In relation extraction tasks， distant supervision is a common method for automatic data labeling. However， this method will introduce a large amount of noisy data， which affects the performance of the model. In order to solve the problem of noisy data， a relation extraction method based on negative training and transfer learning was proposed. Firstly， a noisy data recognition model was trained through negative training method. Then， the noisy data were filtered and relabeled according to the predicted probability value of the sample， Finally， a transfer learning method was used to solve the domain shift problem existing in distant supervision tasks， and the precision and recall of the model were further improved. Based on Thangka culture， a relation extraction dataset with national characteristics was constructed. Experimental results show that the F1 score of the proposed method reaches 91.67%， which is 3.95 percentage points higher than that of SENT （Sentence level distant relation Extraction via Negative Training） method， and is much higher than those of the relation extraction methods based on BERT （Bidirectional Encoder Representations from Transformers）， BiLSTM+ATT（Bi-directional Long Short-Term Memory and Attention）， and PCNN （Piecewise Convolutional Neural Network）.

The existing endoscopic image highlight removal algorithms often have some problems such as unreasonable removal structure and color distortion， which leads to the wrong results of the focus recognition algorithms and image enhancement algorithms. In order to solve the above problems， in the aspect of highlight localization， a method based on the combination of growth in dark region and Scharr filtering was proposed to locate relative highlight； in the aspect of highlight filling， an improved Crinminisi algorithm was proposed. Firstly， through the statistics on a huge amount of data， the search scope was limited and the filling efficiency was increased. Secondly， the statistical scope of priority was improved to avoid repeated meaningless calculations. Finally， the reasonable reconstruction of texture was performed according to the adaptive templates of different regions. Experiments were carried out on endoscopic image dataset of different human tissues， compared with the dichromatic reflection model based method， the Robust Principle Component Analysis （RPCA） method， the thermal diffusion method and the original Criminisi algorithm， the Natural Image Quality Evaluator （NIQE） value of the proposed algorithm was the lowest. Compared with the RPCA method， the thermal diffusion method and the original Crimnisi algorithm， the running time of the proposed algorithm was the lowest. Experimental results show that the proposed algorithm not only has better objective image indicators than other algorithms， but also has a nearly 100-fold improvement in efficiency compared to the original Criminisi algorithm.

The complexity of pedestrian interaction is a challenge for pedestrian trajectory prediction， and the existing algorithms are difficult to capture meaningful interaction information between pedestrians， which cannot intuitively model the interaction between pedestrians. To address this problem， a multi-head soft attention graph convolutional network was proposed. Firstly， a Multi-head Soft ATTention （MS ATT） combined with involution network was used to extract sparse spatial adjacency matrix and sparse temporal adjacency matrix from spatial and temporal graph inputs respectively to generate sparse spatial directed graph and sparse temporal directed graph. Then， a Graph Convolutional Network （GCN） was used to learn interaction and motion trend features from sparse spatial and sparse temporal directed graphs. Finally， the learned trajectory features were input into a Temporal Convolutional Network （TCN） to predict double Gaussian distribution parameters， thereby generating the predicted pedestrian trajectories. Experiments on Eidgenossische Technische Hochschule （ETH） and University of CYprus （UCY） datasets show that， compared with Space-time sOcial relationship pooling pedestrian trajectory Prediction Model （SOPM）， the proposed algorithm reduces the Average Displacement Error （ADE） by 2.78%， and compared to Sparse Graph Convolution Network （SGCN）， the proposed algorithm reduces the Final Displacement Error （FDE） by 16.92%.

With the rapid development of e-commerce and the popularity of the Internet， it is more convenient to exchange and return goods. Therefore， the customers’ demands for goods show the characteristics of timeliness， variety， small batch， exchanging and returning. Aiming at Location-Routing Problem with Simultaneous Pickup and Delivery （LRPSPD） with capacity and considering the characteristics of customers’ diversified demands， a mathematical model of LRPSPD & Time Window （LRPSPDTW） was established. Improved FireWorks Algorithm （IFWA） was used to solve the model， and the corresponding neighborhood operations were carried out for the fireworks explosion and mutation. The performance of the fireworks algorithm was evaluated with some benchmark LRPSPD examples. The correctness and effectiveness of the proposed model and algorithm were verified by a large number of numerical experiments. Experimental results show that compared with Branch and Cut algorithm （B&C）， the average error between the result of IFWA and the standard solution is reduced by 0.33 percentage points. The proposed algorithm shortens the time to find the optimal solution， and provides a new way of thinking for solving location-routing problems.

The virtual try-on technologies based on image synthesis mask strategy can better retain details of the clothing when the warped clothing is fused with the human body. However， because the position and structure of the human body and the clothing are difficult to align during the try-on process， the try-on result is likely to produce severe occlusion， affecting visual effect. In order to solve the occlusion in the try-on process， a U-Net based generator was proposed. In the generator， a cascaded spatial attention module and a channel attention module were added to the U-Net decoder， thereby achieving the cross-domain fusion between local features of warped clothes and global features of the human body. Formally， first， by predicting the Thin Plate Spline （TPS） conversion using the convolutional network， the clothing was distorted according to the target human body pose. Then， the dressed-on person representation information and the warped clothing were input into the proposed generator， and the mask image of the corresponding clothing area was obtained to render the intermediate result. Finally， the strategy of mask synthesis was used to synthesize the warped clothing with the intermediate result through mask processing to obtain the final try-on result. Experimental results show that the proposed method can not only reduce occlusion， but also enhance image details. Compared with Characteristic-Preserving Virtual Try-On Network （CP-VTON） method， the proposed method has the generated image with the average Peak Signal-to-Noise Ratio （PSNR） increased by 10.47%， the average Fréchet Inception Distance （FID） decreased by 47.28%， and the average Structural SIMilarity （SSIM） increased by 4.16%.

Aiming at the problem of poor visual effects of block-based compressed sensing reconstructed images at low sampling rates， a compressed sensing image reconstruction method that fused Spatial Location and Structure Information （SLSI） was proposed. Firstly， observations were linearly mapped to obtain initial estimated values of image blocks. Then， based on block grouping reconstruction branch and whole image reconstruction branch， the spatial location information and structure information of the image were extracted， enhanced and fused. Finally， weighted strategy was used to fuse the outputs of the two branches to obtain final reconstructed whole image. In the block grouping reconstruction branch， reconstruction resources were allocated according to the data characteristics of the image blocks. In the whole image reconstruction branch， information exchange between adjacent image block pixels was mainly carried out through bilateral filtering and structural feature interaction module. Experimental results show that compared with compressed sensing reconstruction methods based on non-iterative Reconstruction Network （ReconNet） and Multi-scale Reconstruction neural Network with Non-Local constraint （NL-MRN）， due to the combination of the image prior with strong autocorrelation between pixels， when sampling rate is 0.05， the average Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity index （SSIM） of the proposed method on the test image data commonly used in the compressed sensing field increase 2.617 5 dB and 0.105 3 respectively， and the visual effects of reconstructed images are better.

Considering different blockchains being isolated and the data interaction and sharing difficulties in the current rapid development process of blockchain technology， a cross-chain mechanism based on Spark blockchain was proposed. Firstly， common cross-chain technologies and current mainstream cross-chain projects were analyzed， the implementation principles of different technologies and projects were studied， and their differences， advantages and disadvantages were summarized. Then， using the blockchain architecture maned main-sub blockchain mode， the key core components such as smart contract component， transaction verification component， transaction timeout component were designed， and the four stages of cross-chain process were elaborated in detail， including transaction initiation， transaction routing， transaction verification and transaction confirmation. Finally， the feasible experiments were designed for performance test and security test， and the security was analyzed. Experimental results show that Spark blockchain has significant advantages compared to other blockchains in terms of transaction delay， throughput and spike testing. Besides， when the proportion of malicious nodes is low， the success rate of cross-chain transactions is 100%， and different sub chains can conduct cross-chain transactions safely and stably. This mechanism solves the problem of data interaction and sharing between blockchains， and provides technical reference for the design of Spark blockchain application scenarios in the next step.

Focused on the issue that different blockchains are independent from and difficult to communicate with each other， a new type of permissioned public blockchain architecture of "main chain + sub chain" was proposed. Firstly， based on the existing algorithms such as Delegated Proof Of Stake （DPOS）， Verifiable Random Function （VRF） and Practical Byzantine Fault Tolerance （PBFT）， an innovative two-layer consensus algorithm was designed. And a trusted permission mechanism was added to make the blockchain have both permission and public characteristics. Secondly， the design process of the main and sub chains was described in detail. The management of the chain group and public services was provided by the main chain， while the sub chains were designed independently for different business scenarios， and cross-chain data communication was realized by connecting the main chain relay， thereby realizing the data secure isolation. Finally， an experimental environment was built for testing to verify the feasibility of the permissioned public blockchain design. Experimental results show that compared with some existing blockchains such as the Hyperledger Fabric， the proposed permissioned public blockchain has significant advantages， including a throughput of up to 25 000 times per second and an average delay time of about 8 s. It can be seen that this permissioned public blockchain provides technical support for further research on cross-chain data interconnection of different types of blockchains.

The main task of text segmentation is to divide the text into several relatively independent text blocks according to the topic relevance. Aiming at the shortcomings of the existing text segmentation models in extracting fine-grained features such as text paragraph structural information， semantic correlation and context interaction， a text segmentation model TS-GCN （Text Segmentation-Graph Convolutional Network） based on Graph Convolutional Network （GCN） was proposed. Firstly， a text graph based on the structural information and semantic logic of text paragraphs was constructed. Then， the semantic similarity attention was introduced to capture the fine-grained correlation between text paragraph nodes， and the information transmission between high-order neighborhoods of text paragraph nodes was realized with the help of GCN， so that the model ability of multi-granularity extraction of text paragraph topic feature representations was enhanced. The proposed model was compared with the representative model CATS （Coherence-Aware Text Segmentation）， and its basic model TLT-TS （Two-Level Transformer model for Text Segmentation）， which were commonly used as benchmarks for text segmentation task. Experimental results show that TS-GCN’s evaluation index P_k is 0.08 percentage points lower than that of TLT-TS without any auxiliary module on Wikicities dataset. And the proposed model has the P_k value decreased by 0.38 percentage points and 2.30 percentage points respectively on Wikielements dataset compared with CATS and TLT-TS. It can be seen that TS-GCN achieves good segmentation effect.

Aiming at the low embedding capacity of Reversible Data Hiding （RDH） in encrypted videos， a high-capacity RDH scheme in encrypted videos based on histogram shifting was proposed. Firstly， 4×4 luminance intra-prediction mode and the sign bits of Motion Vector Difference （MVD） were encrypted by stream cipher， and then a two-dimensional histogram of MVD was constructed， and （0，0） symmetric histogram shifting algorithm was designed. Finally， （0，0） symmetric histogram shifting algorithm was carried out in the encrypted MVD domain to realize separable RDH in encrypted videos. Experimental results show that the embedding capacity of the proposed scheme is increased by 263.3% on average compared with the comparison schemes， the average Peak Signal-to-Noise Ratio （PSNR） of encrypted video is less than 15.956 dB， and the average PSNR of decrypted video with secret can reach more than 30 dB. The proposed scheme effectively improves the embedding capacity and is suitable for more types of video sequences.

Existing product and service quality analysis is often based on questionnaire survey or product reviews， but there are problems such as difficulty in questionnaire collection and invalid data in product reviews. As a bridge between customers and businesses， the customer service dialogue contains rich customer opinions from product to service perspective， however， there are still few studies using customer service dialogues to analyze product and service quality. A product and service quality analysis method based on customer service dialogues was proposed， which firstly combined the product features and service blueprint to determine product and service quality evaluation factors， and used the Important?Performance Analysis （IPA） method to define the importance and performance index of evaluation factors. Then， quantitative analysis of the importance and satisfaction of products and services was performed by using the dialogue topic extraction and sentiment analysis. The method was applied on the real customer service dialogues of a Taobao flagship store which sells disinfection and sterilization products， and 18 evaluation factors were established， whose importance and performance were quantified based on more than 900 thousand real historical customer service dialogues， thereby analyzing the quality of products and services of the flagship store. Finally， a questionnaire on the professional customer service employees was carried out to verify the effectiveness of the proposed method.

Aiming at the problem that most existing directed network link prediction methods only focus on the directed local and reciprocal link information and ignore the directed global structure information， a High-order Self-included Collaborative Filtering （HSCF） link prediction framework was proposed. Firstly， random walk method was used to calculate the high-order similarity matrix to preserve the high-order path information of the directed network. Secondly， an HSCF framework was constructed by combining the high-order similarity matrix with collaborative filtering method. Finally， the proposed framework was integrated with four typical directed structure similarity indices including Directed Common Neighbor （DCN）， Directed Adamic-Adar （DAA）， Directed Resource Allocation （DRA） and potential theory （Bifan）， and four directed network prediction indices HSCF-DCN， HSCF-DAA， HSCF-DRA and HSCF-Bifan were proposed on this basis. Compared with the baseline indices on ten real directed networks， the experimental results show that the AUC （Area Under Curve of Receiver Operating Characteristic （ROC）） values of HSCF-DCN， HSCF-DAA， HSCF-DRA and HSCF-Bifan are increased by an average of 8.16%， 8.85%， 9.64% and 10.33% respectively and the F-score values of them are increased by an average of 66.62%， 68.32%， 68.95% and 76.18% respectively.

In view of the small target of cigarette butts in surveillance videos of public places and the easy divergence of smoke generated by smoking， it is difficult to determine the smoking behavior only by target detection algorithm. Considering that the algorithm of posture estimation using skeleton key points is becoming more and more mature， a smoking behavior detection algorithm was proposed by using the relationship between human skeleton key points and smoking behavior. Firstly， AlphaPose and RetinaFace were used to detect the key points of human skeleton and face respectively. According to the ratio of distance between wrist and middle point of two corners of mouth and between wrist and the eye on the same side， a method for calculating whether the Smoking Action Ratio （SAR） in humans falls within the Golden Ratio of Smoking Actions （GRSA） to distinguish smoking from non-smoking behaviors was proposed. Then， YOLOv4 was used to detect whether cigarette butts existed in the video. The results of GRSA determination and YOLOv4 were combined to determine the possibility of smoking behavior in the video and make a determination of whether smoking behavior was present. The self-recorded dataset test shows that the proposed algorithm can accurately detect smoking behavior with the accuracy reached 92%.

Concerning that the traditional saliency detection algorithm has low segmentation accuracy and the deep learning-based saliency detection algorithm has strong dependence on pixel-level manual annotation data， an unsupervised saliency object detection algorithm based on graph cut refinement and differentiable clustering was proposed. In the algorithm， the idea of “coarse” to “fine” was adopted to achieve accurate salient object detection by only using the characteristics of a single image. Firstly， Frequency-tuned algorithm was used to obtain the salient coarse image according to the color and brightness of the image itself. Then， the candidate regions of the salient object were obtained by binarization according to the image’s statistical characteristics and combination of the central priority hypothesis. After that， GrabCut algorithm based on single image for graph cut was used for segmenting the salient object finely. Finally， in order to overcome the difficulty of imprecise detection when the background was very similar to the object， the unsupervised differentiable clustering algorithm with good boundary segmentation effect was introduced to further optimize the saliency map. Experimental results show that compared with the existing seven algorithms， the optimized saliency map obtained by the proposed algorithm is closer to the ground truth， achieving an Mean Absolute Error （MAE） of 14.3% and 23.4% on ECSSD and SOD datasets， respectively.

Service description texts for Internet of Things （IoT） are short in length and sparse in text features， and direct modeling the IoT service by using traditional topic model has poor clustering effect， so that the best service cannot be discovered. To solve this problem， an IoT service discovery method based on Biterm Topic Model （BTM） was proposed. Firstly， BTM was employed to mine the latent topic of the existing IoT services， and the service document-topic probability distribution was calculated and deduced through global topic distribution and theme-word distribution. Then， K-means algorithm was used to cluster the services and return the best matching results of service requests. Experimental results show that the proposed method can improve the clustering effect of services for IoT and thus obtain the matched best service. Compared with the methods of HDP （Hierarchical Dirichlet Process） and LDA-K （Latent Dirichlet Allocation based on K-means）， the proposed method achieves better performance in terms of Precision and Normalized Discounted Cumulative Gain （NDCG） for best service discovery.

In view of maintenance difficulties and high cost in large-scale development of Wireless Local Access Network (WLAN), the Control and Provisioning of Wireless Access Points (CAPWAP) protocol that applied to communication between Access Controller (AC) and Wireless Terminator Point (WTP) was researched and implemented. In Linux environment, main features were realized, such as state machine management, and WTP centralized configuration. A platform of WLAN centralized management system based on local Medium Access Control (MAC) framework was built up. Wireshark capture tool, Chariot and Iperf were used to test the platform. The capture test results verify the feasibility of the framework, and the results of throughput and User Datagram Protocol (UDP) test also show that network performance is efficient and stable.

The exiting methods of the nearest neighbor query can not search the simple continuous near neighbor chain in the constrained regions. To remedy the deficiency of the existing work, according to the complexity of the constrained regions and the obstacles, the simple continuous near neighbor chain query with non obstacles and with obstacles were studied respectively. The VOR_NB_CRSCNNC algorithm and the VOR_CB_CRSCNNC algorithm were presented. The spatial data were filtered and computed based on the Voronoi diagram and the judging circles. The calculations of each query were reduced by only considering the points which lay in the Voronoi polygon and the juding circles. The theatrical study and the experimental results show that the redundant calculation is reduced and the query efficiency is less affected by the constrained regions.

The existing methods of constructing Voronoi diagram have low efficiency and high complexity, to remedy the disadvantages, a new method of constructing and updating Voronoi diagram based on the hybrid methods was given to query the nearest neighbor of the given spatial data effectively, and a new method of searching the nearest neighbor based on Voronoi diagram and the minimum inscribed circle was presented. To deal with the frequent, changes of the query point position, the method based on Voronoi diagram and the minimum bounding rectangle was proposed. To improve the efficiency of the dual nearest neighbor pair and closest pair query, a new method was given based on Voronoi polygons and their minimum inscribed circles. The experimental results show that the proposed methods reduce the additional computation caused by the uneven distribution of data and have a large advantage for the big dataset and the frequent query.

The current Radio Frequency IDentification (RFID) system just simply integrates the collision algorithm and security mechanism together. Based on the analysis of classical adaptive dynamic anti-collision algorithm, an anti-collision strategy of embedded security mechanism was proposed. It combined the first traversal mechanism and Boolean mutual authentication protocol to solve the problem that traditional RFID tag identification system is not efficient and has high cost; it also has high security. Compared with the backward binary, dynamic adaptive and binary tree search algorithms, the proposed strategy can greatly reduce the times of the system search and improve the label throughput.

In this paper, the grey constrained nonlinear programming problems were investigated. With the help of mean, this paper firstly transformed the original grey optimization problem into a determinate constrained nonlinear programming problem. Then, based on the estimation of distribution algorithm, a stochastic search method was developed to solve the determinate constrained nonlinear programming problem. The key technique of the proposed method was explained in detail and the steps of the proposed method were described concretely. Finally, the elementary numerical examples show the proposed stochastic search method is feasible and effective.

Complex background, critical noise and fuzzy boundary made the performance of the available cell image segmentation methods disappointing. Thus, a new method that can locate and detect nucleus effectively was proposed in this paper. A coarse-to-fine segmentation strategy was adopted to extract the edge of nucleus gradually. First, by using C-means clustering algorithm, the image was divided to three parts: nucleus, cytoplasm and cell intercellular substance. Second, the center of cell was located by calculating the circularity of Canny edge image. Finally, a reformed level set evolution was introduced to extract the edge of nucleus. The experimental results show that, nucleus can be located accurately; even if the cell image has a complex background and is disturbed by much stuff. Moreover, the edge of nucleus extracted by this method has a higher accuracy.

The X-ray phase contrast Computed Tomography (CT) can produce high contrast images by the X-ray phase information alteration, which comes forth after the X-ray passes through the sample, and it is highly favorable to the imaging of light elements and can get much higher contrast resolution than the absorption contrast CT. Grating-based Differential Phase Contrast CT (DPC-CT) shows great clinical prospects due to the possibility of using a conventional X-ray source, but the X-ray radiation dose issue limits its clinical applications. Concerning such inadequacies, an image reconstruction method for DPC-CT named DD-L1 was proposed. This algorithm combined Compressive Sensing (CS) theory with CT iterative reconstruction technique and introduced distance driven forward and backward projection computation strategy. The experimental results show that DD-L1 algorithm can generate tomographic images of higher quality even when the projection data is incomplete.

In this paper, the main idea was to improve the foreground detection method based on statistical model. On one hand, historical maximum probability of which feature vector belongs to background was recorded in the background model, which could improve the matched vectors updating speed and make it blended into the background quickly. On the other hand, a method using spatial feature match was proposed to reduce the shadow effect in the foreground detection. The experimental results show that, compared with the MoG method and Lis statistical model method, the method proposed in this paper has obvious improvement in shadow remove and obscured background restoration of big target object.

Specific to the need of performance prediction in communication networks, a connection rate prediction method based on fuzzy Auto-Regressive (AR) model was proposed and improved, and the fuzzy AR model based on adaptive fitting degree threshold was studied. The median filtering method was applied to pre-process the data of fuzzy AR model. On this basis, for the uncertain thresholds of some applications, the fitting degree threshold formula was added to the prediction model to make it adaptive. The simulation results show that the predistion method based on fuzzy AR model can be used to predict the connection rate with a higher fitting degree.

To solve the problems of distributed control and lack of geographic information technical assistance in the process of integrated automation development for Supervisory Control and Data Acquisition (SCADA) systems, system integration technology was applied to achieve comprehensive design of the Geographic Information System (GIS) and SCADA system in terms of data, interface and function. The comprehensive monitoring system was developed based on the multilayer structure of C/S. ActiveX technology was used in the developmental process to complete the system integration, and the SCADA system was used as the basic means of monitoring and control, while the GIS was used to provide the necessary geographic information analysis and processing functions. With the establishment of integrated database, the seamless connection of GIS with SCADA and sharing were realized, and the data consistency and real-time performance of the integrated system were ensured. The communication between the devices and remote clients helped the system to realize on-site video monitoring. Safety measures were also integrated in the system. The system is now successfully applied in actual working environment, and all functions are verified with stable operation and high scalability.

Lithology identification is the most critical procedure in the logging data interpretation field, while the traditional lithology identification methods have a lot of defects such as slow explain efficiency, low accuracy, and big influenced human factors. To resolve these problems, a new kind lithology identification method was put forward using genetic optimized Radial Basis Probability Neural Network (RBPNN). Probabilistic Neural Network (PNN) and the Radial Basis Function Neural Network (RBFNN) were combined to construct RBPNN. To optimize network structure, upgrade convergence speed and accuracy, Genetic Algorithm (GA) was used to search for the optimal hidden center vector and matching kernel function control parameters of the RBPNN structure which must satisfy minimum error of RBPNN training and form genetic optimized RBPNN network model. The case study shows that lithology identification based on genetic optimized RBPNN can achieve the actual application standards, and it is feasible and effective, it also can provide scientific theoretical supports and dependences for oil geological exploration field.

Lossless video coding is increasingly used because of the need of high quality videos in digital video areas. For this reason, a lossless video compression algorithm based on fuzzy logic was designed in this paper. It utilized fuzzy-logic-based method to calculate the correlation between two subblocks from neighbor frames and the interior correlation in the subblock, which can be used to decide the selection between temporal prediction and spatial prediction. A new matching rule of motion estimation was defined in temporal prediction. At last, the correlation can be adopted to estimate the parameter of Golomb coding and realize fast and efficient Golomb coding without complex calculation of estimation. The experimental results show that the proposed method has significant improvement in coding efficiency compared with the JPEG-LS.

The multi-coverage will appear with the sensors of Wireless Sensor Network (WSN) being randomly and high-densely distributed on the fields that will waste the energy of sensors and the entire network. Concerning this problem, a sleeping algorithm based Voronoi diagram was improved. Sleeping sensors were estimated and the energy of the whole network cost was reduced with calculating distance of sensors and their neighbors and distance of sensors and vertex of their Voronoi diagrams. The simulation results show that the improved sleeping algorithm can save energy of the whole network, and extend the lifetime of network.

To improve the speed and anti-noise performance of face recognition based on sparse representation, the Cross-And-Bouquet (CAB) model and Compressed Sensing (CS) reconstruction algorithm were studied. Concerning the large matrix inversion of reconstruction algorithm, a Fast Orthogonal Matching Pursuit (FOMP) algorithm was proposed. The proposed algorithm could convert the high complexity operations of matrix inversion into the lightweight operation of vector-matrix computation. To increase the amount of effective information in dense noise pictures, several practical and efficient methods were put forward. The experimental results verify that these methods can effectively improve the face recognition rate in dense noise cases, and identifiable noise ratio can reach up to 75%. These methods are of practical values.

Granular Computing is an emerging conceptual in information processing. It plays an important role in information processing for fuzzy, uncertainty. Interval-valued intuitionistic fuzzy information granularity is an important tool, to measure the uncertainty of interval-valued intuitionistic fuzzy information systems. Based on interval-valued intuitionistic fuzzy information systems, this paper constructs intersection, union, subtraction and complement four operators among granular structures, introduces three new partial order relations in interval-valued intuitionistic fuzzy information systems and establishes the relationships among them. It defines an interval-valued intuitionistic fuzzy information granularity and an axiomatic approach to interval-valued intuitionistic fuzzy information granularity in interval-valued intuitionistic fuzzy information systems. Finally, it investigates the properties of interval-valued intuitionistic fuzzy information granularity.