As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the...As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.展开更多
Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence(AI)systems,due to its advantages of adaptive learning and parallel computing.Meanwhile,biocomputing h...Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence(AI)systems,due to its advantages of adaptive learning and parallel computing.Meanwhile,biocomputing has seen ongoing development with the rise of synthetic biology,becoming the driving force for new generation semiconductor synthetic biology(SemiSynBio)technologies.DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks(ANNs),providing the possibility of executing neuromorphic computing at the molecular level.Herein,we briefly outline the principles of neuromorphic computing,describe the advances in DNA computing with a focus on synthetic neuromorphic computing,and summarize the major challenges and prospects for synthetic neuromorphic computing.We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing,which would be of widespread use in biocomputing,DNA storage,information security,and national defense.展开更多
A simple quasi-distributed fiber sensing interrogation system based on random speckles is proposed for weak fiber Bragg gratings(WFBGs) in this work. Without using tunable lasers or spectrometers, a piece of multimode...A simple quasi-distributed fiber sensing interrogation system based on random speckles is proposed for weak fiber Bragg gratings(WFBGs) in this work. Without using tunable lasers or spectrometers, a piece of multimode fiber is applied to interrogate the WFBGs relying on the wavelength sensitivity of speckles. Instead of the CCD sensor, an InGaAs quadrant detector serves as the receiver to capture the fast-changing speckle patterns. A supervised deep learning algorithm of the multilayer perceptron architecture is implemented to process speckle data and to interrogate temperature changes or dynamic strains. The proposed demodulation system is experimentally demonstrated for WFBGs with 0.1% reflectivity.The experimental results demonstrate that the new system is capable of measuring temperature change with an accuracy of 1℃ and achieving dynamic frequency of 100 Hz. This speckle-based interrogation system paves a new way for distributed WFBGs sensing with a simple design.展开更多
Soil formation and ecological rehabilitation is the most promising strategy to eliminate environmental risks of bauxite residue disposal areas. Its poor physical structure is nevertheless a major limitation to plant g...Soil formation and ecological rehabilitation is the most promising strategy to eliminate environmental risks of bauxite residue disposal areas. Its poor physical structure is nevertheless a major limitation to plant growth. Organic materials were demonstrated as effective ameliorants to improve the physical conditions of bauxite residue. In this study, three different organic materials including straw(5% W/W), humic acid(5% W/W), and humic acidacrylamide polymer(0.2% and 0.4%, W/W) were selected to evaluate their effects on physical conditions of bauxite residue pretreated by phosphogypsum following a 120-day incubation experiment. The proportion of 2-1 mm macro-aggregates, mean weight diameter(MWD) and geometric mean diameter(GWD) increased following organic materials addition, which indicated that organic materials could enhance aggregate stability. Compared with straw, and humic acid, humic acid-acrylamide polymer application had improved effects on the formation of water-stable aggregates in the residues. Furthermore, organic materials increased the total porosity, total pore volume and average pore diameter, and reduced the micropore content according to nitrogen gas adsorption(NA) and mercury intrusion porosimetry(MIP)analysis, whilst enhancing water retention of the residues based on water characteristic curves. Compared with traditional organic wastes, humic acid-acrylamide polymer could be regarded as a candidate according to the comprehensive consideration of the additive amount and the effects on physical conditions of bauxite residue. These findings could provide a novel application to both Ca-contained acid solid waste and high-molecular polymers on ecological rehabilitation at disposal areas.展开更多
The construction process for a similarity matrix has an important impact on the performance of spectral clustering algorithms. In this paper, we propose a random walk based approach to process the Gaussian kernel simi...The construction process for a similarity matrix has an important impact on the performance of spectral clustering algorithms. In this paper, we propose a random walk based approach to process the Gaussian kernel similarity matrix. In this method, the pair-wise similarity between two data points is not only related to the two points, but also related to their neighbors. As a result, the new similarity matrix is closer to the ideal matrix which can provide the best clustering result. We give a theoretical analysis of the similarity matrix and apply this similarity matrix to spectral clustering. We also propose a method to handle noisy items which may cause deterioration of clustering performance. Experimental results on real-world data sets show that the proposed spectral clustering algorithm significantly outperforms existing algorithms.展开更多
基金supported by the First Batch of Teaching Reform Projects of Zhejiang Higher Education“14th Five-Year Plan”(jg20220434)Special Scientific Research Project for Space Debris and Near-Earth Asteroid Defense(KJSP2020020202)+1 种基金Natural Science Foundation of Zhejiang Province(LGG19F030010)National Natural Science Foundation of China(61703183).
文摘As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.
文摘Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence(AI)systems,due to its advantages of adaptive learning and parallel computing.Meanwhile,biocomputing has seen ongoing development with the rise of synthetic biology,becoming the driving force for new generation semiconductor synthetic biology(SemiSynBio)technologies.DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks(ANNs),providing the possibility of executing neuromorphic computing at the molecular level.Herein,we briefly outline the principles of neuromorphic computing,describe the advances in DNA computing with a focus on synthetic neuromorphic computing,and summarize the major challenges and prospects for synthetic neuromorphic computing.We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing,which would be of widespread use in biocomputing,DNA storage,information security,and national defense.
基金supported by the National Key Research and Development Program of China(No.2021YFC3340400)Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province(No.MEDH202209)+1 种基金Natural Science Foundation of Zhejiang Province(No.LY22F050004)Zhejiang Xinmiao Talents Program(No.2022R409043).
文摘A simple quasi-distributed fiber sensing interrogation system based on random speckles is proposed for weak fiber Bragg gratings(WFBGs) in this work. Without using tunable lasers or spectrometers, a piece of multimode fiber is applied to interrogate the WFBGs relying on the wavelength sensitivity of speckles. Instead of the CCD sensor, an InGaAs quadrant detector serves as the receiver to capture the fast-changing speckle patterns. A supervised deep learning algorithm of the multilayer perceptron architecture is implemented to process speckle data and to interrogate temperature changes or dynamic strains. The proposed demodulation system is experimentally demonstrated for WFBGs with 0.1% reflectivity.The experimental results demonstrate that the new system is capable of measuring temperature change with an accuracy of 1℃ and achieving dynamic frequency of 100 Hz. This speckle-based interrogation system paves a new way for distributed WFBGs sensing with a simple design.
基金supported by the National Natural Science Foundation of China(No.42077379)。
文摘Soil formation and ecological rehabilitation is the most promising strategy to eliminate environmental risks of bauxite residue disposal areas. Its poor physical structure is nevertheless a major limitation to plant growth. Organic materials were demonstrated as effective ameliorants to improve the physical conditions of bauxite residue. In this study, three different organic materials including straw(5% W/W), humic acid(5% W/W), and humic acidacrylamide polymer(0.2% and 0.4%, W/W) were selected to evaluate their effects on physical conditions of bauxite residue pretreated by phosphogypsum following a 120-day incubation experiment. The proportion of 2-1 mm macro-aggregates, mean weight diameter(MWD) and geometric mean diameter(GWD) increased following organic materials addition, which indicated that organic materials could enhance aggregate stability. Compared with straw, and humic acid, humic acid-acrylamide polymer application had improved effects on the formation of water-stable aggregates in the residues. Furthermore, organic materials increased the total porosity, total pore volume and average pore diameter, and reduced the micropore content according to nitrogen gas adsorption(NA) and mercury intrusion porosimetry(MIP)analysis, whilst enhancing water retention of the residues based on water characteristic curves. Compared with traditional organic wastes, humic acid-acrylamide polymer could be regarded as a candidate according to the comprehensive consideration of the additive amount and the effects on physical conditions of bauxite residue. These findings could provide a novel application to both Ca-contained acid solid waste and high-molecular polymers on ecological rehabilitation at disposal areas.
文摘The construction process for a similarity matrix has an important impact on the performance of spectral clustering algorithms. In this paper, we propose a random walk based approach to process the Gaussian kernel similarity matrix. In this method, the pair-wise similarity between two data points is not only related to the two points, but also related to their neighbors. As a result, the new similarity matrix is closer to the ideal matrix which can provide the best clustering result. We give a theoretical analysis of the similarity matrix and apply this similarity matrix to spectral clustering. We also propose a method to handle noisy items which may cause deterioration of clustering performance. Experimental results on real-world data sets show that the proposed spectral clustering algorithm significantly outperforms existing algorithms.
