bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategy Based on Michael Addition Reaction for the Development of Bioinspired Multilayered and Multiphasic 3D Constructs
Authors: Olaru Mihaela , Simionescu Natalia , Doroftei Florica , David Geta
Published: March 2023

The high incidence of osteochondral defects has increased the interest in the development of improved repairing alternatives, with tissue engineering being considered a promising approach. The hierarchical, complex structure of osteochondral tissue requires the design of a biomimetic multilayered scaffold. Here, a multilayered and multiphasic 3D macroporous structure was achieved at subzero temperature by the Michael addition reaction of amino functionalities of collagen with acryloyl groups of a bifunctionalized poly(ε-caprolactone). This green approach has been successfully applied to crosslink layers of different composition, both for their efficient sequential formation and connection. Polyethylenimine functionalized nano-hydroxyapatite (nHApLPEI) was added to the bottom layer. The resulting hybrid cryogels were characterized by morphology, equilibrium swelling ratios, compressive strength analysis, and MTS assay. They presented good stability, integrity, and biocompatibility. The results revealed that the properties of the prepared constructs may be tuned by varying the composition, number, and thickness of the layers. The Young modulus values were between 3.5 ± 0.02 and 10.5 ± 0.6 kPa for the component layers, while for the multilayered structures they were more than 7.3 ± 0.2 kPa. The equilibrium swelling ratio varied between 4.6 and 14.2, with a value of ~10.5 for the trilayered structure, correlated with the mean pore sizes (74–230 µm).
Study on the Difference of Superhydrophobic Characteristics of Different Wood Furniture Substrates
Authors: Yao Xingzhou , Kong Zhangqian , Yang Feng , Wu Xinyu , Wu Yan
Published: March 2023

To enhance the stability of wood and decrease restrictions on its use in the furniture industry, hydrophobic modification can be employed to confer waterproof, anti-fouling, and self-cleaning properties. The present study outlines the preparation of silica sol using the sol–gel method, followed by impregnation and chemical vapor deposition methods to modify the sol. After grafting 1H,1H,2H,2H-perfluoro-decyl trichlorosilane (FDTS), hydrophobic and superhydrophobic properties were imparted to the wood substrate. To explore the correlation between the surface properties of the wood substrate and superhydrophobic coatings, the densities, porosities, and surface roughness of various tree species were compared. The results showed that the sol–gel method successfully constructed hydrophobic coatings on different wood substrates, with six samples (poplar, elm, toon wood, paulownia, ashtree, and black walnut) achieving superhydrophobic surfaces, with densities ranging from 0.386 to 0.794 g/cm3, porosity ranging from 13.66 to 42.36%, roughness ranging from 4.660 to 11.244 um, and maximum water contact angle of 165.2°. Whereas beech and rosewood only reach the hydrophobic surface. Although the coatings demonstrated good resistance to water, pollutants, self-cleaning, and chemical agents, further improvements are necessary to enhance mechanical wear resistance.
Investigation of CFRP-Countersunk Bolted Assembly Fatigue Damage under Three-Point Bending via Experimental and Numerical Analysis
Authors: Qin Zhengqi , He Ying , Wang Shengwu , Meng Cunying
Published: March 2023

In this research, the fatigue damage behavior under three-point bending of a composite joint incorporating a single countersunk fastener is investigated. Firstly, a self-developed fatigue test system was set up to test the fatigue characteristics of CFRP-countersunk bolted assembly under the displacement amplitude cycles of 103 to 106 to study the formation and expansion rule of damage and cracks. It found two typical damage processes, both of which involve some formal interface damage between fiber and matrix. Based on the experiment, a finite element fatigue damage analysis on this assembly was carried out according to the Hashin failure criterion. The simulation result shows an identical fatigue damage location and fatigue life with the experimental phenomenon. Moreover, it predicted the final fatigue life of the specimen under 10 hz cyclic loading with 1 mm displacement and 10 Nm bolt preloading. This research provides guidance for the engineering fatigue issues of single-bolted joint composite connection structures and provides a reference for the corresponding technical specifications formulation.
Recent Developments and Formulations for Hydrophobic Modification of Carrageenan Bionanocomposites
Authors: Mavelil-Sam Rubie , Ouseph Elizabeth Mariya , Morreale Marco , Scaffaro Roberto , Thomas Sabu
Published: March 2023

Versatility of the anionic algal polysaccharide carrageenan has long been discussed and explored, especially for their affinity towards water molecules. While this feature is advantageous in certain applications such as water remediation, wound healing, etc., the usefulness of this biopolymer is extremely limited when it comes to applications such as food packaging. Scientists around the globe are carrying out research works on venturing diverse methods to integrate hydrophobic nature into these polysaccharides without compromising their other functionalities. Considering these foregoing studies, this review is designed to have an in-depth understanding of diverse methods and techniques adopted for tuning the hydrophobic nature of carrageenan-based bionanocomposites, both via surface alterations or by changes made to their chemical structure and attached functional groups. This review article mainly focuses on how the hydrophobicity of carrageenan bionanocomposites varies as a function of the type and refinement of carrageenan, and with the incorporation of additives including plasticisers, nanofillers, bioactive agents, etc. Incorporation of nanofillers such as polysaccharide-based nanoparticles, nanoclays, bioceramic and mineral based nanoparticles, carbon dots and nanotubes, metal oxide nanoparticles, etc., along with their synergistic effects in hybrid bionanocomposites are also dealt with in this comprehensive review article.
Mechanical and Gas Barrier Properties of Poly(Lactic Acid) Modified by Blending with Poly(Butylene 2,5-Furandicarboxylate): Based on Molecular Dynamics
Authors: Wang Ye , Jiang Gongliang , Shao Xiancheng , Pu Shikun , Jiang Dengbang , Lan Yaozhong
Published: March 2023

Three blends of Poly(butylene 2,5-furandicarboxylate) (PBF) and Poly(lactic acid) (PLA) blends were modeled using molecular dynamics simulations, with PBF contents of 10%, 20%, and 30%, respectively. The study investigated the compatibilities of the blends, as well as the mechanical and gas barrier properties of the composite systems. The molecular dynamics simulation results show that: (1) PLA and PBF have good compatibility in the blend system; (2) the optimal toughness modification was achieved with a 20% PBF content, resulting in a 17.3% increase in toughness compared to pure PLA; (3) the barrier properties of the blend for O2, CO2, and N2 increased when increasing the PBF content. Compared to pure PLA, the diffusion coefficients of the O2, CO2, and N2 of the blends with 30% PBF decreased by 75%, 122%, and 188%, respectively. Our simulation results are in good agreement with the actual experimental results.
Novel Environmentally Friendly Covalent Organic Framework/Polylactic Acid Composite Material with High Chemical Stability for Sand-Control Material
Authors: Yang Wanjia , Du Yongling , Liu Benli
Published: March 2023

A new high-strength, thermally stable, and degradable covalent organic framework (COF) -modified polylactic acid fiber (PLA) material (COF-PLA) was constructed for reinforcing the PLA material, to be used to produce environmentally friendly sand barriers. The micrographs, structure, thermal stability, and photodegradation products of COF-PLA were investigated. The results indicated that the COF material was compatible with PLA, and that the COF-PLA material took on the merits of the COF, so that it had a more regular arrangement, smoother surface, and smaller size, and was more thermostable than PLA alone. The successful incorporation of the COF improved the thermal stability of PLA. The initial pyrolysis temperature of the COF-PLA material is 313.7 °C, higher than that of the PLA material at 297.5 °C. The photodegradation products of COF-PLA and PLA indicated that the COF and PLA materials were mixed in a complex manner. After photodegradation, the COF-PLA material can produce melamine molecules that can neutralize the lactic acid and CO2 produced by PLA, which can maintain the acid–base balance in sandy soil and is beneficial to plant growth. Therefore, COF-PLA degradation does not cause pollution, making it a promising sand-control material.
Nickel Oxide Nanoparticles on KIT-6: An Efficient Catalyst in Methane Combustion
Authors: Huang Xiuhui , Yang Wenkai , Li Zeqiu , Lou Qin , Tian Ying , Li Junfeng
Published: March 2023

KIT-6 silica with well-ordered three–dimensional (3D) mesopores has been synthesized as a support for nickel-based catalysts. Transmission Electron Microscopy (TEM) and low-angle X-ray Diffraction (XRD) analysis are used to ensure that the ordered 3D mesostructure is stable after NiO incorporation. In this study, the catalytic activities of the NiO/KIT-6 samples are investigated. Additionally, the results show that a 10 wt% NiO/KIT-6 catalyst exhibits high catalytic performance in methane combustion, with T10, T50 and T90 being only 386 °C, 456 °C and 507 °C, respectively. Hydrogen Temperature Programmed Reduction (H2-TPR) studies have shown that the interaction between NiO and KIT-6 in the 10 wt% NiO/KIT-6 catalyst is weak. Methane Temperature programmed Surface Reaction (CH4-TPSR) results show that the surface oxygen of the NiO/KIT-6 catalyst allows it to exhibit a high catalytic performance. NiO/KIT-6 catalysts exhibit superior activities to SBA-15, MCF and SiO2 support catalysts because KIT-6 has a higher surface area and ordered 3D mesopore connectivity, which is favorable for better NiO dispersion and peculiar diffusion for reactant and products. Furthermore, the used catalyst maintained an ordered mesostructure and reduction property.
Analysis of Sediment and Water Flow and Erosion Characteristics of Large Pelton Turbine Injector
Authors: Liu Jitao , Pang Jiayang , Liu Xiaobing , Huang Yu , Deng Huiming
Published: March 2023

Based on the solid–liquid two-phase flow model, SST k-ω model, and Lagrangian equation model of particle motion, numerical simulations of the sediment–water flow in the injector of a large Pelton turbine were conducted. The distribution rules of pressure, velocity, erosion rate, and erosion location of the injector were obtained by analyzing the sediment–water flow characteristics and sediment erosion distribution characteristics of the injector. The results revealed that the velocity distribution trend of the water inside the cylindrical jet exhibited a nonlinear distribution, and the phenomenon of “velocity deficit” occurred at the end of the needle guide and needle tip, resulting in a decrease in the jet quality of the injector. The sediment particle diameter affected the erosion rate of the needle and erosion location of the needle and nozzle port ring. This study provided guidance for sediment erosion analysis and the prediction of the utility of large Pelton turbines.
Positioning Control of Robotic Manipulators Subject to Excitation from Non-Ideal Sources
Authors: Tusset Angelo M. , Pereira Amarildo E. B. , Balthazar Jose M. , Janzen Frederic C. , Oliveira Clivaldo , Fuziki Maria E. K. , Lenzi Giane G.
Published: March 2023

The present work proposes the use of a hybrid controller combining concepts of a PID controller with LQR and a feedforward gain to control the positioning of a 2 DOF robotic arm with flexible joints subject to non-ideal excitations. To characterize the performance of the controls, two cases were studied. The first case considered the positioning control of the two links in fixed positions, while the second case considered the situation in which the second link is in rotational movement and the first one stays in a fixed position, representing a system with a non-ideal excitation source. In addition to the second case, the sensitivity of the proposed controls for changes in the length and mass of the second link in the rotational movement was analyzed. The results of the simulations showed the effectiveness of the controls, demonstrating that the PID control combined with feedforward gain provides the lowest error for both cases studied; however, it is sensitive to variations in the mass of the second link, in the case of rotational movements. The numerical results also revealed the effectiveness of the PD control obtained by LQR, presenting results similar to the PID control combined with feedforward gain, demonstrating the importance of the optimal control design.
Spatiotemporal Landscape Pattern Analyses Enhanced by an Integrated Index: A Study of the Changbai Mountain National Nature Reserve
Authors: Zhang Ying , Zhang Jingxiong , Wang Fengyan , Yang Wenjing
Published: March 2023

The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite landscape metrics rarely consider the effects of spatial correlation. A more objective approach to formulating composite landscape metrics involves proper weighting that incorporates spatial structure information into integrating individual conventional metrics selected for building a composite metric. This paper proposes an integrated spatial landscape index (ISLI) based on variogram modeling and entropy weighting. It was tested through a case study, which sought to analyze spatiotemporal changes in the landscape pattern in the Changbai Mountains over 30 years based on six global land-cover products with a fine classification system at 30 m resolution (GLC_FCS30). The test results confirm: (1) spatial structure information is useful for weighting conventional landscape pattern metrics when constructing ISLI as validated by correlation analysis between the incorporated conventional metrics and their variogram ranges. In terms of the range parameters of different land cover types, broadleaf forest and needleleaf forest have much larger range values than those of other land cover types; (2) DIVISION and PLAND, two of the conventional landscape metrics considered for constructing ISLI, were assigned the greatest weights in computing ISLI for this study; and (3) ISLI values can be used to determine the dominant landscape types. For the study area, ISLI values of broadleaf forests remained the largest until 2020, indicating that forest landscape characteristics were the most prominent during that period. After 2020, the dominance of needleleaf forest gradually increased, with its ISLI value reaching a maximum of 0.91 in 2025. Therefore, the proposed ISLI not only functions as an extension and complement to conventional landscape metrics but also provides more comprehensive information concerning landscape pattern dynamics.
Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data
Authors: Fahle Lukas , Petruska Andrew J. , Walton Gabriel , Brune Jurgen F. , Holley Elizabeth A.
Published: March 2023

Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces.
Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation
Authors: Agbona Afolabi , Montesinos-Lopez Osval A. , Everett Mark E. , Ruiz-Guzman Henry , Hays Dirk B.
Published: March 2023

Many processes concerning below-ground plant performance are not fully understood, such as spatial and temporal dynamics and their relation to environmental factors. Accounting for these spatial patterns is very important as they may be used to adjust for the estimation of cassava fresh root yield masked by field heterogeneity. The yield of cassava is an important characteristic that every breeder seeks to maintain in their germplasm. Ground-Penetrating Radar (GPR) has proven to be an effective tool for studying the below-ground characteristics of developing plants, but it has not yet been explored with respect to its utility in normalizing spatial heterogeneity in agricultural field experiments. In this study, the use of GPR for this purpose was evaluated in a cassava field trial conducted in Momil, Colombia. Using the signal amplitude of the GPR radargram from each field plot, we constructed a spatial plot error structure using the variance of the signal amplitude and developed GPR-based autoregressive (AR) models for fresh root yield adjustment. The comparison of the models was based on the average standard error (SE) of the Best Linear Unbiased Estimator (BLUE) and through majority voting (MV) with respect to the SE of the genotype across the models. Our results show that the GPR-based AR model outperformed the other models, yielding an SE of 9.57 and an MV score of 88.33%, while the AR1 × AR1 and IID models had SEs of 10.15 and 10.56% and MV scores of 17.37 and 0.00%, respectively. Our results suggest that GPR can serve a dual purpose in non-destructive yield estimation and field spatial heterogeneity normalization in global root and tuber crop programs, presenting a great potential for adoption in many applications.
P2FEViT: Plug-and-Play CNN Feature Embedded Hybrid Vision Transformer for Remote Sensing Image Classification
Authors: Wang Guanqun , Chen He , Chen Liang , Zhuang Yin , Zhang Shanghang , Zhang Tong , Dong Hao , Gao Peng
Published: March 2023

Remote sensing image classification (RSIC) is a classical and fundamental task in the intelligent interpretation of remote sensing imagery, which can provide unique labeling information for each acquired remote sensing image. Thanks to the potent global context information extraction ability of the multi-head self-attention (MSA) mechanism, visual transformer (ViT)-based architectures have shown excellent capability in natural scene image classification. However, in order to achieve powerful RSIC performance, it is insufficient to capture global spatial information alone. Specifically, for fine-grained target recognition tasks with high inter-class similarity, discriminative and effective local feature representations are key to correct classification. In addition, due to the lack of inductive biases, the powerful global spatial context representation capability of ViT requires lengthy training procedures and large-scale pre-training data volume. To solve the above problems, a hybrid architecture of convolution neural network (CNN) and ViT is proposed to improve the RSIC ability, called P2FEViT, which integrates plug-and-play CNN features with ViT. In this paper, the feature representation capabilities of CNN and ViT applying for RSIC are first analyzed. Second, aiming to integrate the advantages of CNN and ViT, a novel approach embedding CNN features into the ViT architecture is proposed, which can make the model synchronously capture and fuse global context and local multimodal information to further improve the classification capability of ViT. Third, based on the hybrid structure, only a simple cross-entropy loss is employed for model training. The model can also have rapid and comfortable convergence with relatively less training data than the original ViT. Finally, extensive experiments are conducted on the public and challenging remote sensing scene classification dataset of NWPU-RESISC45 (NWPU-R45) and the self-built fine-grained target classification dataset called BIT-AFGR50. The experimental results demonstrate that the proposed P2FEViT can effectively improve the feature description capability and obtain outstanding image classification performance, while significantly reducing the high dependence of ViT on large-scale pre-training data volume and accelerating the convergence speed. The code and self-built dataset will be released at our webpages.
Quantifying the Effects of Climate Change and Revegetation on Erosion-Induced Lateral Soil Organic Carbon Loss on the Chinese Loess Plateau
Authors: Han Jianqiao , Pan Yawen , Xiao Peiqing , Ge Wenyan , Sun Pengcheng
Published: March 2023

Erosion-induced soil organic carbon (SOC) loss substantially affects the redistribution of global organic carbon. The Chinese Loess Plateau, the most severely eroded region on Earth, has experienced notable soil erosion mitigation over the last few decades, making it a hotspot for soil erosion studies. However, the overall rate of SOC loss and spatiotemporal evolution under changing environments remain unclear. In this study, we investigated SOC loss from 1982 to 2015 in the severely eroded Hetong region of the Chinese Loess Plateau by combining the Revised Universal Soil Loss Equation (RUSLE) model and the localized enrichment ratio function derived from field observations and attributed the changes in SOC loss to climate- and human-induced vegetation changes. The results showed that SOC loss in the Hetong region was 64.73 t·km−2·yr−1, 16.79 times higher than the global average. Over the past 34 years, SOC loss decreased by 23.84%, with a total reduction of more than 105.64 Tg C since the change-point year. Moreover, our study found that vegetation changes dominated the changes in SOC loss in the Hetong region, contributing 89.67% of the total reduction in SOC loss in the Hetong region. This study can inform carbon accounting and sustainable catchment management in regions that have experienced large-scale ecological restoration.
A Polarimetric Scattering Characteristics-Guided Adversarial Learning Approach for Unsupervised PolSAR Image Classification
Authors: Dong Hongwei , Si Lingyu , Qiang Wenwen , Miao Wuxia , Zheng Changwen , Wu Yuquan , Zhang Lamei
Published: March 2023

Highly accurate supervised deep learning-based classifiers for polarimetric synthetic aperture radar (PolSAR) images require large amounts of data with manual annotations. Unfortunately, the complex echo imaging mechanism results in a high labeling cost for PolSAR images. Extracting and transferring knowledge to utilize the existing labeled data to the fullest extent is a viable approach in such circumstances. To this end, we are introducing unsupervised deep adversarial domain adaptation (ADA) into PolSAR image classification for the first time. In contrast to the standard learning paradigm, in this study, the deep learning model is trained on labeled data from a source domain and unlabeled data from a related but distinct target domain. The purpose of this is to extract domain-invariant features and generalize them to the target domain. Although the feature transferability of ADA methods can be ensured through adversarial training to align the feature distributions of source and target domains, improving feature discriminability remains a crucial issue. In this paper, we propose a novel polarimetric scattering characteristics-guided adversarial network (PSCAN) for unsupervised PolSAR image classification. Compared with classical ADA methods, we designed an auxiliary task for PSCAN based on the polarimetric scattering characteristics-guided pseudo-label construction. This approach utilizes the rich information contained in the PolSAR data itself, without the need for expensive manual annotations or complex automatic labeling mechanisms. During the training of PSCAN, the auxiliary task receives category semantic information from pseudo-labels and helps promote the discriminability of the learned domain-invariant features, thereby enabling the model to have a better target prediction function. The effectiveness of the proposed method was demonstrated using data captured with different PolSAR systems in the San Francisco and Qingdao areas. Experimental results show that the proposed method can obtain satisfactory unsupervised classification results.
Synergistic Change and Driving Mechanisms of Hydrological Processes and Ecosystem Quality in a Typical Arid and Semi-Arid Inland River Basin, China
Authors: Chen Hongguang , Meng Fanhao , Sa Chula , Luo Min , Zhang Huiting , Bao Shanhu , Liu Guixiang , Bao Yuhai
Published: March 2023

Global warming and human activities are complicating the spatial and temporal relationships between basin hydrologic processes and ecosystem quality (EQ), especially in arid and semi-arid regions. Knowledge of the synergy between hydrological processes and ecosystems in arid and semi-arid zones is an effective measure to achieve ecologically sustainable development. In this study, the inland river basin Ulagai River Basin (URB), a typical arid and semi-arid region in Northern China, was used as the study area; based on an improved hydrological model and remote-sensing and in situ measured data, this URB-focused study analyzed the spatial and temporal characteristics of hydrological process factors, such as precipitation, evapotranspiration (ET), surface runoff, lateral flow, groundwater recharge, and EQ and the synergistic relationships between them. It was found that, barring snowmelt, the hydrological process factors such as precipitation, ET, surface runoff, lateral flow, and groundwater recharge had a rising trend in the URB, since the 20th century. The rate of change was higher in the downstream areas when compared with what it was in the upstream and midstream areas. The multi-year average of EQ in the basin is 53.66, which is at a medium level and has an overall improving trend, accounting for 95.14% of the total area, mainly in the upstream, downstream southern, and downstream northern areas of the basin. The change in relationship between the hydrological process factors and EQ was found to have a highly synergistic effect. Temporally, EQ was consistent with the interannual trends of precipitation, surface runoff, lateral flow, and groundwater recharge. The correlation between the hydrological process factors and EQ was found to be higher than 0.7 during the study period. Spatially, the hydrological process factors had a synergistic relationship with EQ from strong to weak upstream, midstream, and downstream, respectively. In addition, ecosystem improvements were accelerated by government initiatives such as the policy of Returning Grazing Land to Grassland Project, which has played an important role in promoting soil and water conservation and EQ. This study provides theoretical support for understanding the relationship between hydrological processes and ecological evolution in arid and semi-arid regions, and it also provides new ideas for related research.
Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation
Authors: Huang Xinyu , Shirahama Kimiaki , Irshad Muhammad Tausif , Nisar Muhammad Adeel , Piet Artur , Grzegorzek Marcin
Published: March 2023

The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset—Sleep-EDFX—to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.
Highly Robust Adaptive Sliding Mode Trajectory Tracking Control of Autonomous Vehicles
Authors: Xie Fengxi , Liang Guozhen , Chien Ying-Ren
Published: March 2023

Autonomous driving technology has not yet been widely adopted, in part due to the challenge of achieving high-accuracy trajectory tracking in complex and hazardous driving scenarios. To this end, we proposed an adaptive sliding mode controller optimized by an improved particle swarm optimization (PSO) algorithm. Based on the improved PSO, we also proposed an enhanced grey wolf optimization (GWO) algorithm to optimize the controller. Taking the expected trajectory and vehicle speed as inputs, the proposed control scheme calculates the tracking error based on an expanded vector field guidance law and obtains the control values, including the vehicle’s orientation angle and velocity on the basis of sliding mode control (SMC). To improve PSO, we proposed a three-stage update function for the inertial weight and a dynamic update law for the learning rates to avoid the local optimum dilemma. For the improvement in GWO, we were inspired by PSO and added speed and memory mechanisms to the GWO algorithm. Using the improved optimization algorithm, the control performance was successfully optimized. Moreover, Lyapunov’s approach is adopted to prove the stability of the proposed control schemes. Finally, the simulation shows that the proposed control scheme is able to provide more precise response, faster convergence, and better robustness in comparison with the other widely used controllers.
Tackling Age of Information in Access Policies for Sensing Ecosystems
Authors: Zancanaro Alberto , Cisotto Giulia , Badia Leonardo
Published: March 2023

Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system.
Theoretical and Experimental Investigations on High-Precision Micro-Low-Gravity Simulation Technology for Lunar Mobile Vehicle
Authors: Hou Weijie , Hao Yongbo , Wang Chang , Chen Lei , Li Guangping , Zhao Baoshan , Wang Hao , Wei Qingqing , Xu Shuo , Feng Kai , Zang Libin
Published: March 2023

With the development of space technology, the functions of lunar vehicles are constantly enriched, and the structure is constantly complicated, which puts forward more stringent requirements for its ground micro-low-gravity simulation test technology. This paper puts forward a high-precision and high-dynamic landing buffer test method based on the principle of magnetic quasi-zero stiffness. Firstly, the micro-low-gravity simulation system for the lunar vehicle was designed. The dynamic model of the system and a position control method based on fuzzy PID parameter tuning were established. Then, the dynamic characteristics of the system were analyzed through joint simulation. At last, a prototype of the lunar vehicle’s vertical constant force support system was built, and a micro-low-gravity landing buffer test was carried out. The results show that the simulation results were in good agreement with the test results. The sensitivity of the system was better than 0.1%, and the constant force deviation was 0.1% under landing impact conditions. The new method and idea are put forward to improve the micro-low-gravity simulation technology of lunar vehicles.
Patient–Therapist Cooperative Hand Telerehabilitation through a Novel Framework Involving the Virtual Glove System
Authors: Placidi Giuseppe , Di Matteo Alessandro , Lozzi Daniele , Polsinelli Matteo , Theodoridou Eleni
Published: March 2023

Telerehabilitation is important for post-stroke or post-surgery rehabilitation because the tasks it uses are reproducible. When combined with assistive technologies, such as robots, virtual reality, tracking systems, or a combination of them, it can also allow the recording of a patient’s progression and rehabilitation monitoring, along with an objective evaluation. In this paper, we present the structure, from actors and functionalities to software and hardware views, of a novel framework that allows cooperation between patients and therapists. The system uses a computer-vision-based system named virtual glove for real-time hand tracking (40 fps), which is translated into a light and precise system. The novelty of this work lies in the fact that it gives the therapist quantitative, not only qualitative, information about the hand’s mobility, for every hand joint separately, while at the same time providing control of the result of the rehabilitation by also quantitatively monitoring the progress of the hand mobility. Finally, it also offers a strategy for patient–therapist interaction and therapist–therapist data sharing.
3D LiDAR Point Cloud Registration Based on IMU Preintegration in Coal Mine Roadways
Authors: Yang Lin , Ma Hongwei , Nie Zhen , Zhang Heng , Wang Zhongyang , Wang Chuanwei
Published: March 2023

Point cloud registration is the basis of real-time environment perception for robots using 3D LiDAR and is also the key to robust simultaneous localization and mapping (SLAM) for robots. Because LiDAR point clouds are characterized by local sparseness and motion distortion, the point cloud features of coal mine roadway environments show a weak texture and degradation. Therefore, for these environments, the traditional point cloud registration method to register directly will lead to problems, such as a decline in registration accuracy, z-axis drift, and map ghosting. To solve the above problems, we propose a point cloud registration method based on IMU preintegration with the sensor characteristics of LiDAR and IMU. The system framework of this method mainly consists of four modules: IMU preintegration, point cloud preprocessing, point cloud frame matching and point cloud registration. First, IMU sensor data are introduced, and IMU linear interpolation is used to correct the motion distortion in LiDAR scanning, and the IMU preintegration error function is constructed. Second, the point cloud segmentation is performed using the ground segmentation method of RANSAC to provide additional ground constraints for the z-axis displacement and to remove the unstable flawed points from the point cloud. On this basis, the LiDAR point cloud registration error function is constructed by extracting the feature corner points and feature plane points. Finally, the Gaussian Newton solution is used to optimize the constraint relationship between the LiDAR odometry frames to minimize the error function, complete the LiDAR point cloud registration and better estimate the position and pose of the mobile robot. The experimental results show that compared with the traditional point cloud registration method, the proposed method has a higher point cloud registration accuracy, success rate and computational efficiency. The LiDAR odometry constructed using this method can better reflect the authenticity of the robot trajectory and has higher trajectory accuracy and smaller absolute position and pose error.
A CNN-Based Wearable System for Driver Drowsiness Detection
Authors: Li Yongkai , Zhang Shuai , Zhu Gancheng , Huang Zehao , Wang Rong , Duan Xiaoting , Wang Zhiguo
Published: March 2023

Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and require complex algorithms to accommodate user (e.g., sunglasses) and environmental (e.g., lighting conditions) constraints. This paper presents a lightweight convolution neural network that measures eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples. The experimental results showed that the wearable glass prototype, with the neural network in its core, was highly effective in detecting eye blinks. The blink rate derived from the glass output was highly consistent with an industrial gold standard EyeLink eye-tracker. As eye blink characteristics are sensitive measures of driver drowsiness, the glass prototype and the lightweight neural network presented in this paper would provide a computationally efficient yet viable solution for real-world applications.
SliceLRF: A Local Reference Frame Sliced along the Height on the 3D Surface
Authors: Zhong Bin , Li Dong
Published: March 2023

The local reference frame (LRF) plays a vital role in local 3D shape description and matching. Numerous LRF methods have been proposed in recent decades. However, few LRFs can achieve a balance between repeatability and robustness under exposure to a variety of nuisances, including Gaussian noise, mesh resolution variation, clutter, and occlusion. Additionally, most LRFs are heuristic and lack generalizability to different applications and data modalities. In this paper, we first define the degree of distinction to describe the distribution of 2D point clouds and explore the relationship between the relative deviation of the distinction degree and the LRF error through experiments. Based on Gaussian noise and a random sampling analysis, several factors that affect the relative deviation of the distinction degree and result in the LRF error are identified. A scoring criterion is proposed to evaluate the robustness of the point cloud distribution. On this basis, we propose an LRF method (SliceLRF) based on slicing along the Z-axis, which selects the most robust adjacent slices in the point cloud region by scoring criteria for X-axis estimation to improve the repeatability and robustness. SliceLRF is rigorously tested on four public benchmark datasets which have different applications and involve different data modalities. It is also compared with the state-of-the-art LRFs. The experimental results show that the SliceLRF has more comprehensive repeatability and robustness than the other LRFs under exposure to Gaussian noise and random sampling.
Real-Time Safe Landing Zone Identification Based on Airborne LiDAR
Authors: Massoud Ali , Fahmy Ahmed , Iqbal Umar , Givigi Sidney , Noureldin Aboelmagd
Published: March 2023

Over the past two decades, there has been a growing demand for generating digital surface models (DSMs) in real-time, particularly for aircraft landing in degraded visual environments. Challenging landing environments can hinder a pilot’s ability to accurately navigate, see the ground, and avoid obstacles that may lead to equipment damage or loss of life. While many accurate and robust filtering algorithms for airborne laser scanning (ALS) data have been developed, they are typically computationally expensive. Moreover, these filtering algorithms require high execution times, making them unsuitable for real-time applications. This research aims to design and implement an efficient algorithm that can be used in real-time on limited-resource embedded processors without the need for a supercomputer. The proposed algorithm effectively identifies the best safe landing zone (SLZ) for an aircraft/helicopter based on processing 3D LiDAR point cloud data collected from a LiDAR mounted on the aircraft/helicopter. The algorithm was successfully implemented in C++ in real-time and validated using professional software for flight simulation. By comparing the results with maps, this research demonstrates the ability of the developed method to assist pilots in identifying the safest landing zone for helicopters.
Aligning Superconducting Transition-Edge Sensors by Reflected Wave Intensity Measurement
Authors: Ma Pei-Sa , Zhang Hong-Fan , Zhou Xingxiang
Published: March 2023

It is critical to accurately align a quantum photon detector such as a superconducting transition-edge sensor (TES) to an optical fiber in order to optimize its detection efficiency. Conventionally, such alignment requires advanced infrared imaging equipment or sophisticated microfabrication. We introduce a novel technique based on the simple idea of reflected wave intensity measurement which allows to determine the boundary of the sensor and align it accurately with the fiber. By routing a light wave through an optical fiber for normal incidence on the surface of the sensor chip, and separating the reflected wave coupled back into the fiber from the input signal with a circulator, we can observe the variation in the reflected wave intensity when the beam spot of the fiber crosses the boundary between the sensor and substrate that have different reflectivity, and adjust the position of the fiber such that its output falls on the sensor. We evaluate quantitatively the precision of our alignment method, as well as the conditions that must be met to avoid photon loss caused by light beam divergence. After demonstrating the working principle of our scheme and verifying the alignment result experimentally, we employ it for efficient input signal coupling to a TES device, which is used for photon-number-resolving measurement to showcase the successful application of our alignment method in practice. Relying on only ordinary and widely used optical elements that are easy to operate and low in cost, our solution is much less demanding than conventional methods. Dramatically easier to implement and not restricted by the detection mechanism of the sensor, it is accessible to a much broader community.
Threshold-Responsive Colorimetric Sensing System for the Continuous Monitoring of Gases
Authors: Mo Manni , Fu Bo , Hota Piyush , Cay-Durgun Pinar , Wang Ran , Cheng Edward H. , Wiktor Peter , Tsow Francis , Thomas Leslie , Lind Mary Laura , Forzani Erica
Published: March 2023

Colorimetric sensors are widely used because of their inherent advantages including accuracy, rapid response, ease-of-use, and low costs; however, they usually lack reusability, which precludes the continuous use of a single sensor. We have developed a threshold-responsive colorimetric system that enables repeated analyte measurements by a single colorimetric sensor. The threshold responsive algorithm automatically adjusts the sensor exposure time to the analyte and measurement frequency according to the sensor response. The system registers the colorimetric sensor signal change rate, prevents the colorimetric sensor from reaching saturation, and allows the sensor to fully regenerate before the next measurement is started. The system also addresses issues common to colorimetric sensors, including the response time and range of detection. We demonstrate the benefits and feasibility of this novel system, using colorimetric sensors for ammonia and carbon dioxide gases for continuous monitoring of up to (at least) 60 detection cycles without signs of analytical performance degradation of the sensors.
A Virtual Multi-Ocular 3D Reconstruction System Using a Galvanometer Scanner and a Camera
Authors: Han Zidong , Zhang Liyan
Published: March 2023

A novel visual 3D reconstruction system, composed of a two-axis galvanometer scanner, a camera with a lens, and a set of control units, is introduced in this paper. By changing the mirror angles of the galvanometer scanner fixed in front of the camera, the boresight of the camera can be quickly adjusted. With the variable boresight, the camera can serve as a virtual multi-ocular system (VMOS), which captures the object at different perspectives. The working mechanism with a definite physical meaning is presented. A simple and efficient method for calibrating the intrinsic and extrinsic parameters of the VMOS is presented. The applicability of the proposed system for 3D reconstruction is investigated. Owing to the multiple virtual poses of the camera, the VMOS can provide stronger constraints in the object pose estimation than an ordinary perspective camera does. The experimental results demonstrate that the proposed VMOS is able to achieve 3D reconstruction performance competitive with that of a conventional stereovision system with a much more concise hardware configuration.