bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Volatile Constituents Present in Commercial and Lab-Distilled Frankincense (Boswellia carteri) Essential Oils for Authentication
Authors: Ojha Pawan Kumar , Poudel Darbin Kumar , Rokaya Anil , Satyal Rakesh , Setzer William N. , Satyal Prabodh
Published: August 2022

A comparative analysis of the chemical constituents present in twenty-one commercial and two lab-distilled frankincense (Boswellia carteri) essential oils was carried out using gas chromatography-mass spectrometry (GC-MS) and chiral gas chromatography-mass spectrometry (CGC-MS) for authentication. Out of the twenty-one commercial samples, six were adulterated with synthetic limonene, three were contaminated with synthetic octyl acetate, three were adulterated with castor oil, and two samples each were contaminated with frankincense resin and Boswellia occulta species, respectively, and one was contaminated with the Boswellia serrata species. Additionally, one sample was contaminated with phthalates as well as a cheap essential oil with similar compositions. Furthermore, one sample was adulterated with copaiba resin and frankincense resin in combination with synthetic octyl acetate. Additionally, one was contaminated with Boswellia serrata species, which was further adulterated with castor oil and frankincense resin. To the best of our knowledge, this is the first report to compare the enantiomeric distribution of chiral terpenoids present in commercial frankincense essential oil with lab-distilled frankincense oil for authentication. The CGC-MS analysis showed the presence of a total of eight chiral terpenoids in lab-distilled frankincense essential oils, which can be used as chemical fingerprints for the authentication of frankincense essential oil.
Thermal Behaviors, Interfacial Microstructure and Molecular Orientation of Shape Memory Polyurethane/SiO2 Based Sealant for Concrete Pavement
Authors: Shi Shuang , Ma Tao , Gu Linhao , Zhang Yanning
Published: August 2022

Expansion joint failure is one of the main causes that lead to the damages of concrete pavement. The silicon dioxide/shape memory polyurethane (SiO2/SMPU) is a new kind of sealant which can use its shape memory performance to adapt to the width of the expansion joint with the change of pavement temperature, and it can effectively prolong the service life of the pavement and reduce maintenance costs. In this study, the effects of programming and the addition of SiO2 particles to the thermodynamic properties of the specimens were detected using differential scanning calorimetry (DSC), the optimal shape memory programming temperature of which is 72.9 °C. Combined with scanning electron microscopy (SEM) and shape memory effect test, the particles are evenly distributed between the two phases, and the shape fixation rate (Rf) of 98.15% and the shape recovery rate (Rr) of 97.31% show that the composite has a good shape memory effect. Fourier transform infrared spectroscopy (FTIR) and dynamic infrared dichroism illustrate the change of the hydrogen bond of soft and hard segments with the SiO2 particles in the shape memory cycle, revealing the optimal shape memory programming process. This study provides an insight into the reinforcement mechanism of SiO2 nanoparticles in SMPU matrix and verify whether it can meet the engineering requirements of expansion joints when used as a sealant of concrete pavement.
Long-Term Effect of Modified Glass Ionomer Cement with Mimicked Biological Property of Recombinant Translationally Controlled Protein
Authors: Sangsuwan Prawichaya , Chotigeat Wilaiwan , Tannukit Sissada , Kedjarune-Leggat Ureporn
Published: August 2022

This study modified glass ionomer cement (GIC) by adding mimicked biological molecules to reduce cell death. GIC was modified to BIOGIC by adding chitosan and bovine serum albumin for enhancing protein release. The BIOGIC was supplemented with tricalcium phosphate (TCP) and recombinant translationally controlled tumor protein (TCTP) to improve its biological properties. Four groups of materials, GIC, BIOGIC, BIOGIC+TCP, and BIOGIC + TCP + TCTP, were examined by XRD and SEM-EDX. TCTP released from the specimens was determined by an ELISA method. Human dental pulp stem cells (hDPSCs) were harvested and analyzed by MTT assay, apoptosis, gene expression, and cell differentiation. All groups had the same crystallization characteristic peaks of La2O3. The elemental compositions composed of La, Si, and Al are the main inorganic components. The results show that BIOGIC + TCP + TCTP presented significantly higher percentages of cell viability than other groups on day 1 to day 23 (p < 0.05), but were not different after day 24 to day 41 and had reduced cell apoptosis including BAX, TPT1, BCL-2, and Caspase-3. The BIOGIC + TCP + TCTP demonstrated higher odontoblast mineralization and differentiation markers including ALP activity, DSPP, DMP-1, ALP, BMP-2, and OPN. It enhanced cell proliferation and differentiation as well as mineralization with down-regulation of genes related to apoptosis compared with other groups.
Effects of the Diamine Chain End Functionalized Liquid Butadiene Rubber as a Processing Aid on the Properties of Carbon-Black-Filled Rubber Compounds
Authors: Song Sanghoon , Yeom Gyeongdong , Kim Donghyuk , Ryu Gyeongchan , Hwang Kiwon , Ahn Byungkyu , Choi Haeun , Paik Hyun-Jong , Chung Sungwook , Kim Wonho
Published: August 2022

The implementation of vehicle electrification and autonomous driving technologies has recently emphasized the importance of abrasion resistance and fuel efficiency of truck bus radial (TBR) tire treads that undergo high loads and long driving times. In this study, a functionalized liquid butadiene rubber (F-LqBR) was introduced to replace the treated distillate aromatic extracted (TDAE) oil as a way to improve abrasion resistance and fuel efficiency in the TBR tire tread compound and to solve the oil migration. First, radical polymerization was used to synthesize nonfunctionalized LqBR (N-LqBR) and amino-LqBR with an amine group at the chain ends. The synthesized LqBRs were then substituted in place of TDAE oil to manufacture carbon-black-filled natural rubber (NR) compounds and to evaluate their physical properties. The results show that LqBRs improved the migration resistance and enhanced the abrasion resistance by lowering the glass transition temperature (Tg) of the compound. In particular, amino-LqBR improved carbon black dispersion in the rubber matrix through a chemical bond between the functional group of the carbon black surface and the base rubber. In conclusion, amino-LqBR successfully served as a processing aid in a carbon black-filled NR compound while simultaneously enhancing its fuel efficiency and abrasion resistance.
From Sweet Corn By-Products to Carotenoid-Rich Encapsulates for Food Applications
Authors: Vulić Jelena , Šeregelj Vanja , Tumbas Šaponjac Vesna , Karadžić Banjac Milica , Kovačević Strahinja , Šovljanski Olja , Ćetković Gordana , Čanadanović-Brunet Jasna , Jevrić Lidija , Podunavac-Kuzmanović Sanja
Published: August 2022

In the present study, carotenoids were recovered from processing sweet corn by-products (SCB). The total carotenoid content determined in the SCB extract was 1.19 mg/100 g DW, and the principal carotenoids identified by the HPLC technique were zeaxanthin, β-cryptoxanthin and lutein. Freeze- and spray-drying techniques were applied for the encapsulation of SCB extract; for this purpose, four different wall materials were used: two proteins (soy and pea) and two carbohydrates (maltodextrin and inulin). The physicochemical characteristics of eight encapsulates were determined to assess their stability. The obtained results indicate that, by using the freeze-drying method, better water activity, moisture content as well as encapsulation efficiency were achieved. Spray-drying resulted in better flowing properties. All obtained encapsulates were microbiologically safe for food applications due to the fact that the obtained results are in agreement with the requirements for consumer safety, i.e., for further food applications and scale-up processes. Chemometric classification and ranking techniques were applied to observe potential grouping among the investigated encapsulates and to select the most favorable encapsulates regarding the used wall materials and encapsulation techniques for the assessment of sustainability in food products. The most suitable wall material and encapsulation technique for the assessment of sustainability in food products was produced by freeze-drying pea protein as a wall material (FDP).
The Anti-Fatigue Design of 80 t Depressed-Center Gondola Car Body
Authors: Liu Wenfei , Zhang Liang , Bi Cheng , Huo Yanling , Zhang Ren , Wang Zhengchu
Published: August 2022

In view of the problem that special gondola cars are generally insufficient for the actual transportation of coke, and in order to improve the transportation efficiency and reduce the transportation cost, the 80 t depressed-center gondola car for coke transportation is designed by the anti-fatigue design method in this paper. The car body with a supporting bar and the car body without a supporting bar are simulated and analyzed by the finite element method; the results show that there are stress mutations at the transition area of the two key welds, especially at the 110 mm of weld 1. The fatigue lives of the two car body schemes are evaluated by Miner linear cumulative damage theory, spectrum and S-N curve in AAR standard, and load spectrum of the Daqin line and S-N curve in BSI standard. The results show that the 80 t depressed-center gondola car body with a supporting bar is the best scheme. In addition, the fatigue damage results show that the vertical load spectrum of AAR is worse than that of the Daqin line, and the longitudinal load spectrum of the Daqin line is worse than that of AAR. This conclusion will provide a basis for an anti-fatigue design of heavy haul wagon bodies or bogies.
Application of Non-Dominated Sorting Genetic Algorithm (NSGA-II) to Increase the Efficiency of Bakery Production: A Case Study
Authors: Babor Majharulislam , Pedersen Line , Kidmose Ulla , Paquet-Durand Olivier , Hitzmann Bernd
Published: August 2022

Minimizing the makespan is an important research topic in manufacturing engineering because it accounts for significant production expenses. In bakery manufacturing, ovens are high-energy-consuming machines that run throughout the production time. Finding an optimal combination of makespan and oven idle time in the decisive objective space can result in substantial financial savings. This paper investigates the hybrid no-wait flow shop problems from bakeries. Production scheduling problems from multiple bakery goods manufacturing lines are optimized using Pareto-based multi-objective optimization algorithms, non-dominated sorting genetic algorithm (NSGA-II), and a random search algorithm. NSGA-II improved NSGA, leading to better convergence and spread of the solutions in the objective space, by removing computational complexity and adding elitism and diversity strategies. Instead of a single solution, a set of optimal solutions represents the trade-offs between objectives, makespan and oven idle time to improve cost-effectiveness. Computational results from actual instances show that the solutions from the algorithms significantly outperform existing schedules. The NSGA-II finds a complete set of optimal solutions for the cases, whereas the random search procedure only delivers a subset. The study shows that the application of multi-objective optimization in bakery production scheduling can reduce oven idle time from 1.7% to 26% while minimizing the makespan by up to 12%. Furthermore, by penalizing the best makespan a marginal amount, alternative optimal solutions minimize oven idle time by up to 61% compared to the actual schedule. The proposed strategy can be effective for small and medium-sized bakeries to lower production costs and reduce CO2 emissions.
Constrained Reinforcement Learning for Vehicle Motion Planning with Topological Reachability Analysis
Authors: Gu Shangding , Chen Guang , Zhang Lijun , Hou Jing , Hu Yingbai , Knoll Alois
Published: August 2022

Rule-based traditional motion planning methods usually perform well with prior knowledge of the macro-scale environments but encounter challenges in unknown and uncertain environments. Deep reinforcement learning (DRL) is a solution that can effectively deal with micro-scale unknown and uncertain environments. Nevertheless, DRL is unstable and lacks interpretability. Therefore, it raises a new challenge: how to combine the effectiveness and overcome the drawbacks of the two methods while guaranteeing stability in uncertain environments. In this study, a multi-constraint and multi-scale motion planning method is proposed for automated driving with the use of constrained reinforcement learning (RL), named RLTT, and comprising RL, a topological reachability analysis used for vehicle path space (TPS), and a trajectory lane model (TLM). First, a dynamic model of vehicles is formulated; then, TLM is developed on the basis of the dynamic model, thus constraining RL action and state space. Second, macro-scale path planning is achieved through TPS, and in the micro-scale range, discrete routing points are achieved via RLTT. Third, the proposed motion planning method is designed by combining sophisticated rules, and a theoretical analysis is provided to guarantee the efficiency of our method. Finally, related experiments are conducted to evaluate the effectiveness of the proposed method; our method can reduce 19.9% of the distance cost in the experiments as compared to the traditional method. Experimental results indicate that the proposed method can help mitigate the gap between data-driven and traditional methods, provide better performance for automated driving, and facilitate the use of RL methods in more fields.
Analysis of the Feasibility of UAS-Based EMI Sensing for Underground Utilities Detection and Mapping
Authors: Hartshorn Caylin A. , Isaacson Sven D. , Barrowes Benjamin E. , Perren Lee J. , Lozano David , Shubitidze Fridon
Published: August 2022

This paper investigates the feasibility of using a linear current sensing (LCS) technique integrated on an unmanned aerial system (UAS) for detecting and mapping underground infrastructure rapidly and cost-effectively. The LCS technique is based on data from a wide band of electromagnetic induction frequencies (50 kHz to 2 MHz) using a vector magnetic field gradiometer. This technique takes advantage of a slowly decaying secondary magnetic field in order to achieve greater standoff detection distance (1R2 vs. 1R6 for compact metallic targets during EMI sensing, where R is the distance from a target to the sensor). These secondary magnetic fields are produced by the excite current on long conductors, allowing detection at a distance of 10 meters or more. The system operates between tens of kHz to a few MHz and uses either an active EMI source or existing EM fields to excite this linear current on a long metallic subsurface target. Once excited, these linear currents produce a secondary magnetic field that is detected with an above ground triaxial magnetic field gradiometer. By moving and tracking its geolocation, the system outputs rich datasets sufficient to support high-fidelity forward and inverse EMI models for estimating the depth and orientation of deep underground long linear metallic infrastructure. The system’s hardware and its integration to a UAS system are outlined, along with the formulation of LCS theory, and numerical and experimental data are presented. The results illustrate that the LCS technique offers large standoff detection, is adaptable to UAS, and could be used effectively for detecting deep underground infrastructure such as wires and pipes.
Image-Based Adaptive Staring Attitude Control for Multiple Ground Targets Using a Miniaturized Video Satellite
Authors: Song Chao , Fan Caizhi , Wang Mengmeng
Published: August 2022

A miniaturized video satellite can observe the ground targets by recording real-time video clips in staring control mode and therefore obtains a unique advantage over traditional remote sensing techniques. To further extend the application of a video satellite, a strategy for simultaneously observing a group of ground targets is to be developed. To cope with the impacts of an uncalibrated camera on the pointing accuracy which can lead to the failure of a multi-target observation task, an adaptive attitude control method is to be exploited. Hence, to observe multiple ground targets using an onboard uncalibrated camera, this paper proposes an image-based adaptive staring attitude controller. First, a target-selection strategy is proposed to realize a more balanced staring observation of the target group. Second, an updating law is proposed to estimate the camera parameters according to the projection equations. At last, an adaptive staring controller based on the estimated parameters is formulated, so that the center of mass of the ground targets on the image can be controlled towards its desired location, which is normally the image center. The stability of the proposed staring controller is proved using Barbalat’s Lemma. The simulation results show that even though the camera parameters are uncertain, the adaptive control method effectively achieves the staring observation for multiple ground targets by keeping their midpoint at the image center.
A Novel Method for Ground-Based Cloud Image Classification Using Transformer
Authors: Li Xiaotong , Qiu Bo , Cao Guanlong , Wu Chao , Zhang Liwen
Published: August 2022

In recent years, convolutional neural networks (CNNs) have achieved competitive performance in the field of ground-based cloud image (GCI) classification. Proposed CNN-based methods can fully extract the local features of images. However, due to the locality of the convolution operation, they cannot well establish the long-range dependencies between the images, and thus they cannot extract the global features of images. Transformer has been applied to computer vision with great success due to its powerful global modeling capability. Inspired by it, we propose a Transformer-based GCI classification method that combines the advantages of the CNN and Transformer models. Firstly, the CNN model acts as a low-level feature extraction tool to generate local feature sequences of images. Then, the Transformer model is used to learn the global features of the images by efficiently extracting the long-range dependencies between the sequences. Finally, a linear classifier is used for GCI classification. In addition, we introduce a center loss function to address the problem of the simple cross-entropy loss not adequately supervising feature learning. Our method is evaluated on three commonly used datasets: ASGC, CCSN, and GCD. The experimental results show that the method achieves 94.24%, 92.73%, and 93.57% accuracy, respectively, outperforming other state-of-the-art methods. It proves that Transformer has great potential to be applied to GCI classification tasks.
Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data
Authors: Bueso-Bello Jose-Luis , Carcereri Daniel , Martone Michele , González Carolina , Posovszky Philipp , Rizzoli Paola
Published: August 2022

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation.
Genetic Programming for High-Level Feature Learning in Crop Classification
Authors: Lu Miao , Bi Ying , Xue Bing , Hu Qiong , Zhang Mengjie , Wei Yanbing , Yang Peng , Wu Wenbin
Published: August 2022

Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree’s width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM), and was then used for crop classification based on MODIS time series data in Heilongjiang Province, China. The performance of the GP features was compared with the traditional features of vegetation indices (VIs) and the advanced feature learning method Multilayer Perceptron (MLP) to show GP effectiveness. The experiments indicated that high-level features learned by GP improved the classification accuracies, and the accuracies were higher than those using VIs and MLP. GP was more robust and stable for diverse classifiers, different feature numbers, and various training sample sets compared with classification using VI features and the classifier MLP. The proposed GP approach automatically selects valuable features from the original data and uses them to construct high-level features simultaneously. The learned features are explainable, unlike those of a black-box deep learning model. This study demonstrated the outstanding performance of GP for feature learning in crop classification. GP has the potential of becoming a mainstream method to solve complex remote sensing tasks, such as feature transfer learning, image classification, and change detection.
Visual Localization and Target Perception Based on Panoptic Segmentation
Authors: Lv Kefeng , Zhang Yongsheng , Yu Ying , Zhang Zhenchao , Li Lei
Published: August 2022

Visual localization is a core part of many computer vision and geospatial perception applications; however, the ever-changing time phase and environment present challenges. Moreover, the ever-enriching spatial data types and sensors create new conditions for visual localization. Based on the prior 3D model and the location sensor, the current study proposes a visual localization method using semantic information. This method integrates panoptic segmentation and the matching network to refine the sensor’s position and orientation and complete the target perception. First, the panoptic segmentation and the match network are used together to segment and match the 3D- model-rendered image and the truth image. The matching results are then optimized based on the semantic results. Second, the semantic consistency score is introduced in the RANSAC process to estimate the optimal 6 degree-of-freedom (6DOF) pose. In the final stage, the estimated 6DOF pose, the instance segmentation results, and the depth information are used to locate the target. Experimental results show that the proposed method is a significant improvement on advanced methods for the long-term visual localization benchmark dataset. Additionally, the proposed method is seen to provide improved localization accuracy and is capable of accurately perceiving the target for self-collected data.
Stable Regional Reference Frame for Reclaimed Land Subsidence Study in East China
Authors: Peng Yu , Dong Danan , Chen Wen , Zhang Chenglong
Published: August 2022

This study implemented a stable Regional Reference Frame in Shanghai, East China (called SHRRF), using seven years of continuous GNSS observations from the Shanghai Continuously Operating Reference System stations (SHCORS) to examine reclaimed coast–land subsidence. A well−distributed core station network suitable for regional applications was derived. The instantaneous station coordinates and seven frame parameters (translations, rotations, and scale) were estimated at each epoch through minimum constraint during the process of aligning SHRRF to the International Terrestrial Reference Frame (ITRF14). The average root mean square error (RMSE) of all stations under SHRRF was within 1.5 mm horizontally and 5 mm vertically for most epochs. Simultaneously, compared with the ITRF14 solutions, the average RMSE for each site at all epochs was reduced by ~30% horizontally and ~10% vertically. A temporal consolidation settlement model of the reclaimed soil under self−weight was established by combining a geotechnical−derived model with high precision permanent GNSS vertical solutions under SHRRF. The model indicates that ~50% of settlements occurred within 3.6 years, with the whole subsidence time being 46 years. SHRRF provides a precise regional reference frame for use in many East China geophysical applications besides reclaimed coast–land settlement including hydrologic loading, microplate motions, and critical structure deformation monitoring.
Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method
Authors: Zhang Jie , Song Xiaoyu , Jing Xia , Yang Guijun , Yang Chenghai , Feng Haikuan , Wang Jiaojiao , Ming Shikang
Published: August 2022

Grain protein content (GPC) is an important indicator of nutritional quality of rice. In this study, nitrogen fertilization experiments were conducted to monitor GPC for high-quality Indica rice varieties Meixiangzhan 2 (V1) and Wufengyou 615 (V2) in 2019 and 2020. Three types of parameters, including photosynthetic sensitive vegetation indices (VIs), canopy leaf area index (LAI), and crop plant nitrogen accumulation (PNA), obtained from UAV hyperspectral images were used to estimate rice GPC. Two-dimensional and three-dimensional GPC indices were constructed by combining any two of the three types of parameters and all three, respectively, based on the Euclidean distance method. The R2 and RMSE of the two-dimensional GPC index model for variety V1 at the tillering stage were 0.81 and 0.40% for modeling and 0.95 and 0.38% for validation, and 0.91 and 0.27% for modeling and 0.83 and 0.36% for validation for variety V2. The three-dimensional GPC index model for variety V1 had R2 and RMSE of 0.86 and 0.34% for modeling and 0.78 and 0.45% for validation, and 0.97 and 0.17% for modeling and 0.96 and 0.17% for validation for variety V2 at the panicle initiation stage. At the heading stage, the R2 and RMSE of the three-dimensional model for variety V1 were 0.92 and 0.26% for modeling and 0.91 and 0.37% for validation, and 0.96 and 0.20% for modeling and 0.99 and 0.15% for validation for variety V2. These results demonstrate that the GPC monitoring models incorporating multiple crop growth parameters based on Euclidean distance can improve GPC estimation accuracy and have the potential for field-scale GPC monitoring.
Dual Guided Aggregation Network for Stereo Image Matching
Authors: Wang Ruei-Ping , Lin Chao-Hung
Published: August 2022

Stereo image dense matching, which plays a key role in 3D reconstruction, remains a challenging task in photogrammetry and computer vision. In addition to block-based matching, recent studies based on artificial neural networks have achieved great progress in stereo matching by using deep convolutional networks. This study proposes a novel network called a dual guided aggregation network (Dual-GANet), which utilizes both left-to-right and right-to-left image matchings in network design and training to reduce the possibility of pixel mismatch. Flipped training with a cost volume consistentization is introduced to realize the learning of invisible-to-visible pixel matching and left–right consistency matching. In addition, suppressed multi-regression is proposed, which suppresses unrelated information before regression and selects multiple peaks from a disparity probability distribution. The proposed dual network with the left–right consistent matching scheme can be applied to most stereo matching models. To estimate the performance, GANet, which is designed based on semi-global matching, was selected as the backbone with extensions and modifications on guided aggregation, disparity regression, and loss function. Experimental results on the SceneFlow and KITTI2015 datasets demonstrate the superiority of the Dual-GANet compared to related models in terms of average end-point-error (EPE) and pixel error rate (ER). The Dual-GANet with an average EPE performance = 0.418 and ER (>1 pixel) = 5.81% for SceneFlow and average EPE = 0.589 and ER (>3 pixels) = 1.76% for KITTI2005 is better than the backbone model with the average EPE performance of = 0.440 and ER (>1 pixel) = 6.56% for SceneFlow and average EPE = 0.790 and ER (>3 pixels) = 2.32% for KITTI2005.
Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security
Authors: Lukens Joseph M. , Passian Ali , Yoginath Srikanth , Law Kody J. H. , Dawson Joel A.
Published: August 2022

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.
Smart Manufacturing and Digitalization of Metrology: A Systematic Literature Review and a Research Agenda
Authors: Barbosa Carlos Roberto H. , Sousa Manuel C. , Almeida Maria Fatima L. , Calili Rodrigo F.
Published: August 2022

Smart manufacturing comprises fully integrated manufacturing systems that respond in real time to meet the changing demands and conditions in industrial activities, supply networks and customer needs. A smart manufacturing environment will face new challenges, including those concerning metrological issues, i.e., analysis of large quantities of data; communication systems for digitalization; measurement standards for automated process control; digital transformation of metrological services; and simulations and virtual measurement processes for the automatic assessment of measured data. Based on the assumption that the interplay between smart manufacturing and digitalization of metrology is an emerging research field, this paper aims to present a systematic literature review (SLR) based on a bibliographic data collection of 160 scientific articles retrieved from the Web of Science and Scopus databases over the 2016–2022 time frame. The findings presented in this review and recommendations for building a research agenda can help policy makers, researchers and practitioners by providing directions for the evolution of digital metrology and its role in the digitalization of the economy and society.
A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar
Authors: Dang Xiaochao , Zhang Jinlong , Hao Zhanjun
Published: August 2022

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland–Altman consistency analysis statistical method with the algorithm’s respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.
Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization
Authors: Alterazi Hassan A. , Kshirsagar Pravin R. , Manoharan Hariprasath , Selvarajan Shitharth , Alhebaishi Nawaf , Srivastava Gautam , Lin Jerry Chun-Wei
Published: August 2022

High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network’s external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent.
Powering UAV with Deep Q-Network for Air Quality Tracking
Authors: Mohammed Alaelddin F. Y. , Sultan Salman Md , Cho Seokheon , Pyun Jae-Young
Published: August 2022

Tracking the source of air pollution plumes and monitoring the air quality during emergency events in real-time is crucial to support decision-makers in making an appropriate evacuation plan. Internet of Things (IoT) based air quality tracking and monitoring platforms have used stationary sensors around the environment. However, fixed IoT sensors may not be enough to monitor the air quality in a vast area during emergency situations. Therefore, many applications consider utilizing Unmanned Aerial Vehicles (UAVs) to monitor the air pollution plumes environment. However, finding an unhealthy location in a vast area requires a long navigation time. For time efficiency, we employ deep reinforcement learning (Deep RL) to assist UAVs to find air pollution plumes in an equal-sized grid space. The proposed Deep Q-network (DQN) based UAV Pollution Tracking (DUPT) is utilized to guide the multi-navigation direction of the UAV to find the pollution plumes’ location in a vast area within a short duration of time. Indeed, we deployed a long short-term memory (LSTM) combined with Q-network to suggest a particular navigation pattern producing minimal time consumption. The proposed DUPT is evaluated and validated using an air pollution environment generated by a well-known Gaussian distribution and kriging interpolation. The evaluation and comparison results are carefully presented and analyzed. The experiment results show that our proposed DUPT solution can rapidly identify the unhealthy polluted area and spends around 28% of the total time of the existing solution.
Rotation Grids for Improved Electrical Properties of Inkjet-Printed Strain Gauges
Authors: Rehberger Matthias , Mertin Jonas , Vedder Christian , Stollenwerk Jochen , Schleifenbaum Johannes Henrich
Published: August 2022

We report an image data driven approach for inkjet printing (IJP) to improve the electrical properties of printed metallic strain gauges (SGs). The examined SGs contain narrow conducting paths of multiple orientations and therefore suffer from two challenges: 1. The printing direction of inkjet printed conducting paths has an impact on film formation and electrical properties. 2. A loss-free rotation algorithm for IJP image data is lacking. New ways of IJP image data processing are required to compensate for quality-reducing effects. Novel grid types in terms of loss-free rotation algorithms are introduced. For this purpose, a new grid (e.g., 45° tilted) with a different grid constant is placed over a given pixel grid in such a way that all cell centers of the given pixel grid can be transferred to the rotated grid. Via straightening the tilt, the image data is rotated without interpolation and information loss. By applying these methods to measurement gratings of a full bridge with two perpendicular grating orientations, the influence on the manufacturing quality is investigated. It turns out that the electrical detuning of full bridges can be reduced by one order of magnitude compared to state-of-the-art printing by using so-called diagonal rotation grids.
An eDiary App Approach for Collecting Physiological Sensor Data from Wearables together with Subjective Observations and Emotions
Authors: Petutschnig Andreas , Reichel Steffen , Měchurová Kristýna , Resch Bernd
Published: August 2022

Field measurement campaigns with traffic participants using wearable sensors and questionnaires can be challenging to carry out because of inconsistent interfaces across manufacturers for accessing sensor data and campaign-specific questionnaire contents bear large potential for errors. We present an app able to consolidate data from multiple technical sensors and questionnaires. The functionality includes providing feedback for correct sensor platform mounting, accessing and storing all sensor and questionnaire data in a uniform data structure. To do this, the app implements a sensor data bus class that unifies data from technical sensors and questionnaires. The app can also be extended to accommodate other sensor platforms provided they have a suitable API. We also describe a database structure holding the data from multiple campaigns and test subjects in a privacy preserving fashion. Finally, we identify some potential issues and hints that practitioners may encounter when conducting a measurement campaign.
Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models
Authors: Sopelsa Neto Nemesio Fava , Stefenon Stefano Frizzo , Meyer Luiz Henrique , Ovejero Raúl García , Leithardt Valderi Reis Quietinho
Published: August 2022

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 ×10−3, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 ×10−19, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.
Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage
Authors: Hamed Salah Beni , Hamed Mouna Ben , Sbita Lassaad , Bajaj Mohit , Blazek Vojtech , Prokop Lukas , Misak Stanislav , Ghoneim Sherif S. M.
Published: August 2022

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions.
Flow Pattern Identification of Oil–Water Two-Phase Flow Based on SVM Using Ultrasonic Testing Method
Authors: Su Qian , Li Jie , Liu Zhenxing
Published: August 2022

A flow pattern identification method combining ultrasonic transmission attenuation with an ultrasonic reflection echo is proposed for oil–water two-phase flow in horizontal pipelines. Based on the finite element method, two-dimensional geometric simulation models of typical oil–water two-phase flow patterns are established, using multiphysics coupling simulation technology. An ultrasonic transducer test system of a horizontal pipeline with an inner 50 mm diameter was built, and flow pattern simulation experiments of oil–water two-phase flow were carried out in the tested field area. The simulation results show that the ultrasonic attenuation coefficient is extracted to identify the W/O&O/W dispersion flow using the ultrasonic transmission attenuation method, and the identification accuracy is 100%. By comparison, using the ultrasonic reflection echo method, the echo duration is extracted as an input feature vector of support vector machine (SVM), and the identification accuracy of the stratified flow and dispersed flow is 95.45%. It was proven that the method of the ultrasonic transmission attenuation principle combined with the ultrasonic reflection echo principle can identify oil–water two-phase flow patterns accurately and effectively, which provides a theoretical basis for the flow pattern identification of liquid–liquid multiphase flow.
Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets
Authors: Nanni Loris , Brahnam Sheryl , Paci Michelangelo , Ghidoni Stefano
Published: August 2022

CNNs and other deep learners are now state-of-the-art in medical imaging research. However, the small sample size of many medical data sets dampens performance and results in overfitting. In some medical areas, it is simply too labor-intensive and expensive to amass images numbering in the hundreds of thousands. Building Deep CNN ensembles of pre-trained CNNs is one powerful method for overcoming this problem. Ensembles combine the outputs of multiple classifiers to improve performance. This method relies on the introduction of diversity, which can be introduced on many levels in the classification workflow. A recent ensembling method that has shown promise is to vary the activation functions in a set of CNNs or within different layers of a single CNN. This study aims to examine the performance of both methods using a large set of twenty activations functions, six of which are presented here for the first time: 2D Mexican ReLU, TanELU, MeLU + GaLU, Symmetric MeLU, Symmetric GaLU, and Flexible MeLU. The proposed method was tested on fifteen medical data sets representing various classification tasks. The best performing ensemble combined two well-known CNNs (VGG16 and ResNet50) whose standard ReLU activation layers were randomly replaced with another. Results demonstrate the superiority in performance of this approach.