bluuebunny/crossref_metadata_2025 huggingface dataset:
Instability Monitoring and Numerical Analysis of Typical Coal Mines in Southwest China Based on DS-InSAR
Authors: Liu Maoqi , Long Sichun , Wu Wenhao , Liu Ping , Zhang Liya , Zhu Chuanguang
Published: October 2022

Most of the coal mines in Southwest China are located in mountainous areas with high vegetation coverage, and most activities are carried out under the mountains. The deformation monitoring and mechanical behavior analysis of the mining area helps reveal the typical mountain deformation and failure mechanism caused by underground mining activities and reduce the risk of mountain collapse in the mining area. In this manuscript, a research method for mountain stability in mining areas is proposed, which combines InSAR deformation monitoring with numerical analysis. Based on the high-precision deformation information obtained by DS-InSAR and the landslide range, a three-dimensional explicit finite difference numerical analysis method was used to reconstruct the landslide model. According to the layout of the coal mining working face, the variation mechanism of overlying stratum stress and the mountain slip in the coal mining process is inverted, and the mechanism of mountain failure and instability in the mining area is analysed. Based on the sentinel data, the experiment performed time series monitoring and inversion analysis of the mountain collapse in Nayong, Guizhou, China. The results show that mining activities a certain distance from the mountain will affect mountain stability, and there are specific mechanisms. From 2015 to 2017, the stress redistribution of overlying strata above the goaf area resulted in dense longitudinal cracks in the landslide body due to coal mining. The mountain is in a continuous damage state, and the supporting force to prevent collapse continues to decrease, resulting in a gradual decrease in landslide stability. Both the time series DS-InSAR monitoring results and numerical simulation results verify the actual occurrence and development of the on-site subsidence.
Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research
Authors: Jiang Sheng , Ma Junwei , Liu Zhiyang , Guo Haixiang
Published: October 2022

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields.
Bearing Fault Diagnosis of Hot-Rolling Mill Utilizing Intelligent Optimized Self-Adaptive Deep Belief Network with Limited Samples
Authors: Peng Rongrong , Zhang Xingzhong , Shi Peiming
Published: October 2022

Given the complexity of the operating conditions of rolling bearings in the actual rolling process of a hot mill and the difficulty in collecting data pertinent to fault bearings comprehensively, this paper proposes an approach that diagnoses the faults of a rolling mill bearing by employing the improved sparrow search algorithm deep belief network (ISAA-DBN) with limited data samples. First, the fast spectral kurtosis approach is adopted to convert the non-stationary original vibration signals collected by the acceleration sensors installed at the axial and radial ends of the rolling mill bearings into two-dimensional (2D) spectral kurtosis time–frequency images with higher feature recognition, and the principal component analysis (PCA) technique is used to decrease the dimension of the data in order to achieve a high diagnosis rate with a limited number of samples. Subsequently, the sparrow search algorithm (SSA) is used to realize the intelligent optimized self-adaptive function of a deep belief network (DBN). Furthermore, the firefly disturbance algorithm is employed to improve the spatial search capability and robustness of SSA-DBN in order to achieve better performance of the ISSA-DBN method. Finally, the proposed approach is experimentally compared to other approaches used for diagnosis. The results show that the proposed approach not only retains the useful features of the data through dimension reduction but also improves the efficiency of the diagnosis and achieves the highest diagnosis accuracy with limited data samples. In addition, the optimal position of the sensor for diagnosing rolling mill roll faults is identified.
Smart Homes Supporting the Wellness of One or Two-Person Households
Authors: Cho Myung Eun , Kim Mi Jeong
Published: October 2022

The reduction in face-to-face contact and the increase in time spent at home during the ongoing coronavirus disease pandemic have resulted in increasing interest and demand for smart homes. Further, the rapid increase in the number of one-person and two-person households in Korea recently has led to these becoming representative household types. This study identifies the wellness characteristics of such households and proposes a direction for smart home development to help them lead healthy, happy lives. It focuses on mapping residents’ perceptions and experiences to scenarios and on identifying the functions required in smart homes and the technologies needed to provide these functions. It uses data from a survey to investigate and analyze the wellness characteristics of one- and two-person households in five dimensions and develops five scenarios of representative household types. By analyzing the developed scenarios, this study proposes smart homes that support the wellness of such households in six categories: exercise/sports, hobby/entertainment, social communications, occupation/work, self-development/education, and energy conservation. These households are exposed to digital environments from an early age and are familiar with the internet and technologies. Therefore, they are likely to adopt innovative technologies in housing. Thus, the smart home development proposed in this study is a promising strategic approach to housing planning.
Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing
Authors: Munawar Shoaib , Javaid Nadeem , Khan Zeshan Aslam , Chaudhary Naveed Ishtiaq , Raja Muhammad Asif Zahoor , Milyani Ahmad H. , Ahmed Azhari Abdullah
Published: October 2022

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.
Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis
Authors: Iqbal Umair , Barthelemy Johan , Perez Pascal , Davies Tim
Published: October 2022

The increased global waste generation rates over the last few decades have made the waste management task a significant problem. One of the potential approaches adopted globally is to recycle a significant portion of generated waste. However, the contamination of recyclable waste has been a major problem in this context and causes almost 75% of recyclable waste to be unusable. For sustainable development, efficient management and recycling of waste are of huge importance. To reduce the waste contamination rates, conventionally, a manual bin-tagging approach is adopted; however, this is inefficient and requires huge labor effort. Within household waste contamination, plastic bags have been found to be one of the main contaminants. Towards automating the process of plastic-bag contamination detection, this paper proposes an edge-computing video analytics solution using the latest Artificial Intelligence (AI), Artificial Intelligence of Things (AIoT) and computer vision technologies. The proposed system is based on the idea of capturing video of waste from the truck hopper, processing it using edge-computing hardware to detect plastic-bag contamination and storing the contamination-related information for further analysis. Faster R-CNN and You Only Look Once version 4 (YOLOv4) deep learning model variants are trained using the Remondis Contamination Dataset (RCD) developed from Remondis manual tagging historical records. The overall system was evaluated in terms of software and hardware performance using standard evaluation measures (i.e., training performance, testing performance, Frames Per Second (FPS), system usage, power consumption). From the detailed analysis, YOLOv4 with CSPDarkNet_tiny was identified as a suitable candidate with a Mean Average Precision (mAP) of 63% and FPS of 24.8 with NVIDIA Jetson TX2 hardware. The data collected from the deployment of edge-computing hardware on waste collection trucks was used to retrain the models and improved performance in terms of mAP, False Positives (FPs), False Negatives (FNs) and True Positives (TPs) was achieved for the retrained YOLOv4 with CSPDarkNet_tiny backbone model. A detailed cost analysis of the proposed system is also provided for stakeholders and policy makers.
Accuracy of Ground Reaction Force and Muscle Activation Prediction in a Child-Adapted Musculoskeletal Model
Authors: Daunoraviciene Kristina , Ziziene Jurgita
Published: October 2022

(1) Background: Significant advances in digital modelling worldwide have been attributed to the practical application of digital musculoskeletal (MS) models in clinical practice. However, the vast majority of MS models are designed to assess adults’ mobility, and the range suitable for children is very limited. (2) Methods: Seventeen healthy and 4 cerebral palsy (CP) children were recruited for the gait measurements. Surface electromyography (EMG) and ground reaction forces (GRFs) were acquired simultaneously. The MS model of the adult was adapted to the child and simulated in AnyBody. The differences between measured and MS model-estimated GRFs and muscle activations were evaluated using the following methods: the root-mean-square error (RMSE); the Pearson coefficient r; statistical parametric mapping (SPM) analysis; the coincidence of muscle activity. (3) Results: For muscle activity, the RMSE ranged from 10.4% to 35.3%, the mismatch varied between 16.4% and 30.5%, and the coincidence ranged between 50.7% and 68.4%; the obtained strong or very strong correlations between the measured and model-calculated GRFs, with RMSE values in the y and z axes ranged from 7.1% to 17.5%. (4) Conclusions: Child-adapted MS model calculated muscle activations and GRFs with sufficient accuracy, so it is suitable for practical use in both healthy children and children with limited mobility.
On Power Line Positioning Systems
Authors: Lovisolo Lisandro , Cruz-Roldán Fernando , Blanco-Velasco Manuel
Published: October 2022

Power line infrastructure is available almost everywhere. Positioning systems aim to estimate where a device or target is. Consequently, there may be an opportunity to use power lines for positioning purposes. This survey article reports the different efforts, working principles, and possibilities for implementing positioning systems relying on power line infrastructure for power line positioning systems (PLPS). Since Power Line Communication (PLC) systems of different characteristics have been deployed to provide communication services using the existing mains, we also address how PLC systems may be employed to build positioning systems. Although some efforts exist, PLPS are still prospective and thus open to research and development, and we try to indicate the possible directions and potential applications for PLPS.
Statistical Characterization of Wireless Power Transfer via Unmodulated Emission
Authors: Galmés Sebastià
Published: October 2022

In the past few years, the ability to transfer power wirelessly has experienced growing interest from the research community. Because the wireless channel is subject to a large number of random phenomena, a crucial aspect is the statistical characterization of the energy that can be harvested by a given device. For this characterization to be reliable, a powerful model of the propagation channel is necessary. The recently proposed generalized-K model has proven to be very useful, as it encompasses the effects of path loss, shadowing, and fast fading for a broad set of wireless scenarios, and because it is analytically tractable. Accordingly, the purpose of this paper is to characterize, from a statistical point of view, the energy harvested by a static device from an unmodulated carrier signal generated by a dedicated source, assuming that the wireless channel obeys the generalized-K propagation model. Specifically, by using simulation-validated analytical methods, this paper provides exact closed-form expressions for the average and variance of the energy harvested over an arbitrary time period. The derived formulation can be used to determine a power transfer plan that allows multiple or even massive numbers of low-power devices to operate continuously, as expected from future network scenarios such as the Internet of things or 5G/6G.
Experimental and Numerical Validation of Whispering Gallery Resonators as Optical Temperature Sensors
Authors: Paz-Buclatin Franzette , Perera-Suárez Ylenia , Martín Inocencio R. , Ríos Susana , de Varona Omar , Ródenas Airán , Martin Leopoldo L.
Published: October 2022

This study experimentally and numerically validates the commonly employed technique of laser-induced heating of a material in optical temperature sensing studies. Furthermore, the Er3+-doped glass microspheres studied in this work can be employed as remote optical temperature sensors. Laser-induced self-heating is a useful technique commonly employed in optical temperature sensing research when two temperature-dependent parameters can be correlated, such as in fluorescence intensity ratio vs. interferometric calibration, allowing straightforward sensor characterization. A frequent assumption in such experiments is that thermal homogeneity within the sensor volume, that is, a sound hypothesis when dealing with small volume to surface area ratio devices such as microresonators, but has never been validated. In order to address this issue, we performed a series of experiments and simulations on a microsphere supporting whispering gallery mode resonances, laser heating it at ambient pressure and medium vacuum while tracking the resonance wavelength shift and comparing it to the shift rate observed in a thermal bath. The simulations were done starting only from the material properties of the bulk glass to simulate the physical phenomena of laser heating and resonance of the microsphere glass. Despite the simplicity of the model, both measurements and simulations are in good agreement with a highly homogeneous temperature within the resonator, thus validating the laser heating technique.
An Adaptive Prescribed Performance Tracking Motion Control Methodology for Robotic Manipulators with Global Finite-Time Stability
Authors: Vo Anh Tuan , Truong Thanh Nguyen , Kang Hee-Jun
Published: October 2022

In this paper, the problem of an APPTMC for manipulators is investigated. During the robot’s operation, the error states should be kept within an outlined range to ensure a steady-state and dynamic attitude. Firstly, we propose the modified PPFs. Afterward, a series of transformed errors is used to convert “constrained” systems into equivalent “unconstrained” ones, to facilitate control design. The modified PPFs ensure position tracking errors are managed in a pre-designed performance domain. Especially, the SSE boundaries will be symmetrical to zero, so when the transformed error is zero, the tracking error will be as well. Secondly, a modified NISMS based on the transformed errors allows for determining the highest acceptable range of the tracking errors in the steady-state, finite-time convergence index, and singularity elimination. Thirdly, a fixed-time USOSMO is proposed to directly estimate the lumped uncertainty. Fourthly, an ASTwCL is applied to deal with observer output errors and chattering. Finally, an observer-based-control solution is synthesized from the above techniques to achieve PCP in the sense of finite-time Lyapunov stability. In addition, the precision, robustness, as well as harmful chattering reduction of the proposed APPTMC are improved significantly. The Lyapunov theory is used to analyze the stability of closed-loop systems. Throughout simulations, the proposed PPTMC has been shown to perform well and be effective.
Multi-Object Detection in Security Screening Scene Based on Convolutional Neural Network
Authors: Sun Fan , Zhang Xiangfeng , Liu Yunzhong , Jiang Hong
Published: October 2022

The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a coupled multi-scale feature extraction and multi-scale attention architecture. We integrate this architecture into the Single Shot MultiBox Detector (SSD) algorithm and find that it can significantly improve the effectiveness of target detection. Firstly, ResNet is used as the backbone network to replace the original VGG network to improve the feature extraction capability of the convolutional neural network for images. Secondly, a multi-scale feature extraction (MSE) structure is designed to enrich the information contained in the multi-stage prediction feature layer. Finally, the multi-scale attention architecture (MSA) is fused onto the prediction feature layer to eliminate the redundant features’ interference and extract effective contextual information. In addition, a combination of Adaptive-NMS and Soft-NMS is used to output the final prediction anchor boxes when performing non-maximum suppression. The results of the experiments show that the improved method improves the mean average precision (mAP) value by 7.4% compared to the original approach. New modules make detection much more accurate while keeping the detection speed the same.
Transforming Linear to Circular Polarization on Horn Antennas by Using Multiple-Layer Frequency Selective Surfaces
Authors: Heiman Adelaida , Tamas Razvan D.
Published: October 2022

This article presents a technique for transforming the polarization of a pyramidal horn antenna by adding multiple layers of frequency-selective surfaces in front of the aperture, in order to rotate the direction of the electric field. Thus, two orthogonal components with the same magnitude, phase-shifted by 90°, are generated. Each frequency-selective surface consists of skewed λ/2 dipoles. Compared to other similar structures, our antenna system combines the field radiated from the horn aperture with the field scattered by parallel frequency-selective surface structures spaced on the same principle as that for designing a Yagi-Uda antenna array. The proposed horn antenna with multiple frequency-selective surfaces can be used as a feed element for a parabolic reflector antenna for maritime satellite communication systems in the X-band or in the lower part of the Ku band, or as part of a sensor for finding the direction of arrival of a wave, in order to orientate an antenna system. The concept was successfully validated on the basis of simulation and measurements. The proposed technique provides a close to unit axial ratio together with a 3 dB increase in gain compared to the conventional horn antenna, at low manufacturing costs.
A Focus on Electromobility within Smart City Solutions—Charging Stations, Renewable Energy, and Air Quality Monitoring
Authors: Zavorka Radek , Paar Martin
Published: October 2022

This paper reviews some of the existing methods for charging electric vehicles, generating renewable energy, and storing it. Plans of practical implementation in the city of Brno are compared with the situation in Glasgow. Moreover, it is essential to pay attention to integrated solutions in order to increase efficiency. Energy harvesting and charging systems are combined with an air quality measurement system and integrated into LED street lights. The collected data are sent to a central server for evaluation. The use of smart solutions is a modern approach to saving energy and reducing CO2 emissions in many sectors. As an example, the described solutions can be applied dually, in both civilian and military sectors. Considering the potential benefits of easier logistics or quiet operation, the potential military exploitation of technological capabilities is discussed from the perspective of enhancing citizens’ security and safety in cities.
A High-Capacity Steganography Algorithm Based on Adaptive Frequency Channel Attention Networks
Authors: Zhang Shanqing , Li Hui , Li Li , Lu Jianfeng , Zuo Ziqian
Published: October 2022

Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.
Optimal Tasking of Ground-Based Sensors for Space Situational Awareness Using Deep Reinforcement Learning
Authors: Siew Peng Mun , Linares Richard
Published: October 2022

Space situational awareness (SSA) is becoming increasingly challenging with the proliferation of resident space objects (RSOs), ranging from CubeSats to mega-constellations. Sensors within the United States Space Surveillance Network are tasked to repeatedly detect, characterize, and track these RSOs to retain custody and estimate their attitude. The majority of these sensors consist of ground-based sensors with a narrow field of view and must be slew at a finite rate from one RSO to another during observations. This results in a complex combinatorial problem that poses a major obstacle to the SSA sensor tasking problem. In this work, we successfully applied deep reinforcement learning (DRL) to overcome the curse of dimensionality and optimally task a ground-based sensor. We trained several DRL agents using proximal policy optimization and population-based training in a simulated SSA environment. The DRL agents outperformed myopic policies in both objective metrics of RSOs’ state uncertainties and the number of unique RSOs observed over a 90-min observation window. The agents’ robustness to changes in RSO orbital regimes, observation window length, observer’s location, and sensor properties are also examined. The robustness of the DRL agents allows them to be applied to any arbitrary locations and scenarios.
FPGA Applied to Latency Reduction for the Tactile Internet
Authors: Junior José C. V. S. , Silva Sérgio N. , Torquato Matheus F. , Mahmoodi Toktam , Dohler Mischa , Fernandes Marcelo A. C.
Published: October 2022

Tactile internet applications allow robotic devices to be remotely controlled over a communication medium with an unnoticeable time delay. In bilateral communication, the acceptable round trip latency is usually 1 ms up to 10 ms, depending on the application requirements. The communication network is estimated to generate 70% of the total latency, and master and slave devices produce the remaining 30%. Thus, this paper proposes a strategy to reduce 30% of the total latency produced by such devices. The strategy is to use FPGAs to minimize the execution time of device-associated algorithms. With this in mind, this work presents a new hardware reference model for modules that implement nonlinear positioning and force calculations and a tactile system formed by two robotic manipulators. In addition to presenting the implementation details, simulations and experimental tests are performed in order to validate the hardware proposed model. Results associated with the FPGA sampling rate, throughput, latency, and post-synthesis occupancy area are analyzed.
Deep HDR Deghosting by Motion-Attention Fusion Network
Authors: Xiao Yifan , Veelaert Peter , Philips Wilfried
Published: October 2022

Multi-exposure image fusion (MEF) methods for high dynamic range (HDR) imaging suffer from ghosting artifacts when dealing with moving objects in dynamic scenes. The state-of-the-art methods use optical flow to align low dynamic range (LDR) images before merging, introducing distortion into the aligned LDR images from inaccurate motion estimation due to large motion and occlusion. In place of pre-alignment, attention-based methods calculate the correlation between the reference LDR image and non-reference LDR images, thus excluding misaligned regions in LDR images. Nevertheless, they also exclude the saturated details at the same time. Taking advantage of both the alignment and attention-based methods, we propose an efficient Deep HDR Deghosting Fusion Network (DDFNet) guided by optical flow and image correlation attentions. Specifically, the DDFNet estimates the optical flow of the LDR images by a motion estimation module and encodes that optical flow as a flow feature. Additionally, it extracts correlation features between the reference LDR and other non-reference LDR images. The optical flow and correlation features are employed to adaptably combine information from LDR inputs in an attention-based fusion module. Following the merging of features, a decoder composed of Dense Networks reconstructs the HDR image without ghosting. Experimental results indicate that the proposed DDFNet achieves state-of-the-art image fusion performance on different public datasets.
Sub-Block Urban Function Recognition with the Integration of Multi-Source Data
Authors: Liu Baihua , Deng Yingbin , Li Xin , Li Miao , Jing Wenlong , Yang Ji , Chen Zhehua , Liu Tao
Published: October 2022

The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8–36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.
Monitoring Injected CO2 Using Earthquake Waves Measured by Downhole Fibre-Optic Sensors: CO2CRC Otway Stage 3 Case Study
Authors: Shashkin Pavel , Gurevich Boris , Yavuz Sinem , Glubokovskikh Stanislav , Pevzner Roman
Published: October 2022

Monitoring changes of formation properties along the well bore associated with the presence of carbon dioxide can be important for both tracking the plume inside of the primary containment and detecting leakage into the zone located above the reservoir. This can be achieved with time lapse wireline logging, but this approach requires well intervention and is not always possible. If the well is permanently instrumented with an optical fibre, it can be used as a distributed seismic receiver array to detect gas behind the casing by monitoring changes in amplitude of the seismic waves generated by active or passive seismic sources. Previous research showed the efficacy of this technique using continuous seismic sources. The Stage 3 Otway Project presented an opportunity to test this technique using passive seismic recording, as downhole fibre-optic arrays recorded numerous regional earthquakes over the period of nearly 2 years before, during, and after CO2 injection. Analysis of P-wave amplitudes extracted from these downhole gathers shows a consistent amplitude anomaly at the injection level, visible in all events that occurred after the start of injection. This indicates that the anomaly is caused by changes in elastic properties in the reservoir caused by CO2 saturation. However, extracted amplitudes show significant variability between earthquakes even without subsurface changes; thus, multiple events are required to distinguish the time-lapse anomaly from time-lapse noise. Ubiquity of these events even in a tectonically quiet region (such as Australia) makes this technique a viable and cost-effective option for downhole monitoring.
Testing of Homogeneity of Coordinates of Various Permanent GNSS Reference Stations Networks of the Republic of Serbia According to the Common Requirements for Proving Competence
Authors: Gučević Jelena , Vasović Šimšić Olivera , Delčev Siniša , Kuburić Miroslav
Published: October 2022

The validity of the results obtained within different permanent GNSS reference station networks (GNSS Network) must be periodically controlled using criteria that are generally known from statistical analyzes or prescribed by International Standards. Procedures for evaluating the uncertainty of measurements are defined in accordance with the purpose of the GNSS Network. The authors of this paper want to point out the need to establish requirements for periodical and systematical control of GNSS coordinates within the same permanent GNSS Network and control of GNSS coordinates between different permanent GNSS Networks measured on the same/unique point on the ground. This paper presents control procedures for three permanent GNSS reference station Networks established and operating in the Republic of Serbia. Special attention is on the analysis of data consistency within one permanent GNSS Network and the mutual consistency of GNSS data between different networks. The paper aims to promote reliance on the different GNSS Networks and contains suggestions on how GNSS Networks may prove that they are performing competently and that they can provide valid results for field measurements. Particularly highlighted is the need to plan and implement measures related to increasing the effectiveness of the GNSS system, achieving improved results, and preventing negative effects while performing field measurements. The paper presents the results for comparison, selected according to the rules for creating a Digital Cadastral Map features, i.e., points, lines, and polygon. The results for comparing point features are the GNSS coordinates. The results for comparing line features are the lengths of the line, i.e., distances, and the results for comparing polygon features are the areas of the polygons.
Deep Learning for LiDAR Point Cloud Classification in Remote Sensing
Authors: Diab Ahmed , Kashef Rasha , Shaker Ahmed
Published: October 2022

Point clouds are one of the most widely used data formats produced by depth sensors. There is a lot of research into feature extraction from unordered and irregular point cloud data. Deep learning in computer vision achieves great performance for data classification and segmentation of 3D data points as point clouds. Various research has been conducted on point clouds and remote sensing tasks using deep learning (DL) methods. However, there is a research gap in providing a road map of existing work, including limitations and challenges. This paper focuses on introducing the state-of-the-art DL models, categorized by the structure of the data they consume. The models’ performance is collected, and results are provided for benchmarking on the most used datasets. Additionally, we summarize the current benchmark 3D datasets publicly available for DL training and testing. In our comparative study, we can conclude that convolutional neural networks (CNNs) achieve the best performance in various remote-sensing applications while being light-weighted models, namely Dynamic Graph CNN (DGCNN) and ConvPoint.
Developing Disposable EEG Cap for Infant Recordings at the Neonatal Intensive Care Unit
Authors: Asayesh Amirreza , Ilen Elina , Metsäranta Marjo , Vanhatalo Sampsa
Published: October 2022

Long-term EEG monitoring in neonatal intensive care units (NICU) is challenged with finding solutions for setting up and maintaining a sufficient recording quality with limited technical experience. The current study evaluates different solutions for the skin–electrode interface and develops a disposable EEG cap for newborn infants. Several alternative materials for the skin–electrode interface were compared to the conventional gel and paste: conductive textiles (textured and woven), conductive Velcro, sponge, super absorbent hydrogel (SAH), and hydro fiber sheets (HF). The comparisons included the assessment of dehydration and recordings of signal quality (skin interphase impedance and powerline (50 Hz) noise) for selected materials. The test recordings were performed using snap electrodes integrated into a forearm sleeve or a forehead band along with skin–electrode interfaces to mimic an EEG cap with the aim of long-term biosignal recording on unprepared skin. In the hydration test, conductive textiles and Velcro performed poorly. While the SAH and HF remained sufficiently hydrated for over 24 h in an incubator-mimicking environment, the sponge material was dehydrated during the first 12 h. Additionally, the SAH was found to have a fragile structure and was electrically prone to artifacts after 12 h. In the electrical impedance and recording comparisons of muscle activity, the results for thick-layer HF were comparable to the conventional gel on unprepared skin. Moreover, the mechanical instability measured by 1–2 Hz and 1–20 Hz normalized relative power spectrum density was comparable with clinical EEG recordings using subdermal electrodes. The results together suggest that thick-layer HF at the skin–electrode interface is an effective candidate for a preparation-free, long-term recording, with many advantages, such as long-lasting recording quality, easy use, and compatibility with sensitive infant skin contact.
Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest
Authors: Srivastava Sanjeev Kumar , Seng Kah Phooi , Ang Li Minn , Pachas Anibal ‘Nahuel’ A. , Lewis Tom
Published: October 2022

This paper investigated the utility of drone-based environmental monitoring to assist with forest inventory in Queensland private native forests (PNF). The research aimed to build capabilities to carry out forest inventory more efficiently without the need to rely on laborious field assessments. The use of drone-derived images and the subsequent application of digital photogrammetry to obtain information about PNFs are underinvestigated in southeast Queensland vegetation types. In this study, we used image processing to separate individual trees and digital photogrammetry to derive a canopy height model (CHM). The study was supported with tree height data collected in the field for one site. The paper addressed the research question “How well do drone-derived point clouds estimate the height of trees in PNF ecosystems?” The study indicated that a drone with a basic RGB camera can estimate tree height with good confidence. The results can potentially be applied across multiple land tenures and similar forest types. This informs the development of drone-based and remote-sensing image-processing methods, which will lead to improved forest inventories, thereby providing forest managers with recent, accurate, and efficient information on forest resources.
Single-Stage Underwater Target Detection Based on Feature Anchor Frame Double Optimization Network
Authors: Ge Huilin , Dai Yuewei , Zhu Zhiyu , Zang Xu
Published: October 2022

Objective: The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally, typical object identification algorithms struggle to maintain high resilience in underwater environments due to picture domain offset, making underwater object detection problematic. Methods: This paper proposes a single-stage detection method with the double enhancement of anchor boxes and features. The feature context relevance is improved by proposing a composite-connected backbone network. The receptive field enhancement module is introduced to enhance the multi-scale detection capability. Finally, a prediction refinement strategy is proposed, which refines the anchor frame and features through two regressions, solves the problem of feature anchor frame misalignment, and improves the detection performance of the single-stage underwater algorithm. Results: We achieved an effect of 80.2 mAP on the Labeled Fish in the Wild dataset, which saves some computational resources and time while still improving accuracy. On the original basis, UWNet can achieve 2.1 AP accuracy improvement due to the powerful feature extraction function and the critical role of multi-scale functional modules. At an input resolution of 300 × 300, UWNet can provide an accuracy of 32.4 AP. When choosing the number of prediction layers, the accuracy of the four and six prediction layer structures is compared. The experiments show that on the Labeled Fish in the Wild dataset, the six prediction layers are better than the four. Conclusion: The single-stage underwater detection model UWNet proposed in this research has a double anchor frame and feature optimization. By adding three functional modules, the underwater detection of the single-stage detector is enhanced to address the issue that it is simple to miss detection while detecting small underwater targets.
Development and Validation of a Near-Infrared Spectroscopy Method for Multicomponent Quantification during the Second Alcohol Precipitation Process of Astragali radix
Authors: Li Wenlong , Luo Yu , Wang Xi , Gong Xingchu , Huang Wenhua , Wang Guoxiang , Qu Haibin
Published: October 2022

The objective of this study was to develop and validate a near-infrared (NIR) spectroscopy based method for in-line quantification during the second alcohol precipitation process of Astragali radix. In total, 22 calibration experiments were carefully arranged using a Box–Behnken design. Variations in the raw materials, critical process parameters, and environmental temperature were all included in the experimental design. Two independent validation sets were built for method evaluation. Validation set 1 was used for optimization. Different spectral pretreatments were compared using a “trial-and-error” approach. To reduce the calculation times, the full-factorial design was applied to determine the potential optimal combinations. Then, the best parameters for the pretreatment algorithms were compared and selected. Partial least squares (PLS) regression models were obtained with low complexity and good predictive performance. Validation set 2 was used for a thorough validation of the NIR spectroscopy method. Based on the same validation set, traditional chemometric validation and validation using accuracy profiles were conducted and compared. Conventional chemometric parameters were used to obtain the overall predictive capability of the established models; however, these parameters were insufficient for pharmaceutical regulatory requirements. Then, the method was fully validated according to the ICH Q2(R1) guideline and using the accuracy profile approach, which enabled visual and reliable representation of the future performances of the analytical method. The developed method was able to determine content ranges of 8.44–39.8% at 0.541–2.26 mg/mL, 0.118–0.502 mg/mL, 0.220–0.940 mg/mL, 0.106–0.167 mg/mL, 0.484–0.879 mg/mL, and 0.137–0.320 mg/mL for total solid, calycosin glucoside, formononetin glucoside, 9, 10-dimethoxypterocarpan glucopyranoside, 2′-dihydroxy -3′, 4′-dimethoxyisoflavan glucopyranoside, astragloside II, and astragloside IV, respectively. These ranges were specific to the early and middle stages of the second alcohol precipitation process. The method was confirmed to be capable of achieving an in-line prediction with a very acceptable accuracy. The present study demonstrates that accuracy profiles offer a potential approach for the standardization of NIR spectroscopy method validation for traditional Chinese medicines (TCMs).
Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection
Authors: Christou Vasileios , Tsoulos Ioannis , Arjmand Alexandros , Dimopoulos Dimitrios , Varvarousis Dimitrios , Tzallas Alexandros T. , Gogos Christos , Tsipouras Markos G. , Glavas Evripidis , Ploumis Avraam , Giannakeas Nikolaos
Published: October 2022

Hemiplegia is a condition caused by brain injury and affects a significant percentage of the population. The effect of patients suffering from this condition is a varying degree of weakness, spasticity, and motor impairment to the left or right side of the body. This paper proposes an automatic feature selection and construction method based on grammatical evolution (GE) for radial basis function (RBF) networks that can classify the hemiplegia type between patients and healthy individuals. The proposed algorithm is tested in a dataset containing entries from the accelerometer sensors of the RehaGait mobile gait analysis system, which are placed in various patients’ body parts. The collected data were split into 2-second windows and underwent a manual pre-processing and feature extraction stage. Then, the extracted data are presented as input to the proposed GE-based method to create new, more efficient features, which are then introduced as input to an RBF network. The paper’s experimental part involved testing the proposed method with four classification methods: RBF network, multi-layer perceptron (MLP) trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm, support vector machine (SVM), and a GE-based parallel tool for data classification (GenClass). The test results revealed that the proposed solution had the highest classification accuracy (90.07%) compared to the other four methods.
University Teachers’ Digital Competence: A Case Study from Portugal
Authors: Dias-Trindade Sara , Albuquerque Cristina
Published: October 2022

It has become increasingly important to train for an educational culture that makes use of digital technology to enhance its practices. Frameworks such as DigCompEdu enable identification of weaknesses and areas which require more training. This paper presents the results of a case study with 249 participants from a Portuguese University examining differences in the digital competence of teachers according to gender, age, faculty and experience. Based on the data obtained, it was found that there were no significant differences and that it was, therefore, more important to consider training for digital empowerment according to the different areas of the DigCompEdu framework and to address weaknesses found in each of these areas.