bluuebunny/crossref_metadata_2025 huggingface dataset:
Fluid Identification Method of Nuclear Magnetic Resonance and Array Acoustic Logging for Complex Oil and Water Layers in Tight Sandstone Reservoir
Authors: Bai Ze , Tan Maojin , Li Bo , Shi Yujiang , Zhang Haitao , Li Gaoren
Published: October 2023

In order to improve the logging interpretation accuracy for complex oil and water layers developed in tight sandstone reservoirs, this study takes the Chang 8 member of the Yanchang Formation in the Huanxian area as the research object, and two new fluid identification methods were constructed based on nuclear magnetic resonance (NMR) logging and array acoustic logging. Firstly, the reservoir characteristics of physical properties and conductivity were studied in the research area, and the limitations of conventional logging methods in identifying complex oil and water layers were clarified. Then, the sensitive parameters for identifying different pore fluids were established by analyzing the relationship between NMR logging and array acoustic logging with different pore fluids. On this basis, the fluid identification plate, composed of movable fluid apparent diffusion coefficient and effective porosity difference (Da-Δφe) by NMR logging data of D9TWE3 observation mode, and the other fluid identification plate, composed of apparent bulk modulus of pore fluid and elastic parameter sensitive factor (Kf-Fac), were constructed, respectively. Finally, these two fluid identification methods were used for reservoir interpretation of actual logging data. This study shows that the two new fluid identification methods constructed by NMR logging and array acoustic logging can effectively eliminate the interference of rock skeleton on logging interpretation, which make them more effective in identifying complex oil and water layers than the conventional logging method. Additionally, the two methods have their own advantages and disadvantages when used separately for interpreting complex oil and water layers, and the comprehensive interpretation of the two methods provides a technical development direction for further improving the accuracy of logging the interpretation of complex oil and water layers.
Crucial Development Technologies for Volcanic Hydrocarbon Reservoirs: Lessons Learned from Asian Operations
Authors: Liu Songxia , Zhang Yaoyuan , Wu Qilin , Ayers Walter B. , Wang Yanquan , Ott William K.
Published: October 2023

Oil and gas reservoirs in volcanic rocks are a particular type of unconventional reservoir and present unique challenges for exploration and production engineers. To help the oil industry understand volcanic reservoirs and solutions to complex development problems, we reviewed their key engineering technologies as well as their geological characteristics. The distinctive geological characteristics of volcanic hydrocarbon reservoirs are strong heterogeneity, low porosity and permeability, complex fracture systems, etc. The volcanic reservoir rock types in order of hydrocarbon abundance are basalt (38.5%), andesite (15.9%), volcaniclastic (12.1%), and rhyolite (11.5%). The porosity ranges from 0.1 to 70%, and permeability ranges from 0.0007 to 762 md. In some commercially developed volcanic reservoirs of China, the average porosity is 7.7–13%; the average permeability is 0.41–3.4 md. Engineers have applied a variety of adapted technologies to produce volcanic reservoir economically. Horizontal wells can increase production and reserves by 4–6 times those of vertical wells, and longer wells are preferred. Specialized hydraulic fracturing techniques are suggested, including small or mixed proppant size, second HF treatment after proppant slugging, high-viscosity frac fluid with high-temperature resistance, special fluid loss reducer, high pump pressure, Extreme Overbalance Perforating, limited-entry fracturing, matrix acidizing, etc. Water control measures include producing below critical rates, partial perforation or penetration, controlling hydraulic fracture height, using horizontal wells, implementing complete cementing job, etc. Well productivity evaluation should be conducted to understand well performance and appropriately allocate production rates among wells, using the modified AOF method and other productivity prediction models considering breakdown fracture gradient, gas slippage effect, non-Darcy effect, etc. Well sites need to be selected based on recognizing profitable lithologies, lithofacies, high porosity and permeability, relatively developed fracture systems, thick net pay zones, etc. The critical questions for the industry are how to enhance volcanic reservoir recovery with more efficient and economic hydraulic fracturing and water control techniques. This is one of the first papers systematically summarizing the engineering technologies and unique solutions to develop volcanic reservoirs. Further and more complete reviews can be carried out in the future, and more novel and effective techniques can be explored and tested in the field.
A Combined Use of Custom-Made Partial Pelvic Replacement and Proximal Femur Megaprosthesis in the Treatment of Severe Bone Loss after Multiple Total Hip Arthroplasty Revisions
Authors: Fiore Michele , Paolucci Azzurra , Zunarelli Renato , Bortoli Marta , Montanari Andrea , Pace Andrea , Di Prinzio Lorenzo , Parisi Stefania Claudia , De Cristofaro Roberto , De Paolis Massimiliano , Sambri Andrea
Published: October 2023

Hip arthroplasty failures (either septic or aseptic) often require multiple revisions, thus leading to severe bone defects. The most common reconstruction methods do not allow the management of severe defects. For this reason, in recent years, techniques borrowed from surgical oncology have been applied in the field of revision surgery to deal with both acetabular and femoral bone losses. In this article, two cases of severe bone deficiency following multiple hip arthroplasty revisions that were treated with a custom-made hip prosthesis combined with a proximal femur megaprosthesis are presented. Both implants were silver coated. A review of the literature was conducted to analyze similar cases treated with either a custom-made prosthesis or a proximal femur megaprosthesis. At the 2-year follow-up, all prostheses were in site without clinical or radiographic signs of implant loosening. No postoperative complications occurred. At the last follow-up, both patients resumed their daily life activities with an MSTS score of 23 and 21, respectively. The combined approach of a proximal femur megaprosthesis with a custom-made partial pelvic replacement is a solution that allows severe bone deficiency cases to be tackled with good functional results. Additionally, silver coating may help prevent recurrence of infection.
Patient-Specific Instrumentation with Laser-Guide-Navigated THA: Clinical and CT Evaluation of the First 100 Cases
Authors: Previ Leonardo , Viglietta Edoardo , Giuliani Veronica , Corsetti Federico , Redler Andrea , Speranza Attilio , De Carli Angelo , Iorio Raffaele
Published: October 2023

Obtaining a proper position for total hip arthroplasty components is a crucial aspect of implant performance and consequently of patient outcomes. Restoring the original hip center and maintaining the limb length are key factors in reaching the optimal implant positioning. The aim of this study was to assess the accuracy and safety of a computed dynamic analysis system that, through patient-specific guides, tries to improve implant positioning and functional orientation according to patients’ spinopelvic mobility and anatomy. A total of 100 consecutive patients were prospectively enrolled. All patients received an Optimized Positioning System dynamic hip preoperative planning schedule. A CT scan protocol follow-up analysis was performed 6 months after surgery. The mean deviations from the planned acetabular inclination and anteversion were 4.3° and 3.8°, respectively. In total, 98% of cases were within ± 10° of the Lewinnek safe zone, both for inclination and anteversion. The height of osteotomy deviated, on average, 1.6 mm. In total, 100% of cases were included within 4 mm of osteotomy. Patient-specific and laser-guided instrumentation was found to be safe and accurately reproduced dynamic planning in terms of the component orientation, osteotomy level, leg length and offset.
Interaction Map: A Visualization Tool for Personalized Learning Based on Assessment Data
Authors: Ho Eric , Jeon Minjeong
Published: October 2023

Personalized learning is the shaping of instruction to meet students’ needs to support student learning and improve learning outcomes. While it has received increasing attention in education, limited resources are available to help educators properly leverage assessment data to foster personalized learning. Motivated by this need, we introduce a new visualization tool, the interaction map, to foster personalized learning based on assessment data. The interaction map approach is engineered by the latent space item response model, a recent development in assessment data-leveraging social network analysis methodologies. In the interaction map, students and test items are mapped into a two-dimensional geometric space, in which their distances tell us about the student’s strengths and weaknesses with individual or groups of test items given their overall ability levels. Student profiles can be generated based on these distances to display individual student strengths and weaknesses. Finally, we introduce a user-friendly, free web-based software IntMap in which users can upload their own assessment data and view the customizable interaction map and student profiles based on settings that users can adjust. We illustrate the use of the software with an educational assessment example.
Chernobyl’s Aftermath: Multiple Manifestations of Basalioma in a Patient after Radioactive Contamination in 1986
Authors: Ebeling Marcel , Steinestel Konrad , Grunert Michael , Schramm Alexander , Wilde Frank , Pietzka Sebastian , Sakkas Andreas
Published: October 2023

Background: The Chernobyl nuclear disaster is still considered the worst nuclear accident in history. The particles were dispersed over the former USSR and large parts of Western Europe, leading to radioactive exposure to more than 10 million people. Radioactivity is a risk factor for the development of basal cell carcinoma (BCC), since radiation-induced mutations in both Sonic hedgehog (Shh) signaling pathway genes and TP53 have been described. Methods: We present the case of a patient with a history of radiation exposure following the 1986 Chernobyl accident who presented to our outpatient clinic with recurrent basal cell carcinoma in the facial region. Case: The patient presented to our clinic with two facial lesions suspicious for BCC. Although there were no typical risk factors, 11 BCCs had previously been removed. The patient had been building shelters for the construction workers working on the sarcophagus around the destroyed reactor immediately after the 1986 accident. Staging using an 18F-FDG-PET/CT as well as ultrasound of the abdomen revealed no other tumor manifestations. Diagnostic excision of the two facial lesions was performed, and a histopathological workup revealed BCC at the right temporal region and acanthopapillomatosis with no sign of malignancy at the corner of the mouth. After presentation to the tumor board, complete resection of the BCC was initiated. Conclusions: This case demonstrates the value of early use of 18F-FDG-PET/CT in staging/restaging to visualize BCC location, local spread and potential metastases or secondary tumors and to aid in the decision for therapeutic management.
Investigation of the Fiber Length and the Mechanical Properties of Waste Recycled from Continuous Glass Fiber-Reinforced Polypropylene
Authors: MohammadKarimi Shiva , Neitzel Benedikt , Lang Maximilian , Puch Florian
Published: October 2023

This paper explores the mechanical recycling of continuous fiber-reinforced thermoplastics (CFRTPs) waste into injection molded products, focusing on the influence of recycling parameters on fiber length and mechanical properties. CFRTPs are gaining attention for their promising attributes, including weight-specific mechanical properties, short cycle times, storability, and recyclability, making them suitable for diverse applications. However, as CFRTP production rates rise, recycling strategies become crucial for sustainability. This study investigates the processability of CFRTP waste, defines size reduction conditions, and evaluates the impact of various compounding parameters such as temperature, screw speed, and fiber volume content during extrusion. The research findings indicate that higher screw speeds lead to fiber length reduction, whereas elevated temperatures result in longer fibers. Increased fiber volume intensifies interactions, resulting in shorter lengths. Additionally, the study examines the influence of injection molding parameters such as back pressure, screw speed, and initial fiber length on the resulting fiber length and mechanical properties of injection molded specimens, emphasizing the need for precise parameter control to optimize performance in recycled CFRTPs. Key findings are that increasing the initial fiber length from 260 μm to 455 μm results in an average fiber length after injection molding of 225 μm and 341 μm, respectively. This implies that longer initial fibers are more prone to breakage. Regarding the mechanical properties, increasing back pressure from 20 bar to 60 bar results in a reduction in Young’s modulus of approximately 40 MPa. Higher screw speed also reduces modulus by approximately 70 MPa due to intensified fiber–screw interactions. However, back pressure and screw speed have neutral effects on the tensile strength and the elongation at break.
A U-Net Approach for InSAR Phase Unwrapping and Denoising
Authors: Vijay Kumar Sachin , Sun Xinyao , Wang Zheng , Goldsbury Ryan , Cheng Irene
Published: October 2023

The interferometric synthetic aperture radar (InSAR) imaging technique computes relative distances or surface maps by measuring the absolute phase differences of returned radar signals. The measured phase difference is wrapped in a 2π cycle due to the wave nature of light. Hence, the proper multiple of 2π must be added back during restoration and this process is known as phase unwrapping. The noise and discontinuity present in the wrapped signals pose challenges for error-free unwrapping procedures. Separate denoising and unwrapping algorithms lead to the introduction of additional errors from excessive filtering and changes in the statistical nature of the signal. This can be avoided by joint unwrapping and denoising procedures. In recent years, research efforts have been made using deep-learning-based frameworks, which can learn the complex relationship between the wrapped phase, coherence, and amplitude images to perform better unwrapping than traditional signal processing methods. This research falls predominantly into segmentation- and regression-based unwrapping procedures. The regression-based methods have poor performance while segmentation-based frameworks, like the conventional U-Net, rely on a wrap count estimation strategy with very poor noise immunity. In this paper, we present a two-stage phase unwrapping deep neural network framework based on U-Net, which can jointly unwrap and denoise InSAR phase images. The experimental results demonstrate that our approach outperforms related work in the presence of phase noise and discontinuities with a root mean square error (RMSE) of an order of magnitude lower than the others. Our framework exhibits better noise immunity, with a low average RMSE of 0.11.
Mapping of Rubber Forest Growth Models Based on Point Cloud Data
Authors: Zhou Hang , Zhang Gan , Zhang Junxiong , Zhang Chunlong
Published: October 2023

The point cloud-based 3D model of forest helps to understand the growth and distribution pattern of trees, to improve the fine management of forestry resources. This paper describes the process of constructing a fine rubber forest growth model map based on 3D point clouds. Firstly, a multi-scale feature extraction module within the point cloud column is used to enhance the PointPillars learning capability. The Swin Transformer module is employed in the backbone to enrich the contextual semantics and acquire global features with the self-attention mechanism. All of the rubber trees are accurately identified and segmented to facilitate single-trunk localisation and feature extraction. Then, the structural parameters of the trunks calculated by RANSAC and IRTLS cylindrical fitting methods are compared separately. A growth model map of rubber trees is constructed. The experimental results show that the precision and recall of the target detection reach 0.9613 and 0.8754, respectively, better than the original network. The constructed rubber forest information map contains detailed and accurate trunk locations and key structural parameters, which are useful to optimise forestry resource management and guide the enhancement of mechanisation of rubber tapping.
Spatial Downscaling of Near-Surface Air Temperature Based on Deep Learning Cross-Attention Mechanism
Authors: Shen Zhanfei , Shi Chunxiang , Shen Runping , Tie Ruian , Ge Lingling
Published: October 2023

Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLDASSD that utilizes a Shuffle–nonlinear-activation-free block (SNBlock) and Swin cross-attention mechanism (SCAM), and is named SNCA-CLDASSD, for the China Meteorological Administration Land Data Assimilation System (CLDAS). This method aims to achieve a more accurate spatial downscaling of a temperature product from 0.05° to 0.01° for the CLDAS. To better utilize the digital elevation model (DEM) for reconstructing the spatial texture of the temperature field, a module named SCAM is introduced, which can activate more input pixels and enable the network to correct and merge the extracted feature maps with DEM information. We chose 90% of the CLDAS temperature data with DEM and station observation data from 2016 to 2020 (excluding 2018) as the training set, 10% as the verification set, and chose the data in 2018 as the test set. We validated the effectiveness of each module through comparative experiments and obtained the best-performing model. Then, we compared it with traditional interpolation methods and state-of-the-art deep learning super-resolution algorithms. We evaluated the experimental results with HRCLDAS, national stations, and regional stations, and the results show that our improved model performs optimally compared to other methods (RMSE of 0.71 °C/0.12 °C/0.72 °C, BIAS of −0.02 °C/0.02 °C/0.002 °C), with the most noticeable improvement in mountainous regions, followed by plains. SNCA-CLDASSDexhibits the most stable performance in intraday hourly bias at temperature under the conditions of improved feature extraction capability in the SNBlock and a better utilization of the DEM by the SCAM. Due to the replacement of the upsampling method from sub pixels to CARAFE, it effectively suppresses the checkerboard effect and shows better robustness than other models. Our approach extends the downscaling model for CLDAS data products and significantly improves performance in this task by enhancing the model’s feature extraction and fusion capabilities and improving upsampling methods. It offers a more profound exploration of historical high-resolution temperature estimation and can be migrated to the downscaling of other meteorological elements.
Editorial on the Advances, Innovations and Applications of UAV Technology for Remote Sensing
Authors: Mohsan Syed Agha Hassnain , Khan Muhammad Asghar , Ghadi Yazeed Yasin
Published: October 2023

Currently, several kinds of Unmanned Aerial Vehicles (UAVs) or drones [...]
Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network
Authors: Chang Zhu , Li Hu , Chen Donghua , Liu Yufeng , Zou Chen , Chen Jian , Han Weijie , Liu Saisai , Zhang Naiming
Published: October 2023

Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape.
A Multimodal Robust Simultaneous Localization and Mapping Approach Driven by Geodesic Coordinates for Coal Mine Mobile Robots
Authors: Li Menggang , Hu Kun , Liu Yuwang , Hu Eryi , Tang Chaoquan , Zhu Hua , Zhou Gongbo
Published: October 2023

Mobile robots in complex underground coal mine environments are still unable to achieve accurate pose estimation and the real-time reconstruction of scenes with absolute geographic information. Integrated terrestrial-underground localization and mapping technologies have still not been effectively developed. This paper proposes a multimodal robust SLAM method based on wireless beacon-assisted geographic information transmission and lidar-IMU-UWB elastic fusion mechanism (LIU-SLAM). In order to obtain the pose estimation and scene models consistent with the geographic information, the construction of two kinds of absolute geographic information constraints based on UWB beacons is proposed. An elastic multimodal fusion state estimation mechanism is designed based on incremental factor graph optimization. A tightly coupled lidar-inertial odometry is firstly designed to construct the lidar-inertial local transformation constraints, which are further integrated with the absolute geographic constraints by UWB anchors through a loosely coupled approach. Extensive field tests based on coal mine robots have been conducted in scenarios such as underground garages and underground coal mine laneways. The results show that the proposed geodesic-coordinate driven multimode robust SLAM method can obtain absolute localization accuracy within 25 cm with practical robustness and real-time performance in different underground application scenarios. The wireless beacon-assisted geodesic-coordinate transmission strategy can provide a plug-and-play customized solution for precise positioning and scene modeling in complex scenarios of various coal mine robot application.
Integration of Geophysical and Geospatial Techniques to Evaluate Geothermal Energy at Siwa Oasis, Western Desert, Egypt
Authors: Ghoneim Eman , Healey Colleen , Hemida Mohamed , Shebl Ali , Fahil Amr
Published: October 2023

Environmental degradation is reducing crop productivity in many regions of Egypt. Moreover, unsustainable surface water drainage contributes to salinized soil conditions, which negatively impact crops. Egypt is seeking solutions to mitigate the problem of surface water drawdown and its consequences by exploring renewable and sustainable sources of energy. Geothermal energy and the desalination of saline water represent the only solutions to overcoming the fresh water shortage in agricultural industry and to providing sustainable fresh water and electricity to villages and the Bedouin livelihood. In Egypt, the Siwa Oasis contains a cluster of thermal springs, making the area an ideal location for geothermal exploration. Some of these thermal springs are characterized by high surface temperatures reaching 20 °C to 40 °C, and the bottom-hole temperatures (BHT) range from 21 °C to 121.7 °C. Pre-Cambrian basement rocks are usually more than 440 m deep, ranging from 440 m to 4724.4 m deep. It is this feature that makes the Siwa Oasis locality sufficient for geothermal power production and industrial processes. This study utilized both the Horner and the Gulf of Mexico correction methods to determine the formation temperatures from BHT data acquired from 27 deep oil wells. The present study revealed a geothermal gradient ranging from 18 to 42 °C/km, a heat flux of 24.7–111.3 mW/m2, and a thermal conductivity of 1.3–2.65 W/m/k. The derived geothermal, geophysical, and geological layers were combined together with space data and the topographic layer to map relevant physiographic variables including land surface elevation, depth to basement, lineament density, land surface temperature, and geologic rock units. The ten produced variables were integrated in GIS to model the geothermal potential map (GTP) for the Siwa Oasis region. According to the model, both the eastern side and north and northeastern portions of the study region contain high and very high geothermal potential energy. Combining bottom-hole temperature measurements with satellite remote sensing and geospatial analysis can considerably enhance geothermal prospecting in Egypt and other East African areas that have geologically and tectonically similar settings. In addition to identifying sustainable resources needed for food production, this research has implications for renewable energy resources as well.
How to Simulate Carbon Sequestration Potential of Forest Vegetation? A Forest Carbon Sequestration Model across a Typical Mountain City in China
Authors: Guan Dongjie , Nie Jialong , Zhou Lilei , Chang Qiongyao , Cao Jiameng
Published: October 2023

Due to a series of human activities like deforestation and land degradation, the concentration of greenhouse gases has risen significantly. Forest vegetation is an important part of forest ecosystems with high carbon sequestration potential. Estimates of the carbon sequestration rate of forest vegetation in various provinces and districts are helpful to the regional and global Carbon cycle. How to build an effective carbon sequestration potential model and reveal the spatiotemporal evolution trend and driving factors of carbon sequestration potential is an urgent challenge to be solved in carbon cycle simulation and prediction research. This study characterized the carbon sequestration status of forest vegetation using the modified CASA (Carnegie-Ames Stanford Approach) model and estimated the carbon sequestration potential from 2010 to 2060 using the FCS (Forest Carbon Sequestration) model combined with forest age and biomass under the four future Shared Socioeconomic Pathways (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, then proposes natural, social, and economic perspectives. This study found that the average NPP of the forest vegetation in Chongqing from 2000 to 2020 was 797.95 g C/m2, and the carbon storage by 2060 was 269.94 Tg C. The carbon sequestration rate varied between <0.01 Tg C/a and 0.20 Tg C/a in various districts and counties. Over time, forest growth gradually slowed, and carbon sequestration rates also decreased. Under the four future climate scenarios, the SSP5-8.5 pathway had the highest carbon sequestration rate. Natural factors had the greatest influence on changes in carbon sequestration rate. This result provides data support and scientific reference for the planning and control of forests and the enhancement of carbon sequestration capacity in Chongqing.
GPU Implementation of the Improved CEEMDAN Algorithm for Fast and Efficient EEG Time–Frequency Analysis
Authors: Wang Zeyu , Juhasz Zoltan
Published: October 2023

Time–frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time–frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000–8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations.
An Overview to Molecularly Imprinted Electrochemical Sensors for the Detection of Bisphenol A
Authors: Pan Ying , Wu Mengfan , Shi Mingjiao , Shi Peizheng , Zhao Ningbin , Zhu Yangguang , Karimi-Maleh Hassan , Ye Chen , Lin Cheng-Te , Fu Li
Published: October 2023

Bisphenol A (BPA) is an industrial chemical used extensively in plastics and resins. However, its endocrine-disrupting properties pose risks to human health and the environment. Thus, accurate and rapid detection of BPA is crucial for exposure monitoring and risk mitigation. Molecularly imprinted electrochemical sensors (MIES) have emerged as a promising tool for BPA detection due to their high selectivity, sensitivity, affordability, and portability. This review provides a comprehensive overview of recent advances in MIES for BPA detection. We discuss the operating principles, fabrication strategies, materials, and methods used in MIES. Key findings show that MIES demonstrate detection limits comparable or superior to conventional methods like HPLC and GC-MS. Selectivity studies reveal excellent discrimination between BPA and structural analogs. Recent innovations in nanomaterials, novel monomers, and fabrication techniques have enhanced sensitivity, selectivity, and stability. However, limitations exist in reproducibility, selectivity, and stability. While challenges remain, MIES provide a low-cost portable detection method suitable for on-site BPA monitoring in diverse sectors. Further optimization of sensor fabrication and characterization will enable the immense potential of MIES for field-based BPA detection.
An Optical System of Star Sensors with Accuracy Performance Varying with the Field of View
Authors: Wang Xiaoheng , Chen Xinrou , Li Zhikun , Zhu Jun
Published: October 2023

The field of view and single-star measurement accuracy are crucial metrics for assessing the performance of a star sensor. The field of view determines the spatial range of stars that can be captured by the sensor, while the single-star measurement accuracy determines the precision of attitude determination and control for the star sensor. The optical system of conventional star sensors is constrained by imaging relationships. Once the detector is determined, improving either the field of view or the single-star measurement accuracy will result in the degradation of the other. To address this issue, we propose an optical system for star sensors with accuracy performance varying with the field of view. By controlling the relationship between the field focal length of the optical system and the field of view, it is possible to simultaneously enhance both the field of view and the single-star measurement accuracy. We have designed corresponding optical systems to address the requirements for improving the single-star measurement accuracy and field of view. The design results confirm the feasibility of this star sensor. The star sensors are capable of simultaneously meeting the requirements for star pattern recognition and attitude determination, presenting broad application prospects in fields such as space navigation.
SiamHSFT: A Siamese Network-Based Tracker with Hierarchical Sparse Fusion and Transformer for UAV Tracking
Authors: Hu Xiuhua , Zhao Jing , Hui Yan , Li Shuang , You Shijie
Published: October 2023

Due to high maneuverability as well as hardware limitations of Unmanned Aerial Vehicle (UAV) platforms, tracking targets in UAV views often encounter challenges such as low resolution, fast motion, and background interference, which make it difficult to strike a compatibility between performance and efficiency. Based on the Siamese network framework, this paper proposes a novel UAV tracking algorithm, SiamHSFT, aiming to achieve a balance between tracking robustness and real-time computation. Firstly, by combining CBAM attention and downward information interaction in the feature enhancement module, the provided method merges high-level and low-level feature maps to prevent the loss of information when dealing with small targets. Secondly, it focuses on both long and short spatial intervals within the affinity in the interlaced sparse attention module, thereby enhancing the utilization of global context and prioritizing crucial information in feature extraction. Lastly, the Transformer’s encoder is optimized with a modulation enhancement layer, which integrates triplet attention to enhance inter-layer dependencies and improve target discrimination. Experimental results demonstrate SiamHSFT’s excellent performance across diverse datasets, including UAV123, UAV20L, UAV123@10fps, and DTB70. Notably, it performs better in fast motion and dynamic blurring scenarios. Meanwhile, it maintains an average tracking speed of 126.7 fps across all datasets, meeting real-time tracking requirements.
Sensor Scheduling for Remote State Estimation with Limited Communication Resources: A Time- and Event-Triggered Hybrid Approach
Authors: Ni  Yuqing , Liu  Xiaochen , Yang  Chao
Published: October 2023

This paper proposes a time- and event-triggered hybrid scheduling for remote state estimation with limited communication resources. A smart sensor observes a physical process and decides whether to send the local state estimate to a remote estimator via a wireless communication channel; the estimator computes the state estimate of the process according to the received data packets and the known scheduling mechanism. Based on the existing optimal time-triggered scheduling, we employ a stochastic event trigger to save precious communication chances and further improve the estimation performance. The minimum mean-squared error (MMSE) state estimate is derived since the Gaussian property is preserved. The estimation performance upper bound and communication rate are analyzed. The main results are illustrated by numerical examples.
A Rust Extraction and Evaluation Method for Navigation Buoys Based on Improved U-Net and Hue, Saturation, and Value
Authors: Hu Shunan , Duan Haiyan , Zhao Jiansen , Zhao Hailiang
Published: October 2023

Abnormalities of navigation buoys include tilting, rusting, breaking, etc. Realizing automatic extraction and evaluation of rust on buoys is of great significance for maritime supervision. Severe rust may cause damage to the buoy itself. Therefore, a lightweight method based on machine vision is proposed for extracting and evaluating the rust of the buoy. The method integrates image segmentation and processing. Firstly, image segmentation technology is used to extract the metal part of the buoy based on an improved U-Net. Secondly, the RGB image is converted into an HSV image by preprocessing, and the transformation law of HSV channel color value is analyzed to obtain the best segmentation threshold and then the pixels of the rusted and the metal parts can be extracted. Finally, the rust ratio of the buoy is calculated to evaluate the rust level of the buoy. Results show that both the segmentation precision and recall are above 0.95, and the accuracy is nearly 1.00. Compared with the rust evaluation algorithm directly using the image processing method, the accuracy and processing speed of rust grade evaluation are greatly improved.
Autonomous State Estimation and Observability Analysis for the Taiji Formation Using High-Precision Optical Sensors
Authors: Wen Bo , Tang Wenlin , Peng Xiaodong , Yang Zhen
Published: October 2023

In certain observation periods of navigation missions for the Taiji formation, ground observation stations are unable to observe the spacecraft, while the state of the spacecraft can be estimated through the utilization of dynamic equations simulated on prior knowledge. However, this method cannot accurately track the spacecraft. In this paper, we focus on appropriately selecting the available onboard measurement to estimate the state of the spacecraft of the Taiji formation. We design two schemes to explore the performance of the state estimation based on the interspacecraft interferometry measurements and the measurements obtained from the Sun sensor and the radial velocity sensor. The observability of the system is numerically analyzed using the singular value decomposition method. Furthermore, we analyze error covariance propagation using the cubature Kalman filter. The results show that using high-precision interspacecraft angle measurement can improve significantly the observability of the system. The absolute position and velocity of the spacecraft can be estimated respectively with an accuracy of about 3.1 km and 0.14 m/s in the first scheme, where the prior information of the precision of the position and velocity is respectively 100 km and 1 m/s. When the measurement from the radial velocity sensor is used in the second scheme, the estimation accuracy of the velocity can be improved about 18 times better than that in the first scheme.
Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data
Authors: Rodriguez Bryan , Rangarajan Prasanna , Zhang Xinxiang , Rajan Dinesh
Published: October 2023

One of the challenges of using Time-of-Flight (ToF) sensors for dimensioning objects is that the depth information suffers from issues such as low resolution, self-occlusions, noise, and multipath interference, which distort the shape and size of objects. In this work, we successfully apply a superquadric fitting framework for dimensioning cuboid and cylindrical objects from point cloud data generated using a ToF sensor. Our work demonstrates that an average error of less than 1 cm is possible for a box with the largest dimension of about 30 cm and a cylinder with the largest dimension of about 20 cm that are each placed 1.5 m from a ToF sensor. We also quantify the performance of dimensioning objects using various object orientations, ground plane surfaces, and model fitting methods. For cuboid objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 4% and 9% using the bounding technique and between 8% and 15% using the mirroring technique across all tested surfaces. For cylindrical objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 2.97% and 6.61% when the object is in a horizontal orientation and between 8.01% and 13.13% when the object is in a vertical orientation using the bounding technique across all tested surfaces.
A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset
Authors: Gan Qihong , Gong Lang , Hu Dasha , Jiang Yuming , Ding Xuefeng
Published: October 2023

Batch process monitoring datasets usually contain missing data, which decreases the performance of data-driven modeling for fault identification and optimal control. Many methods have been proposed to impute missing data; however, they do not fulfill the need for data quality, especially in sensor datasets with different types of missing data. We propose a hybrid missing data imputation method for batch process monitoring datasets with multi-type missing data. In this method, the missing data is first classified into five categories based on the continuous missing duration and the number of variables missing simultaneously. Then, different categories of missing data are step-by-step imputed considering their unique characteristics. A combination of three single-dimensional interpolation models is employed to impute transient isolated missing values. An iterative imputation based on a multivariate regression model is designed for imputing long-term missing variables, and a combination model based on single-dimensional interpolation and multivariate regression is proposed for imputing short-term missing variables. The Long Short-Term Memory (LSTM) model is utilized to impute both short-term and long-term missing samples. Finally, a series of experiments for different categories of missing data were conducted based on a real-world batch process monitoring dataset. The results demonstrate that the proposed method achieves higher imputation accuracy than other comparative methods.
Deep Neural Network-Based Fusion Localization Using Smartphones
Authors: Yan Suqing , Su Yalan , Xiao Jianming , Luo Xiaonan , Ji Yuanfa , Ghazali Kamarul Hawari Bin
Published: October 2023

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.
Exploitation Perspective Index as a Support of the Management of the Transformer Fleet
Authors: Kunicki Michał , Borucki Sebastian , Fulneček Jan
Published: October 2023

This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and the second shows the condition of the unit in the context of the entire fleet. The objective of the EPI method is to support the decision-making process regarding the technical condition assessment of each of the transformers in the target population, considering not only technical but also economic aspects of transformer maintenance. Application of the method is described step by step, including input data, parametrization of the weights, and interpretation of the output results it provides. The proposed method is evaluated by two representative use cases and compared with two other methods. As a result, EPI confirms its applicability, and it has already been successfully implemented by the electric power industry. EPI can be potentially freely adopted for any transformer fleet, as well as for the specific situation of the utility, by adjusting the relevant parameters.
Design of a Technique for Accelerating the WSN Convergence Process
Authors: Papan Jozef , Bridova Ivana , Filipko Adam
Published: October 2023

A wireless sensor network (WSN) is a network that monitors the physical environment using small and energy-efficient sensor devices. The wide application of WSNs has caused them to be used in critical applications that require a quick response, even at the cost of higher consumption. In recent years, Fast Reroute (FRR) technology has been developed, which accelerates network recovery after line or node failure. This technology plays an important role in connection recovery and data recovery, which helps speed up detection and redirect traffic. In our work, we created a new modification of the Ad hoc On-Demand Distance Vector (AODV) routing protocol, where we added the fast detection of link failure used in the FRR area. This modification rapidly increased connection recovery time and was tested in the OMNET++ simulation environment. The modification was implemented based on an additional RFC 5880 Bidirectional Forwarding Detection (BFD) module, which speeds up failure detection by sending quick “Hello” messages.
Proactive Fault Diagnosis of a Radiator: A Combination of Gaussian Mixture Model and LSTM Autoencoder
Authors: Lee Jeong-Geun , Kim Deok-Hwan , Lee Jang Hyun
Published: October 2023

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model.