Results 41 to 50 of about 522,389 (335)

Stochastic Optimal Control of Parallel Hybrid Electric Vehicles

Energies, 2017
Energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are highly related to the fuel economy and emission performances. However, EMS constitutes a challenging problem due to the complex structure of a HEV and the unknown or partially ...
Feiyan Qin, Guoqing Xu, Yue Hu, Kun Xu, Weimin Li   +4 more
doaj   +1 more source

Receding horizon control strategy for an electric vehicle with dual-motor coupling system in consideration of stochastic vehicle mass. [PDF]

PLoS ONE, 2018
Additional degrees of freedom existed in dual-motor coupling system bring considerable challenge to the optimal control of electric vehicles. Moreover, the stochastic characteristic of vehicle mass can further increase this challenge.
Hongqiang Guo, Jinyong Shangguan, Juan Tang, Qun Sun, Hongting Wu   +4 more
doaj   +1 more source

Maximum principle for a stochastic delayed system involving terminal state constraints [PDF]

, 2017
We investigate a stochastic optimal control problem where the controlled system is depicted as a stochastic differential delayed equation; however, at the terminal time, the state is constrained in a convex set.
Shi, Yufeng, Wen, Jiaqiang
core   +3 more sources

A Comprehensive Overview of the Clinical, Electrophysiological, and Neuroimaging Features of BPAN: Insights From a New Case Series

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Neurodegeneration with brain iron accumulation (NBIA) comprises a genetically and clinically heterogeneous group of rare neurological disorders characterized particularly by iron accumulation in the basal ganglia. To date, 15 genes have been associated with NBIA.
Seda Susgun, Ozgu Kizek, Sibel Aylin Ugur Iseri, Ibrahim Kamaci, Ayse Deniz Elmali, Pinar Iscen, Berfin Gulkaya Guzel, Gul Yalcin Cakmakli, Bulent Elibol, Berril Donmez, Raif Cakmur, Pinar Topaloglu, Turkish NBIA Study Group, Abdullah Acar, Ahmet Acarer, Arzu Karabay, Asuman Ali, Ayla Barlas, Aysegul Gunduz, Banu Ozen Barut, Baris Baslo, Bilge Kocer, Bilgehan Mus, Birsen Karaman, Burcu Gokce Cokal, Cem Ismail Kucukali, Cenk Akbostanci, Ceyhun Sayman, Cagla Turan, Dilek Ince Gunal, Ebru Bilge Dirik, Ebru Erzurumluoglu, Elif Kocasoy Orhan, Enes Demiryurek, Emrah Yucesan, Ercan Kose, Erdem Tuzun, Esen Saka Topcuoglu, Esra Okuyucu, Fatma Betul Ozdilek, Feriha Ozer, Gencer Genc, Gozde Unal, Gulay Kenangil, Gullu Tarhan, Gunes Kiziltan, Halil Onder, Hamit Genc, Hasmet Hanagasi, Hatice Yuksel, Hulya Apaydin, Koray Kirimtay, Mehmet Guney Senol, Melisa Kilic, Meltem Demirkiran, Mert Karaca, Miray Erdem, Muhammet Bilgehan Mus, Murat Gultekin, Nalan Capan, Nazan Karagoz Sakalli, Nazli Basak, Nihan Hande Akcakaya, Ozan Ezer, Ozge Uygun, Ozge Yilmaz Kuspeci, Ozgur Oztop Cakmak, Pervin Iseri, Petek Ballar Kirmizibayrak, Pinar Elkoca, Recep Alp, Remzi Yigiter, Rezzak Yilmaz, Sadika Ozdemir, Selda Keskin, Selen Ilhan Alp, Selen Soylu, Serdar Ceylaner, Serhat Ozkan, Sevda Erer Ozbek, Sevgin Gundogan, Sevil Yasufli, Sezin Alpaydin Baslo, Sibel Ertan, Sultan Cagirici, Seyma Aykac, Vuslat Yilmaz, Yaprak Secil, Yasar Kutukcu, Yeliz Ciftci, Yesim Sucullu Karadag, Yildiz Değirmenci, Zeliha Matur, Nerses Bebek, Murat Emre, Zuhal Yapici   +95 more
wiley   +1 more source

Stochastic optimal control via Bellman's principle [PDF]

Automatica, 2003
Consider a stochastic nonlinear controlled continuous-time system with the dynamics \(x(t)\) given by the equation \[ dx(t)=m(x(t),u(t))+ \sigma(x(t),u(t))dB(t),\quad t\in[0,T], \] where \(dB(t)\) is an \(m\)-dimensional standard Brownian motion, \(u(t)\in \mathbb R^m\) is a control at time \(t\), the functions \(m(x,u)\) and \(\sigma(x,u)\) are ...
Crespo, Luis G., Sun, Jian-Qiao
openaire   +1 more source

Tractable Dual Optimal Stochastic Model Predictive Control: An Example in Healthcare

, 2017
Output-Feedback Stochastic Model Predictive Control based on Stochastic Optimal Control for nonlinear systems is computationally intractable because of the need to solve a Finite Horizon Stochastic Optimal Control Problem.
Bitmead, Robert R., Sehr, Martin A.
core   +1 more source

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source

Optimal Control Problems of Forward-Backward Stochastic Volterra Integral Equations [PDF]

, 2014
Optimal control problems of forward-backward stochastic Volterra integral equations (FBSVIEs in short) are formulated and studied. A general duality principle is established for linear backward stochastic integral equation and linear stochastic Fredholm ...
Shi, Yufeng, Wang, Tianxiao, Yong, Jiongmin   +2 more
core  

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Multimodal Mechanical Testing of Additively Manufactured Ti6Al4V Lattice Structures: Compression, Bending, and Fatigue

Advanced Engineering Materials, EarlyView.
In this experimental study, the mechanical properties of additively manufactured Ti‐6Al‐4V lattice structures of different geometries are characterized using compression, four point bending and fatigue testing. While TPMS designs show superior fatigue resistance, SplitP and Honeycomb lattice structures combine high stiffness and strength. The resulting
Klaus Burkart, Jonas Schmidt, Anastasiya Toenjes, Johanna Eisenträger   +3 more
wiley   +1 more source