A highly sensitive LITES sensor based on a multi-pass cell with dense spot pattern and a novel quartz tuning fork with low frequency

A highly sensitive light-induced thermoelectric spectroscopy(LITES)sensor based on a multi-pass cell(MPC)with dense spot pattern and a novel quartz tuning fork(QTF)with low resonance frequency is reported in this manuscript.An erbi-um-doped fiber amplifier(EDFA)was employed to amplify the output optical power so that the signal level was further enhanced.The optical path length(OPL)and the ratio of optical path length to volume(RLV)of the MPC is 37.7 m and 13.8 cm^(-2),respectively.A commercial QTF and a self-designed trapezoidal-tip QTF with low frequency of 9461.83 Hz were used as the detectors of the sensor,respectively.The target gas selected to test the performance of the system was acetylene(C2H2).When the optical power was constant at 1000 mW,the minimum detection limit(MDL)of the C2H2-LITES sensor can be achieved 48.3 ppb when using the commercial QTF and 24.6 ppb when using the trapezoid-al-tip QTF.An improvement of the detection performance by a factor of 1.96 was achieved after replacing the commer-cial QTF with the trapezoidal-tip QTF.

Diffusion and reaction mechanism of limestone and quartz in fluxed iron ore pellet roasting process

The increase to the proportion of fluxed pellets in the blast furnace burden is a useful way to reduce the carbon emissions in the ironmaking process.In this study,the interaction between calcium carbonate and iron ore powder and the mineralization mechanism of fluxed iron ore pellet in the roasting process were investigated through diffusion couple experiments.Scanning electron microscopy with energy dispersive spectroscopy was used to study the elements’diffusion and phase transformation during the roasting process.The results indicated that limestone decomposed into calcium oxide,and magnetite was oxidized to hematite at the early stage of preheating.With the increase in roasting temperature,the diffusion rate of Fe and Ca was obviously accelerated,while the diffusion rate of Si was relatively slow.The order of magnitude of interdiffusion coefficient of Fe_(2)O_(3)-CaO diffusion couple was 10^(−10) m^(2)·s^(−1) at a roasting temperature of 1200℃for 9 h.Ca_(2)Fe_(2)O_(5) was the initial product in the Fe_(2)O_(3)-CaO-SiO_(2) diffusion interface,and then Ca_(2)Fe_(2)O_(5) continued to react with Fe_(2)O_(3) to form CaFe_(2)O_(4).With the expansion of the diffusion region,the sillico-ferrite of calcium liquid phase was produced due to the melting of SiO_(2) into CaFe_(2)O_(4),which can strengthen the consolidation of fluxed pellets.Furthermore,andradite would be formed around a small part of quartz particles,which is also conducive to the consolidation of fluxed pellets.In addition,the principle diagram of limestone and quartz diffusion reaction in the process of fluxed pellet roasting was discussed.

Improving creep strength of the fine-grained heat-affected zone of novel 9Cr martensitic heat-resistant steel via modified thermo-mechanical treatment

The infamous type Ⅳ failure within the fine-grained heat-affected zone (FGHAZ) in G115 steel weldments seriously threatens the safe operation of ultra-supercritical (USC) power plants.In this work,the traditional thermo-mechanical treatment was modified via the replacement of hot-rolling with cold rolling,i.e.,normalizing,cold rolling,and tempering (NCT),which was developed to improve the creep strength of the FGHAZ in G115 steel weldments.The NCT treatment effectively promoted the dissolution of preformed M_(23)C_(6)particles and relieved the boundary segregation of C and Cr during welding thermal cycling,which accelerated the dispersed reprecipitation of M_(23)C_(6) particles within the fresh reaustenitized grains during post-weld heat treatment.In addition,the precipitation of Cu-rich phases and MX particles was promoted evidently due to the deformation-induced dislocations.As a result,the interacting actions between precipitates,dislocations,and boundaries during creep were reinforced considerably.Following this strategy,the creep rupture life of the FGHAZ in G115 steel weldments can be prolonged by 18.6%,which can further push the application of G115 steel in USC power plants.

Etching of quartz crystals in liquid phase environment:A review

Quartz crystals are the most widely used material in resonant sensors,owing to their excellent piezoelectric and mechanical properties.With the development of portable and wearable devices,higher processing efficiency and geometrical precision are required.Wet etching has been proven to be the most efficient etching method for large-scale production of quartz devices,and many wet etching approaches have been developed over the years.However,until now,there has been no systematic review of quartz crystal etching in liquid phase environments.Therefore,this article provides a comprehensive review of the development of wet etching processes and the achievements of the latest research in thisfield,covering conventional wet etching,additive etching,laser-induced backside wet etching,electrochemical etching,and electrochemical discharge machining.For each technique,a brief overview of its characteristics is provided,associated problems are described,and possible solutions are discussed.This review should provide an essential reference and guidance for the future development of processing strategies for the manufacture of quartz crystal devices.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

Flume experiments to study fine-grain migration and its impact on slope stability

Fine grains migration is a primary cause of landslides and debris flows.This study investigates the effect of fine-grain migration on slope failure through flume experiments,focusing on the spatiotemporal characteristics and mechanisms of slope stability.A series of artificial rainfall flume experiments with varying rainfall intensities and slopes were conducted using soil samples collected from Wei Jia Gully.The experiments monitored pore-water pressure,grain migration,and failure sequences.Grain-size distribution parameters(μand Dc)were analyzed to understand the migration path and accumulation of fine grains.The experiments reveal that fine-grain migration significantly alters soil structure,leading to random blockage and interconnection of internal pore channels.These changes result in fluctuating pore-water pressure distributions and uneven fine-grain accumulation,critical factors in slope stability.Slope failures occur randomly and intermittently,influenced by fine-grain content in runoff and resulting pore-water pressure variations.This study highlights that fine-grain migration plays a vital role in slope stability,with significant implications for predicting and mitigating slope failures.The stochastic nature of fine-grain migration and its impact on soil properties should be incorporated into predictive models to enhance their accuracy and reliability.

Unveiling Nb-Ta mineralization processes:Insight from quartz textural and chemical characteristics in the Songshugang deposit,Jiangxi Province,South China

The Songshugang deposit is a large Ta-Nb deposit in South China,with Ta-Nb mineralization associated genetically with the granite and pegmatite.A diversity of quartz from topaz-albite granite,quartz-mica pegmatite,quartz-feldspar pegmatite,and quartz-fluorite pegmatite at Songshugang was studied by CL and LA-ICP-MS in order to constrain enrichment mechanisms of Nb and Ta and to find geochemical indicators of quartz for rare metal deposits.Cathodoluminescence image illuminates a canvas of complexity,the quartz from topaz-albite granite,quartz-mica pegmatite,quartz-feldspar pegmatite,and quartz-fluorite pegmatite,exhibits numerous dark CL streaks,patches,and a series of healed fractures.These textures suggest that the rocks were fractured because of deep crustal pressure,and underwent later hydrothermal metasomatism and quartz filling.The quartz from quartz-fluorite pegmatite present limited patches or fractures but distinct growth bands,indicating that the melt fluid composition during the formation of quartz at this stage varies greatly and is less aff ected by mechanical fragmentation.The LA-ICP-MS analysis of quartz shows that there is a positive correlation between Al and Li in the quartz from topaz-albite granite,quartz-mica pegmatite,quartz-feldspar pegmatite,to quartz-fluorite pegmatite,indicating that Al mainly enters the quartz lattice through charge compensation substitution mechanism with Li.However,our data deviate from the theoretical Li:Al mass ratio of~1:3.89 in quartz,indicating that there may be competition between H^(+)and Li in a water-rich magmatic environment.The quartz from topaz-albite granite is enriched in K and Na elements,and the quartz from quartz-fluorite pegmatite is enriched in fluorite with a low Ca content in quartz,further elucidating that these rocks were subjected to hydrothermal metasomatism.From topaz-albitite granite to quartz-fluorite pegmatite,Al,Li and Ge content and Al/Ti,Ge/Ti,Sb/Ti ratios in quartz gradually increased,but Ti content gradually decreased,reflecting the high evolution of magma,which can enrich rare metal elements.Based on the characteristics of quartz CL textures and trace elements in topaz-albite granite,quartz-mica pegmatite,quartz-feldspar pegmatite,and quartz-fluorite pegmatite,combined with the albitization and K-feldspathization of rocks,it is suggested that the Nb-Ta mineralization in Songshugang may be influenced by the combined action of magmatic crystallization differentiation and fluid metasomatism.By comparing the quartz in the Songshugang pluton with the quartz in the granite type and pegmatite type rare metal deposits recognized in the world,the Songshugang pegmatite share similarities with the LCT-type pegmatite.Combined with previous studies,the Ge/Ti>0.1 and Ti<10 ppm,as well as Al,Li,Ge,Sb,K,Na contents and Al/Ti,Sb/Ti ratios in quartz have the potential to be a powerful exploration marker for identifying granite-like pegmatitic Nb-Ta deposits in other places.

Trace elements in magmatic and hydrothermal quartz:Implications on the genesis of the Xingluokeng Tungsten Deposit,South China

The Xingluokeng deposit is the largest gran-ite-related tungsten deposit within the Wuyi metallogenic belt in South China.The Xingluokeng intrusion primarily consists of porphyritic biotite granite,biotite granite,andfine-grained granite.The deposit is represented by veinlet-disseminated mineralization with K-feldspathization and biotitization,alongside quartz-vein mineralization with gre-isenization and sericitization.This study investigates in-situ analyses of quartz compositions from both the intrusion and hydrothermal veinlets and veins.Trace element correlations indicate that trivalent Al^(3+)and Fe^(3+)replace Si^(4+)within the quartz lattice,with monovalent cations(such as Li^(+),Na^(+),and K^(+))primarily serving as charge compensators.Low Ge/Al ratios(<0.013)of quartz from granites suggest a mag-matic origin.The low Al/Ti and Ge/Ti ratios,accompanied by high Ti contents in quartz,suggest that the porphyritic biotite granite and biotite granite are characterized by rela-tively low levels of differentiation and high crystallization temperatures.In contrast,thefine-grained granite exhibits a higher degree of fractionation,lower crystallization tem-peratures,and a closer association with tungsten miner-alization.Ti contents in quartz from quartz veins indicate Qz-Ⅰformed at temperatures above 400°C,while Qz-Ⅱto Qz-Ⅴformed at temperatures below 350°C.Variations in different generations of quartz,as indicated by Al content and(Al+Fe)/(Li+Na+K)ratio,suggest that Qz-Ⅰprecipi-tated from a less acidicfluid with a stable pH,whereas Qz-Ⅱto Qz-Ⅴoriginated from a more acidicfluid with notable pH variations.Consequently,alkaline alteration and acidic alteration supplied the essential Ca and Fe for the precipita-tion of scheelite and wolframite,respectively,highlighting a critical mechanism in tungsten mineralization at the Xin-gluokeng deposit.

Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Synergistic strengthening mechanism of Ca^(2+)-sodium silicate to selective separation of feldspar and quartz

Inhibitors are important for flotation separation of quartz and feldspar.In this study,a novel combined inhibitor was used to separate quartz and feldspar in near-neutral pulp.Selective inhibition of the combined inhibitor was assessed by micro-flotation experiments.And a series of detection methods were used to detect differences in the surface properties of feldspars and quartz after flotation reagents and put forward the synergistic strengthening mechanism.The outcomes were pointed out that pre-mixing combined inhibitors were more effective than the addition of Ca^(2+)and SS in sequence under the optimal proportion of 1:5.A concentrate from artificial mixed minerals that was characterized by a high quartz grade and a high recovery was acquired,and was found to be 90.70wt% and 83.70%,respectively.It was demonstrated that the combined inhibitor selectively prevented the action of the collector and feldspar from Fourier-transform infrared(FT-IR)and adsorption capacity tests.The results of X-ray photoelectron spectroscopy(XPS)indicated that Ca^(2+)directly interacts with the surface of quartz to increase the adsorption of collectors.In contrast,the chemistry property of Al on the feldspar surface was altered by combined inhibitor due to Na^(+)and Ca^(2+)taking the place of K^(+),resulting in the composite inhibitor forms a hydrophilic structure,which prevents the adsorption of the collector on the surface of feldspar by interacting with the Al active site.The combination of Ca^(2+)and SS synergically strengthens the difference of collecting property between quartz and feldspar by collector,thus achieving the effect of efficient separation.A new strategy for flotation to separate quartz from feldspar in near-neutral pulp was provided.

Experimental Study on the Effect of Fine-Grained Soil Content on the Freezing Strength of Aeolian Sand-Cement Interface

In cold regions,understanding the freezing strength of the interface between soil and structure is crucial for designing frost-resistant foundations.To investigate how the content of cement powder in aeolian sand affects this strength,we conducted direct shear tests under various conditions such as different fine-grained soil content,normal stress,and initial moisture content of the soil.By analyzing parameters like soil properties,and volume of ice content,and using the Mohr-Coulomb strength theory to define interface strength,we aimed to indirectly measure the cementation strength of the interface.Our findings revealed that as the particle content increased,the interface stress-strain curves became noticeably stiffer.We also observed a positive linear relationship between freezing strength and silt content,while the initial moisture content of the soil did not significantly impact the strengthening effect of fine-grained soil on freezing strength.Moreover,we discovered that as the powder content increased,the force binding the ice to the interface decreased,while the friction angle at the interface increased.However,the cohesion force at the interface remained relatively unchanged.Overall,our analysis suggests that the increase in freezing strength due to fine-grained soil content is primarily due to the heightened friction between aeolian sand and the interface.

Novel polyhydroxy cationic collector N-(2,3-propanediol)-Ndodecylamine: Synthesis and flotation performance to hematite and quartz

To enhance the performance of traditional cationic collector,a novel polyhydroxy amine collector N-(2,3-Propanediol)-N-dodecylamine(PDDA)was designed by introducing one propylene glycol group into dodecylamine(DDA).It was prepared by a nucleophilic substitution reaction,which showed better solubility and hydrophobicity than DDA and was firstly employed as the collector for the separation of hematite and quartz.Flotation tests showed that PDDA had an excellent flotation performance and significantly better selectivity than DDA.In addition,the flotation performance and adsorption mechanism of PDDA on hematite and quartz surfaces were studied using Fourier transform infrared spectroscopy(FTIR),zeta potential and X-ray photoelectron spectroscopy(XPS)tests.These results demonstrated that the interaction between PDDA and the minerals’surfaces was mainly electrostatic adsorption and hydrogen bond,while PDDA tended to adsorb on the surfaces of quartz more than that of hematite.Performance optimization of amine collectors by introducing hydroxyl was also verified,which was of great meaning to the design,development,and application of the polyhydroxy cationic collector.In conclusion,PDDA could be used as a potential collector in the flotation separation of quartz and hematite.

Geochronology,Geochemistry and Petrogenesis of the Bangbule Quartz Porphyry:Implications for Metallogenesis

The Bangbule skarn lead-zinc(Pb-Zn)deposit(>1 Mt Zn+Pb)is located in the western Nyainqentanglha polymetallic metallogenetic belt,central Tibet.Lenticular orebodies are all hosted in skarn and developed in the contact zone between the quartz porphyry and carbonate strata of the mid Paleozoic Middle to Upper Chaguoluoma Formation as well as in carbonate and sandstone beds of the Upper Paleozoic Laga Formation.As a newly discovered skarn deposit,the geological background and metallogenesis of this deposit remain poorly understood.Detailed petrological,geochemical and geochronological data of the ore-related quartz porphyry,helps constrain the mineralization age and contributes to discussion on the ore genesis of the Bangbule deposit.Both endoskarn and exoskarn are identified in the Bangbule deposit.From quartz porphyry to carbonate formation,the exoskarn is zoned from proximal garnet skarn to distal pyroxene skarn.Zircon U-Pb dating results show that the quartz porphyry formed at 73.9±0.8 Ma.Geochemical analysis results show that the quartz porphyry has high contents of SiO_(2)(71.40–74.94 wt%)and K_(2)O+Na_(2)O(3.76–8.46 wt%)with A/CNK values of 0.69 to 1.06.Besides,the quartz porphyry is enriched in large ion lithophile elements(LILEs)and light rare earth elements(LREEs)and have lowεNd(t)(from-8.25 to-8.19)and high initial(^(87)Sr/^(86)Sr)i values(0.713611–0.714478).Major,trace elements and whole-rock F concentration analysis results from the endoskarn samples show higher TFe_(2)O_(3),MgO,CaO,Pb+Zn,W,Sn,Mo and F etc.,and lower alkalis(K_(2)O,Na_(2)O,Sr and Ba)than those of fresh quartz porphyry,indicating that the early ore-forming fluids were an Ca-Fe-F-enriched fluid.Massive ore in the proximal skarn might be related to the high F content in the magma,which lowered the solidus temperature of the quartz porphyry magma and caused a lower temperature of the ore-forming fluids,as well as facilitating the precipitation of sphalerite and galena.Based on the geochemical characteristics presented in this study,we propose that the ore-related quartz porphyry was formed by partial melting of crust materials with some juvenile crustal component input.The partial melting of the middle-upper crust after the initial enrichment of lead and zinc elements are important for the formation of Pb-Zn deposits.The case study of the Bangbule deposit has proven that there is still a crust-derived magmatic source region in the western segment of the central Lhasa terrane.Therefore,there is still great potential for Pb-Zn mineralization and Pb-Zn exploration.

Fine-grained gravity flow sedimentation and its influence on development of shale oil sweet sections in lacustrine basins in China

The geological conditions and processes of fine-grained gravity flow sedimentation in continental lacustrine basins in China are analyzed to construct the model of fine-grained gravity flow sedimentation in lacustrine basin,reveal the development laws of fine-grained deposits and source-reservoir,and identify the sweet sections of shale oil.The results show that fine-grained gravity flow is one of the important sedimentary processes in deep lake environment,and it can transport fine-grained clasts and organic matter in shallow water to deep lake,forming sweet sections and high-quality source rocks of shale oil.Fine-grained gravity flow deposits in deep waters of lacustrine basins in China are mainly fine-grained high-density flow,fine-grained turbidity flow(including surge-like turbidity flow and fine-grained hyperpycnal flow),fine-grained viscous flow(including fine-grained debris flow and mud flow),and fine-grained transitional flow deposits.The distribution of fine-grained gravity flow deposits in the warm and humid unbalanced lacustrine basins are controlled by lake-level fluctuation,flooding events,and lakebed paleogeomorphology.During the lake-level rise,fine-grained hyperpycnal flow caused by flooding formed fine-grained channel–levee–lobe system in the flat area of the deep lake.During the lake-level fall,the sublacustrine fan system represented by unconfined channel was developed in the flexural slope breaks and sedimentary slopes of depressed lacustrine basins,and in the steep slopes of faulted lacustrine basins;the sublacustrine fan system with confined or unconfined channel was developed on the gentle slopes and in axial direction of faulted lacustrine basins,with fine-grained gravity flow deposits possibly existing in the lower fan.Within the fourth-order sequences,transgression might lead to organic-rich shale and fine-grained hyperpycnal flow deposits,while regression might cause fine-grained high-density flow,surge-like turbidity flow,fine-grained debris flow,mud flow,and fine-grained transitional flow deposits.Since the Permian,in the shale strata of lacustrine basins in China,multiple transgression-regression cycles of fourth-order sequences have formed multiple source-reservoir assemblages.Diverse fine-grained gravity flow sedimentation processes have created sweet sections of thin siltstone consisting of fine-grained high-density flow,fine-grained hyperpycnal flow and surge-like turbidity flow deposits,sweet sections with interbeds of mudstone and siltstone formed by fine-grained transitional flows,and sweet sections of shale containing silty and muddy clasts and with horizontal bedding formed by fine-grained debris flow and mud flow.The model of fine-grained gravity flow sedimentation in lacustrine basin is significant for the scientific evaluation of sweet shale oil reservoir and organic-rich source rock.

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.

Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models

A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research.One hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets,respectively.The performance and accuracy of the models were measured by root mean square error(RMSE),coefficient of determination(R2),Pearson product-moment correlation coefficient(r),mean absolute error(MAE),variance accounted for(VAF),mean absolute percentage error(MAPE),weighted mean absolute percentage error(WMAPE),a20-index,index of scatter(IOS),and index of agreement(IOA).Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression(GPR)and model MD 101 in support vector machine(SVM)can achieve over 96%of accuracy in predicting the optimum moisture content(OMC)and maximum dry density(MDD)of soil,and outperformed other standalone models.The comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory(LSTM)predict OMC and MDD with higher accuracy than ANN models.However,the LSTM models outperformed the GPR models in predicting the compaction parameters.The sensitivity analysis illustrates that fine content(FC),specific gravity(SG),and liquid limit(LL)highly influence the prediction of compaction parameters.

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

Fine-Grained Action Recognition Based on Temporal Pyramid Excitation Network

Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.

Fine-Grained Features for Image Captioning

Image captioning involves two different major modalities(image and sentence)that convert a given image into a language that adheres to visual semantics.Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences.The Convolutional Neural Network(CNN)is often used to extract image features in image captioning,and the use of object detection networks to extract region features has achieved great success.However,the region features retrieved by this method are object-level and do not pay attention to fine-grained details because of the detection model’s limitation.We offer an approach to address this issue that more properly generates captions by fusing fine-grained features and region features.First,we extract fine-grained features using a panoramic segmentation algorithm.Second,we suggest two fusion methods and contrast their fusion outcomes.An X-linear Attention Network(X-LAN)serves as the foundation for both fusion methods.According to experimental findings on the COCO dataset,the two-branch fusion approach is superior.It is important to note that on the COCO Karpathy test split,CIDEr is increased up to 134.3%in comparison to the baseline,highlighting the potency and viability of our method.