bluuebunny/crossref_metadata_2025 huggingface dataset:
Joule Heating and Arc-Fault-Induced Electrical Fires for Commercial-Grade Copper and Brass in Low-Voltage Electrical Systems
Authors: Wangwiwattana Sittichai , Yoshikazu Koike
Published: May 2022

Electrical fires are usually caused by contact heating, for which the mechanism of heat accumulation is currently not well understood. Previous studies done by many researchers were able to recognize the pattern of the electrical waveform, but the actual mechanism behind such phenomenon is still a mystery. This research focuses on electrical plugs and sockets made from copper and electrical brass, which are the main components where unsupervised electrical fires often occur due to mechanical wear and electrical breakdown. We performed experiments using commercial-grade replacement electrical sockets and plugs made primarily to be compliant with Japan Industrial Standards. Oxides of the materials were formed by subjecting the samples to variable amounts of current and repeatedly making and breaking contact to create an electrical discharge. The copper(I) oxide that formed due to electrical discharge was found to have intrinsic semiconductor properties that played a critical role in electrical fires involving copper components. Poor contacts exhibited distinct behavior, such as contact vibration which also exacerbated the deterioration. From these findings, we concluded that any contact surface made of copper or a copper alloy may have a greater risk of starting an electrical fire than other types of material when a certain arcing condition is met.
A Method to Improve the Accuracy of Pavement Crack Identification by Combining a Semantic Segmentation and Edge Detection Model
Authors: Li Peigen , Xia Haiting , Zhou Bin , Yan Feng , Guo Rongxin
Published: May 2022

In recent years, deep learning-based detection methods have been applied to pavement crack detection. In practical applications, surface cracks are divided into inner and edge regions for pavements with rough surfaces and complex environments. This creates difficulties in the image detection task. This paper is inspired by the U-Net semantic segmentation network and holistically nested edge detection network. A side-output part is added to the U-Net decoder that performs edge extraction and deep supervision. A network model combining two tasks that can output the semantic segmentation results of the crack image and the edge detection results of different scales is proposed. The model can be used for other tasks that need both semantic segmentation and edge detection. Finally, the segmentation and edge images are fused using different methods to improve the crack detection accuracy. The experimental results show that mean intersection over union reaches 69.32 on our dataset and 61.05 on another pavement dataset group that did not participate in training. Our model is better than other detection methods based on deep learning. The proposed method can increase the MIoU value by up to 5.55 and increase the MPA value by up to 10.41 when compared to previous semantic segmentation models.
An S/C/X-Band 4-Bit Digital Step Attenuator MMIC with 0.25 μm GaN HEMT Technology
Authors: Kim Jeong-Geun , Lee Kang-Hee
Published: May 2022

In this paper, a 4-bit digital step attenuator using 0.25 μm GaN HEMT technology for wideband radar systems is presented. A switched-path attenuator topology with resistive T-type attenuators and double-pole double-throw (DPDT) switches was used to achieve both low insertion loss and phase/amplitude error. The measured insertion loss of the reference state is 2.8–8.3 dB at DC-12 GHz. The input and output return loss are less than 12 dB at DC-12 GHz. An attenuation coverage of 30 dB with a least significant bit of 2 dB was achieved at DC-12 GHz. A root mean square (RMS) amplitude error of 1 dB and a phase error of 8.5° were achieved, respectively. The attenuator chip size is 2.45 mm × 1.75 mm including pads. To the best of the authors’ knowledge, this is the first demonstration of a GaN-based digital step attenuator.
Service Innovation and Quality Assessment of Industry 4.0 Microservice through Data Modeling and System Simulation Evaluation Approaches
Authors: Wen Yen-Chun , Chen Wun-Hwa
Published: May 2022

This study proposes a system construction approach under Industry 4.0 infrastructure that is validated by the proposed framework of microservice quality assessment with framework with data modeling and simulation methodology to achieve innovation and value cocreation goals. The framework, which combines a dynamic process flow with service-dominant logic design and reliability assessment using a multilayer perceptron (MLP) prediction model can assist decision makers in optimizing their service innovation and decision-making processes. The service innovation and evaluation approaches have implications for optimizing the corporation cooperation. The corporation can form a much more comprehensive manufacturing infrastructure or system by considering the requirements and assessment results of third parties. To help the corporation redefine its value proposition and system structure, we must examine the system interaction between different hierarchical layers within the Industry 4.0 system infrastructure. This study used a production dataset from the NASDAQ-listed electronics corporation and two top German and Japanese automobile firms. The proposed system framework had already been validated and introduced to improve 12% of service quality. The system integrated with anticipated functions will accelerate service innovation and optimization by combining MLP and Kaplan–Meier estimation methodologies by extracting the characteristics of realistic datasets.
Processing the Artificial Edge-Effects for Finite-Difference Frequency-Domain in Viscoelastic Anisotropic Formations
Authors: Yang Jixin , He Xiao , Chen Hao
Published: May 2022

Real sedimentary media can usually be characterized as transverse isotropy. To reveal wave propagation in the true models and improve the accuracy of migrations and evaluations, we investigated the algorithm of wavefield simulations in an anisotropic viscoelastic medium. The finite difference in the frequency domain (FDFD) has several advantages compared with that in the time domain, e.g., implementing multiple sources, multi-scaled inversion, and introducing attenuation. However, medium anisotropy will lead to the complexity of the wavefield in the calculation. The damping profile of the conventional absorption boundary is only defined in one single direction, which produces instability when the wavefields of strong anisotropy are reflected on that truncated boundary. We applied the multi-axis perfectly matched layer (M-PML) to the wavefield simulations in anisotropic viscoelastic media to overcome this issue, which defines the damping profiles along different axes. In the numerical examples, we simulated seismic wave propagation in three viscous anisotropic media and focused on the wave attenuation in the absorbing layers using time domain snapshots. The M-PML was more effective for wave absorption compared to the conventional perfectly matched layer (PML). In strongly anisotropic media, the PML became unstable, and prominent reflections appeared at truncated boundaries. In contrast, the M-PML remained stable and efficient in the same model. Finally, the modeling of the stratified cross-well model showed the applicability of this proposed algorithm to heterogeneous viscous anisotropic media. The numerical algorithm can analyze wave propagation in viscoelastic anisotropic media. It also provides a reliable forward operator for waveform inversion, wave equation travel-time inversion, and seismic migration in anisotropic viscoelastic media.
Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network
Authors: Shi Cheng , Zheng Zhijie , Pan Jun , Ni Zhi-Kang , Ye Shengbo , Fang Guangyou
Published: May 2022

Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.
Development of a Novel Passive-Dynamic Custom AFO for Drop-Foot Patients: Design Principles, Manufacturing Technique, Mechanical Properties Characterization and Functional Evaluation
Authors: Caravaggi Paolo , Zomparelli Alessandro , Rogati Giulia , Baleani Massimiliano , Fognani Roberta , Cevolini Franco , Fanciullo Cristina , Cinquepalmi Arianna , Lullini Giada , Berti Lisa , Leardini Alberto
Published: May 2022

Ankle foot orthoses (AFOs) are medical devices prescribed to support the foot and ankle of drop-foot patients. Passive-dynamic AFOs (PD-AFOs) are an effective solution for less severe cases. While off-the-shelf PD-AFOs are rather inexpensive, they provide poor anatomical fit and do not account for the required patient-specific biomechanical support. Three-dimensional (3D) scanning and manufacturing technologies allow manufacturing PD-AFOs customized for the patient’s anatomy and functional needs. This paper aimed to report the overall procedure for designing and manufacturing a novel, fiberglass-reinforced polyamide, custom PD-AFO. The feasibility of the proposed procedure was tested in a case study. The methodology can be divided into the following steps: (i) foot and leg scanning, (ii) 3D design, and (iii) additive manufacturing via selective laser sintering. A custom PD-AFO was designed and manufactured for a 67-year-old male drop-foot patient following paraparesis in severe discarthrosis after spine stabilization surgery. AFO mechanical properties were measured via an ad hoc setup based on a servohydraulic testing machine. The functional outcome was assessed via gait analysis in three conditions: shod (no AFO), wearing an off-the-shelf PD-AFO, and wearing the patient-specific PD-AFO. As expected, wearing the PD-AFO resulted in increased ankle dorsiflexion in the swing phase with respect to the shod condition. Sagittal rotations of the hip, knee, and ankle joints were similar across PD-AFO conditions, but the custom PD-AFO resulted in faster walking speed with respect to the off-the-shelf (walking speed: 0.91 m/s versus 0.85 m/s). Additionally, the patient scored the custom PD-AFO as more comfortable (VAS score: 9.7 vs. 7.3). While the present analysis should be extended to a larger cohort of drop-foot patients, the novel PD-AFO seems to offer a valid, custom solution for drop-foot patients not satisfied with standard orthotics.
Silica Microsphere WGMR-Based Kerr-OFC Light Source and Its Application for High-Speed IM/DD Short-Reach Optical Interconnects
Authors: Salgals Toms , Alnis Janis , Ozolins Oskars , Andrianov Alexey V. , Anashkina Elena A. , Brice Inga , Berkis Roberts , Pang Xiaodan , Udalcovs Aleksejs , Porins Jurgis , Spolitis Sandis , Bobrovs Vjaceslavs
Published: May 2022

Kerr optical frequency combs (OFCs) based on silica microsphere whispering gallery mode resonator (WGMR) have various applications where they are used as a light source. For telecommunication purposes, WGMR-based Kerr-OFC comb generators can be physically realized using silica microsphere resonators and can be used to replace multiple laser arrays. In such a realization, these novel light sources have the potential to demonstrate an attractive solution for intra-datacenter interconnects (DCI). In this paper, we show an experimental demonstration of a silica microsphere WGMR-based Kerr OFC light source where newly generated 400 GHz spaced carriers together with powerful linear equalization techniques, such as a linear symbol-spaced adaptive decision-feedback equalizer (DFE) with feed-forward (FF) and feedback (FB) taps, provide an alternative to individual lasers ensuring low-cost and low-complexity IM/DD scheme for the transmission of NRZ-OOK modulated signals at data rates up to 50 Gbps/λ over 2 km SMF link. Finally, we demonstrate a record 50 Gbps per λ transmission of NRZ-OOK modulated signals with a novel silica microsphere WGMR-based Kerr-OFC as a light source operating in the optical C-band, surpassing the previously demonstrated data rate record by five times.
A New Competitive Neural Architecture for Object Classification
Authors: Madiafi Mohammed , Ezzahar Jamal , Baraka Kamal , Bouroumi Abdelaziz
Published: May 2022

In this paper, we propose a new neural architecture for object classification, made up from a set of competitive layers whose number and size are dynamically learned from training data using a two-step process that combines unsupervised and supervised learning modes. The first step consists in finding a set of one or more optimal prototypes for each of the c classes that form the training data. For this, it uses the unsupervised learning and prototype generator algorithm called fuzzy learning vector quantization (FLVQ). The second step aims to assess the quality of the learned prototypes in terms of classification results. For this, the c classes are reconstructed by assigning each object to the class represented by its nearest prototype, and the obtained results are compared to the original classes. If one or more constructed classes differ from the original ones, the corresponding prototypes are not validated and the whole process is repeated for all misclassified objects, using additional competitive layers, until no difference persists between the constructed and the original classes or a maximum number of layers is reached. Experimental results show the effectiveness of the proposed method on a variety of well-known benchmark data sets.
A Hybrid Self-Seeded Ti:sapphire Laser with a Pumping Scheme Based on Spectral Beam Combination of Continuous Wave Diode and Pulsed DPSS Lasers
Authors: Sonnenschein Volker , Tomita Hideki
Published: May 2022

A wide variety of applications require high peak laser intensity in conjunction with a narrow spectral linewidth. Typically, injection-locked amplifiers have been employed for this purpose, where a continuous wave oscillator is amplified in a secondary external resonant amplifier cavity using a pulsed pump laser. In contrast, here we demonstrate a setup that combines a CW Ti:sapphire oscillator and pulsed amplifier in a single optical cavity, resulting in a compact system. Dichroic beam combination of blue wavelength semiconductor diodes and the green wavelength of a Nd:YAG laser allowed the simultaneous excitation of the Ti:sapphire crystal by both continuous wave and pulsed pump sources. A linewidth of <2 MHz is achieved in continuous wave operation, while the linewidth increases to about 10 MHz in the combined CW+pulsed mode with a pulse duration of 73 ns. A peak pulse intensity of 0.2 kW is achieved, which should enable efficient single-pass second harmonic generation in a nonlinear crystal.
Resilience Evaluation of Shallow Circular Tunnels Subjected to Earthquakes Using Fragility Functions
Authors: Huang Zhongkai
Published: May 2022

The present work aims to introduce an integrated framework for the resilience evaluation of shallow circular tunnels subjected to earthquakes using fragility and restoration functions. A typical shallow circular tunnel in Shanghai city of China is examined in this work and a corresponding numerical model is established using ABAQUS. Then, a set of ground motions are well chosen to implement large numbers of non-linear numerical analyses so as to determine the lining responses of the tunnel structure with various levels of seismic intensities. According to the above numerical results, fragility functions in terms of the peak ground acceleration (PGA) and peak ground velocity (PGV) at the free-field ground surface are generated, accounting for the main sources of uncertainty, and the direct seismic loss for the examined tunnel is obtained. Moreover, according to the developed fragility functions and the existing empirical tunnel restoration functions, the evolution of the resilience index (Re) with various levels of PGA and PGV for the examined tunnel is derived and quantified. The results indicate that the tunnel resilience will decrease significantly as the earthquake intensity measure (IM), i.e., PGA or PGV herein, increases. The proposed framework is expected to help city managers support adaptations to seismic hazards with the development of preventive or retrofitting measures as part of efforts to provide more resilient metro systems.
Microstructural Analysis and Mechanical Properties of a Hybrid Al/Fe2O3/Ag Nano-Composite
Authors: Salman Khansaa Dawood , Al-Maliki Wisam Abed Kattea , Alobaid Falah , Epple Bernd
Published: May 2022

This work aims to define the microstructure and to study the mechanical properties of an Al matrix incorporated with various amounts of Fe2O3 (3, 6, 9, 12 and 15 wt.%) with a constant amount of Ag at 1 wt.%. Al/Fe2O3 + Ag hybrid nano-composite samples are manufactured using powder metallurgy. An aluminum matrix is considered an important alloy, owing to its properties such as being lightweight, strong and corrosion and wear resistant, which enable it to be used in many applications, such as electronics, aerospace and automotive purposes. Various examinations have been performed for the samples of this work, such as Field Emission Scanning Electron Microscopy (FESEM) and X-ray Diffraction (XRD) analysis to estimate the microstructure and phases of manufactured nano-composites. Mechanical testing is also carried out, such as micro-hardness testing, compressive testing and wear testing, to estimate the mechanical properties of the hybrid nano-composites. The results of FESEM and XRD demonstrate that Fe2O3 and Ag nanoparticles are uniformly distributed and dispersed into the Al matrix, whereas the mechanical tests show that enhancement t micro-hardness, compressive strength of 12 wt.% Fe2O3 + 1Ag and wear rate decrease to a minimum value of 12 wt.% of Fe2O3 + 1Ag.
Vehicle Re-Identification in Aerial Imagery Based on Normalized Virtual Softmax Loss
Authors: Qiao Wenzuo , Ren Wenjuan , Zhao Liangjin
Published: May 2022

With the development and popularization of unmanned aerial vehicles (UAVs) and surveillance cameras, vehicle re-identification (ReID) task plays an important role in the field of urban safety. The biggest challenge in the field of vehicle ReID is how to robustly learn the common visual representation of vehicle from different viewpoints while discriminate different vehicles with similar visual appearance. In order to solve this problem, this paper designs the normalized virtual softmax loss to enlarge the inter-class distance and decrease the intra-class distance, and a vehicle ReID model is proposed by jointly training the network with the proposed loss and triplet loss. In addition, we contribute a novel UAV vehicle ReID dataset from multiple viewpoint images to verify the robustness of methods. The experimental results show that comparing with the other softmax-based losses, our method achieves better performance and gets 76.70% and 98.95% in Rank-1 on VRAI and VRAI_AIR dataset, respectively.
The Assessment of the Type of Substrate and LEDs’ Irradiation Influence on Garden Cress Sprouts (Lepidium sativum L.)
Authors: Livadariu Oana , Maximilian Carmen , Raiciu Anca Daniela , Șerbănică Cristian-Paul
Published: May 2022

In the current context of climate change and life on Earth, the purpose of this paper is to establish the effects of different light treatments (i.e., LEDs—white, blue, red, and sunlight) and two different substrates (i.e., gauze and biochar) on garden cress sprouts. In this respect, sprouts’ fresh weight and fresh length were statistically analyzed. In the case of the fresh weight, the results indicated that the most suitable substrate for better growth is biochar under all of the light treatments, with a 5.78% weight increase. On the other hand, the highest particular value regarding weight was obtained for gauze substrate under sunlight treatment, with a 4.08% weight increase compared to biochar substrate and sunlight treatment. Furthermore, red LED and biochar used as substrate improved the development of short radicles, with a decrease in length of 94.80% compared to gauze substrate and red LED treatment. The effect of LED irradiation was completed with biochemical analyses concerning polyphenols, flavonoid content, and antioxidant activity. Results suggest that blue LED is the most suitable source of artificial light to promote the accumulation of secondary metabolites, with a 9.88% higher concentration of flavonoids compared to the other light variants (2.22 ± 0.40 mg rutin equivalent/g fresh weight) and 37.98% higher concentration of polyphenols compared to the other light variants (2.76 ± 1.05 mg gallic acid equivalent/g fresh weight) in garden cress sprouts. The illumination with white LED produced the highest effect concerning antioxidant activity, with a 37.60% higher free radical scavenging activity.
Brassinin Enhances Apoptosis in Hepatic Carcinoma by Inducing Reactive Oxygen Species Production and Suppressing the JAK2/STAT3 Pathway
Authors: Rajendran Peramaiyan , Elsawy Hany , Alfwuaires Manal , Sedky Azza
Published: May 2022

Plants from the family Brassicaceae produce brassinin (BSN), which is an essential indole phytoalexin. BSN can kill certain types of cancer cells. Using hepatocarcinoma (HCC) cells, we examined the molecular mechanisms of BSN. We found that HCC cell growth was suppressed and apoptosis was induced by BSN via the downregulation of the JAK/STAT3 pathway. The cytoplasmic latent transcription factor STAT3, belonging to the STAT family, acted as both a signal transducer and an activator and was linked to tumor progression and decreased survival. BSN incubation caused HCC cells to produce reactive oxygen species (ROS). By activating caspase-9/-3 and PARP cleavage, Bcl-2 was reduced, and apoptosis was increased. BSN inhibited constitutive STAT3, JAK2, and Src phosphorylation. The JAK/STAT signaling cascade was confirmed by siRNA silencing STAT3 in HCC cells. BSN also suppressed apoptosis by Z-Val-Ala-Asp-Fluoromethylketone (Z-VAD-FMK), an apoptotic inhibitor. N-acetylcysteine (NAC) inhibited the production of ROS and diminished BSN-induced apoptosis. Our findings suggested that BSN has potential as a treatment for cancer.
Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
Authors: Liao Zhihao , Fan Neng , Xu Kai
Published: May 2022

Transformer complements convolutional neural network (CNN) has achieved better performance than improved CNN-based methods. Specially, Transformer is utilized to be combined with U-shaped structure, skip-connections, encoder, and even them all together. However, the intermediate supervision network based on the coarse-to-fine strategy has not been combined with Transformer to improve the generalization of CNN-based methods. In this paper, we propose Swin-PANet, which is applying a window-based self-attention mechanism by Swin Transformer in the intermediate supervision network, called prior attention network. A new enhanced attention block based on CCA is also proposed to aggregate the features from skip-connections and prior attention network, and further refine details of boundaries. Swin-PANet can address the dilemma that traditional Transformer network has poor interpretability in the process of attention calculation and Swin-PANet can insert its attention predictions into prior attention network for intermediate supervision learning which is humanly interpretable and controllable. Hence, the intermediate supervision network assisted by Swin Transformer provides better attention learning and interpretability in network for accurate and automatic medical image segmentation. The experimental results evaluate the effectiveness of Swin-PANet which outperforms state-of-the-art methods in some famous medical segmentation tasks including cell and skin lesion segmentation.
Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions
Authors: Quatrini Elena , Costantino Francesco , Li Xiaochuan , Mba David
Published: May 2022

In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector.
The Propagation of Hydraulic Fractures in a Natural Fracture Network: A Numerical Study and Its Implications
Authors: Liu Yiwei , Hu Yi , Kang Yong
Published: May 2022

Natural fractures play a significant role in creating a fracture network simulation treatment. In this work, global cohesive elements were incorporated into the cohesive zone method to realize the unprompted propagation of a hydraulic fracture. The step-by-step propagation patterns of hydraulic fractures in a random natural fracture network were discussed. An effective area was defined to quantitively assess the influenced area of induced fractures. The results showed that the hydraulic fracture tips were attracted by local natural fractures when the horizontal stress difference was low. Bifurcations and secondary fractures occurred at the natural fracture intersections, which contributed to the complexity of the induced fracture network on a local scale. The length of the main hydraulic fracture reached the maximum when the in situ stress ratio was 1.12. The influence of natural fractures on the overall trend of fracture propagation was limited when the in situ stress difference increased. It also suggested that a lower rock tensile strength and natural fractures cementation strength improved the main fracture length. A higher tensile strength of rock increased the initiation pressure of the induced fracture, while the cementing strength of the natural fractures showed no impact on it. The results presented in this paper could improve the basic understanding of the fracture development in a natural network and help to predict a complex fracture network in a real situation.
Optimization of Locking Plate Screw Angle Used to Treat Two-Part Proximal Humerus Fractures to Maintain Fracture Stability
Authors: Castro-Franco Angel D. , Mendoza-Muñoz Ismael , González-Angeles Alvaro , Montoya-Reyes Mildrend I. , Pitalúa-Díaz Nun
Published: May 2022

Proximal humerus fractures increase with the aging of the population. Due to the high failure rates of surgical treatments such as open reduction and internal fixation (ORIF), biomechanical studies seek to optimize the treatments and intervening factors to improve the quality of life of people undergoing these treatments. The aim of the present study was to determine the optimal insertion angle configuration of screws used in a two-part proximal humerus fracture-locking plate osteosynthesis treatment based on finite element analysis (FEA). A series of 3D models of PHILOS locking plates with different screw insertion angle configurations were designed using a matrix system for screw angulation. The locking plate models were evaluated in a two-part proximal humerus fracture with surgical neck fracture under bending and compressive loading conditions using FEA and statistically analyzed using a design of experiments (DOE). The optimal screw insertion angle setting showed an improvement in relation to the interfragmentary strain value of the fracture. Moreover, calcar screws were the most significant feature in fracture stability throughout the tests, followed by the divergence of the most proximal screws and the proximal–distal alignment of the locking plate.
Mixed-Reality-Enhanced Human–Robot Interaction with an Imitation-Based Mapping Approach for Intuitive Teleoperation of a Robotic Arm-Hand System
Authors: Su Yun-Peng , Chen Xiao-Qi , Zhou Tony , Pretty Christopher , Chase Geoffrey
Published: May 2022

This paper presents an integrated mapping of motion and visualization scheme based on a Mixed Reality (MR) subspace approach for the intuitive and immersive telemanipulation of robotic arm-hand systems. The effectiveness of different control-feedback methods for the teleoperation system is validated and compared. The robotic arm-hand system consists of a 6 Degrees-of-Freedom (DOF) industrial manipulator and a low-cost 2-finger gripper, which can be manipulated in a natural manner by novice users physically distant from the working site. By incorporating MR technology, the user is fully immersed in a virtual operating space augmented by real-time 3D visual feedback from the robot working site. Imitation-based velocity-centric motion mapping is implemented via the MR subspace to accurately track operator hand movements for robot motion control and enables spatial velocity-based control of the robot Tool Center Point (TCP). The user control space and robot working space are overlaid through the MR subspace, and the local user and a digital twin of the remote robot share the same environment in the MR subspace. The MR-based motion and visualization mapping scheme for telerobotics is compared to conventional 2D Baseline and MR tele-control paradigms over two tabletop object manipulation experiments. A user survey of 24 participants was conducted to demonstrate the effectiveness and performance enhancements enabled by the proposed system. The MR-subspace-integrated 3D mapping of motion and visualization scheme reduced the aggregate task completion time by 48% compared to the 2D Baseline module and 29%, compared to the MR SpaceMouse module. The perceived workload decreased by 32% and 22%, compared to the 2D Baseline and MR SpaceMouse approaches.
CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes
Authors: Pandey Satyanand , Saha Indrashis , Prakash Om , Mukherjee Tathagata , Iqbal Jawed , Roy Amit Kumar , Wasilewski Marek , Brar Lakhbir Singh
Published: May 2022

Due to the great achievements in the field of optimization of the design of cyclone separators, non-standard solutions are sought to increase their performance. Therefore, in this study, we consider the impact of different cone and cylinder height variants on the performance of cyclone separators. Additionally, we propose non-standard shapes for these sections. Three different heights: H/D = 0.5, 1.0, and 1.5, with D (the main cyclone body diameter), are analyzed. Since the cone is one of the most important geometrical entities, three different shapes viz. a straight (conventional) profile, a concave profile as well as a convex profile are also taken into account. Cyclone performance is rated at three different inlet velocities viz. Uin = 10 m/s, 15 m/s, and 20 m/s. Hence, a total of 27 simulations have been performed using the Reynolds stress model. It becomes apparent from the present study that the pressure loss is lowest in the convex variant, whereas the separation efficiency is better in the conventional design. Furthermore, an increase in the length of the cylindrical section reduces pressure drop with a mild decrease in the collection efficiency in all variants.
Effects of Sidewall Brightness on LED Lighting Environment and Visual Performance in Road Tunnels
Authors: Chen Xuefeng , Feng Shouzhong , Li Jie , Zhou Long , Mao Weixing , Zhu Hehua
Published: May 2022

The lighting design of a road tunnel focuses on the setting of pavement luminance. As for the tunnel sidewall luminance, it simply follows the principle of no less than 60% of the pavement luminance. In fact, the sidewall is one of the important components of the tunnel lighting environment; however, the impact of the improvement of sidewall brightness on the LED lighting environment in a tunnel has not attracted enough attention. In this study, the impact of the improvement of tunnel sidewall brightness on the lighting environment and visual characteristics of human eyes were analyzed based on the lighting experiments when the tunnel sidewalls were decorated with two different types of materials and illuminated by LED lamps with five different color temperatures. The test results show that the tunnel sidewall luminance will increase if the energy-storage and luminescent coating with high reflectance is decorated on tunnel sidewalls, and the pavement luminance increases with the increasing sidewall luminance. After the improvement of the sidewall and pavement luminance, the visible distance for a small object in the tunnel will increase to a certain extent. Furthermore, there is no discomfort for drivers during driving, and the changing of eye pupil diameter is relatively stable, which indicates that the increase in tunnel sidewall luminance has a positive effect on the visual performance of drivers.
A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
Authors: Xiong Mingfu , Gao Zhiyu , Hu Ruimin , Chen Jia , He Ruhan , Cai Hao , Peng Tao
Published: May 2022

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each “descriptor” corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global “feature”, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O(n) for real-time person re-ID applications.
Optimization of Pre-Splitting Blasting Hole Network Parameters and Engineering Applications in Open Pit Mine
Authors: Wang Ziyi , Wu Guiyi , Zhou Lang
Published: May 2022

In order to optimize the parameters of a pre-splitting blasting hole network, taking an open-pit mine in Inner Mongolia as the engineering background, the numerical models of different pore sizes and hole spacing were established by LS-DYNA software. The stress wave propagation law, peak stress change and rock fracture state under various working conditions were analyzed. The optimization formula of a hole network relationship was proposed and verified on site. The results show that the shock wave generated by the explosion propagates rapidly upward from the explosion source and forms a plastic flow zone around the two boreholes. The energy consumption is the largest at this stage. With the propagation of the stress wave, energy dissipates, and its waveform gradually attenuates to a compression wave and seismic wave. In each working condition, a 110 mm aperture first cracked in the stress wave superposition area compared with other working conditions, while a 120 mm aperture delayed evolution to the seismic wave compared with different borehole aperture, and the energy attenuation rate is the slowest. Meanwhile, the fastest energy attenuation rate is with the 130 mm borehole aperture. With the attenuation of the propagation energy of the stress wave, among the four measuring points set at the center of the connection between the two boreholes, the Y-direction stress of the observation points B, C and D is stable between 2.3 and 3.5 MPa, and the Y-direction stress of the observation point A is strenuous between −1.3 and 1.2 MPa. The B, C and D observation points of 90–130 mm aperture conditions showed rock cracking at 7–9 times, 7–9 times, 7–10 times, 7–11 times, and 7–11 times hole spacing, respectively. The cracks of the two boreholes were interconnected. The optimal hole network relationship is obtained by fitting: y = 1.12 + 0.076x, where y is the optimal hole diameter and hole spacing multiple, and x is the hole diameter, which is verified by engineering. After blasting, the slope is smooth and smooth, and the half-hole rate is guaranteed to be above 90%.
Gaussian Processes for Signal Processing and Representation in Control Engineering
Authors: Dudek Adrian , Baranowski Jerzy
Published: May 2022

The Gaussian process is an increasingly well-known type of stochastic process, which is a generalization of the Gaussian probability distribution. It allows us to model complex functions thanks to its flexibility, which would not be possible with the use of other tools. Gaussian processes also have a couple of other features that are used in various branches of automation with positive results, ranging from industrial processes to image processing. There are also many ways of setting up the Gaussian processes, which required knowledge on the topic and depend on the presented problem. Considerations on these topics lead to the conclusion that the current state of practical usefulness of Gaussian processes increases significantly, therefore the deepening of knowledge about the ways of its use is highly suggested. In this review, we present selected technical applications of Gaussian Processes allowing an understanding of their broad applicability.
One Saddle Point and Two Types of Sensitivities within the Lorenz 1963 and 1969 Models
Authors: Shen Bo-Wen , Pielke Roger A. , Zeng Xubin
Published: May 2022

The fact that both the Lorenz 1963 and 1969 models suggest finite predictability is well known. However, less well known is the fact that the mechanisms (i.e., sensitivities) within both models, which lead to finite predictability, are different. Additionally, the mathematical and physical relationship between these two models has not been fully documented. New analyses, along with a literature review, are performed here to provide insights regarding similarities and differences for these two models. The models represent different physical systems, one for convection and the other for barotropic vorticity. From the perspective of mathematical complexities, the Lorenz 1963 (L63) model is limited-scale and nonlinear; and the Lorenz 1969 (L69) model is closure-based, physically multiscale, mathematically linear, and numerically ill-conditioned. The former possesses a sensitive dependence of solutions on initial conditions, known as the butterfly effect, and the latter contains numerical sensitivities due to an ill-conditioned matrix with a large condition number (i.e., a large variance of growth rates). Here, we illustrate that the existence of a saddle point at the origin is a common feature that produces instability in both systems. Within the chaotic regime of the L63 nonlinear model, unstable growth is constrained by nonlinearity, as well as dissipation, yielding time varying growth rates along an orbit, and, thus, a dependence of (finite) predictability on initial conditions. Within the L69 linear model, multiple unstable modes at various growth rates appear, and the growth of a specific unstable mode (i.e., the most unstable mode during a finite time interval) is constrained by imposing a saturation assumption, thereby yielding a time varying system growth rate. Both models were interchangeably applied for qualitatively revealing the nature of finite predictability in weather and climate. However, only single type solutions were examined (i.e., chaotic and linearly unstable solutions for the L63 and L69 models, respectively), and the L69 system is ill-conditioned and easily captures numerical instability. Thus, an estimate of the predictability limit using either of the above models, with or without additional assumptions (e.g., saturation), should be interpreted with caution and should not be generalized as an upper limit for atmospheric predictability.
Crop Yield, Nitrogen Recovery, and Soil Mineral Nitrogen Accumulation in Extremely Arid Oasis Cropland under Long-Term Fertilization Management
Authors: Li Shimin , Wang Xihe , Kou Changlin , Lv Jinling , Gao Jianhua
Published: May 2022

Crop yield stability and soil mineral nitrogen (Nmin) have rarely been evaluated from a long-term perspective in the extremely arid cropland regions of China. Therefore, a nationwide experiment aimed to optimize fertilizer application and increase productivity and nitrogen use efficiency in gray desert soils was initiated in 1990. Eight combinations of chemical fertilizers (CK, N, NK, NP, and NPK), straw return (NPKS), and manure amendments (NPKM and NPKM+) were tested for 24 years on spring wheat, winter wheat, and maize. The results displayed that the yield of three crops from balanced fertilizer treatments (NPK, NPKS, NPKM, and NPKM+) did not differ significantly after 24 years; however, reliable yield stability due to lower coefficient of variation (CV) and higher nitrogen harvest index (NHI) were recorded for manure amendment treatments. Compared to NPKM, NHI was lower for the NPKM+ treatment, but crop yield and stability did not improve, suggesting that the appropriate choice for manure amendment is important for guaranteeing food security in extremely arid regions. Balanced fertilizer treatments resulted in lower Nmin residual in the 300 cm soil profile, compared to unbalanced fertilizer treatments. The NPKS treatment gave the lowest value. In the 0–100 cm soil profile, Nmin was higher in NPKM than in the NPK treatment, suggesting that straw or manure amendment can effectively maintain Nmin in the topsoil undercurrent cropland management in arid areas. The NPKM treatment had the highest crop nitrogen recovery rate and the lowest nitrogen losses, further illustrating that manure amendment has higher N retention potential. Overall, although Nmin residues are relatively high in these regions, balanced fertilizer treatments, especially NPKM and NPKS, are the optimum strategies in extremely arid regions.
Comparison of Wildfire Meteorology and Climate at the Adriatic Coast and Southeast Australia
Authors: Tomašević Ivana Čavlina , Cheung Kevin K. W. , Vučetić Višnjica , Fox-Hughes Paul
Published: May 2022

Wildfire is one of the most complex natural hazards. Its origin is a combination of anthropogenic factors, urban development and weather plus climate factors. In particular, weather and climate factors possess many spatiotemporal scales and various degrees of predictability. Due to the complex synergy of the human and natural factors behind the events, every wildfire is unique. However, there are indeed common meteorological and climate factors leading to the high fire risk before certain ignition mechanismfigures occur. From a scientific point of view, a better understanding of the meteorological and climate drivers of wildfire in every region would enable more effective seasonal to annual outlook of fire risk, and in the long term, better applications of climate projections to estimate future scenarios of wildfire. This review has performed a comparison study of two fire-prone regions: southeast Australia including Tasmania, and the Adriatic coast in Europe, especially events in Croatia. The former is well known as part of the ‘fire continent’, and major resources have been put into wildfire research and forecasting. The Adriatic coast is a region where some of the highest surface wind speeds, under strong topographic effect, have been recorded and, over the years, have coincided with wildfire ignitions. Similar synoptic background and dynamic origins of the meso-micro-scale meteorological conditions of these high wind events as well as the accompanied dryness have been identified between some of the events in the two regions. We have also reviewed how the researchers from these two regions have applied different weather indices and numerical models. The status of estimating fire potential under climate change for both regions has been evaluated. This review aims to promote a global network of information exchange to study the changing anthropogenic and natural factors we have to confront in order to mitigate and adapt the impacts and consequences from wildfire.
Retrieval of High-Resolution Aerosol Optical Depth for Urban Air Pollution Monitoring
Authors: Bai Rui , Xue Yong , Jiang Xingxing , Jin Chunlin , Sun Yuxin
Published: May 2022

Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the currently widely used Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products is too low to meet the application research of atmospheric environment at the regional scale. In 2013, China launched the Gaofen-1 (GF-1) satellite, which provides a new idea for AOD retrieval. In this paper, we apply the synergetic use of TERRA and AQUA satellite MODIS data to calculate the high-resolution AOD over Beijing based on the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) and discussed scale conversion problems between AODs with different resolutions. To obtain the 100 m MODIS data, we use GF-1 wide-field-of-view data to downscale 1 km MODIS data based on mutual information method. The retrieved AOD has a spatial resolution of 100 m and can cover many land surface types. Preliminary validation was carried out with the Aerosol Robotic Network (AERONET) ground observation data. The correlation coefficient is about 0.88, and the root-mean-square error is about 0.15. Due to the high resolution of retrieved results, more detailed features can be provided in the spatial distribution. The experimental results show that the method has high precision, and further verification work is continuing.