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Journal articles on the topic 'Real-Time Dynamic Programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Gehani, Narain, and Krithi Ramamritham. "Real-time Concurrent C: A language for programming dynamic real-time systems." Real-Time Systems 3, no. 4 (1991): 377–405. http://dx.doi.org/10.1007/bf00365999.

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Li, Guoliang, Mohammad N. A. Rana, Jinhong Sun, Yinglei Song, and Junfeng Qu. "Real-time image enhancement with efficient dynamic programming." Multimedia Tools and Applications 79, no. 41-42 (2020): 30883–903. http://dx.doi.org/10.1007/s11042-020-09586-y.

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3

Barto, Andrew G., Steven J. Bradtke, and Satinder P. Singh. "Learning to act using real-time dynamic programming." Artificial Intelligence 72, no. 1-2 (1995): 81–138. http://dx.doi.org/10.1016/0004-3702(94)00011-o.

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4

Tew, A. I., and C. J. Gray. "A real-time gesture recognizer based on dynamic programming." Journal of Biomedical Engineering 15, no. 3 (1993): 181–87. http://dx.doi.org/10.1016/0141-5425(93)90113-d.

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5

Cui, Xi Min, Ke Zheng, Chang Ru Liu, and Qiong Zheng. "Multi-Threaded Technology Apply in Real Time Measurement System of Dual-Camera." Applied Mechanics and Materials 687-691 (November 2014): 966–69. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.966.

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Based on multi-thread technology, completed a set of real-time dynamic measurement system apply on binocular vision principle. Real time measurement require of system efficiency higher, multithreaded programming can greatly improve the efficiency of program. Binocular dynamic real-time detection is based on closed-loop control system, puts forward a multithreaded programming technology which is based on the binocular vision high-speed dynamic measurement method. The data acquisition, processing, display are using multithreading technology.

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Mouawad, Grace, Maroun Nemer, and Khalil El Khoury. "Real Time Dynamic Programming Control of Rapid Thermal Processing Furnaces." Numerical Heat Transfer, Part A: Applications 64, no. 11 (2013): 920–37. http://dx.doi.org/10.1080/10407782.2013.807694.

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Gong, Minglun, and Yee-Hong Yang. "Real-Time Stereo Matching Using Orthogonal Reliability-Based Dynamic Programming." IEEE Transactions on Image Processing 16, no. 3 (2007): 879–84. http://dx.doi.org/10.1109/tip.2006.891344.

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Matthews, B., and I. Elhanany. "Hardware architecture for high-speed real-time dynamic programming applications." IET Computers & Digital Techniques 2, no. 3 (2008): 164. http://dx.doi.org/10.1049/iet-cdt:20070027.

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FAN, Chang-Jie, and Xiao-Ping CHEN. "Optimal Action Criterion and Algorithm Improvement of Real-Time Dynamic Programming." Journal of Software 19, no. 11 (2009): 2869–78. http://dx.doi.org/10.3724/sp.j.1001.2008.02869.

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Korotkova, Yu L., and Yu A. Mezentsev. "The dynamic programming algorithm for management of real-time aircraft assignments." Journal of Physics: Conference Series 1333 (October 2019): 032040. http://dx.doi.org/10.1088/1742-6596/1333/3/032040.

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Teodorović, Dušan, Vijay Varadarajan, Jovan Popović, Mohan Raj Chinnaswamy, and Sharath Ramaraj. "Dynamic programming—neural network real-time traffic adaptive signal control algorithm." Annals of Operations Research 143, no. 1 (2006): 123–31. http://dx.doi.org/10.1007/s10479-006-7376-z.

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Wang, Liang, Ruigang Yang, Minglun Gong, and Miao Liao. "Real-time stereo using approximated joint bilateral filtering and dynamic programming." Journal of Real-Time Image Processing 9, no. 3 (2012): 447–61. http://dx.doi.org/10.1007/s11554-012-0275-4.

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Delgado, Karina V., Leliane N. de Barros, Daniel B. Dias, and Scott Sanner. "Real-time dynamic programming for Markov decision processes with imprecise probabilities." Artificial Intelligence 230 (January 2016): 192–223. http://dx.doi.org/10.1016/j.artint.2015.09.005.

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Fu, Liping, Qing Liu, and Paul Calamai. "Real-Time Optimization Model for Dynamic Scheduling of Transit Operations." Transportation Research Record: Journal of the Transportation Research Board 1857, no. 1 (2003): 48–55. http://dx.doi.org/10.3141/1857-06.

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A new transit operating strategy is presented in which service vehicles operate in pairs with the lead vehicle providing an all-stop local service and the following vehicle being allowed to skip some stops as an express service. The underlying scheduling problem is formulated as a nonlinear integer programming problem with the objective of minimizing the total costs for both operators and passengers. A sensitivity analysis using a real-life example is performed to identify the conditions under which the proposed operating strategy is most advantageous.

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Hu, Ping, Chuan Huang, and Jing Lian. "Efficient dynamic programming in economical cruise control under real-time traffic situations." Transactions of the Institute of Measurement and Control 42, no. 11 (2020): 2044–56. http://dx.doi.org/10.1177/0142331220904552.

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In this paper, dynamic programming (DP) economical cruise control algorithms under discrete distance and discrete time strategies are designed to improve battery electric vehicle (BEV) energy consumption and riding comfort. In our experiment, energy consumption of constant-speed driving is reduced by up to 21.6% using the optimum velocity profiles from discrete distance DP. ‘Variable step length and boundary conditions’ are utilized to reduce discrete distance DP processing time by 90.8% with no effect on accuracy. Under discrete time DP, position is introduced as an additional variable, so discrete time DP allows more inputs including real-time preceding vehicle position and traffic information. The experiment results indicate that under traffic situation, discrete time DP further improves energy consumption and riding comfort by avoiding unnecessary stop at intersections compared with discrete distance DP. Further analysis indicates that under properly controlled velocity, the transfer between potential energy and kinetic energy is more efficient than that between potential energy and electric energy. These DP economical cruise control algorithms are effective for BEVs, hybrid electric vehicles and plug-in hybrid electric vehicles. ‘Variable step length and boundary conditions’ method in DP can also be applied in other domains where DP processing time is crucial.

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Sasinowski, J. E., and J. K. Strosnider. "A dynamic programming algorithm for cache memory partitioning for real-time systems." IEEE Transactions on Computers 42, no. 8 (1993): 997–1001. http://dx.doi.org/10.1109/12.238493.

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Hallek, Mohamed, Fethi Smach, and Mohamed Atri. "Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming." Computational Visual Media 5, no. 1 (2019): 59–71. http://dx.doi.org/10.1007/s41095-019-0133-4.

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OKAMURA, HIROYUKI, KAZUKI IWAMOTO, and TADASHI DOHI. "A DP-BASED CHECKPOINTING SCHEME IN REAL-TIME APPLICATIONS." International Journal of Reliability, Quality and Safety Engineering 13, no. 04 (2006): 323–40. http://dx.doi.org/10.1142/s0218539306002288.

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In this paper, we consider computation algorithms for checkpoint placement in real-time applications. Under the condition that the processing time is bounded by a time limit, we derive sequentially the optimal checkpoint time based on the dynamic programming. In numerical examples, we examine the dependence of optimal checkpoint sequence on both failure time and processing time distributions, and investigate the effectiveness of sequential checkpoint placement.

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Cortes Torres, Carlos C., Ryota Yasudo, and Hideharu Amano. "Body Bias Optimization for Real-Time Systems." Journal of Low Power Electronics and Applications 10, no. 1 (2020): 8. http://dx.doi.org/10.3390/jlpea10010008.

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The energy of real-time systems for embedded usage needs to be efficient without affecting the system’s ability to meet task deadlines. Dynamic body bias (BB) scaling is a promising approach to managing leakage energy and operational speed, especially for system-on-insulator devices. However, traditional energy models cannot deal with the overhead of adjusting the BB voltage; thus, the models are not accurate. This paper presents a more accurate model for calculating energy overhead using an analytical double exponential expression for dynamic BB scaling and an optimization method based on nonlinear programming with consideration of the real-chip parameter constraints. The use of the proposed model resulted in an energy reduction of about 32% at lower frequencies in comparison with the conventional model. Moreover, the energy overhead was reduced to approximately 14% of the total energy consumption. This methodology provides a framework and design guidelines for real-time systems and computer-aided design.

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Singh, Baljeet, Nitin Kumar, Irshad Ahmed, and Karun Yadav. "Real-Time Object Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3159–60. http://dx.doi.org/10.22214/ijraset.2022.42820.

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Abstract: The computer vision field known as real-time acquisition is large, dynamic, and complex. Local image process refers to the acquisition of one object in an image, while Objects refers to the acquisition of multiple objects in an image. In digital photos and videos, this sees semantic class objects. Tracking features, video surveilance, pedestrian detection, census, self-driving cars, face recognition, sports tracking, and many other applications used to find real-time object. Convolution Neural Networks is an in-depth study tool for OpenCV (Opensource Computer Vision), a set of basic computer-assisted programming tasks. Computer visualization, in-depth study, and convolutional neural networks are some of the words used in this paper..

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Gabasov, R., F. M. Kirillova, and Vo Thi Thanh Ha. "Application of linear programming techniques for controlling linear dynamic plants in real time." Computational Mathematics and Mathematical Physics 56, no. 3 (2016): 382–95. http://dx.doi.org/10.1134/s0965542516030064.

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22

Zhang, Weizhe, Yao Hu, Hui He, Yawei Liu, and Allen Chen. "Linear and dynamic programming algorithms for real-time task scheduling with task duplication." Journal of Supercomputing 75, no. 2 (2017): 494–509. http://dx.doi.org/10.1007/s11227-017-2076-9.

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23

Mohiuddin, Abdullah, Tarek Taha, Yahya Zweiri, and Dongming Gan. "Dual-UAV Payload Transportation Using Optimized Velocity Profiles via Real-Time Dynamic Programming." Drones 7, no. 3 (2023): 171. http://dx.doi.org/10.3390/drones7030171.

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In this paper, a real-time dynamic programming (RTDP) approach was developed for the first time to jointly carry a slung load using two unmanned aerial vehicles (UAVs) with a trajectory optimized for time and energy consumption. The novel strategy applies RTDP algorithm, where the journey was discretized into horizons consisting of distance intervals, and for every distance interval, an optimal policy was obtained using a dynamic programming sweep. The RTDP-based strategy is applied for dual-UAV collaborative payload transportation using coordinated motion where UAVs act as actuators on the payload. The RTDP algorithm provides the optimal velocity decisions for the slung load transportation to either minimize the journey time or the energy consumption. The RTDP approach involves minimizing a cost function which is derived after simplifying the combined model of the dual-UAV-payload system. The cost function derivation was also accommodated to dynamically distribute the load/energy between two multi-rotor platforms during a transportation mission. The cost function is used to calculate transition costs for all stages and velocity decisions. A terminal cost is used at the last distance interval during the first phase of the journey when the velocity at the end of the current horizon is not known. In the second phase, the last stage or edge of the horizon includes the destination, hence final velocity is known which is used to calculate the transition cost of the final stage. Once all transition costs are calculated, the minimum cost is traced back from the final stage to the current stage to find the optimal velocity decision. The developed approach was validated in MATLAB simulation, software in the loop Gazebo simulation, and real experiments. The numerical and Gazebo simulations showed the successful optimization of journey time or energy consumption based on the selection of the factor λ. Both simulation and real experiments results show the effectiveness and the applicability of the proposed approach.

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24

Pleau, Martin, François Methot, Andrée Moïra Lebrun, and Hubert Colas. "Minimizing Combined Sewer Overflows in Real-Time Control Applications." Water Quality Research Journal 31, no. 4 (1996): 775–86. http://dx.doi.org/10.2166/wqrj.1996.042.

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Abstract An approach to design and operate real-time control system for combined sewer overflows is proposed. The methodology is based on nonlinear programming with the peculiarity that all the constraints are linear. The problem definition guarantees a non-null optimization space while preserving the physical and dynamical constraints of the state variables. The nonlinear cost function allows standard control objectives to be met and simulation of the behavior associated with storage facilities and actuators. The linear flow dynamic constraints are defined to simulate flow attenuation as well as to reject unknown disturbances.

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Minghai Yuan, Minghai Yuan, Chenxi Zhang Minghai Yuan, Kaiwen Zhou Chenxi Zhang, and Fengque Pei Kaiwen Zhou. "Real-time Allocation of Shared Parking Spaces Based on Deep Reinforcement Learning." 網際網路技術學刊 24, no. 1 (2023): 035–43. http://dx.doi.org/10.53106/160792642023012401004.

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<p>Aiming at the parking space heterogeneity problem in shared parking space management, a multi-objective optimization model for parking space allocation is constructed with the optimization objectives of reducing the average walking distance of users and improving the utilization rate of parking spaces, a real-time allocation method for shared parking spaces based on deep reinforcement learning is proposed, which includes a state space for heterogeneous regions, an action space based on policy selection and a reward function with variable coefficients. To accurately evaluate the model performance, dynamic programming is used to derive the theoretical optimal values. Simulation results show that the improved algorithm not only improves the training success rate, but also increases the Agent performance by at least 12.63% and maintains the advantage for different sizes of parking demand, reducing the user walking distance by 53.58% and improving the parking utilization by 6.67% on average, and keeping the response time less than 0.2 seconds.</p> <p>&nbsp;</p>

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Khan, Rahim, Ihsan Ali, Saleh M. Altowaijri, et al. "LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN Data." Sensors 19, no. 1 (2019): 166. http://dx.doi.org/10.3390/s19010166.

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Multivariate data sets are common in various application areas, such as wireless sensor networks (WSNs) and DNA analysis. A robust mechanism is required to compute their similarity indexes regardless of the environment and problem domain. This study describes the usefulness of a non-metric-based approach (i.e., longest common subsequence) in computing similarity indexes. Several non-metric-based algorithms are available in the literature, the most robust and reliable one is the dynamic programming-based technique. However, dynamic programming-based techniques are considered inefficient, particularly in the context of multivariate data sets. Furthermore, the classical approaches are not powerful enough in scenarios with multivariate data sets, sensor data or when the similarity indexes are extremely high or low. To address this issue, we propose an efficient algorithm to measure the similarity indexes of multivariate data sets using a non-metric-based methodology. The proposed algorithm performs exceptionally well on numerous multivariate data sets compared with the classical dynamic programming-based algorithms. The performance of the algorithms is evaluated on the basis of several benchmark data sets and a dynamic multivariate data set, which is obtained from a WSN deployed in the Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology. Our evaluation suggests that the proposed algorithm can be approximately 39.9% more efficient than its counterparts for various data sets in terms of computational time.

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Shah, Sanket, Meghna Lowalekar, and Pradeep Varakantham. "Neural Approximate Dynamic Programming for On-Demand Ride-Pooling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 507–15. http://dx.doi.org/10.1609/aaai.v34i01.5388.

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On-demand ride-pooling (e.g., UberPool, LyftLine, GrabShare) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies (e.g., Uber). Unlike in Taxi on Demand (ToD) services – where a vehicle is assigned one passenger at a time – in on-demand ride-pooling, each vehicle must simultaneously serve multiple passengers with heterogeneous origin and destination pairs without violating any quality constraints. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current time step without considering the effect such an assignment could have on assignments in future time steps. However, considering the future effects of an assignment that also has to consider what combinations of passenger requests can be assigned to vehicles adds a layer of combinatorial complexity to the already challenging problem of considering future effects in the ToD case.A popular approach that addresses the limitations of myopic assignments in ToD problems is Approximate Dynamic Programming (ADP). Existing ADP methods for ToD can only handle Linear Program (LP) based assignments, however, as the value update relies on dual values from the LP. The assignment problem in ride pooling requires an Integer Linear Program (ILP) that has bad LP relaxations. Therefore, our key technical contribution is in providing a general ADP method that can learn from the ILP based assignment found in ride-pooling. Additionally, we handle the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and show a connection to Deep Reinforcement Learning that allows us to learn this value-function with increased stability and sample-efficiency. We show that our approach easily outperforms leading approaches for on-demand ride-pooling on a real-world dataset by up to 16%, a significant improvement in city-scale transportation problems.

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Min, Seunghwan, Sung Gwan Park, Kunsoo Park, Dora Giammarresi, Giuseppe F. Italiano, and Wook-Shin Han. "Symmetric continuous subgraph matching with bidirectional dynamic programming." Proceedings of the VLDB Endowment 14, no. 8 (2021): 1298–310. http://dx.doi.org/10.14778/3457390.3457395.

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In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critical patterns in such dynamic graphs plays an important role in various application domains such as fraud detection, cyber security, and recommendation systems for social networks. Given a dynamic data graph and a query graph, the continuous subgraph matching problem is to find all positive matches for each edge insertion and all negative matches for each edge deletion. The state-of-the-art algorithm TurboFlux uses a spanning tree of a query graph for filtering. However, using the spanning tree may have a low pruning power because it does not take into account all edges of the query graph. In this paper, we present a symmetric and much faster algorithm SymBi which maintains an auxiliary data structure based on a directed acyclic graph instead of a spanning tree, which maintains the intermediate results of bidirectional dynamic programming between the query graph and the dynamic graph. Extensive experiments with real and synthetic datasets show that SymBi outperforms the state-of-the-art algorithm by up to three orders of magnitude in terms of the elapsed time.

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29

Chiu, Chinchuan, Chia-Yiu Maa, and Michael A. Shanblatt. "AN ARTIFICIAL NEURAL NETWORK ALGORITHM FOR DYNAMIC PROGRAMMING." International Journal of Neural Systems 01, no. 03 (1990): 211–20. http://dx.doi.org/10.1142/s0129065790000114.

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An artificial neural network (ANN) formulation for solving the dynamic programming problem (DPP) is presented. The DPP entails finding an optimal path from a source node to a destination node which minimizes (or maximizes) a performance measure of the problem. The optimization procedure is implemented and demonstrated using a modified Hopfield–Tank ANN. Simulations show that the ANN can provide a near-optimal solution during an elapsed time of only a few characteristic time constants of the circuit for DPPs with sizes as large as 64 stages with 64 states in each stage. An application of the proposed algorithm to an optimal control problem is presented. The proposed artificial neural network dynamic programming algorithm is attractive due to its radically improved speed over conventional techniques especially where real-time near-optimal solutions are required.

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30

Yao, Zhihong, Yibing Wang, Wei Xiao, Bin Zhao, and Bo Peng. "A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control." Algorithms 12, no. 2 (2019): 38. http://dx.doi.org/10.3390/a12020038.

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Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.

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31

Anwar, Aamir, Ijaz Ul Haq, Imdad Ahmad Mian, et al. "Applying Real-Time Dynamic Scaffolding Techniques during Tutoring Sessions Using Intelligent Tutoring Systems." Mobile Information Systems 2022 (June 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/6006467.

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An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of “learning by doing.” Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted at novice authors with minimum technical/programming experience and provides real-time scaffolding to learner’s incomplete/incorrect answers using the best scaffolding techniques. Two evaluation techniques were applied for the evaluation of the performance of the proposed authoring tool, e.g., paired t-test analysis and postexperiment survey. The learning gains obtained from paired t-test contend a significant learning gain and improvement in the learning process with enhanced learning performance with multiple scaffolding techniques as compared to single scaffolding technique experience. The postexperiment survey has a notable result that shows the effectiveness of the tutor model that ensures a very user-friendly interface, deploying scaffolding techniques and adequate control of selecting and deploying scaffolding techniques and making the authoring process easy.

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32

Hallek, Mohamed, Hamdi Boukamcha, Abdellatif Mtibaa, and Mohamed Atri. "Dynamic programming with adaptive and self-adjusting penalty for real-time accurate stereo matching." Journal of Real-Time Image Processing 19, no. 2 (2021): 233–45. http://dx.doi.org/10.1007/s11554-021-01180-1.

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Ali, Ahmed M., Ahmad Ghanbar, and Dirk Soffker. "Optimal Control of Multi-Source Electric Vehicles in Real Time Using Advisory Dynamic Programming." IEEE Transactions on Vehicular Technology 68, no. 11 (2019): 10394–405. http://dx.doi.org/10.1109/tvt.2019.2941523.

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Yin, Biao, Abdellah El Moudni, and Mahjoub Dridi. "Forward search algorithm based on dynamic programming for real-time adaptive traffic signal control." IET Intelligent Transport Systems 9, no. 7 (2015): 754–64. http://dx.doi.org/10.1049/iet-its.2014.0156.

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35

Xu, Xin, Chuanqiang Lian, Lei Zuo, and Haibo He. "Kernel-Based Approximate Dynamic Programming for Real-Time Online Learning Control: An Experimental Study." IEEE Transactions on Control Systems Technology 22, no. 1 (2014): 146–56. http://dx.doi.org/10.1109/tcst.2013.2246866.

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36

Lee, Meng-Tse, Ming-Lung Chuang, Sih-Tse Kuo, and Yan-Ru Chen. "UAV Swarm Real-Time Rerouting by Edge Computing D* Lite Algorithm." Applied Sciences 12, no. 3 (2022): 1056. http://dx.doi.org/10.3390/app12031056.

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Seeking to give unmanned aerial vehicles (UAVs) a higher level of autonomous control, this study uses edge computing systems to replace the ground control station (GCS) commonly used to control UAVs. Since the GCS belongs to the central control architecture, the edge computing system of the distributed architecture can give drones more flexibility in dealing with changing environmental conditions, allowing them to autonomously and instantly plan their flight path, fly in formation, or even avoid obstacles. Broadcast communications are used to realize UAV-to-UAV communications, thus allocating tasks among a swarm of UAVs and ensuring that each individual UAV collaborates as an integrated member of the group. The dynamic path programming problem for UAV swarm missions uses a two-phase tabu search with a 2-Opt exchange method and an A* search as the path programming algorithm. Distance is taken as a cost function for path programming. The turning points of no-fly zones are then increased and expanded based on drone fleet coverage, thus preventing drones from entering prohibited areas. Unlike previous work, which mostly considers only single no-fly zones, this approach accounts for multiple restricted areas, ensuring that a UAV swarm can complete its assigned task without violating no-fly zones. A drone encountering an obstacle while traveling along the route set by the algorithm will update the map information in real time, allowing for instant recharting of the optimal path to the goal as a reverse search using the D* Lite algorithm.

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Nakajima, Natsu, Takeyuki Tamura, Yoshihiro Yamanishi, Katsuhisa Horimoto, and Tatsuya Akutsu. "Network Completion Using Dynamic Programming and Least-Squares Fitting." Scientific World Journal 2012 (2012): 1–8. http://dx.doi.org/10.1100/2012/957620.

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We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified. For this problem, we present a novel method using dynamic programming and least-squares fitting and show that it outputs a network with the minimum sum squared error in polynomial time if the maximum indegree of the network is bounded by a constant. We also perform computational experiments using both artificially generated and real gene expression time series data.

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38

Melnikov, Oleg. "DISCRETE-CONTINUOUS MODEL OF SALES MANAGEMENT IN REAL TIME." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 2 (8) (December 23, 2022): 63–69. http://dx.doi.org/10.20998/2079-0023.2022.02.10.

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The subject of the research is the development of a strategy for dynamic price management when selling products over a fixed time interval. We consider the case when the demand for the seller's products can be represented as a combination of two random processes: 1) Poisson flow of potential consumers; 2) the purchase of goods by an individual consumer, the probability of which is inversely related to the price of the product. Consumers need at most one unit of the good and have independent equally distributed estimates of its consumer value. Such demand structure allows to formalize the choice of the optimal pricing strategy as an optimal control problem. Employing dynamic programming methods to solving this problem yields a system of Riccati differential equations. The optimal solution is obtained in the closedloop form as a function of the time to expiration of the product value and unsold inventory levels. Examples of a practical solution to the optimal pricing problem are given for special cases when it is possible to find an analytical solution. For the general case, it is shown how to find the optimal prices using numerical methods. Calculations show that optimal prices are decreasing functions of time and inventory levels. The combination of these factors, together with the random nature of the product sales, leads to rather complex observed price trajectories, examples of which were obtained using computer simulations. In particular, in many cases, the implementation of the proposed strategy results in cyclical price behavior, the prevalence of which in retail is well documented. The problem of optimizing the expected income of the seller when using constant prices was also solved. Comparison of the expected income of the seller under static and dynamic prices indicates a significant advantage of the latter. The economic effect of using dynamic pricing is most significant near the expiration of the product value.

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39

Kalia, Aman, and Brian Fabien. "On Implementing Optimal Energy Management for EREV Using Distance Constrained Adaptive Real-Time Dynamic Programming." Electronics 9, no. 2 (2020): 228. http://dx.doi.org/10.3390/electronics9020228.

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Extended range electric vehicles (EREVs) operate both as an electric vehicle (EV) and as a hybrid electric vehicle (HEV). As a hybrid, the on-board range extender (REx) system provides additional energy to increase the feasible driving range. In this paper, we evaluate an experimental research EREV based on the 2016 Chevrolet Camaro platform for optimal energy management control. We use model-in-loop and software-in-loop environments to validate the data-driven power loss model of the research vehicle. A discussion on the limitations of conventional energy management control algorithms is presented. We then propose our algorithm derived from adaptive real-time dynamic programming (ARTDP) with a distance constraint for energy consumption optimization. To achieve a near real-time functionality, the algorithm recomputes optimal parameters by monitoring the energy storage system’s (ESS) state of charge deviations from the previously computed optimal trajectory. The proposed algorithm is adaptable to variability resulting from driving behavior or system limitations while maintaining the target driving range. The net energy consumption evaluation shows a maximum improvement of 9.8% over the conventional charge depleting/charge sustaining (CD/CS) algorithm used in EREVs. Thus, our proposed algorithm shows adaptability and fault tolerance while being close to the global optimal solution.

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Cossell, Stephen, and José Guivant. "Concurrent dynamic programming for grid-based problems and its application for real-time path planning." Robotics and Autonomous Systems 62, no. 6 (2014): 737–51. http://dx.doi.org/10.1016/j.robot.2014.03.002.

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van Lon, Rinde R. S., Juergen Branke, and Tom Holvoet. "Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics." Genetic Programming and Evolvable Machines 19, no. 1-2 (2017): 93–120. http://dx.doi.org/10.1007/s10710-017-9300-5.

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Shuai, Hang, Xiaomeng Ai, Jiakun Fang, Tao Ding, Zhe Chen, and Jinyu Wen. "Real-time optimization of the integrated gas and power systems using hybrid approximate dynamic programming." International Journal of Electrical Power & Energy Systems 118 (June 2020): 105776. http://dx.doi.org/10.1016/j.ijepes.2019.105776.

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Pratikakis, Nikolaos E., Matthew J. Realff, and Jay H. Lee. "Strategic capacity decision-making in a stochastic manufacturing environment using real-time approximate dynamic programming." Naval Research Logistics (NRL) 57, no. 3 (2010): 211–24. http://dx.doi.org/10.1002/nav.20384.

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Meng, Yan. "Multi-Robot Searching using Game-Theory Based Approach." International Journal of Advanced Robotic Systems 5, no. 4 (2008): 44. http://dx.doi.org/10.5772/6232.

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This paper proposes a game-theory based approach in a multi–target searching using a multi-robot system in a dynamic environment. It is assumed that a rough priori probability map of the targets' distribution within the environment is given. To consider the interaction between the robots, a dynamic-programming equation is proposed to estimate the utility function for each robot. Based on this utility function, a cooperative nonzero-sum game is generated, where both pure Nash Equilibrium and mixed-strategy Equilibrium solutions are presented to achieve an optimal overall robot behaviors. A special consideration has been taken to improve the real-time performance of the game-theory based approach. Several mechanisms, such as event-driven discretization, one-step dynamic programming, and decision buffer, have been proposed to reduce the computational complexity. The main advantage of the algorithm lies in its real-time capabilities whilst being efficient and robust to dynamic environments.

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Zhao, Hongqian, Honghua Dai, and Zhaohui Dang. "Fast guidance method of lunar landing based on dynamic programming." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 1 (2021): 159–66. http://dx.doi.org/10.1051/jnwpu/20213910159.

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In this paper, a novel multi-stage trajectory transfer and fixed-point landing time optimal guidance method for the lunar surface emergency rescue mission is proposed. Firstly, the whole process motion and dynamics model for the lunar surface emergency rescue with four stages are established. Then, in the initial orbit transfer phase, the Lambert algorithm based on "prediction + correction" is designed for the non spherical gravitational perturbation of the moon. In the powered descent phase, the Hamiltonian function is used to design a time suboptimal explicit guidance law that can be applied in orbit in real time. Finally, aiming at the multi-stage global time optimal guidance, the whole time process guidance law is obtained by establishing the allowable control set for each stage in the whole process. The simulation results show that compared with the piecewise optimal control method, the present method has better optimization effect and shorter whole process time. It is of great significance to the possible emergency rescue mission of manned lunar exploration in the future.

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Wu, Dongli, Hao Zhang, Yunping Liu, Weihua Fang, and Yan Wang. "Real-Time Trajectory Planning and Control for Constrained UAV Based on Differential Flatness." International Journal of Aerospace Engineering 2022 (June 20, 2022): 1–17. http://dx.doi.org/10.1155/2022/8004478.

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The trajectory planning of UAV with nonholonomic constraints is usually taken as differential algebraic equation to solve the optimal control problem of functional extremum under the condition of inequality constraints. However, it can be challenging to meet the requirements of real-time for the high complexity. A differential flat theory based on B-spline trajectory planning can replace the optimal control problem with nonlinear programming and be a good means to achieve the efficient trajectory planning of an UAV under multiple dynamic constraints. This research verifies the feasibility of this theory with actual flight experiments.

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Zhao, Wei, and Ying Zhang. "The Research on Real-Time Collision Detection of Large-Scale Complex Dynamic Scene." Applied Mechanics and Materials 433-435 (October 2013): 1926–29. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1926.

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The rapid real-time collision detection is one of technical difficulties in large-scale simulation of complex dynamic scenes. Complex large-scale real-time scenario, demands of users on the efficiency and the accuracy of collision detection is higher and higher, which has made it become the subject of people to study. Based on the domestic and foreign existing collision detection algorithm, improved the process of collision detection framework, designed and implemented parallel collision detection with method of SIMD technology and multithreading programming, and made combination of these used in the collision detection of complex and dynamic scenes. Experimental results show that the algorithm design made the object in large and complex space of the 3D scene could be achieved real-time simulation. And the method solved the graphic images of moving objects and undetected penetration phenomena, shorten the detection time and response time of the collision for deformation object, increased the realism and immersion.

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Haghish, E. F. "Rethinking Literate Programming in Statistics." Stata Journal: Promoting communications on statistics and Stata 16, no. 4 (2016): 938–63. http://dx.doi.org/10.1177/1536867x1601600408.

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Literate programming is becoming increasingly trendy for data analysis because it allows the generation of dynamic-analysis reports for communicating data analysis and eliminates untraceable human errors in analysis reports. Traditionally, literate programming includes separate processes for compiling the code and preparing the documentation. While this workflow might be satisfactory for software documentation, it is not ideal for writing statistical analysis reports. Instead, these processes should run in parallel. In this article, I introduce the weaver package, which examines this idea by creating a new log system in HTML or LATEX that can be used simultaneously with the Stata log system. The new log system provides many features that the Stata log system lacks; for example, it can render mathematical notations, insert figures, create publication-ready dynamic tables, and style text, and it includes a built-in syntax highlighter. The weaver package also produces dynamic PDF documents by converting the HTML log to PDF or by typesetting the LATEX log and thus provides a real-time preview of the document without recompiling the code. I also discuss potential applications of the weaver package.

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Xie, Zhi-ying, Yuan-Rong He, Yuan-tong Jiang, and Chih-Cheng Chen. "Improved AND/OR Tree Search Algorithm in Analysis of Stochastic and Time-Dependent Shortest Path Problem." Scientific Programming 2021 (July 28, 2021): 1–19. http://dx.doi.org/10.1155/2021/9922466.

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Real-time vehicle guidance effectively reduces traffic jams and improves the operational efficiency of urban transportation. The trip time on a route is considered as a random process that changes with time, and the shortest path selection requires a random dynamic model and the solution of a decision-making problem. Thus, the shortest trip time is the criterion to determine the dynamic path selection by a random dynamic programming (DP) model which discretizes the trip times in the continuous segments on the route. In this study, a numerical model of random dynamic programming is established by using a probability tree model and an AND/OR (AO∗) algorithm to select the path of the shortest trip time. The results show that the branches of the probability tree are only accumulated on the “quantity” and do not cause a “qualitative” change. The inefficient accumulation of “quantity” affects the efficiency of the algorithm, so it is important to separate the accumulation of “quantity” from node expansion. The accumulation of “quantity” changes the trip time according to the entering time into a segment, which demands an improved AO∗ algorithm. The new AO∗ algorithm balances between efficiency and the trip time and provides the optimal real-time vehicle guidance on the road.

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Zhang, Deli, Na Wang, Chunfeng Pang, and Longfei Zhu. "Simulation Research of Simplified Dynamic Matrix Control." Journal of Physics: Conference Series 2355, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1742-6596/2355/1/012036.

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ABSTRACT Aiming at the shortcomings of matrix inversion and large amount of calculation in dynamic matrix control, a simplified algorithm is proposed. The algorithm introduces the expected attenuation factor of control increment to make the control increment gradually close to zero in the control time domain. In this way, the matrix inversion in the algorithm is simplified to pure value inversion (i.e. taking the reciprocal), which greatly reduces the amount of calculation, improves the calculation efficiency and reduces the adjustment of control parameters. Therefore, the real-time performance of the algorithm is strong, It is convenient for microcomputer programming. The simulation results show that the improved DMC algorithm is due to the improved DMC Algorithm in ensuring the real-time performance and the dynamic performance of the controlled object.

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