This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selectio...This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.展开更多
This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data s...This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data sampling methods,such as time bars,often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time.To address this,we propose an alternative approach using information-driven sampling methods,including the CUSUM filter,range bars,volume bars,and dollar bars,and evaluate their performance using tick-level data from January 2018 to June 2023.Additionally,we introduce the Triple Barrier method for target labeling,which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction.We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies.The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction,achieving consistently positive trading performance even after accounting for transaction costs.Moreover,our system enables making trading decisions at any point in time on the basis of market conditions,providing an advantage over traditional methods that rely on fixed time intervals.Furthermore,the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder,FEDformer,and Autoformer—alongside other deep learning architectures and classical machine learning models,revealing insights into their relative performance.展开更多
Smart manufacturing will transform the oil refining and petrochemical sector into a connected, information-driven environment. Using real-time and high-value support systems, smart manufacturing enables a coor-dinated...Smart manufacturing will transform the oil refining and petrochemical sector into a connected, information-driven environment. Using real-time and high-value support systems, smart manufacturing enables a coor-dinated and performance-oriented manufacturing enterprise that responds quickly to customer demandsand minimizes energy and material usage, while radically improving sustainability, productivity, innovation,and economic competitiveness. In this paper, several examples of the application of so-called ＂smart manu-facturing＂ for the petrochemical sector are demonstrated, such as the fault detection of a catalytic crackingunit driven by big data, advanced optimization for the planning and scheduling of oil refinery sites, andmore. Key scientific factors and challenges for the further smart manufacturing of chemical and petrochem-ical orocesses are identified.展开更多
Molecular line emissions are commonly used to trace the distribution and properties of molecular Interstella Medium.However,the emissions are heavily blended on the Galactic disk toward the inner Galaxy because of th ...Molecular line emissions are commonly used to trace the distribution and properties of molecular Interstella Medium.However,the emissions are heavily blended on the Galactic disk toward the inner Galaxy because of th relatively large line widths and the velocity overlaps of spiral arms.Structure identification methods based on voxe connectivity in Position-Position-Velocity(PPV)data cubes often produce unrealistically large structures,which i the“over-linking”problem.Therefore,identifying molecular cloud structures in these directions is not trivial.W propose a new method based on Gaussian decomposition and graph theory to solve the over-linking problem named InterStellar Medium Gaussian Component Clustering(ISMGCC).Using the Milky Way Imaging Scrol Painting(MWISP)^(13)CO(1-0)data in the range of 13°.5≤l≤14°.5,|b|≤0°.5,and-100≤V_(lsr)≤+200 km s^(-1),our method identified three hundred molecular gas structures with at least 16 pixels.These structures contain 92%of the total flux in the raw data cube and show single-peaked line profiles on more than 93%of their pixels.Th ISMGCC method could distinguish gas structures in crowded regions and retain most of the flux without globa data clipping or assumptions on the structure geometry,meanwhile,allowing multiple Gaussian components fo complicated line profiles.展开更多
Molecules reside broadly in the interstellar space and can be detected via spectroscopic observations.To date,more than 271 molecular species have been identified in interstellar medium or circumstellar envelopes.Mole...Molecules reside broadly in the interstellar space and can be detected via spectroscopic observations.To date,more than 271 molecular species have been identified in interstellar medium or circumstellar envelopes.Molecular spectroscopic parameters measured in laboratory make the identification of new species and derivation of physical parameters possible.These spectroscopic parameters are systematically collected into databases,two of the most commonly used being the CDMS and JPL databases.While new spectroscopic parameters are continuously measured/calculated and added to those databases,at any point in time it is the existing spectroscopic data that ultimately limits what molecules can possibly be identified in astronomical data.In this work,we conduct a meta-analysis of the CDMS and JPL databases.We show the statistics of transition frequencies and their uncertainties in these two databases,and discuss the line confusion problem under certain physical environments.We then assess the prospects of detecting molecules in common ISM environments using a few facilities that are expected to be conducting spectroscopic observations in the future.Results show that CSST/HSTDM and SKA1-mid have the potential to detect some complex organic molecules,or even amino acids,with reasonable assumptions about ISM environments.展开更多
The[O iii]λλ4960,5008 emission lines in the optical spectra of galaxies and quasars have been widely used to investigate the possible variation of the fine-structure constantαover cosmic time.In this work,we utiliz...The[O iii]λλ4960,5008 emission lines in the optical spectra of galaxies and quasars have been widely used to investigate the possible variation of the fine-structure constantαover cosmic time.In this work,we utilize the Large Sky Area Multi-object Fiber Spectroscopic Telescope(LAMOST)quasar survey,for the first time,to measure the relativeαvariationΔα/αin time through the[O iii]doublet method.From the LAMOST Data Release 9 quasar catalog,we refine a sample of 209 quasar spectra with strong and narrow[O iii]emission lines over a redshift range of 0<z<0.8.Analysis on all of the 209 spectra obtainsΔα/α=(0.5±3.7)×10^(−4),which suggests that there is no evidence of varyingαon the explored cosmological timescales.Assuming a linear variation,the mean rate of change inΔα/αis limited to be(−3.4±2.4)×10^(−13)yr^(−1)in the last 7.0 Gyr.While our LAMOST-based constraint on Δα/α is not competitive with those of the Sloan Digital Sky Survey(SDSS)quasar observations,our analysis serves to corroborate the results of SDSS with another independent survey.展开更多
文摘This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.
基金support of the University of Warsaw under’New Ideas 3B’competition in POB Ⅲ implemented under the’Excellence Initiative-Research University’Programme.
文摘This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data sampling methods,such as time bars,often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time.To address this,we propose an alternative approach using information-driven sampling methods,including the CUSUM filter,range bars,volume bars,and dollar bars,and evaluate their performance using tick-level data from January 2018 to June 2023.Additionally,we introduce the Triple Barrier method for target labeling,which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction.We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies.The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction,achieving consistently positive trading performance even after accounting for transaction costs.Moreover,our system enables making trading decisions at any point in time on the basis of market conditions,providing an advantage over traditional methods that rely on fixed time intervals.Furthermore,the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder,FEDformer,and Autoformer—alongside other deep learning architectures and classical machine learning models,revealing insights into their relative performance.
文摘Smart manufacturing will transform the oil refining and petrochemical sector into a connected, information-driven environment. Using real-time and high-value support systems, smart manufacturing enables a coor-dinated and performance-oriented manufacturing enterprise that responds quickly to customer demandsand minimizes energy and material usage, while radically improving sustainability, productivity, innovation,and economic competitiveness. In this paper, several examples of the application of so-called ＂smart manu-facturing＂ for the petrochemical sector are demonstrated, such as the fault detection of a catalytic crackingunit driven by big data, advanced optimization for the planning and scheduling of oil refinery sites, andmore. Key scientific factors and challenges for the further smart manufacturing of chemical and petrochem-ical orocesses are identified.
基金sponsored by the National Key R&D Program of China under grants 2023YFA1608000,2017YFA0402701the CAS Key Research Program of Frontier Sciences under grant QYZDJ-SSW-SLH047+1 种基金supported by the National Natural Science Foundation of China(NSFC,grant Nos.U2031202,12373030,and 11873093)the Natural Science Foundation of Jiangsu Province(grant No.BK20231509)。
文摘Molecular line emissions are commonly used to trace the distribution and properties of molecular Interstella Medium.However,the emissions are heavily blended on the Galactic disk toward the inner Galaxy because of th relatively large line widths and the velocity overlaps of spiral arms.Structure identification methods based on voxe connectivity in Position-Position-Velocity(PPV)data cubes often produce unrealistically large structures,which i the“over-linking”problem.Therefore,identifying molecular cloud structures in these directions is not trivial.W propose a new method based on Gaussian decomposition and graph theory to solve the over-linking problem named InterStellar Medium Gaussian Component Clustering(ISMGCC).Using the Milky Way Imaging Scrol Painting(MWISP)^(13)CO(1-0)data in the range of 13°.5≤l≤14°.5,|b|≤0°.5,and-100≤V_(lsr)≤+200 km s^(-1),our method identified three hundred molecular gas structures with at least 16 pixels.These structures contain 92%of the total flux in the raw data cube and show single-peaked line profiles on more than 93%of their pixels.Th ISMGCC method could distinguish gas structures in crowded regions and retain most of the flux without globa data clipping or assumptions on the structure geometry,meanwhile,allowing multiple Gaussian components fo complicated line profiles.
基金financially supported by the National Natural Science Foundation of China through grants 12041305 and 11873094by the China Manned Space Project。
文摘Molecules reside broadly in the interstellar space and can be detected via spectroscopic observations.To date,more than 271 molecular species have been identified in interstellar medium or circumstellar envelopes.Molecular spectroscopic parameters measured in laboratory make the identification of new species and derivation of physical parameters possible.These spectroscopic parameters are systematically collected into databases,two of the most commonly used being the CDMS and JPL databases.While new spectroscopic parameters are continuously measured/calculated and added to those databases,at any point in time it is the existing spectroscopic data that ultimately limits what molecules can possibly be identified in astronomical data.In this work,we conduct a meta-analysis of the CDMS and JPL databases.We show the statistics of transition frequencies and their uncertainties in these two databases,and discuss the line confusion problem under certain physical environments.We then assess the prospects of detecting molecules in common ISM environments using a few facilities that are expected to be conducting spectroscopic observations in the future.Results show that CSST/HSTDM and SKA1-mid have the potential to detect some complex organic molecules,or even amino acids,with reasonable assumptions about ISM environments.
基金supported by the National Natural Science Foundation of China(grant Nos.12422307,12373053,and 12321003)the Key Research Program of Frontier Sciences(grant No.ZDBS-LY-7014)+1 种基金Chinese Academy of Sciences,and the Natural Science Foundation of Jiangsu Province(grant No.BK20221562)supported by the Chinese Academy of Sciences President's International Fellowship Initiative(grant No.2023VMB0001)。
文摘The[O iii]λλ4960,5008 emission lines in the optical spectra of galaxies and quasars have been widely used to investigate the possible variation of the fine-structure constantαover cosmic time.In this work,we utilize the Large Sky Area Multi-object Fiber Spectroscopic Telescope(LAMOST)quasar survey,for the first time,to measure the relativeαvariationΔα/αin time through the[O iii]doublet method.From the LAMOST Data Release 9 quasar catalog,we refine a sample of 209 quasar spectra with strong and narrow[O iii]emission lines over a redshift range of 0<z<0.8.Analysis on all of the 209 spectra obtainsΔα/α=(0.5±3.7)×10^(−4),which suggests that there is no evidence of varyingαon the explored cosmological timescales.Assuming a linear variation,the mean rate of change inΔα/αis limited to be(−3.4±2.4)×10^(−13)yr^(−1)in the last 7.0 Gyr.While our LAMOST-based constraint on Δα/α is not competitive with those of the Sloan Digital Sky Survey(SDSS)quasar observations,our analysis serves to corroborate the results of SDSS with another independent survey.
