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Folk paper-cut art is one of the bright pearls, with a wide range of shapes and a long history, that serves as a model for modern visual communication design in China and encourages designers to think creatively. Integrating emotional elements into a design not only can elicit an emotional response from the viewer but also can make works created using traditional rational thinking more vivid, energetic, and touching. The main focus of this research is on the path and method of incorporating folk paper-cut emotion into visual communication design. The research status of various feature extraction methods and image recognition methods at home and abroad is examined in depth in this paper. For the recognition of distorted images that are not transformed by mathematics, some effective methods are proposed. They are used to recognize artistic images of paper-cut patterns because they can extract effective features that are not affected by translation, rotation, scale change, or small deformation. Experiments have shown that they have a positive impact. The use of folk paper-cut art in visual communication design not only improves the quality of design work but also generates new ideas for the transmission and development of other traditional Chinese cultures. [ABSTRACT FROM AUTHOR]

In the cloud computing era, a paper recommender system is usually deployed on the cloud server and return recommendation results to readers directly. However, considering the paper recommender system, processing tremendous paper citation data on the cloud cannot provide fine-grained personalized and real-time recommendations for each reader because these recommended papers from the cloud are far from readers and probably not correlated strongly with each other for helping each reader research further and deeper in the interested field. Recently, the edge-cloud collaboration-based recommender system has been used for releasing parts of the cloud computing task to the edge and provides the recommendation near the client. Based on the edge computing recommender system, a keywords-driven and weight-aware paper recommendation approach is presented, namely, LP-PRk+ w (link prediction-paper recommendation), to enable intelligent, personalized, and efficient paper recommendation services in the mobile edge computing environment. Specifically, the whole paper recommendation process mainly covers two parts: optimizing the existing paper citation graph via introducing a weighted similarity (i.e., building a weighted paper correlation graph) and then recommending a set of correlated papers according to the weighted paper correlation graph and the users' query keywords. Experiments on a real-world paper correlation dataset, Hep-Th, show the capability of our proposal for improving the paper recommendation performance and its superiority against other related solutions. [ABSTRACT FROM AUTHOR]

Within a few decades of development, Internet is been refreshing the world's awareness of its development and potentials at an unexpected speed. In recent years, virtual reality technology has begun to be applied to more and more fields. Especially in instant transmission of information, the applications of VR are incomparable. Akin to the wide-ranged advantages of the technology in education and health, the applications of VR in the art of artistic image designing need to be addressed. With this research work, a comprehensive study is presented about the applications of virtual reality in the art of image visualization. The research intends to analyze the possibility of intermingling modern image visualization with the emerging VR technology. Following a systematic approach, the history and development of display design are reviewed besides studying the artistry techniques used in the technology of art image visualization design. The interdisciplinary association of the two fields is explored with the help of relevant algorithms. The model proposed in the paper for the design of art image is based on the visual interactive process, rigorous assembly sequence, and appropriate equipment mode. As a result, the whole design process is more controllable and feasible. The suggested design concept is verified dynamically. Through the collection and comparison of feedback information, the hypothesis algorithm of the model is updated to accommodate the dynamic information so that to reflect the complete image design process. To assess performance of the model, evaluation was conducted from various perspectives. The three basic image operations such as illumination change, occlusion change, and color change were performed by using the two technologies such as the traditional image design and VR-based image design. From the comparison of experimental data, it was revealed that the effects of traditional art image visualization products produced by the art image visualization and that of VR technology are quite different. For instance, the visualization score achieved for the lighting change operation using the VR technology was 95.48%, whereas 85.57% was obtained for the same operation using the traditional image design technology. Similarly, while adding visual effects using the VR technology, an average of 9% higher score was obtained for the said image operations. The promising results attained for the VR-based design testify that the technology is feasible and suitable to be intermingled with the visual design of art image. As a whole, the findings justify that the proposed approach is quite applicable in visual design of art image systems. In view of the significance of the research, the study can be extended to related domains of computer-based designing and visualization as well. [ABSTRACT FROM AUTHOR]

With the development of air transport and improved ecological environment, bird strikes on aircraft have become a significant threat to flight safety. Accurate and efficient identification of airport birds can enhance the efficiency of airport staff in bird strike prevention. Public datasets for bird classification are typically limited in size and not specifically tailored for airport birds. Therefore, this paper introduces a self‐built airport bird dataset, ADB‐20, and proposes an image recognition method based on an improved ResNet model. First, this paper replaces traditional convolution in the residual structure with the Pyramidal Convolution method, ensuring the extraction of multiscale features. Second, it introduces the Parallel Convolutional Block Attention Moduleto the backbone network, considering features in both channel and spatial dimensions. Last, the Atrous Spatial Pyramid Pooling module is incorporated to capture contextual information at various scales. Experimental results show that the PPA‐ResNet model achieves an accuracy of 95.2%, with a recall of 93.7%, a precision of 96.8%, and an F1 score of 95.2% on the ADB‐20 dataset. The proposed algorithm significantly enhances classification performance compared to other mainstream image classification algorithms. These indicators confirm that the results of this study can precisely identify airport birds, aid airport personnel in making informed decisions, and guarantee the safety of aviation transport. [ABSTRACT FROM AUTHOR]

Enterprise financial risk refers to the uncertainty of financial status caused by various internal and external factors during investment, financing, operations, and other activities, which may result in losses for the enterprise. Agricultural group enterprises engage in diversified businesses and have a large number of transactions. Characteristics such as natural disasters, financing difficulties, and strong seasonality of income and expenditure make agricultural enterprises face greater financial risks. Based on the perspective of evidence fusion, this paper takes the JS Agricultural Reclamation Group as the research object, puts forward a financial risk early warning model based on evidence theory, and innovatively puts forward the basic probability assignment of evidence obtained by whitening weight function, and uses evidence theory to fuse uncertain risk information. This paper makes an empirical study of 420 subsidiaries of the group and selects 10 subsidiaries as case study objects. The results show that the model proposed in this study can improve the prediction accuracy of the group's intelligent financial decision system and the existing literature and reduce the subjective factors and information loss in the risk assessment process. In addition, this study provides the probability interval of early warning results, which can expose small probability risks and provide a more reliable basis for regulators, enterprise managers, and investors to make efficient and correct decisions. [ABSTRACT FROM AUTHOR]

Modelica is a simulation language that is able to model systems from many different fields of scientific and engineering contexts, which can coexist in individual models. Modelica Standard Library is an open‐source library that contains many ready‐to‐use open‐source models built in Modelica language but does not have any code to determine transmission line parameters from its geometry, thus limiting its usability for power system studies. This paper first describes important issues arising when modelling power transmission lines, then presents how they can be solved using Modelica code. Readers of this paper will, therefore, be able to model power lines from the geometry of conductors using any of the existing (some freely available) Modelica‐capable simulation tools. [ABSTRACT FROM AUTHOR]

Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL). This is a field where applications may be very fruitful. In this paper, the possibility of creating mixed software–hardware experiences (i.e., HIL), through combination of a Modelica program, the related simulation tool, a LabVIEW program, and the corresponding hardware is demonstrated. This demonstration is made using as an example a partial simulator of an electric vehicle running in a stand-alone PC, which communicates via User Datagram Protocol (UDP) packets with another PC running the LabVIEW program, which in turn is physically connected with the hardware-under-test. The obtained results are satisfying, given the inherent delay times due to the UDP communication. [ABSTRACT FROM AUTHOR]

Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of Sci-Tech journals is a widely recognized journal evaluation index that comprehensively reflects the quality and influence of the journals under evaluation. However, traditional journal evaluation methods based on statistical formulas, while relatively simple and fast, have certain limitations. They are not comprehensive enough and do not support the comparison between journals from different disciplines. In recent times, researchers have delved into using multiple suitable indicators for comprehensive journal evaluation, attempting to understand the role each indicator plays in the evaluation process, such as the rank sum ratio. Our paper presents a new dataset constructed from data from journals across various fields obtained from the China Wanfang Literature Platform. We endeavor to explore a series of novel journal evaluation methods based on machine learning, including deep learning models. With these 9 methods, we aim to determine the contribution of 17 journal evaluation indicators to the impact factor and identify important factors that can further enhance the quality and influence of Sci-Tech journals, which has great guiding significance for the future development of journals. [ABSTRACT FROM AUTHOR]

In the bidding process of government investment engineering projects, collusion between the government and bidders occurs repeatedly, which seriously affects the quality of engineering projects and the effectiveness of the government investment. Therefore, it is necessary to analyze and discuss the collusion between the government and bidders in government investment engineering projects, so as to provide a healthy and sustainable environment for the government investment engineering bidding market. There are two main types of collusion in engineering bidding: horizontal collusion and vertical collusion, and this paper focuses on the vertical collusion process in the engineering bidding process. A conceptual framework of the grey interest chain based on three stages of benefit creation, benefit distribution, and benefit realization was established, 15 major nodes in the grey interest chain were identified, and a grey interest chain control model was constructed, which further identified and classified the nodes into four levels: key nodes, important nodes, general nodes, and unimportant nodes. Finally, through the application of the model in the case, measures such as establishing a cracking mechanism for grey resource integration, increasing the supervision of grey interest chain, and strengthening post-bid audit are proposed. Measures such as including the preparation of bidding documents into the work assessment system and entrusting consulting units or third parties to prepare bidding documents are proposed to establish a crack mechanism for grey resource integration. In the benefit distribution stage, the penalties for the government and the bidders can be appropriately increased, the responsibilities of enterprises and project leaders can be implemented in the system on a reciprocal basis, and a perfect reputation mechanism information can be established. At the stage of benefit realization, the bidding system should be improved and post-bid audit should be strengthened to increase the difficulty of grey benefit realization. This paper will provide a reference for the prevention and governance of vertical collusion in bidding and tendering. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of methods: feature selection and feature extraction. However, the two categories of methods have their own advantages and limitation. In this paper, we propose a Hybrid Feature Dimensionality Reduction Approach (HFDRA) for SDP, which combines the two different kinds of methods, to improve the performance of SDP. HFDRA approach can be divided into two stages: feature selection and feature extraction. First, HFDRA divides the original features into several feature subsets through a clustering algorithm in the feature selection stage. Then, in the feature extraction stage, kernel principal component analysis (KPCA) is used to reduce the dimensionality of each feature subset. Finally, the reduced-dimensional data is used to build the prediction model. In the empirical study, we use 22 projects from AEEEM, SOFTLAB, MORP, and ReLink as experiment object. In this paper, we first compare our approach with seven baseline methods and three state-of-the-art methods. Then, we analyze the relationship between FR and prediction performance. Experiment results show that our approach outperforms the state-of-the-art data dimensionality reduction methods for defect prediction. [ABSTRACT FROM AUTHOR]

With people's higher and higher spiritual requirements, fiber art is more common in people's lives. From ordinary fiber materials to handicrafts and daily necessities, there are shadows of fiber art. This paper aims to study the application of two-dimensional images in the visual and tactile dimensions of fiber art design. This paper proposes a three-dimensional simulation of two-dimensional images of art based on deep learning technology to achieve the purpose of convenient display and also analyzes the feature extraction in fiber art. In order to comprehensively compare the performance of the algorithms, this paper compares the performance of four categories: reconstruction speed, number of reconstructed point clouds, diversity of reconstructable categories, and reconstruction stability. The experimental results show that the number of point clouds obtained by the algorithm proposed in this paper far exceeds the other two reconstruction processes, and the average time is 34.4 min. This also shows that such algorithmic processes are more robust to clothing diversity. [ABSTRACT FROM AUTHOR]

With the rapid development and application of internet technology, people are gradually entering a state of digital life. However, it is difficult for digital assets to be circulated with a unified standard protocol. Blockchain has the characteristics of decentralization, trustworthiness, and immutability, which can be used to solve the above problems. Although virtual marketing technology shows great potential, as a form of organization that is difficult to manage, there is still a lack of experience in the form factors and operation mechanism of a virtual business model. Therefore, combined with the realistic background, this paper built a digital twin innovation and entrepreneurship virtual marketing interactive platform based on blockchain. The article analyzed the data structure of the blockchain and completed the construction of the platform operation mode. It studied the encryption and decryption process of cryptography and expanded the ciphertext space, then the blockchain is encrypted and decrypted again, and finally, a chi-square analysis is performed. After building the platform, it conducted market research and analyzed the main operating indicators of the A platform on the market. Then, it compared the uncertain string preprocessing enumeration length change and the index occupied space between the platform constructed in this paper and the platform in the literature. The comparison results have shown that the index proposed in this paper is 320 MB in 8 iterative calculations, while the bibliographic index starts at 10000 MB and keeps increasing during the iterative calculation process. The two are not in the same order of magnitude, illustrating the superiority of the platform in this paper. [ABSTRACT FROM AUTHOR]

The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations. [ABSTRACT FROM AUTHOR]

Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors, which requires expertise in the field of medical imaging and consumes a great deal of doctors' time and energy. Automatic segmentation of breast cancer not only reduces the burden of doctors but also improves work efficiency. Therefore, it is of great significance to study the automatic segmentation technique for breast cancer lesion regions. In this paper, a deep-learning-based automatic segmentation algorithm for TNBC images is proposed. The experimental data were dynamic contrast-enhanced magnetic resonance imaging TNBC dataset provided by the Cancer Hospital of Zhengzhou University. The experiments were analyzed by comparing several models with UNet, Attention-UNet, ResUNet, and SegNet and using evaluation indexes such as Dice score and Iou. Compared to UNet, Attention-UNet, ResUNet, and SegNet, the proposed method improved the Dice score by 2.1%, 1.54%, 0.88%, and 9.65%, respectively. The experimental results show that the proposed deep-learning-based TNBC image segmentation model can effectively improve the segmentation performance of TNBC tumors. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, mankind has entered the era of big data. The Internet records all kinds of information, and the amount of information generated in the future is in a state of explosive growth. With the development of enterprises, the data of accounting information have grown, and its security has also been threatened. Once the accounting information is leaked, it will have a great impact on the company. Therefore, it is very necessary to protect accounting information data. This paper aims to study how to protect accounting information data based on big data and cloud computing. Based on this, this paper proposes an attribute-based encryption method based on big data and cloud computing and analyzes the ABE scheme of a single authority and multiple authorities. The multiauthority ABE scheme can effectively solve the problems of low computational efficiency, poor security performance, and high time overhead in the single-authority ABE scheme. The experimental results of this paper show that, under the single-authority ABE scheme in the information data protection of enterprise A, the confidentiality of data is 54% at the highest and 46% at the lowest, which is generally not very high. In the information data protection of the multiauthority ABE scheme in enterprise A, the highest data confidentiality is 86% and the lowest is 79%. The highest is 32% more than the single-authority ABE scheme. The lowest is also 33% more than that. Generally speaking, the confidentiality of its data is very high. In terms of reliability, integrity, and effectiveness, the single-authority ABE scheme is not as good as the multiauthority ABE scheme. It shows that the multiauthority ABE scheme proposed in this paper has a powerful information data security protection function and can be effectively applied to accounting information data protection. [ABSTRACT FROM AUTHOR]

This paper investigates single-period inventory management problems with uncertain market demand, where the exact possibility distribution of demand is unavailable. In this condition, it is important to order a reliable quantity which can immunize against distribution uncertainty. To model this type of single-period inventory management problem, this paper characterizes the uncertain demand by generalized interval-valued possibility distributions. We present a novel concept about an uncertain distribution set to describe distribution perturbation characterization. First, we introduce a lambda selection of the interval-valued fuzzy variable, and the uncertain distribution set is a collection of all generalized possibility distributions of lambda selection variables. According to the uncertain distribution set, a new distributionally robust fuzzy optimization method is developed for single-period inventory management problems. Under mild assumptions, the robust counterpart of the proposed fuzzy single-period inventory management model is formulated, which is an optimization program with certain linear objectives and infinitely many integral constraints. We discuss the computational issue of integral constraints and reformulate equivalently the robust counterpart as three deterministic inventory submodels under generalized interval-valued trapezoidal possibility distributions. According to the characteristics of three submodels, a domain decomposition method is designed to find the robust optimal solution that can immunize against uncertainty in our single-period inventory management problem. Finally, some computational results demonstrate the efficiency of the proposed distributionally robust fuzzy optimization method. [ABSTRACT FROM AUTHOR]

With the development of the economy, graphic design has become one of the main forms of commercial advertising. Designers design different graphic design schemes for different layouts and design requirements, which puts forward new requirements for graphic design process work. With the advancement of technology, graphic design (or visual communication design) supported by digital media technology is not limited to print media, and network media is also included. This research mainly discusses the visual space system in digital media art design. It can be said that the reconstructed space under virtual reality is based on interaction, has an immersive and imaginative space, and is combined with narrative to a certain extent to create a new form of expression for the information expression of space design. This paper first interprets the concept of "space" from psychology, painting art, and film art produced. Then explain the particularity of digital media art design, and it uses the design works to explain the content of the exposition. Connecting digital technology can bring people the uniqueness of aesthetics. This paper makes related theoretical research on the design practice of visual space. In this paper, visual perception in psychology is introduced into design thinking. The concept of visual space is not only expressed in art and culture but also involves the concept of space and time in physics and the scientific research theory of visual perception in psychology. It breaks through the level of perceptual cognition of space in art design. 31.85% of the respondents think that visual space design is attractive. Now is the era of new media interaction. The development of interactive technology and devices has brought infinite possibilities to visual communication design. We cannot accurately say its future development and changes. Because of the emergence of new technologies and media, visual communication design is also expanding its own types of works, and it is constantly developing and improving through the study of people's emotions and the combination of design and technology. This research has positive significance for the development of the visuospatial system. The advancement of digital media technology has enriched the means of expression in space design creation and expanded people's living spaces. Through the combination of modern digital media technology at multiple levels and multiple channels, various new spaces can be created to further meet the new needs of contemporary people. [ABSTRACT FROM AUTHOR]

In the face of the batch, dynamic access data, or the flow of data that continuous changes over time, the traditional support vector machine algorithm cannot dynamically adjust the previous classification model. To overcome this shortcoming, the incremental support vector machine (ISVM) algorithm is proposed. However, many incremental support vector algorithms still have shortcomings such as low efficiency, memory limitation, and poor generalization. This paper puts forward the new ISVM algorithm, HDFC-ISVM ∗ algorithm, based on the high-dimensional distance and forgetting characteristics. This paper firstly proposes the original HDFC-ISVM algorithm that first learns the distribution characteristics of the samples according to the distance between the samples and the normative hyperplane. Then, it introduces the forgetting factor. In the incremental learning process, the classifier gradually accumulates the spatial distribution knowledge of samples, eliminates the samples that have no contributions to the classifier, and selectively forgets some useless samples according to the forgetting factor, which overcomes the shortcomings such as low efficiency and poor accuracy of some algorithms. But, the original HDFC-ISVM algorithm is sensitive to parameters, and different settings of the parameters have a great impact on the final classification accuracy of the algorithm. Therefore, on the basis of the original algorithm, an improved algorithm HDFC-ISVM ∗ based on the adjustments to the initialization strategy and updating rules of the forgetting factor is proposed. The initialization strategy and updating rules of the forgetting factor are adjusted to adapt datasets with different distributions in this improved algorithm. The rationality of the improved strategy about the forgetting factor is discussed theoretically. At the same time, the proposed algorithm has better classification accuracy, classification efficiency, and better generalization ability than other algorithms, which is verified by experiments. [ABSTRACT FROM AUTHOR]

Speed skating serves as a significant application domain for multiobject tracking (MOT), presenting unique challenges such as frequent occlusion, highly similar appearances, and motion blur. To address these challenges, this paper constructs an MOT dataset called SKT-MOT for speed skating and analyzes the shortcomings of existing datasets and methods. Accordingly, we propose a dynamic MOT method called DyTracker. The method builds upon the DeepSORT baseline and enhances three key modules. At the global level, we design the track dynamic management (TDM) algorithm. In the motion branch, a novel metric is proposed to evaluate occlusion and Kalman filter dynamic update (KFDU) is implemented. In the appearance branch, we account for the difference in human posture and propose the feature dynamic selection and updating (FDSU) strategy. This makes our DyTracker flexible and efficient to achieve a multiobject tracking accuracy (MOTA) of 93.70% and identification F1 (IDF1) score of 92.39% on SKT-MOT, which is a significant advantage over existing SOTA methods. To validate the generalization of our proposed module, two dynamic update modules are inserted into other methods and validated on the public dataset MOT17, and the accuracy is generally improved by 0.2%–0.6%. [ABSTRACT FROM AUTHOR]

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect. [ABSTRACT FROM AUTHOR]

Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause many losses to customers. So far, great outcomes have been obtained when using machine learning techniques in terms of equal error rates and calculations. This paper presents a comprehensive review of the latest studies and results in the last 10 years in the field of online and offline handwritten signature verification. More than 20 research papers were used to make a comparison between datasets, feature extraction, and classification techniques used in each system, taking into consideration the problems that occur in each. In addition, the general limitations and advantages of machine-learning techniques that are used to classify or extract signature features were summarized in the form of a table. We also present the general steps of the verification system and a list of the most considerable datasets available in online and offline fields. [ABSTRACT FROM AUTHOR]

With the increasing import volume of China's oil and natural gas, the structure of China's energy supply chain (ESC) has become more and more complex. How to use the "Belt and Road" (OBOR) layout to promote China's regional energy cooperation and ensure China's energy security has become an important international issue facing China. This paper constructs an ESC network model in the context of the OBOR, and finds out the position of China in this network model. This paper also finds out the role law of each risk influencing factor, analyzes the degree of its influence, and puts forward some suggestions on the risk management of ESC according to the analysis results. This paper proposes to use decision tree algorithm to guide the development and construction of Sino-Lao economic and trade cooperation, which will help analyze and help the construction and research of Sino-Lao economic and trade cooperation and sustainable energy freight corridor. Based on the energy cooperation between China and the countries along the "Belt and Road," this paper analyzes the network structure of energy cooperation under the "Belt and Road" background based on the small-world network theory. This paper analyzes the important role of core countries in the ESC through the analysis of the basic parameters of the small-world network, the efficiency of energy cooperation, and the stability of energy cooperation. According to the value of each vertex degree obtained by the calculation formula of entropy value, the entropy value of the ESC network structure under the background of OBOR can be obtained as 2.408. It calculates that the maximum and minimum entropy of the ESC network structure at this time are 3.09 and 2.21 respectively. [ABSTRACT FROM AUTHOR]

Our work deals with the problems of communication delays, packets dropouts, and quantization errors in the signal transmitted in network-controlled systems (NCS). Although NCS provides a paradigm for adapting to frequent changes in the manufacturing industry, modeling and managing operations are difficult issues due to the complex interactions between system entities and the effect of the network-induced delay on the closed-loop system. Therefore, our paper is about detecting degradations resulting from the insertion of a network in the regulation loop, which can even lead in some cases to the destabilization of the NCS. This paper mainly studies the problem of the graphic modeling, based on the colored Petri nets (CPN), of the NCS under communication constraints. Therefore, models of the communication network (Ethernet) and system entities are presented. Then, the influence of network-induced delay is detected. Finally, as an idea for future work, we propose a solution based on an SDN controller in order to avoid precedent degradations. [ABSTRACT FROM AUTHOR]

3D printing or additive manufacturing (AM) is considered to be the most important technology among the emerging technologies. 3D printing technology is considered as an alternative to the conventional manufacturer machine traditionally used in the manufacturing sector. 3D printing technology is generally classified into seven types. Each type of 3D printing technology has its separate own uniqueness (i.e., operation, material usage, and no wastage). The price of a manufactured item includes all its costs. The most important of these is to take into account the price of the machine being manufactured and the features of the machine. Moreover, the price of the product produced in AM will depend on all the costs required to produce it. Then, it is possible to reduce the cost of the product by choosing the AMM that has significant features and the right price. Therefore, this paper aims to solve a decision-making problem from the AMM selection by using one of the multicriteria decision-making (MCDM) tools, i.e., analytical hierarchy process (AHP). This paper outcome is meant to meet the expectation of end-users. As an initial step, the Micro, Small, and Medium Enterprise (MSME) company gets quotations from some AM companies to choose a type of AM machine known FDM for its structure product and doll product. The first step is to select the most appropriate machines based on cost, size/volume, extruder type, and weight of the machine. Criteria for AHP are derived from decision-makers. Also, in AHP, the pair-wise matrix is obtained from the decision-makers by answering the standard Saaty's scale criteria questions. In this paper, such a selection method is explored. The outcome of this paper may vary depending on the expectations of the decision-makers. The end of this paper helps to choose the AMM with the right price and features to suit the decision-makers. [ABSTRACT FROM AUTHOR]

With the core and small-scale family structure, parents and children have less time and opportunities to interact, which leads to the lack of care and emotional companionship for preschool children, which is easy to cause physical and mental disorders. In response to this phenomenon, a series of child care robot products have emerged on the market and have continued to be the focus of attention in the past two years. However, such products are still generally deficient in terms of interaction and content. It is difficult for users to choose and use. This paper takes the design and application of remote parent-child interaction of preschool children's escort machine as the research object. First, it examines the physical, psychological, and behavioral characteristics of escort robots, namely, preschool children. To sum up, the remote parent-child interaction design strategy of children's escort robot products is proposed for the design practice and industry reference of this paper. The experimental results show that the children in the experimental group pay more attention to the main content and off-topic content than the control group, and the attention rate is more than 95%. To a certain extent, it can be said that the games in the system have the ability of parent-child interaction. This paper abandons the traditional research direction of pursuing human-computer interaction between childcare products and high technology. Instead, it studies parent-child emotional interaction, so that the emotional interaction between parents and children is not replaced by technology products but helps parents deepen the relationship with their children. Emotional interaction allows parents to truly accompany their children to grow up. [ABSTRACT FROM AUTHOR]

With the rapid development of agricultural economy, people are paying more and more attention to how to apply high-efficiency technologies that save resources to improve agricultural production efficiency, so water-saving irrigation technology has gradually developed. China's agricultural irrigation technology is relatively backward. In addition to the inappropriate irrigation methods and irrigation systems, problems such as siltation, seepage, and frost heave damage in irrigation canals have seriously affected the durability and service life of the canals. The backward irrigation technology seriously restricts the development of agricultural water-saving irrigation. Compared with large irrigation channels, the fabricated reinforced concrete small irrigation channels studied in this paper are less prone to frost heave damage and infiltration problems, and have the advantages of standardized production, simple transportation and installation, and convenient maintenance. In order to study the durability issues such as the basic characteristics, frost heave damage, and service life of fabricated irrigation channels, this paper takes the channel concrete and the formed channel as the research objects, and discusses the research on the properties of the channel concrete through theoretical research, numerical analysis, experiments, and other methods. Strength properties, water penetration resistance, cyanide ion penetration resistance and frost resistance; simulate the seasonal frost heave failure process of the channel; finally, on the basis of the test data, the service life aims to explore the safety and applicability of the fabricated reinforced concrete irrigation channel during the design use period. [ABSTRACT FROM AUTHOR]

The comprehensive reasons for the cross-regional flow of enterprises in their own economy are studied in this paper. Most traditional enterprise migration research focuses on the impact of a single factor. Even the research on the influence of multiple factors often ignores the interaction of various factors. Therefore, this paper proposes and logicalises the "push and pull" mechanism of enterprise migration to address the limitations of previous research. Through bibliometric analysis and investigation, we systematically think about the factors that affect the corporate migration mechanism and improve the Push-Pull Theory. Besides, by deploying a conceptual system dynamics model, this paper explains how various factors affect enterprise migration. In addition, we discussed how to adopt systems thinking to promote the development of emerging economies. We selected and tested 6 companies that migrated in China, and the results are in line with reality. The conclusion supports the theory that systematic thinking and decision-making influence corporate behaviour. [ABSTRACT FROM AUTHOR]

The financial status of an enterprise is related to its healthy and long-term development, and whether the interests of investors and bank loans can be guaranteed. To improve the prediction accuracy of corporate financial risk, this paper proposes a prediction model for corporate financial risk that integrates GRA-TOPSIS and SMOTE-CNN. First, using GRA-TOPSIS to make a comprehensive evaluation of the financial situation of listed companies. Second, the evaluation results are clustered to obtain the scientific level and interval of financial risk, which lays the foundation for the supervised learning of the convolutional neural network. Then, the SMOTE algorithm is introduced to solve the problem of data imbalance of enterprises at all levels, and the focal loss function is used instead of the cross-entropy loss function to further balance the data. Finally, the listed companies in A shares are randomly selected, and experiments were designed to verify the performance of the model built in this paper. The results show that the prediction accuracy of the financial risk prediction model based on GRA-TOPSIS and SMOTE-CNN is 98.57%, which indicates that the model is feasible and has certain reference value. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) has been shown to be effective in the treatment of diseases such as malaria, being better understood and accepted by the world. TCM physical health management is based on the policy of "preventive disease," comprehensively collects patients' information, and provides timely and appropriate rehabilitation guidance to achieve the best nursing effect. However, the current TCM physical health management has not been understood by the public, and the effect of its nursing evaluation has not been concluded yet. Therefore, this paper aims to design a TCM physical health management training and learning system based on digital twin technology and to evaluate and analyze the nursing effect. For TCM physical health management training, this paper designed a training system based on the VIKOR algorithm. Based on digital twin technology, the training can be carried out at different times and places, and the teaching content can also be displayed in real time through the cloud platform, which intuitively and comprehensively reflects the teaching content. For the evaluation of nursing effect, this paper selected 100 patients and divided them into two groups to compare the nursing effect of TCM physical health management and general Western medicine nursing. The experimental results of this paper found that the nursing effect of TCM physical health management is 20%–60% better than that of Western medicine nursing in terms of blood pressure, TCM syndromes, exercise tolerance, and quality of life. [ABSTRACT FROM AUTHOR]

This paper has explored the relationship between CO2 emissions, eco-efficiency, and economic growth, which has important theoretical and practical significance for realizing high-quality development of the Yellow River Basin (YRB). Based on the data of 99 prefecture-level cities in the YRB from 2003 to 2017, this paper analyses the temporal and spatial variation and agglomeration characteristics of CO2 emissions and eco-efficiency in the YRB. The spatial Durbin model (SDM) with fixed effects is used to test the relationship between CO2 emissions, eco-efficiency, and economic growth in the YRB by using new structural economics theory and the EKC hypothesis. The results show that (1) the YRB is in a stage of extensive economic development with the coexistence of high CO2 emissions and low eco-efficiency and shows a trend of transformation and upgrading to a stage of high-quality economic development; (2) CO2 emissions and eco-efficiency are spatially heterogeneous; CO2 emissions and eco-efficiency in the upper reaches are low, and the middle maintains relatively high eco-efficiency and high CO2 emissions while the lower reaches have both high CO2 emissions and low eco-efficiency; (3) the relationship between CO2 emissions and economic growth is N-shaped, and there is an inflection point of accelerated increase (per capita GDP is 45,558 yuan). The YRB has crossed this inflection point, and CO2 emissions are accelerating with economic development at this stage. The relationship between eco-efficiency and economic growth is U-shaped, with the inflection point of per capita GDP at 46,483 yuan. The YRB has just crossed the point, and eco-efficiency will subsequently steadily rise with economic development. [ABSTRACT FROM AUTHOR]

With the rapid development of modern technology, due to the light-weight, small size, and good concealment, unmanned aerial vehicle (UAV) has received much attention from the society and has been vigorously promoted. Photoelectric tracking detection system is an important means in the field of modern detection. The combination of a UAV and photoelectric detection system can effectively play an important role in reconnaissance and exploration, target positioning, communication, and navigation. At present, the relevant personnel have higher and higher requirements for the accuracy of the photoelectric detection function of UAV, and the original POS data relied on by the existing photoelectric detection devices of UAV has systematic errors, which leads to the low accuracy of photoelectric detection control. Therefore, in order to achieve the purpose of improving the high-precision control of the photoelectric detection device of UAV, this paper designed an optimization method for the high-precision control device of photoelectric detection of UAV based on POS data. Firstly, the improved PID control algorithm is applied to the optimization of the UAV control device, and secondly the error correction model is established by analyzing the error source, and the original POS data is corrected by the model. This paper used the designed high-precision control device optimization algorithm and the traditional algorithm to compare the stability control experiments of the UAV platform, respectively. The experimental results showed that the application of the improved UAV photoelectric detection control device optimization method could effectively improve the control device optimization accuracy of UAV photoelectric detection by 8.23%, which was conducive to the efficient implementation of the project. [ABSTRACT FROM AUTHOR]

Machine translation is different from written translation. How to improve the performance of machine translation has been a research hotspot in current research on machine translation. In this paper, based on the semantic analysis and research of Japanese passive, a joint optimization algorithm of scheduling has been proposed, and the machine translation of Japanese passive has been studied. At present, machine translation is more and more widely used. Machine translation has solved many vocabulary problems, and it can complete a large amount of translation work and save a lot of manual translation time. While improving the translation speed, in the process of Japanese passive translation, it is also found that direct machine translation shows many shortcomings, and the quality of passive translation is not particularly ideal, exposing the basic problems of machine translation, such as semantic errors, syntactic errors, unclear and rigid expressions, and messy structures. In response to the problems above, this paper has improved the machine translation model for scheduling joint optimization algorithms. The paper has proposed several optimization algorithms and used resource awareness and computing power scheduling algorithms to conduct experimental analysis of translation performance. Finally, it is found that, among the two scheduling optimization algorithms, the resource-aware scheduling algorithm has better performance. With the same data, the resource-aware scheduling algorithm has saved 15.5% of the time compared with the computing power scheduling algorithm, and the accuracy of Japanese passive translation was 6%, 5%, and 21% higher than the computing power scheduling algorithm under different data volumes. Not only has the time taken been shortened, but the translation accuracy has also been improved. [ABSTRACT FROM AUTHOR]

Fiber reinforced composites can meet the needs of lightweight, heavy load, long-span, high strength, and modern structures and work under strict conditions. Therefore, it is widely used in various fields. Sports equipment generally cannot meet the requirements of high-strength use, so there is an urgent need for new fiber materials to make these instruments. Aiming at improving the efficiency of measurement and the reliability of measurement results, this paper studies the quantitative characterization method of the microstructure of short fiber reinforced composites. The swept-frequency OCT system is a newly developed high-resolution biomedical imaging system. The method of this paper is to study the performance parameters of swept frequency OCT system, deduce the application of image processing technology in fiber material detection, and then study the detection of residual strength and residual stiffness of composites, so as to obtain a composite material detection method that can be popularized. On this basis, image processing and performance analysis of the composites were carried out, and the composites were tested. And the prospect of application in this field has been analyzed. The experimental results show that better fiber length can be obtained by this method, and the maximum relative error is only 4.5%, thus ensuring the accurate determination of fiber length. Calculations were carried out using the experimental data, and the results showed that the performance of the sports product increased from 15.4% to 48.6% with the graphite-rubber composite. Using this material can improve the comprehensive performance of sports equipment by 2.1%∼4.5%. [ABSTRACT FROM AUTHOR]

Innovation-driven transformation and the upgrading of traditional industries is an important task in the present. This paper attempts to further study the existing basis. Based on the perspective of continuous innovation driving, this paper divides the transformation and upgrading of traditional industries into two stages, namely, industrial transformation and industrial upgrading, using the interprovincial data from 2008 to 2019, OLS, HAUSMAN, and SYS-GMM. This paper analyzes the macro- and micromechanism driven by continuous innovation in the transformation and upgrading of traditional industries. The results show that in the macromechanism, the innovation drive has a significant positive effect on the transformation of traditional industries but not on the upgrading of industries, entrepreneurship, network capability, and organizational learning, which significantly affect the transformation and upgrading of traditional industries. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, the protection of buildings has attracted the attention of many researchers. Although there is no such massive demolition in the past, natural damage still exists. Identify the collected historical building protection data through multifeature deep learning, and provide protection plans through the information in the database. In order to solve the problem of restoration of natural damage more professionally and efficiently, this paper collects the architectural features and restoration methods of each building in different processes through multifeature deep learning based on the current state of building information in China. Based on the collected information, this paper establishes the building information model, and stores and manages the building information. According to the Newton deep learning optimization algorithm, this paper enhances the algorithm to accurately collect building information and uses the collaborative filtering algorithm to provide users with a repair plan. This paper uses the GRU-based recommendation model to pass the threshold cycle unit algorithm for the probability of each building being selected in the list of similar buildings at a time point. Under the two conditions of 10 and 20 recommended numbers, the user coverage rate of the recommended case of deatomized building photos can reach 100%. And this paper recommends high-probability solutions for users to achieve automation, diversification, and intelligence. [ABSTRACT FROM AUTHOR]

The improvement of microelectronics innovation, programmed control innovation, and correspondence innovation has brought human culture into a universe of electronic data, and different electronic control frameworks are applied to each edge of life. Among them, the intelligent and energy-saving living environment has become more and more popular, and the development of electronic technology has greatly facilitated people's lives. This paper aims to study how to analyze and study indoor lighting control based on the Internet of Things technology and describe the acquisition of human body signals. This paper puts forward the problem of indoor lighting control, which is based on the Internet of Things technology, then elaborates on its related concepts and related algorithms, and designs and analyzes a case of indoor lighting control system. The experimental results showed that 100 orders are normal, and the intelligent lighting node can correctly receive and process the order information and respond accordingly. During the brightness reduction process, the sensitivity and smoothness of the touch screen slider are good, and the brightness reduction is successful. [ABSTRACT FROM AUTHOR]

The pressure relief gas drainage in goaf is the main control method of mine gas. This paper has been designed to study how to analyze and study the gas drainage of goaf pressure relief based on the perception layer of the Internet of Things. The intelligent evaluation of pressure relief gas drainage in goaf is described. This paper has raised the problem of gas extraction, which is based on the Internet of Things, so it has elaborated on the data-level fusion-related algorithms for sensing coal mine safety, and the case design and analysis of the prediction model and intelligent evaluation have been carried out. Aiming at the problem of intelligent grading of gas drainage evaluation in goaf, data preprocessing is performed on the drainage metering data. Using a deep learning evaluation method based on a convolutional neural network (CNN), an intelligent evaluation model is constructed for gas extraction. Compared with the classification model of the shallow neural network, the CNN classification model is more suitable for gas intelligence evaluation and has higher accuracy due to the good learning ability and accuracy of the deep neural network. When the learning rate is 0.1 and the batch is 256, the prediction effect of the CNN pressure relief gas intelligent classification model is the best, which can effectively provide classification results. [ABSTRACT FROM AUTHOR]

Lacking human-computer collaborative capability is one of the key problems commonly faced by the existing escort robots. To this end, a helping elderly escort interactive system based on reverse active integration of multimodal intentions (MES) is proposed in this paper in response to the elderly's decline in language expression ability, memory, and other abilities. This system can understand the intention of the elderly based on scene perception and three-modal information: speech, gesture, and posture. In detail, the system can extract the interactive intention from the nondeterministic multimodal data input by the elderly and evaluate the trust degree of the extracted intention. The evaluation of intention trust degree is such a process that the system autonomously judges the feasibility of the elderly's intention and corrects the wrong intention expressed by the elderly due to memory decline by reverse thinking of "find reasons based on the results"; when the intention cannot be extracted, the system will take the initiative to ask the elderly for enhanced information conducive to the intention extraction, so as to quickly and correctly extract the interactive intention of the elderly. This design aims at improving the quality of elderly care, making the interaction between the elderly and the robot more natural, improving the accuracy in intention extraction from fuzzy expression, as well as breaking the traditional "master-slave" human-computer interaction and improving the harmony of human-computer interaction. Further, the implementation principle of the system is detailed, and the system is evaluated in this paper. The evaluation experiment was conducted by a robot Pepper embedded with the system. Through experiments, it is verified that Pepper can quickly and accurately get the real intention of the elderly in the interaction with the elderly. In a challenging environment (such as the unclear expression of the elderly), it can still correctly extract the real intention of the elderly with an accuracy of 97% and can effectively avoid the wrong intention expressed by the elderly. This puts forward a valuable research path for the challenge in human-computer collaborative interaction. [ABSTRACT FROM AUTHOR]

As an important resource under the background of digital design technology, 3D assembly model plays a great role in many complex product design occasions. Based on the existing 3D model database, how to find and reuse the effective information that meets their own needs in many models is one of the important ways to provide design efficiency. The research content of this paper is to build a general assembly model according to the requirements of 3D assembly model. Based on the common structure, the general structure aims to integrate as much model information as possible to meet the design needs of designers in different periods and conditions. Due to the large number of components in the 3D assembly model, blindly improving hardware cost is the root of the problem. To improve quality and efficiency from the source, we need to start with the source of input. This paper simplifies the analysis of 3D assembly model information, which can not only improve the construction efficiency of the general structure of 3D assembly model but also highlight the assembly characteristics of 3D assembly model and facilitate the matching of assembly features during the construction process. Firstly, based on the attributes of assembly features and matching relationship, combined with the idea of conjugate, the matching problem between two features in the model is solved by using the idea of conjugate subgraph matching. Secondly, based on the Ullmann algorithm, combined with the definition of conjugate subgraph and related optimization operations, a conjugate subgraph matching algorithm based on vertex screening is proposed. Finally, the construction process of the general structure is proposed, and the general structure of 3D assembly model is established. [ABSTRACT FROM AUTHOR]

This paper firstly conducts a theoretical analysis on the digital transformation of enterprises. Theories such as Marxism and new institutional economics related to the digital transformation of enterprises are sorted out. Secondly, it conducts an empirical analysis on the digital transformation of high-tech enterprises. This paper summarizes the three major transformation paths of the existing production service digitization, marketing model digitization, and industrial digitization. Based on the data analysis of the questionnaire, this paper summarizes the difficulties of digital transformation of high-tech enterprises, mainly due to the lack of transformation ability, resulting in "cannot transfer"; the lack of self-development conditions and digital transformation, resulting in "will not transfer"; digital transformation lacks full guarantee, resulting in "do not dare to turn." And based on the analytic hierarchy process, the influencing factors of the digital transformation of high-tech enterprises are analyzed, and the relative importance of each factor is obtained. [ABSTRACT FROM AUTHOR]

Motivation. In the environment of day and night video surveillance, in order to improve the accuracy of machine vision dense crowd counting and target detection, this paper designs a day and night dual-purpose crowd counting and crowd detection network based on multimode image fusion. Methods. Two sub-models, RGBD-Net and RGBT-Net, are designed in this paper. The depth image features and thermal imaging features are effectively fused with the features of visible light images, so that the model has stronger anti-interference characteristics and robustness to the light noise interference caused by the sudden fall of light at night. The above models use density map regression-guided detection method to complete population counting and detection. Results. The model completed daytime training and testing on MICC dataset. Through verification, the average absolute error of the model was 1.025, the mean square error was 1.521, and the recall rate of target detection was 97.11%. Night vision training and testing were completed on the RGBT-CC dataset. After verification, the average absolute error of the network was 18.16, the mean square error was 32.14, and the recall rate of target detection was 97.65%. By verifying the effectiveness of the multimode medium-term fusion network, it is found to exceed the current most advanced bimodal fusion method. Conclusion. The experimental results show that the proposed multimodal fusion network can solve the counting and detection problem in the video surveillance environment during day and night. The ablation experiment further proves the effectiveness of the parameters of the two models. [ABSTRACT FROM AUTHOR]

The key to solving the problem of redundant financial indicators in addressing financial warning issues is to reduce the dimensionality of the original financial indicators. This paper proposes a model based on the whale optimization algorithm with mixed strategy (IWOA) combined with support vector machine (SVM), namely, the IWOA-SVM early warning model, which simultaneously performs index optimization and dimensionality reduction, and financial risk early warning identification. This paper takes a total of 302 enterprises specially treated in Shanghai and Shenzhen stock exchanges and normal enterprises of the same specification as the research samples to design the model. The results show that the improved whale optimization algorithm has better optimization speed and accuracy and improves the search ability of the original algorithm for the optimal solution. Compared with other dimensionality reduction methods, the IWOA-SVM model has the lowest index dimension after dimensionality reduction and has more excellent recognition effect. The dimensionality reduction results have certain universality for different classifiers, which provides a new idea for the selection of indicators for financial early warning. [ABSTRACT FROM AUTHOR]

With the development of remote sensing technology, remote sensing image data plays an active role in the dynamic monitoring of global resource changes and land cover utilization. Remote sensing image land cover classification is an important application direction of remote sensing data; how to further improve the accuracy of remote sensing image land cover classification is very important for the effective application of remote sensing data. The traditional remote sensing image land cover classification is mainly to classify remote sensing data according to the spectral data of ground objects. However, due to the complex environment of remote sensing images and the dynamic changes of the environment, traditional classification methods based only on pixel spectral data are often unable to achieve. A satisfactory classification result is achieved. In addition, some researchers have also proposed to combine pixel neighborhood texture information to supplement spectral feature data. Although the traditional classification method based on spectral features solves the problem of time-consuming visual interpretation, to a certain extent, due to the limited semantic expression ability and poor generalization ability of the design features, the classification accuracy is still not very satisfactory. This paper mainly studies the classification method of land cover remote sensing image based on pattern recognition. This paper is based on the experimental results of remote sensing data in Nanjing Yuhuatai District in 2018 and 2019. The ground resolution of the data is 2.5 meters. Data is projected, corrected, and equalized. Half of the images covering 43.75 square kilometers are used as training samples, and the remaining 50 square kilometers are used for detection. In the classification results of this IndianPines data, OA only increased by nearly 10% to 86.2%, AA increased by 13%, r was 82.77%, and Kappa coefficient was 0.84. In the classification results of Salinas data, both OA and AA increase by about 5%, and the optimization effect is not obvious. [ABSTRACT FROM AUTHOR]

With the increasing development of information science and technology and the vigorous use and promotion of new technologies, profound changes have taken place in all aspects of our daily life. With this huge change, the IoT industry was born. And it analyzes and processes the large amount of data generated between them, and finally, it helps the development of the economy. The RFID system studied in this paper is the radio frequency identification system, which is an automatic signal identification system. The relationship between the RFID system and the Internet of Things is that the former will obtain a large amount of Internet of Things information data by identifying the Internet of Things. However, it is difficult to guarantee the analysis and processing of data and the security of data in the RFID system. This paper aims to study the effective processing and security guarantee of a large amount of data obtained by the RFID system after processing the identification of the Internet of Things. It is expected to overcome the problems of conventional related art. This paper proposes the encryption technology for the Internet of Things RFID system, as well as the corresponding algorithm, and establishes a processing system for information data. The experimental results of this paper show that the cryptographic mechanism run by the algorithm PECC has better security performance compared with other cryptographic mechanisms, and its computational complexity can be reduced by 28.35%. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of information technology has affected the operation of the world economy, and the emergence of e-commerce has decreased significantly. The development of modern information technology, especially the development of Internet technology, provides a solid foundation for e-commerce. The time and space gap between economic operators is shortened, and resources are shared as widely as possible. Based on a comprehensive and in-depth study of language recognition technology. However, the current e-commerce financial management still uses the traditional accounting method, which is inefficient and cannot be integrated with new technologies. This paper proposes a design scheme for a nonspecific person and small vocabulary language recognition system based on SPCE061A processor. This integrated system can be combined with data acquisition and language input technology and constitute a data acquisition and input system. It is used for data collection and input of instruments and meters in the workplace to improve work efficiency. This paper proposes algorithms based on time-domain energy and zero-crossing rate detection, detection based on entropy function method, and detection based on the double-threshold method. The speech recognition technology is analyzed in detail, and a new model of financial management is constructed. Accounting information disclosure is a virtuous circle of "promoting capital management with appropriate decentralization." The experimental results in this paper show that through model construction, the calculated WER is 0 dm, 7 dm, 15 dm, and 11 dm, respectively, and it is concluded that the system exhibits good antinoise performance. [ABSTRACT FROM AUTHOR]

The 3-D mechanical model of the inclined shaft pumping pump and the optimization model of anti-partial wear structure and security position are established in this paper. Based on the real data of well, 148-inclined 2 slope measurement, the ordinary pipe rod combination, inclined well pumping pump lining oil pipe, and oil pump laser oil pipe are optimized and calculated. In order to improve the computing power and reduce computational time consumption, this paper uses cloud computing technology in the computing process. The results show that the combination method of the lining oil pipe and the ordinary continuous pumping rod has less axial force and lateral force, and finally, the combination method of different well pipes is obtained. [ABSTRACT FROM AUTHOR]

Data integrity verification means that the data in the cloud are uploaded by the user. In addition to the user's own update of the data, any external factors including the cloud service provider's data are destroyed, tampered with, and lost, and the data are not updated in a timely manner. Any inconsistencies in the actual data required can be detected by the user. This article aims to study cloud data integrity verification algorithms based on data mining and accounting informatization. This article proposes data mining technology, accounting information to help business managers assist management work. The article uses Company H as an example to illustrate the accounting information system and proposes a new information management strategy based on it. The CBF algorithm and data integrity verification algorithm are used to study the cloud storage data integrity verification protocol, the cloud data integrity verification model is constructed, the data program flow design is analyzed, and the time-consuming operation of file data insertion is analyzed. The experiment in this paper uses 16 standard mathematical calculation formulas to strengthen the analysis. The results show that the study of cloud data integrity verification algorithms based on data mining and accounting information is beneficial to the integrity and protection of data. When the number of documents added increases from 0 to 400, the document agreement shows an upward trend, and the agreement in this paper basically fluctuates between 10 and 80. [ABSTRACT FROM AUTHOR]

Based on the interactive food packaging design method based on the user's emotional experience, the specific food packaging design scheme is completed. On the basis of analyzing the current situation of the food industry, this paper discusses the competitive driving factors between the demand side and the enterprise side for food packaging, integrates the user's emotional experience into the reality of food packaging, and focuses on the specific practical solutions based on the user's emotional experience. Incorporating user emotional experience into food packaging design Yuan, 2022 and Liu, 2020 is beneficial to both the impact of food brands and the promotion of product sales. Incorporating user emotional experience has played a very important role in food market competition. At the same time, through practical cases, closely focusing on the user's emotional experience, it innovatively tried to convert users into core designers of product packaging and adopted a small-batch customized production and packaging mode. This plays a certain guiding and reference role for food packaging design. This paper takes the user's emotional experience as the research perspective and interactive packaging as the research object, uses the three-level theory of emotionality as the guide, explores the user's emotional experience path for packaging by means of literature analysis, fieldwork, case analysis, and experiments, and summarizes the design, principles, and methods. [ABSTRACT FROM AUTHOR]

Cities need to develop, people's lives need to be improved, and the living environment needs to be enhanced. With the strengthening of the national policy of paying attention to people's livelihood, the speed of development, the increase of the number of cars, and the major demolition and construction of streets are in full swing everywhere. However, traffic congestion has become a serious social problem, which greatly affects economic development and people's daily life. Therefore, the study of signal coordination control of urban traffic arteries has become an urgent problem for all countries. In this paper, after a brief introduction of the basic concepts and related technologies of traffic control systems, we focus on the arterial coordinated control system. The adaptive control model studied in this paper reveals the vehicle arrival dynamics from a microscopic perspective, which helps to improve the adaptive control effect and alleviate urban intersection congestion. The use of traffic information acquisition and control systems can promote the application of vehicle networking technology, while moreover helping to provide important model simulations for future street simulation renovation. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and wireless communications, smart multi-sensor networks are gradually being used in various fields, and the Internet of vehicles technology combined with smart multi-sensor network came into being. However, although the Internet of vehicles technology has greatly facilitated people's travel in traffic, its problems in security and privacy protection have also been amplified and became a major threat to the Internet of vehicles technology. Consistency algorithms are an important solution to the trust problem and are a prominent representative of distributed algorithms. In order to solve the problem of security and privacy protection of smart multi-sensors in car networking technology, in this paper, consistent algorithm and group signature verification technology are used to study the security and privacy protection performance of the Internet of vehicles in a connected random network composed of 300 nodes through artificially synthesized simulation data. Using the method in this paper, collect and analyze the experimental data of the number of messages, verification delay, traffic load, and message loss rate of randomly generated vehicles within 200 seconds and evaluate the performance of privacy protection in the maximum consistent algorithm; the results show that the verification delay will increase with the increase of the number of messages, and the more traffic load, the higher the message loss and verification delay, and the maximum consistent algorithm has good privacy protection performance. In terms of data, the RSU-based auxiliary scheme consumes 25.59% of the communication of the KPI-based signature scheme and 25.64% of the swarm scheme. [ABSTRACT FROM AUTHOR]