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Parker, Michael J., Mark A. Lovich, Amy C. Tsao, Abraham E. Wei, Matthew G. Wakim, Mikhail Y. Maslov, Hisashi Tsukada, and Robert A. Peterfreund. "Computer Control of Drug Delivery by Continuous Intravenous Infusion." Anesthesiology 122, no. 3 (March 1, 2015): 647–58. http://dx.doi.org/10.1097/aln.0000000000000519.
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Abstract Background: Intravenous drug infusion driven by syringe pumps may lead to substantial temporal lags in achieving steady-state delivery at target levels when using very low flow rates (“microinfusion”). This study evaluated computer algorithms for reducing temporal lags via coordinated control of drug and carrier flows. Methods: Novel computer control algorithms were developed based on mathematical models of fluid flow. Algorithm 1 controlled initiation of drug infusion and algorithm 2 controlled changes to ongoing steady-state infusions. These algorithms were tested in vitro and in vivo using typical high and low dead volume infusion system architectures. One syringe pump infused a carrier fluid and a second infused drug. Drug and carrier flowed together via a manifold through standard central venous catheters. Samples were collected in vitro for quantitative delivery analysis. Parameters including left ventricular max dP/dt were recorded in vivo. Results: Regulation by algorithm 1 reduced delivery delay in vitro during infusion initiation by 69% (low dead volume) and 78% (high dead volume). Algorithmic control in vivo measuring % change in max dP/dt showed similar results (55% for low dead volume and 64% for high dead volume). Algorithm 2 yielded greater precision in matching the magnitude and timing of intended changes in vivo and in vitro. Conclusions: Compared with conventional methods, algorithm-based computer control of carrier and drug flows can improve drug delivery by pump-driven intravenous infusion to better match intent. For norepinephrine infusions, the amount of drug reaching the bloodstream per time appears to be a dominant factor in the hemodynamic response to infusion.
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Ekanayake, E. M. U. S. B., W. B. Daundasekara, and S. P. C. Perera. "New Approach to Obtain the Maximum Flow in a Network and Optimal Solution for the Transportation Problems." Modern Applied Science 16, no. 1 (January 21, 2022): 30. http://dx.doi.org/10.5539/mas.v16n1p30.
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The maximum flow problem is also one of the highly regarded problems in the field of optimization theory in which the objective is to find a feasible flow through a flow network that obtains the maximum possible flow rate from source to sink. The literature demonstrates that different techniques have been developed in the past to handle the maximum amount of flow that the network can handle. The Ford-Fulkerson algorithm and Dinic's Algorithm are the two major algorithms for solving these types of problems. Also, the Max-Flow Min-Cut Theorem, the Scaling Algorithm, and the Push–relabel maximum flow algorithm are the most acceptable methods for finding the maximum flows in a flow network. In this novel approach, the paper develops an alternative method of finding the maximum flow between the source and target nodes of a network based on the "max-flow." Also, a new algorithmic approach to solving the transportation problem (minimizing the transportation cost) is based upon the new maximum flow algorithm. It is also to be noticed that this method requires a minimum number of iterations to achieve optimality. This study's algorithmic approach is less complicated than the well-known meta-heuristic algorithms in the literature.
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Wang, Yipu. "Max Flows in Planar Graphs with Vertex Capacities." ACM Transactions on Algorithms 18, no. 1 (January 31, 2022): 1–27. http://dx.doi.org/10.1145/3504032.
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We consider the maximum flow problem in directed planar graphs with capacities on both vertices and arcs and with multiple sources and sinks. We present three algorithms when the capacities are integers. The first algorithm runs in O ( min { k 2 n , n log 3 n + kn }) time when all capacities are bounded by a constant, where n is the number of vertices in the graph, and k is the number of terminals. This algorithm is the first to solve the vertex-disjoint paths problem in linear time when k is fixed but larger than 2. The second algorithm runs in O ( k 5 Δ n polylog ( nU )) time, where each arc capacity and finite vertex capacity is bounded by U , and Δ is the maximum degree of the graph. Finally, when k = 3, we present an algorithm that runs in O ( n log n ) time; this algorithm works even when the capacities are arbitrary reals. Our algorithms improve on the fastest previously known algorithms when k and Δ are fixed and U is bounded by a polynomial in n . Prior to this result, the fastest algorithms ran in O ( n 4/3+ o (1) ) time for unit capacities; in the smallest of O ( n 3/2 log n log U ), Õ( n 10/7 U 1/7 ), O ( n 11/8+o(1) U 1/4 ), and O ( n 4/3 + o(1) U 1/3 ) time for integer capacities; and in O ( n 2 /log n ) time for real capacities, even when k = 3.
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Chen, Li, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, and Sushant Sachdeva. "Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow." Communications of the ACM 66, no. 12 (November 17, 2023): 85–92. http://dx.doi.org/10.1145/3610940.
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We present an algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with m edges and polynomially bounded integral demands, costs, and capacities in m 1+ o (1) time. Our algorithm builds the flow through a sequence of m 1+ o (1) approximate undirected minimum-ratio cycles, each of which is computed and processed in amortized m o (1) time using a new dynamic graph data structure. Our framework extends to algorithms running in m 1+ o (1) time for computing flows that minimize general edge-separable convex functions to high accuracy. This gives almost-linear time algorithms for several problems including entropy-regularized optimal transport, matrix scaling, p -norm flows, and p -norm isotonic regression on arbitrary directed acyclic graphs.
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Ali, Gohar, Fernando Moreira, Omar Alfandi, Babar Shah, and Mohammed Ilyas. "A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks." Electronics 9, no. 4 (April 23, 2020): 683. http://dx.doi.org/10.3390/electronics9040683.
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Real-time flows using time division multiple access (TDMA) scheduling in cluster-based wireless sensor networks try to schedule more flows per time frame to minimize the schedule length to meet the deadline. The problem with the previously used cluster-based scheduling algorithm is that intra-cluster scheduling does not consider that the clusters may have internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not utilize their empty time-slots and thus increase schedule length. In this paper, we propose a new intra-cluster scheduling algorithm by considering that clusters may have having internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not differentiate the intra-cluster time slots and utilize their empty time slots. The objective is to schedule more flows per time frame, to reduce schedule length and improve the acceptance rate of flows. Simulation results show that the acceptance rate of the proposed scheme has a higher performance than the previous scheme.
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Bt Ismail, Shafinaz, Darmawaty Bt Mohd Ali, and Norsuzila Ya’acob. "Performance Analysis of Uplink Scheduling Algorithms in LTE Networks." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (February 1, 2018): 373. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp373-379.
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Scheduling is referring to the process of allocating resources to User Equipment based on scheduling algorithms that is located at the LTE base station. Various algorithms have been proposed as the execution of scheduling algorithm, which represents an open issue in Long Term Evolution (LTE) standard. This paper makes an attempt to study and compare the performance of three well-known uplink schedulers namely, Maximum Throughput (MT), First Maximum Expansion (FME), and Round Robin (RR). The evaluation is considered for a single cell with interference for three flows such as Best effort, Video and VoIP in a pedestrian environment using the LTE-SIM network simulator. The performance evaluation is conducted in terms of system throughput, fairness index, delay and packet loss ratio (PLR). The simulations results show that RR algorithm always reaches the lowest PLR, delivering highest throughput for video and VoIP flows among all those strategies. Thus, RR is the most suitable scheduling algorithm for VoIP and video flows while MT and FME is appropriate for BE flows in LTE networks.
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Joung, Jinoo, Yunki Choi, and Sunghoon Son. "An improved algorithm for Detection of Elephant Flows." Journal of Korea Information and Communications Society 37B, no. 9 (September 30, 2012): 849–58. http://dx.doi.org/10.7840/kics.2012.37b.9.849.
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Begouen Demeaux, Charlotte, and Emmanuel Boss. "Validation of Remote-Sensing Algorithms for Diffuse Attenuation of Downward Irradiance Using BGC-Argo Floats." Remote Sensing 14, no. 18 (September 9, 2022): 4500. http://dx.doi.org/10.3390/rs14184500.
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Estimates of the diffuse attenuation coefficient (Kd) at two different wavelengths and band-integrated (PAR) were obtained using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B). The resultant Kds were compared to those inferred from measurements of radiometry from sensors on board autonomous profiling floats (BGC-Argo). Advantages of BGC-Argo measurements compared to ship-based ones include: 1. uniform sampling in time throughout the year, 2. large spatial coverage, and 3. lack of shading by platform. Over 5000 quality-controlled matchups between Kds derived from float and from satellite sensors were found with values ranging from 0.01 to 0.67 m−1. Our results show that although all three algorithm types provided similarly ranging values of Kd to those of the floats, for most sensors, a given algorithm produced statistically different Kd distributions from the two others. Algorithm results diverged the most for low Kd (clearest waters). Algorithm biases were traced to the limitations of the datasets the algorithms were developed and trained with, as well as the neglect of sun angle in some algorithms. This study highlights: 1. the importance of using comprehensive field-based datasets (such as BGC-Argo) for algorithm development, 2. the limitation of using radiative-transfer model simulations only for algorithm development, and 3. the potential for improvement if sun angle is taken into account explicitly to improve empirical Kd algorithms. Recent augmentation of profiling floats with hyper-spectral radiometers should be encouraged as they will provide additional constraints to develop algorithms for upcoming missions such as NASA’s PACE and SBG and ESA’s CHIME, all of which will include a hyper-spectral radiometer.
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Wu, Tian-Yu, Jianfei Zhang, Yanjun Dai, Tao-Feng Cao, Kong Ling, and Wen-Quan Tao. "Implementation of IDEAL algorithm based on Delaunay triangular mesh for 2D-compressible flows." Engineering Computations 41, no. 3 (May 7, 2024): 630–54. http://dx.doi.org/10.1108/ec-02-2023-0071.
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PurposeTo present the detailed implementation processes of the IDEAL algorithm for two-dimensional compressible flows based on Delaunay triangular mesh, and compare the performance of the SIMPLE and IDEAL algorithms for solving compressible problems. What’s more, the implementation processes of Delaunay mesh generation and derivation of the pressure correction equation are also introduced.Design/methodology/approachProgramming completely in C++.FindingsFive compressible examples are used to test the SIMPLE and IDEAL algorithms, and the comparison with measurement data shows good agreement. The IDEAL algorithm has much better performance in both convergence rate and stability over the SIMPLE algorithm.Originality/valueThe detail solution procedure of implementing the IDEAL algorithm for compressible flows based on Delaunay triangular mesh is presented in this work, seemingly first in the literature.
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Tsekeris, Theodore, and Antony Stathopoulos. "Real-Time Dynamic Origin-Destination Matrix Adjustment with Simulated and Actual Link Flows in Urban Networks." Transportation Research Record: Journal of the Transportation Research Board 1857, no. 1 (January 2003): 117–27. http://dx.doi.org/10.3141/1857-14.
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The efficiency and robustness of different real-time dynamic origin–destination (O-D) matrix adjustment algorithms were investigated when implemented in large-scale transportation networks. The proposed algorithms produce time-dependent O-D trip matrices based on the maximum-entropy trip departure times with simulated and actual observed link flows. Implementation of the algorithms, which are coupled with a quasi-dynamic traffic assignment model, indicated their convergent behavior and their potential for handling realistic urban-scale network problems in terms of both accuracy and computational time. The main factors influencing the numerical performance of each algorithm were identified and analyzed. Their relative efficiency was found to be particularly dependent on the level at which the assigned flows approximate the observed link flows. These results may provide insights into the suitability of each algorithm for diverse application domains, including freeways, small networks, and large-scale urban networks, where a different quality of O-D information is usually available.
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Held, Lukas, Felicitas Mueller, Sina Steinle, Mohammed Barakat, Michael R. Suriyah, and Thomas Leibfried. "An Optimal Power Flow Algorithm for the Simulation of Energy Storage Systems in Unbalanced Three-Phase Distribution Grids." Energies 14, no. 6 (March 15, 2021): 1623. http://dx.doi.org/10.3390/en14061623.
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An optimal power flow algorithm for unbalanced three-phase distribution grids is presented in this paper as a new tool for grid planning on low voltage level. As additional equipment like electric vehicles, heat pumps or solar power systems can sometimes cause unbalanced power flows, existing algorithms have to be adapted. In comparison to algorithms considering balanced power flows, the presented algorithm uses a complete model of a three-phase four-wire low voltage grid. Additionally, a constraint for the voltage unbalance in the grid is introduced. The algorithm can be used to optimize the operation of energy storage systems in unbalanced systems. The used grid model, constraints, objective function and solver are explained in detail. A validation of the algorithm using a commercial tool is done. Additionally, three exemplary optimizations are performed to show possible applications for this tool.
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Davoodi, Ali, Mark Wallace, and Daniel Harabor. "Fast Traffic Assignment by Focusing on Changing Edge Flows (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (July 17, 2022): 267–69. http://dx.doi.org/10.1609/socs.v15i1.21781.
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This paper presents a novel algorithm for solving the traffic assignment problem (TAP). Contrary to traditional algorithms, which use the one-to-all shortest path algorithm to solve the problem for all origin destinations (OD) pairs, this algorithm tracks the changes of the edges and (at certain iterations) solves the problem only for critical edges whose flows have changed substantially using a state-of-the-art edge p2p shortest path algorithm. When additionally, only OD pairs with larger flows are considered, this enhancement halves the time needed to optimize the solution with a very small error in a large-scale network.
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SURKOVA, Kateryna, Kostiantyn SURKOV, and Maryna LOMAKINA. "Formalized description of flight dispatchers’ activities with slot messages." Scientific Bulletin of Flight Academy. Section: Pedagogical Sciences 11 (2022): 82–88. http://dx.doi.org/10.33251/2522-1477-2022-11-82-88.
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The article is devoted to the analysis of the flight dispatchers’ complex activities while working with messages on the organization of air traffic flows. Based on expert information, typical operation situations in the airline have been identified, the probabilities of their occurrence have been determined. The decomposition scheme and algorithms of flight dispatchers’ activity while working with the messages concerning slots have been made. The description of flight dispatchers’ activity with messages on the organization of air traffic flows using the decomposition procedure and flowcharts of algorithms has been analysed and worked out. Flight dispatchers process a lot of different information, while performing many professional tasks: obtaining information, encoding and decoding data and disseminating processed information as air traffic flows messages; interaction with the Aeronautical Information Service and others and it can often occur in a time and deficit mode. The training factor while dealing with professional duties is important to reduce and avoid errors, so it is necessary to analyze this complex activity of compiling, transmitting and disseminating information on the organization of air traffic flows and systematize it. Such systematization will be the basis for the creation of algorithms and models of flight dispatchers’ activity concerning messages on the organization of air traffic flows. Decomposition schemes and algorithmic descriptions have been developed and they characterize the field of air traffic flows organization in terms of the airline specialists’activities at two main levels of planning: pre-tactical and operational. The decomposition has been analyzed at some levels such as airline specialists dealing with the organization of air traffic flows according to the planning stage, typical operation situations, type of work with the message (receipt and analysis; formation of the relevant message). These developments can be used in conducting training lessons with cadets during their professional training. Further areas of work are considered to be methodical work with the obtained research results in order to implement them in the training of future flight dispatchers. Key words: flight dispatchers, slot, algorithm, decomposition, typical operation situations
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GHIYASVAND, MEHDI. "A NEW APPROACH FOR SOLVING THE MINIMUM COST FLOW PROBLEM WITH INTERVAL AND FUZZY DATA." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, no. 01 (February 2011): 71–88. http://dx.doi.org/10.1142/s0218488511006897.
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In particular, imprecise observations or possible perturbations mean that data in a network flows may well be better represented by intervals or fuzzy numbers than crisp quantities. In this paper we first consider the minimum cost flow problem with compact interval-valued lower and upper bounds, flows, and costs. We present a new method that shows this problem is solved using two minimum cost flow problems with crisp data. Then this result is extended to networks with fuzzy lower and upper bounds, flows, and costs. One of the best algorithms to solve the minimum cost flow problem with crisp data is the cost scaling algorithm of Goldberg and Tarjan.17 In this paper, the cost scaling algorithm is modified for fuzzy lower and upper bounds, flows and costs. The running time of the modified algorithm is equal to the running time of the cost scaling algorithm with crisp data.
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Jiang, Siya, and Song Fu. "Modifications to the SIMPLE algorithm with the MDCD approach for incompressible flow simulation." International Journal of Numerical Methods for Heat & Fluid Flow 28, no. 9 (September 3, 2018): 2208–30. http://dx.doi.org/10.1108/hff-02-2018-0054.
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Purpose The purpose of the paper is to propose some modifications to the SIMPLE (semi-implicit method for pressure-linked equations) algorithm. These modifications can ensure the numerical robustness and optimize computational efficiency. They remarkably promote the ability of the SIMPLE algorithm for incompressible DNS (direct numerical simulation) of multiscale problems, such as transitional flows and turbulent flows, by improving the properties of dispersion and dissipation. Design/methodology/approach The MDCD (minimized dispersion and controllable dissipation) scheme and MMIM (modified momentum interpolation method) are introduced. Six typical test cases are used to validate the modified algorithm, including the linear convective flow, lid-driven cavity flow, laminar boundary layer, Taylor vortex and DHIT (decaying homogenous isotropic turbulence). Particularly, a highly unsteady DNS of separated-flow transition in turbomachinery is precisely predicted by the modified algorithm. Findings The numerical examples show the distinct superiority of the modified algorithm in both internal flows and external flows. The advantages of the MDCD scheme and MMIM make the SIMPLE algorithm a promising method for DNS. Originality/value Some effective modifications to the SIMPLE algorithm are addressed. It is the first attempt to introduce the MDCD approach into the SIMPLE-type algorithms. The new algorithm is especially suitable for the incompressible DNS of convection-dominated flows.
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Wu, Yulong, Weizhe Zhang, Hui He, and Yawei Liu. "A New Method of Priority Assignment for Real-Time Flows in the WirelessHART Network by the TDMA Protocol." Sensors 18, no. 12 (December 3, 2018): 4242. http://dx.doi.org/10.3390/s18124242.
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WirelessHART is a wireless sensor network that is widely used in real-time demand analyses. A key challenge faced by WirelessHART is to ensure the character of real-time data transmission in the network. Identifying a priority assignment strategy that reduces the delay in flow transmission is crucial in ensuring real-time network performance and the schedulability of real-time network flows. We study the priority assignment of real-time flows in WirelessHART on the basis of the multi-channel time division multiple access (TDMA) protocol to reduce the delay and improve the ratio of scheduled. We provide three kinds of methods: (1) worst fit, (2) best fit, and (3) first fit and choose the most suitable one, namely the worst-fit method for allocating flows to each channel. More importantly, we propose two heuristic algorithms—a priority assignment algorithm based on the greedy strategy for C (WF-C) and a priority assignment algorithm based on the greedy strategy for U(WF-U)—for assigning priorities to the flows in each channel, whose time complexity is O ( m a x ( N ∗ m ∗ l o g ( m ) , ( N − m ) 2 ) ) . We then build a new simulation model to simulate the transmission of real-time flows in WirelessHART. Finally, we compare our two algorithms with WF-D and HLS algorithms in terms of the average value of the total end-to-end delay of flow sets, the ratio of schedulable flow sets, and the calculation time of the schedulability analysis. The optimal algorithm WF-C reduces the delay by up to 44.18 % and increases the schedulability ratio by up to 70.7 % , and it reduces the calculation time compared with the HLS algorithm.
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Ganesh, N., and N. Balakrishnan. "A h-Adaptive Algorithm Using Residual Error Estimates for Fluid Flows." Communications in Computational Physics 13, no. 2 (February 2013): 461–78. http://dx.doi.org/10.4208/cicp.170811.210212a.
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AbstractAlgorithms for adaptive mesh refinement using a residual error estimator are proposed for fluid flow problems in a finite volume framework. The residual error estimator, referred to as the ℜ-parameter is used to derive refinement and coarsening criteria for the adaptive algorithms. An adaptive strategy based on the ℜ-parameter is proposed for continuous flows, while a hybrid adaptive algorithm employing a combination of error indicators and the ℜ-parameter is developed for discontinuous flows. Numerical experiments for inviscid and viscous flows on different grid topologies demonstrate the effectiveness of the proposed algorithms on arbitrary polygonal grids.
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Fu, Sijie, Pascal Henry Biwole, Christian Mathis, and Philippe Maïssa. "Numerical and experimental comparison of complete three-dimensional particle tracking velocimetry algorithms for indoor airflow study." Indoor and Built Environment 27, no. 4 (December 6, 2016): 528–43. http://dx.doi.org/10.1177/1420326x16682294.
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The experimental data retrieved from three-dimensional particle tracking velocimetry (3D PTV) are crucial for indoor environment engineering when designing ventilation strategies or monitoring airborne pollutants dispersion in inhabited spaces as well as in livestock compounds. The performance of the measurement algorithm, as the core part of the 3D PTV technology, has a strong influence on the final results. This study presents a method to select the better 3D PTV algorithm depending on the application targeted. This study first compared the performance of seven 3D PTV algorithms on two types of 3D laminar macro scale flows of known Navier–Stokes solutions, namely the Kovasznay and Beltrami flows. The comparison was based on the accuracy, the measuring volume coverage and the achievable trajectory length of each algorithm with respect to the particle tracking density and the total number of frames. Then, the measurement algorithms with better performances were further compared with the experimental measurement of real 3D airflows in a cubic cavity. The results suggest that different measurement algorithms have different advantages and drawbacks depending on the application targeted, and that the algorithms using the epipolar constraint as the method of spatial matching generally perform better.
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Raj, K. Viswak, M. Mukesh, and J. Kalaivani. "Anomaly Detection and Attribution in Network." International Journal of Engineering and Advanced Technology 9, no. 4 (April 30, 2020): 686–87. http://dx.doi.org/10.35940/ijeat.c6335.049420.
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In this article, we address the problem of not only id entifying phenomena, but also attributing the phenomenon to the movement that induces it. This causes to a combinatorial optimisation problem, which is prohibitively expensive. Instead we design two anomaly detection algorithms that are small in complexity. The first is based on the system for cross-entropy (CE), which identifies flow anomalies and labels flow anomalies. The second algorithm detects anomalies through GLRT on aggregated flow transformation a compact low-dimensional representation of raw traffic flows. The two algorithms complement each other and allow the network operator to use the algorithm for flow aggregation first so that device irregularities can be identified easily. After discovery of an exception, the user Can analyse further that individual flows are anomalous using CE-based algorithm. We perform extensive performance tests and trials on synthetic and semi-synthetic data with our algorithms, as well as real Internet traffic data gathered from the MAWI database, and finally make recommendations as to their usability.
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Cao, Lijia, Pinde Song, Yongchao Wang, Yang Yang, and Baoyu Peng. "An Improved Lightweight Real-Time Detection Algorithm Based on the Edge Computing Platform for UAV Images." Electronics 12, no. 10 (May 18, 2023): 2274. http://dx.doi.org/10.3390/electronics12102274.
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Unmanned aerial vehicle (UAV) image detection algorithms are critical in performing military countermeasures and disaster search and rescue. The state-of-the-art object detection algorithm known as you only look once (YOLO) is widely used for detecting UAV images. However, it faces challenges such as high floating-point operations (FLOPs), redundant parameters, slow inference speed, and poor performance in detecting small objects. To address the above issues, an improved, lightweight, real-time detection algorithm was proposed based on the edge computing platform for UAV images. In the presented method, MobileNetV3 was used as the YOLOv5 backbone network to reduce the numbers of parameters and FLOPs. To enhance the feature extraction ability of MobileNetV3, the efficient channel attention (ECA) attention mechanism was introduced into MobileNetV3. Furthermore, in order to improve the detection ability for small objects, an extra prediction head was introduced into the neck structure, and two kinds of neck structures with different parameter scales were designed to meet the requirements of different embedded devices. Finally, the FocalEIoU loss function was introduced into YOLOv5 to accelerate bounding box regression and improve the localization accuracy of the algorithm. To validate the performance of the proposed improved algorithm, we compared our algorithm with other algorithms in the VisDrone-Det2021 dataset. The results showed that compared with YOLOv5s, MELF-YOLOv5-S achieved a 51.4% reduction in the number of parameters and a 38.6% decrease in the number of FLOPs. MELF-YOLOv5-L had 87.4% and 47.4% fewer parameters and FLOPs, respectively, and achieved higher detection accuracy than YOLOv5l.
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Ghosh, Soumyadip, Jiacai Lu, Vijay Gupta, and Gretar Tryggvason. "Communication-efficient algorithms for solving pressure Poisson equation for multiphase flows using parallel computers." PLOS ONE 17, no. 11 (November 22, 2022): e0277940. http://dx.doi.org/10.1371/journal.pone.0277940.
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Numerical solution of partial differential equations on parallel computers using domain decomposition usually requires synchronization and communication among the processors. These operations often have a significant overhead in terms of time and energy. In this paper, we propose communication-efficient parallel algorithms for solving partial differential equations that alleviate this overhead. First, we describe an asynchronous algorithm that removes the requirement of synchronization and checks for termination in a distributed fashion while maintaining the provision to restart iterations if necessary. Then, we build on the asynchronous algorithm to propose an event-triggered communication algorithm that communicates the boundary values to neighboring processors only at certain iterations, thereby reducing the number of messages while maintaining similar accuracy of solution. We demonstrate our algorithms on a successive over-relaxation solver for the pressure Poisson equation arising from variable density incompressible multiphase flows in 3-D and show that our algorithms improve time and energy efficiency.
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Zhang, Yang, Yuan Wang, and Bin Yang. "Improvement of the Delaunay-Tessellation Particle Tracking Algorithm in the Flow Visualization Research." Applied Mechanics and Materials 411-414 (September 2013): 2134–37. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2134.
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The particle tracking velocimetry (PTV) algorithm is one of the most important branches in the flow visualization research. An efficient two-frame PTV based on Delaunay tessellation was updated by a novel concept called Dual Computation. The updated algorithm was tested using CFD flows with changeable parameters and random erasing of particles as perturbation. In addition to the simple structure and the minimal dependence on algorithmic assumptions, the advantages of this updated algorithm also include the high accuracy in addressing complex flows with noticeable ratio of particles having no match.
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Yang, W., K. Hou, F. Yu, Z. Liu, and T. Sun. "A novel algorithm with heuristic information for extracting drainage networks from raster DEMs." Hydrology and Earth System Sciences Discussions 7, no. 1 (January 20, 2010): 441–59. http://dx.doi.org/10.5194/hessd-7-441-2010.
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Abstract. Extracting drainage networks from raster DEMs is a necessary requirement in almost all hydrological and environmental analyses and determining surface water flow direction is a fundamental problem. In a raster environment, surface water flow direction of each cell can be directed to the neighboring cell with the steepest downslope drop (The basic D8, deterministic eight-neighbour method), which is inadequate for routing flow over pits and flats. Several improved algorithms are proposed to find the outlet of pits and flats, which typically use entirely different procedures for processing pits and flats. Being different from others, this paper presents a new method to route flow through the pits and flats by searching for the outlet using the heuristic information to compensate inadequate searching information of other methods. Heuristic information can reveal the general trend of the DEM and help the proposed algorithm find the outlet of pits and flats accurately. Furthermore, the proposed algorithm can handle pits and flats effectively in one procedure. This new algorithm is implemented in Pascal and experiments are carried out on actual DEM data. It can be seen from the comparison of the drainage networks generated by the proposed algorithm and ArcGIS 9.2, the proposed algorithm with heuristic information can get a closer match result with existing river networks and avoid the generation of the unrealistic parallel drainage lines, unreal drainage lines and spurious terrain features.
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Karaś, Piotr. "Heuristic algorithms in topological design of telecommunication networks." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2003): 73–82. http://dx.doi.org/10.26636/jtit.2003.3.188.
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The paper addresses the generic topological network design problem and considers the use of various heuristic algorithms for solving the problem. The target of the optimisation is to determine a network structure and demand allocation pattern that would minimise the cost of the network, which is given by fixed installation costs of nodes and links and variable link capacity costs described by linear or concave functions. Input data for the optimisation consists of a list of potential node and link locations and their costs and a set of demands defined between the nodes. Since the problem is known to be NP-hard, the use of specialised heuristic algorithms is proposed. The presented approaches encompass original ideas as well as selected methods described in literature and their enhancements. The algorithms are based on the following ideas and methods: shifting of individual flows, local and global restoration of flows from chosen links or nodes, Yaged algorithm for finding local minima, Minoux greedy algorithm, simulated allocation and genetic algorithms. Efficiency of each of the proposed methods is tested on a set of numerical examples.
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Song, Biao, Yue Chang, Xinchang Zhang, Abdullah Al-Dhelaan, and Mohammed Al-Dhelaan. "Mixed-Flow Load-Balanced Scheduling for Software-Defined Networks in Intelligent Video Surveillance Cloud Data Center." Applied Sciences 12, no. 13 (June 26, 2022): 6475. http://dx.doi.org/10.3390/app12136475.
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As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput.
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Keyes, D. E., H. Ltaief, and G. Turkiyyah. "Hierarchical algorithms on hierarchical architectures." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (January 20, 2020): 20190055. http://dx.doi.org/10.1098/rsta.2019.0055.
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A traditional goal of algorithmic optimality, squeezing out flops, has been superseded by evolution in architecture. Flops no longer serve as a reasonable proxy for all aspects of complexity. Instead, algorithms must now squeeze memory, data transfers, and synchronizations, while extra flops on locally cached data represent only small costs in time and energy. Hierarchically low-rank matrices realize a rarely achieved combination of optimal storage complexity and high-computational intensity for a wide class of formally dense linear operators that arise in applications for which exascale computers are being constructed. They may be regarded as algebraic generalizations of the fast multipole method. Methods based on these hierarchical data structures and their simpler cousins, tile low-rank matrices, are well proportioned for early exascale computer architectures, which are provisioned for high processing power relative to memory capacity and memory bandwidth. They are ushering in a renaissance of computational linear algebra. A challenge is that emerging hardware architecture possesses hierarchies of its own that do not generally align with those of the algorithm. We describe modules of a software toolkit, hierarchical computations on manycore architectures, that illustrate these features and are intended as building blocks of applications, such as matrix-free higher-order methods in optimization and large-scale spatial statistics. Some modules of this open-source project have been adopted in the software libraries of major vendors. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.
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Wang, Guanyu. "A Comparative Study of Cuckoo Algorithm and Ant Colony Algorithm in Optimal Path Problems." MATEC Web of Conferences 232 (2018): 03003. http://dx.doi.org/10.1051/matecconf/201823203003.
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Finding the optimal path can be realized through a wide range of algorithms, which is demanded in many fields. Among countless algorithms that are used for solving the optimal path problem, the ant colony optimization (ACO) is one of the algorithms used to solve the approximate optimal path solution, while the cuckoo search (CS) algorithm is a swarm intelligence algorithm featuring Levy flight, whose core idea is derived from the cuckoo nesting property. In order to provide more ideas and directions for future research on optimal path problems, this paper discusses in detail the advantages and disadvantages of the two algorithms for solving the optimal path problem and their scopes of application by comparing principles and flows of the two algorithms.
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Rabbani, Masoud, Azadeh Arjmand, Mohammad Mahdi Saffar, and Moeen Sammak Jalali. "Application of Three Meta-Heuristic Algorithms for Maximizing the Net Present Value of a Resource-Constrained Project Scheduling Problem with Respect to Delay Penalties." International Journal of Applied Industrial Engineering 3, no. 1 (January 2016): 1–15. http://dx.doi.org/10.4018/ijaie.2016010101.
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The Resource Constrained Project Scheduling Problem (RCPSP) is been studied under different kinds of constraints and limitations. In this paper, we are going to consider the discounted cash flows for project activities, and delay penalties which occur when the project make span exceeds its deadline as the objective function of the RCPSP. To solve the model, we will take advantage of three different meta-heuristic algorithms - Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Shuffled Frog Leaping Algorithm (SFLA) - to achieve the optimal solution of the problem. The evaluation of the algorithms performance reveals that, in comparison with ICA and SFLA, GA performs better, especially in large-scale problems.
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Niccolini, Alyssa D., and Nancy Lesko. "Algorithmic Noise: Ed Reform 2.0 and Work/Think/Playing With Methods of the Present." Qualitative Inquiry 24, no. 9 (December 15, 2017): 624–35. http://dx.doi.org/10.1177/1077800417742413.
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As we become more and more algorithmic subjects, algorithms shape the news we access, our political and social views, how and what we buy, as well as the emotional tenor of our lives. Tracking how algorithms work on and with our senses and activate our bodies in different ways requires experimental approaches. To explore affect, algorithms, and method, we work/think/play via two reassemblages of algorithmic tools borrowed from the corporate and art worlds: Moodlens and the Listening Machine. Our algorithmic play with educational reformers’ images and tweets suggests a strategy for fattening our methods and theories to respond to ever-increasing data flows and thinking additively, fractally, and exponentially.
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Litvinov, A. "PROBABILISTIC ANALYSIS OF MULTICHANNEL BUS ARBITRATORS OF COMPUTER NETWORK INTERFACES." Municipal economy of cities 6, no. 180 (December 4, 2023): 27–32. http://dx.doi.org/10.33042/2522-1809-2023-6-180-27-32.
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Probabilistic analysis was conducted on the functioning algorithms of bus arbiters within the ‘Common Bus’ interface of computer networks. The research employed the theory of random impulse flows as its apparatus. Analytical dependencies were derived to calculate the average waiting time for service requests from network subscribers. The first-come-first-serve and round-robin service algorithms provided better service uniformity than priority algorithms. Keywords: algorithm, service, request–impulse, interface, bus arbiter, common bus.
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Lenherr, Christian, Martin Oschwald, Anestis I. Kalfas, and Reza S. Abhari. "Flow adaptive aerodynamic probe for turbomachinery flows." E3S Web of Conferences 345 (2022): 01007. http://dx.doi.org/10.1051/e3sconf/202234501007.
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In order to enable turbomachinery research to obtain data highly resolved in space and time, a novel flow adaptive aerodynamic probe concept has been developed and presented in this paper. The algorithm selects the measurement positions of the probe automatically and therefore provides higher measurement fidelity compared to traditional methods. The development of the adaptive algorithm has been done in several steps. First an automatic 1Dtraversing algorithm has been developed. The following steps dealt with the subject of a 2D adaptive flow concept development, whereas primarily visual programming language-computer package simulations of the new 2D algorithm have been done based on data from previous test series at the Turbomachinery Laboratory. The new 2D traversing algorithm is fully selfcontrolled and requires minimal input such as blade count and hub and tip diameters. Furthermore, areas of interest (e.g. secondary flows, wake) are detected automatically and higher measuring point resolutions are ensured in these regions. After the successful simulations, the intelligent 2D algorithm has been adapted to an object oriented programming environment used for automated data acquisition and reduction. An evaluation of the flow adaptive aerodynamic flow concept has been done on a pressure turbine facility by means of a steady pneumatic probe. The measurement results show that the new 2D algorithm has the potential to detect new flow phenomena. In contrast to traditional algorithms, which in case of a possible enhancement demand a knowledge of the position of interesting areas such as the wake and vortical structures before starting the measurement, the new algorithm detects the right areas and enhances the resolution fully self controlled in these areas. Furthermore, the new 2D flow adaptive probe concept shows a significant improvement regarding the needed time for one measurement.
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Rathnayake, Namal, Upaka Rathnayake, Tuan Linh Dang, and Yukinobu Hoshino. "Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka." PLOS ONE 18, no. 4 (April 26, 2023): e0282847. http://dx.doi.org/10.1371/journal.pone.0282847.
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Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions.
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Bi, Xia-an, Xiaohui Wang, Luyun Xu, Sheng Chen, and Hong Liu. "Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network." Discrete Dynamics in Nature and Society 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/801934.
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With the development of wireless mesh networks and distributed computing, lots of new P2P services have been deployed and enrich the Internet contents and applications. The rapid growth of P2P flows brings great pressure to the regular network operation. So the effective flow identification and management of P2P applications become increasingly urgent. In this paper, we build a multilevel bloom filters data structure to identify the P2P flows through researches on the locality characteristics of P2P flows. Different level structure stores different numbers of P2P flow rules. According to the characteristics values of the P2P flows, we adjust the parameters of the data structure of bloom filters. The searching steps of the scheme traverse from the first level to the last level. Compared with the traditional algorithms, our method solves the drawbacks of previous schemes. The simulation results demonstrate that our algorithm effectively enhances the performance of P2P flows identification. Then we deploy our flow identification algorithm in the traffic monitoring sensors which belong to the network traffic monitoring system at the export link in the campus network. In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.
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Chen, Xiao Mei, Dang Gang, and Tian Yang. "PBP: A Type of BP Neural Network Algorithm Based on Priori Triggering." Advanced Materials Research 271-273 (July 2011): 441–47. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.441.
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The algorithm of anomaly detection for large scale networks is a key way to promptly detect the abnormal traffic flows. In this paper, priori triggered BP neural network algorithm(PBP) is analyzed for the purpose of dealing with the problems caused by typical algorithms that are not able to adapt and learn; detect with high precision; provide high level of correctness. PBP uses K-Means and PCA to trigger self-adapting and learning ability, and also, it uses historical neuron parameter to initialize the neural network, so that it use the trained network to detect the abnormal traffic flows. According to experiments, PBP can obtain a higher level of correctness of detection than priori algorithm, and it can adapt itself according to different network environments.
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Gayibov, Tulkin, and Behzod Pulatov. "Taking into account the constraints in power system mode optimization by genetic algorithms." E3S Web of Conferences 264 (2021): 04045. http://dx.doi.org/10.1051/e3sconf/202126404045.
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Over the past decades, many publications on the use of genetic algorithms, which offer a new and powerful approach for solving the problem of power system mode optimization, have appeared. Despite this, the issues of effectively taking into account various constraints when solving such problems with genetic algorithms remain opened. In this regard, this article proposes an algorithm for optimizing power system modes by genetic algorithm, taking into account functional constraints in the form of equalities and inequalities by various penalty functions. The results of effectiveness research of the given algorithm in the example of optimization of 8-nodal power system with four thermal power plants and three lines with controlled power flows are presented.
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Gu, Jiqing, Chao Song, Haipeng Dai, Lei Shi, Jinqiu Wu, and Li Lu. "ACM: Accuracy-Aware Collaborative Monitoring for Software-Defined Network-Wide Measurement." Sensors 22, no. 20 (October 18, 2022): 7932. http://dx.doi.org/10.3390/s22207932.
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Software-defined measurement (SDM) is a simple and efficient way to deploy measurement tasks and collect measurement data. With SDM, it is convenient for operators to implement fine-grained network-wide measurements at the flow level, from which many important functions can benefit. The prior work provides mechanisms to distribute flows to monitors, such that each monitor can identify its non-overlapped subset of flows to measure, and a certain global performance criterion is optimized, such as load balance or flow coverage. Many applications of network management can benefit from a function that can find large flows efficiently, such as congestion control by dynamically scheduling large flows, caching of forwarding table entries, and network capacity planning. However, the current network-wide measurements neglect the diversity of different flows as they treat large flows and small flows equally. In this paper, we present a mechanism of accuracy-aware collaborative monitoring (ACM) to improve the measurement accuracies of large flows in network-wide measurements at the flow level. The structure of the sketch is an approximate counting algorithm, and a high-measurement accuracy can be achieved by merging the results from multiple monitors with sketches, which is termed as collaborative monitoring. The core idea of our method is to allocate more monitors to large flows and achieve the load balance to provide accuracy-aware monitoring. We modeled our problem as an integer–linear programming problem, which is NP-hard. Thus, we propose an approximation algorithm, named the improved longest processing time algorithm (iLPTA); we proved that its approximation ratio is (12+nl). We propose a two-stage online distribution algorithm (TODA). Moreover, we proved that its approximation ratio is (1+nl−1). The iLPTA is an offline approximation algorithm used to assign monitors for each flow, which prove the validity and feasibility of the core idea. The TODA is an online algorithm that attempts to achieve the load balance by selecting the monitor with the smallest load to a large flow. Our extensional experiment results verify the effectiveness of our proposed algorithms.
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Choi, Seong Woo. "Scheduling Algorithm for Reentrant Flexible Flow Shop with the Objective Function of Minimizing Orders’ Total Flow Time." Korean Production and Operations Management Society 34, no. 1 (February 28, 2023): 129–43. http://dx.doi.org/10.32956/kopoms.2023.34.1.129.
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This study focuses on a scheduling problem in a flexible flow shop, where there are serial stages, each with identical parallel machines. We suggest heuristic algorithms for the problem to minimize the total flow time of a given set of orders. Each order is composed of multiple lots and is processed on any parallel machines at each stage. This shop has reentrant flows since products for specific orders should visit the processing stages twice. We suggest algorithms of two types. The algorithms of the first type are three-phase algorithms, and the second type's algorithm is one-phase. In the first phase of three-phase algorithms, an initial sequence is obtained using priority dispatching rules, and then a construction algorithm is used to obtain sequences for each stage in the second phase. Finally, in the third phase, lots that visit the stages for the second time are scheduled using priority dispatching rules. Computational experiments are performed on randomly generated test problems to evaluate the performance of the suggested algorithms. Results show that the order-based algorithms perform better than the lot-based algorithms and an algorithm used in practice.
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Chaudhry, M. Hanif, and Andreas M. Schulte. "Computation of steady-state, gradually varied flows in parallel channels." Canadian Journal of Civil Engineering 13, no. 1 (February 1, 1986): 39–45. http://dx.doi.org/10.1139/l86-006.
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To compute the water-surface profiles in steady-state, gradually varied flows in open channels, an algorithm is presented that allows calculation of the flow depths and discharges at all sections of a parallel-channels system simultaneously instead of by the presently used trial-and-error computations. This algorithm is accurate, efficient, and suitable for computer solution. As compared to other methods, it may be used directly for computing flows in parallel channels without necessitating graphical plots or other trial-and-error manual computations. A procedure is outlined so that the resulting matrix for a parallel-channels system may be banded in order to increase accuracy and to reduce computer time and storage. A parallel-channels system is solved for illustration purposes and the results are compared with those obtained by the fourth-order Runge–Kutta method. Key words: backwater profiles, computer algorithms, gradually varied flows, mathematical models, Newton–Raphson method, open-channel flows, parallel channels, water surface profiles.
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Vasudevan, Ayyem Pillai, and R. Sudhakar. "A Low Complexity Near-Optimal MIMO Antenna Subset Selection Algorithm for Capacity Maximisation." International Journal of Antennas and Propagation 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/956756.
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Multiple input multiple output (MIMO) wireless systems employ a scheme called antenna subset selection for maximising the data rate or reliability for the prevailing channel conditions with the available or affordable number of radio frequency (RF) chains. In this paper, a low-complexity, and near-optimal performance fast algorithm is formulated and the detailed algorithm statements are stated with the exact complexity involved for capacity-maximising receive-only selection. The complexities of other receive-only selection comparable algorithms are calculated. Complexities have been stated in terms of both complex-complex flops and real-real flops. Significantly, all the algorithms are seen in the perspective of linear increase of capacity with the number of selected antennas up to one less than the total number of receive antennas. It is shown that our algorithm will be a good choice in terms of both performance and complexity for systems, which look for linear increase in capacity with the number of selected antennas up to one less than the total receive antennas. Our algorithm complexity is much less dependent on the number of transmit antennas and is not dependent on the number of selected antennas and it strikes a good tradeoff between performance and speed, which is very important for practical implementations.
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de Lima, Camila, and Elias Salomão Helou. "Fast projection/backprojection and incremental methods applied to synchrotron light tomographic reconstruction." Journal of Synchrotron Radiation 25, no. 1 (January 1, 2018): 248–56. http://dx.doi.org/10.1107/s1600577517015715.
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Iterative methods for tomographic image reconstruction have the computational cost of each iteration dominated by the computation of the (back)projection operator, which take roughlyO(N3) floating point operations (flops) forN×Npixels images. Furthermore, classical iterative algorithms may take too many iterations in order to achieve acceptable images, thereby making the use of these techniques unpractical for high-resolution images. Techniques have been developed in the literature in order to reduce the computational cost of the (back)projection operator toO(N2logN) flops. Also, incremental algorithms have been devised that reduce by an order of magnitude the number of iterations required to achieve acceptable images. The present paper introduces an incremental algorithm with a cost ofO(N2logN) flops per iteration and applies it to the reconstruction of very large tomographic images obtained from synchrotron light illuminated data.
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Pan, Wansu, Haibo Tan, Xiru Li, and Xiaofeng Li. "Improved RTT Fairness of BBR Congestion Control Algorithm Based on Adaptive Congestion Window." Electronics 10, no. 5 (March 6, 2021): 615. http://dx.doi.org/10.3390/electronics10050615.
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To alleviate the lower performance of Transmission Control Protocol (TCP) congestion control over complex network, especially the high latency and packet loss scenario, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm. In contrast with other TCP congestion control algorithms, BBR adjusted transfer data by maximizing delivery rate and minimizing delay. However, some evaluation experiments have shown that the persistent queues formation and retransmissions in the bottleneck can lead to serious fairness issues between BBR flows with different round-trip times (RTTs). They pointed out that small RTT differences cause unfairness in the throughput of BBR flows and flows with longer RTT can obtain higher bandwidth when competing with the shorter RTT flows. In order to solve this fairness problem, an adaptive congestion window of BBR is proposed, which adjusts the congestion window gain of each BBR flow in network load. The proposed algorithms alleviate the RTT fairness issue by controlling the upper limit of congestion window according to the delivery rate and queue status. In the Network Simulator 3 (NS3) simulation experiment, it shows that the adaptive congestion window of BBR (BBR-ACW) congestion control algorithm improves the fairness by more than 50% and reduces the queuing delay by 54%, compared with that of the original BBR in different buffer sizes.
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He, Binjie, Dong Zhang, and Chang Zhao. "Hidden Markov Model-based Load Balancing in Data Center Networks." Computer Journal 63, no. 10 (December 7, 2019): 1449–62. http://dx.doi.org/10.1093/comjnl/bxz142.
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Abstract Modern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.
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Jošić, Hrvoje, and Berislav Žmuk. "A Machine Learning Approach to Forecast International Trade: The Case of Croatia." Business Systems Research Journal 13, no. 3 (October 1, 2022): 144–60. http://dx.doi.org/10.2478/bsrj-2022-0030.
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Abstract Background: This paper presents a machine learning approach to forecast Croatia’s international bilateral trade. Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in the case of Croatia. Methods/Approach: The dataset on Croatian bilateral trade with over 180 countries worldwide from 2001 to 2019 is assembled using main variables from the gravity trade model. To forecast values of Croatian bilateral exports and imports for a horizon of one year (the year 2020), machine learning algorithms (Gaussian processes, Linear regression, and Multilayer perceptron) have been used. Each forecasting algorithm is evaluated by calculating mean absolute percentage errors (MAPE). Results: It was found that machine learning algorithms have a very good predicting ability in forecasting Croatian bilateral trade, with neural network Multilayer perceptron having the best performance among the other machine learning algorithms. Conclusions Main findings from this paper can be important for economic policymakers and other subjects in this field of research. Timely information about the changes in trends and projections of future trade flows can significantly affect decision-making related to international bilateral trade flows.
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Karaeva, M., N. Napkhonenko, and V. Perevozniuk. "Managing Dynamically Changing Traffic Flows." International Journal of Engineering & Technology 7, no. 4.3 (September 15, 2018): 222. http://dx.doi.org/10.14419/ijet.v7i4.3.19791.
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The brief analysis of publications in the field of transport development, the organization and management of urban passenger traffic in the conditions of constantly changing passenger traffics is carried out. The efficiency of using logistic methods is proved by optimization of public passenger transport of the megalopolis. The factors influencing regularity of passenger traffics formation and their dynamic spatial distribution taking into account the main communications between passenger formed points and service providers are revealed. For improvement of traffic flows management the relevance of development of new mathematical models and methods considering relationship of cause and effect between the input and output parameters is established. Methods of solving large-size problems in the conditions of difficult criterion function are considered. Advantages and the prospects of meta-heuristic methods application – genetic algorithms – are shown at the solution of the transport tasks characteristic of unstable streams.For solving the objective using a set of genes is proposed which elements represent a chromosome with a certain set of decisions. Restrictions are formulated and operators of casual changes for a work of genetic algorithm are defined. The algorithm allowing to define and range the key input parameters influencing process of providing transport services and to receive parameters for the output correlation analysis is obtained.
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Movius, SJ, and JH Van Vuuren. "Self-organisation in traffic signal control algorithms under light traffic conditions." ORiON 35, no. 1 (June 28, 2019): 57–87. http://dx.doi.org/10.5784/35-1-605.
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Fixed-time control and vehicle-actuated control are two distinct types of traffic signal control. The latter control method involves switching traffic signals based on detected traffic flows and thus offers more flexibility (appropriate for lighter traffic conditions) than the former, which relies solely on cyclic, predetermined signal phases that are better suited for heavier traffic conditions. The notion of self-organisation has relatively recently been proposed as an alternative approach towards improving traffic signal control, particularly under light traffic conditions, due to its flexible nature and its potential to result in emergent behaviour. The effectiveness of five existing self-organising traffic signal control strategies from the literature and a fixed-control strategy are compared in this paper within a newly designed agent-based, microscopic traffic simulation model. Various shortcomings of three of these algorithms are identified and algorithmic improvements are suggested to remedy these deficiencies. The relative performance improvements resulting from these algorithmic modifications are then quantified by their implementation in the aforementioned traffic simulation model. Finally, a new self-organising algorithm is proposed that is particularly effective under lighter traffic conditions.
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FONTAINE, Matthew C. "Tidal Flow: A Fast and Teachable Maximum Flow Algorithm." Olympiads in Informatics 12 (May 15, 2018): 25–41. http://dx.doi.org/10.15388/ioi.2018.03.
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Among the most interesting problems in competitive programming involve maximum flows. However, efficient algorithms for solving these problems are often difficult for students to understand at an intuitive level. One reason for this difficulty may be a lack of suitable metaphors relating these algorithms to concepts that the students already understand. This paper introduces a novel maximum flow algorithm, Tidal Flow, that is designed to be intuitive to undergraduate andpre-university computer science students.
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Lujano, Efrain, Rene Lujano, Juan Carlos Huamani, and Apolinario Lujano. "Hydrological modeling based on the KNN algorithm: An application for the forecast of daily flows of the Ramis river, Peru." Tecnología y ciencias del agua 14, no. 2 (March 1, 2023): 169–203. http://dx.doi.org/10.24850/j-tyca-14-02-05.
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The forecast of river stream flows is of significant importance for the development of early warning systems. Artificial intelligence algorithms have proven to be an effective tool in hydrological modeling data-driven, since they allow establishing relationships between input and output data of a watershed and thus make decisions data-driven. This article investigates the applicability of the k-nearest neighbor (KNN) algorithm for forecasting the mean daily flows of the Ramis river, at the Ramis hydrometric station. As input to the KNN machine learning algorithm, we used a data set of mean basin precipitation and mean daily flow from hydrometeorological stations with various lags. The performance of the KNN algorithm was quantitatively evaluated with hydrological ability metrics such as mean absolute percentage error (MAPE), anomaly correlation coefficient (ACC), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE') and the spectral angle (SA). The results for forecasting the flows of the Ramis river with the k-nearest neighbor machine learning algorithm reached high levels of reliability with flow lags of one and two days and precipitation with three days. The algorithm used is simple but robust to make short-term flow forecasts and can be integrated as an alternative to strengthen the daily hydrological forecast of the Ramis river.
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Nataraj, P. S. V., R. Satyanarayan, and S. Sheela. "A Polynomial-Time Algorithm for Efficient Extraction of Boundary Rectangles From Interval Templates." Journal of Dynamic Systems, Measurement, and Control 125, no. 4 (December 1, 2003): 654–57. http://dx.doi.org/10.1115/1.1595669.
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A polynomial-time On2 algorithm is presented for extracting the boundary rectangles from a given interval template having no “inner windings.” The algorithm involves only comparisons and list operations. The performance of the algorithm is tested and compared with those of four boundary extraction algorithms existing in the QFT literature. The testing is done on a benchmark suite of eleven transfer function examples, using computational time and effort (flops) as the performance metrics. The test results show the proposed algorithm to be superior in every example. The typical improvement in terms of these metrics is by several orders of magnitude.
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Shafiq, Muhammad, Xiangzhan Yu, Asif Ali Laghari, and Dawei Wang. "Effective Feature Selection for 5G IM Applications Traffic Classification." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6805056.
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Recently, machine learning (ML) algorithms have widely been applied in Internet traffic classification. However, due to the inappropriate features selection, ML-based classifiers are prone to misclassify Internet flows as that traffic occupies majority of traffic flows. To address this problem, a novel feature selection metric named weighted mutual information (WMI) is proposed. We develop a hybrid feature selection algorithm named WMI_ACC, which filters most of the features with WMI metric. It further uses a wrapper method to select features for ML classifiers with accuracy (ACC) metric. We evaluate our approach using five ML classifiers on the two different network environment traces captured. Furthermore, we also apply Wilcoxon pairwise statistical test on the results of our proposed algorithm to find out the robust features from the selected set of features. Experimental results show that our algorithm gives promising results in terms of classification accuracy, recall, and precision. Our proposed algorithm can achieve 99% flow accuracy results, which is very promising.
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Souza, B. C. B. N., R. A. M. Junior, M. G. Nascimento, C. S. Gonçalves, D. V. B. Siqueira, and R. A. Câmara. "Particle Swarm Algorithm Applied to Image Reconstruction on Multiphase Flows." International Journal of Advanced Engineering Research and Science 11, no. 1 (2024): 018–24. http://dx.doi.org/10.22161/ijaers.11.4.
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This work presents a methodology for reconstructing multiphase flow electrical capacitive tomography (ECT) images, using a particle swarm optimization (PSO) algorithm in the parallel processing paradigm. Intended is to improve the efficiency of the inverse problem algorithm in ECT, increasing the resolution of the reconstructed images, without necessarily increasing the processing time of these reconstruction technique. A limitation found is that, for inverse problem-type reconstruction techniques for ECT, the response of the sensor system is non-linear and, therefore, the processing time grows faster than any increase in resolution, imposing a high computational cost. For real-time applications, the first contribution is the removal of unnecessary processing from the usual code; the second is the creation of a new PSO algorithm for image reconstruction that is more efficient than normal. The new parallel processing routine present the physical principles of ECT, the heuristic algorithms used in the reconstruction process and the main concepts for parallel computing.