Decoding topological XYZ^(2) codes with reinforcement learning based on attention mechanisms

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ^(2) code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ^(2) codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ^(2) codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding.However,these approaches have some limitations.For example,a cover image lacks self-adaptability,information leakage,or weak concealment.To address these issues,this study proposes a universal and adaptable image-hiding method.First,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image domain.Second,to improve perceived human similarity,perceptual loss is incorporated into the training process.The experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality output.Furthermore,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at 0.0001.Moreover,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.

Lightweight Cross-Modal Multispectral Pedestrian Detection Based on Spatial Reweighted Attention Mechanism

Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.

An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.

ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data.Concate-nating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method.This fusion method does not utilize the correlation information between modalities.To solve this problem,this paper proposes amodel based on amulti-head attention mechanism.First,after preprocessing the original data.Then,the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence.Next,the input coding sequence is fed into the transformer model for further processing and learning.At the transformer layer,a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between modalities.Finally,the processed results are input into the feedforward neural network to obtain the emotional output through the classification layer.Through the above processing flow,the model can capture semantic information and contextual relationships and achieve good results in various natural language processing tasks.Our model was tested on the CMU Multimodal Opinion Sentiment and Emotion Intensity(CMU-MOSEI)and Multimodal EmotionLines Dataset(MELD),achieving an accuracy of 82.04% and F1 parameters reached 80.59% on the former dataset.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

The underlying mechanism of variety–water–nitrogen–stubble damage interactions on yield formation in ratoon rice with low stubble height under mechanized harvesting

Agronomic measures are the key to promote the sustainable development of ratoon rice by reducing the damage from mechanical crushing to the residual stubble of the main crop, thereby mitigating the impact on axillary bud sprouting and yield formation in ratoon rice. This study used widely recommended conventional rice Jiafuzhan and hybrid rice Yongyou 2640 as the test materials to conduct a four-factor block design field experiment in a greenhouse of the experimental farm of Fujian Agricultural and Forestry University, China from 2018 to 2019.The treatments included fertilization and no fertilization, alternate wetting and drying irrigation and continuous water flooding irrigation, and plots with and without artificial crushing damage on the rice stubble. At the same time, a 13C stable isotope in-situ detection technology was used to fertilize the pot experiment. The results showed significant interactions among varieties, water management, nitrogen application and stubble status.Relative to the long-term water flooding treatment, the treatment with sequential application of nitrogen fertilizer coupled with moderate field drought for root-vigor and tiller promotion before and after harvesting of the main crop, significantly improved the effective tillers from low position nodes. This in turn increased the effective panicles per plant and grains per panicle by reducing the influence of artificial crushing damage on rice stubble and achieving a high yield of the regenerated rice. Furthermore, the partitioning of 13C assimilates to the residual stubble and its axillary buds were significantly improved at the mature stage of the main crop, while the translocation rate to roots and rhizosphere soil was reduced at the later growth stage of ratooning season rice. This was triggered by the metabolism of hormones and polyamines at the stem base regulated by the interaction of water and fertilizer at this time. We therefore suggest that to achieve a high yield of ratoon rice with low stubble height under mechanized harvesting, the timely application of nitrogen fertilizer is fundamental,coupled with moderate field drying for root-vigor preservation and tiller promotion before and after the mechanical harvesting of the main crop.

Image Inpainting Technique Incorporating Edge Prior and Attention Mechanism

Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

Microdynamic mechanical properties and fracture evolution mechanism of monzogabbro with a true triaxial multilevel disturbance method

The far-field microdynamic disturbance caused by the excavation of deep mineral resources and underground engineering can induce surrounding rock damage in high-stress conditions and even lead to disasters.However,the mechanical properties and damage/fracture evolution mechanisms of deep rock induced by microdynamic disturbance under three-dimensional stress states are unclear.Therefore,a true triaxial multilevel disturbance test method is proposed,which can completely simulate natural geostress,excavation stress redistribution(such as stress unloading,concentration and rotation),and subsequently the microdynamic disturbance triggering damaged rock failure.Based on a dynamic true triaxial test platform,true triaxial microdynamic disturbance tests under different frequency and amplitudes were carried out on monzogabbro.The results show that increasing amplitude or decreasing frequency diminishes the failure strength of monzogabbro.Deformation modulus gradually decreases during disturbance failure.As frequency and amplitude increase,the degradation rate of deformation modulus decreases slightly,disturbance dissipated energy increases significantly,and disturbance deformation anisotropy strengthens obviously.A damage model has been proposed to quantitatively characterize the disturbance-induced damage evolution at different frequency and amplitude under true triaxial stress.Before disturbance failure,the micro-tensile crack mechanism is dominant,and the micro-shear crack mechanism increases significantly at failure.With the increase of amplitude and frequency,the micro-shear crack mechanism increases.When approaching disturbance failure,the acoustic emission fractal dimension changes from a stable value to local large oscillation,and finally increases sharply to a high value at failure.Finally,the disturbance-induced failure mechanism of surrounding rock in deep engineering is clearly elucidated.

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.

An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.

A reduced combustion mechanism of ammonia/diesel optimized with multi-objective genetic algorithm

For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.

Video expression recognition based on frame-level attention mechanism

Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

Dynamic analysis of a novel multilink-spring mechanism for vibration isolation and energy harvesting

Due to technical limitations,existing vibration isolation and energy harvesting(VIEH)devices have poor performance at low frequency.This paper proposes a new multilink-spring mechanism(MLSM)that can be used to solve this problem.The VIEH performance of the MLSM under harmonic excitation and Gaussian white noise was analyzed.It was found that the MLSM has good vibration isolation performance for low-frequency isolation and the frequency band can be widened by adjusting parameters to achieve a higher energy harvesting power.By comparison with two special cases,the results show that the MLSM is basically the same as the other two oscillators in terms of vibration isolation but has better energy harvesting performance under multistable characteristics.The MLSM is expected to reduce the impact of vibration on high-precision sensitive equipment in some special sites such as subways and mines,and at the same time supply power to structural health monitoring devices.

Insight into the experiment and extraction mechanism for separating carbazole from anthracene oil with quaternary ammonium-based deep eutectic solvents

Carbazole is an irreplaceable basic organic chemical raw material and intermediate in industry.The separation of carbazole from anthracene oil by environmental benign solvents is important but still a challenge in chemical engineering.Deep eutectic solvents (DESs) as a sustainable green separation solvent have been proposed for the separation of carbazole from model anthracene oil.In this research,three quaternary ammonium-based DESs were prepared using ethylene glycol (EG) as hydrogen bond donor and tetrabutylammonium chloride (TBAC),tetrabutylammonium bromide or choline chloride as hydrogen bond acceptors.To explore their extraction performance of carbazole,the conductor-like screening model for real solvents (COSMO-RS) model was used to predict the activity coefficient at infinite dilution (γ^(∞)) of carbazole in DESs,and the result indicated TBAC:EG (1:2) had the stronger extraction ability for carbazole due to the higher capacity at infinite dilution (C^(∞)) value.Then,the separation performance of these three DESs was evaluated by experiments,and the experimental results were in good agreement with the COSMO-RS prediction results.The TBAC:EG (1:2) was determined as the most promising solvent.Additionally,the extraction conditions of TBAC:EG (1:2) were optimized,and the extraction efficiency,distribution coefficient and selectivity of carbazole could reach up to 85.74%,30.18 and 66.10%,respectively.Moreover,the TBAC:EG (1:2) could be recycled by using environmentally friendly water as antisolvent.In addition,the separation performance of TBAC:EG (1:2) was also evaluated by real crude anthracene,the carbazole was obtained with purity and yield of 85.32%,60.27%,respectively.Lastly,the extraction mechanism was elucidated byσ-profiles and interaction energy analysis.Theoretical calculation results showed that the main driving force for the extraction process was the hydrogen bonding ((N–H...Cl) and van der Waals interactions (C–H...O and C–H...π),which corresponding to the blue and green isosurfaces in IGMH analysis.This work presented a novel method for separating carbazole from crude anthracene oil,and will provide an important reference for the separation of other high value-added products from coal tar.