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27th Euromicro Conference on Digital System Design

作者： Sun, Zhifang Jin, Shengjie Duan, Jinxue Jiang, Junqiang Peng, Zebo Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang Peoples R China Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden

ISBN: (纸本)9798350380392;9798350380385

Integrated Mapping and Scheduling (IMS) problems can be found in many domains, such as electronic design automation (EDA) and modern manufacturing systems. Optimization algorithms to solve the IMS problems can be used to minimize execution time, implementation cost, energy consumption, etc. Genetic Algorithms (GAs) are powerful evolutionary algorithms for tackling many of such IMS optimization problems. By utilizing biological principles like selection, crossover, and mutation, GAs excel in generating high-quality solutions. Chromosome encoding and decoding, in addition to evolutionary operators, significantly influence GA's efficiency. This paper introduces a relative-priority genetic algorithm (RPGA), a novel GA for IMS problems, such as those in EDA. RPGA employs a unique encoding scheme tailored for IMS problems, especially those with OR nodes representing alternative operation paths. It encodes the relative priority of an operation in a chromosome, which can be divided into two parts: one for path selections and the other for operation scheduling and mappings. Efficient decoding of every chromosome into a solution is facilitated through the concept of a ready operation set. The study extensively compares RPGA and established meta-heuristics using a benchmark set. The experimental results demonstrate that RPGA achieves high solution quality and rapid convergence.

关键词： Design optimization encoding scheme Genetic algorithms Mapping and scheduling

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Modified Football Game Algorithm for Multimodal Optimization of Test Task Scheduling Problems Using Normalized Factor Random Key encoding scheme 1

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10th International Conference on Bioinspired Optimization Methods and Their Applications (BIOMA)

作者： Fadakar, Elyas Beihang Univ BUAA Beijing Peoples R China

ISBN: (数字)9783031210945

ISBN: (纸本)9783031210938;9783031210945

Test Task Scheduling Problems (TTSPs) are a type of scheduling problems that are very important in many big and complicated test systems in automotive industries where the reliability of the final product is fundamentally dependent on those tests while the time, workload, and agility of the production is dependent on the optimal scheduling. Scheduling problems are highly multimodal problems with high number of local and global optimum solutions. Availability of different promising solutions provide more freedom for engineering and management decisions based on other criteria which cannot be modeled easily in a design software. In this study we reviewed important researches in the field of multimodal optimization in general and specifically for scheduling problems and then proposed a major modification on Football Game Algorithm (FGA). Then we applied modified FGA for solving some practical TTSPs using a new proposed encoding scheme called Normalized Factor Random Key (NF-RK). The experimental study shows that the modified FGA in combination with NF-RK is promising in solving continuous multimodal TTSPs and shows a notable out performance in comparison to the best-known reported results in the literature.

关键词： Test task scheduling problem Multimodal optimization Niching algorithm encoding scheme

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encoding scheme for data storage and retrieval on DNA computers

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IET NANOBIOTECHNOLOGY 2020年第7期14卷 635-641页

作者： Sharma, Dolly Kumar, Ranjit Gupta, Mayuri Saxena, Tanisha Shiv Nadar Univ NCR Sch Engn Dept Comp Sci & Engn Greater Noida India Amity Univ Uttar Pradesh Amity Inst Nanotechnol Noida India

There has been exponential growth in the amount of data being generated on a daily basis. Such a huge amount of data creates a need for efficient data storage techniques. Due to the limitations of existing storage media, new storage solutions have always been of interest. There have been recent developments in order to efficiently use synthetic deoxyribonucleic acid (DNA) for information storage. DNA storage has attracted researchers because of its extremely high data storage density, about 1 exabyte/mm(3)and long life under easily achievable conditions. This work presents an encoding scheme for DNA-based data storage system with controllable redundancy and reliability, the authors have also talked about the feasibility of the proposed method. The authors have also analysed the proposed algorithm for time and space complexity. The proposed encoding scheme tries to minimise the bases per letter ratio while controlling the redundancy. They have experimented with three different types of data with a value of redundancy as 0.75. In the randomised simulation setup, it was observed that the proposed algorithm was able to correctly retrieve the stored data in our experiments about 94% of the time. In the situation, where redundancy was increased to 1, the authors were able to retrieve all the information correctly in the proposed experiments.

关键词： DNA computational complexity digital storage storage media biocomputing encoding scheme DNA computers exponential growth storage media synthetic deoxyribonucleic acid information storage extremely high data storage density DNA-based data storage system controllable redundancy stored data

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Huffman-based lossless image encoding scheme

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JOURNAL OF ELECTRONIC IMAGING 2021年第5期30卷 053004-053004页

作者： Erdal, Erdal Kirikkale Univ Dept Comp Engn Fac Engn & Architecture Kirikkale Turkey

The data produced in today's Internet and computer world are expanding their wings day by day. With the passage of time, storage and archiving of this data are becoming a significant problem. To overcome this problem, attempts have been made to reduce data sizes using compression methods. Therefore, compression algorithms have received great attention. In this study, two efficient encoding algorithms are presented and explained in a crystal-clear manner. In all compression algorithms, frequency modulation is used. In this way, the characters with the highest frequency after each character are determined and the Huffman encoding algorithm is applied to them. In this study, the compression ratio (CR) is 49.44%. Moreover, 30 randomly selected images in three different datasets consisting of USC-SIPI, UCID, and STARE databases have been used to evaluate the performance of the algorithms. Consequently, excellent results have been obtained in all test images according to well-known comparison algorithms such as the Huffman encoding algorithm, arithmetic coding algorithm, and LPHEA. (C) 2021 SPIE and IS&T

关键词： image compression encoding scheme Huffman encoding algorithm compression algorithm lossless compression

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An encoding scheme Capturing Generic Priors and Properties of Amino Acids Improves Protein Classification

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IEEE ACCESS 2019年 7卷 7348-7356页

作者： Zhou, Xinrui Yin, Rui Zheng, Jie Kwoh, Chee-Keong Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

Feature engineering aims at representing non-numeric data with numeric features that keep the essential information of the underlying problem, and it is a non-trivial process in building a predictive model. In bioinformatics, there is a profound scale of DNA and protein sequences available, but far from being fully utilized. Computational models can facilitate the analyses of large-scale data. However, most computational models require a numeric representation as input. Expert knowledge can help design features to cast the raw symbolic data effectively. But generally, the features vary from case to case and have to be redesigned for a problem. Automated feature engineering, i.e., an encoding scheme automating the construction of features, saves the redesigning process and allows the researchers to try different representations with minimal effort. This is more in line with the explosion of data and the goal of building an intelligent system. In this paper, we introduce an encoding scheme for protein sequences, which encodes the representative sequence dataset into a numeric matrix that can be fed into a downstream learning model. The method, Context-Free encoding scheme (CFreeEnS), was proposed for a dataset with labels for pairwise sequences. Here, we improve the method by making it applicable to a batch of protein sequences, requiring no sequence alignment beforehand. The improved method is applied to protein classification at the functional level, including identifying antimicrobial peptides, screening tumor homing peptides, and detecting hemolytic peptides and phage virion proteins. Compared with the traditional methods using task-specific designed features, CFreeEnS improves the predicting accuracy, with an increase ranging from 5.54% to 14.14%. The results indicate that the improved CFreeEnS, free from dependence on carefully designed features, is promising in capturing generic priors and essential properties of amino acids, thereby serving as an automated fea

关键词： encoding scheme feature engineering information representation machine learning

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A new encoding scheme of LBP based on maximum run length of state "1" for texture classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第20期77卷 26469-26484页

作者： Pan, Zhibin Li, Zhengyi Wu, Xiuquan Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China

As a simple and efficient local feature descriptor, local binary pattern (LBP) is mainly made up of two steps: extraction step and encoding step. In the extraction step, a local region is denoted by a difference vector between the center pixel and its neighbors. In the encoding step, the corresponding binary bit-string of the difference vector is encoded for the following texture classification. Though encoding step plays a vital role in the whole process of LBP, two current widely used encoding schemes of LBPriu2 and LBC still have some limitations. Different from these two current encoding schemes, in this paper, we propose a new LBP encoding scheme based on the maximum run length of state "1" (LBPmr1) in a binary bit-string. The maximum run length of state "1" reflects the most important part of the binary bit-string structure and it is used as the LBP code of a binary bit-string for the first time. Experimental results on four representative texture databases of Outex, UIUC, CUReT and UMD show that the proposed LBPmr1 achieves better classification accuracy compared with other related LBP encoding schemes.

关键词： Local binary pattern (LBP) Local binary count (LBC) encoding scheme Maximumrun length State "1"

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An Adaptive Cycle Decrease encoding scheme for Short Length LT Codes 10

An Adaptive Cycle Decrease Encoding Scheme for Short Length ...

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10th International Conference on Communication Software and Networks (ICCSN)

作者： Song, Xin Liao, Yurong Ding, Dan Teng, Fei Space & Engn Univ Dept Grad Management Beijing Peoples R China Space & Engn Univ Dept Elect & Opt Engn Beijing Peoples R China

ISBN: (纸本)9781538672242

This paper proposes a novel cycle decrease encoding (CDE) scheme for short length LT codes over binary additive white gaussian (BIAWGN) channels. Two thresholds are introduced, which are used to decrease the number of cycles of length 4 in LT codes generator matrix. During the encoding process, information nodes whose number of cycles exceeding the threshold are screened continuously, and consequently the probabilities of their participation in latter encoding process are decreased and the degree distribution used by these information nodes are changed. The upper bound and lower bound of the number of cycles in any generator matrix are derived. The analysis is then used to calculate thresholds, enabling a sharp decrease of the number of cycles in generator matrix. Simulation results show that the short length CDE LT codes (k = 50 and 200) perform better than the conventional LT codes over BIAWGN channels.

关键词： codes BIAWGN channels thresholds encoding scheme

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A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses

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BMC GENOMICS 2018年第10-Sup期19卷 936-936页

作者： Zhou, Xinrui Yin, Rui Kwoh, Chee-Keong Zheng, Jie Nanyang Technol Univ Sch Comp Sci & Engn Nanyang Ave Singapore 639798 Singapore ShanghaiTech Univ Sch Informat Sci & Technol 393 Middle Huaxia Rd Shanghai 201210 Peoples R China

BackgroundThe evolution of influenza A viruses leads to the antigenic changes. Serological diagnosis of the antigenicity is usually labor-intensive, time-consuming and not suitable for early-stage detection. Computational prediction of the antigenic relationship between emerging and old strains of influenza viruses using viral sequences can facilitate large-scale antigenic characterization, especially for those viruses requiring high biosafety facilities, such as H5 and H7 influenza A viruses. However, most computational models require carefully designed subtype-specific features, thereby being restricted to only one *** this paper, we propose a Context-Freeencoding scheme (CFreeEnS) for pairs of protein sequences, which encodes a protein sequence dataset into a numeric matrix and then feeds the matrix into a downstream machine learning model. CFreeEnS is not only free from subtype-specific selected features but also able to improve the accuracy of predicting the antigenicity of influenza. Since CFreeEnS is subtype-free, it is applicable to predicting the antigenicity of diverse influenza subtypes, hopefully saving the biologists from conducting serological assays for highly pathogenic *** accuracy of prediction on each subtype tested (A/H1N1, A/H3N2, A/H5N1, A/H9N2) is over 85%, and can be as high as 91.5%. This outperforms existing methods that use carefully designed subtype-specific features. Furthermore, we tested the CFreeEnS on the combined dataset of the four subtypes. The accuracy reaches 84.6%, much higher than the best performance 75.1% reported by other subtype-free models, i.e. regional band-based model and residue-based model, for predicting the antigenicity of influenza. Also, we investigate the performance of CFreeEnS when the model is trained and tested on different subtypes (i.e. transfer learning). The prediction accuracy using CFreeEnS is 84.3% when the model is trained on the A/H1N1 dataset and tested on the A/H5N1, b

关键词： encoding scheme Influenza Antigenicity Classification

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Partition based real-valued encoding scheme for evolutionary algorithms

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NATURAL COMPUTING 2016年第3期15卷 477-492页

作者： Font, Jose M. Manrique, Daniel Ramos-Criado, Pablo del Rio, David Ctr Univ Tecnol & Arte Digital Calle Playa Liencres 2 Madrid 28290 Spain Univ Politecn Madrid Dept Inteligencia Artificial E-28660 Madrid Spain

encoding feasible solutions is one of the most important aspects to be taken into account in the field of evolutionary computation in order to solve search or optimization problems. This paper proposes a new encoding scheme for real-coded evolutionary algorithms. It is called partition based encoding scheme, and satisfies two restrictions. Firstly, each of the components of a decoded vector that conforms a candidate solution to a problem at hand belongs to a predefined interval. Secondly, the sum of the components of each of these decoded vectors is always equal to a predefined constant. The proposed encoding scheme inherently guarantees these constraints for all the individuals that are generated within the evolution process as a consequence of applying the genetic operators. Partition based encoding scheme is successfully applied to learning conditional probability tables for a given discrete Bayesian network topology, where each row of the tables must exactly add up to one, and the components of each row belong to the interval [0,1] as they are probability values. The results given by the proposed encoding system for this learning problem is compared to a deterministic algorithm and another evolutionary approach. Better results are shown in terms of accuracy with respect to the former one, and accuracy and convergence speed with respect to the later one.

关键词： encoding scheme encoding constraints Real-valued evolutionary algorithms Discrete Bayesian networks Conditional probability tables

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An effective encoding scheme of obtaining radial topology structures in distribution networks

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SPRINGERPLUS 2016年第1期5卷 1页

作者： Wen, Juan Tan, Yanghong Zhang, Jianmin Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Nanhua Univ Coll Elect & Informat Engn Hengyang 421000 Hunan Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China

The structure of a distribution network has great effects on economy, power supply reliability and investment of a power system. To obtain an optimal topology from possible topologies, we need to solve an optimisation problem which aims to find a radial structure satisfying operating constraints. As a basis of solving this optimisation problem, the encoding scheme, is to represent the candidate configurations by a series of codes. Numerical candidate topologies and unfeasible codes would lead low efficiency or premature convergence. This paper presents an effective scheme which can rapidly produce all radial configurations of a distribution network. In order to reduce the computational requirement of solution space, initial network is simplified as a topological graph which reserves loop branches and T-nodes. And a loop-branch chain incidence matrix is derived from analyzing the relationship between any two loops. Then the principles of selecting switches of each variable are designed to determine the ranges of the variables. All radial candidate solutions are available rapidly through applying the theory of combination. The scheme presented minimizes the number of solutions and avoids tedious radial checking procedure in view of avoiding any infeasible solutions. The validity of the proposed scheme is verified by illustrative examples.

关键词： encoding scheme Loop-switch matrix Radial topology structure Distribution network

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