figures

Published
2020
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47. Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization

Author

Khosravi, Mohammad, Behrunani, Varsha, Smith, Roy S., Rupenyan, Alisa, and Lygeros, John

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Cascaded controller tuning is a multi-step iterative procedure that needs to be performed routinely upon maintenance and modification of mechanical systems. An automated data-driven method for cascaded controller tuning based on Bayesian optimization is proposed. The method is tested on a linear axis drive, modeled using a combination of first principles model and system identification. A custom cost function based on performance indicators derived from system data at different candidate configurations of controller parameters is modeled by a Gaussian process. It is further optimized by minimization of an acquisition function which serves as a sampling criterion to determine the subsequent candidate configuration for experimental trial and improvement of the cost model iteratively, until a minimum according to a termination criterion is found. This results in a data-efficient procedure that can be easily adapted to varying loads or mechanical modifications of the system. The method is further compared to several classical methods for auto-tuning, and demonstrates higher performance according to the defined data-driven performance indicators. The influence of the training data on a cost prior on the number of iterations required to reach optimum is studied, demonstrating the efficiency of the Bayesian optimization tuning method., Comment: 8 pages, 5 figures, to appear in IFAC World Congress, 2020

Published
2020

48. Nonlinear System Identification with Prior Knowledge of the Region of Attraction

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

We consider the problem of nonlinear system identification when prior knowledge is available on the region of attraction (ROA) of an equilibrium point. We propose an identification method in the form of an optimization problem, minimizing the fitting error and guaranteeing the desired stability property. The problem is approached by joint identification the dynamics and a Lyapunov function verifying the stability property. In this setting, the hypothesis set is a reproducing kernel Hilbert space, and with respect to each point of the given subset of the ROA, the Lie derivative inequality of the Lyapunov function imposes a constraint. The problem is a non-convex infinite-dimensional optimization with infinite number of constraints. To obtain a tractable formulation, only a suitably designed finite subset of the constraints are considered. The resulting problem admits a solution in form of a linear combination of the sections of the kernel and its derivatives. An equivalent optimization problem with a quadratic cost function subject to linear and bilinear constraints is derived. A suitable change of variable gives a convex reformulation of the problem. To reduce the number of hyperparameters, the optimization problem is adapted to the case of diagonal kernels. The method is demonstrate by means of an example., Comment: 19 pages, 2 figures

Published
2020

49. Convex Nonparametric Formulation for Identification of Gradient Flows

Author

Khosravi, Mohammad and Smith, Roy S.

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

In this paper, we develop a nonparametric system identification method for the nonlinear gradient-flow dynamics. In these systems, the vector field is the gradient field of a potential energy function. This fundamental fact about the dynamics of system plays the role of a structural prior knowledge as well as a constraint in the proposed identification method. While the nature of the identification problem is an estimation in the space of functions, we derive an equivalent finite dimensional formulation, which is a convex optimization in form of a quadratic program. This gives scalability of the problem and provides the opportunity for utilizing recently developed large-scale optimization solvers. The central idea in the proposed method is representing the energy function as a difference of two convex functions and estimating these convex functions jointly. Based on necessary and sufficient conditions for function convexity, the identification problem is formulated, and then, the existence, uniqueness and smoothness of the solution is addressed. We also illustrate the method numerically for a demonstrative example., Comment: 18 pages, 2 figures

Published
2020

50. Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization

Author

Yin, Mingzhou, Iannelli, Andrea, Khosravi, Mohammad, Parsi, Anilkumar, and Smith, Roy S.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.

Published
2020
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