Journal articles: 'Average consensus algorithm'

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Feng, Yong Xiu, Ai Qin Bao, and Deng Yin Zhang. "Distributed Cooperative Spectrum Sensing Algorithm Based on Average Consensus." Applied Mechanics and Materials 713-715 (January 2015): 1090–93. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1090.

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The existing distributed spectrum sensing algorithms usually assume that the information in interaction channel is totally correct and did not consider noise effect. To solve these problems, a new distributed cooperative spectrum sensing scheme based on average consensus is investigated in this paper. Based on minimum mean square deviation criterion, we design an iterative matrix suitable for consensus algorithm with considering the noise of interaction channel. Simulation results show that the proposed method achieves better detection performance under noise effect of interaction channel and outperforms conventional scheme by 11% at-5dB signal to noise ratio (SNR) and 0.1 false alarm probability.

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He, Jianping, Lin Cai, Chengcheng Zhao, Peng Cheng, and Xinping Guan. "Privacy-Preserving Average Consensus: Privacy Analysis and Algorithm Design." IEEE Transactions on Signal and Information Processing over Networks 5, no. 1 (March 2019): 127–38. http://dx.doi.org/10.1109/tsipn.2018.2866342.

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Wang, Wen Kai, and Huan Xin Peng. "Pseudo Multi-Hop Distributed Consensus with Adaptive Quantization." Advanced Materials Research 591-593 (November 2012): 1432–35. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1432.

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The convergence accuracy of distributed consensus with quantization communication depends on the quantization error and the convergence rate of the distributed consensus algorithm. In order to improve the accuracy and the convergence rate of distributed consensus under quantized communication, in the paper, based on the adaptively quantized scheme, we propose the pseudo multi-hop adaptively quantized distributed consensus algorithm. We analyze the convergence performance of the pseudo multi-hop adaptively quantized distributed consensus algorithm, and the algorithm can achieves a consensus in a mean square sense. Simultaneously, Simulations are present. Results show that the pseudo multi-hop adaptively quantized distributed consensus algorithm can reach an average consensus, and its convergence rate is higher than those of the other adaptive quantized distributed consensus algorithms.

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Palomares, A., M. Rebollo, and C. Carrascosa. "Supportive consensus." PLOS ONE 15, no. 12 (December 17, 2020): e0243215. http://dx.doi.org/10.1371/journal.pone.0243215.

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The paper is concerned with the consensus problem in a multi-agent system such that each agent has boundary constraints. Classical Olfati-Saber’s consensus algorithm converges to the same value of the consensus variable, and all the agents reach the same value. These algorithms find an equality solution. However, what happens when this equality solution is out of the range of some of the agents? In this case, this solution is not adequate for the proposed problem. In this paper, we propose a new kind of algorithms called supportive consensus where some agents of the network can compensate for the lack of capacity of other agents to reach the average value, and so obtain an acceptable solution for the proposed problem. Supportive consensus finds an equity solution. In the rest of the paper, we define the supportive consensus, analyze and demonstrate the network’s capacity to compensate out of boundaries agents, propose different supportive consensus algorithms, and finally, provide some simulations to show the performance of the proposed algorithms.

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Carli, Ruggero, Fabio Fagnani, Paolo Frasca, and Sandro Zampieri. "A probabilistic analysis of the average consensus algorithm with quantized communication." IFAC Proceedings Volumes 41, no. 2 (2008): 8062–67. http://dx.doi.org/10.3182/20080706-5-kr-1001.01361.

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Li, Gangqiang, Sissi Xiaoxiao Wu, Shengli Zhang, and Qiang Li. "Neural Networks-Aided Insider Attack Detection for the Average Consensus Algorithm." IEEE Access 8 (2020): 51871–83. http://dx.doi.org/10.1109/access.2020.2978458.

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He, Xing, Junzhi Yu, Tingwen Huang, Chuandong Li, and Chaojie Li. "Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework." IEEE Transactions on Cybernetics 50, no. 1 (January 2020): 351–60. http://dx.doi.org/10.1109/tcyb.2018.2869249.

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Priolo, Attilio, Andrea Gasparri, Eduardo Montijano, and Carlos Sagues. "A distributed algorithm for average consensus on strongly connected weighted digraphs." Automatica 50, no. 3 (March 2014): 946–51. http://dx.doi.org/10.1016/j.automatica.2013.12.026.

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Nozari, Erfan, Pavankumar Tallapragada, and Jorge Cortés. "Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design." Automatica 81 (July 2017): 221–31. http://dx.doi.org/10.1016/j.automatica.2017.03.016.

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Kim, Won Il, Rong Xiong, Qiuguo Zhu, and Jun Wu. "Average Consensus Analysis of Distributed Inference with Uncertain Markovian Transition Probability." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/505848.

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The average consensus problem of distributed inference in a wireless sensor network under Markovian communication topology of uncertain transition probability is studied. A sufficient condition for average consensus of linear distributed inference algorithm is presented. Based on linear matrix inequalities and numerical optimization, a design method of fast distributed inference is provided.

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Moradian, Hossein, and Solmaz S. Kia. "On Robustness Analysis of a Dynamic Average Consensus Algorithm to Communication Delay." IEEE Transactions on Control of Network Systems 6, no. 2 (June 2019): 633–41. http://dx.doi.org/10.1109/tcns.2018.2863568.

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Wang, Gang, Zhiyue Wang, and Jie Wu. "A local average broadcast gossip algorithm for fast global consensus over graphs." Journal of Parallel and Distributed Computing 109 (November 2017): 301–9. http://dx.doi.org/10.1016/j.jpdc.2017.05.008.

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Kenyeres, Martin, and Jozef Kenyeres. "Average Consensus over Mobile Wireless Sensor Networks: Weight Matrix Guaranteeing Convergence without Reconfiguration of Edge Weights." Sensors 20, no. 13 (June 30, 2020): 3677. http://dx.doi.org/10.3390/s20133677.

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Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained. Over recent years, the average consensus algorithm has found a wide application in this technology. In this paper, we present a weight matrix simplifying the average consensus algorithm over mobile wireless sensor networks, thereby prolonging the network lifetime as well as ensuring the proper operation of the algorithm. Our contribution results from the theorem stating how the Laplacian spectrum of an undirected simple finite graph changes in the case of adding an arbitrary edge into this graph. We identify that the mixing parameter of Best Constant weights of a complete finite graph with an arbitrary order ensures the convergence in time-varying topologies without any reconfiguration of the edge weights. The presented theorems and lemmas are verified over evolving graphs with various parameters, whereby it is demonstrated that our approach ensures the convergence of the average consensus algorithm over mobile wireless sensor networks in spite of no edge reconfiguration.

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Woodcock, Frank, and Chermelle Engel. "Operational Consensus Forecasts." Weather and Forecasting 20, no. 1 (February 1, 2005): 101–11. http://dx.doi.org/10.1175/waf-831.1.

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Abstract The objective consensus forecasting (OCF) system is an automated operational forecasting system that adapts to underlying numerical model upgrades within 30 days and generally outperforms direct model output (DMO) and model output statistics (MOS) forecasts. It employs routinely available DMO and MOS guidance combined after bias correction using a mean absolute error (MAE)-weighted average algorithm. OCF generates twice-daily forecasts of screen-level temperature maxima and minima, ground-level temperature minima, evaporation, sunshine hours, and rainfall and its probability for day 0 to day 6 for up to 600 Australian sites. Extensive real-time trials of temperature forecasts yielded MAEs at days 0–2 about 40% lower than those from its component MOS and DMO forecasts. MAEs were also lower at day 1 than matching official forecasts of maxima and minima by 8% and 10% and outperformed official forecasts at over 71% and 75% of sites, respectively. MAEs of weighted average consensus outperformed simple average forecasts by about 5%.

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Wang, Wen Kai, and Huan Xin Peng. "High-Order Distributed Consensus with One-Bit Adaptive Quantization." Advanced Materials Research 591-593 (November 2012): 1299–302. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1299.

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The convergence performance of distributed consensus algorithm with adaptive quantization communication depends on the convergence rate of the distributed consensus algorithm. In order to improve the convergence performance of distributed consensus under adaptive quantization communication, based on the one-bit adaptive quantization scheme, we propose the high-order distributed consensus to update the state of every node. We analyze the convergence performance and calculate the mean square error of the high-order distributed consensus algorithm with one-bit adaptive quantization. The high-order distributed consensus with one-bit adaptive quantization achieves a consensus in a mean square sense. Simultaneously, Simulations are done about the high-order distributed consensus based on one-bit adaptive quantization. Results show that the high-order distributed consensus algorithm based on one-bit adaptive quantization can reach an average consensus, and its convergence rate is higher than those of the first-order adaptive quantized distributed consensus algorithm and second-order adaptive quantized distributed consensus algorithm.

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Peng, Huan Xin, Wen Kai Wang, and Bin Liu. "Pseudo Two-Hop Distributed Consensus with Adaptive Quantization." Advanced Materials Research 889-890 (February 2014): 662–65. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.662.

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In order to improve the accuracy and the convergence rate of distributed consensus under quantized communication, in the paper, based on adaptive quantization scheme, we propose the pseudo two-hop distributed consensus algorithm. By analyses and simulations, Results show that the pseudo two-hop distributed consensus algorithm based on adaptive quantization can reach an average consensus, and its convergence rate is higher than that of the first-order adaptive quantized distributed consensus algorithm, moreover, the mean square errors are smaller within the finite steps.

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Yuan, Deming, Shengyuan Xu, Huanyu Zhao, and Yuming Chu. "Distributed average consensus via gossip algorithm with real-valued and quantized data for." Systems & Control Letters 59, no. 9 (September 2010): 536–42. http://dx.doi.org/10.1016/j.sysconle.2010.06.010.

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Oróstica, Boris, and Felipe Núñez. "A multi-cast algorithm for robust average consensus over internet of things environments." Computer Communications 140-141 (May 2019): 15–22. http://dx.doi.org/10.1016/j.comcom.2019.04.007.

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Wei, Baishen, and Brett Nener. "Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering." Sensors 18, no. 9 (September 7, 2018): 3005. http://dx.doi.org/10.3390/s18093005.

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Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized δ -generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors with different observation volumes. A consensus algorithm is used to achieve the global average by iterative regional averages. The sensor network can have unknown or time-varying topology. The proposed space debris tracking algorithm provides an efficient solution to the key challenges (e.g., detection uncertainty, data association uncertainty, clutter, etc.) for space situational awareness. The performance of the proposed algorithm is verified by simulation results.

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Wang, Junwei, Kairui Chen, and Yun Zhang. "Consensus of High-Order Nonlinear Multiagent Systems with Constrained Switching Topologies." Complexity 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5340642.

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The relationship between control and communication constraints is becoming of central importance in the consensus problem of networked agents. In this paper, we investigate such a problem for nonlinear multiagent systems with Lipschitz dynamics. To reflect communication constraints, the topology is assumed to switch within a finite set of digraphs characterised by an average dwell time switching signal. By constructing a suitable multiple Lyapunov function, we show that consensus can be reached under the designed consensus protocol. A multistep algorithm for designing consensus protocol is then developed by solving the Lyapunov equation and algebraic Riccati equation. Finally, simulation examples are delineated to substantiate the effectiveness of the proposed algorithms.

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Mustafa, Ali, Muhammad Najam ul Islam, Salman Ahmed, and Muhammad Ahsan Tufail. "Asynchronous Communication under Reliable and Unreliable Network Topologies in Distributed Multiagent Systems: A Robust Technique for Computing Average Consensus." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/4635701.

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Nearly all applications in multiagent systems demand precision, robustness, consistency, and rapid convergence in designing of distributed consensus algorithms. Keeping this thing in our sight, this research suggests a robust consensus protocol for distributed multiagent networks, continuing asynchronous communications, where agent’s states values are updated at diverse interval of time. This paper presents an asynchronous communication for both reliable and unreliable network topologies. The primary goal is to delineate local control inputs to attain time synchronization by processing the update information received by the agents associated in a communication topology. Additionally in order to accomplish the robust convergence, modelling of convergence analysis is conceded by commissioning the basic principles of graph and matrix theory alongside the suitable lemmas. Moreover, statistical examples presenting four diverse scenarios are provided in the end; produced results are the recognisable indicator to authenticate the robust effectiveness of the proposed algorithm. Likewise, a simulation comparison of the projected algorithm with the other existing approaches is conducted, considering different performance parameters are being carried out to support our claim.

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Mustafa, Ali, Muhammad Najam ul Islam, Salman Ahmed, and Muhammad Ahsan Tufail. "Unreliable communication in high-performance distributed multi-agent systems: A ingenious scheme in high computing." International Journal of Distributed Sensor Networks 14, no. 2 (February 2018): 155014771875921. http://dx.doi.org/10.1177/1550147718759218.

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Designing of distributed consensus algorithms featuring accuracy, robustness, reliability, and speed of convergence is in high demand for various multi-agent applications. In this research, it has been investigated to device a novel design of distributed estimation algorithm which can tackle the problem of unreliable communication among multi-agents to achieve consensus on the average value of their initial values and must be capable of computing the total number of agents in the system under dynamically changing interaction topologies. A dynamically changing network topology is considered in this research with unreliable communication links, and four different scenarios are established to be analyzed for the proposed consensus-based distributed estimation algorithm. This study established a consensus for a dynamically changing interaction topology among agents, for addition of agents in the network with dynamically switching topology at any instant in communication, for removal of agents from the network with dynamically switching topology at any instant in communication, and for a fixed topology with link failure and a reconnection with the same agent after each iteration. The proposed algorithm paces up the rate of convergence by reducing the number of iteration, along with sure convergence of the designed algorithm using the concepts of stochastic differential equation theory, control system theory, algebraic graph theory, and algebraic matrix theory. Finally, in the end, simulation results are provided which are clear evidence to validate the effectiveness of theoretical results of the proposed algorithm in comparison to previously known consensus algorithms in terms of different performance parameters.

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Liu, Wei, Wei Gu, Yinliang Xu, Shuai Xue, Ming Chen, Bo Zhao, and Miao Fan. "Improved average consensus algorithm based distributed cost optimization for loading shedding of autonomous microgrids." International Journal of Electrical Power & Energy Systems 73 (December 2015): 89–96. http://dx.doi.org/10.1016/j.ijepes.2015.04.006.

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Li, Yang, and Jin Yuan. "A Consensus Algorithm with Buffer in Second-Order Multi-Agent with Time-Varying Delayed Communication." Advanced Materials Research 1039 (October 2014): 305–12. http://dx.doi.org/10.4028/www.scientific.net/amr.1039.305.

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In this paper, we discuss consensus problems for a network of dynamic agents with fixed topologies. A consensus algorithm for multi-agent systems with time-varying delay is presented. With the consideration of the effects of network conditions, such as network-induced delays, packet dropouts, mis-sequence, etc., a communication buffer is introduced for avoiding agents’ communication error, and a method for choosing buffer length is proposed. Using this buffer design, received data is rearranged and transferred in the original order. With the presented consensus algorithm, agents’ consensus is well-performance, and all the agents match the average speed finally. Simulation oriented results verify the effectiveness of the proposed algorithm.

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HU, CHAOLANG, RONGJUN WU, and JIAYONG LIU. "STUDY ON THE MODEL OF CONSENSUS FORMATION IN INTERNET BASED ON THE DIRECTED GRAPH." International Journal of Modern Physics C 23, no. 06 (June 2012): 1250041. http://dx.doi.org/10.1142/s0129183112500416.

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This paper constructs a model of the consensus formation in Internet based on the directed graph after analyzing the classical models of the social consensus formation, sets up the rules for the evolvement of opinions of agents and induces the evolving algorithm of consensus in Internet. The paper presents some key parameters such as the influence area of the mainstream media, the average influence of the mainstream media, the average self-persisting ability of agents and etc. Simulation results on a small-world networks show that the less the average self-persisting capability of the agents is, the easier the guidance of the media will be. The stronger the average influence of the main stream media is, the easier the mainstream media guides the consensus. These results reflect the formation law of the network consensus and are consistent approximately with the real circumstance.

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Zheng, Xiandong, Wenlong Feng, Mengxing Huang, and Siling Feng. "Optimization of PBFT Algorithm Based on Improved C4.5." Mathematical Problems in Engineering 2021 (March 3, 2021): 1–7. http://dx.doi.org/10.1155/2021/5542078.

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Aiming at the problems of PBFT algorithm of consortium blockchain, such as high communication overhead, low consensus efficiency, and random selection of leader nodes, an optimized algorithm of PBFT is proposed. Firstly, the algorithm improves C4.5 and introduces weighted average information gain to overcome the mutual influence between conditional attributes and improve the classification accuracy. Then classify the nodes with improved C4.5, and select the ones with a high trust level to form the main consensus group. Finally, the integral voting mechanism is introduced to determine the leader node. Experimental results show that compared with traditional PBFT algorithm, the communication times of the improved PBFT algorithm are reduced greatly, which effectively alleviates the problem that the number of nodes in traditional PBFT algorithm increases and the traffic volume is too large, and significantly reduces the probability of the leader node doing evil and improves the consensus efficiency.

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Saraoğlu, Mustafa, Aydın Polat, İlhan Mutlu, and Mehmet Turan Söylemez. "A Practical Framework for the Analysis of Average Consensus Problem: Laplacian Generator and Analyzer Algorithm." IFAC-PapersOnLine 49, no. 3 (2016): 341–46. http://dx.doi.org/10.1016/j.ifacol.2016.07.057.

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Xu, De Hong, Huan Xin Peng, and Bin Liu. "Non-Uniform Probabilistically Quantized Distributed Consensus Applied on Sensors Network." Applied Mechanics and Materials 577 (July 2014): 921–25. http://dx.doi.org/10.4028/www.scientific.net/amm.577.921.

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In order to improve the accuracy of distributed consensus under digital communication, in the paper, assuming the initial states of nodes following uniform distribution, a non-uniform quantization scheme based on probabilistic quantization is proposed, and the entire data range is divided based on µ-law non-uniform quantization scheme. The quantization step-size near the average of initial states is smaller, and the corresponding quantization errors are smaller. Base on the proposed quantization scheme, a non-uniform probabilistically quantized distributed consensus algorithm is proposed. The performance and the mean square errors of the non-uniform probabilistically quantized distributed consensus algorithm is analyzed, by analyses and simulations, the results show the non-uniform probabilistically quantized distributed consensus can reach a consensus, and the mean square error is far smaller than that of probabilistically quantized distributed consensus.

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Li, Bin, Yu Yang, Chengshuai Qin, Xiao Bai, and Lihui Wang. "Improved random sampling consensus algorithm for vision navigation of intelligent harvester robot." Industrial Robot: the international journal of robotics research and application 47, no. 6 (August 12, 2020): 881–87. http://dx.doi.org/10.1108/ir-03-2020-0055.

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Purpose Focusing on the problem that the visual detection algorithm of navigation path line in intelligent harvester robot is susceptible to interference and low accuracy, a navigation path detection algorithm based on improved random sampling consensus is proposed. Design/methodology/approach First, inverse perspective mapping was applied to the original images of rice or wheat to restore the three-dimensional spatial geometric relationship between rice or wheat rows. Second, set the target region and enhance the image to highlight the difference between harvested and unharvested rice or wheat regions. Median filter is used to remove the intercrop gap interference and improve the anti-interference ability of rice or wheat image segmentation. The third step is to apply the method of maximum variance to thresholding the rice or wheat images in the operation area. The image is further segmented with the single-point region growth, and the harvesting boundary corner is detected to improve the accuracy of the harvesting boundary recognition. Finally, fitting the harvesting boundary corner point as the navigation path line improves the real-time performance of crop image processing. Findings The experimental results demonstrate that the improved random sampling consensus with an average success rate of 94.6% has higher reliability than the least square method, probabilistic Hough and traditional random sampling consensus detection. It can extract the navigation line of the intelligent combine robot in real time at an average speed of 57.1 ms/frame. Originality/value In the precision agriculture technology, the accurate identification of the navigation path of the intelligent combine robot is the key to realize accurate positioning. In the vision navigation system of harvester, the extraction of navigation line is its core and key, which determines the speed and precision of navigation.

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Li, Lingyun, Jie Zhou, Demin Li, Jinde Cao, and Xiaolu Zhang. "Gossip Consensus Algorithm Based on Time-Varying Influence Factors and Weakly Connected Graph for Opinion Evolution in Social Networks." Abstract and Applied Analysis 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/940809.

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We provide a new gossip algorithm to investigate the problem of opinion consensus with the time-varying influence factors and weakly connected graph among multiple agents. What is more, we discuss not only the effect of the time-varying factors and the randomized topological structure but also the spread of misinformation and communication constrains described by probabilistic quantized communication in the social network. Under the underlying weakly connected graph, we first denote that all opinion states converge to a stochastic consensus almost surely; that is, our algorithm indeed achieves the consensus with probability one. Furthermore, our results show that the mean of all the opinion states converges to the average of the initial states when time-varying influence factors satisfy some conditions. Finally, we give a result about the square mean error between the dynamic opinion states and the benchmark without quantized communication.

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Carneiro, Joao, Diogo Martinho, Luís Conceição, Goreti Marreiros, and Paulo Novais. "How the ability to analyse tendencies influences decision satisfaction." Inteligencia Artificial 20, no. 59 (February 14, 2017): 8. http://dx.doi.org/10.4114/intartif.vol20iss59pp8-20.

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Using agents to represent decision-makers is a complex task. It is important that agents can understand the context and be more proactive. Here we propose a model and an algorithm that will allow the agent to analyse tendencies regarding the number of supporters for each alternative along the process. It is intended that agents can be more dynamic and intelligent and can evaluate different contexts throughout the decision-making process. We believe agents will achieve better and consensual decisions more easily. We tested our model in three simulation environments with different complexity levels. Our model proved that agents that use it will obtain higher average consensus and satisfaction levels. Besides that, agents using this model will obtain those higher consensus and satisfaction levels in most of the times compared to agents that do not use it.

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Nguyen, Cao-Khang, Thai-Thanh Nguyen, Hyeong-Jun Yoo, and Hak-Man Kim. "Consensus-Based SOC Balancing of Battery Energy Storage Systems in Wind Farm." Energies 11, no. 12 (December 16, 2018): 3507. http://dx.doi.org/10.3390/en11123507.

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Multiple battery energy storage systems (BESSs) are used to compensate for the fluctuation in wind generations effectively. The stage of charge (SOC) of BESSs might be unbalanced due to the difference of wind speed, initial SOCs, line impedances and capabilities of BESSs, which have a negative impact on the operation of the wind farm. This paper proposes a distributed control of the wind energy conversion system (WECS) based on dynamic average consensus algorithm to balance the SOC of the BESSs in a wind farm. There are three controllers in the WECS with integrated BESS, including a machine-side controller (MSC), the grid-side controller (GSC) and battery-side controller (BSC). The MSC regulates the generator speed to capture maximum wind power. Since the BSC maintains the DC link voltage of the back-to-back (BTB) converter that is used in the WECS, an improved virtual synchronous generator (VSG) based on consensus algorithm is used for the GSC to control the output power of the WECS. The functionalities of the improved VSG are designed to compensate for the wind power fluctuation and imbalance of SOC among BESSs. The average value of SOCs obtained by the dynamic consensus algorithm is used to adjust the wind power output for balancing the SOC of batteries. With the proposed controller, the fluctuation in the output power of wind generation is reduced, and the SOCs of BESSs are maintained equally. The effectiveness of the proposed control strategy is validated through the simulation by using a MATLAB/Simulink environment.

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Xie, Jiangjian, Xingguang Li, Zhaoliang Xing, Bowen Zhang, Weidong Bao, and Junguo Zhang. "Improved Distributed Minimum Variance Distortionless Response (MVDR) Beamforming Method Based on a Local Average Consensus Algorithm for Bird Audio Enhancement in Wireless Acoustic Sensor Networks." Applied Sciences 9, no. 15 (August 2, 2019): 3153. http://dx.doi.org/10.3390/app9153153.

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Currently, wireless acoustic sensor networks (WASN) are commonly used for wild bird monitoring. To better realize the automatic identification of birds during monitoring, the enhancement of bird audio is essential in nature. Currently, distributed beamformer is the most suitable method for bird audio enhancement of WASN. However, there are still several disadvantages of this method, such as large noise residue and slow convergence rate. To overcome these shortcomings, an improved distributed minimum variance distortionless response (IDMVDR) beamforming method for bird audio enhancement in WASN is proposed in this paper. In this method, the average metropolis weight local average consensus algorithm is first introduced to increase the consensus convergence rate, then a continuous spectrum update algorithm is proposed to estimate the noise power spectral density (PSD) to improve the noise reduction performance. Lastly, an MVDR beamformer is introduced to enhance the bird audio. Four different network topologies of the WASNs were considered, and the bird audio enhancement was performed on these WASNs to validate the effectiveness of the proposed method. Compared with two classical methods, the results show that the Segmental signal to noise ratio (SegSNR), mean square error (MSE), and perceptual evaluation of speech quality (PESQ) obtained by the proposed method are better and the consensus rate is faster, which means that the proposed method performs better in audio quality and convergence rate, and therefore it is suitable for WASN with dynamic topology.

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Straat, A. Carlien, Pieter Coenen, Denise J. M. Smit, Gerben Hulsegge, Esther V. A. Bouwsma, Judith A. F. Huirne, Rutger C. van Geenen, et al. "Development of a Personalized m/eHealth Algorithm for the Resumption of Activities of Daily Life Including Work and Sport after Total and Unicompartmental Knee Arthroplasty: A Multidisciplinary Delphi Study." International Journal of Environmental Research and Public Health 17, no. 14 (July 9, 2020): 4952. http://dx.doi.org/10.3390/ijerph17144952.

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Evidence for recommendations concerning the resumption of activities of daily life, including work and sport, after knee arthroplasty is lacking. Therefore, recommendations vary considerably between hospitals and healthcare professionals. We aimed to obtain multidisciplinary consensus for such recommendations. Using a Delphi procedure, we strived to reach consensus among a multidisciplinary expert panel of six orthopaedic surgeons, three physical therapists, five occupational physicians and one physician assistant on recommendations regarding the resumption of 27 activities of daily life. The Delphi procedure involved three online questionnaire rounds and one face-to-face consensus meeting. In each of these four rounds, experts independently decided at what time daily life activities could feasibly and safely be resumed after knee arthroplasty. We distinguished patients with a fast, average and slow recovery. After four Delphi rounds, the expert panel reached consensus for all 27 activities. For example, experts agreed that total knee arthroplasty patients with a fast recovery could resume cycling six weeks after the surgery, while those with an average and slow recovery could resume this activity after nine and twelve weeks, respectively. The consensus recommendations will subsequently be integrated into an algorithm of a personalized m/eHealth portal to enhance recovery among knee arthroplasty patients.

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Abdelkader, Eslam Mohammed. "A Hybrid Decision Support System for Holistic Evaluation of Material Alternatives." International Journal of Strategic Decision Sciences 12, no. 1 (January 2021): 18–36. http://dx.doi.org/10.4018/ijsds.2021010102.

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Selection of materials is pivotal for the success of engineering design applications. Their proper selection is an exhaustive task as a result of the presence of a wide range of material alternatives and performance attributes. Thus, this research proposes an integrated multi-criteria decision-making method for the sake of evaluating a set of material alternatives. The material alternatives are assessed with regards to their surface hardness, core hardness, surface fatigue limit, bending fatigue limit, ultimate tensile strength, and cost. The weights of attributes are obtained based on criteria importance through inter-criteria correlation (CRITIC) algorithm. This research then employs six types of multi-criteria decision-making algorithms to rank the material alternatives. Average ranking algorithm is then applied to generate a full consensus prioritization of material alternatives. A sensitivity analysis is also carried out to determine the most robust and sensitive multi-criteria decision-making algorithm.

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Priolo, Attilio, Andrea Gasparri, Eduardo Montijano, and Carlos Sagues. "Corrigendum to “A distributed algorithm for average consensus on strongly connected weighted digraphs” [Automatica 50(3) (2014) 946–951]." Automatica 59 (September 2015): 264. http://dx.doi.org/10.1016/j.automatica.2015.03.024.

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Zhang, Cui, Wang, Ding, and Xia. "Distributed Joint Cooperative Self-Localization and Target Tracking Algorithm for Mobile Networks." Sensors 19, no. 18 (September 4, 2019): 3829. http://dx.doi.org/10.3390/s19183829.

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: Location information is a key issue for applications of the Internet of Things. In this paper, we focus on mobile wireless networks with moving agents and targets. The positioning process is divided into two phases based on the factor graph, i.e., a prediction phase and a joint self-location and tracking phase. In the prediction phase, we develop an adaptive prediction model by exploiting the correlation of trajectories within a short period to formulate the prediction message. In the joint positioning phase, agents calculate the cooperative messages according to variational message passing and locate themselves. Simultaneously, the average consensus algorithm is employed to realize distributed target tracking. The simulation results show that the proposed prediction model is adaptive to the random movement of nodes. The performance of the proposed joint self-location and tracking algorithm is better than the separate cooperative self-localization and tracking algorithms.

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SAIDANE, LEILA AZOUZ, and FAROUK KAMOUN. "MODELLING AND PERFORMANCE EVALUATION OF THE CIRCULATING MULTISEQUENCER, THE MULTI-TOKENS AND THE CONSENSUS ALGORITHMS IN A REAL TIME DISTRIBUTED TRANSACTIONAL SYSTEM." International Journal of Foundations of Computer Science 12, no. 06 (December 2001): 719–49. http://dx.doi.org/10.1142/s0129054101000837.

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In a real-time distribute transactional system, customers generate transactions, which should be scheduled to be executed on different servers. The transactions have temporal constraints and must be executed before their deadlines. To schedule these transactions the circulating mutisequencer, the multi-tokens and the consensus algorithms have been considered to obtain a global view of the system. In this paper, mathematical models are developed to obtain the average stay time of a transaction within the system. These models introduce a bulk arrival M/G/1 station with K classes of customers where bulks are considered according to FIFO discipline and customers (actions) are scheduled according to EDF within a group and with the HOL discipline for the algorithm operating. The response time distribution is also computed. This allowed us to determine the minimum relative deadline, to affect to a generated transaction, to guarantee a given probability p that the transaction does not miss its deadline. The system is then called p-feasible. This study enables to determine the number of tokens to use for the multi-tokens algorithm, for a given number of servers and shows that the circulating mutisequencer algorithm presents the best results.

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Na, Dongjun, and Sejin Park. "Fusion Chain: A Decentralized Lightweight Blockchain for IoT Security and Privacy." Electronics 10, no. 4 (February 5, 2021): 391. http://dx.doi.org/10.3390/electronics10040391.

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As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI).

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Cao, Song, Song, Xiao, and Peng. "Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments." Sensors 19, no. 14 (July 18, 2019): 3166. http://dx.doi.org/10.3390/s19143166.

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Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

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Kumar, Adarsh, Deepak Kumar Sharma, Anand Nayyar, Saurabh Singh, and Byungun Yoon. "Lightweight Proof of Game (LPoG): A Proof of Work (PoW)’s Extended Lightweight Consensus Algorithm for Wearable Kidneys." Sensors 20, no. 10 (May 19, 2020): 2868. http://dx.doi.org/10.3390/s20102868.

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In healthcare, interoperability is widely adopted in the case of cross-departmental or specialization cases. As the human body demands multiple specialized and cross-disciplined medical experiments, interoperability of business entities like different departments, different specializations, the involvement of legal and government monitoring issues etc. are not sufficient to reduce the active medical cases. A patient-centric system with high capability to collect, retrieve, store or exchange data is the demand for present and future times. Such data-centric health processes would bring automated patient medication, or patient self-driven trusted and high satisfaction capabilities. However, data-centric processes are having a huge set of challenges such as security, technology, governance, adoption, deployment, integration etc. This work has explored the feasibility to integrate resource-constrained devices-based wearable kidney systems in the Industry 4.0 network and facilitates data collection, liquidity, storage, retrieval and exchange systems. Thereafter, a Healthcare 4.0 processes-based wearable kidney system is proposed that is having the blockchain technology advantages. Further, game theory-based consensus algorithms are proposed for resource-constrained devices in the kidney system. The overall system design would bring an example for the transition from the specialization or departmental-centric approach to data and patient-centric approach that would bring more transparency, trust and healthy practices in the healthcare sector. Results show a variation of 0.10 million GH/s to 0.18 million GH/s hash rate for the proposed approach. The chances of a majority attack in the proposed scheme are statistically proved to be minimum. Further Average Packet Delivery Rate (ADPR) lies between 95% to 97%, approximately, without the presence of outliers. In the presence of outliers, network performance decreases below 80% APDR (to a minimum of 41.3%) and this indicates that there are outliers present in the network. Simulation results show that the Average Throughput (AT) value lies between 120 Kbps to 250 Kbps.

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Hossein Pouraghdam, M., M. Saadatseresht, H. Rastiveis, A. Abzal, and M. Hasanlou. "BUILDING FLOOR PLAN RECONSTRUCTION FROM SLAM-BASED POINT CLOUD USING RANSAC ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 483–88. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-483-2019.

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Abstract. In recent years, the applications of interior and exterior model of buildings have been increased in the field of surveying and mapping. This paper presents a new method for extracting a two-dimensional (2D) floor plan of a building from Simultaneous localization and mapping (SLAM)-based point clouds. In the proposed algorithm, after preprocessing, the voxel space is generated for the point cloud. Then, the optimal section of the voxel cube to generate building floor plan is identified. Finally, the linear structures and walls are extracted using the random sample consensus (RANSAC) algorithm. The proposed algorithm was examined on a collected point clouds of a building, and the walls of this building were automatically extracted. To evaluate the proposed method, the obtained walls by the algorithm were compared with the manually extracted walls. The algorithm successfully extracted almost 90% of the walls in the test area. Moreover, the average error of 3 cm for the extracted walls proved the high accuracy of the proposed method for building floor plan modeling.

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Wang, Mengchi, David Wang, Kai Zhang, Vu Ngo, Shicai Fan, and Wei Wang. "Motto: Representing Motifs in Consensus Sequences with Minimum Information Loss." Genetics 216, no. 2 (August 19, 2020): 353–58. http://dx.doi.org/10.1534/genetics.120.303597.

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Sequence analysis frequently requires intuitive understanding and convenient representation of motifs. Typically, motifs are represented as position weight matrices (PWMs) and visualized using sequence logos. However, in many scenarios, in order to interpret the motif information or search for motif matches, it is compact and sufficient to represent motifs by wildcard-style consensus sequences (such as [GC][AT]GATAAG[GAC]). Based on mutual information theory and Jensen-Shannon divergence, we propose a mathematical framework to minimize the information loss in converting PWMs to consensus sequences. We name this representation as sequence Motto and have implemented an efficient algorithm with flexible options for converting motif PWMs into Motto from nucleotides, amino acids, and customized characters. We show that this representation provides a simple and efficient way to identify the binding sites of 1156 common transcription factors (TFs) in the human genome. The effectiveness of the method was benchmarked by comparing sequence matches found by Motto with PWM scanning results found by FIMO. On average, our method achieves a 0.81 area under the precision-recall curve, significantly (P-value < 0.01) outperforming all existing methods, including maximal positional weight, Cavener’s method, and minimal mean square error. We believe this representation provides a distilled summary of a motif, as well as the statistical justification.

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Lertampaiporn, Supatcha, Sirapop Nuannimnoi, Tayvich Vorapreeda, Nipa Chokesajjawatee, Wonnop Visessanguan, and Chinae Thammarongtham. "PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins." BioMed Research International 2019 (November 19, 2019): 1–11. http://dx.doi.org/10.1155/2019/5617153.

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Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellular localization. Hence, to address this issue, this work proposes the use of the particle swarm optimization (PSO) algorithm to combine the prediction outputs from multiple different subcellular localization predictors with the aim of integrating diverse prediction models to enhance the final predictions. Herein, we present PSO-LocBact, a consensus classifier based on PSO that can be used to combine the strengths of several preexisting protein localization predictors specially designed for bacteria. Our experimental results indicate that the proposed method can resolve inconsistency problems in subcellular localization prediction for both Gram-negative and Gram-positive bacterial proteins. The average accuracy achieved on each test dataset is over 98%, higher than that achieved with any individual predictor.

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Kuo, C. Y., Y. R. Lu, and S. M. Yang. "On the Image Sensor Processing for Lane Detection and Control in Vehicle Lane Keeping Systems." Sensors 19, no. 7 (April 8, 2019): 1665. http://dx.doi.org/10.3390/s19071665.

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Lane keeping systems for a keeping a vehicle in the desired lane is key to advanced driving assistance system in autonomous vehicles. This paper presents a cost-effective image sensor with efficient processing algorithm for lane detection and lane control applications to autonomous delivery systems. The algorithm includes (1) lane detection by inverse perspective mapping and random sample consensus parabola fitting and (2) lane control by pure pursuit steering controller and classical proportional integral speed controller based on a nonholonomic kinematic model. The image sensor experiments conducted on a 1/10 scale model car maneuvering in a straight–curve–straight lane validate the better processing performance before, during, and after the turning section over previous work. The image sensor with the processing algorithm achieves the average lane detection error within 5% and maximum cross-track error within 9% in real-time. The development shall pave the way to cost-effective autonomous delivery systems.

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Woo, Seungjun, Francisco Yumbla, Chanyong Park, Hyouk Ryeol Choi, and Hyungpil Moon. "Plane-based stairway mapping for legged robot locomotion." Industrial Robot: the international journal of robotics research and application 47, no. 4 (April 23, 2020): 569–80. http://dx.doi.org/10.1108/ir-09-2019-0189.

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Purpose The purpose of this study is to propose a novel plane-based mapping method for legged-robot navigation in a stairway environment. Design/methodology/approach The approach implemented in this study estimates a plane for each step of a stairway using a weighted average of sensor measurements and predictions. It segments planes from point cloud data via random sample consensus (RANSAC). The prediction uses the regular structure of a stairway. When estimating a plane, the algorithm considers the errors introduced by the distance sensor and RANSAC, in addition to stairstep irregularities, by using covariance matrices. The plane coefficients are managed separately with the data structure suggested in this study. In addition, this data structure allows the algorithm to store the information of each stairstep as a single entity. Findings In the case of a stairway environment, the accuracy delivered by the proposed algorithm was higher than those delivered by traditional mapping methods. The hardware experiment verified the accuracy and applicability of the algorithm. Originality/value The proposed algorithm provides accurate stairway-environment mapping and detailed specifications of each stairstep. Using this information, a legged robot can navigate and plan its motion in a stairway environment more efficiently.

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Abdelkader, Eslam Mohammed, Abobakr Al-Sakkaf, and Ghasan Alfalah. "Optimizing Material Selection Using a Hybridized Multi-attribute Decision Making Model." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (July 26, 2021): 404–21. http://dx.doi.org/10.37394/23203.2021.16.36.

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Material selection is a very entangled and decisive stage in the design and development of products. There are large numbers of on hand and newly developed materials available in the market. In addition, inability to select the correct materials adversely affects the reputation and profitability of the company. Thus, designers need to study and trace the performance of available materials with appropriate functionalities. Thus, this research aims at establishing an efficient and systematic platform for the optimum selection of materials while accommodating the designated conflicting performance requirements. The developed model encompasses designing a hybrid decision support system in an attempt to circumvent the shortcomings of single multi-criteria decision making-based (MCDM) models. First, the objective relative importance weights of attributes are interpreted capitalizing on Shannon entropy algorithm. Then, an integrated model that encompasses the utilization of six different types of multi-criteria decision making algorithms is designed to create a reliable selection of material alternatives. The utilized MCDM algorithms comprise weighted product method (WPM), simple additive weighting (SAW), additive ratio assessment (ARAS), new combinative distance-based assessment (CODAS), complex proportional assessment (COPRAS) and technique for order of preference by similarity to ideal solution (TOPSIS). Afterwards, COPELAND algorithm is exploited to generate a consensus and distinct ranking of material alternatives. Eventually, Spearman’s rank correlation analysis is used to evaluate the rankings obtained from the MCDM algorithms. Five numerical examples in diverse fields of material selection are tackled to examine the features and efficiency of the developed integrated model. Results illustrated that the developed model was able to solve the five material selection problems efficiently. On the other hand, no individual MCDM algorithm was able to solve all the assigned material selection problems. For instance, CODAS and TOPSIS only succeeded in solving one and two material selection problems, respectively. It was also inferred that notable differences and perturbations are encountered between the rankings of MCDM algorithms in the first, third, fourth and fifth numerical examples, which necessitates the implementation of COPELAND algorithm. It was also revealed that the highest correlation lied between COPRAS and WPM with an average Spearman’s rank correlation coefficient of 92.67%. On the other hand, the correlation between TOPSIS and CODAS attained the lowest rank with an average Spearman’s rank correlation coefficient of 18.95%. Results also demonstrated that COPRAS accomplished the highest Spearman’s rank correlation coefficient with 59.54%. Hence, it is the most efficient MCDM algorithm among the five algorithms which can serve as a reference for solving material selection problems. It can be also deduced that CODAS and TOPSIS are not advised to be implemented in solving similar material selection problems.

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Hong, Yinqiu, Jihua Xie, and Fang Fang. "Distributed Secondary Control of Islanded Microgrids for Fast Convergence Considering Arbitrary Sampling." Processes 9, no. 6 (May 29, 2021): 971. http://dx.doi.org/10.3390/pr9060971.

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This paper proposes a novel distributed secondary control of MGs for fast convergence considering asynchronous sampling. With the employment of the algorithm, optimal power sharing and voltage restoration are implemented simultaneously. First, the hierarchical control objectives concerned with economic operation and voltage quality are introduced. Then, the execution process of the fast convergence algorithm is described for weighted average state estimation, with the illustration of corresponding features and the application in cooperative control. Further, the relevant stability issue is discussed based on large-signal dynamic modeling and a sufficient stability condition is derived based on the Lyapunov–Krasovskii theory. Our approach offers superior reliability, flexibility and robustness because of the loose implementation in terms of its performance concern, which is essential when the distributed consensus protocol is likely to yield toward deviation or even instability under arbitrary sampling and delays. The effectiveness of the proposed methodology is verified via simulations.

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George, Jemin, and Randy A. Freeman. "Robust Dynamic Average Consensus Algorithms." IEEE Transactions on Automatic Control 64, no. 11 (November 2019): 4615–22. http://dx.doi.org/10.1109/tac.2019.2901819.

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Daliakopoulos, Ioannis N., and Ioannis K. Tsanis. "A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs." Sensors 19, no. 5 (March 5, 2019): 1123. http://dx.doi.org/10.3390/s19051123.

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A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m Geoeye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements.

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Journal articles on the topic 'Average consensus algorithm'

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