bluuebunny/crossref_metadata_2025_split 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

AbstractIn 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

AbstractIn 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

AbstractResearch 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.
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

AbstractGenomic 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

AbstractIn 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

AbstractWe 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

AbstractBecause 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

AbstractWhether 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

AbstractThe 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

AbstractDeep 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

AbstractThe 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

AbstractThe 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

AbstractA 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

AbstractIn 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

AbstractWith 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

AbstractIn 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

AbstractThe 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

AbstractSingle 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

AbstractAs 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%.