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3,664 results on '"Chen, Lijun"'

101. Validity and Reliability of a Pictorial Instrument for Assessing Movement Skill Perceived Competence in Chinese Children

Author

Li, Xuanxi, Chen, Lijun, Diao, Yucui, Li, Jing, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Kuan, Garry, editor, Chang, Yu-Kai, editor, Morris, Tony, editor, Eng Wah, Teo, editor, Musa, Rabiu Muazu, editor, and P. P. Abdul Majeed, Anwar, editor

Published
2023
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107. Consistency analysis of PD-L1 immunohistochemistry antibodies in esophageal squamous cell carcinoma

Author

CHEN Lijun, WANG Yichen, ZHENG Qiang, WANG Yue, JIN Yan, LI Yuan

Subjects
esophageal squamous cell carcinoma, programmed death ligand-1, immunohistochemistry, consistency analysis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background and purpose: Immunohistochemistry (IHC) expression of programmed death ligand-1 (PD-L1) is the most widely used biomarker for predicting the efficacy of esophageal cancer immunotherapy, and accurate and reliable PD-L1 testing is crucial for screening potential beneficiaries of immunotherapy. This study aimed to investigate the expression of PD-L1 in patients with esophageal squamous cell carcinoma (ESCC) and the consistency and correlation of IHC detection of seven different clones of PD-L1 antibody, and to provide reference for the gradual standardization of PD-L1 IHC detection and follow-up clinical research of esophageal cancer. Methods: This study collected surgically resected and pathologically confirmed specimens from 146 ESCC patients in Fudan University Shanghai Cancer Center from January 2020 to December 2021, and specimens were stained with seven antibodies: PD-L1 22C3, SP263, 28-8, E1L3N XP, CST E1L3N, BP6099 and MXR006, respectively. The consistency of the antibodies was statistically analyzed in different cut-off values using three scoring criteria: comprehensive positive score (CPS), tumor cell proportional score (TPS) and immune cell score (IPS). Results: The results of PD-L1 22C3, SP263 and 28-8 antibodies demonstrated that the consistency between the three antibodies was substantial (Kappa: 0.66-0.80) when CPS cut-off was 10, and the consistency of PD-L1 28-8 with 22C3 and SP263 antibodies was substantial (Kappa=0.80, 0.65) whereas the consistency between PD-L1 22C3 and SP263 was moderate (Kappa=0.49) when CPS cut-off was 1. The three antibodies showed a high consistency when TPS cut-off was 10% and 1% (Kappa: 0.79-0.89, 0.71-0.91). The companion diagnostic PD-L1 22C3 clone antibody and the four domestic clone antibodies E1L3N XP, CST E1L3N, BP6099 and MXR006 consistency study showed that the consistency between the five antibodies was substantial (Kappa: 0.67-0.88, 0.65-0.79) when CPS cut-off was 10 and TPS cut-off was 10%, and the consistency between the five antibodies was substantial or moderate when CPS cut-off was 1 (Kappa: 0.58-0.88). When TPS cut-off was 1%, there was an excellent consistency between the five antibodies (Kappa: 0.85-0.92). Using IPS cut-off of 1% or 10%, the five antibodies showed moderate or poor consistency (Kappa: 0.30-0.62). Conclusion: PD-L1 22C3, SP263 and 28-8 antibodies showed high consistency. PD-L1 22C3 and four domestic antibodies PD-L1 E1L3N XP, CST E1L3N, BP6099 and MXR006 antibodies showed moderate consistency in CPS and TPS, while the consistency was poor in IPS. These antibodies are interchangeable with each other, providing more selectivity for clinical testing needs.

Published
2023
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108. Research Progress on Preventive and Interventional Effects of Dairy Products on Osteoporosis

Author

WANG Qishan, ZHAO Junying, WEI Xinyue, LIU Yanpin, YANG Baoyu, CHEN Lijun

Subjects
dairy products, calcium, milk protein, milk fat globule membrane, vitamin d, intestinal flora, osteoporosis, Food processing and manufacture, TP368-456
Abstract

Osteoporosis afflicts 200 million patients worldwide. With the aging of the global population, the incidence of osteoporosis will continue to increase, and the prevention of osteoporosis has become an urgent problem in the field of public health. This paper analyzes the mechanisms by which milk and dairy products as well as their components such as minerals, vitamins, protein, fat and lactose modulate the intestinal flora and in turn affect calcium absorption and bone health. The interventional effects of dairy products fortified with vitamin D, prebiotics and/or probiotics in different populations and their effect in reducing the risk of osteoporosis are reviewed. The interventional effects of dairy products and plant-based milk are compared. This review hopes to provide a scientific reference for precision intervention with dairy products to improve bone health.

Published
2023
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110. Economic Dispatch With Distributed Energy Resources: Co-Optimization of Transmission and Distribution Systems

Author

Zhou, Xinyang, Chang, Chin-Yao, Bernstein, Andrey, Zhao, Changhong, and Chen, Lijun

Subjects
Mathematics - Optimization and Control
Abstract

The increasing penetration of distributed energy resources (DERs) in the distribution networks has turned the conventionally passive load buses into active buses that can provide grid services for the transmission system. To take advantage of the DERs in the distribution networks, this letter formulates a transmission-and-distribution (T&D) systems co-optimization problem that achieves economic dispatch at the transmission level and optimal voltage regulation at the distribution level by leveraging large generators and DERs. A primal-dual gradient algorithm is proposed to solve this optimization problem jointly for T&D systems, and a distributed market-based equivalent of the gradient algorithm is used for practical implementation. The results are corroborated by numerical examples with the IEEE 39-Bus system connected with 7 different distribution networks.

Published
2020
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111. Cooperative Control of Mobile Robots with Stackelberg Learning

Author

Koh, Joewie J., Ding, Guohui, Heckman, Christoffer, Chen, Lijun, and Roncone, Alessandro

Subjects
Computer Science - Robotics, Computer Science - Computer Science and Game Theory, Computer Science - Machine Learning, Computer Science - Multiagent Systems, I.2.9, I.2.6, I.2.11
Abstract

Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry in capabilities and individual objectives. To accomplish this goal, we propose a method named SLiCC: Stackelberg Learning in Cooperative Control. SLiCC models the problem as a partially observable stochastic game composed of Stackelberg bimatrix games, and uses deep reinforcement learning to obtain the payoff matrices associated with these games. Appropriate cooperative actions are then selected with the derived Stackelberg equilibria. Using a bi-robot cooperative object transportation problem, we validate the performance of SLiCC against centralized multi-agent Q-learning and demonstrate that SLiCC achieves better combined utility., Comment: 8 pages, 7 figures

Published
2020
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112. A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem

Author

Liu, Zhiyuan, Wang, Huazheng, Waggoner, Bo, Youjian, Liu, and Chen, Lijun

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We investigate the sparse linear contextual bandit problem where the parameter $\theta$ is sparse. To relieve the sampling inefficiency, we utilize the "perturbed adversary" where the context is generated adversarilly but with small random non-adaptive perturbations. We prove that the simple online Lasso supports sparse linear contextual bandit with regret bound $\mathcal{O}(\sqrt{kT\log d})$ even when $d \gg T$ where $k$ and $d$ are the number of effective and ambient dimension, respectively. Compared to the recent work from Sivakumar et al. (2020), our analysis does not rely on the precondition processing, adaptive perturbation (the adaptive perturbation violates the i.i.d perturbation setting) or truncation on the error set. Moreover, the special structures in our results explicitly characterize how the perturbation affects exploration length, guide the design of perturbation together with the fundamental performance limit of perturbation method. Numerical experiments are provided to complement the theoretical analysis., Comment: 16 pages, 2 figures

Published
2020

113. Optimal Power Flow with State Estimation In the Loop for Distribution Networks

Author

Guo, Yi, Zhou, Xinyang, Zhao, Changhong, Chen, Lijun, and Summers, Tyler H.

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

We propose a framework for integrating optimal power flow (OPF) with state estimation (SE) in the loop for distribution networks. Our approach combines a primal-dual gradient-based OPF solver with a SE feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on a few appropriate online state measurements and noisy "pseudo-measurements". We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least squares (WLS) estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudo measurement variability and inherent sensor measurement noise., Comment: arXiv admin note: text overlap with arXiv:1909.12763

Published
2020

114. Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation

Author

Ding, Guohui, Koh, Joewie J., Merckaert, Kelly, Vanderborght, Bram, Nicotra, Marco M., Heckman, Christoffer, Roncone, Alessandro, and Chen, Lijun

Subjects
Computer Science - Robotics, Computer Science - Computer Science and Game Theory, Computer Science - Machine Learning, Computer Science - Multiagent Systems, I.2.9, I.2.6, I.2.11
Abstract

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems., Comment: 3 pages, 3 figures

Published
2020

115. Quantum Experiments and Hypergraphs: Multi-Photon Sources for Quantum Interference, Quantum Computation and Quantum Entanglement

Author

Gu, Xuemei, Chen, Lijun, and Krenn, Mario

Subjects
Quantum Physics
Abstract

We introduce the concept of hypergraphs to describe quantum optical experiments with probabilistic multi-photon sources. Every hyperedge represents a correlated photon source, and every vertex stands for an optical output path. Such general graph description provides new insights for producing complex high-dimensional multi-photon quantum entangled states, which go beyond limitations imposed by pair creation via spontaneous parametric down-conversion. Furthermore, properties of hypergraphs can be investigated experimentally. For example, the NP-Complete problem of deciding whether a hypergraph has a perfect matchin can be answered by experimentally detecting multi-photon events in quantum experiments. By introducing complex weights in hypergraphs, we show a general many-particle quantum interference and manipulating entanglement in a pictorial way. Our work paves the path for the development of multi-photon high-dimensional state generation and might inspire new applications of quantum computations using hypergraph mappings., Comment: 14pages, 11figures

Published
2020
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116. Multi-Level Optimal Power Flow Solver in Large Distribution Networks

Author

Zhou, Xinyang, Chen, Yue, Liu, Zhiyuan, Zhao, Changhong, and Chen, Lijun

Subjects
Mathematics - Optimization and Control
Abstract

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multi-level implementation of the primal-dual gradient algorithm to solve the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verify that the proposed algorithm can significantly improve the computational speed without compromising any optimality., Comment: 2020 IEEE SmartGridComm

Published
2020

117. 100Mbps Reconciliation for Quantum Key Distribution Using a Single Graphics Processing Unit

Author

Guo, Yu, Gao, Chaohui, Jiang, Dong, and Chen, Lijun

Subjects
Quantum Physics, Computer Science - Cryptography and Security
Abstract

An efficient error reconciliation scheme is important for post-processing of quantum key distribution (QKD). Recently, a multi-matrix low-density parity-check codes based reconciliation algorithm which can provide remarkable perspectives for high efficiency information reconciliation was proposed. This paper concerns the improvement of reconciliation performance. Multi-matrix algorithm is implemented and optimized on the graphics processing unit (GPU) to obtain high reconciliation throughput. Experimental results indicate that GPU-based algorithm can highly improve reconciliation throughput to an average 85.67 Mbps and a maximum 102.084 Mbps with typical code rate and efficiency. This is the best performance of reconciliation on GPU platform to our knowledge., Comment: 8pages, 3figures, 4tables

Published
2020

118. Multi-matrix rate-compatible reconciliation for quantum key distribution

Author

Gao, Chaohui, Guo, Yu, Jiang, Dong, and Chen, Lijun

Subjects
Quantum Physics, Computer Science - Information Theory
Abstract

Key reconciliation of quantum key distribution (QKD) is the process of correcting errors caused by channel noise and eavesdropper to identify the keys of two legitimate users. Reconciliation efficiency is the most important figure for judging the quality of a reconciliation scheme. To improve reconciliation efficiency, rate-compatible technologies was proposed for key reconciliation, which is denoted as the single-matrix ratecompatible reconciliation (SRCR). In this paper, a recently suggested technique called multi-matrix reconciliation is introduced into SRCR, which is referred to as the multi-matrix rate-compatible reconciliation (MRCR), to further improve reconciliation efficiency and promote the throughput of SRCR. Simulation results show that MRCR we proposed outperforms SRCR in reconciliation efficiency and throughput., Comment: 8 pages, 4 figures

Published
2020
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120. Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity

Author

Yang, Wanshan, Huang, Ting, Zeng, Junlin, Chen, Lijun, Mishra, Shivakant, Youjian, and Liu

Subjects
Computer Science - Human-Computer Interaction
Abstract

In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime.

Published
2019

121. Incentivized Exploration for Multi-Armed Bandits under Reward Drift

Author

Liu, Zhiyuan, Wang, Huazheng, Shen, Fan, Liu, Kai, and Chen, Lijun

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward. We seek to understand the impact of this drifted reward feedback by analyzing the performance of three instantiations of the incentivized MAB algorithm: UCB, $\varepsilon$-Greedy, and Thompson Sampling. Our results show that they all achieve $\mathcal{O}(\log T)$ regret and compensation under the drifted reward, and are therefore effective in incentivizing exploration. Numerical examples are provided to complement the theoretical analysis., Comment: 10 pages, 2 figures, AAAI 2020

Published
2019

130. Psychometric properties of the Alcohol Use Disorders Identification Test (AUDIT) across cross-cultural subgroups, genders, and sexual orientations: Findings from the International Sex Survey (ISS)

Author

Horváth, Zsolt, Nagy, Léna, Koós, Mónika, Kraus, Shane W., Demetrovics, Zsolt, Potenza, Marc N., Ballester-Arnal, Rafael, Batthyány, Dominik, Bergeron, Sophie, Billieux, Joël, Briken, Peer, Burkauskas, Julius, Cárdenas-López, Georgina, Carvalho, Joana, Castro-Calvo, Jesús, Chen, Lijun, Ciocca, Giacomo, Corazza, Ornella, Csako, Rita, Fernandez, David P., Fujiwara, Hironobu, Fernandez, Elaine F., Fuss, Johannes, Gabrhelík, Roman, Gewirtz-Meydan, Ateret, Gjoneska, Biljana, Gola, Mateusz, Grubbs, Joshua B., Hashim, Hashim T., Islam, Md. Saiful, Ismail, Mustafa, Jiménez-Martínez, Martha C., Jurin, Tanja, Kalina, Ondrej, Klein, Verena, Költő, András, Lee, Sang-Kyu, Lewczuk, Karol, Lin, Chung-Ying, Lochner, Christine, López-Alvarado, Silvia, Lukavská, Kateřina, Mayta-Tristán, Percy, Miller, Dan J., Orosová, Oľga, Orosz, Gábor, Ponce, Fernando P., Quintana, Gonzalo R., Quintero Garzola, Gabriel C., Ramos-Diaz, Jano, Rigaud, Kévin, Rousseau, Ann, Scanavino, Marco De Tubino, Schulmeyer, Marion K., Sharan, Pratap, Shibata, Mami, Shoib, Sheikh, Sigre-Leirós, Vera, Sniewski, Luke, Spasovski, Ognen, Steibliene, Vesta, Stein, Dan J., Strizek, Julian, Tsai, Meng-Che, Ünsal, Berk C., Vaillancourt-Morel, Marie-Pier, Van Hout, Marie Claire, and Bőthe, Beáta

Published
2023
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133. Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Author

Antczak, Andrzej, Anurag, Meenakshi, Bauer, Thomas, Birger, Chet, Birrer, Michael J., Borucki, Melissa, Cai, Shuang, Calinawan, Anna, Carr, Steven A., Castro, Patricia, Cerda, Sandra, Chan, Daniel W., Chesla, David, Cieslik, Marcin P., Cottingham, Sandra, Dhir, Rajiv, Domagalski, Marcin J., Druker, Brian J., Duffy, Elizabeth, Edwards, Nathan J., Edwards, Robert, Ellis, Matthew J., Eschbacher, Jennifer, Fam, Mina, Fevrier-Sullivan, Brenda, Francis, Jesse, Freymann, John, Gabriel, Stacey, Getz, Gad, Gillette, Michael A., Godwin, Andrew K., Goldthwaite, Charles A., Grady, Pamela, Hafron, Jason, Hariharan, Pushpa, Hindenach, Barbara, Hoadley, Katherine A., Huang, Jasmine, Ittmann, Michael M., Johnson, Ashlie, Jones, Corbin D., Ketchum, Karen A., Kirby, Justin, Le, Toan, Ma'ayan, Avi, Madan, Rashna, Mareedu, Sailaja, McGarvey, Peter B., Modugno, Francesmary, Montgomery, Rebecca, Nyce, Kristen, Paulovich, Amanda G., Pruetz, Barbara L., Qi, Liqun, Richey, Shannon, Schadt, Eric E., Shutack, Yvonne, Singh, Shilpi, Smith, Michael, Tansil, Darlene, Thangudu, Ratna R., Tobin, Matt, Um, Ki Sung, Vatanian, Negin, Webster, Alex, Wilson, George D., Wright, Jason, Zaalishvili, Kakhaber, Zhang, Zhen, Zhao, Grace, Dou, Yongchao, Katsnelson, Lizabeth, Gritsenko, Marina A., Hu, Yingwei, Reva, Boris, Hong, Runyu, Wang, Yi-Ting, Kolodziejczak, Iga, Lu, Rita Jui-Hsien, Tsai, Chia-Feng, Bu, Wen, Liu, Wenke, Guo, Xiaofang, An, Eunkyung, Arend, Rebecca C., Bavarva, Jasmin, Chen, Lijun, Chu, Rosalie K., Czekański, Andrzej, Davoli, Teresa, Demicco, Elizabeth G., DeLair, Deborah, Devereaux, Kelly, Dhanasekaran, Saravana M., Dottino, Peter, Dover, Bailee, Fillmore, Thomas L., Foxall, McKenzie, Hermann, Catherine E., Hiltke, Tara, Hostetter, Galen, Jędryka, Marcin, Jewell, Scott D., Johnson, Isabelle, Kahn, Andrea G., Ku, Amy T., Kumar-Sinha, Chandan, Kurzawa, Paweł, Lazar, Alexander J., Lazcano, Rossana, Lei, Jonathan T., Li, Yi, Liao, Yuxing, Lih, Tung-Shing M., Lin, Tai-Tu, Martignetti, John A., Masand, Ramya P., Matkowski, Rafał, McKerrow, Wilson, Mesri, Mehdi, Monroe, Matthew E., Moon, Jamie, Moore, Ronald J., Nestor, Michael D., Newton, Chelsea, Omelchenko, Tatiana, Omenn, Gilbert S., Payne, Samuel H., Petyuk, Vladislav A., Robles, Ana I., Rodriguez, Henry, Ruggles, Kelly V., Rykunov, Dmitry, Savage, Sara R., Schepmoes, Athena A., Shi, Tujin, Shi, Zhiao, Tan, Jimin, Taylor, Mason, Thiagarajan, Mathangi, Wang, Joshua M., Weitz, Karl K., Wen, Bo, Williams, C.M., Wu, Yige, Wyczalkowski, Matthew A., Yi, Xinpei, Zhang, Xu, Zhao, Rui, Mutch, David, Chinnaiyan, Arul M., Smith, Richard D., Nesvizhskii, Alexey I., Wang, Pei, Wiznerowicz, Maciej, Ding, Li, Mani, D.R., Zhang, Hui, Anderson, Matthew L., Rodland, Karin D., Zhang, Bing, Liu, Tao, and Fenyö, David

Published
2023
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137. The eleven-item Alcohol, Smoking and Substance Involvement Screening Test (ASSIST-11): Cross-cultural psychometric evaluation across 42 countries

Author

Lee, Chih-Ting, Lin, Chung-Ying, Koós, Mónika, Nagy, Léna, Kraus, Shane W., Demetrovics, Zsolt, Potenza, Marc N., Ballester-Arnal, Rafael, Batthyány, Dominik, Bergeron, Sophie, Billieux, Joël, Burkauskas, Julius, Cárdenas-López, Georgina, Carvalho, Joana, Castro-Calvo, Jesús, Chen, Lijun, Ciocca, Giacomo, Corazza, Ornella, Csako, Rita I., Fernandez, David P., Fernandez, Elaine F., Fujiwara, Hironobu, Fuss, Johannes, Gabrhelík, Roman, Gewirtz-Meydan, Ateret, Gjoneska, Biljana, Gola, Mateusz, Grubbs, Joshua B., Hashim, Hashim T., Islam, Md. Saiful, Ismail, Mustafa, Jiménez-Martínez, Martha, Jurin, Tanja, Kalina, Ondrej, Klein, Verena, Költő, András, Lee, Sang-Kyu, Lewczuk, Karol, Lochner, Christine, López-Alvarado, Silvia, Lukavská, Kateřina, Mayta-Tristán, Percy, Milea, Ionut, Miller, Dan J., Orosová, Oľga, Orosz, Gábor, Ponce, Fernando P., Quintana, Gonzalo R., Garzola, Gabriel C.Quintero, Ramos-Diaz, Jano, Rigaud, Kévin, Rousseau, Ann, Scanavino, Marco De Tubino, Schulmeyer, Marion K., Sharan, Pratap, Shibata, Mami, Shoib, Sheikh, Sigre-Leirós, Vera, Sniewski, Luke, Spasovski, Ognen, Steibliene, Vesta, Stein, Dan J., Strizek, Julian, Ünsal, Berk C., Vaillancourt-Morel, Marie-Pier, Van Hout, Marie Claire, and Bőthe, Beáta

Published
2023
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143. Early-Life Outcomes in Relation to Social Determinants of Health for Children Born Extremely Preterm

Author

Jobe, Alan H., Caplan, Michael S., Polin, Richard A., Laptook, Abbot R., Keszler, Martin, Hensman, Angelita M., Alksninis, Barbara, Bishop, Carmena, Burke, Robert T., Caskey, Melinda, Hoffman, Laurie, Johnson, Katharine, Keszler, Mary Lenore, Knoll, Andrea M., Lamberson, Vita, Leach, Teresa M., Little, Emilee, McGowan, Elisabeth C., Stephens, Bonnie E., Vieira, Elisa, St. Pierre, Lucille, Ventura, Suzy, Watson, Victoria E., Hibbs, Anna Maria, Walsh, Michele C., Wilson-Costello, Deanne E., Newman, Nancy S., Bhola, Monika, Payne, Allison H., Siner, Bonnie S., Yalcinkaya, Gulgun, Truog, William E., Pallotto, Eugenia K., Kilbride, Howard W., Gauldin, Cheri, Holmes, Anne, Johnson, Kathy, Scott, Allison, Parimi, Prabhu S., Gaetano, Lisa, Poindexter, Brenda B., Schibler, Kurt, Kallapur, Suhas G., Donovan, Edward F., Merhar, Stephanie, Grisby, Cathy, Yolton, Kimberly, Alexander, Barbara, Beiersdorfer, Traci, Bridges, Kate, Cahill, Tanya E., Dudley, Juanita, Fischer, Estelle E., Gratton, Teresa L., Hayes, Devan, Hessling, Jody, Jackson, Lenora D., Kirker, Kristin, Mincey, Holly L., Muthig, Greg, Stacey, Sara, Steichen, Jean J., Tepe, Stacey, Thompson, Julia, Wuertz, Sandra, Cotten, C. Michael, Goldberg, Ronald N., Goldstein, Ricki F., Malcolm, William F., Mago-Shah, Deesha, Ashley, Patricia L., Finkle, Joanne, Auten, Kathy J., Fisher, Kimberley A., Grimes, Sandra, Gustafson, Kathryn E., Lohmeyer, Melody B., Laughon, Matthew M., Bose, Carl L., Bernhardt, Janice, Bose, Gennie, Clark, Cindy, Talbert, Jennifer, Warner, Diane, Trembath, Andrea, O'Shea, T. Michael, Wereszczak, Janice, Kicklighter, Stephen D., Rhodes-Ryan, Ginger, White, Donna, Patel, Ravi M., Carlton, David P., Stoll, Barbara J., Hale, Ellen C., Loggins, Yvonne C., Adams-Chapman, Ira, Blackwelder, Ann, Bottcher, Diane I., Carter, Sheena L., Kendrick-Allwood, Salathiel, Laursen, Judith, LaRossa, Maureen Mulligan, Mackie, Colleen, Sanders, Amy, Seabrook, Irma, Smikle, Gloria, Wineski, Lynn C., Higgins, Rosemary D., Bremer, Andrew A., Archer, Stephanie Wilson, Sokol, Gregory M., Dusick, Anna M., Papile, Lu Ann, Gunn, Susan, Hamer, Faithe, Harmon, Heidi M., Herron, Dianne E., Hines, Abbey C., Lytle, Carolyn, Miller, Lucy C., Minnich, Heike M., Richard, Leslie, Smiley, Lucy, Wilson, Leslie Dawn, Tyson, Jon E., Kennedy, Kathleen A., Khan, Amir M., Duncan, Andrea, Mosquera, Ricardo, Stephens, Emily K., McDavid, Georgia E., Alaniz, Nora I., Allain, Elizabeth, Arldt-McAlister, Julie, Burson, Katrina, Dempsey, Allison G., Eason, Elizabeth, Evans, Patricia W., Garcia, Carmen, Green, Charles, Hall, Donna, Harris, Beverly Foley, Jiminez, Margarita, John, Janice, Jones, Patrick M., Lillie, M. Layne, Lis, Anna E., Martin, Karen, Martin, Sara C., Mason, Carrie M., McKee, Shannon, Morris, Brenda H., Rennie, Kimberly, Rodgers, Shawna, Siddiki, Saba Khan, Simmons, Maegan C., Sperry, Daniel, Pierce Tate, Patti L., Wright, Sharon L., Sánchez, Pablo J., Nelin, Leif D., Jadcherla, Sudarshan R., Slaughter, Jonathan L., Yeates, Keith O., Keim, Sarah, Maitre, Nathalie L., Timan, Christopher J., Luzader, Patricia, Clark, Erna, Fortney, Christine A., Gutentag, Julie, Park, Courtney, Shadd, Julie, Sullivan, Margaret, Stein, Melanie, Nelin, Mary Ann, Newton, Julia, Small, Kristi, Burkhardt, Stephanie, Purnell, Jessica, Pietruszewski, Lindsay, Levengood, Katelyn, Batterson, Nancy, Morehead, Pamela, Carey, Helen, Yoseff-Salameh, Lina, Sullivan, Rox Ann, Hague, Cole, Grothause, Jennifer, Fearns, Erin, Fowler, Aubrey, Notestine, Jennifer, Tonneman, Jill, Hay, Krystal, Chao, Michelle, Warnimont, Kyrstin, Marzec, Laura, Miller, Bethany, Beckford, Demi R., Baugher, Hallie, DeSantis, Brittany, Hanlon, Cory, McCool, Jacqueline, Das, Abhik, Gantz, Marie G., Bann, Carla M., Wallace, Dennis, Crawford, Margaret M., Gabrio, Jenna, Leblond, David, Newman, Jamie E., Petrie Huitema, Carolyn M., O'Donnell Auman, Jeanette, Poole, W. Kenneth, Zaterka-Baxter, Kristin M., Van Meurs, Krisa P., Chock, Valerie Y., Stevenson, David K., Adams, Marian M., Ball, M. Bethany, Bentley, Barbara, Bruno, Elizabeth, Davis, Alexis S., Elena DeAnda, Maria, DeBattista, Anne M., Huffman, Lynne C., Ismael, Magdy, Kohn, Jean G., Krueger, Casey, Lowe, Janice, Lucash, Ryan E., Palmquist, Andrew W., Patel, Jessica, Proud, Melinda S., Reichert, Elizabeth N., John, Nicholas H. St., Sivakumar, Dharshi, Taylor, Heather L., Wager, Natalie, Williams, R. 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Rebecca, Miller, Nancy A., Morgan, Janet S., Pavageau, Lara, Sepulveda, Pollieanna, Tolentino-Plata, Kristine, Boatman, Cathy Twell, Vera, Azucena, Waterbury, Jillian, Yoder, Bradley A., Baserga, Mariana, Faix, Roger G., Winter, Sarah, Minton, Stephen D., Sheffield, Mark J., Rau, Carrie A., Baker, Shawna, Bird, Karie, Burnett, Jill, Christensen, Susan, Cole-Bledsoe, Laura, Davis, Brandy, Elmont, Jennifer O., Jensen, Jennifer J., Loertscher, Manndi C., Jordan, Jamie, Marchant, Trisha, Maxson, Earl, McGrath, Kandace M., Osborne, Karen A., Parry, D. Melody, Reich, Brixen A., Schaefer, Susan T., Spencer, Cynthia, Steffen, Michael, Tice, Katherine, Weaver-Lewis, Kimberlee, Woodbury, Kathryn D., Zanetti, Karen, Dillard, Robert G., Washburn, Lisa K., Jackson, Barbara G., Peters, Nancy, Chiu, Korinne, Allred, Deborah Evans, Goldstein, Donald J., Halfond, Raquel, Peterson, Carroll, Waldrep, Ellen L., Welch, Cherrie D., Morris, Melissa Whalen, Hounshell, Gail Wiley, Shankaran, Seetha, Sood, Beena G., Natarajan, Girija, Pappas, Athina, Abramczyk, Katherine, Agarwal, Prashant, Bajaj, Monika, Bara, Rebecca, Billian, Elizabeth, Chawla, Sanjay, Childs, Kirsten, De Jesus, Lilia C., Driscoll, Debra, February, Melissa, Goldston, Laura A., Johnson, Mary E., Muran, Geraldine, Panaitescu, Bogdan, Prentiss, Jeannette E., White, Diane, Woldt, Eunice, Barks, John, Wiggins, Stephanie A., Christensen, Mary K., Carlson, Martha D., Ehrenkranz, Richard A., Jacobs, Harris, Butler, Christine G., Cervone, Patricia, Greisman, Sheila, Konstantino, Monica, Poulsen, JoAnn, Taft, Janet, Williams, Joanne, Romano, Elaine, Vohr, Betty R., Travers, Colm P., and Hintz, Susan R.

Published
2023
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147. Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm

Author

Liu, Zhiyuan, Ding, Guohui, Chen, Lijun, and Yeung, Enoch

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

We propose an alternating optimization algorithm to the nonconvex Koopman operator learning problem for nonlinear dynamic systems. We show that the proposed algorithm will converge to a critical point with rate $O(1/T)$ and $O(\frac{1}{\log T})$ for the constant and diminishing learning rates, respectively, under some mild conditions. To cope with the high dimensional nonlinear dynamical systems, we present the first-ever distributed Koopman operator learning algorithm. We show that the distributed Koopman operator learning has the same convergence properties as the centralized Koopman operator learning, in the absence of optimal tracker, so long as the basis functions satisfy a set of state-based decomposition conditions. Numerical experiments are provided to complement our theoretical results., Comment: 8 pages, 2 figures

Published
2019

148. Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback

Author

Guo, Yi, Zhou, Xinyang, Zhao, Changhong, Chen, Yue, Summers, Tyler, and Chen, Lijun

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

Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.

Published
2019

149. Gradient-Based Multi-Area Distribution System State Estimation

Author

Zhou, Xinyang, Liu, Zhiyuan, Guo, Yi, Zhao, Changhong, Huang, Jianqiao, and Chen, Lijun

Subjects
Mathematics - Optimization and Control
Abstract

The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy.

Published
2019

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