To overcome the problem of easily falling into local extreme values of the whale swarm algorithm to solve the material emergency dispatching problem with changing road conditions, an improved whale swarm algorithm is ...
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To overcome the problem of easily falling into local extreme values of the whale swarm algorithm to solve the material emergency dispatching problem with changing road conditions, an improved whale swarm algorithm is proposed. First, an improved scan and Clarke-Wright algorithm is used to obtain the optimal vehicle path at the initial time. Then, the group movement strategy is designed to generate offspring individuals with an improved quality for refining the updating ability of individuals in the population. Finally, in order to maintain population diversity, a different weights strategy is used to expand individual search spaces, which can prevent individuals from prematurely gathering in a certain area. The experimental results show that the performance of the improved whale swarm algorithm is better than that of the ant colony system and the adaptive chaotic genetic algorithm, which can minimize the cost of material distribution and effectively eliminate the adverse effects caused by the change of road conditions.
Soft-cancellation(scan) is a soft output iterative algorithm widely used in polar decoding. This algorithm has better decoding performance than reduced latency soft-cancellation(RLSC) algorithm, which can effectively ...
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Soft-cancellation(scan) is a soft output iterative algorithm widely used in polar decoding. This algorithm has better decoding performance than reduced latency soft-cancellation(RLSC) algorithm, which can effectively reduce the decoding delay of scan algorithm by 50% but has obvious performance loss. A modified reduced latency soft-cancellation(MRLSC) algorithm is presented in the paper. Compared with RLSC algorithm, LLR information storage required in MRLSC algorithm can be reduced by about 50%, and better decoding performance can be achieved with only a small increase in decoding delay. The simulation results show that MRLSC algorithm can achieve a maximum block error rate(BLER) performance gain of about 0.4 dB compared with RLSC algorithm when code length is 2048. At the same time, compared with the performance of several other algorithms under(1024, 512) polar codes, the results show that the throughput of proposed MRLSC algorithm has the advantage at the low and medium signal-to-noise ratio(SNR) and better BLER performance at the high SNR.
As an useful and important graph clustering algorithm for discovering meaningful clusters, scan has been used in a lot of different graph analysis applications, such as mining communities in social networks and detect...
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ISBN:
(纸本)9781665414852
As an useful and important graph clustering algorithm for discovering meaningful clusters, scan has been used in a lot of different graph analysis applications, such as mining communities in social networks and detecting functional clusters of genes in computational biology. scan generates clusters in light of two parameters epsilon and mu. Due to the users lack the necessary professional knowledge, however, the parameter epsilon needs to be changed multiple times to obtain the desired clustering results. Every time the parameter epsilon changes, the new clustering result R' can be obtained by executing the scan algorithm once, which takes a lot of time. To address this problem, in this paper, based on the previously clustering result R, we explore an effective incremental clustering strategy when the parameter epsilon changes dynamically to epsilon'. Although the scan results are affected by the parameters epsilon and mu, some vertices in R are unaffected when the clustering parameter epsilon increases or decreases. Moreover, some useful information for clustering is constant, such as the structural neighborhood of any vertex, and the degree of any vertex. In this case, these information can be stored for use in the process of obtaining the new clustering result R' with regard to the changed parameter epsilon' and the original parameter mu. Therefore, we explore two effective incremental clustering algorithms for the dynamically changing parameter epsilon', which avoids re-executing the scan algorithm based on the new parameter epsilon' and mu. Finally, we conduct comprehensive experimental studies, which illustrates that the incremental clustering model can effectively obtain the new clustering results when the clustering parameter epsilon changes dynamically.
In the applications of analyzing graph data, scan algorithm is an effective clustering algorithm for detecting meaningful clusters, which is widely used in many different graph applications. The problem of refining st...
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ISBN:
(纸本)9781665414852
In the applications of analyzing graph data, scan algorithm is an effective clustering algorithm for detecting meaningful clusters, which is widely used in many different graph applications. The problem of refining structural graph clustering parameters for unexpected node is to explain why an unexpected node is included in the specified cluster of the scan results and how to make the unexpected node disappear in the specified cluster. It is obvious that the scan results are very sensitive to the two clustering parameters, one is the similarity threshold epsilon, the other one is the density constraint mu, when they are input unreasonable, some unexpected nodes would be included in the specified clusters. To address this problem, how the parameters affect the scan results is analyzed firstly, then two effective refining algorithms for making the unexpected vertices disappear in the specified cluster are proposed, which optimize the initial scan parameters with minimum penalty from two aspects: one is to refine the parameter epsilon;and the other is to refine the parameter mu. Moreover, to retain the original scan results as much as possible in the refined scan results, one penalty function is proposed. Finally, comprehensive experiments on real datasets show that our refining model can efficiently refine clustering parameters for the unexpected nodes of the clustering results of scan.
To effectively manage and analyze graph data in many real-life graph applications, scan algorithm can efficiently help users to detect useful clusters, such as social networks, communication networks, gene networks, a...
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ISBN:
(纸本)9781665414852
To effectively manage and analyze graph data in many real-life graph applications, scan algorithm can efficiently help users to detect useful clusters, such as social networks, communication networks, gene networks, and so on. However, dirty data exist in graph data, for example, some edges are missing in graph data. In this case, the clustering results of scan over the dirty graph data cannot meet the users' requirements, such as, the missing nodes are not included in the desired clusters. To address this kind of problem, in this paper, we explore an effective explanation model to make the missing nodes be included in the desired clusters. The problem of explaining missing nodes in light of data modification is to explain why the missing nodes are not included in the desired clusters and how to make the missing nodes appear in the corresponding desired clusters by modifying the original graph dataset. To achieve this purpose, first, the clustering rational of scan algorithm is analyzed, and an unified explanation framework is proposed based on the analysis above. Moreover, based on the common explanation principle, the original clustering results should be maintained as much as possible in the new clustering results of scan, we design a penalty function to achieve this purpose. Then, we propose two explanation algorithms for making the missing nodes appear in the desired clusters by modifying the original graph dataset with minimum penalty value. Finally, comprehensive experiments are conducted, which demonstrate that the explored explanation model can efficiently explain the missing nodes on structural graph clustering.
Sparse code multiple access (SCMA) and polar codes (PC) have been considered as two promising techniques for future systems and have attracted growing research interests in order to meet the targets of the next genera...
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ISBN:
(纸本)9789532900880
Sparse code multiple access (SCMA) and polar codes (PC) have been considered as two promising techniques for future systems and have attracted growing research interests in order to meet the targets of the next generation of wireless communication networks. In this paper, we develop a novel detection and decoding scheme for SCMA systems combined with channel coding candidate polar codes. The performance of decoding methods of polar codes, such as the Successive Cancellation (SC), belief propagation (BP), Successive Cancellation List (SCL) and Soft Successive Cancellation (scan) decoding, over Additive White Gaussian Noise (AWGN) channel are presented. Simulation results indicate that decoding algorithms of polar codes outperforms conventional SCMA system. In fact, the performances of the estimated Bit Error Rate (BER) are below the order of 10(-5).
Sparse code multiple access (SCMA) and polar codes (PC) are two promising candidates for Future communication systems since they are capable of achieving high system capacity. In this paper, we develop a novel detecti...
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ISBN:
(纸本)9781538677476
Sparse code multiple access (SCMA) and polar codes (PC) are two promising candidates for Future communication systems since they are capable of achieving high system capacity. In this paper, we develop a novel detection and decoding scheme for SCMA systems combined with channel coding candidate polar codes. First, we propose a separate detection and decoding (SDD) receiver for uplink communications. Then, we introduce a joint detection and decoding (JDD) receiver scheme. The investigation of system receiver is decomposed on message passing algorithm (MPA) based SCMA multiuser detection and soft cancellation (scan) algorithm based polar codes decoder. The separate and joint schemes are studied over additive white gaussian noise (AWGN) channels. JDD scheme yields a better performance gain. Moreover, the joint scheme has a lower computational complexity compared to the separate one. Numerical results show that when polar code length polar(N) = 1024 and R = 1/2, under system loading 150%, JDD outperforms the SDD 1.8dB at BER = 10(-2) and 3.3dB at BER = 10(-6) over AWGN channels.
In this paper we introduce the concept of a BDA-generated model of the genetic code which is based on binary dichotomic algorithms (BDAs). A BDA-generated model is based on binary dichotomic algorithms (BDAs). Such a ...
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In this paper we introduce the concept of a BDA-generated model of the genetic code which is based on binary dichotomic algorithms (BDAs). A BDA-generated model is based on binary dichotomic algorithms (BDAs). Such a BDA partitions the set of 64 codons into two disjoint classes of size 32 each and provides a generalization of known partitions like the Rumer dichotomy. We investigate what partitions can be generated when a set of different BDAs is applied sequentially to the set of codons. The search revealed that these models are able to generate code tables with very different numbers of classes ranging from 2 to 64. We have analyzed whether there are models that map the codons to their amino acids. A perfect matching is not possible. However, we present models that describe the standard genetic code with only few errors. There are also models that map all 64 codons uniquely to 64 classes showing that BDAs can be used to identify codons precisely. This could serve as a basis for further mathematical analysis using coding theory, for example. The hypothesis that BDAs might reflect a molecular mechanism taking place in the decoding center of the ribosome is discussed. The scan demonstrated that binary dichotomic partitions are able to model different aspects of the genetic code very well. The search was performed with our tool Beady-A. This software is freely available at http://***/beady-a. It requires a JVM version 6 or higher. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
Recent studies show hard disk drives fail much more often in real systems than specified in their data-sheets, and RAID-5 may not be able to provide necessary reliability for practical systems. It is desirable to have...
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ISBN:
(纸本)9780769544892
Recent studies show hard disk drives fail much more often in real systems than specified in their data-sheets, and RAID-5 may not be able to provide necessary reliability for practical systems. It is desirable to have disk arrays and clustered storage systems with higher data redundancy, such as RAID-6. Meanwhile, latest research also indicates that sector failures become a threat to data reliability in storage systems. As a result, disk failures in RAID-6 systems become complex, and call for efficient decoding approaches to recover data when disk failures take place. This paper proposes a simple and efficient decoding algorithm to reconstruct data from disk failures for RAID-6 systems. First, for many well known RAID-6 codes, we provide the conditions to determine the recoverability of disk failures by using Tanner graph. The covered RAID-6 codes include X-code, EVENODD, and RDP. Then, a generic failure decoding algorithm called scan algorithm is derived. The scan algorithm is able to efficiently reconstruct data for any recoverable disk failures. Extensive performance evaluation shows the scan algorithm achieves higher performance than Matrix Method, another general decoding algorithm. Hence, the scan algorithm is an attractive decoding algorithm to be integrated into RAID-6 systems.
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