bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Implementation of Semi-Physical Platform for Label Based Frame Switching in Integrated Satellite Terrestrial Networks
Authors: Zhou Wei , Jiang Xing , Luo Qingsong , Huang Shanguo , Guo Bingli , Sun Xiang , Li Shaobo , Tan Xiaochuan , Ma Mingyi , Fu Tianwen
Published: July 2022

With the explosion of traffic demand in recent years, the integration of satellite optical networks and terrestrial networks (ISTN) creates a promising networking solution for future low-latency, high-rate, and high-capacity communications. Owing to the high cost of deploying and maintaining a satellite optical network, it is critical to carefully design and plan the network to ensure the performance of the network. Thus, a semi-physical simulation platform based on software-defined networks (SDNs) is developed to simulate a satellite optical network and evaluate the performance of the proposed label-based advanced orbiting system (AOS) frame switching method that adheres to the Consultative Committee for Space Data Systems’ recommended standard (CCSDS). The semi-physical simulation platform has two major innovations: (1) adapting and integrating network protocols between the CCSDS and open system interconnect (OSI) reference models, particularly at the data link layer, and (2) the foundation for an SDN-based satellite optical network. In the control plane, real-time VxWorks Simulators serve as controllers to establish and manage various network protocols and the link manager protocol (LMP). Here, network protocols include open shortest path first (OSPF) for routing managing and controlling messages, constraint shortest path first–traffic engineering (CSPF-TE), and constraint-label distribution protocol (CR-LDP) for routing data services. LMP is used to assign and reserve satellite optical link resources. The performance of the architecture and protocols is evaluated via a semi-physical simulation platform.
A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery
Authors: Zhang Sulan , Huang Hong , Huang Yunbiao , Cheng Dongdong , Huang Jinlong
Published: July 2022

Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a global destructive threat to forests and has led to serious economic losses all over the world. Therefore, it is necessary to establish a feasible and effective method to accurately monitor and estimate PWD infection. In this study, we used hyperspectral imagery (HI) collected by an unmanned airship with a hyperspectral imaging spectrometer to detect PWD in healthy, early, middle and serious infection stages. To avoid massive calculations on the full spectral dimensions of the HI, 16 spectral features were extracted from the HI, and a genetic algorithm (GA) was implemented to identify the optimal ones with the least fitness. Simultaneously, a support vector machine (SVM) classifier was established to predict the PWD infection stage for an individual pine tree. The following results were obtained: (1) the spectral characteristics for pine trees in different PWD infection stages were distinctive in the green region (510–580 nm), red edge (680–760 nm) and near-infrared (780–1000 nm) spectra; (2) the six optimal spectral features (Dgreen, SDgreen, Dred, DRE, DNIR, SDNIR) selected with the GA effectively distinguished the PWD infection stages of pine trees with a lower calculation cost; (3) compared with the traditional classifiers, such as k-nearest neighbor (KNN), random forest (RF) and single SVM, the proposed GA and SVM classifier achieved the highest overall accuracy (95.24%) and Kappa coefficient (0.9234). The approach could also be employed for monitoring and detecting other forest pests.
CO2 Adsorption Reactions of Synthetic Calcium Aluminum Ferrite (CAF)
Authors: Lee Woong-Geol , Song Myong-Shin
Published: July 2022

In this study, we investigated a mechanism of carbonation reaction by CO2 capture through synthesis of ternary (CaO-Al2O3-Fe2O3) compounds. As for the composition of the sintered calcium aluminum ferrite (SCAF), the proportions of CF-based product and CA-based product were high, at 87.3% and 64.6%, at sintering temperatures of 1000 °C and 1100 °C, respectively. In addition, in the process of both dry and wet carbonation, the carbonation reaction occurred in the synthetic SCAF regardless of the sintering temperature conditions. In particular, in the carbonation with the wet method, CAH and CAFH, which are hydrates, were produced in up to 1 h of the reaction time with CO2, but from 3 h of reaction time, carbo compounds such as calcium carbo aluminate and calcium carbo alumino-ferrite compounds were produced. That is, with increasing reaction time, the carbo reaction becomes more active in the process. Therefore, SCAF synthesized in this study easily produced carbo compounds through carbonation reactions and formed carbonates by reaction with CO2. Thus, it is expected that the compounds can be effectively utilized as an excellent material for CO2 capture capable of CO2 absorption and fixation.
Developing Microservice-Based Applications Using the Silvera Domain-Specific Language
Authors: Suljkanović Alen , Milosavljević Branko , Inđić Vladimir , Dejanović Igor
Published: July 2022

Microservice Architecture (MSA) is a rising trend in software architecture design. Applications based on MSA are distributed applications whose components are microservices. MSA has already been adopted with great success by numerous companies, and a significant number of published papers discuss its advantages. However, the results of recent studies show that there are several important challenges in the adoption of microservices such as finding the right decomposition approach, heterogeneous technology stacks, lack of relevant skills, out-of-date documentation, etc. In this paper, we present Silvera, a Domain-Specific Language (DSL), and a compiler for accelerating the development of microservices. Silvera is a declarative language that allows users to model the architecture of microservice-based systems. It is designed so that it can be used both by inexperienced and experienced developers. The following characteristics distinguish Silvera from similar tools: (i) lightweight and editor-agnostic language, (ii) built with heterogeneity in mind, (iii) uses microservice-tailored metrics to evaluate the architecture of the designed system, and (iv) automatically generates the documentation. Silvera’s retargetable compiler transforms models into runnable code and produces the documentation for each microservice in the model. The compiler can produce code for any programming language or framework since code generators are registered as plugins. We present a case study that illustrates the use of Silvera and also discuss some current limitations and development directions. To evaluate Silvera, we conducted a survey based on A Framework for Qualitative Assessment of DSLs (FQAD), where we focused on the following DSL characteristics: functional suitability, usability, reliability, productivity, extendability, and expressiveness. Overall, the survey results show that Silvera satisfies these characteristics.
Dynamic Analysis and Trajectory Tracking Control for a Parallel Manipulator with Joint Friction
Authors: Chen Shibiao , Cheng Gang , Pang Yusong
Published: July 2022

To overcome the bearing capacity deficiencies of traditional serial hip joint simulators, complex trajectory simulation, among others, as well as a parallel manipulator with two pairs of artificial hip joints and two moving platforms are proposed. The movements and driving forces of the parallel manipulator under the required motion and loading are studied to provide a basis for further research. In this study, the modeling and analysis of inverse kinematics and dynamics for a parallel manipulator with joint friction are derived. In the inverse kinematic model, kinematic relationships between the linear module slider and the moving platform are established, and expressions for the slider are deduced. Subsequently, by analyzing the frictional forces of the artificial hip joint and thrust ball bearing, a rigid body dynamics model of the parallel manipulator with joint friction is established, which is subsequently decomposed into four driving torques associated with the moving platform, joint lever, slider, and screw. Finally, the difference in the kinematic performance between the two moving platforms is analyzed using numerical simulations and experiments, and the accuracy of the established model is verified.
BERTOEIC: Solving TOEIC Problems Using Simple and Efficient Data Augmentation Techniques with Pretrained Transformer Encoders
Authors: Lee Jeongwoo , Moon Hyeonseok , Park Chanjun , Seo Jaehyung , Eo Sugyeong , Lim Heuiseok
Published: July 2022

Recent studies have attempted to understand natural language and infer answers. Machine reading comprehension is one of the representatives, and several related datasets have been opened. However, there are few official open datasets for the Test of English for International Communication (TOEIC), which is widely used for evaluating people’s English proficiency, and research for further advancement is not being actively conducted. We consider that the reason why deep learning research for TOEIC is difficult is due to the data scarcity problem, so we therefore propose two data augmentation methods to improve the model in a low resource environment. Considering the attributes of the semantic and grammar problem type in TOEIC, the proposed methods can augment the data similar to the real TOEIC problem by using POS-tagging and Lemmatizing. In addition, we confirmed the importance of understanding semantics and grammar in TOEIC through experiments on each proposed methodology and experiments according to the amount of data. The proposed methods address the data shortage problem of TOEIC and enable an acceptable human-level performance.
Research on a Fiber Corner Compensation Algorithm in a 3D Printing Layer of Continuous Fiber-Reinforced Composite Materials
Authors: Liu Jiang , Kang Yuzhu , Ma Chenyu , Wang Yesong
Published: July 2022

Fused filament fabrication (FFF) 3D printing technology for continuous fiber-reinforced composite (CFRC) printing has become a trend. This article is based on ‘independent extrusion’ FFF CFRC printing. The continuous fiber-reinforced filament (CFRF) printing solution is the contour offset method for obtaining executable g-code. When the CFRCF prints at the corner, it is found that the actual CFRC printing trajectory is inconsistent with the ideal laying trajectory. The causes of the error are analyzed, and an angle optimization algorithm is proposed. The corner optimization algorithm is verified by theoretical analysis and experimental analysis. From the experimental results, the corner optimization algorithm improves the 30° angle fit of CFRF printing by 90% and reliability has also been improved. When the printing length is 127,200 mm, there are 960 printing corners, and the failure rate is 0.
High Pulse Energy, Narrow Linewidth 6.45 μm from an Optical Parametric Oscillator in BaGa4Se7 Crystal
Authors: Shen Yu , Wen Ning , Li Chunxiao , Zong Nan , Yao Jiyong , Chang Jinquan , Wen Ya , Yang Feng , Li Wenlong , Gao Hongwei , Bo Yong , Peng Qinjun , Cui Dafu
Published: July 2022

This paper presents a high pulse energy, narrow linewidth, mid-infrared (MIR) laser at 6.45 μm, based on a BaGa4Se7 (BGSe) crystal optical parametric oscillator (OPO) pumped by 1.064 μm laser. The maximum pulse energy at 6.45 μm was up to 1.23 mJ, with a pulse width of 24.3 ns and repetition rate of 10 Hz, corresponding to an optical–optical conversion efficiency of 2.1%, from pump light 1.064 μm to idler light 6.45 μm. The idler light linewidth was about 6.8 nm. Meanwhile, we accurately calculated the OPO phase-matching condition at BGSe crystal pumped by 1.064 μm laser, and a numerical simulation system was performed to analyze the input–output characteristics at 6.45 μm, as well as the effect of crystal length on the conversion efficiency. Good agreement was found between measurement and simulation. To the best of our knowledge, this is the highest pulse energy at 6.45 μm, with the narrowest linewidth for any all-solid-state MIR ns laser in BGSe-OPO pumped by simple 1.064 μm oscillator. This simple and compact 6.45 μm OPO system, with high pulse energy and narrow linewidth, can meet the requirements for tissue cutting and improve tissue ablation accuracy.
Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms
Authors: Dai Yue , Dai Wujiao , Yu Wenkun , Bai Dongxin
Published: July 2022

Numerical simulation has emerged as a powerful technique for landslide failure mechanism analysis and accurate stability assessment. However, due to the bias of simplified numerical models and the uncertainty of geomechanical parameters, simulation results often differ greatly from the actual situation. Therefore, in order to ensure the accuracy and rationality of numerical simulation results, and to improve landslide hazard warning capability, techniques and methods such as displacement back-analysis, machine learning, and numerical simulation are combined to create a novel landslide warning method based on DBA-LSTM (displacement back-analysis based on long short-term memory networks), and a numerical simulation algorithm is proposed, i.e., the DBA-LSTM algorithm is used to invert the equivalent physical and mechanical parameters of the numerical model, and the modified numerical model is used for stability analysis and failure simulation. Taking the Shangtan landslide as an example, the deformation mechanism of the landslide was analyzed based on the field monitoring data, and subsequently, the superiority of the DBA-LSTM algorithm was verified by comparing it with DBA-BPNN (displacement back-analysis based on back-propagation neural network); finally, the stability of the landslide was analyzed and evaluated a posteriori using the warning threshold calculated by the proposed method. The analytical results show that the displacement back-analysis based on the machine learning (DBA-ML) algorithm can achieve more than 95% accuracy, and the deep learning algorithm exemplified by LSTM had higher accuracy compared to the classical BPNN algorithm, meaning that it can be used to further improve the existing intelligent inversion theory and method. The proposed method calculates the landslide’s factor of safety (FOS) before the accelerated deformation to be 1.38 and predicts that the landslide is in a metastable state after accelerated deformation rather than in failure. Compared to traditional empirical warning models, our method can avoid false warnings and can provide a new reference for research on landslide hazard warnings.
Study and Design of a Machine Learning-Enabled Laser-Based Sensor for Pure and Sea Water Determination Using COMSOL Multiphysics
Authors: Mourched Bachar , Ferko Ndricim , Abdallah Mariam , Neji Bilel , Vrtagic Sabahudin
Published: July 2022

Accurate detection of salt in water is crucial in many applications. Numerous techniques, using direct and indirect methods, have been employed to design seawater sensors. Among the indirect sensing methods, optical sensors are known to be the most accurate, easy to implement, and suitable for application where the chemical properties of the solution to be tested should stay unchanged. This research presents a novel method for real-time label-free biochemical detection of salty water combining various optics concepts with a machine learning system. COMSOL Multiphysics has been employed to design and simulate the proposed sensor. The designed device uses a laser light emitted from the top of a water container, with a sensing part located on the bottom surface. The laser light initially propagates in the air portion, then refracts when it comes into contact with the air-water interface. Different parameters, including the laser beam wavelength λ and its incident angles θi, the temperature, and the air-water levels are employed to generate a set of data and the multilayer perceptron classifier (MLP) to model prediction. The obtained results validated the concept of the proposed sensor using machine learning. The sensor’s prediction precision under various temperature conditions is R2 = 0.844, the equivalent of an MSE of 0.155.
Micromagnetic Properties of Powder Metallurgically Produced Al Composites as a Fundamental Study for Additive Manufacturing
Authors: Gräbner Maraike , Wiche Henning , Treutler Kai , Wesling Volker
Published: July 2022

Resource-efficient manufacturing with a high degree of freedom in terms of component shape can be realised through additive manufacturing. The focus can lie not only on the manufacturing process in terms of geometrical correctness, stability, etc., but also on the targeted development of specific material properties. This study shows the development of hybrid material systems made of aluminium and the ferromagnetic particles iron, cobalt, and nickel. The aim is to use the ferromagnetic properties as sensor properties to enable the easy sensing of material properties such as the microstructure, fatigue, or occurring stresses. To easily adopt different compositions, hot isostatic pressing was selected for the characterisation of the material composites Al-Fe, Al-Ni, and Al-Co with regard to their magnetic properties. Subsequently, transfer to the additive manufacturing process of wire and arc additive manufacturing gas metal arc welding was carried out by mixing the powder separately into the weld pool. The study shows that it is possible to prevent a complete transformation of Ni and Co into intermetallic phases with Al by adjusting the influencing variables in the HIP process. Magnetic properties could be detected in the composites of Al-Co and Al-Fe. This work serves as a preliminary work to realise additive components made of hybrid material systems of Al-Fe, Al-Co, and Al-Ni with the GMA welding process.
Impact and a Novel Representation of Spatial Data Uncertainty in Debris Flow Susceptibility Analysis
Authors: Kurilla Laurie Jayne , Fubelli Giandomenico
Published: July 2022

In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows demonstrates the impact of uncertainty in defining appropriate predisposing factors and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points and localized distribution, and hence susceptibility results are likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent, and the context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility for the continent of Europe. A novel representation of the uncertainty is expressed and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.
Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction
Authors: Kim Donghyun , Han Heechan , Wang Wonjoon , Kang Yujin , Lee Hoyong , Kim Hung Soo
Published: July 2022

Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3), sulfurous acid gas (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.
User Experience Quantification Model from Online User Reviews
Authors: Hussain Jamil , Azhar Zahra , Ahmad Hafiz Farooq , Afzal Muhammad , Raza Mukhlis , Lee Sungyoung
Published: July 2022

Due to the advancement in information technology and the boom of micro-blogging platforms, a growing number of online reviews are posted daily on product distributed platforms in the form of spontaneous and insightful user feedback, and these can be used as a significant data source to understand user experience (UX) and satisfaction. However, despite the vast amount of online reviews, the existing literature focuses on online ratings and ignores the real textual context in reviews. We proposed a three-step UX quantification model from online reviews to understand customer satisfaction using the effect-based Kano model. First, the relevant online reviews are selected using various filter mechanisms. Second, UX dimensions (UXDs) are extracted using a proposed method called UX word embedding Latent Dirichlet allocation (UXWE-LDA) and sentiment orientation using a transformer-based pipeline. Then, the casual relationships are identified for the extracted UXDs. Third, the UXDs are mapped on the customer satisfaction model (effect-based Kano) to understand the user perspective about the system, product, or services. Finally, the different parts of the proposed quantification model are evaluated to examine the performance of this method. We present different results of the proposed method in terms of accuracy, topic coherence (TC), Topic-wise performance, and expert-based evaluation for the proposed framework validation. For review quality filters, we achieved 98.49% accuracy for the spam detection classifier and 95% accuracy for the relatedness detection classifier. The results show that the proposed method for the topic extractor module always gives a higher TC value than other models such as WE-LDA and LDA. Regarding topic-wise performance measures, UXWE-LDA achieves a 3% improvement on average compared to LDA due to the incorporation of semantic domain knowledge. We also compute the Jaccard coefficient similarity between the extracted dimensions using UXWE-LDA and UX experts-based analysis for checking the mutual agreement, which is 0.3, 0.5, and 0.4, respectively. Based on the Kano model, the presented study has potential implications concerning issues and knowing the product’s strengths and weaknesses in product design.
Comparative Study of Fresh and Frozen Broiler Neck Skin Sampled for Process Hygiene Purposes
Authors: Langkabel Nina , Oswaldi Verena , Merle Roswitha , Dzierzon Janine , Meemken Diana
Published: July 2022

The objective of the study was to determine the effect of freezing broiler neck skin samples before their microbial analysis, compared to freshly examined samples regarding total viable count (TVC) and Enterobacteriaceae count (EC). For this, 300 neck skin samples were taken at a German commercial broiler abattoir and each neck skin sample was cut into two parts. One randomly selected part underwent microbial examination after storage at 4 °C overnight; the other part was frozen at −30 °C for eight weeks before analysis in the same laboratory. Log cfu/g values of TVC and EC were separately compared between the fresh and frozen neck skin samples. A difference up to 0.5 log values was set as acceptable, i.e., fresh and frozen samples with counts that differed by this amount were considered as not different. The differences between the grouped samples of fresh and frozen broiler neck skin regarding both TVC and EC levels were less than 0.5 log values. Thus, it can be assumed that broiler neck skin samples, both fresh and frozen for eight weeks, are suitable for microbiological examination, as the TVC and EC results showed equivalence. Therefore, freezing broiler neck skin samples can be an option to maintain viable bacteria levels in broiler neck skin samples taken for microbiological examination in process control, when freezing and later examination is necessary due to insufficient laboratory capacity for the examination of fresh neck skin samples.
Study on the Route-Selection Behavior of Bus Passengers Based on an Evolutionary Game under the Condition of Information Guidance
Authors: Hu Xinghua , Qin Hongbin , Guo Jianpu , Xu Yimei , Liu Wei , Zhou Xiaochuan
Published: July 2022

Bus guidance information supplied by advanced traveler information systems provide a reference for route selection by bus passengers. This study analyzes the interaction between bus passengers’ route-choice behavior and the relationship in a game. The game revenue of the passengers waiting on the bus platform is quantified by parameters such as the number of passengers waiting on the platform, the number of passengers on the bus, and the passenger capacity of the bus. Based on the characteristics of passenger-bounded rationality, an evolutionary game model of bus choice behavior under information-induced conditions is established. Through the evolutionary game model, the distribution of the proportion of passengers receiving the guidance strategy is obtained when the evolution is stable. Finally, according to the distribution results, the passenger guidance strategy is discussed to achieve a win-win situation between passengers and bus operators.
Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction
Authors: Ahern Michael , O’Sullivan Dominic T. J. , Bruton Ken
Published: July 2022

The disparity between public datasets and real industrial datasets is limiting the practical application of advanced data analysis. Therefore, industry is stuck in a reactive mode regarding their maintenance strategy and cannot transition to cost-effective and energy-efficient proactive maintenance approaches. In this paper, an integration-type adaptation of the CRISP-DM data mining process model is proposed to combine domain expertise with data science techniques to address the pervasive data issues in industrial datasets. The development of the Industrial Data Analysis Improvement Cycle (IDAIC) framework led to the novel repurposing of knowledge-based fault detection and diagnosis (FDD) techniques for data quality assessment. Through interdisciplinary collaboration, the proposed framework facilitates a transition from reactive to proactive problem solving by firstly resolving known faults and data issues using domain expertise, and secondly exploring unknown or novel faults using data analysis.
Design of a Single-Stage Transformerless Buck–Boost Inverter for Electric Vehicle Chargers
Authors: Wang Lixia , Liu Chenming , Fang Jingyang
Published: July 2022

This paper details the design and implementation of a single-stage transformerless buck–boost inverter for electric vehicle (EV) chargers. Being different from conventional H-bridge inverters, the proposed inverter operates like buck–boost dc/dc converters instead of buck dc/dc converters. As a consequence, the advantages of a buck–boost dc/dc converter, i.e., the arbitrary relationships between its input voltage and output voltage, are still applicable to the proposed inverter. Specifically, it remains in normal operation even when the peak ac output voltage is higher than the dc-link voltage. Simulation results are finally presented to illustrate its effectiveness.
Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning
Authors: Lopez-Rodriguez Pedro , Avina-Cervantes Juan Gabriel , Contreras-Hernandez Jose Luis , Correa Rodrigo , Ruiz-Pinales Jose
Published: July 2022

Online handwriting recognition has been the subject of research for many years. Despite that, a limited number of practical applications are currently available. The widespread use of devices such as smartphones, smartwatches, and tablets has not been enough to convince the user to use pen-based interfaces. This implies that more research on the pen interface and recognition methods is still necessary. This paper proposes a handwritten character recognition system based on 3D accelerometer signal processing using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). First, a user wearing an MYO armband on the forearm writes a multi-stroke freestyle character on a touchpad by using the finger or a pen. Next, the 3D accelerometer signals generated during the writing process are fed into a CNN, LSTM, or CNN-LSTM network for recognition. The convolutional backbone obtains spatial features in order to feed an LSTM that extracts short-term temporal information. The system was evaluated on a proprietary dataset of 3D accelerometer data collected from multiple users with an armband device, corresponding to handwritten English lowercase letters (a–z) and digits (0–9) in a freestyle. The results show that the proposed system overcomes other systems from the state of the art by 0.53%.
Deep Bite Treatment with Aligners: A New Protocol
Authors: Pasciuti Enzo , Coloccia Giovanni , Inchingolo Alessio Danilo , Patano Assunta , Ceci Sabino , Bordea Ioana Roxana , Cardarelli Filippo , Di Venere Daniela , Inchingolo Francesco , Dipalma Gianna
Published: July 2022

Deep bites can be treated by the intrusion of anterior teeth and the extrusion of the posterior teeth, or both, according to the exposure of the incisors in the smile. The aim of this paper is to propose a protocol to improve the effectiveness of deep bite treatments. Orthodontic movements are programmed following the “frog staging” protocol for the anterior teeth, the application of retention attachments and the overengineered final setup. Two case reports described the clinical and radiographic changes with orthodontic treatment using Invisalign clear aligners by applying this protocol.
A Variety of Choice Methods for Image-Based Artistic Rendering
Authors: Lin Chiu-Chin , Hsu Chih-Bin , Lee Jen-Chun , Chen Chung-Hsien , Tu Te-Ming , Huang Huang-Chu
Published: July 2022

Neural style transfer (NST) is a technique based on the deep learning of a convolutional neural network (CNN) to create entertaining pictures by cleverly stylizing ordinary pictures with the predetermined visual art style. However, three issues must be carefully investigated during the generation of neural-stylized artwork: the color scheme, the strength of style of the strokes, and the adjustment of image contrast. To solve these problems and select image colorization based on personal preference, in this paper, we propose modified universal-style transfer (UST) method combined with the image fusion and color enhancement methods to design a good post-processing framework to tackle the three above-mentioned issues simultaneously. This work provides more visual effects for stylized images, and also can integrate into the UST method effectively. In addition, the proposed method is suitable for stylized images generated by any NST method, but it also works similarly to the Multi-Style Transfer (MST) method, which mixes two different stylized images. Finally, our proposed method successfully combined the modified UST method and post-processing method to meet personal preference.
A High-Level Representation of the Navigation Behavior of Website Visitors
Authors: Huidobro Alicia , Monroy Raúl , Cervantes Bárbara
Published: July 2022

Knowing how visitors navigate a website can lead to different applications. For example, providing a personalized navigation experience or identifying website failures. In this paper, we present a method for representing the navigation behavior of an entire class of website visitors in a moderately small graph, aiming to ease the task of web analysis, especially in marketing areas. Current solutions are mainly oriented to a detailed page-by-page analysis. Thus, obtaining a high-level abstraction of an entire class of visitors may involve the analysis of large amounts of data and become an overwhelming task. Our approach extracts the navigation behavior that is common among a certain class of visitors to create a graph that summarizes class navigation behavior and enables a contrast of classes. The method works by representing website sessions as the sequence of visited pages. Sub-sequences of visited pages of common occurrence are identified as “rules”. Then, we replace those rules with a symbol that is given a representative name and use it to obtain a shrinked representation of a session. Finally, this shrinked representation is used to create a graph of the navigation behavior of a visitor class (group of visitors relevant to the desired analysis). Our results show that a few rules are enough to capture a visitor class. Since each class is associated with a conversion, a marketing expert can easily find out what makes classes different.
An Improved Method for Evaluating Image Sharpness Based on Edge Information
Authors: Liu Zhaoyang , Hong Huajie , Gan Zihao , Wang Jianhua , Chen Yaping
Published: July 2022

In order to improve the subjective and objective consistency of image sharpness evaluation while meeting the requirement of image content irrelevance, this paper proposes an improved sharpness evaluation method without a reference image. First, the positions of the edge points are obtained by a Canny edge detection algorithm based on the activation mechanism. Then, the edge direction detection algorithm based on the grayscale information of the eight neighboring pixels is used to acquire the edge direction of each edge point. Further, the edge width is solved to establish the histogram of edge width. Finally, according to the performance of three distance factors based on the histogram information, the type 3 distance factor is introduced into the weighted average edge width solving model to obtain the sharpness evaluation index. The image sharpness evaluation method proposed in this paper was tested on the LIVE database. The test results were as follows: the Pearson linear correlation coefficient (CC) was 0.9346, the root mean square error (RMSE) was 5.78, the mean absolute error (MAE) was 4.9383, the Spearman rank-order correlation coefficient (ROCC) was 0.9373, and the outlier rate (OR) as 0. In addition, through a comparative analysis with two other methods and a real shooting experiment, the superiority and effectiveness of the proposed method in performance were verified.
Micro-Raman Analysis of the Pigments on a Crucifix in Calabria
Authors: Cazzanelli Enzo , Grande Danilo , Rizzuto Carmen , Nucera Antonello , Barberi Riccardo Cristoforo , Castriota Marco
Published: July 2022

This study was carried out on a crucifix located inside the church of St. Mary Major in Acri. The story of this crucifix is not very clear, and its dating is still uncertain, as well; however, it ranges between the 15th and the 14th century. The wooden sculpture of the crucified body of Jesus was painted and the conservation state of the color appeared good. A detailed analysis of the pigments was considered very useful in verifying the restoration history of this crucifix, and to discriminate between the original painting and later restoration activities. A micro-Raman analysis reveals the presence of the classical pigments expected for the estimated age of the crucifix, such as gypsum, terra di Siena, vermilion, carbon black, and others. In addition, other pigments of more recent use were found, such as Prussian blue, titanium white, lithopone, and chromium yellow, mostly in the thong around body of Jesus, which appears green. This is a clear indication of a relevant modification of the artifact in modern times, when the thong was totally painted again; smaller modifications were surely revealed by the presence of “modern” pigments on other points of the body.
Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
Authors: Li Zan , Liu Songyu , Wu Kai , Lu Taishan
Published: July 2022

With the development of urbanization, numerous excavations are carried out to facilitate the development of underground space. As a support for tunnel structures, uplift piles are often installed prior to tunnel excavation. The excavation inevitably causes disturbance to the soil below the excavation surface, changing the soil’s mechanical behavior and stress state significantly. However, there is still a lack of a method to evaluate the change in pile capacity due to excavation. This paper proposes a semi-analytical approach for estimating the change in load-settlement behavior of an uplift pile considering the effects of excavation. A hyperbolic model was adopted to simulate the nonlinear interaction of the pile–soil interface. The nonlinear shear-induced soil displacement outside the pile–soil interface is introduced to obtain a more realistic load-displacement behavior of the uplift pile. An effective iterative program was implemented based on the proposed semi-analytical approach. The comparisons between the results from the proposed methods, well-documented field tests, centrifuge tests, and other analytical methods showed that the proposed approach is suitable for analyzing an uplift pile considering excavation effects. A parametric study was conducted to investigate the effects of main soil properties on the pile capacity loss caused by excavation. The results showed that the soil friction angle and the ratio of the excavation depth to the pile effective length have a great influence on the loss in pile uplift capacity caused by excavation. However, the loss of pile uplift capacity caused by excavation is not affected by the soil’s shear modulus or Poisson’s ratio.
A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network
Authors: Zheng Shaoxiong , Gao Peng , Wang Weixing , Zou Xiangjun
Published: July 2022

In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model. First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained DCNN models were used to extract features from forest fire images. Second, principal component analysis (PCA) reconstruction technology was used in the appropriate subspace. The constructed 15-layer forest fire risk identification DCNN model named “DCN_Fire” could accurately identify core fire insurance areas. Moreover, the original and enhanced image data sets were used to evaluate the impact of data enhancement on the model’s accuracy. The traditional DCNN model was improved and the recognition speed and accuracy were compared and analyzed with the other three DCNN model algorithms with different architectures. The difficulty of using DCNN to monitor forest fire risk was solved, and the model’s detection accuracy was further improved. The true positive rate was 7.41% and the false positive rate was 4.8%. When verifying the impact of different batch sizes and loss rates on verification accuracy, the loss rate of the DCN_Fire model of 0.5 and the batch size of 50 provided the optimal value for verification accuracy (0.983). The analysis results showed that the improved DCNN model had excellent recognition speed and accuracy and could accurately recognize and classify the risk of a forest fire under natural light conditions, thereby providing a technical reference for preventing and tackling forest fires.
Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
Authors: Tsai Cheng-Ying , Chen Chieh-Li
Published: July 2022

In recent decades, haze has become an environmental issue due to its effects on human health. It also reduces visibility and degrades the performance of computer vision algorithms in autonomous driving applications, which may jeopardize car driving safety. Therefore, it is extremely important to instantly remove the haze effect on an image. The purpose of this study is to leverage useful modules to achieve a lightweight and real-time image-dehazing model. Based on the U-Net architecture, this study integrates four modules, including an image pre-processing block, inception-like blocks, spatial pyramid pooling blocks, and attention gates. The original attention gate was revised to fit the field of image dehazing and consider different color spaces to retain the advantages of each color space. Furthermore, using an ablation study and a quantitative evaluation, the advantages of using these modules were illustrated. Through existing indoor and outdoor test datasets, the proposed method shows outstanding dehazing quality and an efficient execution time compared to other state-of-the-art methods. This study demonstrates that the proposed model can improve dehazing quality, keep the model lightweight, and obtain pleasing dehazing results. A comparison to existing methods using the RESIDE SOTS dataset revealed that the proposed model improves the SSIM and PSNR metrics by at least 5–10%.
Semi-Supervised Medical Image Classification Based on Attention and Intrinsic Features of Samples
Authors: Zhou Zhuohao , Lu Chunyue , Wang Wenchao , Dang Wenhao , Gong Ke
Published: July 2022

The training of deep neural networks usually requires a lot of high-quality data with good annotations to obtain good performance. However, in clinical medicine, obtaining high-quality marker data is laborious and expensive because it requires the professional skill of clinicians. In this paper, based on the consistency strategy, we propose a new semi-supervised model for medical image classification which introduces a self-attention mechanism into the backbone network to learn more meaningful features in image classification tasks and uses the improved version of focal loss at the supervision loss to reduce the misclassification of samples. Finally, we add a consistency loss similar to the unsupervised consistency loss to encourage the model to learn more about the internal features of unlabeled samples. Our method achieved 94.02% AUC and 72.03% Sensitivity on the ISIC 2018 dataset and 79.74% AUC on the ChestX-ray14 dataset. These results show the effectiveness of our method in single-label and multi-label classification.
Preprocessing Acoustic Emission Signal of Broken Wires in Bridge Cables
Authors: Li Guangming , Zhao Zhen , Li Yaohan , Li Chun-Yin , Lee Chi-Chung
Published: July 2022

Bridges, especially cable-stayed bridges, play an important role in modern transportation systems. The safety status of bridge cables, as an important component of cable-stayed bridges, determines the health status of the entire bridge. As a non-destructive real-time detection technology, acoustic emission has the advantages of high detection efficiency and low cost. This paper focuses on the issue that a large amount of data are generated during the process of health monitoring of bridge cables. A novel acoustic emission signal segmentation algorithm is proposed with the aim to facilitate the extraction of acoustic emission signal characteristics. The proposed algorithm can save data storage space efficiently. Moreover, it can be adapted to different working conditions according to the adjustment of parameters in order to accurately screen out the target acoustic emission signal. Through the acoustic emission signal acquisition experiments of three bridges, the characteristics of the noise signal in the acquisition process are extracted. A comprehensive analysis of the signal in the time domain, frequency domain and time-frequency domain is carried out. The noise signal filtering parameter thresholds are proposed according to the analysis results.
Study the Effect of eHMI Projection Distance and Contrast on People Acceptance in Blind-Spot Detection Scenario
Authors: Shah Ali Hassan , Sun Xiaodong , Lin Yandan
Published: July 2022

External human-machine interaction (eHMI) road projections are a new feature for automotive lighting to improve vehicle communication with other road users. These modalities are used to draw users’ attention and awareness to specific situations. However, such advanced capabilities are still being debated to be used on the road in the context of whether or not such road projections can provide a clear and understandable message to road users in a specific scenario or lead to anticipation and change in the driving behavior. Therefore, it is necessary to investigate human factors aspects, such as the feeling of safety, useability, understanding, acceptability, and driver behavior. This study investigates the change in distance and luminance contrast and its effect on human driving behavior and acceptability in blind spot detection scenarios on the highway. A lab experiment with 12 participants is performed to analyze: understanding, satisfaction, usability, visibility, safety, workload, and driving behavior towards eHMI projection while varying projecting distance and luminance contrast. Video recordings and a designed questionnaire were used during the whole process. Results show that ego vehicle drivers prefer a projection distance between 5 to 10 m. However, a distance of 5 m is preferred by overtaking vehicle drivers in terms of visibility and safety. Luminance contrasts have no significant effect on the symbol’s visibility in 5 m and 10 m projection distances. In contrast, participants in overtaking vehicles feel difficult to understand the situation for 15 m condition, which increases their overall workload significantly (p < 0.019). No significant effect is recorded in terms of change in driving behavior.
Use and Management in the Heritage Conservation of the Historic Water Supply of Canal de Isabel II, Madrid
Authors: Bernabéu-Larena Jorge , Cabau-Anchuelo Beatriz , Plasencia-Lozano Pedro , Hernández-Lamas Patricia
Published: July 2022

The historic water supply to large cities constitutes a constructed heritage characterised by comprising a range of public structures—dams, canals, tanks, siphons and aqueducts—over a large geographical area. Within this international context, this paper looks at the case of Canal de Isabel II (CYII) and its historic infrastructure, built in the second half of the 19th century and the early 20th century. The purpose of this study is to analyse how these water supply public works, which maintain their original use, have also taken on new functions through the conversion of some of their parts and added new values to the existing ones. In order to do this, an inventory was drawn up with the location and cultural value of each structure based on its historic, technological, landscape and symbolic features, as well as its use. The results establish the significance of the overall system, not only in functional terms but also as a cultural resource. It is essential to understand the historic water supply infrastructure as a whole, not just as individual components but rather as pieces of a network. This is also essential for the management and preservation of the system, both where the structures are still in use as part of the water supply and where they have been converted for other uses.