Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-...Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.展开更多
This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model...This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.展开更多
In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence(AI)technology,enabling intelligent vision sensing and extensive ...In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence(AI)technology,enabling intelligent vision sensing and extensive data processing.These architectures must demonstrate potential advantages in terms of mass production and complementary metal oxide semiconductor compatibility.Here,we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network(ANN),employing a single neuro-inspired indium-g allium-zinc-oxide photo transistor(NIP)featuring an aluminum sensitization layer(ASL).By methodically adjusting the ASL coverage on IGZO phototransistors,a fast-switching response-type and a synaptic response-type of IGZO photo transistors are successfully developed.Notably,the fabricated NIP shows a remarkable retina-like photoinduced synaptic plasticity under wavelengths up to 635 nm,with over256-states,weight update nonlinearity below 0.1,and a dynamic range of 64.01.Owing to this technology,a 6×6 neuro-inspired optical image sensor array with the NIP can perform highly integrated sensing,memory,and preprocessing functions,including contrast enhancement,and handwritten digit image recognition.The demonstrated prototype highlights the potential for efficient hardware implementations in in-sensor AI technologies.展开更多
The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For ...The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).展开更多
As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the ...As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the preprocessing meth- ods reported in the literatures were not the same. In order to screen the best pre- processing method, this paper took three typical treatments to explore the effect of different preprocessing methods on soil microbial community functional diversity. The results showed that, method B＇s overall trend of AWCD values was better than A and C＇s. Method B＇s microbial utilization of six carbon sources was higher, and the result was relatively stable. The Simpson index, Shannon richness index and Car- bon source utilization richness index of the two treatments were B〉C〉A, while the Mclntosh index and Shannon evenness were not very stable, but the difference of variance analysis was not significant, and the method B was always with a smallest variance. Method B＇s principal component analysis was better than A and C＇s. In a word, the method using 250 r/min shaking for 30 minutes and cultivating at 28 ℃ was the best one, because it was simple, convenient, and with good repeatability.展开更多
This paper discusses some aspects of finite element computation,such as the automatic generation of finite element ,refinement of mesh,process of node density, distribution of load,optimum design and the drawing o...This paper discusses some aspects of finite element computation,such as the automatic generation of finite element ,refinement of mesh,process of node density, distribution of load,optimum design and the drawing of stress contour, and describes the developing process of software for a planar 8 node element.展开更多
Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps oft...Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.展开更多
目的现有数据浓缩后门攻击方法将含有触发器的中毒样本和干净样本浓缩为小的数据集,中毒数据中真实数据的强信号掩盖触发器的弱信号,并且未考虑将非目标类浓缩数据与中毒数据特征分离,非目标类浓缩数据残留触发器特征。因此,提出分离触...目的现有数据浓缩后门攻击方法将含有触发器的中毒样本和干净样本浓缩为小的数据集,中毒数据中真实数据的强信号掩盖触发器的弱信号,并且未考虑将非目标类浓缩数据与中毒数据特征分离,非目标类浓缩数据残留触发器特征。因此,提出分离触发器和多重对比的数据浓缩后门攻击。方法首先将触发器与真实数据进行分离。分离的触发器作为样本与真实数据并行嵌入浓缩数据,减少真实数据对触发器的干扰。然后,对分离的触发器进行优化,将触发器接近目标类真实数据的特征,提高触发器的嵌入效果,同时对触发器进行了分区放大预处理来增加触发器像素的数量,使其在优化过程获取大量的梯度用于指导学习。在数据浓缩阶段,通过多重对比将目标类浓缩数据与触发器特征投影在同一空间,将非目标类浓缩数据与触发器特征分离,进一步提高后门攻击的成功率。结果为了验证所提出方法的有效性,将所提出方法在FashionMNIST(Fashion Modified National Institute of Standards and Technology database)、CIFAR10(Canadian Institute for Advances Research’s ten categories dataset)、STL10(Stanford letter-10)、SVHN(street view house numbers)与其他4种方法进行对比实验。所提出的方法在5个数据集和6个不同的模型上均达到100%的攻击成功率,同时未降低干净样本在模型上的准确率。结论所提出的方法通过解决现有方法存在的问题,实现了性能的显著提高。本文方法具体代码见:https://github.com/tfuy/STMC。展开更多
基金supported by the Gas Hydrate R&D Organization and the Korea Institute of Geoscience and Mineral Resources(KIGAM)(GP2021-010)supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1C1C1004460)Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korean government(MOTIE)(20214000000500,Training Program of CCUS for Green Growth).
文摘Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.
基金2024 Anqing Normal University University-Level Key Project(ZK2024062D)。
文摘This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(Grant No.RS-2023-00256917)Samsung Display。
文摘In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence(AI)technology,enabling intelligent vision sensing and extensive data processing.These architectures must demonstrate potential advantages in terms of mass production and complementary metal oxide semiconductor compatibility.Here,we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network(ANN),employing a single neuro-inspired indium-g allium-zinc-oxide photo transistor(NIP)featuring an aluminum sensitization layer(ASL).By methodically adjusting the ASL coverage on IGZO phototransistors,a fast-switching response-type and a synaptic response-type of IGZO photo transistors are successfully developed.Notably,the fabricated NIP shows a remarkable retina-like photoinduced synaptic plasticity under wavelengths up to 635 nm,with over256-states,weight update nonlinearity below 0.1,and a dynamic range of 64.01.Owing to this technology,a 6×6 neuro-inspired optical image sensor array with the NIP can perform highly integrated sensing,memory,and preprocessing functions,including contrast enhancement,and handwritten digit image recognition.The demonstrated prototype highlights the potential for efficient hardware implementations in in-sensor AI technologies.
基金The support provided by the Natural Science Foundation of Hubei Province(Grant No.2021CFA081)the National Natural Science Foundation of China(Grant No.42277160)the fellowship of China Postdoctoral Science Foundation(Grant No.2022TQ0241)is gratefully acknowledged.
文摘The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).
基金Supported by National and International Scientific and Technological Cooperation Project"The application of Microbial Agents on Mining Reclamation and Ecological Recovery"(2011DFR31230)Key Project of Shanxi academy of Agricultural Science"The Research and Application of Bio-organic Fertilizer on Mining Reclamation and Soil Remediation"(2013zd12)Major Science and Technology Programs of Shanxi Province"Key Technology Research and Demonstration of mining waste land ecosystem Restoration and Reconstruction"(20121101009)~~
文摘As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the preprocessing meth- ods reported in the literatures were not the same. In order to screen the best pre- processing method, this paper took three typical treatments to explore the effect of different preprocessing methods on soil microbial community functional diversity. The results showed that, method B＇s overall trend of AWCD values was better than A and C＇s. Method B＇s microbial utilization of six carbon sources was higher, and the result was relatively stable. The Simpson index, Shannon richness index and Car- bon source utilization richness index of the two treatments were B〉C〉A, while the Mclntosh index and Shannon evenness were not very stable, but the difference of variance analysis was not significant, and the method B was always with a smallest variance. Method B＇s principal component analysis was better than A and C＇s. In a word, the method using 250 r/min shaking for 30 minutes and cultivating at 28 ℃ was the best one, because it was simple, convenient, and with good repeatability.
文摘This paper discusses some aspects of finite element computation,such as the automatic generation of finite element ,refinement of mesh,process of node density, distribution of load,optimum design and the drawing of stress contour, and describes the developing process of software for a planar 8 node element.
基金National Natural Science Foundation of China(62402020,62303022)Beijing Nova Program(20240484720)+1 种基金Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions(BPHR202203043)BTBU Digital Business Platform Project byBMEC.
文摘Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.
文摘目的现有数据浓缩后门攻击方法将含有触发器的中毒样本和干净样本浓缩为小的数据集,中毒数据中真实数据的强信号掩盖触发器的弱信号,并且未考虑将非目标类浓缩数据与中毒数据特征分离,非目标类浓缩数据残留触发器特征。因此,提出分离触发器和多重对比的数据浓缩后门攻击。方法首先将触发器与真实数据进行分离。分离的触发器作为样本与真实数据并行嵌入浓缩数据,减少真实数据对触发器的干扰。然后,对分离的触发器进行优化,将触发器接近目标类真实数据的特征,提高触发器的嵌入效果,同时对触发器进行了分区放大预处理来增加触发器像素的数量,使其在优化过程获取大量的梯度用于指导学习。在数据浓缩阶段,通过多重对比将目标类浓缩数据与触发器特征投影在同一空间,将非目标类浓缩数据与触发器特征分离,进一步提高后门攻击的成功率。结果为了验证所提出方法的有效性,将所提出方法在FashionMNIST(Fashion Modified National Institute of Standards and Technology database)、CIFAR10(Canadian Institute for Advances Research’s ten categories dataset)、STL10(Stanford letter-10)、SVHN(street view house numbers)与其他4种方法进行对比实验。所提出的方法在5个数据集和6个不同的模型上均达到100%的攻击成功率,同时未降低干净样本在模型上的准确率。结论所提出的方法通过解决现有方法存在的问题,实现了性能的显著提高。本文方法具体代码见:https://github.com/tfuy/STMC。
文摘高通量分析(High-throughput analysis,HTA)在分析测试、材料科学、药物发现及生命科学等领域发挥着关键作用。随着HTA技术的广泛应用,产生了海量、高维且结构复杂的实验数据,如何从中高效提取关键信息和重要特征已成为当前研究的重要挑战。然而,传统分析方法在处理大规模且高维的高通量数据集时往往能力有限,难以深入挖掘深层信息和重要特征之间的复杂关系。人工智能(AI)技术凭借其高效数据处理、深度信息挖掘等优点,目前成为实验室高通量分析的重要解决方案。因此,本文系统梳理了高通量分析中常用的AI核心技术,重点阐述了高通量数据分析中的预处理(包括智能化预处理平台建设)、特征提取、特征选择等方法的研究进展,旨在处理高通量数据分析中常见的标准化、批次效应和高维度问题;延伸总结了机器学习(Machine learning,ML)和大语言模型(Large language model,LLM)对高通量分析的应用效果,展现了其在智能设计预测与自主实验开展等方面的应用潜力;最后探讨了高通量数据分析仍面临的数据规模、样本标注、多模态数据融合等瓶颈问题,并对未来发展提出了对策和建议,为光谱分析、材料分析等多领域高通量数据分析的研究提供支持和参考。
