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Journal articles on the topic 'Dynamic Priority'

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Published: 4 June 2021
Last updated: 8 June 2024

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1

Maymir-Ducharme, Fred. "Dynamic priorities, priority scheduling and priority inheritance." ACM SIGAda Ada Letters X, no. 9 (November 1990): 39–45. http://dx.doi.org/10.1145/102456.102467.

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2

Bin Chen, S. K. Bose, and Wen-De Zhong. "Priority enabled dynamic traffic grooming." IEEE Communications Letters 9, no. 4 (April 2005): 366–68. http://dx.doi.org/10.1109/lcomm.2005.1413636.

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3

颛, 孙盈. "Dynamic Scheduling Strategy Based on Dynamic Priority Algorithm." Computer Science and Application 09, no. 06 (2019): 1126–33. http://dx.doi.org/10.12677/csa.2019.96127.

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4

Fratini, Stephen S. "Analysis of a dynamic priority queue." Communications in Statistics. Stochastic Models 6, no. 3 (January 1990): 415–44. http://dx.doi.org/10.1080/15326349908807155.

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5

Tung, T. Y., and J. F. Chang. "Analysis of dynamic priority cell discarding." IEE Proceedings - Communications 145, no. 4 (1998): 249. http://dx.doi.org/10.1049/ip-com:19982137.

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6

Chen, Chin-Ling, and Ruay-Shiung Chang. "Dynamic priority transmission mechanism for DQDB." Computer Communications 22, no. 5 (April 1999): 483–90. http://dx.doi.org/10.1016/s0140-3664(98)00263-1.

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7

Chen, T. M., J. Walrand, and D. G. Messerschmitt. "Dynamic priority protocols for packet voice." IEEE Journal on Selected Areas in Communications 7, no. 5 (June 1989): 632–43. http://dx.doi.org/10.1109/49.32327.

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8

Bagchi, Uttarayan, and Robert S. Sullivan. "Dynamic, Non-Preemptive Priority Queues with General, Linearly Increasing Priority Function." Operations Research 33, no. 6 (December 1985): 1278–98. http://dx.doi.org/10.1287/opre.33.6.1278.

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9

Zhu, Chengming, Yanyan Chen, and Changxi Ma. "The Theory of Dynamic Public Transit Priority with Dynamic Stochastic Park and Ride." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/525460.

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Public transit priority is very important for relieving traffic congestion. The connotation of dynamic public transit priority and dynamic stochastic park and ride is presented. Based on the point that the travel cost of public transit is not higher than the travel cost of car, how to determine the level of dynamic public transit priority is discussed. The traffic organization method of dynamic public transit priority is introduced. For dynamic stochastic park and ride, layout principle, scale, and charging standard are discussed. Traveler acceptability is high through the analysis of questionnaire survey. Dynamic public transit priority with dynamic stochastic park and ride has application feasibility.
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10

TSUCHIYA, T. "Usage of Network-Level Dynamic Priority and Its Comparison with Static Priority." IEICE Transactions on Communications E88-B, no. 4 (April 1, 2005): 1549–58. http://dx.doi.org/10.1093/ietcom/e88-b.4.1549.

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11

Ekeila, Wael, Tarek Sayed, and Mohamed El Esawey. "Development of Dynamic Transit Signal Priority Strategy." Transportation Research Record: Journal of the Transportation Research Board 2111, no. 1 (January 2009): 1–9. http://dx.doi.org/10.3141/2111-01.

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12

Lamotte, Raphaël, André de Palma, and Nikolas Geroliminis. "Impacts of Metering-Based Dynamic Priority Schemes." Transportation Science 56, no. 2 (March 2022): 358–80. http://dx.doi.org/10.1287/trsc.2021.1091.

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Several works published over the last two decades have shown for a stylized set-up with homogeneous users that metering-based priority (MBP) schemes may generate Pareto improving departure time adjustments similar to those induced by congestion pricing, but without any financial transaction. We investigate whether MBP (i) still generates significant savings and (ii) remains Pareto-improving, with various sources of heterogeneity (in schedule flexibility, desired arrival time, and capacity usage). We consider two types of schemes: one where the priority status is allocated randomly (R-MBP) and another (HOV-MBP), which only prioritizes users with small capacity usage (e.g., carpoolers). We find that the relative total cost savings of R-MBP decrease with heterogeneity in flexibility, but may increase with heterogeneity in desired arrival time. It fails however to be Pareto-improving, as nonprioritized users are almost systematically worse-off. HOV-MBP circumvents this issue by generating an ordering effect and a modal shift, which both contribute to a better distribution of benefits among users. Under favorable circumstances, they may even restore a Pareto improvement. Overall, MBP appears as a realistic way to alleviate congestion, scoring well both in terms of efficiency and social acceptability.
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13

KumarPradhan, Rakesh, and Mark A Gregory. "Priority based Energy Efficient Dynamic Power Scaling." International Journal of Computer Applications 54, no. 12 (September 25, 2012): 37–41. http://dx.doi.org/10.5120/8620-2484.

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14

Browne, Sid, and Uri Yechiali. "Dynamic priority rules for cyclic-type queues." Advances in Applied Probability 21, no. 2 (June 1989): 432–50. http://dx.doi.org/10.2307/1427168.

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A cyclic service system is composed of K channels (queues) and a single cyclically roving server who typically takes a positive amount of time to switch between channels. Research has previously focused on evaluating and computing performance measures (notably, waiting times) of fixed template routing schemes under three main service disciplines, the exhaustive, gated and limited service regimes.In this paper, probabilistic results are derived that allow control strategies and optimal policies to be considered for the first time. By concentrating on a new objective function, we are able to derive rules of index form amenable for direct implementation to dynamically control the system at suitably defined decision epochs. These rules utilize current system information, are of an adaptive nature, and are shown to emanate from a general physical principle.
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15

Dag, Tamer, and Sezer Uzungenc. "Dynamic Multi Threshold Priority Packet Scheduling Algorithms." MATEC Web of Conferences 75 (2016): 06004. http://dx.doi.org/10.1051/matecconf/20167506004.

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16

Browne, Sid, and Uri Yechiali. "Dynamic priority rules for cyclic-type queues." Advances in Applied Probability 21, no. 02 (June 1989): 432–50. http://dx.doi.org/10.1017/s0001867800018620.

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A cyclic service system is composed of K channels (queues) and a single cyclically roving server who typically takes a positive amount of time to switch between channels. Research has previously focused on evaluating and computing performance measures (notably, waiting times) of fixed template routing schemes under three main service disciplines, the exhaustive, gated and limited service regimes. In this paper, probabilistic results are derived that allow control strategies and optimal policies to be considered for the first time. By concentrating on a new objective function, we are able to derive rules of index form amenable for direct implementation to dynamically control the system at suitably defined decision epochs. These rules utilize current system information, are of an adaptive nature, and are shown to emanate from a general physical principle.
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17

Spuri, Marco, and Giorgio Buttazzo. "Scheduling aperiodic tasks in dynamic priority systems." Real-Time Systems 10, no. 2 (March 1996): 179–210. http://dx.doi.org/10.1007/bf00360340.

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18

Chakravarthy, S. "A finite capacity dynamic priority queuing model." Computers & Industrial Engineering 22, no. 4 (October 1992): 369–85. http://dx.doi.org/10.1016/0360-8352(92)90013-a.

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19

Erdelyi, Alexander, and Huseyin Topaloglu. "Approximate dynamic programming for dynamic capacity allocation with multiple priority levels." IIE Transactions 43, no. 2 (November 30, 2010): 129–42. http://dx.doi.org/10.1080/0740817x.2010.504690.

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20

Wang, Zhongbin, Luyi Yang, Shiliang Cui, and Jinting Wang. "In-queue priority purchase: a dynamic game approach." Queueing Systems 97, no. 3-4 (March 5, 2021): 343–81. http://dx.doi.org/10.1007/s11134-021-09694-y.

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AbstractPay-for-priority is a common practice in congestion-prone service systems. The extant literature on this topic restricts attention to the case where the only epoch for customers to purchase priority is upon arrival, and if customers choose not to upgrade when they arrive, they cannot do so later during their wait. A natural alternative is to let customers pay and upgrade to priority at any time during their stay in the queue, even if they choose not to do so initially. This paper builds a queueing-game-theoretic model that explicitly captures self-interested customers’ dynamic in-queue priority-purchasing behavior. When all customers (who have not upgraded yet) simultaneously decide whether to upgrade, we find in our model that pure-strategy equilibria do not exist under some intuitive criteria, contrasting the findings in classical models where customers can only purchase priority upon arrival. However, when customers sequentially decide whether to upgrade, threshold-type pure-strategy equilibria may exist. In particular, under sufficiently light traffic, if the number of ordinary customers accumulates to a certain threshold, then it is always the second last customer who upgrades, but in general, it could be a customer from another position, and the queue-length threshold that triggers an upgrade can also vary with the traffic intensity. Finally, we find that in-queue priority purchase subject to the sequential rule yields less revenue than upon-arrival priority purchase in systems with small buffers.
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21

Di Lillo, Paolo, Gianluca Antonelli, and Ciro Natale. "Effects of Dynamic Model Errors in Task-Priority Operational Space Control." Robotica 39, no. 9 (February 1, 2021): 1642–53. http://dx.doi.org/10.1017/s0263574720001411.

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SUMMARYControl algorithms of many Degrees-of-Freedom (DOFs) systems based on Inverse Kinematics (IK) or Inverse Dynamics (ID) approaches are two well-known topics of research in robotics. The large number of DOFs allows the design of many concurrent tasks arranged in priorities, that can be solved either at kinematic or dynamic level. This paper investigates the effects of modeling errors in operational space control algorithms with respect to uncertainties affecting knowledge of the dynamic parameters. The effects on the null-space projections and the sources of steady-state errors are investigated. Numerical simulations with on-purpose injected errors are used to validate the thoughts.
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22

T, Kannan. "QoS Improvement in MIMO-OFDM System with Priority based Dynamic Resource Allocation Algorithm." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (March 20, 2020): 202–6. http://dx.doi.org/10.5373/jardcs/v12i3/20201183.

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23

Gao, Zhibo, Kejun Long, Chaoqun Li, Wei Wu, and Lee D. Han. "Bus Priority Control for Dynamic Exclusive Bus Lane." Computers, Materials & Continua 61, no. 1 (2019): 345–61. http://dx.doi.org/10.32604/cmc.2019.06235.

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24

Ryzhikov, Yuriy I. "Multi-channel Queuing Systems with the Dynamic Priority." Journal of Automation and Information Sciences 41, no. 8 (2009): 49–54. http://dx.doi.org/10.1615/jautomatinfscien.v41.i8.50.

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25

Min Chen, Jian-Qiang Hu, and Michael C. Fu. "Perturbation Analysis of a Dynamic Priority Call Center." IEEE Transactions on Automatic Control 55, no. 5 (May 2010): 1191–96. http://dx.doi.org/10.1109/tac.2010.2041979.

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26

Viswanathan, Arvind, Garimella Rama Murthy, and Naveen Chilamkurti. "Heterogeneous Dynamic Priority Scheduling in Time Critical Applications." International Journal of Wireless Networks and Broadband Technologies 2, no. 2 (April 2012): 47–54. http://dx.doi.org/10.4018/ijwnbt.2012040104.

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In the unlicensed band, the notion of primary user and secondary user (To implement cognitive radio) is not explicit. By dynamic priority assignment the authors propose to implement cognitive radio in the unlicensed band. In time critical events, the data which is most important, has to be given the time slots. Wireless Sensor nodes in the authors’ case are considered to be mobile, and hence make it difficult to prioritize one over another. A node may be out of the reach of the cluster head or base station by the time it is allotted a time slot and hence mobility is a constraint. With the data changing dynamically and factors such as energy and mobility, which are major constraints, assigning priority to the nodes becomes difficult. In this paper, the authors have discussed about how Wireless Sensor Networks are able to allocate priorities to nodes in the unlicensed band with multiple parameters being posed. They have done simulations on NS-2 and have shown the implementation results.
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27

Sadeghian, Hamid, Luigi Villani, Mehdi Keshmiri, and Bruno Siciliano. "Dynamic multi-priority control in redundant robotic systems." Robotica 31, no. 7 (May 22, 2013): 1155–67. http://dx.doi.org/10.1017/s0263574713000416.

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SUMMARYThis paper presents a dynamic-level control algorithm to meet simultaneously multiple desired tasks based on allocated priorities for redundant robotic systems. It is shown that this algorithm can be treated as a general framework to achieve control over the whole body of the robot. The control law is an extension of the well-known acceleration-based control to the redundant robots, and considers also possible interactions with the environment occurring at any point of the robot body. The stability of this algorithm is shown and some of the previously developed results are formulated using this approach. To handle the interaction on robot body, null space impedance control is developed within the multi-priority framework. The effectiveness of the proposed approaches is evaluated by means of computer simulation.
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28

XIE, Zhi-Qiang, Jing YANG, Guang YANG, and Guang-Yu TAN. "Dynamic Job-Shop Scheduling Algorithm with Dynamic Set of Operation Having Priority." Chinese Journal of Computers 31, no. 3 (September 28, 2009): 502–8. http://dx.doi.org/10.3724/sp.j.1016.2008.00502.

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29

Muller, Stefan K., Kyle Singer, Devyn Terra Keeney, Andrew Neth, Kunal Agrawal, I.-Ting Angelina Lee, and Umut A. Acar. "Responsive Parallelism with Synchronization." Proceedings of the ACM on Programming Languages 7, PLDI (June 6, 2023): 712–35. http://dx.doi.org/10.1145/3591249.

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Many concurrent programs assign priorities to threads to improve responsiveness. When used in conjunction with synchronization mechanisms such as mutexes and condition variables, however, priorities can lead to priority inversions, in which high-priority threads are delayed by low-priority ones. Priority inversions in the use of mutexes are easily handled using dynamic techniques such as priority inheritance, but priority inversions in the use of condition variables are not well-studied and dynamic techniques are not suitable. In this work, we use a combination of static and dynamic techniques to prevent priority inversion in code that uses mutexes and condition variables. A type system ensures that condition variables are used safely, even while dynamic techniques change thread priorities at runtime to eliminate priority inversions in the use of mutexes. We prove the soundness of our system, using a model of priority inversions based on cost models for parallel programs. To show that the type system is practical to implement, we encode it within the type systems of Rust and C++, and show that the restrictions are not overly burdensome by writing sizeable case studies using these encodings, including porting the Memcached object server to use our C++ implementation.
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30

M Fahri Aditya Nasution, Suendri, and Aninda Muliani Harahap. "Customer Service Information System Using Dynamic Priority Scheduling Algorithm At PT Sumatra Sistem Integrasi." Journal of Information Systems and Technology Research 2, no. 1 (January 31, 2023): 25–37. http://dx.doi.org/10.55537/jistr.v2i1.324.

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The development of information technology can make it easier to do various things, including the provision of services that use technology in providing information to customers who need and receive information. The service at PT Sumatera Sistem Integrasi is that the process of complaining about network disturbances from customers is still manual, so that customers still come directly to the company or by telephone to make complaints and prepare reports still in the form of ledgers and customers. queues are not yet systematic and integrated. Based on these problems, the authors built a website-based customer complaint application using the Dynamic Priority Scheduling algorithm as a queue priority. The Dynamic Priority Scheduling Algorithm is a dynamic approach to the priority scheduling algorithm. In the dynamic approach, this algorithm focuses on the process of determining the queue based on predetermined priority rules. With this application, it is hoped that it can simplify and improve the quality of service to customers optimally at PT Sumatra Sistem Integrasi.
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31

Ouyang, Yiming, Yang Zhao, Kun Xing, Zhengfeng Huang, Huaguo Liang, and Jianhua Li. "Design of Wireless Network on Chip with Priority-Based MAC." Journal of Circuits, Systems and Computers 28, no. 08 (July 2019): 1950124. http://dx.doi.org/10.1142/s021812661950124x.

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The wireless network on chip WiNoC introduces wireless links in the traditional network on chip (NoC), which reduces the network diameter and enables high-throughput, low-latency data communications. In addition, if wireless nodes can dynamically request data transmission, wireless bandwidth will be more effectively utilized. In order to implement a conflict-free, adaptive bandwidth allocation strategy, a priority-based dynamic media access control mechanism has been designed. In this work, a dynamic priority calculation method has been proposed based on the packets’ transmission time and the waiting time in the queue. Then, a priority calculating unit is designed to calculate the dynamic priority of the packet. Finally, the central control unit designed obtains the dynamic priority of the packets, and dynamically authorizes the use rights of the wireless medium according to the priority of the data packet. Simulation experiments show that the media access control mechanism proposed in this paper has significant improvements in throughput, delay, and power consumption performances compared with other mechanisms [S.Deb et al., Wireless NoC as interconnection backbone for multicore chips: promises and challenges, IEEE J. Emerg. Sel. Topics Circuits Syst. 2 (2012) 228–239].
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32

Ran, Wenxue, Sen Liu, and Zhe Zhang. "A Polling-Based Dynamic Order-Picking System considering Priority Orders." Complexity 2020 (July 24, 2020): 1–15. http://dx.doi.org/10.1155/2020/4595316.

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Nowadays, how to offer extremely fast response to customer orders has become a major challenge for warehouse management, especially in e-commerce. Due to the time urgency aspect of some “VIP” orders that need priority processing, one of the most important issues for logistics distribution centres is how to improve the VIP order-picking priority without reducing the common order-picking efficiency. With this consideration, this article put forward a new priority polling model to describe and analyse this problem. We divide orders into priority and common categories according to their time urgency. A mathematical model is established for such a system by applying polling theory, a probability generating function, and an embedded Markov chain. Numerical analysis shows that this priority polling-based picking system can improve the picking efficiency and is well suited to practical operations.
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33

Khamooshi, Homayoun. "Dynamic Priority–Dynamic Programming Scheduling Method (DP)2SM: a dynamic approach to resource constraint project scheduling." International Journal of Project Management 17, no. 6 (December 1999): 383–91. http://dx.doi.org/10.1016/s0263-7863(98)00052-0.

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34

DU, Shuangzhi, Yong WANG, and Xiaoling TAO. "Dual priority dynamic scheduling algorithm based on multi-FPGA." Journal of Computer Applications 33, no. 3 (September 26, 2013): 862–65. http://dx.doi.org/10.3724/sp.j.1087.2013.00862.

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35

G Harkut, Dinesh, and Ali M.S. "Dynamic HW Priority Queue Based Schedulers for Embedded System." International Journal of Embedded Systems and Applications 5, no. 4 (December 30, 2015): 01–13. http://dx.doi.org/10.5121/ijesa.2015.5401.

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36

Setyawati, Rizki, and Adam Bachtiar Maulachela. "Penerapan Algoritma Dynamic Priority Scheduling pada Antrian Pencucian Mobil." JTIM : Jurnal Teknologi Informasi dan Multimedia 2, no. 1 (May 27, 2020): 29–35. http://dx.doi.org/10.35746/jtim.v2i1.85.

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Queue is a condition where the number of service recipients is higher than the number ofservice providers. This condition can cause a buildup of service recipients, and eventually,bottlenecks will occur. It faced by all service organizations that focus on service to customers.No exception is a car wash business whose business processes focus on providing fast andquality car wash services. But unfortunately, many car wash businesses get complaints fromcustomers, especially related to the queue buildup and unclear queue information received bycustomers. Therefore this study aims to produce a mobile-based car wash queue application,which includes a dynamic priority scheduling algorithm that functions as a queue manager.To carry out these objectives, a research methodology that is sequential and iterative used,namely, the software development methodology using the Rapid Application Development(RAD) model. This model consists of four phases: planning needs, prototype development,system development, and finally, testing. The Application test is finished with two approaches,namely testing of application code, specifically the application of dynamic priority schedulingalgorithm and testing of the overall functional system. From the test results, it knows that thecar wash queue application managed to sort customer orders based on the specified priorityrules based on the distance and time of the law. While the results of testing the systemfunctionality show that the application successfully manages a variety of errors, both causedby system failures and due to human error.
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37

Yan-Fang, Fu, and Liu Jin-Xuan. "A Study on Priority-based Dynamic TDMA Protocol Simulation." Information Technology Journal 13, no. 3 (January 15, 2014): 493–500. http://dx.doi.org/10.3923/itj.2014.493.500.

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38

Zhao, Zixiang, Quanwei Zhou, Xiaoguang Han, and Lili Wang. "Dynamic targets searching assistance based on virtual camera priority." Virtual Reality & Intelligent Hardware 3, no. 6 (December 2021): 484–500. http://dx.doi.org/10.1016/j.vrih.2021.10.001.

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39

Wang, Jia Si, Song Yang Du, Xiong Gao Wang, and Di Ming Ai. "Study on Message Transmission Technology Based on Dynamic Priority." Applied Mechanics and Materials 263-266 (December 2012): 1759–63. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1759.

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Through analyzing the unique requirements of military command and control software and the characteristics of wireless message transmission, this paper studies a new message transmission mechanism based on wireless narrow band transmission, bring forward the proposal of improving wireless message transmission efficiency through improving the message queue transmission mechanism of the software, and verifies it in the new software version. The results show that new transmission technology greatly improves large-scale network transmission effect.
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40

Ahmad, I., M. K. Dhodhi, and R. Ul–Mustafa. "DPS: dynamic priority scheduling heuristic for heterogeneous computing systems." IEE Proceedings - Computers and Digital Techniques 145, no. 6 (1998): 411. http://dx.doi.org/10.1049/ip-cdt:19982345.

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41

van den Heuvel, Martijn M. H. P., Reinder J. Bril, Stefan Schiemenz, and Christian Hentschel. "Dynamic resource allocation for real-time priority processing applications." IEEE Transactions on Consumer Electronics 56, no. 2 (May 2010): 879–87. http://dx.doi.org/10.1109/tce.2010.5506015.

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42

Karamti, Walid, and Adel Mahfoudhi. "States Graph Generation from dynamic Priority Time Petri Nets." International Journal of Open Problems in Computer Science and Mathematics 6, no. 2 (June 2013): 85–100. http://dx.doi.org/10.12816/0006172.

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43

Smith, Stephen L., Marco Pavone, Francesco Bullo, and Emilio Frazzoli. "Dynamic Vehicle Routing with Priority Classes of Stochastic Demands." SIAM Journal on Control and Optimization 48, no. 5 (January 2010): 3224–45. http://dx.doi.org/10.1137/090749347.

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44

Cheung, Raymond K., and B. Muralidharan. "Dynamic Routing for Priority Shipments in LTL Service Networks." Transportation Science 34, no. 1 (February 2000): 86–98. http://dx.doi.org/10.1287/trsc.34.1.86.12279.

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45

Xu, Yunjian, Feng Pan, and Lang Tong. "Dynamic Scheduling for Charging Electric Vehicles: A Priority Rule." IEEE Transactions on Automatic Control 61, no. 12 (December 2016): 4094–99. http://dx.doi.org/10.1109/tac.2016.2541305.

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46

Güçdemir, Hülya, and Hasan Selim. "Dynamic dispatching priority setting in customer-oriented manufacturing environments." International Journal of Advanced Manufacturing Technology 92, no. 5-8 (March 22, 2017): 1861–74. http://dx.doi.org/10.1007/s00170-017-0258-5.

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47

Niño-Mora, José. "Dynamic priority allocation via restless bandit marginal productivity indices." TOP 15, no. 2 (September 27, 2007): 161–98. http://dx.doi.org/10.1007/s11750-007-0025-0.

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48

Browne, Sid, and Gideon Weiss. "Dynamic priority rules when polling with multiple parallel servers." Operations Research Letters 12, no. 3 (September 1992): 129–37. http://dx.doi.org/10.1016/0167-6377(92)90096-l.

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49

Chen, Jiming, and Youxian Sun. "Dynamic priority scheduling-based MAC for wireless sensor networks." International Journal of Sensor Networks 2, no. 1/2 (2007): 3. http://dx.doi.org/10.1504/ijsnet.2007.012976.

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50

Xu, Yan, and Yanlei Shang. "Dynamic Priority based Weighted Scheduling Algorithm in Microservice System." IOP Conference Series: Materials Science and Engineering 490 (April 12, 2019): 042048. http://dx.doi.org/10.1088/1757-899x/490/4/042048.

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