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Instance-Specific Algorithm Selection via Multi-Output Learning 认领 引用 被引量：1
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作者 Kai Chen Yong Dou +1 位作者 Qi Lv Zhengfa Liang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2017年第2期210-217,共8页
Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential rel... Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods. 展开更多
关键词 algorithm selection multi-output learning extremely randomized trees performance prediction constraint satisfaction
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Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems 认领 引用 被引量：4
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作者 Jin-hui Yang Liang Sun +2 位作者 Heow Pueh Lee Yun Qian Yan-chun Liang 《Journal of Bionic Engineering》 SCIE EI 2008年第2期111-119,共9页
A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exp... A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm. 展开更多
关键词 job shop scheduling problem clonal selection algorithm simulated annealing global search local search
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A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery 认领 引用 被引量：4
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作者 窦唯 刘占生 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2009年第1期5-10,共6页
To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery usin... To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness. 展开更多
关键词 artificial immune system negative selection algorithm abnormality monitor image recognition rotating machinery
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OPTIMIZATION OF AIRPORT TAXIING PLANNING DURING CONGESTED HOURS BASED ON IMMUNE CLONAL SELECTION ALGORITHM 认领 引用 被引量：1
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作者 柳青 吴桐水 宋祥波 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2012年第3期294-301,共8页
In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical j... In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations. 展开更多
关键词 aircraft taxiing schedule airport operation control hub airport congested hours immune clonal selection algorithm（ICSA）
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The distribution modeling and analysis of Antarctic krill:impacts of algorithm and spatial resolution 认领 引用
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作者 LI Wenxiong YING Yiping +5 位作者 ZHANG Jichang ZHAO Yunxia ZHU Jiancheng FAN Gangzhou MU Xiuxia WANG Xinliang 《Advances in Polar Science》 CSCD 2025年第4期373-391,共19页
Antarctic krill(Euphausia superba),widely distributes around Antarctica,is a key species supporting the biodiversity of the Southern Ocean ecosystem.The Commission for the Conservation of Antarctic Marine Living Resou... Antarctic krill(Euphausia superba),widely distributes around Antarctica,is a key species supporting the biodiversity of the Southern Ocean ecosystem.The Commission for the Conservation of Antarctic Marine Living Resources(CCAMLR)has thus managed the krill fishery according to a precautionary way.Currently,CCAMLR is making effort to develop a refined krill fishery management approach based on more solid science,which requires accurate predictions of krill distribution.To address this need,this study investigated the effects of algorithm and spatial resolution on the performance of Antarctic krill distribution modelling.We integrated acoustic data from 4 surveys conducted in the waters adjacent to the Antarctic Peninsula with 11 environmental variables characterizing krill prey conditions,water mass properties,and seafloor topography.These data were processed at 4 spatial resolutions(5,10,15,and 20 km)to fit distribution models using 4 algorithms:Random Forests(RF),Generalized Additive Models(GAM),Extreme Gradient Boosting(XGBoost),and Artificial Neural Networks(ANN).Model performance was assessed and compared in terms of goodness-of-fit and predictive accuracy.The results showed that RF achieved the highest predictive performance at most resolutions,whereas GAM performed best at the coarsest resolution(20 km).XGBoost closely following RF in accuracy and demonstrated robustness as evidenced by the highly consistent partial dependence curves across resolutions.In contrast,ANN exhibited limitations with smaller sample sizes,resulting in comparatively poorer predictive performance.The analysis revealed a trade-off whereby reducing spatial resolution improved model fit and mitigated zero-inflation at the expense of fine-scale information and overall predictive accuracy.Ensemble models,integrating RF,GAM,and XGBoost,are proposed as potential balanced solutions to improve predictive stability,offering a more robust scientific basis for the refinement of krill management. 展开更多
关键词 Antarctic krill species distribution model algorithm selection spatial resolution machine learning
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Research on a randomized real-valued negative selection algorithm 认领 引用
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作者 张凤斌 王胜文 郝忠孝 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2006年第6期745-747,共3页
A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of dete... A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance. 展开更多
关键词 intrusion detection immune systems negative selection algorithm
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A Cuckoo Search Detector Generation-based Negative Selection Algorithm 认领 引用
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作者 Ayodele Lasisi Ali M.Aseere 《Computer Systems Science & Engineering》 SCIE EI 2021年第8期183-195,共13页
The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificia... The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved. 展开更多
关键词 Negative selection algorithm detector generation cuckoo search optimization
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An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers 认领 引用 被引量：2
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作者 Danasingh Asir Antony Gnana Singh Subramanian Appavu Alias Balamurugan Epiphany Jebamalar Leavline 《International Journal of Automation and computing》 CSCD 2015年第5期511-517,共7页
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is ... Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers. 展开更多
关键词 Feature selection algorithm classification cluster
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An Improved Immune Clone Selection Algorithm for Parameters Optimization of Marine Electric Power System Stabilizer 认领 引用
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作者 Zong Bi Weifeng Shi Tiewei Song 《Energy Engineering》 EI 2022年第3期907-928,共22页
In themarine electric power system,the marine generators will be disturbed by the large change of loads or the fault of the power system.The marine generators usually installed power system stabilizers to damp power s... In themarine electric power system,the marine generators will be disturbed by the large change of loads or the fault of the power system.The marine generators usually installed power system stabilizers to damp power system oscillations through the excitation control.This paper proposes a novel method to obtain optimal parameter values for Power System Stabilizer(PSS)to suppress low-frequency oscillations in the marine electric power system.In this paper,a newly developed immune clone selection algorithm was improved from the three aspects of the adaptive incentive degree,vaccination,and adaptive mutation strategies.Firstly,the typical PSS implementation type of leader-lag structure was adopted and the objective function was set in the optimization process.The performance of PSS tuned by improved immune clone selection algorithm was compared with PSS tuned by basic immune clone selection algorithm(ICSA)under various operating conditions and disturbances.Then,an improved immune clone selection algorithm(IICSA)optimization technique was implemented on two test systems for test purposes.Based on the simulations,it is found that an improved immune clone selection algorithm demonstrates superiority over the basic immune clone selection algorithm in getting a smaller number of iterations and fast convergence rates to achieve the optimal parameters of the power system stabilizers.Moreover,the proposed approach improves the stability and dynamic performance under various loads conditions and disturbances of the marine electric power system. 展开更多
关键词 Marine electric power system excitation system immune clone selection algorithm low frequency oscillations power system stability
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Fault Detection Using Negative Selection and Genetic Algorithms 认领 引用 被引量：3
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作者 Anam ABID Zia Ul HAQ Muhammad Tahir KHAN 《Instrumentation》 EI 2019年第3期39-51,共13页
In this paper,negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm detectors.The main aim of the optima... In this paper,negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm detectors.The main aim of the optimal detector generation technique is maximal nonself space coverage with reduced number of diversified detectors.Conventionally,researchers opted clonal selection based optimization methods to achieve the maximal nonself coverage milestone;however,detectors cloning process results in generation of redundant similar detectors and inefficient detector distribution in nonself space.In approach proposed in the present paper,the maximal nonself space coverage is associated with bi-objective optimization criteria including minimization of the detector overlap and maximization of the diversity factor of the detectors.In the proposed methodology,a novel diversity factorbased approach is presented to obtain diversified detector distribution in the nonself space.The concept of diversified detector distribution is studied for detector coverage with 2-dimensional pentagram and spiral self-patterns.Furthermore,the feasibility of the developed fault detection methodology is tested the fault detection of induction motor inner race and outer race bearings. 展开更多
关键词 Detector Coverage Diversity Factor Fault Detection Genetic Algorithm Negative Selection Algorithm
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Greedy Algorithm Applied to Relay Selection for Cooperative Communication Systems in Amplify-and-Forward Mode 认领 引用 被引量：1
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作者 Cheng-Ying Yang Yi-Shan Lin Jyh-Horng Wen 《Journal of Electronic Science and Technology》 CAS 2014年第1期49-53,共5页
Using a relaying system to provide spatial diversity and improve the system performance is a tendency in the wireless cooperative communications. Amplify-and-forward （AF） mode with a low complexity is easy to be imp... Using a relaying system to provide spatial diversity and improve the system performance is a tendency in the wireless cooperative communications. Amplify-and-forward （AF） mode with a low complexity is easy to be implemented. Under the consideration of cooperative communication systems, the scenario includes one information source, M relay stations and N destinations. This work proposes a relay selection algorithm in the Raleigh fading channel. Based on the exhaustive search method, easily to realize, the optimal selection scheme can be found with a highly complicated calculation. In order to reduce the computational complexity, an approximate optimal solution with a greedy algorithm applied for the relay station selection is proposed. With different situations of the communication systems, the performance evaluation obtained by both the proposed algorithm and the exhaustive search algorithm are given for comparison. It shows the proposed algorithm could provide a solution approach to the optimal one. 展开更多
关键词 Amplify-and-forward mode cooperativecommunication exhaustive search greedy algorithm,relay selection.
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A review of dynamic monitoring methods for intermittent rivers:Integrating remote sensing and machine learning 认领 引用
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作者 XIE Chaoshuai Lv Aifeng 《Journal of Geographical Sciences》 SCIE CSCD 2026年第3期763-796,共34页
Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the c... Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the context of climate change and escalating human activities.Recent advances in machine learning(ML)techniques have significantly improved the analysis of dynamic changes in IRES.Various ML models,including random forest(RF),long short-term memory(LSTM),and U-Net,demonstrate clear advantages in processing complex hydrological data,enhancing the efficiency and accuracy of IRES extraction from remote sensing data.Furthermore,hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms.However,ML methods still face challenges,including high data dependence,computational complexity,and scalability issues with models.This review proposes an IRES monitoring framework that combines satellite data with ML algorithms,integrating remote sensing technologies such as optical imaging and synthetic aperture radar,and evaluates the advantages and limitations of different ML methods.It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics,conduct ecological assessments,and support sustainable water management,offering a scientific foundation for addressing environmental and anthropogenic pressures. 展开更多
关键词 machine learning intermittent rivers and ephemeral streams remote sensing framework algorithm selection
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Enhancing Epileptic Seizure Classification via Multi-Feature Fusion in a Transformer-LSTM Architecture 认领 引用
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作者 Gaoteng Yuan Ping Qiu +3 位作者 Qika Lin Jianchu Lin Xiang Li Dongping Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1134-1154,共21页
Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective interventio... Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective intervention.This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram(EEG)signals.The proposed method begins with signal preprocessing,including denoising,segmentation,and label construction.Subsequently,a comprehensive set of temporal,spectral,and wavelet-based features—such as signal mean,power,heart rate,and wavelet coefficients—is extracted.Feature selection is then performed using the Maximal Information Coefficient(MIC)to identify the most discriminative inputs.A hybrid model combining a Transformer encoder and a Long Short-Term Memory(LSTM)network is developed to effectively capture both long-range dependencies and temporal dynamics in EEG sequences for seizure classification.Evaluated on the Bonn dataset using 5-fold cross-validation,the proposed method achieves an accuracy of 96.43%in distinguishing between epileptic patients and healthy subjects,with a sensitivity of 97.53%in detecting seizure states.It also attains a multi-class classification accuracy of 90.14%across different epileptic signal types.Ablation studies confirm that MICbased feature selection improves accuracy by over 2O%compared to using raw features without selection.The results demonstrate that the integration of multi-feature analysis with the Transformer-LSTM architecture offers an effective and reliable solution for EEG-based seizure classification. 展开更多
关键词 Epileptic classification EEG signal wavelet analysis feature selection transformer-LSTM Algorithm 2:MIC algorithm feature selection
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A zoning-based machine learning framework for accurate soil organic matter prediction across Mollisol and non-Mollisol regions 认领 引用
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作者 Xue Li Bo Jiang +5 位作者 Depiao Kong Deqiang Zang Ya Chen Changkun Wang Huanjun Liu Chong Luo 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第8期3453-3468,共16页
Soil organic matter(SOM)is a core indicator of soil fertility and ecosystem function.However,in regions where Mollisol and non-Mollisol coexist,high-precision spatial mapping faces significant challenges due to pronou... Soil organic matter(SOM)is a core indicator of soil fertility and ecosystem function.However,in regions where Mollisol and non-Mollisol coexist,high-precision spatial mapping faces significant challenges due to pronounced terrain heterogeneity and redundancy in high-dimensional covariates.This study proposes a"remote sensing zoning-feature selection optimization-random forest(RSZ-FSO-RF)"framework.By integrating Landsat-8 multi-temporal imagery from 2014-2023 with topographic and climatic factors,and leveraging the Google Earth Engine(GEE)platform,it achieves highprecision remote sensing zoning of Mollisol and non-Mollisol areas(overall accuracy:92.13%,Kappa coefficient:0.70).Subsequently,local Random Forest(RF)regression models were established within each zone for SOM prediction,with predictive variables optimized using recursive feature elimination(RFE).Results demonstrate that compared to FAOzone-based modeling,the RSZ-FSO-RF framework significantly enhances prediction accuracy(R2=0.619,RMSE=6.849 g kg-1).And further feature optimization continued to enhance model performance(R2=0.627,RMSE=6.781 g kg-1).Notably,optimal predictor combinations varied significantly across zones,with SOM spatial variability generally higher in non-Mollisol areas than in Mollisol regions.By organically integrating remote sensing zoning with feature selection,this framework effectively mitigates covariate redundancy while accounting for local heterogeneity,significantly enhancing the accuracy and stability of high-resolution SOM mapping.Furthermore,this study provides scientific basis and decision support for soil resource management and sustainable agricultural development under complex topographic conditions. 展开更多
关键词 soil organic matter remote sensing zoning feature selection algorithm random forest algorithm prediction
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Energy-efficient virtual machine consolidation algorithm in cloud data centers 认领 引用 被引量：3
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作者 ZHOU Zhou HU Zhi-gang +2 位作者 YU Jun-yang Jemal Abawajy Morshed Chowdhury 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第10期2331-2341,共11页
Cloud data centers consume a multitude of power leading to the problem of high energy consumption.In order to solve this problem,an energy-efficient virtual machine(VM)consolidation algorithm named PVDE(prediction-bas... Cloud data centers consume a multitude of power leading to the problem of high energy consumption.In order to solve this problem,an energy-efficient virtual machine(VM)consolidation algorithm named PVDE(prediction-based VM deployment algorithm for energy efficiency)is presented.The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center,based on the predicted host load,into four classes for the purpose of VMs migration.We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated.We performed extensive performance analysis of the proposed algorithms.Experimental results show that,in contrast to other energy-saving algorithms,the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA)violations. 展开更多
关键词 cloud computing energy consumption linear weighted method virtual machine consolidation virtual machine selection algorithm
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A Proximity-Aware BitTorrent System via Tracker-Side Biased Neighbor Selection 认领 引用 被引量：2
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作者 吕晓鹏 王文东 +1 位作者 龚向阳 马建 《China Communications》 SCIE CSCD 2011年第2期75-85,共11页
To address cross-ISP traffic problem caused by BitTorrent,we present our design and evaluation of a proximity-aware BitTorrent system. In our approach,clients generate global proximity-aware information by using landm... To address cross-ISP traffic problem caused by BitTorrent,we present our design and evaluation of a proximity-aware BitTorrent system. In our approach,clients generate global proximity-aware information by using landmark clustering;the tracker uses this proximity to maintain all peers in an orderly way and hands back a biased subset consisting of the peers who are physically closest to the requestor. Our approach requires no co-operation between P2P users and their Internet infra structures,such as ISPs or CDNs,no constantly path monitoring or probing their neighbors. The simulation results show that our approach can not only reduce unnecessary cross-ISP traffic,but also allow downloadsing fast. 展开更多
关键词 P2P ISP cross-ISP traffic neighbor selection algorithm
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Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System 认领 引用 被引量：6
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作者 LI Jie SHEN Shi-tuan 《Chinese Journal of Aeronautics》 SCIE EI CAS 2007年第3期223-229,共7页
Based on the fuzzy expert system fault diagnosis theory,the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis.The knowledge base is constructed by fault quer... Based on the fuzzy expert system fault diagnosis theory,the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis.The knowledge base is constructed by fault query network,of which the basic ele-ment is the test-diagnosis fault unit.Every underlying fault cause's membership degree is calculated using fuzzy product inference algorithm,and the fault answer best selection algorithm is developed,to which the deep knowledge is applied.Using some examples the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle. 展开更多
关键词 fuzzy expert system fault query network fault answer best selection algorithm fuzzy theory test-diagnosis fault unit
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A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm 认领 引用 被引量：1
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作者 Jinyin Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第6期1627-1637,共11页
Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,... Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms. 展开更多
关键词 Artificial immunity center optimized hybrid detect negative detector negative selection algorithm(NSA) radius adaptive
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Classification for Glass Bottles Based on Improved Selective Search Algorithm 认领 引用
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作者 Shuqiang Guo Baohai Yue +2 位作者 Manyang Gao Xinxin Zhou Bo Wang 《Computers, Materials & Continua》 SCIE EI 2020年第7期233-251,共19页
The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection.At present,the classification of recycled glass bottles is difficult due to the many differences in spe... The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection.At present,the classification of recycled glass bottles is difficult due to the many differences in specifications and models.This paper proposes a classification algorithm for glass bottles that is divided into two stages,namely the extraction of candidate regions and the classification of classifiers.In the candidate region extraction stage,aiming at the problem of the large time overhead caused by the use of the SIFT(scale-invariant feature transform)descriptor in SS(selective search),an improved feature of HLSN(Haar-like based on SPP-Net)is proposed.An integral graph is introduced to accelerate the process of forming an HBSN vector,which overcomes the problem of repeated texture feature calculation in overlapping regions by SS.In the classification stage,the improved SS algorithm is used to extract target regions.The target regions are merged using a non-maximum suppression algorithm according to the classification scores of the respective regions,and the merged regions are classified using the trained classifier.Experiments demonstrate that,compared with the original SS,the improved SS algorithm increases the calculation speed by 13.8%,and its classification accuracy is 89.4%.Additionally,the classification algorithm for glass bottles has a certain resistance to noise. 展开更多
关键词 Classification of glass bottle HBSN feature improved selective search algorithm LightGBM
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Object Recognition Algorithm Based on an Improved Convolutional Neural Network 认领 引用 被引量：1
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作者 Zheyi Fan Yu Song Wei Li 《Journal of Beijing Institute of Technology》 EI CAS 2020年第2期139-145,共7页
In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted... In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition. 展开更多
关键词 object recognition selective search algorithm improved convolutional neural network(CNN)
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