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Journal of Sensors
Journal Prestige (SJR): 0.288
Citation Impact (citeScore): 1
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  This is an Open Access Journal Open Access journal
ISSN (Print) 1687-725X - ISSN (Online) 1687-7268
Published by Hindawi Homepage  [339 journals]
  • Nonlinear Dynamic Analysis of Hybrid Piezoelectric-Magnetostrictive
           Energy-Harvesting Systems

    • Abstract: To progress the proficiency and broaden the action bandwidth of vibration energy harvesters, this paper presents a cantilever piezoelectric-magnetostrictive bistable hybrid energy harvester with a dynamic magnifier. The hybrid energy-harvesting system comprises two vibration degrees of freedom and two electrical degrees of freedom. It consists of a composite cantilever beam made of three layers, in which the magnetostrictive and piezoelectric layers are attached to the top and bottom of the base layer. The electromechanically coupled vibration equations of the whole hybrid structure were established with the lumped-parameter model while taking into account the magnetic interaction of two magnets. The nonlinear frequency-response of vibrations for the hybrid harvester is calculated using the harmonic balance method, and the model has been validated by literature. The time response and phase portraits of oscillation for the cantilever harvester and its performance in generating electrical power under different magnet distances, dynamic magnifier features, and excitation levels are analyzed. Numerical results have shown that the hybrid structure can harvest additional electrical power and operates at larger bandwidth than routine bistable piezoelectric or magnetostrictive energy harvesters.
      PubDate: Fri, 05 Aug 2022 07:50:02 +000
  • Research on Home Product Design and Intelligent Algorithm Recommendation
           considering Ergonomics

    • Abstract: Under the modern design concept, consider ergonomics to design home products. With the progress of civilization and technology, the improvement of life quality in the process of urbanization, and the increasing abundance of home life and home products, people’s requirements for living environment and environmental products are continuously improving. In order to further meet the necessities of life and solve the reasons such as limited living space at home, people are no longer satisfied with purchasing household products in large quantities but are more suitable for household needs. According to the user’s requirements for ergonomic home product design, a criterion layer is established, and the weight of the criterion layer is calculated to obtain its corresponding weight value. It can be obtained that consumers think that safety is the most important, followed by ease of use, functionality, and aesthetics. In the second criterion level, the order of importance is stable operation, safe use of materials, invisible circuit, strong practicability, massage function, safety guardrail, convenient installation, easy cleaning, intelligent operation, home style, structural strength, easy to move, natural materials, air purification, easy disassembly, suitable size, simple shape, convenient function, timely after-sales, soft color tone, noise reduction, simple decoration, single color matching, and comfortable function. The addition of the nearest neighbors improves the accuracy of the CFCNN-CL algorithm and the REPREDICT PCC algorithm in terms of smart algorithm recommendations for home products considering ergonomics. But compared between the two, the CFCNN-CL algorithm has better performance and better accuracy than the REPREDICT PCC algorithm. In terms of the influence of data sparseness, UCF-Jaccard has a smaller MAE value than other methods in general and is less susceptible to the influence of sparse data, and the MAE value does not change much. Among the group filtering methods, the RRP-UICL method has better prediction accuracy than the commonly used group filtering methods.
      PubDate: Fri, 05 Aug 2022 07:50:02 +000
  • Distributed Soccer Training Smart Sensors for Multitarget Localization and

    • Abstract: This paper presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance.
      PubDate: Fri, 05 Aug 2022 07:50:02 +000
  • Online Fault Detection of Dry Reactor Based on Improved Kalman Filter

    • Abstract: In order to meet the requirements of online fault detection for dry reactor, an online fault detection technology based on improved Kalman filter is proposed. The main content of the technology is based on the dry reactor detection technology, through the study of improved Kalman filter, the use of fault diagnosis and other methods, and finally through the experiments and analysis to build improved Kalman filter dry reactor online fault detection research means. The experimental results show that the maximum relative error of the improved Kalman filter is 6.039%, and the average relative error is 2.388%. The improved algorithm is very effective and greatly improves the prediction accuracy. The research based on improved Kalman filter can meet the demand of online fault detection of reactor.
      PubDate: Thu, 04 Aug 2022 10:35:01 +000
  • TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint
           Learning of Topic Recognition and Named Entity Recognition

    • Abstract: There is a centralization of the core content in the text information of the new crown epidemic notification. This paper proposes a joint learning text information extraction method: TBR-NER (topic-based recognition named entity recognition) based on topic recognition and named entity recognition to predict the labeled risk areas and epidemic trajectory information in text information. Transfer learning and data augmentation are used to solve the problem of data scarcity caused by the initial local outbreak of the epidemic, and mutual understanding is achieved by topic self-labeling without introducing additional labeled data. Taking the epidemic cases in Hebei and Jilin provinces as examples, the reliability and effectiveness of the method are verified by five types of topic recognition and 15 types of entity information extraction. The experimental results show that, compared with the four existing NER methods, this method can achieve optimality faster through the mutual learning of each task at the early stage of training. The optimal accuracy in the independent test set can be improved by more than 20%, and the minimum loss value is significantly reduced. This also proves that the joint learning algorithm (TBR-NER) mentioned in this paper performs better in such tasks. The TBR-NER model has specific sociality and applicability and can help in epidemic prediction, prevention, and control.
      PubDate: Thu, 04 Aug 2022 09:05:02 +000
  • Construction and Effect Analysis of College Students’ Physical Education
           Teaching Mode Based on Data Mining Algorithm

    • Abstract: With the wide application of computer technology, communication technology, network technology, and multimedia technology in modern education, teaching methods tend to be diversified and scientific. Based on the DM (data mining) algorithm, this paper implements a sports achievement management system. Through this system, the efficiency of inputting and counting students’ achievements can be improved, and teachers can be freed from the complicated achievement management. Aiming at the problem that SVM (Support Vector Machine) is slow in training large sample sets in DM classification, a DM classification algorithm based on improved SVM, PSO_SVM, is proposed, which applies the density of adjacent samples to the design of membership function to reduce the influence of noise points on classification. The results show that the training time of this algorithm is increased by 54.26 s and 55.69 s compared with SVM and -means, respectively, and the accuracy is increased by 35.62%. The results obtained by the DM algorithm will be helpful for teachers to diagnose teaching problems and construct PET (physical education teaching) model for college students with characteristics.
      PubDate: Thu, 04 Aug 2022 07:35:02 +000
  • Simulation and Application of Urban Road Landscape Based on Geographic
           Information Data

    • Abstract: In this paper, we study and analyze the urban road landscape by integrating big data with GIS and designing a simulation system. Underwood is selected as the base model and calibrated for optimal density and capacity. The traffic flow model is optimized, and the minimum flow rate of each class of road is corrected; the traffic volume of highway and expressway in the early morning and at night is adjusted to make up for the discrepancy between the calculated flow rate of the model and the actual situation due to the unstable speed. Investigate and analyze the pollutants produced by enterprises along the road and select suitable and targeted plants to enhance the ecological protection of road landscape and optimize and improve the park environment; combine the regional culture and apply the ceramic culture to the design of road landscape vignettes; select suitable water and moisture-resistant plants for slope protection plant landscape design, to form a trinity of ecological protection, landscape road, and riverfront green space of industrial park road landscape. As a new basic geospatial infrastructure and carrier, 3D city has a unique spatiotemporal view to plan and manage the operation and maintenance information of the whole city. This part of the study takes urban planning, community management, urban publicity, and tourism management as demonstration cases and focuses on the elaboration and verification of how 3D city data and 3D urban geographic information are applied in the refined urban construction and management. Typical road measurement data are selected, and the applicability of the road traffic flow model of each level is judged by numerical analysis. An interactive road model editing method with the help of auxiliary information is proposed. With the help of auxiliary information displayed in different ways, the operation of adjusting road height in three-dimensional space is transformed into operation in a two-dimensional plane, which can effectively simplify the operation process and improve the accuracy of model editing.
      PubDate: Thu, 04 Aug 2022 07:35:01 +000
  • Development and Construction of Internet of Things Training Practice
           Platform for Employment Skills Assessment

    • Abstract: In order to solve the problem of employment skills assessment, a method using the Internet of Things training practice platform is proposed. The main content of this method is based on the research and analysis of the practical training platform of the Internet of Things. According to the data characteristics of the Internet of Things and through the construction of the practical platform, it is concluded that the development and construction of the practical training platform of the Internet of Things is highly feasible for the evaluation of employment skills. The experimental results show that the average RI value of data mining accuracy is 0.95, and the accuracy of data mining algorithm is high. Conclusion. It proves that the development and construction of the practical training platform of the Internet of things is feasible and accurate for the evaluation of employment skills.
      PubDate: Thu, 04 Aug 2022 06:20:06 +000
  • High-Concurrency Big Data Precision Marketing and Advertising
           Recommendation under 5G Wireless Communication Network Environment

    • Abstract: With the rise of 5G wireless communication networks, information technology has challenged the traditional practice of marketing. The impact of enterprise precision marketing theory on traditional marketing theory was brought by massive user data in the era of big data. With the sudden rise and rapid development of mobile Internet and its derived big data, in the fierce market competition of new media and we-media, how to provide accurate information push for users has become the primary subject of research. The design of advertising precision marketing system mainly uses 5G network as the basic tool and makes precise positioning of consumer demand through high-concurrency big data analysis system. -means clustering algorithm improves the data analysis system to provide personalized demand customization and deeply excavates the segmentation of the consumer market and the real demand of consumers. The results show that the model effectively enhances its adaptability. When value is between 0.01 and 0.05, the variable has a high significance, and precision marketing improves the accuracy of recommendation.
      PubDate: Thu, 04 Aug 2022 04:35:01 +000
  • Design and Implementation of Multimedia Network Intelligent Control Robot
           Based on Software Definition

    • Abstract: In order to solve the problem that the delay of wireless network and complex operating environment affects the stability and operating performance of teleoperation system, a method of intelligent control robot based on multimedia network defined by software is proposed in this paper. In the network environment established based on the software definition, the gain of the system control is increased according to the network delay to improve the operating performance of the system, and the output of parameters is dynamically adjusted to adapt to the stability of the system in complex environment. The experimental results show that the robot control system can obtain the best control stability by continuously adjusting the relevant parameters. After the simulation test, the final setting is .Conclusion. Based on the intelligence of gain scheduling control algorithm, the control effect of fuzzy control can be significantly improved when the network delay is large.
      PubDate: Wed, 03 Aug 2022 10:50:02 +000
  • Intelligent Sensor Network Using Internet of Things in Urban Community
           Network Governance

    • Abstract: The purpose is to conform to the development of the times and study a new way of urban community network governance. First, the ways of governance, social governance, urban community governance, and urban community network governance are discussed. Next, the intelligent sensor function of Internet of things (IoT) technology is applied to realize the communication between IoT and urban community network governance. An urban community network governance system based on an intelligent sensor network is established. The system combines Kalman filtering to realize the dynamic monitoring of the mobile Application (APP) of the urban community residents and can update and process the collected information in real time. Finally, a survey is conducted on the satisfaction of residents of a community in Jiaozuo City, Henan Province, in 2019 on community network governance under the IoT. The efficiency and ability of urban community network governance under different network modes are compared and analyzed. The results show that the community has improved the needs of community residents through IoT network governance in multiple aspects, but the community service level of culture is not high. The community needs to strengthen the network publicity of community culture to improve the cultural level of the community. Meanwhile, in urban community governance, the comprehensive governance ability of the network governance model under network organization management is the highest, with an average of 50.93%. It shows that the established system is beneficial to the construction of urban community network governance. This exploration provides the direction for the IoT urban community network governance and constructs the urban community network governance system based on the intelligent sensor network.
      PubDate: Wed, 03 Aug 2022 10:35:01 +000
  • Self-Healing and Shortest Path in Optical Fiber Sensor Network

    • Abstract: In this study, a new square-based fiber Bragg grating (FBG) sensor network model is proposed to address possible link failures in FBG sensor networks and improve their reliability. Graph theory and optical switching are simultaneously applied to these sensor networks to improve their self-healing ability; the FBG sensor network is regarded as a directed graph. Three commonly used self-short-circuit algorithms are compared in terms of the self-healing capabilities that they provide to the optical fiber sensor network. Among these, the shortest-path faster algorithm achieved a high, nearly 90% repair accuracy and had an average repair time of 0.103 s, the shortest in this study. The newly designed FBG self-healing network can be reorganized and repaired when local damage occurs, thereby improving its reliability.
      PubDate: Wed, 03 Aug 2022 10:35:00 +000
  • A Training Method for a Sensor-Based Exercise Rehabilitation Robot

    • Abstract: In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed. A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (IMU), fuzzy adaptive proportion differentiation (PD) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (sEMG) of the affected limb, and power compensation of the outer ring of the affected limb. The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb. The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive PD controller in the variable domain was . The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb. The calculated tracking error of the healthy limb trajectory was . It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.
      PubDate: Tue, 02 Aug 2022 11:35:06 +000
  • Abnormal Concentration Detection Method of Chemical Pollutants Based on
           Multisensor Fusion

    • Abstract: China is a big industrial producer, but also a big producer and user of chemical materials. Although the use of chemical materials has improved the level of industrialization, it has also caused harm to the environment and ecosystem. With the acceleration of China’s industrialization, more and more attention has been paid to the problem of chemical pollution. The pollution of water resources in China has seriously damaged the balance of ecological environment and is also an important factor threatening people’s own health. The detection of chemical pollutants in water resources, especially organic pollutants, has a long way to go. To solve this problem, this paper designs a method of chemical pollutant concentration detection based on multisource information fusion and analyzes the performance of the detection system. Firstly, this paper introduces the main types of situations of chemical pollution at present. Secondly, a multisensor fusion model based on BP neural network is established, and the collected chemical pollutant samples were input into the model. Finally, the quantitative and qualitative analysis of the detected pollutant concentration results shows that the proposed method not only has good detection effect of chemical pollutant concentration but also has good practicability. In a word, the proposed method not only has good theoretical significance but also has certain potential application value.
      PubDate: Tue, 02 Aug 2022 11:35:05 +000
  • Effective Preprocessing and Normalization Techniques for COVID-19 Twitter
           Streams with POS Tagging via Lightweight Hidden Markov Model

    • Abstract: The major focus of this research work is to refine the basic preprocessing steps for the unstructured text content and retrieve the potential conceptual features for further enhancement processes such as semantic enrichment and named entity recognition. Although some of the preprocessing techniques such as text tokenization, normalization, and Part-of-Speech (POS) tagging work exceedingly well on formal text, it has not performed well when it is applied into informal text such as tweets and short messages. Hence, we have given the enhanced text normalization techniques to reduce the complexity persist over the twitter streams and eliminate the overfitting issues such as text anomalies and irregular boundaries while fixing the grammar of the text. The hidden Markov model (HMM) has been pervasively used to extract the core lexical features from the Twitter dataset and suitably adapt the external documents to supplement the extraction techniques to complement the tweet context. Using this Markov process, the POS tags are identified as states of the Markov process, and words are the desired results of the model. As this process is very crucial for the next stage of entity extraction and classification, the effective handling of informal text is considered to be important and therefore proposed the most effective hybrid approach to deal with the issues appropriately.
      PubDate: Tue, 02 Aug 2022 11:20:01 +000
  • Efficient Management and Application of Human Resources Based on Genetic
           Ant Colony Algorithm

    • Abstract: With the increasing demand of human resources, the cost of staffing and management is increasing, and it is difficult to dynamically allocate and adjust personnel among different parts. It is the key of intelligent management technology to realize efficient application and mining in human resource management. In the aspect of human resource allocation and management, this paper puts forward the efficient management and application of human resource based on the genetic ant colony algorithm. Firstly, this paper describes the process management and parameter application of the genetic algorithm and ant colony algorithm and manages the resource allocation and management process under the two algorithms. Secondly, several current test functions are applied to the genetic algorithm and ant colony algorithm to test the efficiency of the algorithm, which has obvious advantages in convergence efficiency. Finally, the paper uses the efficient management configuration of human resources for comprehensive application and management and applies the system uploading and downloading services on human resumes, respectively. The genetic ant colony algorithm has obvious advantages in efficiency. In human resource data matching, the genetic algorithm is slightly better than the ant colony algorithm in the case of relatively few data in the early stage, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. The ACO-GA algorithm is more consistent with the actual value, which not only ensures the stability but also ensures the accuracy of prediction, which is more in line with the actual needs.
      PubDate: Mon, 01 Aug 2022 06:05:01 +000
  • The Evaluation Method of Community Emergency Resource Allocation Based on
           Coordination Sensor Information Collection

    • Abstract: This paper analyzes the information collection data of coordination sensors and designs a corresponding evaluation method to conduct an in-depth assessment of the configuration of community emergency resources. Based on the principles of assessment index system construction and the actual research, an assessment index system of community emergency resource allocation level is established. In this paper, the G1 method was selected to determine the weights of the evaluation indexes, and the gray clustering method was applied to construct the whitening weight function, determine the gray level to which each index belongs and the affiliation degree of each gray class, and establish a comprehensive evaluation model of the community emergency resource allocation level. In response to the problem that the increase of state variables leads to a decrease in real-time map building, a dynamic local window size mechanism is proposed, which can reduce the time consumption and save computational resources under the condition of ensuring the accuracy of positioning and map building. Therefore, it is urgent to design an efficient self-interference cancellation mechanism to resist the influence of self-interference on the signal-to-noise ratio of the first-hop link. For example, the IMU pre integration theory is combined with the wheel range method to solve the problem of frequency synchronization; An initialization algorithm is proposed to recover the scale information of the camera and optimize the external parameters; Design a graphic optimization framework integrating IMU, wheel range finder and camera. The monitoring terminal sends the data information of coordination transportation to the monitoring platform in real-time, and the monitoring platform is responsible for storing and displaying the data information, thus realizing the real-time monitoring of the coordination transportation process. Finally, the functions of the monitoring terminal and the display system of the monitoring platform experiment, respectively, and the test results verify the effectiveness and integrity of the system data communication, which proves the correctness of the terminal design of the integrated coordination monitoring system based on multidimensional information and has practical engineering value.
      PubDate: Mon, 01 Aug 2022 05:50:01 +000
  • Real-Time Detection of Lower Limb Training Stability Function Based on
           Smart Wearable Sensors

    • Abstract: The research of smart wearable sensors in limb training has great application significance. In the face of real-time detection requirements, this paper proposes a hardware solution for the stability function of lower limb training based on the theory of intelligent wearable sensors. For the specific implementation circuit of the device, considering the reliability of the system, the system implements antijamming design for the hardware circuit from three aspects: adding decoupling capacitors, optimizing layout and wiring, and rationally grounding the hardware circuit, and performs moving average filtering on the collected sensor data to remove noise, which solves the problem of sensor data precision issues. During the simulation process, by analyzing the changes of acceleration, angular velocity, and attitude angle under different lower limb training activities and different wearing positions, the characteristics of stability combined acceleration, combined angular velocity, and attitude angle were constructed, and the stability mean, variance, and attitude angle were extracted. The experimental results show that the extracted 57 feature dimensions are first reduced to 21 dimensions by the principal component analysis algorithm, and then, the optimal feature subset is selected by the encapsulation method, and the dimension is reduced to 9. The proposed multifeature fusion algorithm has higher accuracy, and the maximum has increased by 6.5%, effectively improving the accuracy of the lower limb training stability function detection algorithm.
      PubDate: Sun, 31 Jul 2022 06:05:02 +000
  • Detection of Weak Pulse Signal in Chaotic Noise Based on Improved Brain
           Emotional Learning Model and PSO-AGA

    • Abstract: A model for detecting weak pulse signals in chaotic noise was proposed. Firstly, based on the short-term predictability of chaotic signals, according to Takens’s theorem, the phase space of observed signal was reconstructed. Then, an improved brain emotional learning (BEL) model combined with PSO-AGA was proposed to predict chaotic signals, and the one-step prediction error was obtained. In order to optimize the parameters of the BEL model, an algorithm named PSO-AGA combined with particle swarm optimization and adaptive genetic algorithm was adopted to achieve the balance of global search and local search capabilities. Finally, the hypothesis testing method was used to detect whether there existed the pulse signal from the one-step prediction error. The experiments simulated the Lorenz system and the magnetic storm loop current system. In the Lorenz system, the MAD of BEL-PSO-AGA, BP-NN-PSO-AGA, and Wavelet-NN-PSO-AGA were 0.0022, 0.0142, and 0.0076; the MSE were , 0.00034, and 0.00016; the RMSE were 0.0029, 0.0187, and 0.0128; the running times were 410 s, 792 s, and 721 s; the ACC were 0.999, 0.972, and 0.997; the F1 were 0.984, 0.423, and 0.878. It could be seen that the BEL model had better performance, shorter running time and higher values of the ACC and F1, indicated that the BEL model ran faster and had a better predictive effect. The MAD of BEL-PSO-AGA, BEL-WOA, BEL-AGA, and BEL-PSO were 0.0022, 0.0065, 0.0135, and 0.0071; the MSE were , 0.00013, 0.00029, and 0.00014; the RMSE were 0.0029, 0.0115, 0.0173, and 0.0119; the ACC were 0.999, 0.992, 0.990, and 0.997; the F1 were 0.984, 0.733, 0.451, and 0.878. This indicated that the PSO-AGA also had better performance and higher prediction accuracy. In the magnetic storm loop current system, the experimental results were similar to the Lorenz experiment, which also indicated that the BEL-PSO-AGA model was better. To sum up, the detection results of simulations showed that the proposed model and algorithm could effectively detect weak pulse signals from the chaotic noise.
      PubDate: Sun, 31 Jul 2022 06:05:01 +000
  • ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable

    • Abstract: The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains. Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices. To achieve this, the researchers try to solve two problems: (I) reduce the complex feature dependencies and (II) reduce the complexity of the deep learning model without compromising accuracy. In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework. We have introduced knowledge distillation on the Vision Transformer model to study the MIT-BIH Arrhythmia Database. A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% F1 score and 99.3% accuracy. The model was further tested on the Xilinx Alveo U50 FPGA accelerator, and it is found fit for any low-powered wearable device implementation.
      PubDate: Sun, 31 Jul 2022 03:20:01 +000
  • Analysis of Green Financial Policy Utility: A Policy Incentive Financial
           Mechanism Based on State Space Model Theory Algorithm

    • Abstract: In recent years, in the context of “double carbon” and innovation-driven synthesis, the volume of green finance has been growing year by year, and the intensity of environmental regulation has been stabilizing. As green financial technology innovation cannot be separated from the support of financial market and government policies, how to promote green financial technology innovation with green finance and environmental regulation has become a hot issue. How to control the appropriate strength of environmental regulations to promote green financial technology innovation is a matter of continuous exploration by local governments. The research of this paper is about the utility analysis of green finance policy: a policy incentive financial mechanism based on the state space model theory algorithm. Therefore, this paper introduces the theory of green finance based on the state space model algorithm and neural network model algorithm to study China’s green finance policy incentive mechanism, profoundly study the current situation of domestic green finance development, and put forward further strengthen the leading role of the government in green financial innovation. At the same time, suggestions for achieving coordinated regional development were made in terms of giving full play to the role of financial markets in promoting green technology innovation.
      PubDate: Sat, 30 Jul 2022 12:20:02 +000
  • Digital Twin-Driven Machine Condition Monitoring: A Literature Review

    • Abstract: Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.
      PubDate: Sat, 30 Jul 2022 12:20:02 +000
  • Design of Health Detection System for Elderly Smart Watch Based on
           Biosignal Acquisition

    • Abstract: The health monitoring of the elderly has attracted increasing attention of researchers. Based on the biosignal acquisition method, this paper proposes a design structure of the health detection system for smart watches for the elderly and realizes the effective detection of health signals by analyzing the Lipschitz exponent of the maximum value column of the transform. The multiphysiological parameter acquisition and monitoring system of the wearable smart watch designed in this paper can continuously monitor the physiological parameters of the elderly such as body temperature, pulse, and respiration for a long time and solve the problem of the accuracy of the health detection of the elderly. In the simulation process, based on the performance of the synchronization source and the difference of the network path, the model applies the multivariate and multiscale biological signals to collect the human gait acceleration. The experimental results show that, compared with the international recognition rate obtained for this data set, the highest recognition rate obtained by the method in this paper reaches 96.5%, which can provide a calibration accuracy of 1 ~ 50 ms, and the synchronized system time and the national time service center network are given. The error obtained by comparing the published time is within 50 ms, which meets the accuracy requirements of the time protocol. The results fully prove that the algorithm in this paper can effectively extract the biosignal features of the elderly’s health detection and has good statistical features and classification accuracy.
      PubDate: Sat, 30 Jul 2022 08:50:02 +000
  • Analysis and Application of the Ideological and Political Evaluation
           System of College Students Based on Text Mining

    • Abstract: Under the background of the new era, the ideological and political theory courses in universities are the key courses to cultivate people by virtue. It is very important for college students in their own ideological and political construction and consciousness. At the present stage, there is little research on the ideological and political current situation of college students, most of them improve the teaching program from the teacher level, and there is no research on the ideological and political evaluation system of college students. In order to understand the current situation of the ideological and political evaluation of college students, we will study it from the two aspects of the school and the society. This paper excavates and analyzes the ideological and political aspects of college students and those in the society: first of all, the ideological and political evaluation of teachers on large social platforms such as Zhihu and Weibo and the moral quality and political consciousness of college students in the society. It was then processed and analyzed using the Python language. Draw the word frequency and word cloud map of the keywords in the evaluation for analysis. Then, use the text data preprocessing method based on the word frequency statistics law (Data Preprocessing Based on Term Frequency Statistics Rules (DPTFSR)). Processing the text data and finally conducting the relevant emotional analysis show the university ideological and political system to understand the ideological and political situation of college students in the new era and to improve the ideological and political education program according to its performance.
      PubDate: Fri, 29 Jul 2022 10:05:02 +000
  • Multiobjective Optimization Scheduling of Sequential Charging Software for
           Networked Electric Vehicles

    • Abstract: In order to reduce the adverse effects of disordered charging of electric vehicles on the safe and stable operation of the distribution network, a multiobjective optimal scheduling method for the sequential charging software of networked electric vehicles is proposed. Aimed at minimizing charging costs and peak-to-valley differences in distribution network loads, its scheduling strategy will continuously roll and update EV charging schemes over time. The results show that the actual response data collected by the Internet of Vehicles app has corrected the probability distribution of the user’s choice of charging mode and response behavior. On the fifth day, the user’s actual charging response curve is close to the theoretical curve obtained by the optimization algorithm, and the expected charging is basically achieved. Calculations showed that the variance of the total load curve after charging decreased by 24.8 from 169.35 to 127.39. The proposed orderly charging strategy can effectively reduce the charging cost of electric vehicle users and the peak-to-valley difference of the distribution network load, play a good role in peak-valley filling, improve the convergence accuracy of the algorithm, and obtain the optimal solution of the problem.
      PubDate: Fri, 29 Jul 2022 10:05:02 +000
  • Fractal Art Pattern Information System Based on Genetic Algorithm

    • Abstract: In order to solve the problem of fractal art pattern innovative design in specific fields, this paper proposes a new method of fractal technology and visualization technology based on genetic algorithm to support art pattern innovative design. In these models, the function of the fractal structure is represented by the binary structure, while the main function represented by the wooden structure is the function of creating new offspring. Provide high-quality services to meet customer needs faster and better. The experimental results show that after 14 generations, the force curve appears to be more stable, the weight scale is studied, and a new model is developed. At the same time, the pattern elements of interest to users are retained for genetic algorithm. Conclusion. This method can help designers quickly design fractal art patterns appreciated by users.
      PubDate: Fri, 29 Jul 2022 10:05:01 +000
  • A Critical Evaluation of Procedural Content Generation Approaches for
           Digital Twins

    • Abstract: Procedural content generation (PCG) of terrains is one of the main building blocks for creating an automated and real-time virtual world. This study provides and in-depth review of the different tools, algorithms, and engines used for the PCG of terrains for the PCG of digital worlds; we focus especially on terrain generation but address also the main issues related to build structures and characters. It is important to know that the PCG of terrains can be implemented in a multidisciplinary scenario, for instance, modeling of games, simulating industrial maps, urban and rural planning of developments, government, and nongovernmental agency improvement plans. Many problems in current approaches were identified from the literature, where most of the researches studied were merely throwaway prototyping based, i.e., proof of concept-based systems, and were not tested in the real environment. Also, genetic algorithm-based PCG of terrains lack multiple features such as roads and buildings. The virtual cities generated through different engines were lacking a realistic look and feel. Terrain generation through multiobjective evolutionary algorithms (MOEAs) is investigated, and it is deemed the usage was restricted to the gaming domain and not extended to other fields. Findings suggest that the correctness of generated terrain is a big issue in the automatic generation of terrains. Thus, a focus on the automated correctness check is required. Other important content generation in video games such as structure generation and character generation has been extensively studied, and the techniques are analysed in the further sections of this research work.
      PubDate: Fri, 29 Jul 2022 10:05:01 +000
  • Construction of a Multimedia-Assisted Teaching System for English Courses
           in a Multimodal Sensing Environment

    • Abstract: With the advent of the information era, education reform has also sounded the call for in-depth reform and development. In order to cope with the development needs of modern society’s economy and political culture, the importance of English, as the mainstream language of international communication in the world today, is also self-evident. In rapid development of science and technology, network multimedia-assisted teaching has also been developed rapidly, while the teaching methods and teaching outcomes of foreign languages have been the focus of people’s attention. The diversified development of students and changes in science and technology has been driving the improvement and construction of teaching systems based on the English curriculum. In today’s multimodal sensing environment, a personalized recommendation algorithm of cluster analysis algorithm + collaborative filtering is introduced to complete the construction of the teaching system, and the superiority of the system is demonstrated by questionnaire survey and performance analysis comparison with teachers and students as the main research targets. The aim is to fully integrate the new media context and multimodal sensing environment in this environment, and to use in this environment, we aim to fully integrate the new media context and multimodal sensing environment and use multimedia technology to assist the construction of the teaching system.
      PubDate: Fri, 29 Jul 2022 10:05:01 +000
  • Performance Analysis of Logistic Model Tree-Based Ensemble Learning
           Algorithms for Landslide Susceptibility Mapping

    • Abstract: Landslide susceptibility prediction (LSP) is the key technology in landslide monitoring, warning, and evaluation. In recent years, a lot of research on LSP has focused on machine learning algorithms, and the ensemble learning algorithm is a new direction to build the optimal prediction. Logistic model tree (LMT) combines the advantages of decision tree and logistic regression, which is smaller and more robust than ordinary algorithms. The main aim of this study is to construct and test LMT-based random forest (RF) and selected ensemble learning algorithms including bagging and boosting algorithms to compare their performance. Firstly, taking the county of Ziyang, China, as the study area, through historical reports, aerial-photo interpretations, and field investigations, 690 inventory maps of landslide locations were constructed and randomly divided into the 70/30 ratio for a training and validation dataset. Secondly, considering geological conditions, and landslide-induced disease and its characteristics, 14 landslide-conditioning factors was selected. Thirdly, the variance-inflation factor (VIF) and tolerance (TOL) were used to analyze the 14 factors, and the prediction ability was calculated with information-gain technology. Ultimately, the receiver-operating-characteristic (ROC) curve was applied to verify and compare model performance. Results showed that the LMT-RF model (0.897) was superior to other models, and the performance of LMT single model (0.791) was the worst. Therefore, it can be inferred that the LMT-RF model is a promising model, and the outcome of this study will be useful to planners and scientists in landslide sensitivity studies in similar situations.
      PubDate: Thu, 28 Jul 2022 10:50:01 +000
  • Analysis of Key Factors of College Students’ Ideological and Political
           Education Based on Complex Network

    • Abstract: Rapid updating and complex network means bringing more development opportunities for the education industry. As for ideological classes and political science at colleges and universities, how to use sophisticated online technology to educate and educate ideological and political classes and improve the formation of ideological and political classes has been a complex theory in the education industry in recent years. Complex network is an abstract representation of complex systems in the real world, with broad research value and application prospects, and has many advantages in complex network research, with interpretability, expression ability, generalization ability, flexibility, etc., and has been used in various network analysis tasks, such as community discovery, link prediction, network representation, and political learning. The second part of this article focuses on (1) the concept of ideological education, (2) the advantages of ideological education, (3) the contradiction of the mode of ideological education, and (4) how to innovate ideological education. The third part proposes the basic characteristics and models of complex networks and proposes the emergence of fractal structures. The fourth part analyzes complex network models in detail and compares the number of points and sides of the real network with the respective irregular networks, indicating that the real world is not fully defined or completely irregular, and that the real network has the nature of a small world and a high-quality cluster. The key factors of complex networks applied propose theoretical and political education and ultimately influence theoretical and political teachers and students in the complex networks examined through empirical questionnaires. He proposes that large universities should be able to use a network environment to promote ideological and political education.
      PubDate: Thu, 28 Jul 2022 10:50:00 +000
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