bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying Poor Adherence in Outpatients with Bipolar Disorder: A Comparison of Different Measures
Authors: Chauhan Nidhi , Chakrabarti Subho , Grover Sandeep
Published: September 2021

Objective Unlike schizophrenia, comparisons of different methods of estimating inadequate adherence in bipolar disorder (BD) are scarce. This study compared four methods of identifying inadequate adherence among outpatients with BD.

Materials and Methods Two self-reports, the Morisky Medication Adherence Questionnaire (MAQ) and the Drug Attitude Inventory (DAI-10), clinician ratings employing the Compliance Rating Scale (CRS), mood-stabilizer levels, and clinic-based pill counts were compared at intake in 106 outpatients with BD and after 6 months of follow-up (n = 75).

Statistical Analysis Rates of nonadherence were determined for each method. The ability to detect inadequate adherence was based on sensitivity, specificity, positive and negative predictive values (PPV and NPV), and positive and negative likelihood ratios (LR positive and LR negative). Correlation coefficients and Cohen's kappa values were used to determine the agreement between measures. Correlation coefficients were also used to evaluate the determinants of inadequate adherence

Results The MAQ and the DAI-10 (cut-off score of two) yielded higher rates of nonadherence (35–47%) than the other methods. They were better at detecting adherence (specificity, 34–42%; PPV, 40–44%; and LR negative, 0.70–0.96) than other measures and had moderate ability to identify nonadherence compared with them (sensitivity, 63–73%; NPV, 54–70%; and LR positive, 1.02–1.16). They were associated with several established predictors of nonadherence. The MAQ and DAI-10 scores and the MAQ and CRS scores were modestly correlated. Multivariate analysis showed that 20% of the variance in the DAI-10 scores was explained by the MAQ scores. Despite their low yield, serum levels had a high sensitivity (88%) and higher accuracy (55%) in identifying inadequate adherence. CRS ratings and pill counts had high sensitivity but low specificity to detect inadequate adherence.

Conclusion Self-reports appeared to be the most efficient method of ascertaining inadequate adherence among outpatients with BD. However, since none of the measures were adequate by themselves, a combination of different measures is more likely to maximize the chances of identifying inadequate adherence among these patients.
News media coverage of COVID-19 public health and policy information
Authors: Mach Katharine J. , Salas Reyes Raúl , Pentz Brian , Taylor Jennifer , Costa Clarissa A. , Cruz Sandip G. , Thomas Kerronia E. , Arnott James C. , Donald Rosalind , Jagannathan Kripa , Kirchhoff Christine J. , Rosella Laura C. , Klenk Nicole
Published: September 2021

During a pandemic, news media play a crucial role in communicating public health and policy information. Traditional newspaper coverage is important amidst increasing disinformation, yet uncertainties make covering health risks and efforts to limit transmission difficult. This study assesses print and online newspaper coverage of the coronavirus disease COVID-19 for March 2020, when the global pandemic was declared, through August 2020 in three countries: Canada (with the lowest per-capita case and death rates during the study timeframe), the United Kingdom (with a pronounced early spike), and the United States (with persistently high rates). Tools previously validated for pandemic-related news records allow measurement of multiple indicators of scientific quality (i.e., reporting that reflects the state of scientific knowledge) and of sensationalism (i.e., strategies rendering news as more extraordinary than it really is). COVID-19 reporting had moderate scientific quality and low sensationalism across 1331 sampled articles in twelve newspapers spanning the political spectrums of the three countries. Newspapers oriented towards the populist-right had the lowest scientific quality in reporting, combined with very low sensationalism in some cases. Against a backdrop of world-leading disease rates, U.S. newspapers on the political left had more exposing coverage, e.g., focused on policy failures or misinformation, and more warning coverage, e.g., focused on the risks of the disease, compared to U.S. newspapers on the political right. Despite the generally assumed benefits of low sensationalism, pandemic-related coverage with low scientific quality that also failed to alert readers to public-health risks, misinformation, or policy failures may have exacerbated the public-health effects of the disease. Such complexities will likely remain central for both pandemic news media reporting and public-health strategies reliant upon it.
Analysis of the developmental bioelectricity involved in regeneration
Authors: Green Leigh Ann
Published: September 2021

Data collected from regenerating adult flatworms are used to analyze system-level aspects of bioelectrically-regulated anatomical patterns.
Model-driven mitigation measures for reopening schools during the COVID-19 pandemic
Authors: McGee Ryan Seamus , Homburger Julian R. , Williams Hannah E. , Bergstrom Carl T. , Zhou Alicia Y.
Published: September 2021

Significance

As the COVID-19 pandemic drags on, we need strategies for reopening those schools that have remained closed. We developed stochastic network models to study the risks associated with returning to in-person learning during the COVID-19 pandemic, and to explore the value of mitigation measures in reducing these risks. Our models indicate that the risk of school outbreaks increases as community prevalence increases, and that secondary schools pose greater control challenges than primary schools. The models reveal that a number of measures can help substantially: dividing students into multiple cohorts who attend school on an alternating basis, frequently testing teachers and students, and vaccinating teachers and staff. Basic transmission control strategies such as mask use, social distancing, and ventilation remain essential.
Discourse markers at the peripheries of syntax, intonation and
turns
Authors: Degand Liesbeth , Crible Ludivine
Published: September 2021

In this study, we analyze to what extent the type
of unit influences the position and function of discourse markers
(DMs). By comparing DM use across peripheries and across units, we
aim to identify which linguistic level (syntax, intonation, turns)
is most functionally and cognitively motivated. Our corpus-based
analysis reveals that clauses best account for the systematic
variation of DMs: initial uses are dedicated to marking discourse
relations, medial uses express the speaker’s subjectivity and final
uses call out to the addressee. The distribution of DMs in turns is
fairly similar but this interactional unit is not fine-grained
enough. Intonational peripheries, in turn, seem to perform other
functions that are not reflected in a systematic variation of DM
uses.
What is (and could be) thick description in academic writing research?
Authors: Tardy Christine M.
Published: September 2021

One hallmark of ethnographic research is its use of thick description, a concept often associated with the use of multiple sources of data, collected over time, ideally aiding the researcher in understanding participants’ perspectives on the data. This chapter explores the roots of thick description in ethnographic research, and then reviews how thick description has been used in studies of academic writing through close analysis of 21 studies published in international journals. The chapter concludes by considering the implications of varying interpretations and enactments of thick description as well as the potential role of thick participation in helping researchers achieve thick description.
The Importance of Not Being Too Attached: Pharmaceutical Equipment Characteristics and Bacterial Attachment
Authors: Sandle Tim
Published: September 2021

In this article the author provides an overview of the characteristics of microbial attachment and the essential considerations when developing a contamination control strategy. This review paper assesses the factors affecting finish and roughness, primarily in relation to microbial attachment to stainless steel, while considering other related variables like contact height and shape of surface defects
PQ Forum #21 - Validation Initiation Document Template
Authors: Pluta Paul
Published: September 2021

Validation documents are key documents in a pharmaceutical manufacturing facility. Validation documents must be carefully written for internal and external review including audit by regulatory inspectors; they may be accessed numerous times during a product lifetime. The most frequently reviewed validation documents are Stage 2 Process Performance Qualification documents.
Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN
Authors: Zhou Xiaojie , Zhai Pengjun , Fang Yu
Published: September 2021

Knowledge graphs have played a significant role in various applications and knowledge reasoning is one of the key tasks. However, the task gets more challenging when each fact is associated with a time annotation on temporal knowledge graph. Most of the existing temporal knowledge graph representation learning methods exploit structural information to learn the entity and relation representations. By these methods, those entities with similar structural information cannot be easily distinguished. Incorporating other information is an effective way to solve such problems. To address this problem, we propose a temporal knowledge graph representation learning method d-HyTE that incorporates entity descriptions. We learn structure-based representations of entities and relations and explore a deep convolutional neural network with attention to encode description-based representations of entities. The joint representation of two different representations of an entity is regarded as the final representation. We evaluate this method on link prediction and temporal scope prediction. Experimental results showed that our method d-HyTE outperformed the other baselines on many metrics.
Research on Named Entity Recognition Method Based on Improved LSTM-CRF Model
Authors: Gan Yong , Jia Dongwei , Wang Yifan
Published: September 2021

Because the computer cannot directly understand the text corpus in the NLP task, the first thing to do is to represent the characteristics of the natural language numerically, and the word vector technology provides a good way to express it. Because Word2vec considers context and has fewer dimensions, it is now more popular words embedded. However, due to the particularity of Chinese, word2vec cannot accurately identify the polysemy of words. In this paper, a lightweight and effective method is used to merge vocabulary into character representation. This approach avoids designing complex sequence modeling architectures. for any neural network model, simply fine-tuning the character input layer can introduce vocabulary information. The model also uses the modified LSTM to bridge the enormous LSTM and the Transformer model. The interaction between input and context provides a richer modeling space that significantly improves testing on all four public datasets.
Feature Selection and Reduction of Lower Limb Activity Recognition Based on Surface Electromyography and Motion Data
Authors: Wei Chunfeng , Wang Hong , Hu Fo , Chen Jichi , Lu Yanzheng , Qi Yangyang
Published: September 2021

Daily activity recognition of lower limbs is of great significance to the health care of the elderly and patients with hemiplegia. Surface electromyography (sEMG) signal can directly reflect neuromuscular activity and is an important method for non-invasive monitoring of muscle activity on the body surface. In this paper, a novel method based on sEMG signal and inertial measurement unit (IMU) data to recognize daily activities of lower limbs is proposed. Record sEMG signals and IMU data of fifteen subjects using wearable sensor devices. After preprocessing such as filtering and sliding windows on the data, we extracted seventeen features. A feature selection method based on maximal relevance and minimal redundancy maximal relevance (mRMR) to select representative features. The selected features are input into four machine learning classifiers to classify four daily activities. The performance of the classifier is evaluated using accuracy and receiver operating characteristic curve-area under curve (ROC-AUC) score. The results show that the support vector machine has excellent performance in recognizing the daily activities of human lower limbs.
Data Augmentation with Illumination Correction in Sematic Segmentation
Authors: Liu Honghui , Wang Changjian , Peng Yuxing
Published: September 2021

In the training process of the deep learning network, data augmentation can effectively improve accuracy. Presently, the commonly used data augmentation methods have been mainly divided into space transformation and color transformation. However, these methods do not take into account the effects of the image’s illumination factor. Under natural conditions, the color, angle, and strength of the light will cause the color of the object to change, which is likely to cause segmentation errors. In this article, we start from the angle of illumination and perform data augmentation on the image. By applying the illumination correction algorithm based on Retinex theory, we are able to enhance the data and achieved good results in the experiment. However, there still remains some problems within the current algorithms based on Retinex theory. Especially for images with uniform illumination conditions, which can be overexposed after processed by these algorithms. In response to this problem, we have improved the relevant algorithm. The improved algorithm further improves the accuracy of semantic segmentation on the original basis.
A Recommended Method Based on the Weighted RippleNet Network Mode
Authors: Luo Yutai , Sha Baocheng , Xu Tao
Published: September 2021

User preferences were modeled by the RippleNet network and successfully applied in the recommender systems, but the weight of the entity was not considered. This paper proposes a RippleNet model incorporating the influence of complex network nodes. After the construction of complex networks based on knowledge Graphs, we build the maximum subnet model and calculate the influence of nodes in the graph network. We added it to the RippleNet as the weight of entities. The experimental results showed that new method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, solve the problem that entity influence was not considered in the RippleNet network, and made the recommended results more in line with users’ expectations.
Network Anomaly Traffic Detection Method Based on Multi-SAE and LSTM
Authors: Guo Shuai , Chen Bowei , Su Yang
Published: September 2021

For the purpose of faster and more accurate anomalous traffic detection with increasing classes of data traffic in the network, this paper proposes a new anomalous traffic detection method based on stacked auto-encoders and a long short-term memory network model. The method uses Multi-SAE to extract the effective features of sequential traffic, which is obtained by concatenating multiple stacked auto-encoders, and a long short-term memory network to extract the temporal structure of the effective features, with the Multi-SAE and the long short-term memory network in a back-and-forth tandem structure. To further improve the efficiency of the detection, redundant MAC addresses are also removed in the pre-processing. From the experimental results, this paper achieves effective detection of twenty types of data traffic with an accuracy rate of 98.25%, which is higher than that of the same category of research by nearly 2 percentage points, and the parameters of precision, recall and f1-score also reach over 96%, improving the detection results.
Distance of Single-Valued Neutrosophic Set and Its Application in Pattern Recognition
Authors: Wang Tongjuan
Published: September 2021

Single-valued neutrosophic set (SVNS) is an extension of fuzzy set, which combines the truth, indeterminacy, and falsity information. The measurement of distance of single-valued neutrosophic set will bring new ideas to pattern recognition. This paper introduces the distance definitions and properties of SVNS, and proposes the improved distance definition of single-valued neutrosophic set based on decision maker attitude towards indeterminacy information and applied them to pattern recognition. By assigning a value to decision-maker’s attitude towards indeterminacy information, this attitude comes from the historical experience and risk preference etc. The distance calculated in this way can often get a matching result closer to the reality. The results show that the improved distance formula of SVNSs is more effective and more practical for pattern recognition.
Research on Bi-objective Vehicle Routing Problem Considering Empty Loading Ratio
Authors: Sun Lulu , Jia Wei , Jiang Qi , Shi Weixuan , Zhang Meng
Published: September 2021

The vehicle routing plan in logistic distribution minimizing the transportation cost merely may lead to high empty loading ratio and a waste of logistic resources. This research studies the bi-objective vehicle routing problem considering empty loading ratio. The definitions about the empty loading ratio are proposed firstly. A bi-objective vehicle routing model is developed, minimizing the empty loading ratio and transportation cost simultaneously. Then, the solution method based on ɛ constraint method is provided to compute the Pareto optimal solutions. Finally, numerical experiments are reported, and the results show that the ɛ constraint method is better than the weighting method for the proposed problem.
Intelligent Video Surveillance System Based on Cloud Network
Authors: Li Liwei , Xie Haibin , Li Peng
Published: September 2021

There are two shortcomings in traditional video surveillance systems: First, the amount of data is so huge that the processing, analysis, and viewing of massive data requires a lot of manpower, material resources and financial resources. Second, it is difficult to store and transmit video data. Mass video data often needs to be stored and passed to the backstage for analysis, and huge data makes data storage and transmission difficult. In view of the above trouble, this paper proposes a set of intelligent video surveillance system based on cloud network. It contains modules such as face detection, quick retrieval, etc., and uses the cloud network as a medium to transmit data and information. It can complete the automatic detection, capture, enhancement, and coding processing of the human face in the video, search and compare with the background database automatically and quickly.
Business Anomaly Detection Method of Power Dispatching Automation System Based on Clustering Under-Sampling in the Boundary Region
Authors: Li Junliang , Xu Jianhang , Huang Xu , Ren Bing , Dai Tianqi , Zhang Zemin , Su Rui
Published: September 2021

Timely detecting business anomaly in the power dispatching automation system is significant for the steady operation of the power grid. Though the imbalanced binary classification method in machine learning is an effective way to achieve the business anomaly detection of the system, the overlap of boundary samples is an urgent issue affecting the classification effect. An under-sampling method by removing the clustering noises of the majority samples in the boundary region is proposed. Firstly, KNN is used to search adjacent points of the majority class, and the boundary region and the safety region are divided according to the proportion of the majority samples in adjacent points. Secondly, DBSCAN is used to cluster the majority samples in the boundary region, and noise points are removed. Finally, it’s combined with the method based on model dynamic selection driven by data partition hybrid sampling (DPHS-MDS). The purpose of reducing the overlap degree of boundary samples, balancing the dataset and improving the classification effect is achieved. Experimental results show that the proposed method is superior to the relevant mainstream methods under F-measure and G-mean.
Optimizing the Weights and Thresholds in Dendritic Neuron Model Using the Whale Optimization Algorithm
Authors: Xu Weixiang , Li Cunhua , Dou Yuxiang , Zhang Mengnan , Dong Zihao , Jia Dongbao , Ban Xinxin
Published: September 2021

In recent years, with the great success of dendritic neuron model (DNM) in various fields, the application of intelligent optimization algorithms in dendritic neuron model has attracted increasing attention of researchers. The training process of neural network is regarded as one of the great challenges of machine learning because of its non-linear nature and unknown optimal parameters. The traditional training algorithm of DNM is prone to fall into local optimum and speed of convergence slowly and so on, resulting in the problem of accuracy and low efficiency. In this paper, for solving the classification problem of dendritic neural model, an innovative intelligent optimization algorithm which named whale optimization algorithm (WOA), is applied to the training of DNM for the first time. Compared with six traditional and classic intelligent optimization algorithms in four classic datasets, the results indicate that WOA-DNM has good performance in various aspects, and its advantage is remarkable.
BERT Pre-training Acceleration Algorithm Based on MASK Mechanism
Authors: Hu Puzhao , Dong Lanfang , Zhan Yanyan
Published: September 2021

With the development of natural language processing (NLP) science, machines can understand human language better and better. Especially with the emergence of pre-training models in recent years, such as ELMO, GPT, BERT, etc. Researchers can easily transfer model knowledge and apply them to related fields, but at the same time they often ignore the pretraining process. Due to the huge number of parameters and calculations of the pre-training model, it often takes a lot of resources and much time to get a good pre-training model. So many people are daunted by the pre-training process. From a different perspective, this article mainly focuses on the pre-training process of BERT, and conducts in-depth research and related improvements on its own mask mechanism, so as to accelerate the training process. The experimental results show that our algorithm can get better result than the original scheme which the BERT use. Also it is better than the original BERT in terms of training speed and can better understand the training text input. What is more, we have also conducted a detailed analysis of the experimental result.
Research on Optimization of Main Engine Speed of Inland Ship Based on Genetic Algorithm
Authors: Peng Zhongbo , Tan Jianping , Shi Liyong
Published: September 2021

This paper aims at reduced total fuel consumption of voyage by obtaining navigation data of inland ships, and established the fuel consumption prediction model of the main engine in the segment through studying the navigation route by sections according to the characteristics of the navigation environment, and solved the problem with the help of genetic algorithm to obtain the best main engine for each segment main engine speed. The results show that: (1) The error of the neural network model verification data set is mostly within 6%, and the model performance is good; (2) The experimental ship from Nanjing to Jingzhou from June to September adopts the optimized main engine speed to save 0.6232t of fuel, which is 4.06% lower than the original total fuel consumption. This method has practical and guiding significance for reducing the cost of inland ship navigation and improving the ecological environment of the Yangtze River.
An OSM Contributors Classification Method Based on WPCA and GMM
Authors: Zhao Yijiang , Wei Xingcai , Liu Yizhi , Liao Zhuhua
Published: September 2021

Contributors have a significant impact on data quality of OpenStreetMap (OSM) because most of them are the non-professional, so clustering analysis of contributors based on different experiences has practical significance. Firstly, this paper obtained 31 behavioural characteristics of contributors from OSM historical data. Then, a weighted principal component analysis (WPCA) method was used to reduce the dimensions of the contributors’ behaviour in the selected region. By using an unsupervised prototype-based Gaussian mixture model (GMM) clustering algorithm, contributors with similar contribution attributes in the London area were clustered into four groups. Finally, the characteristics of four different types of contributors are analysed, and two types of experienced and professional contributors are found, who contribute a large amount of high-quality data.
Design and Implementation of a Cross-Media Image to Sound Algorithm and Objective Evaluation Algorithm
Authors: Liu Zihao , Wang Botao
Published: September 2021

With the joining of digital media and Internet, the application of the conversion of cross-media information becomes wider and wider, which makes the integration of multimedia information possible. The transformation of image information to sound information is also in the stage of exploration. It can be applied to all aspects of the industry. Most importantly, it can be used to guide the blind. In this paper, an improved image-to-sound algorithm based on original method is proposed to judge whether there are obstacles in front of the road and test the sound segment through human ears. Among different mapping methods, the highest accuracy can reach 84.2%, which is much higher than the original method. At the same time, an objective evaluation algorithm is designed based on the neural network, which can replace the human ear to judge a large number of image information, and the matching degree of this model can reach 68%.
Robust Fuzzy Clustering Algorithm Based on Adaptive Neighbors
Authors: Miao Kehua , Wang Jialin , Gao Yunlong , Cao Chao , Xie Youwei , Gao Peng
Published: September 2021

In pattern recognition, fuzzy logic is widely used in unsupervised classification or clustering methods. Fuzzy c-means (FCM) clustering algorithm is a typical dynamic clustering algorithm of the fuzzy c-means algorithm based on the error square sum criterion. It introduces fuzzy membership and optimizes the objective function to obtain each sample point for all classes. The membership degree of the center is used to automatically classify the data sample. However, the FCM algorithm is susceptible to the noise points and outliers, and the unbalanced data structure reduces the generalization performance of the algorithm. In this paper, we propose a fuzzy c-means clustering algorithm with adaptive neighbors weight learning. Through adaptive neighborhood robust weight learning, an adaptive weight vector with robustness and sparsity is obtained. During the optimization, we only activate the k samples with the shortest distance to the cluster center and eliminate extreme noise samples to improve the global robustness and sparsity of the algorithm. Finally, empirical analysis verifies the superiority and effectiveness of the proposed clustering method.
Optimizing the Flight Route of UAV Using Biology Migration Algorithm
Authors: Zhang Q Y , He X Y , Dong Y Q , Jiang S Y , Song H
Published: September 2021

Optimizing the flight trajectory unmanned aerial vehicles (UAVs) is always a popular optimization problem in computation intelligence field, which aims to search optimal flight path for avoiding detection and complementing some highly difficult missions in complex military environments. This paper mainly utilizes a recent proposed biology migration algorithm (BMA) inspired by the species migration mechanism for dealing with the UAV trajectory optimization problem. The main goal of the UAV model is to search feasible parameters including the flight angle, coordinates and distance for minimizing the flight price computed based on the threat and fuel costs. As one of swarm intelligence techniques, BMA has the characteristics of self-organization, fast convergence and self-adaption in the optimization process, So, it is able to find a safe flight route between the start point and target point while avoiding the dangerous regions and minimum cost. Simulation experiments show that BMA can generate promising and better results with respective to other compared algorithms.
Motion Target Detection and Recognition Based on YOLOv4 Algorithm
Authors: Yu Hailan , Chen Weili
Published: September 2021

With the application of artificial intelligence more and more widely, the target detection of artificial intelligence “eyes” is becoming more and more important, which can give the machine the ability to detect and recognize image content in the environment. In this paper, YOLOv4 deep learning motion target detection algorithm is used to achieve the positioning and recognition of motion targets, to identify and mark the location and type of objects contained in the image, to achieve the detection of pictures, videos and camera realtime image detection, so that the machine has the most basic visual ability. Using YOLOv4 algorithm, the speed requirement of real-time detection can be realized with certain precision, and the model mAP value reached 69.48%.
Optimization Method of Segmented Grain Structure Based on Shortest Distance Proxy Model
Authors: Zhao Jiuling
Published: September 2021

The structural integrity of the grain is very important for the grain with complex geometry. Large section column structure has its particularity, differs from that of a single column, its length to diameter ratio is large and absolute meat the increase of the thickness, particles of geometric shape and size of the key factors to determine the gas production rate and its change rule of the engine, at the same time, the engine thrust and pressure change with time, particle size determines the volume and weight of the combustion chamber,Therefore, the particle design of solid rocket motor determines the quality design index of the engine to a large extent.In order to improve the efficiency of model optimization, an agent model is established in this paper. The prediction accuracy of the model depends on the distance between prediction points and sample points. The shortest distance hierarchical clustering algorithm is used to improve the prediction accuracy.Through the research, the comprehensive effect of the key factors affecting the structural integrity of the propellant and the law of the influence on the structure and safety margin of the propellant are obtained. The research method and law can provide support for the large solid motor grain design, and provide theoretical reference for the solid rocket motor grain structure multi-parameter optimization design.
Site Location Optimization Based on Genetic Algorithm
Authors: Zuo Yahui , Huang Shan , Feng Zijian , Dou Yajie , Xu Xiangqian
Published: September 2021

With the goal of providing food to people in need, the FBST serves nearly 4,000 square miles in six counties in New York State. The MFP program is among the main activities of FBST, using an MFP truck loaded with food to serve 70 sites in New York State. An effective and fair schedule for visiting all 70 sites is necessary to optimize the MFP plan. In order to describe the problem, a triad {G, R, C} of goal, resource and constraint is established according to the decision theory. Based on the established triad, this paper establishes a multi-objective optimization model with the goal of improving validity and fairness, a genetic algorithm has been adopted to solve the problem. The time schedule for visiting sites is finally obtained after the iteration of 2000 times, which took 156.40 seconds. Considering that customers can go to further sites, the number of service sites and the location of sites can be optimized. The K-means clustering algorithm based on the improved demand centroid solution method is used to solve the site location selection. After optimizing the location of the site, the performance of seniorsite and regular-site systems is improved by 30.755% and 36.413% respectively.
Image Super-Resolution Based on Additional Self-Loop Supervision
Authors: Chen Hong , Xu Li , Luo Bing , Du Shuai , Xiao Kunshu , Li Siwei
Published: September 2021

Relying on the prevalence of deep neural networks, only one image super-resolution (SR) research has taken a big step forward. Many network models use the difference between the real image and the generated super-resolution image as a loss function, ignoring the difference between the small-scale image and the super-resolution generated image. We adopt an end-to-end network model with two-way supervision, which can ensure that the image content is similar in both large and small scales. Only real natural images are used for supervision. As the scale of the image becomes larger, the difference becomes larger, and the effect of correcting image details becomes smaller. Adding the loss between the input image and the degraded image of the generated image can not only keep the content of the image similar on a small scale, but also ensure the image details. Compared with several state-of-the-arts, our method obtains the best results.
Research on Floating Object Ranging and Positioning Based on UAV Binocular System
Authors: Guo Peng , Zhang Nengwen , Jiang Bing , Qin Fatao , Zhou Zhiheng
Published: September 2021

Aiming at the ranging and positioning problem of floating objects polluted by water surface, the binocular system of UAV is designed to obtain the parallax and distance information of target objects in the scene. Firstly, Zhang Zhengyou calibration method is used to calibrate binocular camera; Secondly, stereo correction is carried out on the image to obtain a standard binocular stereo vision model; Finally, the depth information of the target object is obtained by stereo matching algorithm. The simulation results show that the depth error is less than 5%, and the actual physical coordinate position of the floating object is obtained by combining the GPS information of the UAV itself.
Residual Shuffle Attention Network for Image Super-Resolution
Authors: Li Zhiwei , Zhang Yaping , Yang Yuwei
Published: September 2021

In order to improve the accuracy of the super-resolution network and reduce the number of model parameters, this paper improves its RCAB module on the basis of RCAN, and builds a reconstruction network RSAN that can improve the quality and efficiency of image super-resolution reconstruction. By replacing the original channel attention module with a more efficient and lightweight shuffle attention, it is mainly used to reduce the number of parameters, supplemented by improving the accuracy; and replacing part of the ordinary convolution in RCAN with split convolution is mainly used to improve accuracy, supplemented by reducing feature redundancy and parameters. The experimental results show that RSAN in this paper can not only obtain better subjective visual evaluation and objective quantitative evaluation, but also reduce the number of network model parameters and improve the efficiency of the network to a certain extent.
Estimation of Nitrogen Content in Citrus Leaves Using Stacking Ensemble Learning
Authors: Wu Tong , Li Yong , Ge Ying , Xi Shunzhong , Ren Mengjie , Yuan Xiaohui , Zhuang Cuizhen
Published: September 2021

Nitrogen is an essential biochemical component of citrus growth, and also an important indication to determine the quality and yield of citrus. The traditional method to obtain Leaf Nitrogen Content (LNC) is costly and time-consuming. Therefore, the rapid and accurate acquisition of nitrogen content information by satellite remote sensing is of great significance for citrus cultivation and production. Empirical models based on vegetation indices (VIs) have been widely used to estimate LNC, while individual model only extracts limited information. The ensemble learning strategy has shown great potential in machine learning, so Landsat8 OLI satellite remote sensing images and ground sample data are used to construct a two-layer Stacking ensemble learning framework for estimating the nitrogen content of citrus leaves in this study. In the proposed model, K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) are utilized as base-models. Linear Regression (LR) is employed as the meta-model. Results show that Stacking model can obtain better estimation results of citrus LNC, thus providing scientific decision support for orchard planting and production.
Dynamic Scheduling Framework of the Flexible Mixed-Model Assembly Line Based on the Internet of Manufacturing Things
Authors: Shi Lei , Zhong Yinghao , Yao Jianfeng , Dai Xiang , Guo Gang
Published: September 2021

In mass customization (MC), the assembly operations of modular products tend to be organized as the form of flexible mixed-model assembly line (FMMAL). The dynamic scheduling problem of FMMAL is quite complex with product sequencing, station allocation, material delivery, disruption events of station failure, product inserting and product reworking. To solve these problems, a comprehensive framework combining the architecture of Internet of Manufacturing Things (IoMT) with the dynamic scheduling algorithms is proposed. Firstly, the IoMT-based FMMAL are constructed via the multi-agent system (MAS) and ubiquitous environment. Secondly, a mathematical model of FMMAL is formulated with the decision variables, optimization objectives and constraint conditions. Thirdly, the IoMT-oriented algorithms for dynamic scheduling are proposed including the fuzzy analytic hierarchy process (FAHP) for normalization, weighted sum of properties-improved genetic algorithm (WSP-IGA) for prescheduling, priority weights search-simulated annealing (PWS-SA) for rescheduling. Lastly, a discrete event simulation of a numerical case is conducted to demonstrate the practicality and validity of proposed theories and algorithms. The results show that the proposed hyper-heuristics (WSP-IGA and PWS-SA) can respectively realize the prescheduling and rescheduling of FMMAL in four modes including the synthesized mode, time-efficient mode, just-in-time mode and energy-saving mode, which are superior to the four referenced meta-heuristics.
Robust ILC of Nonlinearly Parametric Time-Delay Systems with Input Deadzone
Authors: Wang Xidan , Ma Yuhan , Yan Qiuzhen , Zhang Yuntao , Guan Xiaohui
Published: September 2021

The trajectory tracking problem for a class of nonlinearly parametric systems with input deadzone and time-delay is studied in this work. An adaptive iterative learning control (ILC) scheme is developed by using Lyapunov synthesis. The alignment condition is applied to solve the initial position problem of ILC. Robust control and ILC are used to deal with input deadzone, delay and nonlinearly parametric uncertainties, together. As the iteration learning cycle increases, the system state may precisely track the reference signal over the whole interval. A numerical simulation is presented to verify the efficacy of the proposed adaptive iterative learning control method.
String Stability Control Strategy Analysis of Mixed Traffic Flow with the CIVs and NCVs
Authors: Zhang Junjie , Yang Can , Yu Haiyang , Zhang Jun , Wang Zixiao
Published: September 2021

With the development of vehicle-to-infrastructure cooperation system, a mixed traffic phenomenon with non-connected vehicles (NCVs) and connected and intelligent vehicles (CIVs) will exist over a long period of time. Therefore, the mixed traffic flow stability control has become a hot topic in the future. In order to improve the string stability in the complex and changeable internet of vehicles environment, it is necessary to propose the optimal control method of string stability in the mixed traffic flow. In this paper, NCV and CIV car-following modes are employed to propose a local platoon control method of the connected vehicle, which can achieve the purpose of optimizing the mixed traffic flow stability. Two types of local mixed platoon are considered when the effective communication distance with two vehicles in the vehicle-to-vehicle (V2V) communication. Numerical simulations results show that our proposed string stability control strategy has the effectiveness in the improvement of the mixed traffic flow stability.
Reverse Logistics Enterprise Performance Research Based on Super-Efficiency DEA and LMBP Neural Network
Authors: Pan Junlin , Li Guanliang , Luo Haiyan
Published: September 2021

On the basis of considering the characteristics of reverse logistics enterprises, this paper uses stakeholder theory to establish an evaluation system containing 36 indicators in five dimensions, based on the balanced scorecard, and adopts PCA optimization index. In view of the performance evaluation problem, first, the performance data is obtained by using the super-efficiency DEA, then the enterprise performance of A company is fitted by LMBP neural network to obtain the quantitative model of performance evaluation, and finally, according to the resulting weight reference performance benchmark, the direction of reverse logistics enterprise performance improvement is given. The simulation results show that the index system can react to the reverse logistics characteristics very well, and the BP neural network based on LM algorithm can accurately and efficiently evaluate the performance of reverse logistics enterprises which has better generalization, and can find out the key performance indicators and give reasonable optimization direction for enterprises by reverse tracking the weights of the neural network.
Research on Factors Influencing Smart Library Users’ Use Intention in the Era of Artificial Intelligence
Authors: Liu Jingwen , Song Dan , Li Weimo
Published: September 2021

The arrival of the era of artificial intelligence has promoted the development of smart libraries and endowed them with new features such as autonomy, autonomous learning, context perception, and multi-function. Analyzing how these new features affect users’ behavior is of great significance to the application of artificial intelligence and the optimization of services. Based on the TTF model, the four dimensions of product intelligence (autonomy, adaptability, reactivity, and multifunctionality) are introduced to represent the technology characteristics, and the research model is constructed. And the method of SEM is used to conduct empirical analysis. The results show that technology characteristics and individual characteristics are the main factors affecting the task-technology fit (TTF), while the effect of task characteristics on TTF is not significant. Among them, the impact of technology characteristics is greater than the individual characteristics. The relative importance of the four dimensions of technology characteristics is “Autonomy >Multifunctionality, Reactivity> Adaptability”. The TTF significantly positively affects users’ intention to use smart libraries. Therefore, the suggestions on optimizing smart services are put forward.
The Intelligent Robot Path Planning and Navigation System Based on Openlayers3 Online Map
Authors: Yang Liu , Yu Limin , Tao Sha , Yang Zhenghong , Gao Wanlin , Ren Yanzhao
Published: September 2021

The electronic map becomes more and more popular due to its convenience and powerful functions. This paper aims to use map information to build a system for path planning and navigation of an intelligent robot. It can provide a basis for field autonomous identification of crop diseases and insect pests with the robot as the carrier. The open source Openlayers is used as a tool to link online maps. And Dijkstra algorithm is used to obtain the shortest path. And then it can simulate a robot moving along the path by creating a moving point. In addition, it could accomplish a series of fundamental operations of controlling movement and the display of route details. The feasibility of the functions is verified by experiments and it lays a solid foundation for subsequent work.
The Construction Parameter Optimization of High-Speed Pantograph
Authors: Wang Jianpu , Ren Chenglong , Hong Lei
Published: September 2021

The composition structure of high-speed pantograph is analyzed, which is transformed into a four-rod mechanism, and the relative motion relation and its position coordinate equations are built by constructing a kinematics model. Based on the equations of motion of pantograph and the simulation of motion pattern between elevation height and transverse deviation of bow head, angle of balance arm is obtained by programming and running program in Matlab. The main parameters which are affecting high-speed pantograph lateral offset and balance arm rotation angle are analyzed. The relevant parameters are optimized by using the sequential quadratic programming (SQP) algorithm in Matlab. We compare the results after optimization with the results before optimization. Finally, according to the optimized results, the pantograph is modeled in Solidworks. The pantograph model is imported into the finite element analysis software to load, and the displacement cloud diagram of the pantograph bow head is obtained, and compared with the theoretical value to verify the optimization results.
Scheduling Data Flow between Data Centers Based on Software Defined Network
Authors: Hu Siquan , Huang Zhao , Shi Zhiguo
Published: September 2021

For intelligent control of network traffic, to solve the problem of low utilization of public network bandwidth, we put forward scheduling data flow between data center based on SDN technology. First, we connect the data center distributed in different locations by wide area network and exchange or share the research data of high performance. Then, the system can sense the utilization and performance of the network bandwidth resources between the data center. Based on the results of perception, we can dynamically adjust and optimize the path of data and maximize the utilization of network link. SDN is a new software based network structure. This paper uses SDN network architecture to schedule the traffic of data center, and constructs a two-layer network suitable for cloud computing and virtualization environment. It realizes the global, dynamic and intelligent data flow.
Research on Evolutionary Construction Method of Software Service Architecture Based on ACP
Authors: Gao Tilei , Yang Ming , Jia Xiaohui , Zhang Tao
Published: September 2021

In the open-source computing environment, the software service individuals that constitute the system are highly independent. The construction of software system is changed into combination and iteration, which puts forward higher requirements for the system architecture. Based on algebra of communication process, this paper abstracts the individual software service into service items, and uses the operation rules in communication algebra to realize the construction and modification of software architecture, thus ensuring the preciseness of the construction process and the stability of the architecture. Finally, a system with 12 sub services is used as a case to demonstrate the evolution process of service adding, deleting and replacing operation, which also verified the effectiveness and feasibility of the proposed method.
Research on regenerative braking system for linear control chassis platform
Authors: Gao Guowei , Li Xiaopeng , Xu Zheng
Published: September 2021

The regenerative braking system of the electric vehicle with the linear control chassis plays an important role in improving the mileage of the vehicle, which mainly solves the problem of large electricity demand for the linear control chassis. After analyzing the structural characteristics of the linear control chassis, the constraints of regenerative braking and the existing regenerative braking control strategies, a new regenerative braking control strategy is proposed, and the control strategy is modified. The regenerative braking control strategy model is established by Simulink / Stateflow, and the braking effect and energy recovery efficiency are verified by changing the initial braking speed. The simulation results show that the strategy can well complete the braking task of the vehicle, and the braking energy recovery rate increases with the increase of the initial braking speed. Reasonable control strategy can effectively improve vehicle energy recovery efficiency.