Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate trackin...Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.展开更多
Two-dimensional(2D)WSe_(2)has received increasing attention due to its unique optical properties and bipolar behavior.Several WSe_(2)-based heterojunctions exhibit bidirectional rectification characteristics,but most ...Two-dimensional(2D)WSe_(2)has received increasing attention due to its unique optical properties and bipolar behavior.Several WSe_(2)-based heterojunctions exhibit bidirectional rectification characteristics,but most devices have a lower rectification ratio.In this work,the Bi_(2)O_(2)Se/WSe_(2)heterojunction prepared by us has a typeⅡband alignment,which can vastly suppress the channel current through the interface barrier so that the Bi_(2)O_(2)Se/WSe_(2)heterojunction device has a large rectification ratio of about 10^(5).Meanwhile,under different gate voltage modulation,the current on/off ratio of the device changes by nearly five orders of magnitude,and the maximum current on/off ratio is expected to be achieved 106.The photocurrent measurement reveals the behavior of recombination and space charge confinement,further verifying the bidirectional rectification behavior of heterojunctions,and it also exhibits excellent performance in light response.In the future,Bi_(2)O_(2)Se/WSe_(2)heterojunction field-effect transistors have great potential to reduce the volume of integrated circuits as a bidirectional controlled switching device.展开更多
Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately ...Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.展开更多
The water snail Pomacea canaliculata retracts the discoidal and multi-layered operculum to protect the soft body from being attacked by predators,and releases it when threats lifted.However,the duration of the opercul...The water snail Pomacea canaliculata retracts the discoidal and multi-layered operculum to protect the soft body from being attacked by predators,and releases it when threats lifted.However,the duration of the operculum retraction is usually less than that of the operculum protraction.In this paper,we elucidate the biological compliant mechanism of the operculum.By using confocal laser scanning microscopy,we find that the operculum has compliant sandwiched layers between hard layers.The layered structure results in a compliant mechanism with a bidirectional stiffness for the locking and unlocking processes of the operculum.A mathematical model is derived to rationalize the bidirectional stiffness mechanism of the operculum.In addition,we carry out the experiments on the locking and unlocking processes.The experimental results show that the locking tension is about two-fifths of the unlocking tension of the operculum.Moreover,based on the mechanical properties of the operculum with the layered structure,we designed an operculum-inspired structure,which may have a variety of potential applications in combined driving patterns.展开更多
Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any shortcomings.According ...Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any shortcomings.According to the literature,social networks and particularly Twitter are effective platforms for gathering public opinions.Moreover,recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors,including healthcare.The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus.The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022.The tweets were labeled according to sentiment(positive or negative)and sector(public or private),and thereby the Hospital Patient Experiences in Saudi Arabia(HoPE-SA)dataset was produced.A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors.The authors trained five models to distinguish sentiments in tweets automatically with the following schemes:a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture,using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers(BERT),Multi-dialect Arabic BERT(MAR-BERT),and multilingual BERT(mBERT),as well as a pretrained word2vec model with a support vector machine classifier.This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT.The deep-learning fine-tuned MARBERT-based model—the authors’best-performing model—achieved accuracy,micro-F1,and macro-F1 scores of 98.71%,98.73%,and 98.63%,respectively.展开更多
A perfect bidirectional broadband visible light absorber composed of titanium nitride and tungsten nanodisk arrays is proposed.The average absorption of the absorber exceeds 89%at 400 nm–800 nm when light is normally...A perfect bidirectional broadband visible light absorber composed of titanium nitride and tungsten nanodisk arrays is proposed.The average absorption of the absorber exceeds 89%at 400 nm–800 nm when light is normally incident on the front-side.Illumination from the opposite direction(back-side)results in absorption of more than 75%.Through the theoretical analysis of the electric and magnetic fields,the physical mechanism of the broadband perfect absorption is attributed to the synergy of localized surface plasmons,propagating surface plasmons,and plasmonic resonant cavity modes.Furthermore,the absorber also exhibits excellent polarization-independence performance and a high angular tolerance of~30°for both front-and back-side incidence.The designed bidirectional broadband visible light absorber here has wide application prospects in the fields of solar cells and ink-free printing.展开更多
There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement an...There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.展开更多
The bidirectional subduction system,island arc magmatic activities,and thermal structure of the forearc basin in the Molucca Sea are taken into consideration in this study.The active volcanic arcs on both sides of the...The bidirectional subduction system,island arc magmatic activities,and thermal structure of the forearc basin in the Molucca Sea are taken into consideration in this study.The active volcanic arcs on both sides of the bidirectional subduction zone in the Molucca Sea are undergoing arc-arc collisions.We applied a finite element thermal simulation method to reconstruct the thermal evolution history of the Molucca Sea Plate based on geophysical data.Then,we analyzed the thermodynamic characteristics of island arc volcanism on both sides of the bidirectional subduction zone.The results showed that at 10Myr,the oceanic ridge of the Molucca Sea Plate was asymmetrically biased to the west,causing this bidirectional subduction to be deeper in the west than in the east.Furthermore,the oceanic ridge subducted under the Sangihe arc at 5.5Myr,causing intermittent cessation of volcanic activities.Due to the convergence of bidirectional subduction,the geothermal gradient in the top 3km depth of the forearc area between the Sangihe and Halmahera arcs decreased from about 60℃km^(−1) at 4Myr to about 38℃km^(−1) today.Finally,within the 45–100 km depth range of the sliding surface of the subduction,anomalously high-temperature zones formed due to shear friction during the bidirectional subduction.展开更多
As a novel electric demulsification method,bidirectional pulsed electric field(BPEF)was employed to demulsify the surfactant stabilized oil-in-water(SSO/W)emulsion for oil/water separation in this work.The demulsifica...As a novel electric demulsification method,bidirectional pulsed electric field(BPEF)was employed to demulsify the surfactant stabilized oil-in-water(SSO/W)emulsion for oil/water separation in this work.The demulsification behavior,characteristics,and stages under BPEF were explored.It was discovered that BPEF drove SSO/W emulsion to move and form vortexes,during which the oil droplets aggregated and accumulated to generate an oil droplet layer(ODL).ODL subsequently transformed into a continuous oil layer(COL)leading to the demulsification and separation of SSO/W emulsion.The conversion rate of ODL to COL was defined and used to evaluate the demulsification process and reflect the coalescence ability and transformation efficiency of dispersed oil droplets into COL.Furthermore,the effects of BPEF voltage,frequency,duty cycle,ratio of pulse output time,and surfactant type and content on the demulsification performance were examined.The optimal values of BPEF parameters for demulsification operation were 400 V,25 Hz,50%,and 4:1.O/W emulsion containing anionic surfactant was apt to be demulsified by BPEF,nonionic surfactant took the second place and cationic surfactant was the most difficult.A high surfactant content was not conducive to the BPEF demulsification.This work is anticipated to provide useful guidance for oil/water separation and oil recovery from actual emulsified oily wastewater by BPEF.展开更多
Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced...Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.展开更多
3D models are essential in virtual reality,game development,architecture design,engineering drawing,medicine,and more.Compared to digital images,3D models can provide more realistic visual effects.In recent years,sign...3D models are essential in virtual reality,game development,architecture design,engineering drawing,medicine,and more.Compared to digital images,3D models can provide more realistic visual effects.In recent years,significant progress has been made in the field of digital image encryption,and researchers have developed new algorithms that are more secure and efficient.However,there needs to be more research on 3D model encryption.This paper proposes a new 3D model encryption algorithm,called the 1D map with sin and logistic coupling(1D-MWSLC),because existing digital image encryption algorithms cannot be directly applied to 3D models.Firstly,this paper introduce 1D-MWSLC,which has a wider range of parameters compared to traditional 1D chaotic systems.When the parameter exceeds a specific range,the chaotic phenomenon does not weaken.Additionally,1D-MWSLC has two control parameters,which increases the cryptosystem’s parameter space.Next,1D-MWSLC generates keystreams for confusion and diffusion.In the confusion stage,this paper use random confusion,and the keystream generates an index matrix that confuses the integer and decimal parts of the 3D model simultaneously.In the diffusion stage,this paper use parallel bidirectional diffusion to simultaneously diffuse the integer parts of the three coordinates of the 3D model.Finally,this paper verify the proposed algorithm through statistical analysis,and experimental results demonstrate that the proposed 3D model encryption algorithm has robust security.展开更多
Objective:To explore the bidirectional mechanism of Haizao Yuhu decoction(HYD)on goiter and drug-induced liver injury(DILI)based on machine learning and data mining.Methods:Firstly,compounds of HYD were selected from ...Objective:To explore the bidirectional mechanism of Haizao Yuhu decoction(HYD)on goiter and drug-induced liver injury(DILI)based on machine learning and data mining.Methods:Firstly,compounds of HYD were selected from the TCMSP,TCMIP,and BATMAN databases,then the TCMSP was used to acquire the targets of compounds.Targets of goiter and DILI were obtained from the GeneCards database.Secondly,common targets of“HYD-goiter”and“HYD-DILI”as well as related compounds were used to construct the networks and perform Random Walk with Restart(RWR)algorithm and network stability test.Finally,core targets in the“HYD-goiter”and“HYD-DILI”networks were used for molecular docking with core compounds and searched for validation on PubChem,and the relevant experimental data of our group were quoted to verify the analysis results.Results:There were 22 intersection targets of HYD and DILI,326 of HYD and goiter.RWR analysis showed that MAPK1,MAPK3,AKT1,etc.may be the core targets of HYD treating goiter,RELA,TNF,IL4,etc.may be the core targets of the bidirectional effect,and eckol may be the core compound in bidirectional effect.Network stability test indicated that the HYD had a high stability on treating goiter and playing a bidirectional effect.The core targets and core compounds docked well,and 37.3%of targets had been confirmed by experiments and 29.8%core targets had been confirmed.Our previous experimental result confirmed that the HYD could treat goiter usefully by reducing the expression levels of PI3K and AKT mRNA,and down-regulating the expression of Cyclin D1 and Bcl-2 mRNA.Conclusion:HYD containing“sargassum-liquorice”combination may have a bidirectional effect on treating goiter and causing DILI.We offered a new way for more explorations on the therapeutic and toxic bidirectional mechanisms based on machine learning and data mining.展开更多
In this study,dynamic responses of two buildings connected by viscoelastic dampers under bidirectional excitations are extensively investigated.The two buildings are a 10-story building and a 16-story building,with th...In this study,dynamic responses of two buildings connected by viscoelastic dampers under bidirectional excitations are extensively investigated.The two buildings are a 10-story building and a 16-story building,with the shorter building on the left.Viscoelastic dampers are installed at all fl oors of the shorter building.Equations of motion are formulated using a fractional derivative model to represent the viscoelastic dampers.Three cases are considered with mass eccentricities at 0,10% and-10% with respect to the dimensions of the buildings.The responses of the buildings are numerically predicted at different damper properties.The simulation results indicated that the maximum horizontal responses of the buildings without eccentricities are signifi cantly mitigated.However,torsional effects are adversely increased.For asymmetric buildings,the effectiveness of the connecting dampers is affected by building eccentricities.As a result,mass eccentricities must be taken into account in damper selection.When compared with vibrations induced by unidirectional excitations,bidirectional excitations can increase the responses of coupled asymmetric buildings.In addition,installing dampers only at the top fl oor of the shorter building may cause a sudden change in lateral stiffness of the taller building.Consequently,the story shear envelopes of the taller building are changed.展开更多
Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented...Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.展开更多
With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service respons...With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.展开更多
Modelling of bidirectional full bridge DC-DC converter as one of the most applicable converters has received significant attention. Mathematical modelling reduces the simulation time in comparison with detailed circui...Modelling of bidirectional full bridge DC-DC converter as one of the most applicable converters has received significant attention. Mathematical modelling reduces the simulation time in comparison with detailed circuit response;moreover it is convenient for controller design purpose. Due to simple and effective methodology, average state space is the most common method among the modelling methods. In this paper a bidirectional full bridge converter is modelled by average state space and for each mode of operations a controller is designed. Attained mathematical model results are in a close agreement with detailed circuit simulation.展开更多
Nowadays,Internet has become an indispensable part of daily life and is used in many fields.Due to the large amount of Internet traffic,computers are subject to various security threats,which may cause serious economi...Nowadays,Internet has become an indispensable part of daily life and is used in many fields.Due to the large amount of Internet traffic,computers are subject to various security threats,which may cause serious economic losses and even endanger national security.It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data.As machine learning technology matures,deep learning is widely used in various industries.Combining deep learning with network security and intrusion detection is the current trend.In this paper,the problem of data classification in intrusion detection system is studied.We propose an intrusion detection model based on stack bidirectional long short-term memory(LSTM),introduce stack bidirectional LSTM into the field of intrusion detection and apply it to the intrusion detection.In order to determine the appropriate parameters and structure of stack bidirectional LSTM network,we have carried out experiments on various network structures and parameters and analyzed the experimental results.The classic KDD Cup’1999 dataset was selected for experiments so that we can obtain convincing and comparable results.Experimental results derived from the KDD Cup’1999 dataset show that the network with three hidden layers containing 80 LSTM cells is superior to other algorithms in computational cost and detection performance due to stack bidirectional LSTM model’s ability to review time and correlate with connected records continuously.The experiment shows the effectiveness of stack bidirectional LSTM network in intrusion detection.展开更多
Bi Directional体制是德宇航在2012年提出的一种通过单星单次飞行实现秒级重访的新体制,基本原理是利用相控阵电扫描方式生成双波束天线方向图,同时发射两个脉冲照射方位向前后两块成像区域,将同时接收到的脉冲在多普勒域进行带通滤波分...Bi Directional体制是德宇航在2012年提出的一种通过单星单次飞行实现秒级重访的新体制,基本原理是利用相控阵电扫描方式生成双波束天线方向图,同时发射两个脉冲照射方位向前后两块成像区域,将同时接收到的脉冲在多普勒域进行带通滤波分离,并分别成像。该文介绍了一种改进的基于Bi Directional体制的多发单收(Multi Input Single Output,MISO)SAR系统,将传统的双波束同发同收改进为分时先后发射和同时接收,利用发射时较优的方向图抑制方位模糊(AASR),获得了较好的效果。文中给出了频谱分离效果、AASR分析和系统设计流程,给出了改进前后的点目标1维和2维成像结果对比,证明了该改进的有效性,最后给出了Bi Directional体制与其它几种单星短时重访体制的对比结果。展开更多
The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot sprea...The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot spread uniformly, since the Pareto front shows different features, such as concave and convex. To improve the distribution uniformity of non-dominated solutions, a bidirectional decomposition based approach that constructs two search directions is proposed to provide a uniform distribution no matter what features problems have. Since two populations along two search directions show differently on diversity and convergence, an adaptive neighborhood selection approach is presented to choose suitable parents for the offspring generation. In order to avoid the problem of the shrinking search region caused by the close distance of the ideal and nadir points, a reference point update approach is presented. The performance of the proposed algorithm is validated with four state-of-the-art algorithms. Experimental results demonstrate the superiority of the proposed algorithm on all considered test problems.展开更多
In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by...In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.展开更多
基金the National Natural Science Foundation of China(No.52275062)and(No.52075262).
文摘Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.
基金This work was supported by the National Natural Science Foundation of China(61704054,92161115,62374099,and 62022047)the Fundamental Research Funds for the Central Universities(JB2020MS042 and JB2019MS051).
文摘Two-dimensional(2D)WSe_(2)has received increasing attention due to its unique optical properties and bipolar behavior.Several WSe_(2)-based heterojunctions exhibit bidirectional rectification characteristics,but most devices have a lower rectification ratio.In this work,the Bi_(2)O_(2)Se/WSe_(2)heterojunction prepared by us has a typeⅡband alignment,which can vastly suppress the channel current through the interface barrier so that the Bi_(2)O_(2)Se/WSe_(2)heterojunction device has a large rectification ratio of about 10^(5).Meanwhile,under different gate voltage modulation,the current on/off ratio of the device changes by nearly five orders of magnitude,and the maximum current on/off ratio is expected to be achieved 106.The photocurrent measurement reveals the behavior of recombination and space charge confinement,further verifying the bidirectional rectification behavior of heterojunctions,and it also exhibits excellent performance in light response.In the future,Bi_(2)O_(2)Se/WSe_(2)heterojunction field-effect transistors have great potential to reduce the volume of integrated circuits as a bidirectional controlled switching device.
基金This work was supported in part by the National Key R&D Program of China 2021YFE0110500in part by the National Natural Science Foundation of China under Grant 62062021in part by the Guiyang Scientific Plan Project[2023]48-11.
文摘Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.
基金supported by National Natural Science Foundation of China(Grant No.52275298,and No.51905556)Pandeng Plan of Guangdong Province(Grant No.52910001,and No.11220004)Shenzhen Science and Technology Program(Grant No.GXWD2021B03,No.20220817165030002,and No.ZDSYS20210623091808026).
文摘The water snail Pomacea canaliculata retracts the discoidal and multi-layered operculum to protect the soft body from being attacked by predators,and releases it when threats lifted.However,the duration of the operculum retraction is usually less than that of the operculum protraction.In this paper,we elucidate the biological compliant mechanism of the operculum.By using confocal laser scanning microscopy,we find that the operculum has compliant sandwiched layers between hard layers.The layered structure results in a compliant mechanism with a bidirectional stiffness for the locking and unlocking processes of the operculum.A mathematical model is derived to rationalize the bidirectional stiffness mechanism of the operculum.In addition,we carry out the experiments on the locking and unlocking processes.The experimental results show that the locking tension is about two-fifths of the unlocking tension of the operculum.Moreover,based on the mechanical properties of the operculum with the layered structure,we designed an operculum-inspired structure,which may have a variety of potential applications in combined driving patterns.
文摘Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any shortcomings.According to the literature,social networks and particularly Twitter are effective platforms for gathering public opinions.Moreover,recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors,including healthcare.The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus.The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022.The tweets were labeled according to sentiment(positive or negative)and sector(public or private),and thereby the Hospital Patient Experiences in Saudi Arabia(HoPE-SA)dataset was produced.A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors.The authors trained five models to distinguish sentiments in tweets automatically with the following schemes:a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture,using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers(BERT),Multi-dialect Arabic BERT(MAR-BERT),and multilingual BERT(mBERT),as well as a pretrained word2vec model with a support vector machine classifier.This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT.The deep-learning fine-tuned MARBERT-based model—the authors’best-performing model—achieved accuracy,micro-F1,and macro-F1 scores of 98.71%,98.73%,and 98.63%,respectively.
基金the National Key Research and Development Program(Grant No.2022YFB2804602)Shanghai Pujiang Program(Grant No.21PJD048).
文摘A perfect bidirectional broadband visible light absorber composed of titanium nitride and tungsten nanodisk arrays is proposed.The average absorption of the absorber exceeds 89%at 400 nm–800 nm when light is normally incident on the front-side.Illumination from the opposite direction(back-side)results in absorption of more than 75%.Through the theoretical analysis of the electric and magnetic fields,the physical mechanism of the broadband perfect absorption is attributed to the synergy of localized surface plasmons,propagating surface plasmons,and plasmonic resonant cavity modes.Furthermore,the absorber also exhibits excellent polarization-independence performance and a high angular tolerance of~30°for both front-and back-side incidence.The designed bidirectional broadband visible light absorber here has wide application prospects in the fields of solar cells and ink-free printing.
文摘There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR2021MD069)the Strategic Pioneer Science and Technology Special Project of the Chinese Academy of Sciences(No.XDB42020104)+1 种基金the National Natural Science Foundation of China(No.42176052)the Project of Introducing and Cultivating Young Talents in the Universities of Shandong Province(No.LUJIAOKEHAN-2021-51).
文摘The bidirectional subduction system,island arc magmatic activities,and thermal structure of the forearc basin in the Molucca Sea are taken into consideration in this study.The active volcanic arcs on both sides of the bidirectional subduction zone in the Molucca Sea are undergoing arc-arc collisions.We applied a finite element thermal simulation method to reconstruct the thermal evolution history of the Molucca Sea Plate based on geophysical data.Then,we analyzed the thermodynamic characteristics of island arc volcanism on both sides of the bidirectional subduction zone.The results showed that at 10Myr,the oceanic ridge of the Molucca Sea Plate was asymmetrically biased to the west,causing this bidirectional subduction to be deeper in the west than in the east.Furthermore,the oceanic ridge subducted under the Sangihe arc at 5.5Myr,causing intermittent cessation of volcanic activities.Due to the convergence of bidirectional subduction,the geothermal gradient in the top 3km depth of the forearc area between the Sangihe and Halmahera arcs decreased from about 60℃km^(−1) at 4Myr to about 38℃km^(−1) today.Finally,within the 45–100 km depth range of the sliding surface of the subduction,anomalously high-temperature zones formed due to shear friction during the bidirectional subduction.
基金Scientific Platform Project of the Ministry of Education(fykf201907)the Postdoctoral Science Foundation Project of the Natural Science Foundation of Chongqing Municipality(cstc2021jcyjbshX0194)+3 种基金Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202100820 and KJZD-K201900804)Science and Technology Innovation Project of the Construction of the Chengdu-Chongqing Economic Circle of Chongqing Municipal Education Commission(KJCX2020036)Scientific Research Project of Chongqing Technology and Business University(2152016 and 2056006)Chongqing Technical Innovation and Application Project(cstc2019jscx-msxmX0275).
文摘As a novel electric demulsification method,bidirectional pulsed electric field(BPEF)was employed to demulsify the surfactant stabilized oil-in-water(SSO/W)emulsion for oil/water separation in this work.The demulsification behavior,characteristics,and stages under BPEF were explored.It was discovered that BPEF drove SSO/W emulsion to move and form vortexes,during which the oil droplets aggregated and accumulated to generate an oil droplet layer(ODL).ODL subsequently transformed into a continuous oil layer(COL)leading to the demulsification and separation of SSO/W emulsion.The conversion rate of ODL to COL was defined and used to evaluate the demulsification process and reflect the coalescence ability and transformation efficiency of dispersed oil droplets into COL.Furthermore,the effects of BPEF voltage,frequency,duty cycle,ratio of pulse output time,and surfactant type and content on the demulsification performance were examined.The optimal values of BPEF parameters for demulsification operation were 400 V,25 Hz,50%,and 4:1.O/W emulsion containing anionic surfactant was apt to be demulsified by BPEF,nonionic surfactant took the second place and cationic surfactant was the most difficult.A high surfactant content was not conducive to the BPEF demulsification.This work is anticipated to provide useful guidance for oil/water separation and oil recovery from actual emulsified oily wastewater by BPEF.
基金supported by National Natural Science Foundation of China(Grant Nos.52279137,52009090).
文摘Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.
基金Funds for New Generation Information Technology of the Industry-UniversityResearch Innovation Foundation of China University (No.2020ITA03022).
文摘3D models are essential in virtual reality,game development,architecture design,engineering drawing,medicine,and more.Compared to digital images,3D models can provide more realistic visual effects.In recent years,significant progress has been made in the field of digital image encryption,and researchers have developed new algorithms that are more secure and efficient.However,there needs to be more research on 3D model encryption.This paper proposes a new 3D model encryption algorithm,called the 1D map with sin and logistic coupling(1D-MWSLC),because existing digital image encryption algorithms cannot be directly applied to 3D models.Firstly,this paper introduce 1D-MWSLC,which has a wider range of parameters compared to traditional 1D chaotic systems.When the parameter exceeds a specific range,the chaotic phenomenon does not weaken.Additionally,1D-MWSLC has two control parameters,which increases the cryptosystem’s parameter space.Next,1D-MWSLC generates keystreams for confusion and diffusion.In the confusion stage,this paper use random confusion,and the keystream generates an index matrix that confuses the integer and decimal parts of the 3D model simultaneously.In the diffusion stage,this paper use parallel bidirectional diffusion to simultaneously diffuse the integer parts of the three coordinates of the 3D model.Finally,this paper verify the proposed algorithm through statistical analysis,and experimental results demonstrate that the proposed 3D model encryption algorithm has robust security.
基金funded by the National Natural Science Foundation of China(Grant No:82104411).
文摘Objective:To explore the bidirectional mechanism of Haizao Yuhu decoction(HYD)on goiter and drug-induced liver injury(DILI)based on machine learning and data mining.Methods:Firstly,compounds of HYD were selected from the TCMSP,TCMIP,and BATMAN databases,then the TCMSP was used to acquire the targets of compounds.Targets of goiter and DILI were obtained from the GeneCards database.Secondly,common targets of“HYD-goiter”and“HYD-DILI”as well as related compounds were used to construct the networks and perform Random Walk with Restart(RWR)algorithm and network stability test.Finally,core targets in the“HYD-goiter”and“HYD-DILI”networks were used for molecular docking with core compounds and searched for validation on PubChem,and the relevant experimental data of our group were quoted to verify the analysis results.Results:There were 22 intersection targets of HYD and DILI,326 of HYD and goiter.RWR analysis showed that MAPK1,MAPK3,AKT1,etc.may be the core targets of HYD treating goiter,RELA,TNF,IL4,etc.may be the core targets of the bidirectional effect,and eckol may be the core compound in bidirectional effect.Network stability test indicated that the HYD had a high stability on treating goiter and playing a bidirectional effect.The core targets and core compounds docked well,and 37.3%of targets had been confirmed by experiments and 29.8%core targets had been confirmed.Our previous experimental result confirmed that the HYD could treat goiter usefully by reducing the expression levels of PI3K and AKT mRNA,and down-regulating the expression of Cyclin D1 and Bcl-2 mRNA.Conclusion:HYD containing“sargassum-liquorice”combination may have a bidirectional effect on treating goiter and causing DILI.We offered a new way for more explorations on the therapeutic and toxic bidirectional mechanisms based on machine learning and data mining.
文摘In this study,dynamic responses of two buildings connected by viscoelastic dampers under bidirectional excitations are extensively investigated.The two buildings are a 10-story building and a 16-story building,with the shorter building on the left.Viscoelastic dampers are installed at all fl oors of the shorter building.Equations of motion are formulated using a fractional derivative model to represent the viscoelastic dampers.Three cases are considered with mass eccentricities at 0,10% and-10% with respect to the dimensions of the buildings.The responses of the buildings are numerically predicted at different damper properties.The simulation results indicated that the maximum horizontal responses of the buildings without eccentricities are signifi cantly mitigated.However,torsional effects are adversely increased.For asymmetric buildings,the effectiveness of the connecting dampers is affected by building eccentricities.As a result,mass eccentricities must be taken into account in damper selection.When compared with vibrations induced by unidirectional excitations,bidirectional excitations can increase the responses of coupled asymmetric buildings.In addition,installing dampers only at the top fl oor of the shorter building may cause a sudden change in lateral stiffness of the taller building.Consequently,the story shear envelopes of the taller building are changed.
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 52074258, 41941018, and U21A20153)
文摘Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.
基金supported by Science and Technology Project of State Grid Corporation(Research and Application of Intelligent Energy Meter Quality Analysis and Evaluation Technology Based on Full Chain Data)
文摘With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.
文摘Modelling of bidirectional full bridge DC-DC converter as one of the most applicable converters has received significant attention. Mathematical modelling reduces the simulation time in comparison with detailed circuit response;moreover it is convenient for controller design purpose. Due to simple and effective methodology, average state space is the most common method among the modelling methods. In this paper a bidirectional full bridge converter is modelled by average state space and for each mode of operations a controller is designed. Attained mathematical model results are in a close agreement with detailed circuit simulation.
基金This work was supported by Scientific Research Starting Project of SWPU[Zheng,D.,No.0202002131604]Major Science and Technology Project of Sichuan Province[Zheng,D.,No.8ZDZX0143]+1 种基金Ministry of Education Collaborative Education Project of China[Zheng,D.,No.952]Fundamental Research Project[Zheng,D.,Nos.549,550].
文摘Nowadays,Internet has become an indispensable part of daily life and is used in many fields.Due to the large amount of Internet traffic,computers are subject to various security threats,which may cause serious economic losses and even endanger national security.It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data.As machine learning technology matures,deep learning is widely used in various industries.Combining deep learning with network security and intrusion detection is the current trend.In this paper,the problem of data classification in intrusion detection system is studied.We propose an intrusion detection model based on stack bidirectional long short-term memory(LSTM),introduce stack bidirectional LSTM into the field of intrusion detection and apply it to the intrusion detection.In order to determine the appropriate parameters and structure of stack bidirectional LSTM network,we have carried out experiments on various network structures and parameters and analyzed the experimental results.The classic KDD Cup’1999 dataset was selected for experiments so that we can obtain convincing and comparable results.Experimental results derived from the KDD Cup’1999 dataset show that the network with three hidden layers containing 80 LSTM cells is superior to other algorithms in computational cost and detection performance due to stack bidirectional LSTM model’s ability to review time and correlate with connected records continuously.The experiment shows the effectiveness of stack bidirectional LSTM network in intrusion detection.
文摘The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot spread uniformly, since the Pareto front shows different features, such as concave and convex. To improve the distribution uniformity of non-dominated solutions, a bidirectional decomposition based approach that constructs two search directions is proposed to provide a uniform distribution no matter what features problems have. Since two populations along two search directions show differently on diversity and convergence, an adaptive neighborhood selection approach is presented to choose suitable parents for the offspring generation. In order to avoid the problem of the shrinking search region caused by the close distance of the ideal and nadir points, a reference point update approach is presented. The performance of the proposed algorithm is validated with four state-of-the-art algorithms. Experimental results demonstrate the superiority of the proposed algorithm on all considered test problems.
基金supported by the Postgraduate Scientific Research Innovation Project of Hunan Province under Grant QL20210212the Scientific Innovation Fund for Postgraduates of Central South University of Forestry and Technology under Grant CX202102043.
文摘In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.
