bluuebunny/crossref_metadata_2025 huggingface dataset:
Observer‐based adaptive fuzzy hierarchical sliding mode control of uncertain under‐actuated switched nonlinear systems with input quantization
Authors: Zhang Haoyan , Zhao Xudong , Zhang Liang , Niu Ben , Zong Guangdeng , Xu Ning
Published: July 2022

In this article, an observer‐based adaptive fuzzy hierarchical sliding mode control (HSMC) scheme is put forward for uncertain under‐actuated switched nonlinear systems in the presence of quantized input signals. The issues of quantized input signals from a hysteretic quantizer, external disturbances, unmeasured system states and completely unknown nonlinear functions are taken into account for the under‐actuated system. First, the chattering phenomena can be effectively avoided by adopting the considered hysteretic quantizer. Then, fuzzy logic systems (FLSs) is utilized to approximate the unknown smooth nonlinear functions representing the system uncertainties. Furthermore, a switched fuzzy state observer is established to estimate the unmeasured system states. Based on the Lyapunov stability theory, the proposed control scheme can ensure the boundedness of all signals in the closed‐loop system and make the system output track given reference signals well. Finally, a numerical simulation example is given to verify the effectiveness of the proposed control scheme.
Motion planning in complex urban environments: An industrial application on autonomous last‐mile delivery vehicles
Authors: Wang Haiming , Zhang Liangliang , Kong Qi , Zhu Weicheng , Zheng Jie , Zhuang Li , Xu Xin
Published: July 2022

In this article, a motion planning framework for autonomous driving is explored to achieve unmanned last‐mile delivery vehicle application in complicated urban scenario. This approach can dramatically improve the intelligence, driving safety, driving robustness, and scalability of autonomous vehicles. In this framework, a specific High‐Definition (HD) map representation was proposed for last‐mile delivery applications, and a Route Planning layer generates a kinematically feasible reference line with smoothness condition based on Routing and HD Map components. Then a Scenario Planning layer considers routing results and both static and dynamic obstacles into account to select a corresponding scenario to execute, such as cruise on‐road scenario, cross intersection scenario, parking scenario, and so forth. Finally, a Trajectory Planning layer which is classified with trajectory generation and optimization modules is described. In the trajectory generation part, a rough path–speed profile and corresponding decisions, such as nudge, stop, yield, and so forth, are generated. Then, the rough path–speed profile is postprocessed by optimization algorithms in the trajectory optimization part. On the basis of the real road test results from thousands of times for JD.com's real autonomous delivery vehicle operations and approximate 115,475 km of fully autonomous driving mode in an urban scenario, the proposed motion planning framework demonstrates the efficiency of autonomous driving, improves the driving quality and reduces the manual intervention.
Science class as clinic: Why histories of segregated instruction matter for health equity reforms today
Authors: Kirchgasler Kathryn L.
Published: July 2022

Research has recommended centering health disparities to make science instruction relevant to students from minoritized racial and ethnic groups. While promoted as a recent innovation, the repurposing of science instruction to improve the health of demographic groups has a longer history traceable to segregated and colonial schooling. Using a historicizing approach, this study explores how certain U.S. science classrooms have become clinics of preventative care aimed at transforming groups into healthy citizens. Analysis identifies how U.S. science education studies have used psychological, sociological, and anthropological lenses to divide students into populations, classifying some as needing intervention to improve their minds (e.g., basic health knowledge), home lives (e.g., daily habits), and cultural beliefs (e.g., attitudes toward science and medicine). Through systematic analysis of U.S. science education journals and sources cited therein, I map shifts over three periods: the rise of urban, segregated, and colonial schooling (1901–45), postwar desegregation and international development (1946–89), and equity reforms (1990–2021). Despite declines in deficit language, analysis suggests the intervention space of the science‐class‐as‐clinic still demarcates groups as not yet fully reasoning, self‐regulating, or agentic, and as needing the applied relevance of preventative health. Paradoxically, efforts to redress disparities may reinsert older distinctions by positing groups as educationally and medically at‐risk—implicitly locating inequities within students rather than the unjust systemic conditions they face. I conclude with implications for research and practice, highlighting approaches that do not treat educational and health inequities as problems to be fixed in the child, family, or community.
Classification of Stress Coping Styles Based on Time Series Correlation of Face Area Temperature
Authors: Nakamura Ryosuke , Nagumo Kent , Oiwa Kosuke , Nozawa Akio
Published: July 2022

It is hoped that remotely classifying stress‐coping styles can be applied to decisions about how to approach things, learning, etc. In this study, we devised a classification method for stress coping styles based on the time‐series correlation of facial area temperature. As a result, we were able to classify stress coping styles with about 70% probability. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks
Authors: Murad Leite Andrade Mario Henrique , Acharya Janam P. , Benevenuto Juliana , de Bem Oliveira Ivone , Lopez Yolanda , Munoz Patricio , Resende Marcio F. R. , Rios Esteban F.
Published: July 2022

Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high‐quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full‐sib and 35 half‐sib families was bulk‐genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single‐nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.
Introduction
Authors: Hill Lisa , Douglass Max , Baltutis Ravi
Published: July 2022

In this chapter we provide some background to the growing false election information problem in both Australia and other democratic settings and argue that it is an issue in need of urgent attention. We then do some ground-clearing preparatory to the main discussion by clarifying the scope of our inquiry and exploring the kinds of statements that should, and can, be regulated by truth in political advertising (TIPA) laws. We have kept this class of statements to a narrow range for the sake of feasibility and legality. Accordingly, our focus is on the regulation of authorised, demonstrably false statements of purported fact with the potential to influence an election outcome. The provenance of such statements is therefore easily traced to political candidates and others associated with their election campaigns and therefore, so too, is responsibility for them. This means that other classes of mis- and disinformation (such as anonymous online posts) are excluded from our analysis. We also show how the forms of communication that concern us map onto scholarly understandings of common ‘information disorders’ such as misinformation, malinformation and disinformation.
Australia’s Experience
Authors: Hill Lisa , Douglass Max , Baltutis Ravi
Published: July 2022

We now explore legal experiments in truth in election advertising in the Australian context, including two at the Commonwealth level (later repealed) and one at the state level (we explore South Australia’s experience in Chapters 8 and 9). We also show that some jurisdictions have adopted what we refer to as ‘pseudo’ truth in political advertising laws that appear to perform the same function as those we propose but do not due to the operations of an influential precedent. There is also some discussion of cases where states came close to adopting truth in political advertising legislation but failed to; we examine why.
South Australia’s Experience
Authors: Hill Lisa , Douglass Max , Baltutis Ravi
Published: July 2022

Because it has been in operation for some time, the SA model embodied in s 113 of the Electoral Act 1983 (SA) offers us many useful lessons about the benefits and pitfalls of truth in election advertising laws and what to avoid and pursue in an ideal-type regime. We therefore explore in detail how it has operated and the extent to which it has been successful. We also examine, in detail, its constitutionality in a number of important test cases. To improve its efficacy, we suggest a variety of ways in which it could be strengthened by amending specific elements of the law.
From Information Resources Push to Service Aggregation: The Development Trend of Mobile Government Service
Authors: Liu Jinyu , Li Dongze , Wu Yongzhao
Published: July 2022

Whether the mobile government service in this paper can meet the use standard of “4-b”. The mobile government service can be divided into four development stages since its emergence: one is to solve the main problem of how to build the basic framework of mobile government service, which is based on the push stage of government information resources of information offline browsing system; the second is to solve the main problem of how to identify the user’s identity conveniently, which is based on the user identity authentication stage of the mobile client; the third is to solve the main problem of fast interaction between server and client, which is based on the intelligent document processing stage of QR code; fourthly, it solves the main problem of fast access to services, which is based on the service aggregation stage of “App + applet”. These four stages are inherited from each other, which is a process of continuous improvement. With the solution of service aggregation, the mobile government service will fully meet the “4-b” usage standard and become the mainstream form of e-government.
Simulations of Fuzzy PID Temperature Control System for Plant Factory
Authors: Xie Hongmei , Yan Yuxiao , Zeng Tianzi
Published: July 2022

The five key factors that affect plant growth are temperature, humidity, CO2 gas density, nutritious liquid density and light intensity. The monitoring and controlling of these factors are vital. Fuzzy PID controller technology for plant factory environment parameter controlling was proposed and temperature controlling using three different methods were given out. The physical and mathematical models of ordinary differential equation used in temperature subsystem in plant factory was established, traditional PID controller was discussed and specifically the fuzzification interface, membership function, fuzzy inference rule and the defuzzification procedure were designed for mere fuzzy and fuzzy PID controllers. Simulations for temperature controlling using pure PID, mere fuzzy and fuzzy PID control algorithm were performed respectively. The experimental results show that the performance of the novel fuzzy PID controller is best since it outperforms the other controllers in terms of stable error, overshooting and stabling time. The stable error, overshooting and time to stable for fuzzy PID are 0, 0.1% and 170 s respectively, all are the minimum among the three controllers.
An Effective GAN-Based Multi-classification Approach for Financial Time Series
Authors: Liu Lei , Pei Zheng , Chen Peng , Gao Zhisheng , Gan Zhihao , Feng Kang
Published: July 2022

Deep learning has achieved significant success in various applications due to its powerful feature representations of complex data. Financial time series forecasting is no exception. In this work we leverage Generative Adversarial Nets (GAN), which has been extensively studied recently, for the end-to-end multi-classification of financial time series. An improved generative model based on Convolutional Long Short-Term Memory (ConvLSTM) and Multi-Layer Perceptron (MLP) is proposed to effectively capture temporal features and mine the data distribution of volatility trends (short, neutral, and long) from given financial time series data. We empirically compare the proposed approach with state-of-the-art multi-classification methods on real-world stock dataset. The results show that the proposed GAN-based method outperforms its competitors in precision and F1 score.
New Tendencies in Regulating Personal Data. Can We Achieve Balance?
Authors: Bundin Mikhail , Martynov Aleksei , Tereschenko Lyudmila
Published: July 2022

The article seeks to emphasize existing tendencies in regulation personal data in Russia and in foreign countries. The wide use of modern technologies of data processing “big data”, “artificial intelligence”, “internet of things” does not only open new opportunities for business and people but also makes more evident the gap between individual’s interests for control of his data processing and thus protecting its privacy and commercial use of data by Internet companies. The state, on the other hand, seeks to get a more wide and exclusive access to the data collected by business entities, trying to apply a renewed concept of data sovereignty using its citizens’ personal data protection as a legal ground. The author notes the growing desire from both the state and business entities to undermine individual’s right to control his data processing as an inherent right of a data subject in order to facilitate the access to them and guarantee their interests. Awareness by the state and business of the new opportunities given by processing metadata including personal data, as a fundamental resource for the digital economy development can potentially lead to the situation where an individual will no longer be able to participate in determining the key parameters of their use. Most recent changes in Russian legislation on open access personal data that are to come into force in 2021 also leave much ground for uncertainty. In fact, they can shift the balance even more towards the interests of big business and the state.
Fuzzing-Based Office Software Vulnerability Mining on Android Platform
Authors: Huang Yujie , Wang Zhiqiang , Ou Haiwen , Chi Yaping
Published: July 2022

The wide application of mobile terminals that makes the software and hardware of mobile platforms gradually become the important target of malicious attackers. In response to the above problems, this paper proposes a vulnerability mining scheme based on Fuzzing. In this scheme, many methods are used to generate a large number of test cases. After the application receives the corresponding test cases, it analyzes the output results and the exceptions thrown. The experimental results show that the scheme can effectively excavate the vulnerabilities of mobile office software on the Android platform, and has certain reliability.
A Novel Approach for Surface Topography Simulation Considering the Elastic-Plastic Deformation of a Material During a High-precision Grinding Process
Authors: Chen Huiqun , Jin Fenpin
Published: July 2022

A novel simulation approach for 3D surface topography that considers the elastic-plastic deformation of workpiece material during a high-precision grinding process is presented in this paper. First, according to the kinematics analysis for the abrasive grain during the grinding process, the motion trajectory of the abrasive grain can be calculated. Second, the kinematic interaction between the workpiece and the abrasive grains can be established, which integrates the elastic-plastic deformation effect on the workpiece material with the topography, the simulation results are more realistic, and the simulation precision is much higher. Finally, based on an improved surface applied to the grinding wheel, the surface topography of the workpiece is formed by continuously iterating overall motion trajectories from all active abrasive-grains in the process of high-precision grinding. Both the surface topography and the simulated roughness value of this work are found to agree well with those obtained in the experiment. Based on the novel simulation method in this paper, a brand-new approach to predict the quality of the grinding surface by providing machining parameters, selecting effective machining parameters, and further optimizing parameters for the actual plane grinding process, is provided.
Measurement and Evaluation on China’s Cargo Airlines Network Development
Authors: Wang Chaofeng , Li Jiaxin
Published: July 2022

In view of China’s cargo airlines network, taking the airport of each city as the node and the number of flights between cities as the weight of the side, the network topology index and economic index are used to evaluate the current situation of the network and the development potential of the network. Then, the TOPSIS method is used to comprehensively evaluate China’s cargo airlines network. The results show that the network ranking of each airline is: China Cargo Airlines, SF Airlines, China Post Airlines, Jinpeng Airlines, Longhao Airlines, Yuantong Airlines. Finally, considering the development stage of China’s cargo airlines, the sensitivity analysis is conducted by resetting the weight to verify the effectiveness of TOPSIS method. At the same time, according to the different stages of the network of cargo airlines, some suggestions on the development of the network are given.
Exploration of Non-legacy Creative Product Development Based on Information Technology
Authors: Gao Kun , Xun Lijie , Wu Zhenlu
Published: July 2022

With the development of The Times, the development of information technology is accelerating, rapidly into the life, learning in all fields. Under the background of information technology, the dissemination and development of intangible cultural heritage and the development of non-heritage products have been updated. Therefore, a new way of developing intangible cultural heritage should be set up to make it highly compatible with the development of cultural and creative products, so as to build a new development pattern of mutual promotion, integration and reciprocity between intangible cultural heritage culture and cultural and creative products. This paper analyzes the concept of non-heritage products and the value of the combination of intangible cultural heritage and cultural creation, and discusses the development strategy of non-heritage products based on information technology for reference.
A Method of UAV Formation Transformation Based on Reinforcement Learning Multi-agent
Authors: Wang Kunfu , Xing Ruolin , Feng Wei , Huang Baiqiao
Published: July 2022

In the face of increasingly complex combat tasks and unpredictable combat environment, a single UAV can not meet the operational requirements, and UAVs perform tasks in a cooperative way. In this paper, an improved heuristic reinforcement learning algorithm is proposed to solve the formation transformation problem of multiple UAVs by using multi-agent reinforcement learning algorithm and heuristic function. With the help of heuristic back-propagation algorithm for formation transformation, the convergence efficiency of reinforcement learning is improved. Through the above reinforcement learning algorithm, the problem of low efficiency of formation transformation of multiple UAVs in confrontation environment is solved.
On Differential Protection Principle Compatible Electronic Transducer
Authors: Gao Guangling , Pan Keqing , Xu Zheng , Pan Xianghua , Li Xiuhua , Luo Qiang
Published: July 2022

The technology of digital interface compatible electronic transducer is studied. The measuring and protective equipment is explored to make a new application of current and voltage signals from the electronic transducer so that electronic transducer differential signal is directly used as a protection input. The new differential protection principle based on the differential input signal is put forward. And the theoretical analysis and simulation shows that the protection principles proposed are feasible.
Distributed Heterogeneous Parallel Computing Framework Based on Component Flow
Authors: Li Jianqing , Li Hongli , Li Jing , Chen Jianmin , Liu Kai , Chen Zheng , Liu Li
Published: July 2022

Single processor has limited computing performance, slow running speed and low efficiency, which is far from being able to complete complex computing tasks, while distributed computing can solve such huge computational problems well. Therefore, this paper carried out a series of research on the heterogeneous computing cluster based on CPU+GPU, including component flow model, multi-core multi processor efficient task scheduling strategy and real-time heterogeneous computing framework, and realized a distributed heterogeneous parallel computing framework based on component flow. The results show that the CPU+GPU heterogeneous parallel computing framework based on component flow can make full use of the computing resources, realize task parallel and load balance automatically through multiple instances of components, and has the characteristics of good portability and reusability.
Research on Intrusion Detection Technology Based on CNN-SaLSTM
Authors: Li Jiacheng , Du Qiang , Huang Feifei
Published: July 2022

As Internet-connected application devices become more and more popular, more and more services need to be done through the network, which also leads to users paying more attention to network security performance. Due to the continuous iterative development of cyber attack means and attack scale, it is difficult to conduct passive security detection systems such as traditional intrusion detection mechanisms to conduct endless attacks. Later, intrusion detection was studied as an active defense technique to compensate for the shortcomings of traditional safety detection techniques. Active defense and response technology has also attracted the attention of researchers at home and abroad. The complex, engineering and large-scale scenarios presented by network attacks prevent the original passive intrusion detection system to meet the users’ needs for network security performance. With the continuous expansion of network scale, the continuous increase of network traffic scenarios and the rapid iteration of attack means, the performance of network intrusion detection system has put higher requirements. Therefore, we introduced the CNN, LSTM and self attention mechanisms in deep learning into invasion detection and performed experiments in the tensorflow framework, increasing the accuracy to 97.4%.
The Impacts of Cyber Security on Social Life of Various Social Media Networks on Community Users. A Case Study of Lagos Mainland L.G.A of Lagos State
Authors: Diallo Oumar Bella , Xie Paul
Published: July 2022

This study investigates the impacts of cyber Security on social life on community users Lagos L.G.A. The survey research was designed and adopted to describe this study. The sample for this research consists of one hundred and twenty undergraduate students randomly selected from respondents in their different homes using a simple technique. A structured questionnaire titled was developed and validated. It has a reliability coefficient of 0.72 using the test and re-test method. Descriptive statistics of frequency count and simple percentages were used to analysis the research question. This study is based on finding solution, it was affirmed that result from the demographic variable of respondents by age, sex, most visited social networks, duration of visitation, hours spent on social networks daily and people with the gadget that can access the internet. Results show that communities aware of the effects of cyber security on social media platforms are people between the ages of 21–25 years, male and female community dwellers have access to social network platforms, WhatsApp represents the most visited social platforms by people. The research questions show that social media platforms significantly influence the social life of community users in Lagos mainland L.G.A.
Analyzing the Structural Complexity of Software Systems Using Complex Network Theory
Authors: Du Juan
Published: July 2022

Software systems have nearly been used in all walks of life, playing an increasingly important role. Thus, how to understand and measure complex software systems has become an ever-important step to ensure a high-quality software system. The traditional analysis of software system structure focuses on a single module. However, the traditional software structural metrics mainly focus on analyzing the local structure of software systems and fail to characterize the properties of software as a whole. Complex network theory provides us with a new way to understand the internal structure of software systems, and many researchers have introduced the theory of complex networks into the examination of software systems by building software networks from the source code of software systems. In this paper, we combine software structure analysis and complex network theory together and propose a SCANT (Software Complexity Analysis using complex Network Theory) approach to probe the internal complexity of software systems.
Cluster-Based Three-Dimensional Particle Tracking Velocimetry Algorithm: Test Procedures, Heuristics and Applications
Authors: Ma Qimin , Lin Yuanwei , Zhang Yang
Published: July 2022

Particle tracking velocimetry (PTV) algorithm based on the concept of particle cluster is investigated and improved. Firstly, an artificial test flow is constructed, and a dimensionless parameter CPTV is introduced to characterize the difficulty for the PTV reconstruction. Secondly, the heuristics that particle-cluster based algorithms must follow are summarized, and a three-dimensional cluster-based PTV incorporating the Delaunay Tessellation is proposed and tested by using the artificial flow. The criteria property of CPTV is then analysed and verified. Combining the proposed algorithm with a three-dimensional particle detection system, two particle flows are successfully reconstructed, therefore verifying the practicality of the algorithm.
Vehicle Collision Prediction Model on the Internet of Vehicles
Authors: Qian Shenghua
Published: July 2022

An active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorithm optimized by Bayesian parameters is used to predict the vehicle collision state. Finally, for the problem of low accuracy in predicting the collision time, the time prediction is transformed into a classification problem, and the Bayesian optimization K-means algorithm is used to predict the vehicle collision time. The experimental results prove that the vehicle collision prediction model proposed in this paper has better results.
Emotional Analysis and Application of Business Space Based on Digital Design
Authors: Wang Yaying , Dou Jiahui
Published: July 2022

As social science and technology progressing, people pay more attention to themselves. Jewelry, whether as a daily design or exquisite art, deeply carries individual feeling. Commercial space design, as an important embodiment of tolerance and foil, could show its value and meet people's emotional needs. Based on jewelry store design, this paper studies the emotional design contained in digital commercial space to enrich the emotional experience in space design. Through the construction and design of jewelry store space, it can better convey the value and emotion of goods, and apply emotional elements to the layout, color and form of digital commercial space, so as to build a digital commercial space full of emotion and design [1].
Study on the Analysis Method of Ship Surf-Riding/Broaching Based on Maneuvering Equations
Authors: Zhang Baoji , Fu Lupeng
Published: July 2022

In order to understand the mechanism of the surf-riding/broaching profoundly, the four- degree- of-freedom(4DOF) maneuvering equation (surge, sway, yaw and roll) is simplified to a one- degree-of-freedom (1DOF) equation, and the fourth-order Runge-Kutta method is used to integrate a 1DOF surge equation in the time domain to analyze the two motion states of the ship during the surging and surf-riding. The critical Froude number is calculated using the Melnikov method. Taking a fishing boat as an example, the ship’s surf-riding/broaching phenomenon is simulated under the condition of wavelength-to-ship-length ratio and wave steepness, 1 and 1/10 respectively, providing technical support for the formulation of the second generation intact stability criteria.
Research on Virtual and Real Fusion Maintainability Test Scene Construction Technology
Authors: Zhang Yi , Ge Zhexue , Li Qiang
Published: July 2022

The construction of maintenance test scenes is the premise of accurate assessment of equipment maintenance. In order to reduce the cost and simulate the actual maintenance scene of the product with high fidelity, the construction method of virtual and real fusion maintainability test scene based on partial physical devices is studied in depth. The position and posture of the physical equipment are recognized by binocular vision, and the virtual environment is registered around the physical equipment. Firstly, the ORB (Oriented FAST and Rotated BRIEF) feature extraction of the physical product is carried out and compared, the ICP (iterative closest point) method is then used to perform the matching of physical product features and digital prototype features. Secondly, the virtual maintenance environment is register accurately. Thirdly, the experimental evaluation method of qualitative and quantitative indexes of virtual and real fusion maintainability is formulated. Finally, a case study of a virtual and real fusion maintainability test is carried out with an engine as an example, which verifies the effectiveness and feasibility of the maintenance evaluation based on the virtual and real fusion test scene.
Cascaded GLRT Radar/Infrared Lidar Information Fusion Algorithm for Weak Target Detection
Authors: Wu Peixuan , Du Xiaoyong , Hu Weidong
Published: July 2022

To deal with the problem of weak target detection, a cascaded generalized likelihood ratio test (GLRT) radar/infrared lidar heterogeneous information fusion algorithm is proposed in this paper. The algorithm makes full use of the target characteristics in microwave/infrared spectrum and the scanning efficiency of different sensors. According to the correlation of target position in the multi-sensor view field, the GLRT statistic derived from the radar measurements is compared with a lower threshold so as to generate initial candidate targets with high detection probability. Subsequently, the lidar is guided to scan the candidate regions and the final decision is made by GLRT detector to discriminate the false alarm. To get the best detection performance, the optimal detection parameters are obtained by nonlinear optimization for the cascaded GLRT Radar/Infrared lidar heterogeneous information fusion detection algorithm. Simulation results show that the cascaded GLRT heterogeneous information fusion detector comprehensively utilizes the advantages of radar and infrared lidar sensors in detection efficiency and performance, which effectively improves the detection distance upon radar weak targets within the allowable time.
CTran_DA: Combine CNN with Transformer to Detect Anomalies in Transmission Equipment Images
Authors: Zhou Honghui , Qin Ruyi , Wu Jian , Qian Ying , Ju Xiaoming
Published: July 2022

With the development of the State Grid, the power lines, equipment and transmission scale are expanding. In order to ensure the stability and safety of electricity, it is necessary to patrol and inspect the power towers and other equipment. With the help of deep learning, neural networks can be used to learn the features in patrol image. In this paper, feature learning model named CNN Transformer Detect Anomalies (CTran_DA) is proposed to detect anomalies in patrol images. CTran_DA uses CNN to learn local features in the image, and uses Transformer to learn global features. This paper innovatively combines the advantages of CNN and Transformer to learn the local details as well as the global feature associations in images. By comparing experiments on out self-constructed dataset, the model outperforms state-of-the-art baselines. Moreover, the Floating Point Operations (FLOPs) and parameters of the model in this paper are smaller than other algorithms. In general, CTran_DA is an efficient and lightweight model to detect anomalies in images.
Implementation and Application of Embedded Real-Time Database for New Power Intelligent Terminal
Authors: Shi Yingjie , Wang Xiang , Wang Wei , Zhang Huayun , Jiang Shusong
Published: July 2022

An implementation method of embedded real-time database is proposed. The lightweight high matching of power model is realized through tree structure. The resource consumption of real-time database in embedded device environment is reduced by means of separated storage and non independent process deployment. The efficient access of measuring point data is realized through internal mapping rules and improved breadth first search algorithm. Experiments show that the embedded real-time database realized by this method has good performance and low energy consumption, and is suitable for intelligent terminal equipment in new power system.
Analysis of Purchasing Power Data of Department Store Members and Design of Effective Management Model
Authors: Li Bo , Jiang Henry L. , Yan Hanhuan , Qi Yishan , Gan Zhiwang
Published: July 2022

This paper focuses on the consumption situation and discounting strategies of members in large department stores. On this basis, reasonable strategies and suggestions for discounting activities in department stores are proposed. It needs to determine the consumption habits of members, customer value, life cycle, discount effect and other information. The mathematical model was established to calculate the activation rate of non-active members in the life cycle of members, that is, the possibility of transforming from inactive members to active members. Based on the actual sales data, the relationship model between the activation rate and shopping mall promotion was determined. Generally speaking, the higher the commodity price is, the higher the profit will be. IA regression model of activation rate and promotion activities is developed. The appraisal index of market promotion activities is established in terms of both discounts and integral. Lasso regression is used for variable screening, and the correlation between activation rate and the above indicators is studied.
Analysis of Subway Braking Performance Based on Fuzzy Comprehensive Evaluation Method
Authors: Peng Hua , He Yixin
Published: July 2022

In the process of subway operation, the braking system is a complex system, and its state detection is for high data accuracy and state positioning accuracy According to the structure of the braking system and the principle of the braking method, the basic braking performance parameters of the system are analyzed, combined with the abnormal state of the subway brake cylinder pressure data, the braking process is divided into two stages: brake cylinder pressure establishment and peak stability. And define the six characteristic parameters of 90% brake cylinder pressure establishment time, special slope period time, stable pressure value, stable pressure standard deviation, maximum value and minimum value. Aiming at the braking process performance, a data mining theory is proposed, and software based on the fuzzy comprehensive evaluation method is written to analyze the deterioration of the braking performance of subway vehicles. The actual on-board data is used as an example to verify the reliability of the theory.
Some Problems of Complex Signal Representation
Authors: Xu JingBo
Published: July 2022

The time domain signal is based on the decomposition of the unit step signal, the complex signal is represented by the Heaviside Function, and the problem of the definition of the original jump time in the new function is proposed, based on the analysis and comparison of simple signal and complex signal in time domain and frequency domain, the problems needing attention in using $$\varepsilon (t)$$

ε
(
t
)

to express signal are put forward. It is concluded that no definition or special definition of the “0” moment in the original unit step signal does not affect the composition of the composite function.
Design and Finite Element Analysis of Magnetorheological Damper
Authors: Song Yunyun , Yang Xiaolong
Published: July 2022

In order to solve the problem that the output damping force of magnetorheological Damper is not large enough and the adjustable range is small, a bypass magnetorheological Damper is designed in this paper. The Valve is connected in a hydraulic cylinder with pipes to form a controllable magnetorheological Damper device. Two structures are designed by adding non-magnetic materials to the structure so that the magnetic field lines pass vertically through the damping gap as much as possible. One is to use two coils and add a non-magnetic material above the coil, and the other is to use only one coil and add a non-magnetic material above the coil. The finite element method is used to simulate and analyze the parameters of two structures which affect the damping performance, and the results are discussed. The results show that more magnetic force lines can pass through the damping channel vertically by adding non-magnetic material to the structure, which can increase the damping force and adjustable coefficient.
Optimizing Performance of Image Processing Algorithms on GPUs
Authors: Zhou Honghui , Qin Ruyi , Liu Zihan , Qian Ying , Ju Xiaoming
Published: July 2022

The application of machine learning algorithms in the field of power grid improves the service level of power enterprises and promotes the development of power grid. NVIDIA Volta and Turing GPUs powered by Tensor Cores can accelerate training and learning performance for these algorithms. With Tensor Cores enabled, FP32 and FP16 mixed precision matrix multiplication dramatically accelerates the throughput and reduces AI training times. In order to explore the cause of this phenomenon, we choose a convolutional neural network (CNN), which is widely used in computer vision, as an example and show the performance characteristics with tensor core on general matrix multiplications and convolution calculations as benchmark. Building a CNN based on cuDNN and TensorFlow, we analyze the performance of CNN from various aspects and optimize performance of it by changing the shape of convolution kernel and using texture memory, etc. The experimental results prove the effectiveness of our methods.
Point Cloud Registration of Road Scene Based on SAC-IA and ICP Methods
Authors: Liu Yan , Su Hu , Lei Yu , Zou Fan
Published: July 2022

Registration of point cloud data obtained by vehicle-mounted LiDAR is necessary process to establish high-precision road scene 3D model automatically. This paper presents a set of multi-line LiDAR point cloud registration method in road scenarios. Firstly, the obtained original point cloud data are pre-processed according to the characteristics of multi-line LiDAR point cloud. Then an initial registration algorithm (SAC-IA) with sampling consistency based on fast point feature histogram (FPFH) is used to achieve the coarse registration for two frame point clouds. Lastly, ICP algorithm optimized by KD-tree is used for precise registration and global road point cloud model can be obtained by iterative registration. In order to verify the method, actual road point cloud data are collected. The experimental results show that the method is feasible and its registration accuracy can meet the requirements of road model.
Altered m6A RNA methylation contributes to hippocampal memory deficits in Huntington’s disease mice
Authors: Pupak Anika , Singh Ankita , Sancho-Balsells Anna , Alcalá-Vida Rafael , Espina Marc , Giralt Albert , Martí Eulàlia , Ørom Ulf Andersson Vang , Ginés Silvia , Brito Verónica
Published: July 2022

N6-methyladenosine (m6A) regulates many aspects of RNA metabolism and is involved in learning and memory processes. Yet, the impact of a dysregulation of post-transcriptional m6A editing on synaptic impairments in neurodegenerative disorders remains unknown. Here we investigated the m6A methylation pattern in the hippocampus of Huntington’s disease (HD) mice and the potential role of the m6A RNA modification in HD cognitive symptomatology. m6A modifications were evaluated in HD mice subjected to a hippocampal cognitive training task through m6A immunoprecipitation sequencing (MeRIP-seq) and the relative levels of m6A-modifying proteins (FTO and METTL14) by subcellular fractionation and Western blot analysis. Stereotaxic CA1 hippocampal delivery of AAV-shFTO was performed to investigate the effect of RNA m6A dysregulation in HD memory deficits. Our results reveal a m6A hypermethylation in relevant HD and synaptic related genes in the hippocampal transcriptome of Hdh+/Q111 mice. Conversely, m6A is aberrantly regulated in an experience-dependent manner in the HD hippocampus leading to demethylation of important components of synapse organization. Notably, the levels of RNA demethylase (FTO) and methyltransferase (METTL14) were modulated after training in the hippocampus of WT mice but not in Hdh+/Q111 mice. Finally, inhibition of FTO expression in the hippocampal CA1 region restored memory disturbances in symptomatic Hdh+/Q111 mice. Altogether, our results suggest that a differential RNA methylation landscape contributes to HD cognitive symptoms and uncover a role of m6A as a novel hallmark of HD.
Nonresonant bilinear forms for partially dissipative hyperbolic systems violating the Shizuta–Kawashima condition
Authors: Bianchini Roberta , Natalini Roberto
Published: July 2022

In the context of hyperbolic systems of balance laws, the Shizuta–Kawashima coupling condition guarantees that all the variables of the system are dissipative even though the system is not totally dissipative. Hence it plays a crucial role in terms of sufficient conditions for the global in time existence of classical solutions. However, it is easy to find physically based models that do not satisfy this condition, especially in several space dimensions. In this paper, we consider two simple examples of partially dissipative hyperbolic systems violating the Shizuta–Kawashima condition (SK) in 3D, such that some eigendirections do not exhibit dissipation at all. We prove that if the source term is nonresonant (in a suitable sense) in the direction where dissipation does not play any role, then the formation of singularities is prevented, despite the lack of dissipation, and the smooth solutions exist globally in time. The main idea of the proof is to couple Green function estimates for weakly dissipative hyperbolic systems with the space–time resonance analysis for dispersive equations introduced by Germain, Masmoudi and Shatah. More precisely, the partially dissipative hyperbolic systems violating (SK) are endowed, in the nondissipative directions, with a special structure of the nonlinearity, the so-called nonresonant bilinear form for the wave equation (see Pusateri and Shatah, CPAM 2013).
Cardiac Arrest Center – Zertifizierung stärkt Patientenzufluss durch den Rettungsdienst
Authors: Rott Nadine , Wingen Sabine , Müller Dirk , Böttiger Bernd W.
Published: July 2022

Hintergrund

Seit 2015 empfehlen die internationalen Reanimationsleitlinien die Implementierung von spezialisierten Krankenhäusern (sog. Cardiac Arrest Center, CAC) für die Versorgung von Patienten mit außerklinischem Herz-Kreislauf-Stillstand.

Ziel

Ziel der Studie war es, den potenziellen Einfluss der Zertifizierung von Kliniken als CAC auf das Einweisungsverhalten von Notärzten und Rettungsfachpersonal bei prähospital reanimierten Patienten zu untersuchen.

Methoden

Eine webbasierte anonyme Befragung mit 20 Items wurde vom 15.05. bis zum 15.06.2018 in Deutschland durchgeführt. Zielgruppen waren im Rettungsdienst tätige Notärzte sowie Rettungsfachpersonal.

Wesentliche Ergebnisse

Von 437 Teilnehmern wurden die Ergebnisse von 378 Befragten (n = 292 Notärzte, n = 86 Rettungsfachpersonen) in die statistische Analyse eingeschlossen. 75,1 % (n = 284) gaben an, dass die CAC-Zertifizierung von Krankenhäusern bei ihrem künftigen Einweisungsverhalten von Patienten mit präklinischem Herz-Kreislauf-Stillstand eine Rolle spielen würde. 78,3 % (n = 296) erwarteten, dass die CAC-Zertifizierung zu einer Verbesserung der Patientenversorgung führen wird. 78,8 % (n = 298) befürworteten die Einführung der CAC-Zertifizierung. Die Befragten würden eine zusätzliche Transportzeit von 16,3 min (95 %-KI: 15,2–17,3) akzeptierten, um ein CAC zu erreichen.

Fazit

Die Zertifizierung von Kliniken als CAC hat das Potenzial, die Entscheidung des Rettungsdienstpersonals bei der Zuweisung von Patienten mit einem präklinischen Herz-Kreislauf-Stillstand zu beeinflussen. Aufgrund der limitierten zusätzlich akzeptablen Transportzeit zur Erreichung eines CAC bedarf es eines bundesweiten, engen Netzes zertifizierter Kliniken.