Article
Engineering, Geological
Chuanjie Xi, Xiewen Hu, Guotao Ma, Mohammad Rezania, Bo Liu, Kun He
Summary: This study proposes a novel framework combining Monte Carlo and logic tree simulations to predict the permanent displacement of regional coseismic landslides. The framework considers different regression functions and shows better prediction results compared to existing methods. It provides a meaningful measure for decision-making and emergency strategies to mitigate earthquake-induced landslides.
Article
Green & Sustainable Science & Technology
Siti Norsakinah Selamat, Nuriah Abd Majid, Aizat Mohd Taib
Summary: This study aims to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The study considered 12 landslide conditioning factors and evaluated the model using statistical measures and AUC. Based on the model validation results, the predictive model with an 80:20 ratio was chosen as the most suitable model.
Article
Engineering, Electrical & Electronic
Kexin Lv, Fan He, Xiaolin Huang, Jie Yang
Summary: This paper proposes a consensus-based distributed algorithm for GEP in multi-agent systems, which can effectively deal with problems with quadratic inseparable constraints.
Article
Automation & Control Systems
Stefanos Baros, Chin-Yao Chang, Gabriel E. Colon-Reyes, Andrey Bernstein
Summary: We propose an online data-enabled predictive control method (ODeePC) for optimal control of unknown systems. The method utilizes real-time measurement feedback and a special structure of matrices to compute the corresponding real-time optimal control policy, and we provide theoretical guarantees and numerical examples to demonstrate its performance.
Article
Automation & Control Systems
Chen Chen, Lei Pan, Li Sun, Jiong Shen, Junli Zhang, Kwang Y. Lee
Summary: This paper proposes an extended state observer-based stable predictive tracking control method for micro gas turbine combined heating and power systems. The method can overcome various challenges in the system and its effectiveness is verified through simulation experiments.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Environmental Sciences
Natalie Barbosa, Louis Andreani, Richard Gloaguen, Lothar Ratschbacher
Summary: The importance of identifying areas prone to landslides and using landslide susceptibility models, although hindered by the lack of thematic data from developing countries, highlights the reliance on simple morphometric parameters for estimating landslide likelihood.
Article
Engineering, Environmental
Ali Lashgari, Yaser Jafarian, Abdolhosein Haddad
Summary: This study introduces a predictive model based on pulse-like ground motions and coupled stick-slip-rotation analysis to estimate earthquake-triggered displacement of slopes in the near-fault region. By evaluating the efficiency of ground motion intensity measures, a predictive model is developed for seismic sliding displacement with a reasonable standard deviation. The proposed model is compared with two previous models based on pulse-like ground motions and coupled stick-slip-rotation approach.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2021)
Article
Mathematics
Shunichi Ohmori
Summary: This paper discusses the integration of predictive and prescriptive analytics framework, proposing a modeling framework that combines machine learning and robust optimization for deriving decision from data. The algorithm presented in the study uses the k-nearest neighbor model to predict uncertain parameters and forms an uncertainty set for robust optimization. The data-driven decision-making framework is illustrated on a two-stage linear stochastic programming, which can be efficiently solved through convex programming.
Article
Construction & Building Technology
Maximilian Mork, Nick Materzok, Andre Xhonneux, Dirk Mueller
Summary: This paper presents a nonlinear hybrid Model Predictive Control (MPC) approach for building energy systems based on Modelica. The approach considers the nonlinearities and discontinuities commonly found in building energy systems and uses a time-variant linearization approach to approximated nonlinear optimization problems. The proposed approach demonstrates good control quality and integration of multiple integer characteristics in a simulation study.
ENERGY AND BUILDINGS
(2022)
Article
Engineering, Chemical
Iosif Pappas, Nikolaos A. Diangelakis, Efstratios N. Pistikopoulos
Summary: This paper presents a strategy for regulating constrained uncertain discrete-time linear systems by calculating an explicit state feedback policy. The strategy considers uncertain processes affected by box-bounded multiplicative uncertainty and bounded additive uncertainty with linear state and inputs constraints. The proposed method involves calculating a terminal set constraint and reformulating state constraints in the prediction horizon to ensure feasible operation in the presence of uncertainty. Variable and constraint elimination approaches are employed to improve the computational performance of the strategy. The steps and benefits of the proposed developments are demonstrated through a numerical example and a chemical engineering case study.
Article
Construction & Building Technology
Ho Gul Kim, Chan Park, Mingyun Cho
Summary: With the increase in extreme weather phenomena due to climate change, cities are facing various damages caused by landslides. By combining statistical and runout models, this study aims to more accurately predict future landslide hazard areas, with a particular focus on urban areas adjacent to forests where the hazard is predicted to significantly increase.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Automation & Control Systems
Shirin Panahi, Ali Kashani, Claus Danielson
Summary: This paper introduces a primal-dual interior-point (pdIp) optimization algorithm for solving extremescale model predictive control (mpC) problems with linear dynamics, polytopic constraints, and quadratic/linear costs which are all invariant under the symmetric-group. The algorithm exploits symmetry to reduce the computational and memory burden of extreme-scale or fast-paced applications of mpC. It transforms the original inputs, states, and constraints of the mpC problem into a symmetric domain to achieve lower computational and memory complexity in solving the optimization problem. For a hvAC numerical example, the presented symmetry exploiting pdIp algorithm significantly reduces the computation-time and outperforms a state-of-the-art symmetry exploiting optimization algorithm.
Article
Engineering, Mechanical
Ying-Kuan Tsai, Richard J. Malak
Summary: This paper introduces a new technique, called sp-NLPC, for designing a feedback controller that can stabilize intrinsically unstable nonlinear dynamical systems using parametric optimization. The technique is less assumption-heavy and more data-efficient than alternative methods. Results show that sp-NLPC outperforms and is more efficient than competing methods.
JOURNAL OF MECHANICAL DESIGN
(2022)
Article
Mathematics, Applied
Alessandro Alla, Carmen Grassle, Michael Hinze
Summary: The core of the MPC method is to solve a time-dependent optimization problem in the prediction horizon and apply the optimal control to the application horizon. To efficiently solve this problem, a time-adaptive residual based error control concept is proposed. This concept provides adaptive discretization of the prediction and application horizons, improving control performance and robustness.
JOURNAL OF SCIENTIFIC COMPUTING
(2022)
Article
Robotics
Manan S. Gandhi, Bogdan Vlahov, Jason Gibson, Grady Williams, Evangelos A. Theodorou
Summary: The proposed novel decision-making architecture for Robust Model-Predictive Path Integral Control (RMPPI) shows superior performance compared to other model predictive controllers. Experimental results demonstrate the applicability and effectiveness of RMPPI in real hardware, outperforming existing controllers in terms of agility and robustness to disturbances.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)