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The TFAHP-FAHP algorithm model constructed in this article can preferably avoid subjective assumptions caused by human factor. The new energy vehicle power battery is a core component for improving the performance of electric vehicles, and constitutes one of the most valuable parts of the invention patent for new energy vehicles. Patent pledge financing is a brand new form with great potential. However, as a result of the imperfect system of the patent pledge financing, banks and other financial institutions are exposed to an increased risk in the process of the patent pledge financing, which seriously limits the development of the patent pledge financing for power battery. In view of the above issues, this paper uses the SPSS reliability and validity tests in order to develop a relatively complete and accurate index system. TFAHP algorithm model and fuzzy comprehensive are combined to determine the final correction coefficient. In this study, the income method is used to determine the pledge valuation value of a new energy vehicle power battery patent portfolio, and the AHP algorithm method is used to determine the weight of the value of the invention patent portfolio within the patent portfolio. As a result, in this paper, the pledge value of the patent portfolio is multiplied by a proportion of the invention patent portfolio's value, and then multiplied by a correction coefficient to obtain the final new energy vehicle power battery invention patent portfolio pledge value. Using this reference value, technology-based energy companies can pledge financing for new energy vehicle power battery invention patent portfolios. [ABSTRACT FROM AUTHOR]

The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training set, test set and comparison set according to the data characteristics. At last, this paper used the data preprocessing method to verify the LSTM NN algorithm. The experimental results showed that the LSTM NN algorithm analyzed in this paper can effectively improve the generalization ability of financial market trend prediction models while ensuring the prediction accuracy. Through experimental analysis, this paper found that the average accuracy rate of using LSTM NN algorithm was 2.25% higher than that of using traditional NN algorithm. This research is primarily aimed at developing effective methods for predicting stock market trends in the continuously evolving Chinese securities market. The core objective is to empower investors with precise guidance by enabling them to make well-informed investment decisions. Achieving accurate predictions holds the potential to significantly impact economic operations in a positive way. Therefore, this research direction is of paramount importance, offering substantial value both in academic exploration and practical application. [ABSTRACT FROM AUTHOR]

The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters. [ABSTRACT FROM AUTHOR]

With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS trajectory data in lifelogs is typically categorized into continuous and discrete trajectories. Finding a point of interest (POI) from discrete trajectories is a challenging task to do and has caught little attention so far. This paper suggests an LP-DBSCAN model for mining personal trajectories from discrete GPS trajectory data. It makes use of the hierarchical structure information implied in GPS trajectory data and it is suggested a variable-levels, variable-parameters clustering method (LP-DBSCAN) based on the DBSCAN algorithm to increase the precision of finding POI information. Finally, the Liu lifelog dataset is subjected to a systematic evaluation. In terms of GPS data that are not evenly distributed geographically, the experimental results demonstrated that the proposed algorithm could more accurately identify POI information and address the adverse effects caused by the global parameters of the traditional DBSCAN algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a photovoltaic (PV) dynamic maximum power point tracking algorithm based on improved PSO (particle swarm optimization) optimization in response to the problems associated with low tracking accuracy, poor immunity, and the ease of falling into local optimization, as well as the failure of the traditional MPPT algorithm (maximum power point tracking algorithm) under partial shading conditions. Firstly, three traditional MPPT algorithms are compared and analyzed, followed by simulation testing under standard and partial shading conditions. The advantages and disadvantages of three traditional algorithms are analyzed. Secondly, it is proposed that dynamic inertia weights and learning factors be applied synchronously during the optimization process in order to speed up the tracking speed of particle swarm optimization. In order to evaluate the effectiveness of different algorithms, it is best to simulate them under static and dynamic conditions. In comparison to the standard particle swarm algorithm and three other traditional algorithms, the proposed algorithm is capable of tracking the maximum power point quickly and accurately under conditions of uniform illumination and static and dynamic partial shading. There is a faster convergence speed as well as a greater degree of accuracy at steady state. [ABSTRACT FROM AUTHOR]

To solve the problems of high storage resource consumption and low efficiency of the RRT exploration algorithm in the late stage of exploration, this paper proposes an Improved Artificial Fish Swarm based Optimize Rapidly-exploring Random Trees multi-robot Exploration Algorithm. Firstly, the efficiency of a single robot's exploration of nearby unknown regions is improved by dynamically adjusting the step size of the RRT tree.Secondly, the improved artificial fish swarm algorithm is used to delete the redundant nodes in the RRT tree and optimize the node state in the RRT tree, which reduces the occupation of memory resources and improves the exploration efficiency of the RRT tree in the narrow environment.Results from comparative experiments in simulation environments with different degrees of openness show that the optimized exploration algorithm can save significant storage resources and show better exploration performance in narrow environments compared to the original RRT exploration algorithm. [ABSTRACT FROM AUTHOR]

With the raising popularity of digital devices in the current society, the present image detection system is becoming a great threaten. Especially the appearance of the recaptured images. It can be used in traditional invalid digital image detection algorithm. There is a new algorithm in this paper is presented to detect the recaptured and real image. The algorithm obtains low-frequency images, directional filtering images and high-frequency images by multiple application frequency domain filtering. Then the proposed algorithm analyzes the directional filtering images and high-frequency images by means of LBP algorithm to extract features. At last, the recaptured images were classified by the SVM. The experimental results demonstrated the algorithm in this paper could be effectively identify in the recaptured images. [ABSTRACT FROM AUTHOR]

In order to capture autobiographical memory, inspired by the development of human intelligence, a computational AM model for autobiographical memory is proposed in this paper, which is a three-layer network structure, in which the bottom layer encodes the event-specific knowledge comprising 5W1H, and provides retrieval clues to the middle layer, encodes the related events, and the top layer encodes the event set. According to the bottom-up memory search process, the corresponding events and event sets can be identified in the middle layer and the top layer respectively; At the same time, AM model can simulate human memory roaming through the process of rule-based memory retrieval. The computational AM model proposed in this paper not only has robust and flexible memory retrieval, but also has better response performance to noisy memory retrieval cues than the commonly used memory retrieval model based on keyword query method, and can also imitate the roaming phenomenon in memory. [ABSTRACT FROM AUTHOR]

As the current society is increasingly facing major challenges from extremism and terrorism, protecting key urban public facilities and important targets from destruction is an important challenge facing the security departments of all countries. Based on real scene, this paper conducts researches on anti-terrorism security game algorithms and emergency response models in response to the three key links of before, during and after terrorist attacks. First of all, this paper constructs a multi-round joint attack game and emergency response model based on cooperation, establishes the optimization problem of solving the defender's optimal strategy in mathematical form, and then obtains the optimal defense strategy. Secondly, in response to the fact that terrorists are not completely rational, a new hybrid model is constructed to propose an efficient allocation and scheduling algorithm for safe resources in response to terrorist attacks. Thirdly, a model of crowd evacuation strategy after a terrorist attack is built based on the problem of crowd evacuation in multiple rounds of premeditated cooperative attacks. Finally, taking the area of the first ring of a certain city as a real scene, a complete game system of the whole process is constructed, and the game effectiveness evaluation of the existing security resource allocation scheme in the first ring area is carried out. Through the research of this thesis, the author puts forward some new technical ideas for the current society's anti-terrorism governance, and hopes to provide some technical references for the decision-making of security agencies. [ABSTRACT FROM AUTHOR]

With the development of the Intelligent Transportation System, various distributed sensors (including GPS, radar, infrared sensors) process massive data and make decisions for emergencies. Federated learning is a new distributed machine learning paradigm, in which system heterogeneity is the difficulty of fairness design. This paper designs a system heterogeneous fair federated learning algorithm (SHFF). SHFF introduces the equipment influence factor I into the optimization target and dynamically adjusts the equipment proportion with other performance. By changing the global fairness parameter θ , the algorithm can control fairness according to the actual needs. Experimental results show that, compared with the popular q-FedAvg algorithm, the SHFF algorithm proposed in this paper improves the average accuracy of the Worst 10% by 26% and reduces the variance by 61%. [ABSTRACT FROM AUTHOR]

Taking Uyghur character recognition as an example, this paper use a method of image segmentation which combines traditional methods with CNN, and transforms and implements the Uyghur character on intelligent devices. Firstly, after analyzing the application of several general segmentation methods, this paper finds some shortcomings but also some ideas for Uighur segmentation. Then, starting from the characteristics of Uyghur language, such as structure, word formation and input habits, the author studies the idea of Uyghur adhesive language segmentation from the perspective of language characteristics, puts forward the basic algorithm of Uyghur symbol segmentation, and applies the Uyghur character adhesion segmentation based on minimum spanning tree and multi-queue primitive merging model to improve the segmentation efficiency. In addition, in order to solve the limitations of the traditional handwriting recognition framework of "preprocessing + feature extraction + classifier", this paper puts forward a new solution of Uyghur handwriting recognition technology combining prior domain knowledge with CNN, constructs a Uyghur handwriting recognition model on the basis of CNN and random elastic deformation, and also improves the recognition rate of the system by combining domain knowledge with CNN. According to the experimental results, we can conclude that the mixed method proposed in this paper can effectively break the technical bottleneck of traditional methods, thus improving the efficiency of segmentation and recognition. [ABSTRACT FROM AUTHOR]

Taking on the problems of unstable reconstruction performance and high reconstruction method computational cost induced by the unpredictability of the measurement matrix during power quality signal reconstruction, this research provides a power quality reconstruction model based on a self-encoding network and compressed sensing for the first time. The model includes a noise-adding module, an encoder module and a decoder module. The noise-adding module adds a specific amount of white noise in relation to the original signal to simulate the disturbance signal collected in the real scene. The encoder module uses a nonlinear measurement method to observe the power quality signal, and the decoder module completes the signal through a compressed sampling matching tracking algorithm. Refactoring. The outcomes of the experiment reveal that the compressed reconstruction model proposed in this paper significantly improves the efficiency and stability of power quality signal reconstruction. [ABSTRACT FROM AUTHOR]

At present, college students generally choose courses according to their own interests or understanding of the course, which has a certain subjectivity and blindness. In many cases, students know little about the courses before class, and only rely on the course name to guess the course content, so as to decide whether to take this course. However, the existing studies are mainly aiming at online learning resources which are heterogeneous, these methods cannot be effectively applied to the recommendation of university courses. This paper explores improve collaborative filtering for university application environments, provides a knowledge recommendation algorithm for university elective courses. First, we created individual models of the course and the students based on background information. Next, we use context-based recommendation and "Parent Class Filling" method to reduce the impact of Cold Start and Sparsity problem on the initial stage of the system. Then, recommendations are generated based on the course evaluation model and similarity matrix. We select several commonly used algorithms to achieve the recommendation, and the experimental results proved that the proposed algorithm is accurate and effective. [ABSTRACT FROM AUTHOR]

The Internet of Things has a large number of terminal devices and a wide range of deployment. This feature has higher requirements for reliable transmission and parallel connection, and is more prone to network congestion resulting in the loss of power information flow and excessive delay. The traditional congestion control algorithm is not suitable for the high concurrent mass heterogeneous iot terminal access control and mass information flow storage control. In this paper, the Transformer BBR algorithm is proposed, which is a congestion control algorithm based on BBR deep reinforcement learning combined with Transformer's excellent long-term prediction ability. In the BBR algorithm bandwidth detection stage, transformer model is used as an agent to detect the network state and map it to the congestion window, so as to find the network changing state in time and make the corresponding decision action. Firstly, the congestion control strategy is learned in the simulation network environment, and then the efficiency is verified in the simulation environment. Finally, the experiment shows that the algorithm can reduce the delay while ensuring good bandwidth performance, and is superior to the main application algorithms in network throughput. [ABSTRACT FROM AUTHOR]

The automation transformation of container lifting operations is one of the main technical issues in Rail-Truck intermodal transportation. To solve this problem, this paper analyzes the advantages and disadvantages of several existing container positioning methods, and proposed a container corner holes location detection method based on lightweight convolutional neural network and adaptive morphological image processing algorithm. This method locates the container through a visual sensor that shoots from top to bottom. In order to improve the positioning accuracy and calculation speed while maintaining a high recognition rate, this method first uses a lightweight SSD detector to quickly detect the rough coordinates of the container corner hole in the image. On this basis, the smallest rectangle detection method based on adaptive HSV filtering is used to detect the precise coordinates of the corner hole in the image. Experiments show that the recognition rate of this algorithm in the initial positioning process reaches 94.2%, the recognition rate of secondary positioning reaches 87.4%, and the final positioning error is lower than 3.765 pixels and 2.81 pixels in the heading and lateral directions, respectively. The overall calculation time of the algorithm can realize the positioning calculation of 30 frames per second, which shows that this method takes into account the characteristics of high measurement accuracy and high calculation speed on the basis of high recognition rate. [ABSTRACT FROM AUTHOR]

In order to solve the problem of noise sensitivity and low precision in image segmentation, a new image segmentation method based on super-pixel with neighborhood constrained and multi-level FCM clustering is proposed. Firstly, an improved SLIC algorithm of FCM is designed based on the local pixel region of the image to get the refined super-pixel. Then, the improved FCM clustering algorithm is combined with the spatial relationship of neighborhood super-pixel to mark the super-pixel categories. In order to verify the effectiveness of the algorithm, the artificial synthetic image with noise and natural image without noise are used for comparison experiments. The experimental result show the algorithm proposed in this paper can not only suppress the noise, but also improve the processing accuracy. [ABSTRACT FROM AUTHOR]

1) The problem this paper is going to solve is how to determine the optimal number of dimension when using dimensionality reduction methods, and in this paper, we mainly use local linear embedding (LLE) method as example. 2) The solution proposed is on the condition of the parameter k in LLE is set in advance. Firstly, we select the parameter k , and compute the distance matrix of each feature in the source data and in the data after dimensionality reduction. Then, we use the Log-Euclidean metric to compute the divergence of the distance matrix between the features in the original data and in the low-dimensional data. Finally, the optimal low dimension is determined by the minimum Log-Euclidean metric. 3) The performances are verified by a public dataset and a handwritten digit dataset experiments and the results show that the dimension found by the method is better than other dimension number when classifying the dataset. [ABSTRACT FROM AUTHOR]

Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers. [ABSTRACT FROM AUTHOR]

The stator winding inter-turn short fault of permanent magnet synchronous motor (PMSM) is a common motor fault with high probability. If this fault is not detected and handled in time, small initial fault will rapidly develop into other faults such as grounding short, inter-phase short, winding short et al., and even the motor damage. In order to diagnose the inter-turn short fault of PMSM, this paper firstly builds the model of the inter-turn short fault of PMSM, and then proposes a fast and simple fault diagnosis method for extracting the features of the inter-turn short fault under noise. For the first time, by introducing the full-short-circuit resistance R f that relates with the fault severity factor δ and by estimating the inductance of inter-turn short fault motor on-line according to the three principles of the inductance calculation, which is used in the inter-turn short fault of transformer winding, the inter-turn short analysis model of PMSM is deduced. The model can be used to simulate the performance not only the health of PMSM but also the fault of inter-turn short fault of PMSM in one phase winding. This paper analyses the transient and steady state performance of 3-phase unbalanced current, speed and electromagnetic torque ripple and the relationship between full-short-circuit resistance and short current under fault condition. Finally, a fast and simple on-line fault diagnosis method of PMSM with inter-turn short fault is proposed based on phase sensitive detection (PSD) algorithm. The method uses the fundamental and third harmonic features of inter-turn short fault current. The simulation results show that the model can be well suited for the simulation of PMSM with inter-turn short fault, and the method can effectively extract the features and diagnose the fault. [ABSTRACT FROM AUTHOR]

Since rail images are normally uneven gray-scale distributed, both Otsu algorithm and VEM algorithm are therefore difficult to recognize the defects from the background. This paper proposed a VEM (valley-emphasis enhancement) algorithm based on neighborhood mean weighting. First of all, according to the image histogram, calculate the effective traversal interval, and then divide the image into two types by using threshold histogram, and the between-class variance. Secondly, calculates the threshold of neighborhood average probability, with the neighborhood average weighted variance between classes. Generally, in the most efficient traverse range obtained between-class variance and the corresponding threshold value is the optimal threshold, that not only ensure maximizing variance between images, but also meet the best threshold value fell in the bottom of the histogram. Experimental results demonstrate that the algorithm proposed in this paper can segment rail images effectively, which is superior to Otsu algorithm and VEM algorithm in image segmentation performance, and has a certain degree of improvement on anti-noise. It has better image segmentation performance for histogram images with single peak distribution and double peak distribution, and expands the application scope of the algorithm. [ABSTRACT FROM AUTHOR]

The background segmentation of human motion images is the first step in the process of human motion analysis. It is the low-level processing part of human motion analysis. The processing effect at this stage directly affects the progress of the follow-up work. The segmentation results have a great impact on the final human motion analysis results. An important purpose of our research is to endow the computer with the ability being similar to the human vision. So, the computer can feel the motion object in the view and apprehend the behavior of the human more easily. The paper is studied on the representative theories and algorithms of the background subtraction with human motion monocular image. And this paper analyses the predominance and deficiency of these theories and algorithm. These algorithms include differential images, Running Gaussian average, the Mixture of Gaussians and BP neural network. The basic principle and steps of realization are expounded. Also the data of the evaluation is given. Experiment shows that the proposed algorithm of background subtraction is highly effective and it can cast the reflected light, shadow and inverted image well. The algorithm improves the correct rate of target segmentation and is suitable for human motion image segmentation in this complex ice field environment. [ABSTRACT FROM AUTHOR]

During the traditional cultural heritage virtual interaction algorithm in the interaction action recognition, the database is too single, resulting in low recognition accuracy, recognition time-consumer and other issues. Therefore, this paper introduces the multi feature fusion method to optimize the cultural heritage virtual interaction algorithm. Kinect bone tracking technology is applied to identify the movement of the tracking object, 20 joints of the human body are tracked, and interactive action recognition is realized according to the fingertip candidate points. In order to carry out the judgment virtual interactive operation of subsequent recognition actions, a multi feature fusion database is established. The mean shift is used to derive the moving mean of the target's action position and to track the interactive object. The Euclidean distance formula is used to train samples of multi feature fusion database data to realize the judgment of recognition action and virtual interaction. In order to verify the feasibility of the research algorithm, the virtual interactive script of ink painting in a cultural heritage museum is used to simulate the research algorithm, and a comparative experiment is designed. The experimental results show that the proposed algorithm is superior to the traditional virtual interactive algorithm in recognition accuracy and efficiency, which proves the feasibility of this method. [ABSTRACT FROM AUTHOR]

Considering that the current cost control algorithm has the problems of poor control effect and high cost of manpower and material resources in enterprise cost control, a target cost control algorithm based on Hypercycle model is proposed. By analyzing the relationship between capital, price, technology and target cost control, this paper analyzes the influence of uncertain factors on target cost, and constructs a cost control super cycle model to realize enterprise cost control. Examples show that the algorithm can effectively improve the cost effect of enterprises, and has the practicability of reducing enterprise costs and improving enterprise profits. [ABSTRACT FROM AUTHOR]

In multi-sensor data based partial discharge (PD) source location in high voltage (HV) cable, poor reliability of detection of pulse arrival time against noises, waveform distortion, amplitude attenuation is the major barriers to practical application. In this paper, an improved Allen-Bear PD pulse detection algorithm, which combines the advantage of the characteristic functions of the two time-window energy ratio based detection algorithms, is proposed for robust PD location. The most critical contribution of proposed method is the extension of frequency domain component compared to the characteristic functions of the original algorithm. Simulation and physical experiment results demonstrate that the proposed method not only ensures the detection accuracy of the PD pulse arrival time, but also improves the reliability under harsh practical scenarios. It performs significantly better than the existing detection algorithm in terms of anti-noise, anti-waveform distortion, time window length variation, etc. It has significant value for practical applications. [ABSTRACT FROM AUTHOR]

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods. [ABSTRACT FROM AUTHOR]

Each pixel can be classified in the image by the semantic segmentation. The segmentation detection results of pixel level can be got which are similar to the contour of the target object. However, the results of semantic segmentation trained by Fully convolutional networks often lead to the loss of detail information. This paper proposes a CRF-FCN model based on CRF optimization. Firstly, the original image is detected based on feature pyramid networks, and the target area information is extracted, which is used to train the high-order potential function of CRF. Then, the high-order CRF is used as the back-end of the complete convolution network to optimize the semantic image segmentation. The algorithm comparison experiment shows that our algorithm makes the target details more obvious, and improves the accuracy and efficiency of semantic segmentation. [ABSTRACT FROM AUTHOR]

Many defects now appear in ancient murals due to both natural and man-made factors. To better repair the damaged ancient murals, this paper improves the existing intelligent restoration method. It identifies and marks the crack-producing area, categorizes the mural image into two types – texture area and flat area according to the local gradient features, decides the initial sample block and calculates the weight, analyzes the extracted pixel data and applies discrete differential algorithm to supplement image defects. Through experiments, the method is validated in meeting the needs of image continuity law and human vision. It can shorten the repair time and restore mural cracks in an intelligent way. [ABSTRACT FROM AUTHOR]

Currently, the methods for rolling bearing fault diagnosis using acquired signals still have certain deficiencies, such as mode mixing during signal decomposition and selection of the optimum fault feature. To address these problems, this paper used a method based on Hilbert vibration decomposition (HVD) and sample entropy to perform bearing fault diagnosis. This method firstly decomposed the acquired original signals of faulty bearings used the HVD algorithm, then extracted fault features from the processed signals, found the frequency and multiplied frequency of the bearing fault by substituting some experimental parameters and bearing parameters into the calculation formula. The results show that the method proposed in this paper can reduce other interfering signals during signal processing and achieve a fault diagnosis rate significantly higher than that of the original EMD algorithm. [ABSTRACT FROM AUTHOR]

Wireless ad hoc network is popular in the research direction of network, and it has quite good application foreground in both civil and military aspect. However, traditional network and wireless ad hoc network distinguishes from one another on self-organization, which leads to a new problem, that traditional network security protection technology cannot be applied directly. Now whether wireless ad hoc network can be applied and popularized, and whether safe wireless ad hoc network can be constructed to ensure its security becomes vital. In this paper, the author assumes that a model based on ADHOC-ECDSA algorithm can cover all problems, including identity authentication combined with allocation address. Then we can modify basic algorithm by applying elliptic curve, and complete safety single hop, or even complete safety multi hop based on the algorithm of routing security protocol. In this paper, by analyzing the research progress of the network and its requirements of security protection, and by applying testing machine to prevent invasion, we present specific model design plan and analysis of security protection. In the mean time, popularization of wireless ad hoc network becomes possible. [ABSTRACT FROM AUTHOR]

Attribute reduction means that redundant attributes are excluded from decision table and it is an important topic in rough set theory research. Firstly, this paper proposes a new algorithm for computing equivalence classes based on subsection quick sort and obtains a higher efficiency compared with traditional algorithms. On this basis, the algorithm for computing refined decision table is given, which makes it possible to discover attribute reduction by using part objects. Finally, a fast attribute reduction algorithm which uses quantity of information as heuristic information is presented. Time complexity of the algorithm is O(| C | 2 | U/C |). Theoretical analysis and experimental results show that the algorithm proposed in this paper is efficient and provides a good job for follow-up work. [ABSTRACT FROM AUTHOR]

The root-fnding problem of a univariate nonlinear equation is a fundamental and long-studied problem, and has wide applications in mathematics and engineering computation. In this paper, two modified Newton-type methods and two variants of Super-Halley's method to solve nonlinear equations are brought forward. Analytical discussions are reported and the theoretical efficiency of the methods is studied. Both of Algorithms 1 and 2 require three evaluations of the functions and two evaluations of derivatives at each iteration. Therefore the efficiency indices of Algorithms 1 and 2 are 1.431 and 1.476, respectively. The presented variants of Super-Halley's methods require two evaluations of the functions and two evaluations of derivatives at each iteration. Therefore the efficiency indices of Algorithms 3 and 4 are 1.565. Hence, all efficiency indices of the proposed algorithms are better than that of classical Newton's method 1.414. All of the proposed algorithms in this paper are free from second derivatives. Some numerical results are finally provided to support the theoretical discussions of the proposed methods. [ABSTRACT FROM AUTHOR]

Focused crawlers, as fundamental components of vertical search engines, focus on crawling the web pages related to a specific topic. Existing focused crawlers commonly suffer from the problems of low efficiency of crawling pages and subject migration. In this paper, we propose a learning-based focused crawler using a URL knowledge base. To improve the accuracy of similarity, the similarity of the topic is measured with the parent page content, anchor information, and URL content. The URL content is also learned and updated iteratively and continuously. Within the crawler, we implement a crawling mechanism based on a combination of content analysis and simple link analysis crawler strategy, which decreases computational complexity and avoids the locality problem of crawling. Experimental results show that our proposed algorithm achieves a better precision than traditional methods including the shark-search and best-first search algorithms, and avoids the local optimum problem of crawling. [ABSTRACT FROM AUTHOR]

At present, the correct recognition rate of face recognition algorithm is limited under unconstrained conditions. To solve this problem, a face recognition algorithm based on deep learning under unconstrained conditions is proposed in this paper. The algorithm takes LBP texture feature as the input data of deep network, and trains the network layer by layer greedily to obtain optimized parameters of network, and then uses the trained network to predict the test samples. Experimental results on the face database LFW show that the proposed algorithm has higher correct recognition rate than some traditional algorithms under unconstrained conditions. In order to further verify its effectiveness and universality, this algorithm was also tested in YALE and YALE-B, and achieved a high correct recognition rate as well, which indicated that the deep learning method using LBP texture feature as input data is effective and robust to face recognition. [ABSTRACT FROM AUTHOR]

Image inpainting is an effective method to repair damaged image. In this paper, we mainly studied some typical image inpainting models and analyzed these corresponding repair qualities. Firstly, we studied three typical algorithms based on PDE (Partial Differential Equation) model to repair images whose damaged areas are relative small. And then we studied the image inpainting algorithm based on texture synthesis. This kind of inpainting algorithm is suitable for image with large damaged area. Finally, we studied Criminisi inpainting algorithm based on image segmentation in detail, on which we proposed an improved priority formula image repair algorithm, the simulation results indicate that the improved algorithm of this paper showed better performance in repair quality from both subjective and objective aspects. [ABSTRACT FROM AUTHOR]

With the rapid growth of massive data in all walks of life, massive data faces enormous challenges such as storage capacity and computing power. In Chinese universities, traditional data analysis of student course cannot meet the growing demand for increasing data size and real-time computation of big data. In this paper, a parallel FP-Growth algorithm based on split is proposed. The established FP-Tree is split into blocks, and the split FP-Trees are equally divided into different nodes. The monitoring point is set up to monitor the operation of other nodes, dynamically migrate tasks and maintain load balancing. The experiment proves that each node has good load balancing with the given support degree, and the improved algorithm has better running performance than the classic FP-Growth algorithm in parallel processing. Finally, the parallel FP-Growth algorithm based on split is implemented on Hadoop to mine association rules between course grades. The mining process includes data preprocessing, mining results and analysis. The association rules between course grades provide suggestions for the way students learn and the way teachers teach. [ABSTRACT FROM AUTHOR]

In an islanded microgrid with distributed generators (DGs), on account of the influence of feeder impedance, load and other factors, it is difficult to dispatch output power reasonably by means of droop control, therefore the secondary control is employed to solve this problem. However, the convention methods may fail to achieve the expected effect. In this context, an improved distributed control strategy based on multi-agent consensus algorithm is proposed in this paper. A virtual leader is set to track the balance of power. Through the mutual communication between DGs, it can realize the flexible distribution of load power and has good convergence. By introducing the integral controller on the basis of conventional droop control, the droop curve can move adaptively to maintain frequency and voltage stable. Finally, the effectiveness of the proposed scheme and the performance of anti-communication failure are verified in MATLAB/Simulink simulation platform. [ABSTRACT FROM AUTHOR]

Community discovery in complex networks has become a core issue in multidisciplinary cross-disciplinary research and has been successfully applied in many areas, such as social network analysis, protein network analysis, and link prediction. This paper proposes a genetic algorithm based on adaptive cross mutation operator for complex network community mining. The population is generated by establishing fitness value calibration and adaptive cross mutation operator, and a good individual is selected from it. An improved adaptive cross mutation operator is proposed to ensure the convergence of genetic algorithm and accelerate the generation of optimal solution while maintaining the diversity of population. Finally, experiments were carried out in multiple real networks to verify the stability and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Traditional discriminant correlation filters fuse hand-crafted features and deep features fusion for tracking, which will cause features redundancy too much and lead to model overfitting when parameters are updated. Moreover, it is difficult to deal with various complex problems in video by using the same label map for different features. In addition, the model updating strategy of the traditional method is relatively single, the model is updated by one frame or several frames. Different from the traditional methods of response map fusion, this paper proposes a multi-layer features correlation filter algorithm to estimate the target model from multiple perspectives. The corresponding label maps are used for different features, and an adaptive model updating strategy is proposed. The proposed tracker achieving leading performance in OTB2013, OTB2015 and VOT2016 datasets. [ABSTRACT FROM AUTHOR]

Due to the filter and threshold setting method, the traditional Canny algorithm has the disadvantages of limited denoising ability and poor adaptive ability. Focusing on the filtering part and the threshold setting part, a new image edge detection algorithm based on improved Canny was proposed. In the new algorithm, the Gaussian filtering algorithm is replaced by an improved filtering algorithm, in which the filtering method, weighting method and the size of the filtering window are adaptively selected according to the noise density. In addition, the OTSU algorithm is used to figure out the upper threshold. And an evalution function based on the gradient magnitude histogram and intra-class variance minimization is introduced to help determine the lower threshold The Lena images with differernt pepper and salt noise density were taken as the experiment object. Both the subjective and objective evalution were carried out to verify that the algorithm proposed in this paper has good denoising ability and detail retention ability and that when pepper and salt noise density grows, the new algorithm has more advantages over the adaptive median filtering Canny algorithm and the adaptive weighting median filtering Canny algorithm. [ABSTRACT FROM AUTHOR]

Image matching plays an important role in Augmented Reality, Simultaneous Localization and Map ping (SLAM), and unmanned. The key issue of image matching is the accuracy of feature matching between adjacent images. Due to the complicated environment, matching between images through geometry feature runs into bottlenecks. In this paper, we focus on improving the accuracy of feature matching by incorporating instance-aware semantic segmentation into Oriented FAST and Rotated BRIEF (ORB) feature matching, which is broadly utilized in image registration and Visual SLAM. We segment the objects between the adjacent images on pixel level and qualify the features matching procedure using the semantic information. Experiment results show that our novel proposed method increases the accuracy of feature matching. [ABSTRACT FROM AUTHOR]

This paper discusses the binarization restoration algorithms of ancient Chinese Rubbings image. According to the location of the image features, such as color, edge, stroke width and pixel location feature, the binary restoration algorithm of digital rubbings based on threshold segmentation and morphology is discussed. If the difference between the text and the background is larger and more obvious distinguish in images, Otsu threshold segmentation method can be directly used. For images with more background noise, shadows and uneven illumination, threshold segmentation cannot achieve effective segmentation and reasonable denoising, a set of algorithms associated with the pixel position of adaptive segmentation, morphological method, connected domain and mean filtering combined of binarization restoration are needed. The experimental results show that this algorithm is very effective for image with complex and large area noise. [ABSTRACT FROM AUTHOR]

Aiming at the nonlinear and coupling phenomena of temperature and humidity changes in the course of tea fixation, this paper proposes the FNN algorithm and its hardware implementation technology in the process of making tea. STM32F107 microcontroller is as the core of the whole system and the multi-point detection system is used in real-time collection of two parameters of temperature and humidity. In addition, data transmission is implemented between PC and I2C, SPI bus. FNN control is adopted to meet the process requirement of temperature and humidity change in process. The method improves the control precision of temperature and humidity and the quality of tea. [ABSTRACT FROM AUTHOR]

Recommender systems have been very important components to prevent people from dwelling in the overwhelming information. In this paper we analyze the difference between item-based recommendation algorithms and SVR-based collaborative filtering algorithms, and it can be found that item-based method performs much better while the data is not sparse significantly, and SVR-based method performs better while the data is dense and small. On this premise we propose a method that can combine the advantages of these two methods by predicting a small part of ratings using SVR method firstly and then predicting the rest of ratings using the item-based algorithm, which can solve the problem of data sparsity to certain extend. Finally, we evaluate our results compared with the benchmark on different datasets and prove our method's advantages. [ABSTRACT FROM AUTHOR]

Through the researches on various item improvements of particle swarm optimization algorithm, the improved particle swarm optimization algorithm is proposed for the insufficient global searching ability. This paper has improved the parameters of velocity formula and introduced new distance calculation formula to modify various parameters in the velocity formula. The improved particle swarm optimization algorithm has been tested by adopting standard test function, and the result has been compared with other particle swarm optimization algorithms. The experimental result indicated that the proposed improved particle swarm optimization algorithm in this paper got the better result for function optimization problems. [ABSTRACT FROM AUTHOR]

Poor quality and harsh condition can lead to the existence of error and abnormal data in sampling data of sensor nodes. Therefore, sometimes the average value query (AVG query) does not reflect the real average level of the monitoring area. In this case, median query is needed to reflect the average level of the monitoring area. In this paper, we first propose a median query algorithm HMA based on equal height histogram. We can establish the global histogram to determine which interval the median falls, and then we only collect data in the interval, so as to reduce the data we need to transmit. Then we extend HMA algorithm and put forward HFMA algorithm combining histogram and filter. In HFMA we only need to collect the data inside filter and aggregate influence coefficient during each sampling period. The base station can calculate the median according to the collected data and the aggregated value of the influence coefficient. Experimental results have shown that the HFMA algorithm is superior to the NAIVE algorithm and the HMA algorithm, which can effectively save the energy cost and improve the network lifetime. [ABSTRACT FROM AUTHOR]

Community detection is an important way to describe the evolution of network events. In order to study.how to differentiate network community structure accurately and effectively, and solve the problem of the Sync clustering algorithm directly connected nodes without common neighbor nodes existing similarity underestimated tendency, and the synchronization time being too long, we put forward a method which breaks edge disconnection between connected nodes directly and add auxiliary nodes. We can think that the two nodes are connected by the secondary node and the edge of the two nodes connected respectively, and then realize the improvement of node similarity. Secondly this paper uses neighborhood radius to divide community, and detect the community before the node object has reacded fully synchronous. Simulation experiments are done respectively on the artificial data and real data sets generated, and the results show that the improved algorithm is more accurate to effectively find community structure compared with the original algorithm. [ABSTRACT FROM AUTHOR]

In Underwater Acoustic Sensor Networks (UASNs), the path loss of signal is serious due to the harsh underwater environment. Fractional power control algorithm has been widely used for the compensation of path loss. However, the randomness of underwater sensor nodes makes it hard to derive the performance index for UASNs. To address this issue, stochastic geometry theory is introduced to build an analysis model, which immediately gives the outage probability and transmission capacity for the fractional power control algorithm. Furthermore, the minimum transmission capacity for different power control factors is derived. This paper provides a huge incentive to improve the existing approaches to dynamically manage the power control scheme. [ABSTRACT FROM AUTHOR]

In the field of cloud computing, Particle swarm optimization (PSO) is an important intelligent algorithm for solving the task scheduling problem, and has been rapidly developed. In order to improve the overall optimization ability, and get a low cost optimization solution, this paper proposes an improved particle swarm optimization (IPSO) algorithm based on the adaptive inertia weight and random factor correlation. Simulation results show that under the same conditions, IPSO algorithm is less than the sequential scheduling algorithm, the greedy algorithm, the correlation particle swarm optimization (CPSO) algorithm and the new adaptive inertia weight based particle swarm optimization (NewPSO) algorithm in terms of cost consumption (including time cost and virtual machine cost). [ABSTRACT FROM AUTHOR]

Considering the complexity of diagnosing lung cancer, a novel neighborhood covering algorithm based on the stages and diagnosis of lung cancer was put forward. The paper used to process a quantity of data, 1074 cases of lung cancer diagnosed from 2013 to 2017 in the Second Hospital of Anhui Medical University. Modeling and processing the data set by using the improved neighborhood covering algorithm, so as to mine and use the potential information in the data set as much as possible. The Objective is to predict the lung cancer patients reasonably. Experimental results show that the improved algorithm is effective in lung cancer staging prediction, and has faster training speed and better performance. The new method for staging diagnosis of lung cancer and a new reference for the clinical treatment of lung cancer research is provided. [ABSTRACT FROM AUTHOR]

Facial recognition is a technique for biometric identification, generally applied to finance, cybersecurity, public security, property management, and other important management departments. In this paper, the facial recognition system based on improved LBP and PCA mathematical algorithm is used to extract the relationship between the facial centre point and other phase domain points. After using PCA to reduce the dimension, the facial features are obtained. The facial recognition system designed by the thesis covers face image creation, face image pre-processing, face image recognition and result output module. Studies have shown that the system's face recognition accuracy can be as high as 90%. [ABSTRACT FROM AUTHOR]