bluuebunny/crossref_metadata_2025 huggingface dataset:
Comfort Optimization of the Active Collision Avoidance Control System of Electric Vehicles for Green Manufacturing
Authors: Li Ning , Liu Yingshuai , Zhang Tengfei , Yang Yongqi , Wang Chunlin , Wang Xinzhi
Published: February 2023

The vehicle model was built based on MATLAB/Simulink and Carsim, and the multi-objective active collision avoidance control algorithm considering safety and comfort was established based on a model predictive control (MPC) algorithm. The vehicle active collision avoidance control system for comfort and safety was studied by simulation and experimentation. The results show that the active collision avoidance control system based on an MPC algorithm can follow the vehicle under different working conditions and ensure the safety and comfort in the process of following the vehicle while meeting the requirements of the active collision avoidance control system.
Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning
Authors: Wei Yuzhen , Yang Chao , He Liu , Wu Feiyue , Yu Qiangguo , Hu Wenjun
Published: February 2023

The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning.
Comprehensive Kinase Activity Profiling Revealed the Kinase Activity Patterns Associated with the Effects of EGFR Tyrosine Kinase Inhibitor Therapy in Advanced Non-Small-Cell Lung Cancer Patients with Sensitizing EGFR Mutations
Authors: Noguchi Rei , Yoshimura Akihiro , Uchino Junji , Takeda Takayuki , Chihara Yusuke , Ota Takayo , Hiranuma Osamu , Gyotoku Hiroshi , Takayama Koichi , Kondo Tadashi
Published: February 2023

EGFR mutations are strong predictive markers for EGFR tyrosine kinase inhibitor (EGFR-TKI) therapy in patients with non-small-cell lung cancer (NSCLC). Although NSCLC patients with sensitizing EGFR mutations have better prognoses, some patients exhibit worse prognoses. We hypothesized that various activities of kinases could be potential predictive biomarkers for EGFR-TKI treatment among NSCLC patients with sensitizing EGFR mutations. In 18 patients with stage IV NSCLC, EGFR mutations were detected and comprehensive kinase activity profiling was performed using the peptide array PamStation12 for 100 tyrosine kinases. Prognoses were observed prospectively after the administration of EGFR-TKIs. Finally, the kinase profiles were analyzed in combination with the prognoses of the patients. Comprehensive kinase activity analysis identified specific kinase features, consisting of 102 peptides and 35 kinases, in NSCLC patients with sensitizing EGFR mutations. Network analysis revealed seven highly phosphorylated kinases: CTNNB1, CRK, EGFR, ERBB2, PIK3R1, PLCG1, and PTPN11. Pathway analysis and Reactome analysis revealed that the PI3K-AKT and RAF/ MAPK pathways were significantly enriched in the poor prognosis group, being consistent with the outcome of the network analysis. Patients with poor prognoses exhibited high activation of EGFR, PIK3R1, and ERBB2. Comprehensive kinase activity profiles may provide predictive biomarker candidates for screening patients with advanced NSCLC harboring sensitizing EGFR mutations.
Measuring the Effectiveness of Career Education: A Kindergarten Intervention
Authors: Buffoli Alessandro , Rinaldi Teresa , Morici Roberta , Boerchi Diego
Published: February 2023

Several studies have confirmed the importance of career education in promoting career development in children. This study aims to test whether specific career education interventions would develop new conceptions about career choice and career attainment in kindergarten pupils. The intervention was conducted directly by teachers who were adequately trained and supervised. The career conceptions were assessed in experimental and control groups through the Conceptions of Career Choice and Attainment protocol, before and after career education intervention. The results showed that the two groups started from the same level and increased their conceptions over time. However, the experimental groups increased them much more, and statistically significantly, than the control groups.
The Social Networks of Gods in Late Imperial Spirit-Writing Altars
Authors: Goossaert Vincent
Published: February 2023

The late imperial-educated Chinese interacted with a very large array of gods through various means, especially spirit-writing, for which we have abundant detailed records. While a few prominent gods have been studied in this context, there are currently no comprehensive studies of the connections between humans and gods. Using the records of thirteen different spirit-writing altars in various parts of the Chinese world between the sixteenth and nineteenth centuries, this paper maps the 478 gods involved using standard social network analysis visualizations, and identifies the types of gods that played central roles (connecting many different gods and humans) and those that had fewer, more exclusive sociabilities.
Impacts of Green Fraction Changes on Surface Temperature and Carbon Emissions: Comparison under Forestation and Urbanization Reshaping Scenarios
Authors: Mumtaz Faisal , Li Jing , Liu Qinhuo , Tariq Aqil , Arshad Arfan , Dong Yadong , Zhao Jing , Bashir Barjeece , Zhang Hu , Gu Chenpeng , Liu Chang
Published: February 2023

Global land cover dynamics alter energy, water, and greenhouse gas exchange between land and atmosphere, affecting local to global weather and climate change. Although reforestation can provide localized cooling, ongoing land use land cover (LULC) shifts are expected to exacerbate urban heat island impacts. In this study, we monitored spatiotemporal changes in green cover in response to land use transformation associated with the Khyber Pakhtunkhwa (KPK) provincial government’s Billion Tree Tsunami Project (BTTP) and the Ravi Urban Development Plan (RUDP) initiated by the provincial government of Punjab, both in Pakistan. The land change modeler (LCM) was used to assess the land cover changes and transformations between 2000 and 2020 across Punjab and KPK. Furthermore, a curve fit linear regression model (CFLRM) and sensitivity analysis were employed to analyze the impacts of land cover dynamics on land surface temperature (LST) and carbon emissions (CE). Results indicated a significant increase in green fraction of +5.35% under the BTTP, achieved by utilizing the bare land with an effective transition of 4375.87 km2. However, across the Punjab province, an alarming reduction in green fraction cover by −1.77% and increase in artificial surfaces by +1.26% was noted. A significant decrease in mean monthly LST by −4.3 °C was noted in response to the BTTP policy, while an increase of 5.3 °C was observed associated with the RUDP. A substantial increase in LST by 0.17 °C was observed associated with transformation of vegetation to artificial surfaces. An effective decrease in LST by −0.21 °C was observed over the opposite transition. Furthermore, sensitivity analysis suggested that LST fluctuations are affecting the % of CO2 emission. The current findings can assist policymakers in revisiting their policies to promote ecological conservation and sustainability in urban planning.
Prospects of Consumer-Grade UAVs for Overpass Bridges Pier Pads Alignment
Authors: Jaafar Hasan Abdulhussein , Alsadik Bashar
Published: February 2023

The use of Unmanned Aerial Vehicles (UAVs) for surveying is at the forefront of their use in the Architectural Engineering and Construction (AEC) industry. UAVs make accessing hard-to-reach construction regions simpler and more cost-effective because of their small size, ease of mobility, and the wealth of information given by their integrated sensors. Accordingly, their use is thriving in different AEC sectors such as the management and inspection of engineering facilities such as concrete bridges. Overpass bridge engineering inspections are still applied using high accuracy surveying instruments in situ to ensure meeting the quality standards of construction. One important application is to measure the bridge pier caps centerline fitting using total stations, which is costly in terms of time and labor. Therefore, in this article, a new approach based on consumer-grade UAV imaging is proposed for replacing the traditional surveying techniques which are expected to improve automation and reduce time and cost. The proposed method utilized a sequence of processes on the UAV point clouds of the bridge concrete pier caps to finally extract the pier pads center and check their alignment. In two experiments, point clouds are created using DJI Phantom 3 images taken over bridge pier projects under construction, and concrete pad centers are then estimated and compared to the reference total station measurements. The results of both tests reveal the ability of the proposed method to attain the required accuracy for the pads’ alignment, as the root mean square error (RMSE) is one centimeter and two centimeters for the first and second tests, respectively. In addition, the new approach can reduce implementation time and the project budget.
Representing Spatial Data with Graph Contrastive Learning
Authors: Fang Lanting , Kou Ze , Yang Yulian , Li Tao
Published: February 2023

Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for geospatial data. In this paper, we construct a spatial graph from the locations and propose a geospatial graph contrastive learning method to learn the location representations. Firstly, we propose a skeleton graph in order to preserve the primary structure of the geospatial graph to solve the positioning bias problem of remote sensing. Then, we define a novel mixed node centrality measure and propose four data augmentation methods based on the measure. Finally, we propose a heterogeneous graph attention network to aggregate information from both the structural neighborhood and semantic neighborhood separately. Extensive experiments on both geospatial datasets and non-geospatial datasets are conducted to illustrate that the proposed method outperforms state-of-the-art baselines.
A Closed-Loop Network for Single Infrared Remote Sensing Image Super-Resolution in Real World
Authors: Zhang Haopeng , Zhang Cong , Xie Fengying , Jiang Zhiguo
Published: February 2023

Single image super-resolution (SISR) is to reconstruct a high-resolution (HR) image from a corresponding low-resolution (LR) input. It is an effective way to solve the problem that infrared remote sensing images are usually suffering low resolution due to hardware limitations. Most previous learning-based SISR methods just use synthetic HR-LR image pairs (obtained by bicubic kernels) to learn the mapping from LR images to HR images. However, the underlying degradation in the real world is often different from the synthetic method, i.e., the real LR images are obtained through a more complex degradation kernel, which leads to the adaptation problem and poor SR performance. To handle this problem, we propose a novel closed-loop framework that can not only make full use of the learning ability of the channel attention module but also introduce the information of real images as much as possible through a closed-loop structure. Our network includes two independent generative networks for down-sampling and super-resolution, respectively, and they are connected to each other to get more information from real images. We make a comprehensive analysis of the training data, resolution level and imaging spectrum to validate the performance of our network for infrared remote sensing image super-resolution. Experiments on real infrared remote sensing images show that our method achieves superior performance in various training strategies of supervised learning, weakly supervised learning and unsupervised learning. Especially, our peak signal-to-noise ratio (PSNR) is 0.9 dB better than the second-best unsupervised super-resolution model on PROBA-V dataset.
Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations
Authors: Romeo Francesco , Mereu Luigi , Scollo Simona , Papa Mario , Corradini Stefano , Merucci Luca , Marzano Frank Silvio
Published: February 2023

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 1011 kg (MLE method) and 1.32 ± 0.47 × 1011 kg (NN method) for Kelud; 4.48 ± 1.61 × 1010 kg (MLE method) and 4.32 ± 1.56 × 1010 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.
Numerical Study on the Effects of Intraseasonal Oscillations for a Persistent Drought and Hot Event in South China Summer 2022
Authors: Qin Yi , Qin Yujing , Shen Yichen , Li Yonghua , Xiang Bo
Published: February 2023

From 19 July 2022 to 31 August 2022, a rare persistent drought and heat event occurred in the middle of the Yangtze River basin (MYRB). Normalized difference vegetation Index (NDVI) over 25% of the area decreased more than 0.05 compared with the climatology, causing extremely agricultural drought disaster and economic losses to China. Previous studies have shown that the occurrence of compound drought and heat events (CDHEs) in the MYRB was associated with intra-seasonal oscillations (ISOs) from different latitudes. Nevertheless, what was the role of ISOs at different latitudes in the formation of the CDHE? To address this question, this paper designed a numerical simulation experiment of partial lateral forcing to investigate the changes in meteorological elements by removing the signals of ISOs on different lateral boundaries. We found that the wave series formed in the upper troposphere at 200 hPa played a significant role in the occurrence of the CDHE in the northern part of the MYRB in this progress. It was found that the ISO component of the northern boundary caused the mean temperature to rise by 2.4 °C and aggravated the drought in 53.7% of the region. On the other hand, the anticyclone anomaly in the lower troposphere at 800 hPa had a continuous impact on the southern and eastern boundaries. It was found that the ISO component of these two boundaries can increase the average temperature by 1.93 °C in the MYRB and intensify the drought in 49.7% of the area. In the developing period of the CDHE, the South Asian high and the Western North Pacific subtropical high were coupled with each other and jointly controlled the MYRB, so that the significant positive geopotential height anomaly stayed above the MYRB for a long time, which was conducive to the development of local subsidence. The results of this paper will help to better understand the formation mechanism of CDHEs in the MYRB and assist meteorologists to prevent and forecast the occurrence of CDHEs in advance.
Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI
Authors: Yang Liuyin , Van Hulle Marc M.
Published: February 2023

Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain–computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair—the latter raises safety concerns—thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes’ lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.
Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization
Authors: Radmilović Marija , Urukalo Djordje , Janković Milica M. , Dujović Suzana Dedijer , Tomić Tijana J. Dimkić , Trumić Maja , Jovanović Kosta
Published: February 2023

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.
Automated Adjustment of PPE Masks Using IoT Sensor Fusion
Authors: Manchanda Ashish , Lee Kevin , Poznanski Gillud David , Hassani Alireza
Published: February 2023

The COVID-19 pandemic has led to a dramatic increase in the use of PPE by the general public as well as health professionals. Scientists and health organizations have developed measures to protect people and minimize the catastrophic outcomes of COVID, including social distancing, frequent and periodic sanitizing, vaccinations, protective coverings, and face masks. During this time, the usage of protective face masks has increased dramatically. A mask only provides full safety to the user if it is a proper fit on their face. The aim of this paper is to automatically analyze and improve the fit of a face mask using IoT sensors. This paper describes the creation of a 3D-printed smart face mask that uses sensors to determine the current mask fit and then automatically tightens mask straps. This is evaluated using adjustment response time and the quality of fit achieved using the automatic adjustment approach with a range of sensor types.
Sensitivity Enhancement of 2D Material-Based Surface Plasmon Resonance Sensor with an Al–Ni Bimetallic Structure
Authors: Hu Miaosen , Li Min , Li Ming-Yu , Wen Xiaoyan , Deng Shuo , Liu Sisi , Lu Haifei
Published: February 2023

In this paper, a variety of 2D materials on the surface plasmon resonance sensor based on Al–Ni bimetallic layer are compared. Simulation results indicate that lateral position shift, which is calculated according to the real and imaginary parts of the refractive index of material, can be used as an effective parameter to optimize the sensitivity. By using the parameters for optimizing the SPR structures, the results show that the multiple layer models of Al(40 nm)–Ni(22 nm)–black phosphorus (BP)(1 L) and Al(40 nm)–Ni(22 nm)–blue phosphorus (BlueP)/WS2(1 L) exhibit average angular sensitivities of 507.0 °/RIU and 466 °/RIU in the refractive index range of 1.330–1.335, and maximum sensitivity of 542 °/RIU and 489 °/RIU at the refractive index of 1.333, respectively. We expect more applications can be explored based on the highly sensitive SPR sensor in different fields of optical sensing.
Low-Cost Sensors for Monitoring Coastal Climate Hazards: A Systematic Review and Meta-Analysis
Authors: Ahmed Tasneem , Creedon Leo , Gharbia Salem S.
Published: February 2023

Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards. Monitoring the coast is often difficult and expensive, resulting in sparse monitoring equipment lacking in sufficient temporal and spatial coverage. Thus, low-cost methods to monitor the coast at finer temporal and spatial resolution are imperative for climate resilience along the world’s coasts. Exploiting such low-cost methods for the development of early warning support could be invaluable to coastal settlements. This paper aims to provide the most up-to-date low-cost techniques developed and used in the last decade for monitoring coastal hazards and their forcing agents via systematic review of the peer-reviewed literature in three scientific databases: Scopus, Web of Science and ScienceDirect. A total of 60 papers retrieved from these databases through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol were analysed in detail to yield different categories of low-cost sensors. These sensors span the entire domain for monitoring coastal hazards, as they focus on monitoring coastal zone characteristics (e.g., topography), forcing agents (e.g., water levels), and the hazards themselves (e.g., coastal flooding). It was found from the meta-analysis of the retrieved papers that terrestrial photogrammetry, followed by aerial photogrammetry, was the most widely used technique for monitoring different coastal hazards, mainly coastal erosion and shoreline change. Different monitoring techniques are available to monitor the same hazard/forcing agent, for instance, unmanned aerial vehicles (UAVs), time-lapse cameras, and wireless sensor networks (WSNs) for monitoring coastal morphological changes such as beach erosion, creating opportunities to not only select but also combine different techniques to meet specific monitoring objectives. The sensors considered in this paper are useful for monitoring the most pressing challenges in coastal zones due to the changing climate. Such a review could be extended to encompass more sensors and variables in the future due to the systematic approach of this review. This study is the first to systematically review a wide range of low-cost sensors available for the monitoring of coastal zones in the context of changing climate and is expected to benefit coastal researchers and managers to choose suitable low-cost sensors to meet their desired objectives for the regular monitoring of the coast to increase climate resilience.
Sagnac Effect Compensations and Locked States in a Ring Laser Gyroscope
Authors: Choi Woo-Seok , Shim Kyu-Min , Chong Kyung-Ho , An Jun-Eon , Kim Cheon-Joong , Park Byung-Yoon
Published: February 2023

Frequency lock-in-induced deadband phenomena are major problems of ring laser gyroscopes (RLGs), which deteriorate linear responses to changes in the applied rotation rate. In this work, the frequency lock-in phenomenon occurring in the RLG was successfully investigated by compensating for the Sagnac effect through frequency analysis using a newly defined error function. Integrative and generalized viewpoints from the analyzed results provide new possibilities for relevant performance improvements of optical gyroscopes, as well as a deeper understanding of locked states in principle aspects.
Identification and Quantification of Activities Common to Intensive Care Patients; Development and Validation of a Dual-Accelerometer-Based Algorithm
Authors: Dikkema Yvonne , Mouton Noor , Gerrits Koen , Valk Tim , van der Steen-Diepenrink Mariëlle , Eshuis Hans , Houdijk Han , van der Schans Cees , Niemeijer Anuschka , Nieuwenhuis Marianne
Published: February 2023

The aim of this study was to develop and validate an algorithm that can identify the type, frequency, and duration of activities common to intensive care (IC) patients. Ten healthy participants wore two accelerometers on their chest and leg while performing 14 activities clustered into four protocols (i.e., natural, strict, healthcare provider, and bed cycling). A video served as the reference standard, with two raters classifying the type and duration of all activities. This classification was reliable as intraclass correlations were all above 0.76 except for walking in the healthcare provider protocol, (0.29). The data of four participants were used to develop and optimize the algorithm by adjusting body-segment angles and rest-activity-threshold values based on percentage agreement (%Agr) with the reference. The validity of the algorithm was subsequently assessed using the data from the remaining six participants. %Agr of the algorithm versus the reference standard regarding lying, sitting activities, and transitions was 95%, 74%, and 80%, respectively, for all protocols except transitions with the help of a healthcare provider, which was 14–18%. For bed cycling, %Agr was 57–76%. This study demonstrated that the developed algorithm is suitable for identifying and quantifying activities common for intensive care patients. Knowledge on the (in)activity of these patients and their impact will optimize mobilization.
Deep Reinforcement Learning for Edge Caching with Mobility Prediction in Vehicular Networks
Authors: Choi Yoonjeong , Lim Yujin
Published: February 2023

As vehicles are connected to the Internet, various services can be provided to users. However, if the requests of vehicle users are concentrated on the remote server, the transmission delay increases, and there is a high possibility that the delay constraint cannot be satisfied. To solve this problem, caching can be performed at a closer proximity to the user which in turn would reduce the latency by distributing requests. The road side unit (RSU) and vehicle can serve as caching nodes by providing storage space closer to users through a mobile edge computing (MEC) server and an on-board unit (OBU), respectively. In this paper, we propose a caching strategy for both RSUs and vehicles with the goal of maximizing the caching node throughput. The vehicles move at a greater speed; thus, if positions of the vehicles are predictable in advance, this helps to determine the location and type of content that has to be cached. By using the temporal and spatial characteristics of vehicles, we adopted a long short-term memory (LSTM) to predict the locations of the vehicles. To respond to time-varying content popularity, a deep deterministic policy gradient (DDPG) was used to determine the size of each piece of content to be stored in the caching nodes. Experiments in various environments have proven that the proposed algorithm performs better when compared to other caching methods in terms of the throughput of caching nodes, delay constraint satisfaction, and update cost.
Assessment of a Side-Row Continuous Canopy Shaking Harvester and Its Adaptability to the Portuguese Cobrançosa Variety in High-Density Olive Orchards
Authors: Aragon-Rodriguez Fernando , Dias António B. , Pinheiro Anacleto , Peça José , Días Ivo Lourenço , Castro-Garcia Sergio
Published: February 2023

The olive tree is an important crop in Portugal, where different levels of intensification coexist. The traditional olive orchards present profitability problems, mainly due to harvesting, so there has been a drastic reconversion towards high-density or super-high-density olive orchards. The latter present major constraints due to very specific needs for their use, being practically destined for new orchards. Consequently, the possibility of using systems based on canopy shakers in high-density olive orchards with local varieties is promising. The objective of this work is to evaluate a prototype canopy shaker for the harvesting of high-density olive orchards of the Portuguese variety ‘Cobrançosa’. The evaluation is based on the study of canopy shaking in order to adapt canopy training and the adaptability of the machine. For this purpose, the vibration of 72 points of the tree canopy was recorded and a qualitative assessment of the harvest was carried out. Differences were found between the different zones according to the direction of the forward movement of the harvester and the distance to the trunk. These differences were associated with the values obtained for fruit detachment, and a greater quantity of fruit was harvested in the areas of the canopy in contact with the rods.
A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea
Authors: Li Yuanheng , Yang Shengxiong , Gong Yuehua , Cao Jingya , Hu Guang , Deng Yutian , Tian Dongmei , Zhou Junming
Published: February 2023

Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps.
Core versus Surface Sensors for Reinforced Concrete Structures: A Comparison of Fiber-Optic Strain Sensing to Conventional Instrumentation
Authors: Hoult Ryan , Bertholet Alex , de Almeida João Pacheco
Published: February 2023

High-resolution distributed reinforcement strain measurements can provide invaluable information for developing and evaluating numerical and analytical models of reinforced concrete structures. A recent testing campaign conducted at UCLouvain in Belgium used fiber-optic sensors embedded along several longitudinal steel rebars of three reinforced concrete U-shaped walls. The resulting experimental dataset provides an opportunity to evaluate and compare, for different types of loading, the strain measurements obtained with the fiber-optic sensors in the confined core of the structural member against more conventional and state-of-the-practice sensors that monitor surface displacements and deformations. This work highlights the need to average strain measurements from digital image correlation techniques in order to obtain coherent results with the strains measured from fiber optics, and investigates proposals to achieve this relevant goal for research and engineering practices. The longitudinal strains measured by the fiber optics also provide additional detailed information on the behavior of these wall units compared to the more conventional instrumentation, such as strain penetration into the foundation and head of the wall units, which are studied in detail.
Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
Authors: Álvarez David , Nuño Pelayo , González Carlos T. , Bulnes Francisco G. , Granda Juan C. , García-Carrillo Dan
Published: February 2023

The defence-in-depth (DiD) methodology is a defensive approach usually performed by network administrators to implement secure networks by layering and segmenting them. Typically, segmentation is implemented in the second layer using the standard virtual local area networks (VLANs) or private virtual local area networks (PVLANs). Although defence in depth is usually manageable in small networks, it is not easily scalable to larger environments. Software-defined networks (SDNs) are emerging technologies that can be very helpful when performing network segmentation in such environments. In this work, a corporate networking scenario using PVLANs is emulated in order to carry out a comparative performance analysis on defensive strategies regarding CPU and memory usage, communications delay, packet loss, and power consumption. To do so, a well-known PVLAN attack is executed using simulated attackers located within the corporate network. Then, two mitigation strategies are analysed and compared using the traditional approach involving access control lists (ACLs) and SDNs. The results show the operation of the two mitigation strategies under different network scenarios and demonstrate the better performance of the SDN approach in oversubscribed network designs.
A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning
Authors: Ren Hui , Gao Luli , Shen Xiaochen , Li Mengnan , Jiang Wei
Published: February 2023

A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning.
Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
Authors: Hamoud Batol , Kashevnik Alexey , Othman Walaa , Shilov Nikolay
Published: February 2023

One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson’s correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone.
Achromatic and Athermal Design of Aerial Catadioptric Optical Systems by Efficient Optimization of Materials
Authors: Li Jing , Ding Yalin , Liu Xueji , Yuan Guoqin , Cai Yiming
Published: February 2023

The remote sensing imaging requirements of aerial cameras require their optical system to have wide temperature adaptability. Based on the optical passive athermal technology, the expression of thermal power offset of a single lens in the catadioptric optical system is first derived, and then a mathematical model for efficient optimization of materials is established; finally, the mechanical material combination (mirror and housing material) is optimized according to the comprehensive weight of offset with temperature change and the position change of the equivalent single lens, and achieve optimization of the lens material on an athermal map. In order to verify the effectiveness of the method, an example of a catadioptric aerial optical system with a focal length of 350 mm is designed. The results show that in the temperature range of −40 °C to 60 °C, the diffraction-limited MTF of the designed optical system is 0.59 (at 68 lp/mm), the MTF of each field of view is greater than 0.39, and the thermal defocus is less than 0.004 mm, which is within one time of the focal depth, indicating that the imaging quality of the optical system basically does not change with temperature, meeting the stringent application requirements of the aerial camera.
Static-Analysis-Based Solutions to Security Challenges in Cloud-Native Systems: Systematic Mapping Study
Authors: Rahaman Md Shahidur , Islam Agm , Cerny Tomas , Hutton Shaun
Published: February 2023

Security is a significant priority for cloud-native systems, regardless of the system size and complexity. Therefore, one must utilize a set of defensive mechanisms or controls to protect the system from exploitation by potential adversaries. There is an expanding amount of research on security issues, including attacks against individual microservices or overall systems and their corresponding defense mechanism options. This study intends to provide a comprehensive overview of currently used defense mechanisms involving static analysis that can detect and react against associated attacks and vulnerabilities. We present a systematic literature review that extracts current approaches for the security analysis of microservices and the violation of security principles. We gathered 1049 relevant publications, of which 50 were selected as primary studies. We are providing practitioners and developers with a structured survey of the existing literature of defensive solutions for microservice architectures and cloud-native systems to aid them in identifying applicable solutions for their systems.
Revealing the Mutual Information between Body-Worn Sensors and Metabolic Cost in Running
Authors: Baumgartner Tobias , Klatt Stefanie , Donath Lars
Published: February 2023

Running power is a popular measure to gauge objective intensity. It has recently been shown, though, that foot-worn sensors alone cannot reflect variations in the exerted energy that stems from changes in the running economy. In order to support long-term improvement in running, these changes need to be taken into account. We propose leveraging the presence of two additional sensors worn by the most ambitious recreational runners for improved measurement: a watch and a heart rate chest strap. Using these accelerometers, which are already present and distributed over the athlete’s body, carries more information about metabolic demand than a single foot-worn sensor. In this work, we demonstrate the mutual information between acceleration data and the metabolic demand of running by leveraging the information bottleneck of a constrained convolutional neural network. We perform lab measurements on 29 ambitious recreational runners (age = 28 ± 7 years, weekly running distance = 50 ± 25 km, V˙O2max = 60.3 ± 7.4 mL · min−1·kg−1). We show that information about the metabolic demand of running is contained in kinetic data. Additionally, we prove that the combination of three sensors (foot, torso, and lower arm) carries significantly more information than a single foot-worn sensor. We advocate for the development of running power systems that incorporate the sensors in watches and chest straps to improve the validity of running power and, thereby, long-term training planning.
Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms
Authors: Pietrzak Przemyslaw , Wolkiewicz Marcin
Published: February 2023

Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet (PM) of the rotor. PM damage may be mechanical in nature or be related to the phenomenon of demagnetization. This article presents a machine learning (ML) based demagnetization fault diagnosis method for PMSM drives. The time-frequency domain analysis based on short-time Fourier transform (STFT) is applied in the process of PM fault feature extraction from the stator phase current signal. Moreover, two ML-based models are verified and compared in the process of the automatic fault detection of demagnetization fault. These models are k-nearest neighbors (KNN) and multiLayer perceptron (MLP). The influence of the input vector elements, key parameters and structures of the models used on their effectiveness is extensively analyzed. The results of the experimental verification confirm the very high effectiveness of the proposed method.
The Effects of ADAS on Driving Behavior: A Case Study
Authors: Bosurgi Gaetano , Pellegrino Orazio , Ruggeri Alessia , Sollazzo Giuseppe
Published: February 2023

The presence of numerous sensors inside modern vehicles leads to the development of new driving assistance tools, the real usefulness of which depends, however, on the environmental context. This study proposes a procedure capable of quantifying the effectiveness of some warnings produced by an On-Board Unit (OBU) inside the vehicle in a specific environmental context, even if limited only to the considered road. The experimentation was carried out by means of a driving simulator with a sample of young users with sufficiently homogeneous characteristics. The collected data were treated by ANOVA to highlight any differentiation between a traditional driving condition, without any instrumental support, and another involving the OBU was present. The results showed that only in relation to the investigated road, the OBU ensured the advantage of sending information of interest to the driver without invalidating their performance in terms of longitudinal and transverse acceleration, speeding, and steering angle. This research could be of interest to the infrastructure managers who, in case of inappropriate use of a road, could intensify active and passive safety devices for users’ safety.