bluuebunny/crossref_metadata_2025 huggingface dataset:
A Robust Technique for Polymer Damping Identification Using Experimental Transmissibility Data
Authors: Brun Mikel , Cortés Fernando , García-Barruetabeña Jon , Sarría Imanol , Elejabarrieta María Jesús
Published: June 2022

This paper presents a robust method to estimate polymers’ damping, based on modal identification methods on frequency functions. The proposed method presents great advantages compared to other traditional methods such as the HPB method for polymeric materials where high damping or noise levels can limit their use. Specifically, this new method is applied on an experimental transmissibility function measured in a composite cantilever beam and the complex modulus is determined as a function of frequency. From this, a regenerated function is obtained based on the Euler–Bernoulli beam theory, and it is compared with experimental data. It can be concluded that the best way to apply the curve-fitting method for further testing of polymeric materials is when it is used with the whole frequency range by means of the MDOF method considering the residuals. In addition, this has the added advantage that the number of experimental tests to be carried out is much lower compared to using the SDOF method.
Influence of Brown’s Gas on Cracking Behavior of Gas-Phase Tar during Pine Wood Pyrolysis
Authors: Yang Shuo , Fu Yudong , Cui Jie , Liu Zhanzhi , Qin Daocheng , Xu Lin , Xu Youning
Published: June 2022

The effect of Brown’s gas on the gas-phase tar cracking behavior, carbonic oxide (CO) production rate, and gaseous product temperature during the pine wood pyrolysis was preliminarily explored. By the application of cold trapping and gravimetric methods, it was found that Brown’s gas reduces the energy barrier of thermochemical conversion for gas-phase tar, widens the temperature range of gas-phase tar accelerated cracking, and increases the cracking rate. When the pyrolysis temperature increases by 1 °C, the average cracking rate of gas-phase tar increases from C = 4.58 g⋅Nm−3 (flow volume ratio of Brown’s gas to nitrogen, X(Brown’s gas):N2 = 0%) to C = 4.8 g⋅Nm−3 (X:N2 = 1%) and C = 5.02 g⋅Nm−3 (X:N2 = 5%). While participating in the deep cracking of gas-phase tar, Brown’s gas reduces the conversion energy barrier of the gas-phase tar to CO. The CO production rate rises from the initial 1.87% (X:N2 = 0%) to 4.22% (X:N2 = 1%) and 5.52% (X:N2 = 5%) per 1 °C of increased pyrolysis temperature. The consumption of Brown’s gas is 0.32 m3 per 1 g⋅Nm−3 of gas-phase tar cracking within the pyrolysis residence time of 30 min.
A Digital Process for Manufacturing Customized Trays for Dental-Whitening Treatments
Authors: Tamburrino Francesco , Aruanno Beatrice , Razionale Armando V. , Barone Sandro , Martini Marco , Bordegoni Monica
Published: June 2022

This study presents an alternative process for designing and manufacturing customized trays for dental-whitening treatments. The process is based on a digitized approach consisting of three main stages: design of a reference model, its manufacturing by AM, and thermoforming of the tray. The aim of the study was to develop a high-performance tray, able to guarantee comfort, safety, and efficacy for whitening treatments. To evaluate the patient’s experience, some tests under real operating conditions were performed. Twenty people carried out a nighttime treatment of 14 days. Each patient was asked to assess the overall level of satisfaction and the comfort of the tray and its ability to retain the gel. Tooth whitening was also determined according to the VITAPAN scale. All patients involved in the study were satisfied and provided positive feedback about comfort and tightness of the tray. At the end of the treatment, 15 out of 20 patients achieved shade A1 on the VITAPAN scale. The mean improvement in color shades was about 7. These results confirmed the great potential of the proposed dental tray. Its use was proven to guarantee a high level of quality, flexibility, and customization of dental-whitening treatments, improving comfort, safety, and efficacy.
Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis
Authors: Fatima Syeda , Abbas Manzar , Mir Imran , Gul Faiza , Mir Suleman , Saeed Nasir , Alotaibi Abdullah , Althobaiti Turke , Abualigah Laith
Published: June 2022

Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.
Neutronic Study on Ac-225 Production for Cancer Therapy by (n,2n) Reaction of Ra-226 or Th-230 Using Fast Reactor Joyo
Authors: Iwahashi Daiki , Kawamoto Kota , Sasaki Yuto , Takaki Naoyuki
Published: June 2022

Ac-225 has lately drawn considerable attention as a radioisotope for targeted alpha therapy treatment for certain types of prostate, blood-derived, and disseminated cancers, but its supply is limited. Therefore, we investigated the production method of Ac-225 by nuclear transmutation in a fast neutron reactor. The authors investigated irradiation of Ra-226 or Th-230 as a target nuclide in the experimental fast reactor Joyo, owned and operated by Japan Atomic Energy Agency, which has abundant fast neutrons and a large loading region with high heat removal capacity. Ra-226 is in increasing demand as a target nuclide to produce Ac-225. Therefore, as another option, we selected Th-230, which is 50 times more abundant than Ra-226 in natural uranium, as an alternative nuclide. Irradiation of Ra-226 and Th-230 with high energy neutrons above the threshold causes an (n,2n) reaction, producing Ra-225 and Th-229, respectively, which are the parent nuclides of Ac-225. The analyses showed that 47 GBq of Ac-225 can be generated annually by irradiating 1 g of Ra-226, and 6.5 GBq of Ac-225 can be semi-permanently generated every year by one-time irradiation of 50 g of Th-230 for 10 years (5 EFPY). It can be concluded that 100 MWt Joyo has potential to produce more than 70% of the current global supply of Ac-225 and/or to generate the parent nuclide Th-229, which keeps producing Ac-225 for thousands of years.
Absent in Body, Present in Spirit: Apostolic Iconography in Greek Byzantine New Testament Manuscripts
Authors: Soon Isaac T.
Published: June 2022

This article analyzes the phenomena that arise when the images of New Testament authors are placed before, alongside, and within the titles and incipits of New Testament texts in ancient manuscripts. Such images facilitate encounters with “specters” of the authors, invoking their bodily presence in the absence of their physical body. They are encodings of collective memory but also participants in perpetuating and sometimes modifying the physical appearance of apostolic figures. On occasion, the blending of textual incipits with apostolic images sublimate authorial identity and textual identity; the bodies of apostles become frames through which to view their written works. Although they are paratexts, apostolic icons can rearrange and aggregate other paratextual features including titles and even Euthaliana. Images of the apostles further interact with anonymous features of NT manuscripts, such as Euthaliana, providing authorization for works without ascription in the manuscripts themselves. Images of the apostles in NT manuscripts are therefore more than decoration or pious creativity. They are loci of presence, identity, memory, and authority.
Pleasure and Poetics as Tools for Transformation in Aśvaghoṣa’s mahākāvya
Authors: Regan Julie
Published: June 2022

Why does Aśvaghoṣa (c. second century C.E.), the first known author of a Buddhist literary work, choose a literary genre (mahākāvya) with erotic scenes and elaborate poetic language to present the truth that leads to liberation? This question, which has puzzled and fascinated scholars since the first known translations of Buddhacarita and Saundarananda, is often answered by turning to a statement Aśvaghoṣa makes, which suggests that such methods are necessary to reach his worldly audience, who are interested only in pleasure and not liberation. Dismissed as mere sugarcoating for “the bitter truth” of the Buddhist doctrine, the impact of the pleasures and poetics of Aśvaghoṣa’s work upon the reader has rarely been explored. Methods emphasizing a hermeneutic approach to scholarship, focused on interpreting what such works have to say, has meant less attention to what these works do to transform readers (their poetics). However, new attention to the literary aspects of Aśvaghoṣa’s mahākāvya, a genre of long-form narrative literature known for its poetic features, as well as recent scholarship on the Sanskrit courtly culture for which it was produced, suggest pleasure is a central feature. In this article I argue that comparative analysis of the dramatic structure of Buddhacarita and Saundarananda demonstrates that Aśvaghoṣa uses his ability as a dramatist to employ rasa, pleasurable aesthetic experiences, staged to gradually transform the minds of readers. I argue that as the plots of Buddhacarita and Saundarananda unfold, and the Buddha and his brother Nanda go from erotic and ascetic scenes to the sites of liberation, readers are engaged and moved in ways that refine their perceptions, introducing forms of concentration and insight not unlike the Buddhist practices depicted in these works.
View-Agnostic Point Cloud Generation for Occlusion Reduction in Aerial Lidar
Authors: Singer Nina , Asari Vijayan K.
Published: June 2022

Occlusions are one of the leading causes of data degradation in lidar. The presence of occlusions reduces the overall aesthetic quality of a point cloud, creating a signature that is specific to that viewpoint and sensor modality. Typically, datasets consist of a series of point clouds with one type of sensor and a limited range of viewpoints. Therefore, when training a dataset with a particular signature, it is challenging to infer scenes outside of the original range of the viewpoints from the training dataset. This work develops a generative network that can predict the area in which an occlusion occurs and furnish the missing points. The output is a complete point cloud that is a more general representation and agnostic to the original viewpoint. We can then use the resulting point cloud as an input for a secondary method such as semantic or instance segmentation. We propose a learned sampling technique that uses the features to inform the point sampling instead of relying strictly on spatial information. We also introduce a new network structure that considers multiple point locations and augmentations to generate parallel features. The network is tested against other methods using our aerial occlusion dataset, DALES Viewpoints Version 2, and also against other point cloud completion networks on the Point Cloud Network (PCN) dataset. We show that it reduces occlusions visually and outperforms state-of-the-art point cloud completion networks in both Chamfers and Earth Mover’s Distance (EMD) metrics. We also show that using our occlusion reduction method as a pre-processing step improves semantic segmentation results compared to the same scenes processed without using our method.
A Lightweight Self-Supervised Representation Learning Algorithm for Scene Classification in Spaceborne SAR and Optical Images
Authors: Xiao Xiao , Li Changjian , Lei Yinjie
Published: June 2022

Despite the increasing amount of spaceborne synthetic aperture radar (SAR) images and optical images, only a few annotated data can be used directly for scene classification tasks based on convolution neural networks (CNNs). For this situation, self-supervised learning methods can improve scene classification accuracy through learning representations from extensive unlabeled data. However, existing self-supervised scene classification algorithms are hard to deploy on satellites, due to the high computation consumption. To address this challenge, we propose a simple, yet effective, self-supervised representation learning (Lite-SRL) algorithm for the scene classification task. First, we design a lightweight contrastive learning structure for Lite-SRL, we apply a stochastic augmentation strategy to obtain augmented views from unlabeled spaceborne images, and Lite-SRL maximizes the similarity of augmented views to learn valuable representations. Then, we adopt the stop-gradient operation to make Lite-SRL’s training process not rely on large queues or negative samples, which can reduce the computation consumption. Furthermore, in order to deploy Lite-SRL on low-power on-board computing platforms, we propose a distributed hybrid parallelism (DHP) framework and a computation workload balancing (CWB) module for Lite-SRL. Experiments on representative datasets including OpenSARUrban, WHU-SAR6, NWPU-Resisc45, and AID dataset demonstrate that Lite-SRL can improve the scene classification accuracy under limited annotated data, and it is generalizable to both SAR and optical images. Meanwhile, compared with six state-of-the-art self-supervised algorithms, Lite-SRL has clear advantages in overall accuracy, number of parameters, memory consumption, and training latency. Eventually, to evaluate the proposed work’s on-board operational capability, we transplant Lite-SRL to the low-power computing platform NVIDIA Jetson TX2.
Increased Compound Droughts and Heatwaves in a Double Pack in Central Asia
Authors: Wang Chuan , Li Zhi , Chen Yaning , Li Yupeng , Liu Xigang , Hou Yifeng , Wang Xuechun , Kulaixi Zulipiya , Sun Fan
Published: June 2022

Compound droughts and heatwaves (CDHWs) are likely to cause more severe natural disasters than a single extreme event, and they have been exacerbated by rapid global warming. Based on high-resolution grid data, this study combines the daily-scale ERA5-Land dataset and the monthly-scale SPEI dataset with multiple indicators to analyze CDHWs. We calculated and analyzed the temporal and spatial modal distribution of CDHWs in Central Asia from 1981 to 2018, and in this paper, we discuss the sequence relationship between drought events, heatwave events, and CDHWs. The results show that the number of CDHWs in the study region have increased over time and expanded in terms of area, especially in eastern and southwestern Central Asia. The tsum (total frequency of CDHWs) was 0.5 times higher than the total heatwave frequency and it increased at a rate of 0.17/yr. The maximum duration of tmax (maximum duration of CDHWs in days) was 17 days. Furthermore, the occurrence rate of tmax was 96.67%, and the AH (CDHWs’ accumulated heat) had a rate of 97.78%, which, upon examination of the spatial trend pattern, accounted for the largest increase in terms of area. We also found that the TAH (CDHWs’ average temperature anomalies, SPEI < −0.5) shows obvious seasonality, with the increases in winter and spring being significantly greater than the increases in summer and autumn. The intensity of the CDHWs was stronger than that of a single extreme event, the temperature anomaly was higher than the average of 0.4–0.8 °C, and there was a north–south spatial pattern across the study region. In eastern and northwestern Central Asia, the AH and heatwaves (SPEI < −0.5) increased by 15–30 times per year on average. During the transition from the base period to the reference period, CDHWs increased by 25%, and the number of dry days prior to the CDHWs decreased by 7.35 days. The conclusion of our study can provide a theoretical basis for coping with climate change in arid zones.
Coastal Erosion and Flooding Threaten Low-Lying Coastal Tracts at Lipari (Aeolian Islands, Italy)
Authors: Romagnoli Claudia , Bosman Alessandro , Casalbore Daniele , Anzidei Marco , Doumaz Fawzi , Bonaventura Fabiana , Meli Matteo , Verdirame Carmelo
Published: June 2022

Lipari is the largest and most populated island in the Aeolian Archipelago, a UNESCO site, and a highly frequented touristic destination. As in many other insular settings, the low-lying coastal stretches in the E and NE sectors of Lipari are locally exposed to coastal erosion and flooding, enhanced by subsidence effects leading to local sea level rise. Most of these coastal sectors appear critical, being narrow and increasingly threatened by the risk of permanent inundation and beach disappearance. In this study, this setting is placed in the wider context of the decadal evolution of the main beaches, analysed through a multidisciplinary approach, which includes remote sensing techniques (aero-photogrammetry, unmanned aerial vehicle survey, and satellite data), offshore geophysical surveys (high-resolution multibeam bathymetry), and field observations. The results show a variable interaction in space and time between natural and anthropogenic factors in the long- and mid-term evolution of the studied coastal areas. Considering that part of the local economy at Lipari depends on beach tourism, proper future management is required in the view of natural risk reduction and in the light of future climate changes and related impacts.
Changes in Land Relief in Urbanised Areas Using Laser Scanning and Archival Data on the Example of Łódź (Poland)
Authors: Jaskulski Marcin , Jażdżewska Iwona , Szmidt Aleksander
Published: June 2022

The authors undertook to examine and analyse the changes in the relief in Łódź city centre over a period of over one hundred years. Archival cartographic resources containing morphometric information and contemporary laser scanning data (LIDAR) are used to analyse changes. This required appropriate transformation of these data to generate a differential relief map. Information on the geographical environment (waters, relief) is linked to the spatial development of the city. The analyses revealed several characteristic types of changes occurring in the area, which are presented in the form of case studies.
Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images
Authors: da Silva Fabiano G. , Ramos Lucas P. , Palm Bruna G. , Machado Renato
Published: June 2022

This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.
Study on the Uncertainty of Machine Learning Model for Earthquake-Induced Landslide Susceptibility Assessment
Authors: Feng Haixia , Miao Zelang , Hu Qingwu
Published: June 2022

The landslide susceptibility assessment based on machine learning can accurately predict the probability of landslides happening in the region. However, there are uncertainties in machine learning applications. In this paper, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are used to assess the landslide susceptibility in order to discuss the model uncertainty. The model uncertainty is explained in three ways: landslide susceptibility zoning result, risk area (high and extremely high) statistics, and the area under Receiver Operating Characteristic Curve (ROC). The findings indicate that: (1) Landslides are restricted by influence factors and have the distribution law of relatively concentrated and strip-shaped distribution in space. (2) The percentage of real landslide in risk area is 86%, 87%, 82%, and 61% in SVM, RF, LR, and ANN, respectively. The area under ROC of RF, SVM, LR, and ANN, respectively, is 90.92%, 80.45%, 73.75%, and 71.95%. (3) Compared with the prediction accuracy of the training set and test set from the same earthquake, the accuracy of landslide prediction in the different earthquakes is reduced.
Assessing the Applications of Earth Observation Data for Monitoring Artisanal and Small-Scale Gold Mining (ASGM) in Developing Countries
Authors: Moomen Abdul-Wadood , Lacroix Pierre , Benvenuti Antonio , Planque Marion , Piller Thomas , Davis Kenneth , Miranda Manoela , Ibrahim Elsy , Giuliani Gregory
Published: June 2022

This paper discusses opportunities to use remote sensing (RS) technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining (ASGM) sector. The paper uses a systematic literature review to identify, analyze, and synthesize various uses of RS on the detection and monitoring of ASGM activities across the globe. The study covers the use of spaceborne sensors and available opportunities for data access and processing and emphasizes the important role that freely-available data has played in understanding ASGM activities. It discusses applications and opportunities offered in assessing the geospatial and temporal characteristics of ASGM and its impacts on the surrounding environment. Furthermore, it examines different indicators for the detection of ASGM in the landscape. Finally, technological capabilities described in the study are illustrated with case studies in the Democratic Republic of Congo and in Colombia using cloud computing with the Open Data Cube. The case studies demonstrate the identification and quantification of impacts of ASGM activities on land degradation and water turbidity in remote areas and results are dissiminated using the MapX platform. This facilitates policy development, implementation, and evaluation in the ASGM context.
Simulation and Analysis of an FMCW Radar against the UWB EMP Coupling Responses on the Wires
Authors: Chen Kaibai , Liu Shaohua , Gao Min , Zhou Xiaodong
Published: June 2022

An ultra-wideband electromagnetic pulse (UWB EMP) can be coupled to an FMCW system through metal wires, causing electronic equipment disturbance or damage. In this paper, a hybrid model is proposed to carry out the interference analysis of UWB EMP coupling responses on the wires to the FMCW radar. First, a field simulation model of the radar is constructed and the wire coupling responses are calculated. Then, the responses are injected into an FMCW circuit model via data format modification. Finally, we use the FFT transform to identify the spectral peak of the intermediate frequency (IF) output signal, which corresponds to the radar’s detection range. The simulation results show that the type of metal wire has the greatest influence on the amplitude of coupling responses. The spectral peak of the IF output changes to the wrong frequency with the increase of injection power. Applying interference at the end of the circuit can more effectively interfere with the detection of the radar. The investigation provides a theoretical basis for the electromagnetic protection design of the radar.
Chlorine Gas Sensor with Surface Temperature Control
Authors: Krajewski Andrzej , Houshyar Shadi , Wang Lijing , Padhye Rajiv
Published: June 2022

The work describes the design, manufacturing, and user interface of a thin-film gas transducer platform that is able to provide real-time detection of toxic vapor. This proof-of-concept system has applications in the field of real-time detection of hazardous gaseous agents that are harmful to the person exposed to the environment. The small-size gas sensor allows for integration with an unmanned aerial vehicle, thus combining high-level mobility with the ability for the real-time detection of hazardous/toxic chemicals or use as a standalone system in industries that deal with harmful gaseous substances. The sensor was designed based on the ability of thin-film metal oxide sensors to detect chlorine gas in real time. Specifically, a concentration of 10 ppm of Cl2 was tested.
Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain
Authors: Jeong Yoon-Su
Published: June 2022

Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations’ operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average.
A Single-Phase High-Impedance Ground Faulty Feeder Detection Method for Small Resistance to Ground Systems Based on Current-Voltage Phase Difference
Authors: Hou Zequan , Zhang Zhihua , Wang Yizhao , Duan Jiandong , Yan Wanying , Lu Wenchao
Published: June 2022

At present, the small resistance to ground system (SRGS) is mainly protected by fixed-time zero-sequence overcurrent protection, but its ability to detect transition resistance is only about 100 Ω, which is unable to detect single-phase high resistance grounding fault (SPHIF). This paper analyzes the zero-sequence characteristics of SPHIF for SRGS and proposes a SPHIF feeder detection method that uses the current–voltage phase difference. The proposed method is as follows: first, the zero-sequence current phase of each feeder is calculated. Second, the phase voltage root mean square (RMS) value is used to determine the fault phase and obtain its initial phase as the reference value. The introduction of the initial phase of the fault phase voltage can highlight the fault characteristics and improve the sensitivity and reliability of feeder detection, and then CVPD is the difference between each feeder ZSC phase and the reference value. Finally, the magnitude of CVPD is judged. If the CVPD of a particular feeder meets the condition, the feeder is detected as the faulted feeder. Combining the theoretical and practical constraints, the specific adjustment principle and feeder detection logic are given. A large number of simulations show that the proposed method can be successfully detected under the conditions of 5000 Ω transition resistance, –1 dB noise interference, and 40% data missing. Compared with existing methods, the proposed method uses phase voltages that are easy to measure to construct SPHIF feeder detection criteria, without adding additional measurement and communication devices, and can quickly achieve local isolation of SPHIF with better sensitivity, reliability, and immunity to interference.
Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR
Authors: Li Zebin , Gao Lifu , Lu Wei , Wang Daqing , Cao Huibin , Zhang Gang
Published: June 2022

During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc.
Conceptual Modeling of Extended Collision Warning System from the Perspective of Smart Product-Service System
Authors: Wu Chunlong , Lv Hanyu , Zhu Tianming , Liu Yunhe , Pessôa Marcus Vinicius Pereira
Published: June 2022

While Product-Service Systems (PSS) have a potential sustainability impact by increasing a product’s life and reducing resource consumption, the lack of ownership might lead to less responsible user behavior. Smart PSS can overcome this obstacle and guarantee correct and safe PSS use. In this context, intelligent connected vehicles (ICVs) with PSS can effectively reduce traffic accidents and ensure the safety of vehicles and pedestrians by guaranteeing optimal and safe vehicle operation. A core subsystem to support that is the collision-warning system (CWS). Existing CWSs are, however, limited to in-car warning; users have less access to the warning information, so the result of CWS for collision avoidance is insufficient. Therefore, CWS needs to be extended to include more elements and stakeholders in the collision scenario. This paper aims to provide a novel understanding of extended CWS (ECWS), outline the conceptual framework of ECWS, and contribute a conceptual modeling approach of ECWS from the smart PSS perspective at the functional level. It defines an integrated solution of intelligent products and warning services. The function is modeled based on the Theory of Inventive Problem Solving (TRIZ). Functions of an ECWS from the perspective of smart PSS can be comprehensively expressed to form an overall solution of integrated intelligent products, electronic services, and stakeholders. Based on the case illustration, the proposed method can effectively help function modeling and development of the ECWS at a conceptual level. This can effectively avoid delays due to traffic accidents and ensure the safety of vehicles and pedestrians.
Variable Optimization of Seaweed Spectral Response Characteristics and Species Identification in Gouqi Island
Authors: Chen Jianqu , Li Xunmeng , Wang Kai , Zhang Shouyu , Li Jun , Zhang Jian , Gao Weicheng
Published: June 2022

Probing the coverage and biomass of seaweed is necessary for achieving the sustainable utilization of nearshore seaweed resources. Remote sensing can realize dynamic monitoring on a large scale and the spectral characteristics of objects are the basis of remote sensing applications. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions from the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Sargassum fusifarme. The different seaweed spectra were identified and analyzed using a combination of one-way analysis of variance (ANOVA), support vector machines (SVM), and a fusion model comprising extreme gradient boosting (XGBoost) and SVM. In total, 14 common spectral variables were used as input variables, and the input variables were filtered by one-way ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a ratio of 3:1 for input into the SVM and fusion model. The results showed that when the input variables were the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), Vre, Abe, Rg, Lre, Lg, and Lr and the model parameters were g = 1.30 and c = 2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.99%, and the highest accuracy was 93.94% when distinguishing between the different seaweed phyla (g = 6.85 and c = 2.55). The classification of the fusion model also shows similar results: The overall accuracy is 73.98%, and the mean score of the different seaweed phyla is 97.211%. In this study, the spectral data of intertidal seaweed with different dry and wet states were classified to provide technical support for the monitoring of coastal zones via remote sensing and seaweed resource statistics.
PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation
Authors: Han Jubao , Xu Chao , An Ziheng , Qian Kai , Tan Wei , Wang Dou , Fang Qianqian
Published: June 2022

In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural network (parallel residual atrous pyramid network or PRAPNet) based on a parallel residual atrous pyramid module for the segmentation of intestinal polyp detection. We made full use of the global contextual information of the different regions by the proposed parallel residual atrous pyramid module. The experimental results showed that our proposed global prior module could effectively achieve better segmentation results in the intestinal polyp segmentation task compared with the previously published results. The mean intersection over union and dice coefficient of the model in the Kvasir-SEG dataset were 90.4% and 94.2%, respectively. The experimental results outperformed the scores achieved by the seven classical segmentation network models (U-Net, U-Net++, ResUNet++, praNet, CaraNet, SFFormer-L, TransFuse-L).
A Virtual Combustion Sensor Based on Ion Current for Lean-Burn Natural Gas Engine
Authors: Wang Xiaoyan , Zhou Tanqing , Dong Quan , Cheng Zhaolin , Yang Xiyu
Published: June 2022

In this study, an innovative sensor was designed to detect the key combustion parameters of the marine natural gas engine. Based on the ion current, any engine structurally modified was avoided and the real-time monitoring for the combustion process was realized. For the general applicability of the proposed sensor, the ion current generated by a high-energy ignition system was acquired in a wide operating range of the engine. It was found that engine load, excess air coefficient (λ) and ignition timing all generated great influence on both the chemical and thermal phases, which indicated that the ion current was highly correlated with the combustion process in the cylinder. Furthermore, the correlations between the 5 ion current-related parameters and the 10 combustion-related parameters were analyzed in detail. The results showed that most correlation coefficients were relatively high. Based on the aforementioned high correlation, the novel sensor used an on-line algorithm at the basis of neural network models. The models took the characteristic values extracted from the ion current as the inputs and the key combustion parameters as the outputs to realize the online combustion sensing. Four neural network models were established according to the existence of the thermal phase peak of the ion current and two different network structures (BP and RBF). Finally, the predicted values of the four models were compared with the experimental values. The results showed that the BP (with thermal) model had the highest prediction accuracy of phase parameters and amplitude parameters of combustion. Meanwhile, RBF (with thermal) model had the highest prediction accuracy of emission parameters. The mean absolute percentage errors (MAPE) were mostly lower than 0.25, which proved a high accuracy of the proposed ion current-based virtual sensor for detecting the key combustion parameters.
Measuring High Dynamic Range Spectral Reflectance of Artworks through an Image Capture Matrix Hyperspectral Camera
Authors: Gómez Manzanares Ángela , Vázquez Moliní Daniel , Alvarez Fernandez-Balbuena Antonio , Mayorga Pinilla Santiago , Martínez Antón Juan Carlos
Published: June 2022

Commercial hyperspectral imaging systems typically use CCD or CMOS sensors. These types of sensors have a limited dynamic range and non-linear response. This means that when evaluating an artwork under uncontrolled lighting conditions and with light and dark areas in the same scene, hyperspectral images with underexposed or saturated areas would be obtained at low or high exposure times, respectively. To overcome this problem, this article presents a system for capturing hyperspectral images consisting of a matrix of twelve spectral filters placed in twelve cameras, which, after processing these images, makes it possible to obtain the high dynamic range image to measure the spectral reflectance of the work of art being evaluated. We show the developed system and describe all its components, calibration processes, and the algorithm implemented to obtain the high dynamic range spectral reflectance measurement. In order to validate the system, high dynamic range spectral reflectance measurements from Labsphere’s Spectralon Reflectance Standards were performed and compared with the same reflectance measurements but using low dynamic range images. High dynamic range hyperspectral imaging improves the colorimetric accuracy and decreases the uncertainty of the spectral reflectance measurement based on low dynamic range imaging.
Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems
Authors: Li Xianqing , Duan Zhansheng , Tang Qi , Mallick Mahendra
Published: June 2022

The performance evaluation of state estimators for nonlinear regular systems, in which the current measurement only depends on the current state directly, has been widely studied using the Bayesian Cramér-Rao lower bound (BCRLB). However, in practice, the measurements of many nonlinear systems are two-adjacent-states dependent (TASD) directly, i.e., the current measurement depends on the current state as well as the most recent previous state directly. In this paper, we first develop the recursive BCRLBs for the prediction and smoothing of nonlinear systems with TASD measurements. A comparison between the recursive BCRLBs for TASD systems and nonlinear regular systems is provided. Then, the recursive BCRLBs for the prediction and smoothing of two special types of TASD systems, in which the original measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the proposed recursive BCRLBs for the prediction and smoothing of TASD systems.
Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)
Authors: Post Claudia , Heyden Niklas , Reinartz André , Foerderer Aaron , Bruelisauer Simon , Linnemann Volker , Hug William , Amann Florian
Published: June 2022

The entire water cycle is contaminated with largely undetected micropollutants, thus jeopardizing wastewater treatment. Currently, monitoring methods that are used by wastewater treatment plants (WWTP) are not able to detect these micropollutants, causing negative effects on aquatic ecosystems and human health. In our case study, we took collective samples around different treatment stages (aeration tank, membrane bioreactor, ozonation) of a WWTP and analyzed them via Deep-UV laser-induced Raman and fluorescence spectroscopy (LIRFS) in combination with a CNN-based AI support. This process allowed us to perform the spectra recognition of selected micropollutants and thus analyze their reliability. The results indicated that the combination of sensitive fluorescence measurements with very specific Raman measurements, supplemented with an artificial intelligence, lead to a high information gain for utilizing it as a monitoring purpose. Laser-induced Raman spectroscopy reaches detections limits of alert pharmaceuticals (carbamazepine, naproxen, tryptophan) in the range of a few µg/L; naproxen is detectable down to 1 × 10−4 mg/g. Furthermore, the monitoring of nitrate after biological treatment using Raman measurements and AI support showed a reliable assignment rate of over 95%. Applying the fluorescence technique seems to be a promising method in observing DOC changes in wastewater, leading to a correlation coefficient of R2 = 0.74 for all samples throughout the purification processes. The results also showed the influence of different extraction points in a cleaning stage; therefore, it would not be sensible to investigate them separately. Nevertheless, the interpretation suffers when many substances interact with one another and influence their optical behavior. In conclusion, the results that are presented in our paper elucidate the use of LIRFS in combination with AI support for online monitoring.
Stroke Risk Prediction with Machine Learning Techniques
Authors: Dritsas Elias , Trigka Maria
Published: June 2022

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.
Side Channel Analysis of SPECK Based on Transfer Learning
Authors: Zhang Qingqing , Zhang Hongxing , Cui Xiaotong , Fang Xing , Wang Xingyang
Published: June 2022

Although side-channel attacks based on deep learning are widely used in AES encryption algorithms, there is little research on lightweight algorithms. Lightweight algorithms have fewer nonlinear operations, so it is more difficult to attack successfully. Taking SPECK, a typical lightweight encryption algorithm, as an example, directly selecting the initial key as the label can only crack the first 16-bit key. In this regard, we evaluate the leakage of SPECK’s operations (modular addition, XOR, shift), and finally select the result of XOR operation as the label, and successfully recover the last 48-bit key. Usually, the divide and conquer method often used in side-channel attacks not only needs to train multiple models, but also the different bytes of the key are regarded as unrelated individuals. Through the visualization method, we found that different key bytes overlap in the position of the complete electromagnetic leakage signal. That is, when SPECK generates a round key, there is a connection between different bytes of the key. In this regard, we propose a transfer learning method for different byte keys. This method can take advantage of the similarity of key bytes, improve the performance starting-point of the model, and reduce the convergence time of the model by 50%.
Concrete Curing Performance Assessment Based on Gas Permeability Testing in the Lab and on Site
Authors: Ptacek Lisa , Strauss Alfred , Bos Clémence , Peyerl Martin , Torrent Roberto
Published: June 2022

Durability is an essential aspect of the lifetime performance of concrete components. The adequate surface quality and thus the service life of concrete can be achieved, among other things, by appropriate curing during hydration. To measure and control the curing quality, appropriate procedures are required. Gas permeability allows conclusions to be drawn about the porosity of concrete, which has a significant impact on durability. In this contribution, the effect of different curing methods on gas permeability is presented with the help of laboratory and on-site tests, showing that inadequate curing leads to increased permeability in the near-surface area of concrete. The measurement results of concrete samples and components with the same composition but varying curing treatment are compared and evaluated. Influences such as concrete composition and environmental factors on the quality of concrete are observed, and recommendations are made for a reliable assessment of the surface quality as a result of the investigated curing measures.