bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Prediction Process of the Remaining Useful Life of Electric Vehicle Battery Using Real-World Data
Authors: Wang Xu , Li Jian , Shia Ben-Chang , Kao Yi-Wei , Ho Chieh-Wen , Chen Mingchih
Published: December 2021

In modern society, environmental sustainability is always a top priority, and thus electric vehicles (EVs) equipped with lithium-ion batteries are becoming more and more popular. As a key component of EVs, the remaining useful life of battery directly affects the demand of the EV supply chain. Accurate prediction of the remaining useful life (RUL) benefits not only EV users but also the battery inventory management. There are many existing methods to predict RUL based on state of health (SOH), but few of them are suitable for real-world data. There are several difficulties: (1) battery capacity is not easy to obtain in the real world; (2) most of these methods use the individual data for each battery, and the computing processes are difficult to perform in the cloud; (3) there is a lack of approaches for real-time SOH estimating and RUL predicting. This paper adopts several statistical methods to perform the prediction and compars the results of different models on experimental data (NASA dataset). Then, real-world data were implemented for an online process of RUL prediction. The main finding of this research is that the required CPU time was short enough to meet the daily usage after the real-world data was implemented for an online process of RUL prediction. The feasibility and precision of the prediction model can help to support the frequency control in power systems.
Use of Causal Language in Studies on the Relationship between Spiritually-Based Treatments and Substance Abuse and Relapse Prevention
Authors: Sánchez-Iglesias Iván , González-Castaño Marta , Molina Antonio J.
Published: December 2021

The main goal of scientific research is to explain what causes a phenomenon. However, only well-controlled studies guarantee sufficient internal validity to support causal explanations (i.e., experimental and some quasi-experimental designs). The use of causal claims in non-experimental studies can mislead readers into assuming a cause–effect relationship when alternative explanations have not been ruled out, undermining the principle of scientific rigor and the credibility of scientific findings. Although spiritual practices form part of some interventions for health and behavioral problems, their effectiveness cannot often be assessed via experimental methodology. This paper assesses the validity of causal inferences in published non-experimental studies, and more specifically in studies on the relationship between spiritually based treatments and substance abuse improvement and relapse prevention. We conducted a systematic review using Scopus, Pubmed, and several databases included in ProQuest, for the period 2015 to 2020. Out of 16 studies selected, six studies (37.5%) used correct language in the title, abstract, and discussion sections; 10 studies (68.8%) used tendentious or incorrect language in at least one section. Spiritually based treatments show promising results in some health improvement outcomes. Most studies show transparency when reporting results. However, researchers should be careful not to make causal assertions unless the internal validity of the research is sound.
Validation of “Trust/Mistrust in God Scale” for Spanish Cancer Patients
Authors: Almaraz David , Saiz Jesús , Sánchez-Iglesias Iván , Rosmarin David H.
Published: December 2021

Trust/Mistrust in God have turned out to be two constructs that have great relevance in the study of the relationship between religion, spirituality, and health. In Spain, there are no instruments adapted to measure trust/mistrust in God, which limits the work of researchers interested in these aspects. Therefore, the aim of this study was to validate the Brief Trust/Mistrust in God Scale (BTMGS) in Spanish. The scale translated into Spanish was applied in a sample of 178 oncologic patients together with the Positive and Negative Affect Schedule (PANAS) and the significant others subscale of the Multidimensional Scale of Perceived Social Support, due to the existing evidence of relationships between the variables evaluated by these measures. Internal consistency, structural validity, convergent and discriminant validity were evaluated. The Spanish adaptation of the BTMGS obtained high internal consistency, both for trust subscale (α = 0.95) and for the mistrust subscale (α = 0.86). Furthermore, the correlations found between the BTMGS and the measures of positive and negative emotions and social add evidence of convergent and discriminant validity. These results suggest that the Spanish version of the BTMGS is a valid and reliable measure to be used in research on religion, spirituality and health in Spanish-speaking contexts.
An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery
Authors: Chen Shujie , Huang Wenli , Chen Yumin , Feng Mei
Published: December 2021

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.
Continuous Change Mapping to Understand Wetland Quantity and Quality Evolution and Driving Forces: A Case Study in the Liao River Estuary from 1986 to 2018
Authors: Peng Jianwei , Liu Shuguang , Lu Weizhi , Liu Maochou , Feng Shuailong , Cong Pifu
Published: December 2021

Coastal wetland ecosystems, one of the most important ecosystems in the world, play an important role in regulating climate, sequestering blue carbon, and maintaining sustainable development of coastal zones. Wetland landscapes are notoriously difficult to map with satellite data, particularly in highly complex, dynamic coastal regions. The Liao River Estuary (LRE) wetland in Liaoning Province, China, has attracted major attention due to its status as Asia’s largest coastal wetland, with extensive Phragmites australis (reeds), Suaeda heteroptera (seepweed, red beach), and other natural resources that have been continuously encroached upon by anthropogenic land-use activities. Using the Continuous Change Detection and Classification (CCDC) algorithm and all available Landsat images, we mapped the spatial–temporal changes of LRE coastal wetlands (e.g., seepweed, reed, tidal flats, and shallow marine water) annually from 1986 to 2018 and analyzed the changes and driving forces. Results showed that the total area of coastal wetlands in the LRE shrank by 14.8% during the study period. The tidal flats were the most seriously affected type, with 45.7% of its total area lost. One of the main characteristics of wetland change was the concurrent disappearance and emergence of wetlands in different parts of the LRE, creating drastically different mixtures of wetland quality (e.g., wetland age composition) in addition to area change. The reduction and replacement/translocation of coastal wetlands were mainly caused by human activities related to urbanization, tourism, land reclamation, and expansion of aquaculture ponds. Our efforts in mapping annual changes of wetlands provide direct, specific, and spatially explicit information on rates, patterns, and causes of coastal wetland change, both in coverage and quality, so as to contribute to the effective plans and policies for coastal management, preservation, and restoration of coastal ecosystem services.
A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching
Authors: Yu Yang , Ma Yong , Mei Xiaoguang , Fan Fan , Huang Jun , Ma Jiayi
Published: December 2021

Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.
Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)
Authors: Han Te , Tang Yuqi , Yang Xin , Lin Zefeng , Zou Bin , Feng Huihui
Published: December 2021

To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.
A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning
Authors: Man Peng , Ding Chibiao , Ren Wenjuan , Xu Guangluan
Published: December 2021

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples.
Climatology and Long-Term Trends in the Stratospheric Temperature and Wind Using ERA5
Authors: Kozubek Michal , Laštovička Jan , Zajicek Radek
Published: December 2021

This study analyses long-term trends in temperature and wind climatology based on ERA5 data. We study climatology and trends separately for every decade from 1980 to 2020 and their changes during this period. This study is focused on the pressure levels between 100–1 hPa, which essentially covers the whole stratosphere. We also analyze the impact of the sudden stratospheric warmings (SSW), North Atlantic Oscillation (NAO), El Nino Southern Oscillation (ENSO) and Quasi-biennial oscillation (QBO). This helps us to find details of climatology and trend behavior in the stratosphere in connection to these phenomena. ERA5 is one of the newest reanalysis, which is widely used for the middle atmosphere. We identify the largest differences which occur between 1990–2000 and 2000–2010 in both temperature climatology and trends. We suggest that these differences could relate to the different occurrence frequency of SSWs in 1990–2000 versus 2000–2010.
Fishing for Feral Cats in a Naturally Fragmented Rocky Landscape Using Movement Data
Authors: Williamson Sandra D. , van Dongen Richard , Trotter Lewis , Palmer Russell , Robinson Todd P.
Published: December 2021

Feral cats are one of the most damaging predators on Earth. They can be found throughout most of Australia’s mainland and many of its larger islands, where they are adaptable predators responsible for the decline and extinction of many species of native fauna. Managing feral cat populations to mitigate their impacts is a conservation priority. Control strategies can be better informed by knowledge of the locations that cats frequent the most. However, this information is rarely captured at the population level and therefore requires modelling based on observations of a sample of individuals. Here, we use movement data from collared feral cats to estimate home range sizes by gender and create species distribution models in the Pilbara bioregion of Western Australia. Home ranges were estimated using dynamic Brownian bridge movement models and split into 50% and 95% utilisation distribution contours. Species distribution models used points intersecting with the 50% utilisation contours and thinned by spacing points 500 m apart to remove sampling bias. Male cat home ranges were between 5 km2 (50% utilisation) and 34 km2 (95% utilisation), which were approximately twice the size of the female cats studied (2–17 km2). Species distribution modelling revealed a preference for low-lying riparian habitats with highly productive vegetation cover and a tendency to avoid newly burnt areas and topographically complex, rocky landscapes. Conservation management can benefit by targeting control effort in preferential habitat.
On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula
Authors: Rahimi Dalkhani Amin , Zhang Xin , Weemstra Cornelis
Published: December 2021

Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two-step inversion algorithms are employed to solve the tomographic inverse problem. That is, a first inversion results in frequency-dependent, two-dimensional maps of phase velocity, which then serve as input for a series of independent, one-dimensional frequency-to-depth inversions. As such, a set of localized depth-dependent velocity profiles are obtained at the surface points. Stitching these separate profiles together subsequently yields a three-dimensional velocity model. Relatively recently, a one-step three-dimensional non-linear tomographic algorithm has been proposed. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. Specifically, the three-dimensional velocity field is parameterized by means of a polyhedral Voronoi tessellation. In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes Peninsula, southwest Iceland. To that end, we design a number of synthetic tests that take into account the station configuration of the Reykjanes seismic network. We find that the algorithm is able to recover the 3D velocity structure at various scales in areas where station density is high. In addition, we find that the standard deviation of the recovered velocities is low in those regions. At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations. This implies that the algorithm successfully adapts model resolution to the density of rays. It also adapts model resolution to the amount of noise in the travel times. Because the algorithm is computationally demanding, we modify the algorithm such that computational costs are reduced while sufficiently preserving non-linearity. We conclude that the algorithm can now be applied adequately to travel times extracted from station–station cross correlations by the Reykjanes seismic network.
Recent Spatiotemporal Trends in Glacier Snowline Altitude at the End of the Melt Season in the Qilian Mountains, China
Authors: Guo Zhongming , Wang Ninglian , Shen Baoshou , Gu Zhujun , Wu Yuwei , Chen Anan
Published: December 2021

Glaciers in the Qilian Mountains, China, play an important role in supplying freshwater to downstream populations, maintaining ecological balance, and supporting economic development on the Tibetan Plateau. Glacier snowline altitude (SLA) at the end of the melt season is an indicator of the Equilibrium line altitude (ELA), and can be used to estimate the mass balance and climate reconstruction. Here, we employ the height zone-area method to determine the SLA at the end of the melt season during the 1989–2018 period using Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) SLA and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data. The accuracy of glacier SLA obtained in 1989–2018 after adding MODIS SLA data to the years without Landsat data increased by about 78 m. The difference between the remote-sensing-derived SLA and measured equilibrium line altitude (ELA) is mostly within 50 m, suggesting that the SLA can serve as a proxy for the ELA at the end of the melt season. The SLA of Qiyi Glacier in the Qilian Mountains rose from 4690 ± 25 m to 5030 ± 25 m, with an average of 4900 ± 103 m during the 30 year study period. The western, central, eastern sections and the whole range of the Qilian Mountains exhibited an upward trend in SLA during the 30 year study period. The mean glacier SLAs were 4923 ± 137 m, 4864 ± 135 m, 4550 ± 149 m and 4779 ± 149 m for the western, central, eastern sections and the whole range, respectively. From the perspective of spatial distribution, regardless of the different orientation, grid scale and basin scale, the glacier SLA of Qilian Mountains showed an upward trend from 1989 to 2018, and the glacier SLA is in general located at a comparably higher altitude in the southern and western parts of the Qilian Mountains while it is located at a comparably lower altitude in its northern and eastern parts. In an ideal condition, climate sensitivity studies of ELA in Qilian Mountains show that if the summer mean temperature increases (decreases) by 1 °C, then ELA will increase (decrease) by about 102 m. If the annual total solid precipitation increases (decreases) by 10%, then the glacier ELA will decrease (rise) by about 6 m. The summer mean temperature is the main factor affecting the temporal trend of SLA, whereas both summer mean temperature and annual total precipitation influence the spatial change of SLA.
An Improved Fmask Method for Cloud Detection in GF-6 WFV Based on Spectral-Contextual Information
Authors: Yang Xiaomeng , Sun Lin , Tang Xinming , Ai Bo , Xu Hanwen , Wen Zhen
Published: December 2021

GF-6 is the first optical remote sensing satellite for precision agriculture observations in China. Accurate identification of the cloud in GF-6 helps improve data availability. However, due to the narrow band range contained in GF-6, Fmask version 3.2 for Landsat is not suitable for GF-6. Hence, this paper proposes an improved Fmask based on the spectral-contextual information to solve the inapplicability of Fmask version 3.2 in GF-6. The improvements are divided into the following six aspects. The shortwave infrared (SWIR) in the “Basic Test” is replaced by blue band. The threshold in the original “HOT Test” is modified based on the comprehensive consideration of fog and thin clouds. The bare soil and rock are detected by the relationship between green and near infrared (NIR) bands. The bright buildings are detected by the relationship between the upper and lower quartiles of blue and red bands. The stratus with high humidity and fog_W (fog over water) are distinguished by the ratio of blue and red edge position 1 bands. Temperature probability for land is replaced by the HOT-based cloud probability (LHOT), and SWIR in brightness probability is replaced by NIR. The average cloud pixels accuracy (TPR) of the improved Fmask is 95.51%.
Contribution of Changes in Snow Cover Extent to Shortwave Radiation Perturbations at the Top of the Atmosphere over the Northern Hemisphere during 2000–2019
Authors: Chen Xiaona , Yang Yaping , Yin Cong
Published: December 2021

Snow-induced radiative forcing (SnRF), defined as the instantaneous perturbation of the Earth’s shortwave radiation at the top of the atmosphere (TOA), results from variations in the terrestrial snow cover extent (SCE), and is critical for the regulation of the Earth’s energy budget. However, with the growing seasonal divergence of SCE over the Northern Hemisphere (NH) in the past two decades, novel insights pertaining to SnRF are lacking. Consequently, the contribution of SnRF to TOA shortwave radiation anomalies still remains unclear. Utilizing the latest datasets of snow cover, surface albedo, and albedo radiative kernels, this study investigated the distribution of SnRF over the NH and explored its changes from 2000 to 2019. The 20-year averaged annual mean SnRF in the NH was −1.13 ± 0.05 W m−2, with a weakening trend of 0.0047 Wm−2 yr−1 (p < 0.01) during 2000–2019, indicating that an extra 0.094 W m−2 of shortwave radiation was absorbed by the Earth climate system. Moreover, changes in SnRF were highly correlated with satellite-observed TOA shortwave flux anomalies (r = 0.79, p < 0.05) during 2000–2019. Additionally, a detailed contribution analysis revealed that the SnRF in snow accumulation months, from March to May, accounted for 58.10% of the annual mean SnRF variability across the NH. These results can assist in providing a better understanding of the role of snow cover in Earth’s climate system in the context of climate change. Although the rapid SCE decline over the NH has a hiatus for the period during 2000–2019, SnRF continues to follow a weakening trend. Therefore, this should be taken into consideration in current climate change models and future climate projections.
Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks
Authors: Kuck Tahisa Neitzel , Silva Filho Paulo Fernando Ferreira , Sano Edson Eyji , Bispo Polyanna da Conceição , Shiguemori Elcio Hideiti , Dalagnol Ricardo
Published: December 2021

It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.
Rotational Speed Measurement Based on LC Wireless Sensors
Authors: Zhou Yi , Dong Lei , Zhang Chi , Wang Lifeng , Huang Qingan
Published: December 2021

This article presents a method for detecting rotational speed by LC (inductor-capacitor) wireless sensors. The sensing system consists of two identical LC resonant tanks. One is mounted on the rotating part and the other, as a readout circuit, is placed right above the rotating part. When the inductor on the rotating part is coaxially aligned with the readout inductor during rotation, the mutual coupling between them reaches the maximum, resulting in a peak amplitude induced at the readout LC tank. The period of the readout signal corresponds to the rotation speed. ADS (Advanced Design System) software was used to simulate and optimize the sensing system. A synchronous detection circuit was designed. The rotational speed of an electric was measured to validate this method experimentally, and the results indicated that the maximum error of the rotation speed from 16 rps to 41 rps was 0.279 rps.
Prediction of Cow Calving in Extensive Livestock Using a New Neck-Mounted Sensorized Wearable Device: A Pilot Study
Authors: González-Sánchez Carlos , Sánchez-Brizuela Guillermo , Cisnal Ana , Fraile Juan-Carlos , Pérez-Turiel Javier , Fuente-López Eusebio de la
Published: December 2021

In this study, new low-cost neck-mounted sensorized wearable device is presented to help farmers detect the onset of calving in extensive livestock farming by continuously monitoring cow data. The device incorporates three sensors: an inertial measurement unit (IMU), a global navigation satellite system (GNSS) receiver, and a thermometer. The hypothesis of this study was that onset calving is detectable through the analyses of the number of transitions between lying and standing of the animal (lying bouts). A new algorithm was developed to detect calving, analysing the frequency and duration of lying and standing postures. An important novelty is that the proposed algorithm has been designed with the aim of being executed in the embedded microcontroller housed in the cow’s collar and, therefore, it requires minimal computational resources while allowing for real time data processing. In this preliminary study, six cows were monitored during different stages of gestation (before, during, and after calving), both with the sensorized wearable device and by human observers. It was carried out on an extensive livestock farm in Salamanca (Spain), during the period from August 2020 to July 2021. The preliminary results obtained indicate that lying-standing animal states and transitions may be useful to predict calving. Further research, with data obtained in future calving of cows, is required to refine the algorithm.
Howling Detection and Suppression Based on Segmented Notch Filtering
Authors: Li Yanping , Huang Xiangdong , Zheng Yi , Gao Zhongke , Kou Lei , Wan Junhe
Published: December 2021

The existing adaptive echo cancellation based howling (typically in hearing aids) removal methods have several drawbacks such as insufficient attenuation of the howling component, slow response and nonlinear distortion. To solve these problems, we propose a segmented notch filtering based scheme. Specifically, firstly, it is proved that the attenuation value can reach −330 dB at any detected howling frequency; secondly, the filter coefficients can be readily calculated by a closed-form formula, yielding a fast response to the sudden howling accident; thirdly, the closed-form formula of this filter is theoretically an even function, indicating that this filter possesses a linear transfer characteristic. In combination with proper segmentation and precisely removing these transient samples arising from FIR (Finite Impulsive Response) filtering, nonlinear distortion can be entirely avoided. Experimental results show that our proposed scheme can not only accurately estimate the howling frequency, but can also completely remove it, which yields a high-quality output waveform with a recovery SNR of about 22 dB. Therefore, the proposed segmented notching based scheme possesses vast potential for hearing aid development and other relevant applications.
Ultra-Reliable Communication for Critical Machine Type Communication via CRAN-Enabled Multi-Connectivity Diversity Schemes
Authors: Kharel Binod , López Onel Luis Alcaraz , Alves Hirley , Latva-aho Matti
Published: December 2021

This paper focuses on edge-enabled cloud radio access network architecture to achieve ultra-reliable communication, a crucial enabler for supporting mission-critical machine-type communication networks. We propose coordinated multi-point transmission schemes taking advantage of diversity mechanisms in interference-limited downlink cellular networks. The network scenario comprises spatially distributed multiple remote radio heads (RRHs) that may cooperate through silencing, or by using more elaborated diversity strategies such as maximum ratio transmission or transmit antenna selection to serve user equipment in the ultra-reliable operation regime. We derive an exact closed-form expression for the outage probabilities and expected values of signal-to-interference ratio for silencing, transmit antenna selection and maximum ratio transmission schemes. We formulate rate control and energy efficiency under reliability constraints to test the performance and resource usage of the proposed schemes. Furthermore, we study the impact on average system sum throughput with throughput-reliability trade-off under cooperative communication. Extensive numerical analysis shows the feasibility of ultra-reliable communication by implementing diversity schemes with RRHs cooperation.
Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry
Authors: Barreto Leilane C. , Martínez-Arias Rosa , Schechert Axel
Published: December 2021

Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field.
Real-Time Detection of Cook Assistant Overalls Based on Embedded Reasoning
Authors: Sheng Qinghua , Sheng Haixiang , Gao Peng , Li Zhu , Yin Haibing
Published: December 2021

Currently, the target detection based on convolutional neural network plays an important role in image recognition, speech recognition and other fields. However, the current network model features a complex structure, a huge number of parameters and resources. These conditions make it difficult to apply in embedded devices with limited computational capabilities and extreme sensitivity to power consumption. In this regard, the application scenarios of deep learning are limited. This paper proposes a real-time detection scheme for cook assistant overalls based on the Hi3559A embedded processor. With YOLOv3 as the benchmark network, this scheme fully mobilizes the hardware acceleration resources through the network model optimization and the parallel processing technology of the processor, and improves the network reasoning speed, so that the embedded device can complete the task of real-time detection on the local device. The experimental results show that through the purposeful cropping, segmentation and in-depth optimization of the neural network according to the specific processor, the neural network can recognize the image accurately. In an application environment where the power consumption is only 5.5 W, the recognition speed of the neural network on the embedded end is increased to about 28 frames (the design requirement was to achieve a recognition speed of 25 frames or more), so that the optimized network can be effectively applied in the back kitchen overalls identification scene.
Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis
Authors: Yao Yuman , Dai Yiyang , Luo Wenjia
Published: December 2021

The products of a batch process have high economic value. Meanwhile, a batch process involves complex chemicals and equipment. The variability of its operation leads to a high failure rate. Therefore, early fault diagnosis of batch processes is of great significance. Usually, the available information of the sensor data in batch processing is obscured by its noise. The multistage variation of data results in poor diagnostic performance. This paper constructed a standardized method to enlarge fault information as well as a batch fault diagnosis method based on trend analysis. First, an adaptive standardization based on the time window was created; second, utilizing quadratic fitting, we extracted a data trend under the window; third, a new trend recognition method based on the Euclidean distance calculation principle was composed. The method was verified in penicillin fermentation. We constructed two test datasets: one based on an existing batch, and one based on an unknown batch. The average diagnostic rate of each group was 100% and 87.5%; the mean diagnosis time was the same; 0.2083 h. Compared with traditional fault diagnosis methods, this algorithm has better fault diagnosis ability and feature extraction ability.
Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard
Authors: Wieczorek Grzegorz , Chlebus Marcin , Gajda Janusz , Chyrowicz Katarzyna , Kontna Kamila , Korycki Michał , Jegorowa Albina , Kruk Michał
Published: December 2021

The multiclass prediction approach to the problem of recognizing the state of the drill by classifying images of drilled holes into three classes is presented. Expert judgement was made on the basis of the quality of the hole, by dividing the collected photographs into the classes: “very fine,” “acceptable,” and “unacceptable.” The aim of the research was to create a model capable of identifying different levels of quality of the holes, where the reduced quality would serve as a warning that the drill is about to wear down. This could reduce the damage caused by a blunt tool. To perform this task, real-world data were gathered, normalized, and scaled down, and additional instances were created with the use of data-augmentation techniques, a self-developed transformation, and with general adversarial networks. This approach also allowed us to achieve a slight rebalance of the dataset, by creating higher numbers of images belonging to the less-represented classes. The datasets generated were then fed into a series of convolutional neural networks, with different numbers of convolution layers used, modelled to carry out the multiclass prediction. The performance of the so-designed model was compared to predictions generated by Microsoft’s Custom Vision service, trained on the same data, which was treated as the benchmark. Several trained models obtained by adjusting the structure and hyperparameters of the model were able to provide better recognition of less-represented classes than the benchmark.
Development of a Hybrid Method to Generate Gravito-Inertial Cues for Motion Platforms in Highly Immersive Environments
Authors: Riera Jose V. , Casas Sergio , Fernández Marcos , Alonso Francisco , Useche Sergio A.
Published: December 2021

Motion platforms have been widely used in Virtual Reality (VR) systems for decades to simulate motion in virtual environments, and they have several applications in emerging fields such as driving assistance systems, vehicle automation and road risk management. Currently, the development of new VR immersive systems faces unique challenges to respond to the user’s requirements, such as introducing high-resolution 360° panoramic images and videos. With this type of visual information, it is much more complicated to apply the traditional methods of generating motion cues, since it is generally not possible to calculate the necessary corresponding motion properties that are needed to feed the motion cueing algorithms. For this reason, this paper aims to present a new method for generating non-real-time gravito-inertial cues with motion platforms using a system fed both with computer-generated—simulation-based—images and video imagery. It is a hybrid method where part of the gravito-inertial cues—those with acceleration information—are generated using a classical approach through the application of physical modeling in a VR scene utilizing washout filters, and part of the gravito-inertial cues—the ones coming from recorded images and video, without acceleration information—were generated ad hoc in a semi-manual way. The resulting motion cues generated were further modified according to the contributions of different experts based on a successive approximation—Wideband Delphi-inspired—method. The subjective evaluation of the proposed method showed that the motion signals refined with this method were significantly better than the original non-refined ones in terms of user perception. The final system, developed as part of an international road safety education campaign, could be useful for developing further VR-based applications for key fields such as driving assistance, vehicle automation and road crash prevention.
Micrometer Sized Hexagonal Chromium Selenide Flakes for Cryogenic Temperature Sensors
Authors: Buruiana Angel-Theodor , Sava Florinel , Iacob Nicusor , Matei Elena , Bocirnea Amelia Elena , Onea Melania , Galca Aurelian-Catalin , Mihai Claudia , Velea Alin , Kuncser Victor
Published: December 2021

Nanoscale thermometers with high sensitivity are needed in domains which study quantum and classical effects at cryogenic temperatures. Here, we present a micrometer sized and nanometer thick chromium selenide cryogenic temperature sensor capable of measuring a large domain of cryogenic temperatures down to tenths of K. Hexagonal Cr-Se flakes were obtained by a simple physical vapor transport method and investigated using scanning electron microscopy, energy dispersive X-ray spectrometry and X-ray photoelectron spectroscopy measurements. The flakes were transferred onto Au contacts using a dry transfer method and resistivity measurements were performed in a temperature range from 7 K to 300 K. The collected data have been fitted by exponential functions. The excellent fit quality allowed for the further extrapolation of resistivity values down to tenths of K. It has been shown that the logarithmic sensitivity of the sensor computed over a large domain of cryogenic temperature is higher than the sensitivity of thermometers commonly used in industry and research. This study opens the way to produce Cr-Se sensors for classical and quantum cryogenic measurements.
Detection of Collaterals from Cone-Beam CT Images in Stroke
Authors: Abd Aziz Azrina , Izhar Lila Iznita , Asirvadam Vijanth Sagayan , Tang Tong Boon , Ajam Azimah , Omar Zaid , Muda Sobri
Published: December 2021

Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels. This paper presents a technique to objectively identify collateral vessels from non-collateral vessels. In our technique, several filters are used on the CBCT images of stroke patients to remove noises and artifacts, then multiscale top-hat transformation method is implemented on the pre-processed images to further enhance the vessels. Next, we applied three types of feature extraction methods which are gray level co-occurrence matrix (GLCM), moment invariant, and shape to explore which feature is best to classify the collateral vessels. These features are then used by the support vector machine (SVM), random forest, decision tree, and K-nearest neighbors (KNN) classifiers to classify vessels. Finally, the performance of these classifiers is evaluated in terms of accuracy, sensitivity, precision, recall, F-Measure, and area under the receiver operating characteristics curve. Our results show that all classifiers achieve promising classification accuracy above 90% and able to detect the collateral and non-collateral vessels from images.
Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis
Authors: Faysal Atik , Ngui Wai Keng , Lim Meng Hee , Leong Mohd Salman
Published: December 2021

Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis.