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It is important to judge the density of pear flower accurately for automatic thinning. In order to judge the density of pear flowers precisely and achieve the purpose of automatic flower thinning, a density classification of pear flower images method based on improved density peak clustering algorithm was proposed in this study. The pear flower image samples used in the experiment were taken from the research base of Hebei Agricultural University, Niugang Village, Yi County, Baoding City, Hebei Province, and the variety was three-year-old Qiuyue. In this study, the images of pear flowers in natural environment were collected on April 18, 2021, when the pear flowers were in full bloom. The pear flower recognition images with high detection accuracy were obtained by using the depth learning model to detect the pear flower. The central coordinates of the target detection frame were extracted from the recognition images to obtain the data points that needed to be clustered. Secondly, in order to achieve the density classification of pear flower images, the method of selecting the cluster center was improved according to the shortcomings of the original density peak clustering algorithm in the density classification of pear flower and the requirements of pear flower density classification. The decision graph was divided into four parts by four groups of local density and center offset distance segmentation thresholds to select the cluster centers. These four parts corresponded to four grades of high, medium and low density and no thinning treatment. The accurate classification of pear flower images with reasonable density was realized. Finally, to solve the problem of inaccurate clustering classification of pear flower distribution with only cluster distribution, sparse distribution and large-scale close-up, the calculation method of dij parameter of distance between two points was improved. The pear flower scale and density grading standard were unified, and reasonable density grading could be achieved for all distribution types of pear flower images. The experimental results showed that the proposed algorithm could adapt to pear flower images of different scales. The accuracy of scale prediction was 94.89%, and the accuracy of density classification was 94.29%. Compared with the existing methods, the proposed method could achieve the density classification of local flower clusters in natural environment. the algorithm in this paper was compared with K-means algorithm and DBSCAN algorithm, and the accuracy of density classification was 94.29% and 68.57% respectively. Although the density classification accuracy of K-Means clustering algorithm was high, it lacked the algorithm steps to automatically select the clustering centers of different density classification, and was too sensitive to noise points. The density calculation method of DBSCAN algorithm lacked continuity, and the division of density grade was not precise enough. Based on the comprehensive analysis of density classification accuracy and cluster center selection method, the improved density peak clustering algorithm in this paper had the best effect. The clustering centers of different density grade could be automatically selected and the error of random initialization of clustering centers could be reduced. The proposed algorithm was robust to noise points and could eliminate noise points effectively. The proposed algorithm adopted soft statistics method for density calculation, which had continuity and was more accurate for density classification. It provides technical support for analyzing the density of pear flower and machine intelligent flower thinning. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the mirror legs shade the face in the process of trying on 3D glasses on face image， this paper proposes a virtual trying on system of 3D glasses based on face image. The three-dimensional model of the face shape is used to blanking the mirror leg and solve the occlusion problem of the mirror leg. In this paper， the input face image for key point detection， Convex hull of face shape is obtained by Graham scanning method，using translation scanning to construct a three-dimensional model of the face shape， according to the key points in the face image and pose estimation for transforming the 3D glasses ， the glasses model is worn on the face image. The experimental results show that realize virtual try-on effect for face images from multiple perspectives， and solve the occlusion problem of mirror legs in the try-on process of face images. The average occlusion accuracy of the blanking leg during virtual trial is 94.5%， and the occlusion accuracy is high. [ABSTRACT FROM AUTHOR]

In order to improve the technical level of wharf construction, solve the problems of large tidal height difference, underfill caisson packing, irregular fender groove and different positions, and improve the quality of the project entity, this paper analyzes the fender groove construction in No.9 and No.10 berths project in Dalanping South Operation Area of Qinzhou Port, discusses the construction methods and key points of operation and formwork technology, and puts forward specific technical measures, such as mixed adjustable formwork, prefabricated bottom formwork, water -stop steel box, etc. It has accumulated the field construction experience of the fender groove of gravity wharf, ensured that the concrete at the fender groove meets the design and specification requirements, and effectively improved the quality of engineering entities. [ABSTRACT FROM AUTHOR]

This paper mainly introduces the way of optimizating synthesis process of the [Ni(Me3en)(acac)]BPh4 complex and the preliminary results of initial effort. On the basis of guiding students to understand the synthetic principle, conditions and process of [Ni(Me3en)(acac)]BPh4 complex, we take absorbing the free water on the solid surface of Ni(NO3)2·6H2O as the starting point, the effect of water on the synthetic system was discussed. Combined with the experimental principle, synthetic process and conditions, we propose several different synthetic approaches, and explore and verify them through the experiment. Through the above optimization processes, students' problem awareness and ability to solve problems can be cultivated, and we lay the foundation for the experiment to face students with different levels, different teaching needs and different teaching purposes. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the siamese neural network is sensitive to position, complex background and intra-class differences due to the use of image-level feature metrics, this paper proposed a Dual Metric Siamese Neural Network (DM-SiameseNet) . DM-SiameseNet used image-level features and local features (local descriptors) to jointly represent each image, then learned feature maps based on two different levels of similarity measures, and finally used an adaptive fusion strategy to adaptively integrate two different measurement result represented by the level feature. Experimental results showed that the accuracy of the improved model was increased by 5.04% and 9.66%respectively, and was higher than the measurement methods that only use image-level feature representation or local descriptor representation on miniImageNet, TieredImageNet, Stanford Dogs, Stanford Cars and CUB-200 datasets. The experimental results prove that the proposed model not only considers the global features of the image, but also captures more effective local feature information in the image, which improves the generalization ability of the model. [ABSTRACT FROM AUTHOR]

This paper proposed an improved RRT（Rapidly-Explorative Random Tree） algorithm to solve the problem that the traditional target offset RRT was easy to fall into local optimum in the environment where there were many obstacles such as yarn racks, placing platforms and other packages during the package handling process. The collision detection model was established by geometric envelope method, and the distance coefficient g was added to keep a safe distance between the end of the manipulator and the obstacle; A random action selection strategy was proposed to select the expansion action before each expansion of the tree; A weakening target offset strategy was proposed to offset the extension direction according to the position of Xgoal relative to the Xnear and Xrand line; According to the vertical distance limit method and cubic spline interpolation, the initial path was optimized, and finally a smooth path was obtained. Through simulation comparison with RRT algorithm, M-RRT algorithm, RRT algorithm with added gravity coefficient and Improved RRT * algorithm, the maximum decrease in path length was 23.3%, the maximum decrease in time was 82.5%, and the distance from obstacles was always more than 50 mm. The results show that the algorithm improves the efficiency and safety of the manipulator handling. [ABSTRACT FROM AUTHOR]

TiAl intermetallic compound alloy is an important high-perlormance material, and its microstructure is ol great signilicance to the study ol the characteristics ol lamellar growth behavior and microstructure evolution. In the experiment ol high-temperature phase transition precipitation growth, the accurate identilication and positioning ol its microstructure plays a vital role. This paper aims to solve the problems ol low accuracy and strong subjectivity in identifying the microstructure through human observation by applying computer vision technology to the intelligent identilication ol Ti-48Al-2Cr-2Nb alloy high-temperature precipitated phase structure. Alter model selection, data set production, parameter and conliguration selection, and training result analysis, the target detection experiment was completed. The experimental results show that the proposed target detection model lor precipitated phase structure can achieve more than 80% in terms ol precision, recall and average precision, among which mAP can reach up to 88%. Meanwhile, the method has high precision and high elliciency, and the detection speed ol a single image is 17ms. Therefore, this method shows broad application prospects in the identilication and location ol Ti-48Al-2Cr-2Nb alloy high temperature precipitated phase structure. [ABSTRACT FROM AUTHOR]

The forage silage can not only prevent the loss of nutrients, but also reduce the volume of feed and solve the problem that the forage grass is difficult to preserve. However, many grass have the problems of high water and low sugar content. Thus, the quality of direct silage is lower. Additives can improve the quality of silage and the commonly used silage additives include enzyme and lactic acid bacteria preparation. How to play the advantages of different silage additives and improve the quality of silage are crucial to increase the utilization rate of forage grass. This review paper introduced the effects characteristics of silage additives, concluded the effects of additives on fermentation quality, nutritional value and aerobic stability of forage silage, and emphatically summarized the effects of additives on microbial diversity of forage silage. [ABSTRACT FROM AUTHOR]

The data accuracy and anti-interference of inertial measurement unit (IMU) three-axis Euler angle are affected by high frequency noise and strong instantaneous interference. To solve this problem, this paper proposes a dynamic error suppression method with low computational burden, good real-time performance and low cost, which is more suitable for embedded systems. In this method, an infinite impulse response-extended Kalman filter (IR-EKF) is introduced to the front end of the EKF algorithm. The data are preprocessed with a second-order low-pass Butterworth filter (BF) to help EKF suppress high frequency or strong interference. The IIR-EKF algorithm is implemented in the STM32H743 microcontroller. The experimental results show that the data variance is very big when the EKF acts alone, and great discrepancy occurs when strong interferences are encountered. When unscented Kalman filter (UKF)acts alone, the data variance is smaller than that of EKF. Although it does not depend on the initial noise parameters, it does not meet the requirements. After the addition of the second-order BF, the data variance is significantly reduced, the instantaneous error is greatly suppressed, and the stability and anti-interference ability of the system are enhanced. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the existing cross-modal methods of chest X-ray images and diagnostic reports focus on global information alignment, which ignore the fine-grained semantic association between chest X-ray images and diagnostic reports, resulting in low retrieval accuracy and poor matching degree, this paper proposed a method named CDTCR (chest X-ray images and diagnostic reports for twin-towers cross-modal retrieval) Specifically, for fine-grained semantic representation, it proposed an image encoder composed of residual network to learn the fine-grained features of the image and a BERT model composed of Transformer to learn the fine-grained semantic features of the diagnostic report. In order to solve the problem of fine-grained semantic association, it also designed an information alignment strategy of two different granularity modes for the global image to sentence and local region to phrase, which solved the problem of insufficient fine-grained semantic association between different modes. The experimental results on a large-scale medical dataset MIMIC-CXR show that CDTCR has higher retrieval accuracy and better interpretation than the existing cross-modal retrieval methods. [ABSTRACT FROM AUTHOR]

To solve the problem of combustion state recognition in solid oxide fuel cell (SOFC) combustor, this paper proposed a combustion state recognition method based on attention mechanism and image feature pyramid. The method adopted the adaptive gamma correction with weighting distribution (AGCWD) to standardize the input images. It combined two single full connections with 1×1 convolution to replace the squeeze and excitation structure, and proposed a hybrid attention structure combined with spatial attention structure to enhance the ability of feature extraction. To provide multi-scale information communication capability, it constructed the multi-scale bidirectional fusion pyramid by means of bidirectional computation and multi-scale fusion. The experimental results show that the proposed method reaches 99.22% accuracy under the premise of 3.98 M parameters and 397 M floating point operations (FLOPs), and effectively identifies the combustion state in SOFC combustor. [ABSTRACT FROM AUTHOR]

This paper proposed a matching wavelet depth model transfer learning algorithm to solve the problems of low efficiency of feature identification and limited number of samples, which exist in traditional methods for small vibration fault diagnosis. Firstly, using Morse continuous wavelets to capture small changes in a one-dimensional fault signal by matching and up-dimensioning the signal to obtain a visual enhanced feature image. Then, this algorithm effectively transfers the source domain model of the depth transfer network. This model has efficient image learning experience and can reduce the number of training samples in the target domain. Finally, optimize the parameters of the process for this algorithm based on limited data in model transfer. The algorithm has proven to be highly generalisable, allowing the detection and localisation of minute features in multiple operating conditions and effectively reducing the reliance on data, greatly improving the speed of computing and diagnostic accuracy. [ABSTRACT FROM AUTHOR]

At present, Chinese named entity recognition model has errors in recognizing entities with nested structures, so it can not be recognized accurately. The method based on span can find nested entities, but when detecting text, it often gene-rates span without entities, and can not clearly define the span boundary, which increases the model burden. To solve this problem, this paper proposed a Chinese nested named entity recognition model based on vocabulary fusion and span boundary detection. The model used multi-word fusion method to enhance text features, and it merged multiple lexical information related to the characters in the target sentence in the designed injection module. Ant then it integrated into BERT to obtain more comprehensive context information, and provided better span representation. Secondly, it added a span boundary detection method to divide the span boundary by predicting the first and last characters of the span by perceptual classifier. Experiments on public data sets show that the model can effectively improve the recognition accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problems of uneven resource utilization and long task completion time in complex task group scheduling, this paper constructed a complex task group resource scheduling model to minimize the mean square error of resource load and the task group completion time, and proposed a multi-objective optimization algorithm based on boundary range local search and NSGA-Ⅱ, called BRLSN. The algorithm used an effective coding method and cross mutation operator for iterative optimization, and constructed an elite retention strategy based on local search in boundary region to expand the search scope of the algorithm and preserved good individuals in the population. Experimental results show that compared with other multi-objective algorithms, the convergence and diversity of BRLSN are significantly improved. At the same time, the algorithm convergence speed is faster, the population quality is higher, and the result value of the final objective function is obviously optimized. [ABSTRACT FROM AUTHOR]

Objectives: Satellite mission planning is an important way to rationally allocate satellite resources and obtain effective data. In order to solve the problem of resource waste in the absence of user demand for Qimingxing-1(QMX-1), we proposed a semantic description driven autonomous mission planning method for QMX-1. Methods: By analyzing the characteristics of earth observation targets and their requirements for remote sensing data, we have construct a semantic description model for global tasks and form a global earth observation target database. Based on the description model, we design a satellite autonomous mission planning method. By building a mission planning model, we can achieve autonomous planning for high-value targets and maximize the value of satellite in orbit applications. Results and Conclusions: We verifyed the proposed model based on the QMX-1 and proved the feasibility of the method in this paper. The model is applicable to different types of remote sensing satellites and can play a certain value in the future remote sensing constellation real-time intelligent service applications. [ABSTRACT FROM AUTHOR]

Objectives: Sea-land segmentation is of great significance for tasks such as ocean target detection and coastline extraction in synthetic aperture radar (SAR) image. To solve the problem of sea-land segmentation of multi-resolution SAR image in practical applications, this paper presents a sea-land segmentation method based on context and edge attention. Methods: The proposed method uses the channel attention mechanism to fuse context features of different scales and levels, and designs an edge extraction branch to provide edge information for further improving the segmentation result of the boundary area. In addition, a sea-land segmentation dataset of multi-resolution SAR image based on the Gaofen-3 satellite data is provided. The dataset covers multiple resolution images, including various sea-land boundary types such as ports, islands. Using this multi-resolution SAR image coastline segmentation dataset, we perform experimental analyses on the effectiveness of the proposed network and the contributions of each module. Results and Conclusions: Experimental results show that the proposed method can work well for the task of sealand segmentation, the overall classification accuracy and mean intersection over union achieve 98.21% and 96.47%, respectively. [ABSTRACT FROM AUTHOR]

Transition metal phosphate has attracted the attention of researchers in the field of electrolytic water because of its advantages of safety， cleanliness， low cost and high efficiency. Phosphate groups in phosphate have unique atomic geometric structure， strong coordination and various orientations， which are beneficial to stabilize the middle valence state of transition metals and accelerate proton conduction rate. However， its poor conductivity and low porosity have prompted researchers to explore and design more efficient transition metal phosphate electrocatalysts. Although researchers have invested a lot of time and energy， there are still many problems to be solved in the efficient development and utilization of transition metal phosphate electrocatalysts. In this paper， combined with the latest research progress of transition metal phosphate electrocatalysts， the development and design strategies of phosphate by researchers in recent years are introduced from the aspects of morphology control， defect engineering and interface engineering. At the same time， the opportunities and challenges faced by this kind of catalyst in the future material field are discussed from the aspects of scientific research and practical application. [ABSTRACT FROM AUTHOR]

There are various ways for various services to carry out accusation information interoperability. The establishment of a standardized accusation information interoperability framework can provide a way to solve the problem of diversified and complicated implementation of accusation information interoperability between systems. This paper analyzes and compares three types of design methods, centering on the Battle Management Language (BML), combined with the structural concepts of Service-Oriented Architecture(SOA) and Unified Architecture Framework (UAF). Finally, a directly connected structural framework was chosen to build, and a public service supporting structural framework including six levels of system, technology, resources, service, function and organization, and multiple elements such as ontology model, semantic mapping and model generation is constructed, providing theoretical support for the research on standardization of accusative information interoperability. [ABSTRACT FROM AUTHOR]

In order to solve the problem of dynamic evaluation of operational effectiveness of space-based information support equipment system, this paper presents a method of operational effectiveness evaluation of space-based information support equipment system based on dynamic Bayesian network. Firstly, the data samples are obtained by the operational effectiveness evaluation model of system dynamics, then the dynamic network is established based on the static Bayesian network operational effectiveness evaluation model, and the dynamic Bayesian network operational effectiveness evaluation model is established by EM parameter learning algorithm. Finally, the operational effectiveness of space-based information support equipment system is analyzed. Through this model, the relationship and influence degree of many influencing factors in the space- based information support equipment system are deeply analyzed in the battle scenario of attacking moving ship targets in the sea direction. At the same time, simulation experiments verify the effectiveness and feasibility of this method in the operational effectiveness evaluation of space-based information support equipment system, which can provide solid technical support for the operational effectiveness evaluation of space-based information support equipment system. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the driver is difficult to monitor and input complex during the running process, this paper proposed and implemented a driver fuzzing tool DrgenFuzzer based on the fitness and input constraint model. The tool utilized the kernel tracing technology combined with the static analysis of binary programs to realize the information monitoring of the driver operation, analyzed the driver interface parameters, designed a sample constraint scheme, proposed a new fitness calculation scheme and a crossover mutation scheme. Experiments show that compared with the commonly used kernel fuzzing tools, the test success rate of samples generated by this tool after the input constraint model is more than 10 times that of other tools, and the quality of the generated samples is higher. This tool performed a fuzzy test on the driver and exploits the null pointer reference vulnerability in the i2c driver. DrgenFuzzer can effectively guide and standardize sample generation, improve the success rate and operating efficiency of sample testing, and enhance the vulnerability mining ability. [ABSTRACT FROM AUTHOR]

Most existing traditional congestion control algorithms are difficult to adapt to the highly dynamic link environment of wireless Ad hoc network. In order to solve the above problem, this paper proposed a method of improving the performance of congestion control based on deep reinforcement learning, Enhanced-CC. It conducted a preliminary detection of the congestion window by using the traditional congestion control algorithm. On this basis, the method used deep reinforcement technology to learn the real-time optimal congestion window range of the link. When the congestion window calculated by the traditional congestion control algorithm was too large or too small, the method adjusted the congestion window, so that the sending rate could match the highly dynamic link bandwidth, and the method could improve the transmission performance of the traditional congestion control algorithm. The experimental results show that Enhanced-CC can significantly improve the performance of traditional congestion control algorithms such as BBR, CUBIC, Westwood, Reno, and is superior to the performance of fully learning based congestion control algorithms such as PCC and PCC Vivace, and the combination of deep reinforcement learning and traditional congestion control algorithms such as Orca and DeepCC. [ABSTRACT FROM AUTHOR]

Large-scale spiking neural network (SNN) parallel simulation is an important means to explore brain function. The difficulty lies in properly mapping the load to the multi-process parallel computing platform and improving the simulation speed. In order to solve this problem, this paper proposed a SNN load balancing method based on united weight hypergraph partitioning, which could solve the problem of balancing computing load and communication load between processes in parallel computing, and improve the speed of SNN simulation. And it used sparse communication instead of collective communication to solve the problem of data redundancy in the process of event communication and improve communication efficiency. The experimental results show that this method makes the simulation time of the cortical microcircuit model with STDP synapse scale of 20% shortened by about 64.5% compared with the standard cyclic allocation algorithm, and about 57.4% compared with the ordinary supermap allocation algorithm. At the same time, the communication time is reduced by more than 90%. [ABSTRACT FROM AUTHOR]

In the consistency detection of business process discovery research, existing methods of multi-perspective alignment between event logs and process models can only obtain the optimal alignment of one trace with the process model at a time. Meanwhile the computation of heuristic function in obtaining the optimal alignment is complex, leading to low computation efficiency. To solve above problems, the paper proposed a multi-perspective alignment between batch traces of event log and process model based on trace minimum edit distance. Firstly, the study selected multiple traces in the event log to form batch traces, and used a process mining algorithm to obtain the log model of the batch traces. Then it obtained the product model of log model and process model and their transition system, which was the search space of batch traces. Then it designed a heuristic function based on the minimum edit distance between Petri net transition’s sequence set and the remaining traces to speed up the A* algorithm. Finally, the study designed a multi-perspective cost function that could adjust the weight of data and resource perspectives, and proposed a method of multi-perspective optimal alignment between each trace in the batch traces and the process model with the transition system of the product model. Compared with existing work, the simulation results show the proposed method takes up less memory space and uses less running time. This method improves the computation speed of heuristic function for optimal alignment, and obtains all optimal alignments of batch traces at one time, thus improves the efficiency of multi-perspective alignment between event logs and process models. [ABSTRACT FROM AUTHOR]

In order to fix the integer ambiguity quickly and accurately, this paper proposed a new ambiguity search algorithm based on measurement accuracy constraints to solve the problems of the LAMBDA algorithm, such as the wide search range of the integer ambiguity and the low search efficiency. When the least squares problem got optimally weighted, the algorithm took the carrier phase measurement accuracy as the constraint condition to test the fixed ambiguity solution on the basis of the stan-dard differential evolution （DE） algorithm. This algorithm solved the influence of different satellite altitude angles, and could achieve 99% success rate when solving 3D integer ambiguity. Compared with the MLAMBDA algorithm, the standard DE algorithm and the adaptive weighted differential evolution （AWDE） algorithm, the proposed algorithm further improves the efficiency and success rate of ambiguity resolution. [ABSTRACT FROM AUTHOR]

In order to meet customers’ individual needs for delivery methods and specific time in urban distribution, this paper introduced the concept of customer preference to describe the delivery needs of home delivery and self-pickup services, and established vehicles path optimization model with different delivery options with the optimization goal of minimizing the total ope-rating cost. Considering the complexity of the model, it introduced a variety of operators, and designed an adaptive large neighborhood search algorithm and improved to solve the problem. Finally, combined with a case analysis of a distribution case in Nan’an district of Chongqing city, the validity of the model and algorithm is verified. [ABSTRACT FROM AUTHOR]

Acid mine drainage(AMD) is one ol the most serious environmental pollution, mainly caused by pyrite oxidation. AMD treatment mainly has two ways of end-treatment and source control, end-treatment technology cannot lundamentally solve the problem ol pollution, so Irom the source control ol pyrite oxidation is the lundamental way to control AMD. The main source control technologies include covering method, sterilization method and surface passivation method. In this paper, on the basis ol introducing the causes ol AMD, various surlace passivation techniques are reviewed. The research status ol organosilane, carrier-microencapsulation, sell-healing and other technologies is summarized. The advantages and disadvantages ol dilierent methods are analyzed, and the future research direction is proposed in view ol their shortcomings. It provides relerence lor solving the problem ol pyrite oxidation and realizing ellective control of AMD pollution. [ABSTRACT FROM AUTHOR]

Aiming at the current hand pose estimation which doesn’t fully use 2D information to assistance 3D hand mesh reconstruction, this paper further introduced HOG feature of the RGB image based on the use of hand Mask and 2D heatmaps. This paper designed the framework of multi task learning CNN to extract the 2D information, and used the topology of the hand to fused the relevant information. In order to solve two problems: the consistency of hidden Embeddings dimensions in Transformer Encoder as well as requires heavy computational resources, the model introduced a new MLP (Multi-Layer Perception) module into transformer encoder to increase the length of Embeddings and decrease the dimensions of Embeddings, thereby the hand Mesh was completed in the way from coarse to fine. The new method proposed in this paper has achieves better results in two Frei-HAND and RHD datasets. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low accuracy of 6D pose estimation for textured models under occlusion, this paper proposes an end-to-end 6D pose estimation algorithm based on local feature representation. Firstly, this paper proposes a Spatial and Coordinate Attention mechanism to obtain accurate localization information. A YOLOv5-CBE detection network forms by adding the attention mechanism to the backbone network and introducing a weighted Bidirectional Feature Pyramid Network in the detection layer. The Precision, Recall and mAP@0.5 of YOLOV5-CBE algorithm rise by 3.6%, 2.8% and 2.5% respectively, and the coordinate error of local feature center point decreases by 25% at most. Secondly, the YOLOv5-CBE network detects the local feature key points and calculates 6D pose of the model with 3 D Harris key points by Singular Value Decomposition, and the algorithm can guarantee 2D reprojection Accuracy and ADD Accuracy above 95% with 70% occlusion, which has a strong robustness. [ABSTRACT FROM AUTHOR]

Existing task offloading strategy usually makes offloading decision within one time slot without considering the internal relationship between multiple offload time slots, so they cannot be offloaded according to the actual needs of tasks. To solve this problem, this paper proposed a task secondary application offloading strategy based on deep Q network（DQN-TSAO）. Firstly, this paper introduced a three-layer of cloud-edge-end architecture that supports task secondary application offloading, and established priority model, delay model and energy consumption model for task offloading. Second, aiming at minimizing system energy consumption, the energy consumption optimization problem is transformed into a Markov decision process problem of maximum cumulative offloading reward. Finally, DQNTSAO algorithm can extract the task offload characteristics of each time slot, which enabled the task to obtain the optimal offloading decision of multiple time slots in the continuous interaction with the environment. Simulation results validated that DQN-TSAO algorithm can effectively reduce the total energy consumption of the system in a period of time. [ABSTRACT FROM AUTHOR]

Nowadays, human society has entered the ternary spaces, where geographical space, human space and information space are integrated. Driven by new technologies and demands, the three basic elements of map spatial cognition, cognitive subject, object and expression, have shown generalized characteristics. The existing map spatial cognition theories and methods can no longer support the accurate cognitive expression of pan-maps. Therefore, this paper first summarizes the progress of map spatial cognition research at home and abroad and its limitations, including the extension of cognitive subject studies， visualization of information space, usability of novel pan-map representation. Then, we propose a panmap spatial cognition research framework, including experimental methodology, cognitive mechanism, and expression model. To the end, we hope that this paper can provide deep insights for future studies and pro‐ mote the establishment of related theories and methods that adapt to the development trend of pan-maps, so as to solve a series of problems brought by the continuous generalization of elements of map spatial cognition. [ABSTRACT FROM AUTHOR]

Software defined networking (SDN) is a network architecture technology with the functions of network programming and separation of data plane and control plane. The security issues in SDN have attracted much attention in the industry along with the emerging of SDN networks. Due to the characteristics of SDN network architecture, saturation attack is particularly prominent among many security problems. To solve these problems (1) saturation attack detection under traditional networks is difficult to adapt to in the SDN environment, and the detection rate is low; (2) The use of machine learning-based saturation attack detection method requires a large amount of historical data, which has certain requirements for the system’s computing and hardware resources, this paper proposes a saturation attack detection method based on subnet temperature in SDN network environment. Firstly, the network was divided by the concept of subnet, which reduces the detection granularity, improves the detection efficiency and reduces the resources required for detection. After that, the subnet temperature was used as the judgment standard to measure the occurrence of saturation attack, and the detection method of junction and subnet temperature gives a defense scheme for saturation attack. Finally, the simulation comparison was made on the experimental platform of Mininet, Ryu and sFlow. The experimental results showed that compared with other traditional detection technologies, the saturation attack detection algorithm proposed in this paper can ensure higher detection accuracy, higher reliability, complete detection data, high detection rate and low false alarm rate while using fewer system resources. [ABSTRACT FROM AUTHOR]

At present, the research on combining deep learning with Visual SLAM(Simultaneous Localization And Mapping) to construct indoor 3 D pointcloud semantic map is in full swing. In order to solve the problems that traditional Visual SLAM networks have low accuracy and poor speed and lack semantic information, this paper proposed a novel RGB-D semantic segmentation network. The network uses the depth information which less affected by the light in indoor scenes to improve the accuracy of segmentation, and meanwhile designs the lightweight multi-scale residual module(MRAM) and Atrous Spatial Pyramid Pooling(ASPP) module to lightweight the segmentation network and improve the segmentation accuracy. Firstly, ORB-SLAM2 network screens the input image sequences to obtain keyframes. Then, the keyframes go into the proposed semantic segmentation network to get the 2 D semantic label, and then map the 2 D semantic information to 3 D pointcloud space. Finally, the method uses the Bayesian algorithm to update the 3 D map to obtain the globally consistent 3 D pointcloud semantic map. The experiments adopt NYUv2 dataset to verify the performance of semantic segmentation network, and adopt TUM dataset construct pointcloud semantic map. The results show that the performance and speed of the semantic segmentation network in this paper are better than the existing models, and the combination of semantic segmentation network with visual SLAM can meet the requirements of constructing 3 D dense semantic pointcloud map accurately and quickly. [ABSTRACT FROM AUTHOR]

In the actual wireless environment, the influence of shadow and fading will lead to different characteristics of the signals received by the sensor nodes. Therefore, some cooperative nodes in deep fading will have serious missed detection, which will affect the final result of fusion operation. To solve the above problems, this paper proposed a soft decision cooperative spectrum sensing method for cognitive wireless sensor networks(CRSN) based on entropy weight method. The method organized the sensor nodes into logical groups to improve energy efficiency and sensing performance. After receiving the soft sensing information from all member nodes, the cluster head used the equal gain soft fusion to fuse among clusters, and then forwarded the local decision to the fusion center. In the final decisionmaking process, this paper used the entropy weight method to assign the optimal weight to the corresponding clustering local decision. Simulation results show that this method is superior to the typical cooperative spectrum sensing clustering scheme in terms of detection probability and total error probability. [ABSTRACT FROM AUTHOR]

To solve the problems of low convergence accuracy, slow convergence speed and easy to fall into local optimum of traditional particle swarm algorithm, this paper proposed an improved particle swarm algorithm with multistrategy fusion. Firstly, in order to accelerate the convergence speed of free particles, the improved algorithm used a method of updating the position of free particles based on the midperpendicular algorithm. secondly, the improved algorithm designed a strategy of generating exploding particles near the optimal particles to enhance the optimizationseeking accuracy and optimization-seeking speed of the algorithm, and the improved algorithm also designed a particle velocity updating strategy relying only on the global optimal particle position to accommodate the first two strategies. Finally, The algorithm also used the inertia weights and particle position update methods of the simplified particle swarm optimization algorithm based on probabilistic hierarchy. This paper designed a few comparison experiments with other five improved particle swarm algorithms, and the results show that the improved algorithm in this paper has a greater advantage and better performance whether dealing with low-dimensional problems or high-dimensional problems. [ABSTRACT FROM AUTHOR]

This paper proposed an improved unscented Kalman filter(UKF) mobile robot localization algorithm based on Sage-Husa filter to solve the problem of low localization accuracy caused by unknown or inaccurate noise statistical characteristics in multi-sensor fusion localization process. Firstly, it established the mobile robot localization model. Then, according to the time-varying characteristics of noise statistics, this article adaptively estimated the state noise and measurement noise to reduce the error of position calculation caused by perturbation noise. The paper also introduced the convergence factor in the state update to accelerate the convergence speed of the algorithm. Finally, the UKF algorithm and the improved UKF algorithm are applied to the laboratory mobile robot for simulation experiments. The experimental results show that the proposed algorithm has strong resistance to state disturbance, and has remarkable effect on improving the accuracy and stability of robot positioning. [ABSTRACT FROM AUTHOR]

In order to solve the problem of strong popularity bias in recommendation lists generated by Bayesian personalized ranking algorithm under the condition of unbalanced data, this paper designed a hybrid recommendation algorithm based on feature embedding to remove popularity bias. Firstly, this paper used convolutional neural network to extract user and item features, determine user preferences, and fill original unbalanced data according to user preferences. Secondly, this paper embedded the user preference features extracted from convolutional neural network into the Bayesian personalized ranking algorithm for hybrid recommendation. Finally, it trained the mixed recommendation model with score filled data, and obtained the personalized ranking list without popularity bias. In order to verify the performance of the algorithm, it conducted the analysis and comparison experiments on Movielens-100 K and Movielens-1 M. And the experimental results show that the popularity bias is reduced by about 50%～60% and the accuracy is improved by about twice. [ABSTRACT FROM AUTHOR]

Wei Songjie et al. proposed an identity-based signature scheme（named mIBS）, and used a security mediator（SEM） node to realize real-time revocation of entity identity in the mIBS scheme. The SEM held a part of the signature key, and generated a signature by working collaboratively with a signer. This paper analyzed the security of the mIBS scheme, found it had serious security flaws, and presented a specific attack instance. In the attack instance, a signer can stole the key held by the SEM through once normal signature interaction with a SEM, and then bypassed the SEM to implement a signature independently. This paper proposed an improved signature scheme（named mIBSG）. The mIBSG scheme remedied the security flaws of the original scheme by randomizing the private key held by the SEM. Further, this paper established a security model for mIBS scheme, named mEUF-CMIA, and defined a new type of adversary that simulated malicious but legitimate signers. The new adversary had the power to generate a forged signature independently through asking random oracles. Based on the new security model, this paper deduced the unforgeability of the mIBSG scheme as solving the CDH problem on a cyclic group. Comparative analysis showed that the mIBSG scheme achieved provable security with a small calculate efficiency loss. The mIBSG scheme can be used to build an IBC-based cross-domain authentication system. [ABSTRACT FROM AUTHOR]

In order to solve the problems of excessive system cost and long response time of user-oriented service function chain deployment algorithm in mobile edge computing networks, this paper proposed a joint optimization design method for service function chain cost and delay. First, in the construction stage of the service function chain, according to the node location and resource status, the algorithm selected the current best node to reduce the delay between VNFs（virtualized network functions） to improve the response time of the service function chain. Secondly, in the service function chain deployment stage, due to the limited mapping resources, this paper used the node selection algorithm to determine the optimal node mapping order when serving the mapping nodes and selected the shortest weighted path as the communication link between VNFs. The experimental simulation results show that, compared with the existing solutions, the method can effectively reduce the delay and deployment cost, and can significantly improve the success rate of service function chain deployment. [ABSTRACT FROM AUTHOR]

Synthetic minority oversampling technology（SMOTE） is one of the effective methods to solve the classimbalanced problem. However, the linear interpolation mechanism of SMOTE restricts the synthesized samples to the connecting line of the original samples, resulting in a lack of diversity for new samples, and may generate noisy samples when this line passes through the majority class region. In response to the above issues, this paper proposed a generation mechanism for minority samples with hypercuboid constraints. This mechanism constructed a hypercuboid as the generation region of new samples instead of linear interpolation, thereby increasing the variability between the synthesized samples and the original samples. Then, it detected whether there were majority samples in the hypercuboid to determine whether to adjust the hypercuboid, which aimed at preventing the new samples into the region of the majority class. This paper integrated the proposed mechanism into three oversampling methods, i. e., SMOTE, Borderline-SMOTE, and ADASYN, by using it to replace linear interpolation, and then experimentally evaluated the integrated methods on 11 benchmark datasets from KEEL. The results showed that compared to the original methods, the integrated methods could help the classifier to obtain higher F1 and comparable G-mean. It verifies that the hypercuboid generation mechanism can significantly improve the classifier’s ability to recognize minority samples, and meanwhile the majority samples are also taken into account. [ABSTRACT FROM AUTHOR]

The U-turn task is one of the contents of autonomous driving research, and most of the solutions under the standard roads in cities cannot be implemented on non-standard roads. Aiming at solving this problem, this paper establishes a vehicle U-turn dynamical model and designs a multi-scale convolutional neural network to extract feature maps as the input of the agent. In addition, for the sparse reward problem in the U-turn task, this paper proposes a hierarchical proximal policy optimization algorithm that combines hierarchical reinforcement learning and proximal policy optimization algorithm. In experiments with simple and complex scenarios, this algorithm learns policies faster and has a higher success rate of U-turn compared to other algorithms. [ABSTRACT FROM AUTHOR]

The homology analysis of diffusion results aims to identify which historical diffusion results were produced by the same set of diffusion sources. This paper uses Bernoulli Mixture Model to solve this problem: The diffusion results generated by the same group of sources correspond to the same Bernoulli component, and the parameters of the corresponding Bernoulli component reflects the arrival probability of the sources’ influence at each point. Then the log-likelihood function is constructed based on the model and the observed data. To determine the results of homology analysis, the Bernoulli parameters in the log-likelihood function are calculated through using the EM algorithm. A large number of experimental results verify that compared with the traditional clustering algorithm, the algorithm in this paper can get higher accuracy of the homologous clustering. That is to say, the algorithm in this paper can perform homology analysis on the propagation result data more efficiently and accurately. [ABSTRACT FROM AUTHOR]

Due to the numerous but scattered studies on the bias of recommendation systems and few relevant research reviews, according to the subject that causes the bias, this paper divides bias into the bias in user behavior, the bias in item display, and the bias in recommendation algorithm, and then subdivides it into nine specific categories, such as selection bias and conformity bias, and gives the definition and causes of each bias; This paper focuses on sorting out the research on correlation recommendation methods and causal recommendation methods to solve the bias problem, and further classifies them according to the technical means or the first, middle and last three stages of the training model, and analyzes the differences between different types of research; This paper summarizes common datasets and reviewers of bias studies and forecasts the development trend of bias-oriented recommendation methods from different categories, and hopes to provide some help for the follow-up bias research in recommender system. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high noise and insufficient feature extraction of Retinex-Net in low-light image enhancement processing, this paper proposes a new network structure. First, the Retinex-Net network was used as the basic model to decompose the input image, and a residual shrinkage network was introduced in the convolutional layer to remove the noise generated during the decomposition process. Then, in order to preserve the details of the image and suppress noise while enhancing the brightness, the enhancement network was divided into three small sub-networks for processing respectively. Finally, the adjusted images were fused to obtain an enhanced image. Compared with the SIRE, LIME, GLADNet, Retinex-Net algorithm, experiments show that the peak signal-to-noise ratio of the images processed by the algorithm in this paper has an average increase of 3.48 dB, the mean square error has an average increase of 0.082 7, the structural similarity has an average increase of 0.146, and the lightness order error has an average increase of 271.6. [ABSTRACT FROM AUTHOR]

In order to solve the problems of large space span, bad environmental conditions, periodic changes and high safety risks in the measurement and payment management of waterway engineering, and improve the efficiency of measurement and payment management of waterway engineering, this paper analyzes the characteristics of measurement and payment of waterway engineering, and adopts the method of combining project management with BIM technology and information technology, this paper puts forward the idea of measurement and payment management based on BIM cloud platform, builds a BIM cloud platform based on a channel regulation project of the Yangtze River, carries out the practical application of measurement and payment management method of channel project based on BIM cloud platform, and realizes the graphical interactive and multiparty collaborative measurement and payment management based on BIM, it promotes the improvement of refined management level of waterway engineering projects and provides a new idea for measurement and payment management of waterway engineering projects. [ABSTRACT FROM AUTHOR]

It is difficult for traditional highlight removal algorithms to effectively deal with the processing of stepped highlight images in the stepped palletizing of factory robots. To solve this problem, based on the knowledge of conditional generative adversarial network, this paper proposes a stepped surface highlight removal network model named MSDGC-GAN (Multiscale Spatial dense gradient cascade generative adversarial network). In this method, the Spatial Contextual Feature Dense Block (SCFDB) aims to deeply extract the spatial background information between pixel rows and columns. In addition, the multi-scale gradient cascade structure aims to compensate for the scale feature loss in network down sampling, and this structure can endow the model with multi-scale discriminative ability while stabilizing the training gradient distribution. Based on the analysis of the classical two-color reflectance model, this paper apply the maximum diffuse reflectance estimation to the loss function to supervise the network training. The experimental results show that the proposed method outperforms the compared methods in both the classical highlight dataset and the self-made stepped highlight image dataset. [ABSTRACT FROM AUTHOR]

With the rapid development of electronic information technology, the security of data has become increasingly serious. The traditional single cipher algorithm can not meet the requirements any more. In order to solve these problems, this paper proposed a hardware design scheme of high-speed hybrid encryption system based on SM2, SM3, SM4, in which the hardware structure of SM2 and SM4 algorithms is optimized. Besides, this paper optimized the SM2 algorithm with Karatsuba-Ofman modular multiplier and parallel with point operation. For SM4 algorithm, this paper designed a subpipelining hardware architecture based on composite field S-box so as to improve its throughout. The experimental results showed that all the algorithm circuits implemented by the system have high performance: the point multiplication time of SM2 was 68.37 μs; the hash generation of SM3 only took 0.71 μs; the throughput of SM4 was up to 53.76 Gbps. Compared with the SM2 algorithm with the same security, this hybrid system had 26.67% less encryption time. By comparing and analyzing with related work, it can be proved that this scheme has certain advantages in security and encryption and decryption performance. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the results of the gate reassignment algorithm can’t meet habits of different operators, this paper proposed a method that accords with the actual operators’ operating habits. Firstly, this paper established the spatial model of decision-making environment by using flights characteristics and occupancy of gate resources. The model transforms manual operating data into a multi-channel time-space matrix. Then, make use of CNN-based generative adversarial network to match the order decision-making operation strategy. The simulation results show that actions with reliability scores of more than 90% account for up to 84.4%. The model has a good ability to distinguish the operation data from 3 different operators. Compared with dynamic adjustment result under perturbance, this algorithm can obtain an adjustment scheme whose flight-gate attribute characteristics are closer to the original manual operation. [ABSTRACT FROM AUTHOR]

The current service substitution methods mainly focus on the function and quality matching between the substitutive service and invalid one. These methods rarely considered service collaboration. To solve the above problem, this paper proposed a service substitution method integrating service clustering and collaboration. Firstly, this method constructed service collaboration map according to service composition relationships. It used Node2 Vec to sample node sequences and generate collaboration vectors from the service collaboration map. This method computed service collaboration similarity based on these collaboration vectors. Then, this paper introduced service clustering to reduce the search space during service substitution., It generated the candidate substitutive service set from the functional and quality levels. Finally, this method integrated the service quality score and collaboration similarity to achieve the recommendation of substitutive services. Experiments show that the proposed method can effectively improve the efficiency and rationality of service substitution. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the Raft algorithm cannot resist the attacks of Byzantine nodes and the logs are easy to tamper with, this paper proposes an RB-Raft (Resist Byzantine-Raft) algorithm that resists Byzantine nodes. Firstly, this paper uses the method of hash chain to iteratively hash each log. At the same time, verification of the log through the dynamic verification mechanism, so that the malicious behavior of the leader node has a certain fault tolerance rate, which solves the problem of log forgery and verification. Secondly, this paper proposes a "Legacy" mechanism based on threshold encryption, which makes it legal for Candidate nodes to pull votes. This mechanism can prevent Byzantine nodes from randomly the attack of pulling votes to replace leader nodes, and solves the problem that Byzantine nodes affect the system consistency. The experimental results show that the proposed RB-Raft algorithm has the ability to resist Byzantine nodes, and its log recognition rate can reach 100%. At the same time, compared with PBFT, the consensus latency of the algorithm in this paper is reduced by 53.3%, and the throughput is increased by 61.8%. The algorithm proposed in this paper is suitable for consensus in untrusted consortium chains. [ABSTRACT FROM AUTHOR]

In order to solve the problems of large number of parameters and high GPU resource cost in binocular vision stereo matching method based on neural network in robot and unmanned driving fields, this paper proposed a multi-scale aggregation stereo matching method. Firstly, this paper proposed a multi-scale feature extraction network to obtain richer features without changing the resolution by using dilated convolution, and introduced the attention mechanism was. Then, features at different resolutions were cross-fused to improve the feature information. Secondly, the acquisition method of cost volume was changed, the cost volume was obtained by aggregation at low scale, and continuously combined the high-scale similar information to update iteratively, and cross fused multiple cost volumes to obtain the final cost volume. Finally, combined with the refinement module of attention mechanism, the outliers and discontinuous regions in the initial disparity map are corrected to obtain the final disparity map. Experimental results show that the algorithm can run under low parameter number and low cost GPU resources, and obtain good matching accuracy. [ABSTRACT FROM AUTHOR]