bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Conditional Constraint Generative Adversarial Network-Based MR Imaging from CT Scan Data
Authors: Liu Mingjie , Zou Wei , Wang Wentao , Jin Cheng-Bin , Chen Junsheng , Piao Changhao
Published: May 2022

Magnetic resonance (MR) imaging is an important computer-aided diagnosis technique with rich pathological information. The factor of physical and physiological constraint seriously affects the applicability of that technique. Thus, computed tomography (CT)-based radiotherapy is more popular on account of its imaging rapidity and environmental simplicity. Therefore, it is of great theoretical and practical significance to design a method that can construct an MR image from the corresponding CT image. In this paper, we treat MR imaging as a machine vision problem and propose a multi-conditional constraint generative adversarial network (GAN) for MR imaging from CT scan data. Considering reversibility of GAN, both generator and reverse generator are designed for MR and CT imaging, respectively, which can constrain each other and improve consistency between features of CT and MR images. In addition, we innovatively treat the real and generated MR image discrimination as object re-identification; cosine error fusing with original GAN loss is designed to enhance verisimilitude and textural features of the MR image. The experimental results with the challenging public CT-MR image dataset show distinct performance improvement over other GANs utilized in medical imaging and demonstrate the effect of our method for medical image modal transformation.
GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection
Authors: Tanwar Sudeep , Kumari Aparna , Vekaria Darshan , Raboaca Maria Simona , Alqahtani Fayez , Tolba Amr , Neagu Bogdan-Constantin , Sharma Ravi
Published: May 2022

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.
Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations
Authors: Rodríguez-Abreo Omar , Rodríguez-Reséndiz Juvenal , Álvarez-Alvarado José Manuel , García-Cerezo Alfonso
Published: May 2022

This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterations.
Design of Dust-Filtering Algorithms for LiDAR Sensors Using Intensity and Range Information in Off-Road Vehicles
Authors: Afzalaghaeinaeini Ali , Seo Jaho , Lee Dongwook , Lee Hanmin
Published: May 2022

Although the LiDAR sensor provides high-resolution point cloud data, its performance degrades when exposed to dust environments, which may cause a failure in perception for robotics applications. To address this issue, our study designed an intensity-based filter that can remove dust particles from LiDAR data in two steps. In the first step, it identifies potential points that are likely to be dust by using intensity information. The second step involves analyzing the point density around selected points and removing them if they do not meet the threshold criterion. To test the proposed filter, we collected experimental data sets under the existence of dust and manually labeled them. Using these data, the de-dusting performance of the designed filter was evaluated and compared to several types of conventional filters. The proposed filter outperforms the conventional ones in achieving the best performance with the highest F1 score and removing dust without sacrificing the original surrounding data.
Assessment of a Passive Lumbar Exoskeleton in Material Manual Handling Tasks under Laboratory Conditions
Authors: Iranzo Sofía , Piedrabuena Alicia , García-Torres Fernando , Martinez-de-Juan Jose Luis , Prats-Boluda Gema , Sanchis Mercedes , Belda-Lois Juan-Manuel
Published: May 2022

Manual material handling tasks in industry cause work-related musculoskeletal disorders. Exoskeletons are being introduced to reduce the risk of musculoskeletal injuries. This study investigated the effect of using a passive lumbar exoskeleton in terms of moderate ergonomic risk. Eight participants were monitored by electromyogram (EMG) and motion capture (MoCap) while performing tasks with and without the lumbar exoskeleton. The results showed a significant reduction in the root mean square (VRMS) for all muscles tracked: erector spinae (8%), semitendinosus (14%), gluteus (5%), and quadriceps (10.2%). The classic fatigue parameters showed a significant reduction in the case of the semitendinosus: 1.7% zero-crossing rate, 0.9% mean frequency, and 1.12% median frequency. In addition, the logarithm of the normalized Dimitrov’s index showed reductions of 11.5, 8, and 14% in erector spinae, semitendinosus, and gluteus, respectively. The calculation of range of motion in the relevant joints demonstrated significant differences, but in almost all cases, the differences were smaller than 10%. The findings of the study indicate that the passive exoskeleton reduces muscle activity and introduces some changes of strategies for motion. Thus, EMG and MoCap appear to be appropriate measurements for designing an exoskeleton assessment procedure.
Intelligent Fault Detection and Classification Based on Hybrid Deep Learning Methods for Hardware-in-the-Loop Test of Automotive Software Systems
Authors: Abboush Mohammad , Bamal Daniel , Knieke Christoph , Rausch Andreas
Published: May 2022

Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%.
Large-Area and Low-Cost Force/Tactile Capacitive Sensor for Soft Robotic Applications
Authors: Pagoli Amir , Chapelle Frédéric , Corrales-Ramon Juan-Antonio , Mezouar Youcef , Lapusta Yuri
Published: May 2022

This paper presents a novel design and development of a low-cost and multi-touch sensor based on capacitive variations. This new sensor is very flexible and easy to fabricate, making it an appropriate choice for soft robot applications. Materials (conductive ink, silicone, and control boards) used in this sensor are inexpensive and easily found in the market. The proposed sensor is made of a wafer of different layers, silicone layers with electrically conductive ink, and a pressure-sensitive conductive paper sheet. Previous approaches like e-skin can measure the contact point or pressure of conductive objects like the human body or finger, while the proposed design enables the sensor to detect the object’s contact point and the applied force without considering the material conductivity of the object. The sensor can detect five multi-touch points at the same time. A neural network architecture is used to calibrate the applied force with acceptable accuracy in the presence of noise, variation in gains, and non-linearity. The force measured in real time by a commercial precise force sensor (ATI) is mapped with the produced voltage obtained by changing the layers’ capacitance between two electrode layers. Finally, the soft robot gripper embedding the suggested tactile sensor is utilized to grasp an object with position and force feedback signals.
Impact of Chamber/Annealing Temperature on the Endurance Characteristic of Zr:HfO2 Ferroelectric Capacitor
Authors: Choi Yejoo , Han Changwoo , Shin Jaemin , Moon Seungjun , Min Jinhong , Park Hyeonjung , Eom Deokjoon , Lee Jehoon , Shin Changhwan
Published: May 2022

The endurance characteristic of Zr-doped HfO2 (HZO)-based metal–ferroelectric–metal (MFM) capacitors fabricated under various deposition/annealing temperatures in the atomic layer deposition (ALD) process was investigated. The chamber temperature in the ALD process was set to 120 °C, 200 °C, or 250 °C, and the annealing temperature was set to 400 °C, 500 °C, 600 °C, or 700 °C. For the given annealing temperature of 700 °C, the remnant polarization (2Pr) was 17.21 µC/cm2, 26.37 µC/cm2, and 31.8 µC/cm2 at the chamber temperatures of 120 °C, 200 °C, and 250 °C, respectively. For the given/identical annealing temperature, the largest remnant polarization (Pr) was achieved when using the chamber temperature of 250 °C. At a higher annealing temperature, the grain size in the HZO layer becomes smaller, and thereby, it enables to boost up Pr. It was observed that the endurance characteristics for the capacitors fabricated under various annealing/chamber temperatures were quite different. The different endurance characteristics are due to the oxygen and oxygen vacancies in ferroelectric films, which affects the wakeup/fatigue behaviors. However, in common, all the capacitors showed no breakdown for an externally applied pulse (up to 108 cycles of the pulse).
Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks
Authors: Yang Shicheng , Lee Gongwei , Huang Liang
Published: May 2022

This paper investigates the computation offloading problem in mobile edge computing (MEC) networks with dynamic weighted tasks. We aim to minimize the system utility of the MEC network by jointly optimizing the offloading decision and bandwidth allocation problems. The optimization of joint offloading decisions and bandwidth allocation is formulated as a mixed-integer programming (MIP) problem. In general, the problem can be efficiently generated by deep learning-based algorithms for offloading decisions and then solved by using traditional optimization methods. However, these methods are weakly adaptive to new environments and require a large number of training samples to retrain the deep learning model once the environment changes. To overcome this weakness, in this paper, we propose a deep supervised learning-based computational offloading (DSLO) algorithm for dynamic computational tasks in MEC networks. We further introduce batch normalization to speed up the model convergence process and improve the robustness of the model. Numerical results show that DSLO only requires a few training samples and can quickly adapt to new MEC scenarios. Specifically, it can achieve 99% normalized system utility by using only four training samples per MEC scenario. Therefore, DSLO enables the fast deployment of computation offloading algorithms in future MEC networks.
Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis
Authors: Nguyen Cong Dai , Kim Cheol Hong , Kim Jong-Myon
Published: May 2022

Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature’s relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio–based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy.
An Electrochemical Sensor Based on Carbon Paper Modified with Graphite Powder for Sensitive Determination of Sunset Yellow and Tartrazine in Drinks
Authors: Stozhko Natalia Yu. , Khamzina Ekaterina I. , Bukharinova Maria A. , Tarasov Aleksey V.
Published: May 2022

The paper describes the development of an electrochemical sensor to be used for the determination of synthetic food colorants such as Sunset Yellow FCF (SY) and Tartrazine (TZ). The sensor is a carbon paper (CP) electrode, manufactured by using hot lamination technology and volume modified with fine-grained graphite powder (GrP). The sensor (GrP/CP) was characterized by scanning electron microscopy, energy dispersive spectrometry, electrochemical impedance analysis, cyclic, linear sweep and differential pulse voltammetry. The mechanism of SY and TZ electrochemical oxidation on GrP/CP was studied. The developed sensor has good electron transfer characteristics and low electron resistance, high sensitivity and selectivity. Applying the differential pulse mode, linear dynamic ranges of 0.005–1.0 μM and 0.02–7.5 μM with limits of detection of 0.78 nM and 8.2 nM for SY and TZ, respectively, were obtained. The sensor was used to detect SY and TZ in non-alcoholic and alcoholic drinks. The results obtained from drink analysis prove good reproducibility (RSD ≤ 0.072) and accuracy (recovery 96–104%).
A Smart Camera Trap for Detection of Endotherms and Ectotherms
Authors: Corva Dean M. , Semianiw Nathan I. , Eichholtzer Anne C. , Adams Scott D. , Mahmud M. A. Parvez , Gaur Kendrika , Pestell Angela J. L. , Driscoll Don A. , Kouzani Abbas Z.
Published: May 2022

Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals.
A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge
Authors: Spandonidis Christos , Theodoropoulos Panayiotis , Giannopoulos Fotis
Published: May 2022

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The implemented methodology is based on processing the spectrum of vibration signals appearing in the pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is intended to use low frequencies to detect and characterize leakage to increase the range of sensors and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition and data analysis is briefly described. For this matter, two leakage detection methodologies are implemented. A 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed, receiving signals from the accelerometers, and providing an unsupervised leakage detection solution.
B5GEMINI: AI-Driven Network Digital Twin
Authors: Mozo Alberto , Karamchandani Amit , Gómez-Canaval Sandra , Sanz Mario , Moreno Jose Ignacio , Pastor Antonio
Published: May 2022

Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML. First, we present the infrastructural and architectural components that support B5GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5GEMINI for building new AI-powered applications. In addition, we identify the main components of the AI ecosystem of B5GEMINI, outlining emerging research trends and identifying the open challenges that must be solved along the way. Finally, we present two relevant use cases in the application of NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes the use of B5GEMINI to facilitate the design of ML-based attack detectors and the second addresses the design of energy efficient ML components and introduces the modular development of NDTs adopting the Digital Map concept as a novelty.
HERO: Hybrid Effortless Resilient Operation Stations for Flash Flood Early Warning Systems
Authors: Wannachai Autanan , Aramkul Somrawee , Suntaranont Benya , Somchit Yuthapong , Champrasert Paskorn
Published: May 2022

Floods are the most frequent type of natural disaster. Flash floods are one of the most common types of floods, caused by rapid and excessive rainfall. Normally, when a flash flood occurs, the water of the upstream river increases rapidly and flows to the downstream watersheds. The overflow of water increasingly submerges villages in the drainage basins. Flash flood early warning systems are required to mitigate losses. Water level monitoring stations can be installed at upstream river areas. However, telemetry stations face several challenges because the upstream river areas are far away and lack of public utilities (e.g., electric power and telephone lines). This research proposes hybrid effortless resilient operation stations, named HERO stations, in the flash flood early warning system. The HERO station was designed and developed with a modular design concept to be effortlessly customized and maintained. The HERO station adapts its working operation against the environmental changes to maintain a long working period with high data sensing accuracy. Moreover, the HERO station can switch its communication mode between the centralized and decentralized communication modes to increase availability. The network of the HERO stations has already been deployed in the northern part of Thailand. It results in improvements of the telemetry station’s availability. The HERO stations can adapt to environmental changes. The flash flood early warning messages can be disseminated to the villagers to increase the flood preparation time and to reduce flash flood damage.
IoT System Selection as a Fuzzy Multi-Criteria Problem
Authors: Ilieva Galina , Yankova Tania
Published: May 2022

This research aims to analyse the applications of IoT in agriculture and to compare the most widely used IoT platforms. The problem of determining the most appropriate IoT system depends on many factors, often expressed by incomplete and uncertain estimates. In order to find a feasible decision, this study develops a multi-criteria framework for IoT solution selection in a fuzzy environment. In the proposed framework, a new modification of the Multi-Attribute Border approximation Area Comparison (MABAC) method with a specific distance measure via intuitionistic fuzzy values has been presented as a decision analysis method. The new technique is more precise than existing crisp and fuzzy analogues, as it includes the three components of intuitionistic numbers (degree of membership, degree of non-membership and hesitancy degree) and the relationships between them. The effectiveness of the new decision-making framework has been verified through an illustrative example of ranking IoT platforms.
Deep Learning Regression Approaches Applied to Estimate Tillering in Tropical Forages Using Mobile Phone Images
Authors: Santos Luiz , Junior José Marcato , Zamboni Pedro , Santos Mateus , Jank Liana , Campos Edilene , Matsubara Edson Takashi
Published: May 2022

We assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 labeled images with 2 mobile phones 7 days after the harvest of the forage plants. Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89. Our findings suggest that our proposal using deep learning on mobile phone images can successfully be used to estimate regrowth density in forages.
Acoustic Sensor-Based Soundscape Analysis and Acoustic Assessment of Bird Species Richness in Shennongjia National Park, China
Authors: Hou Yanan , Yu Xinwen , Yang Jingyuan , Ouyang Xuan , Fan Dongpu
Published: May 2022

Passive acoustic sensor-based soundscape analysis has become an increasingly important ecological method for evaluation of ecosystem conditions using acoustic indices. Understanding the soundscape composition and correlations between acoustic indices and species richness of birds, the most important sound source in the ecosystem, are of great importance for measuring biodiversity and the level of anthropogenic disturbance. In this study, based on yearlong sound data obtained from five acoustic sensors deployed in Dalongtan, Shennongjia National Park, we analyzed the soundscape composition by comparing the distributions of the soundscape power in different frequency ranges, and examined the correlations between acoustic indices and bird species richness by means of the Spearman rank correlation coefficient method. The diurnal dynamic characteristics of acoustic indices in different seasons were also described. Results showed that the majority of sounds were in the frequency of 2–8 kHz, in which over 50% sounds were in 2–6 kHz, commonly considered the bioacoustic frequency range. The Acoustics Complexity Index, Bioacoustic Index, and Normalized Difference Soundscape Index were significantly correlated with bird species richness, suggesting that these indices can be used for evaluation of bird species richness; Apparent diurnal dynamic patterns of bird acoustic activities were observed in spring, summer, and autumn; however, the intensity and duration of bird acoustic activities in summer is larger/longer than in spring and autumn.
Microwave Antenna System for Muscle Rupture Imaging with a Lossy Gel to Reduce Multipath Interference
Authors: Guerrero Orozco Laura , Peterson Lars , Fhager Andreas
Published: May 2022

Injuries to the hamstring muscles are an increasing problem in sports. Imaging plays a key role in diagnosing and managing athletes with muscle injuries, but there are several problems with conventional imaging modalities with respect to cost and availability. We hypothesized that microwave imaging could provide improved availability and lower costs and lead to improved and more accurate diagnostics. In this paper, a semicircular microwave imaging array with eight antennae was investigated. A key component in this system is the novel antenna design, which is based on a monopole antenna and a lossy gel. The purpose of the gel is to reduce the effects of multipath signals and improve the imaging quality. Several different gels have been manufactured and evaluated in imaging experiments. For comparison, corresponding simulations were performed. The results showed that the gels can effectively reduce the multipath signals and the imaging experiments resulted in significantly more stable and repeatable reconstructions when a lossy gel was used compared to when an almost non-lossy gel was used.
A Model for Predicting Cervical Cancer Using Machine Learning Algorithms
Authors: Al Mudawi Naif , Alazeb Abdulwahab
Published: May 2022

A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming increasingly common in medical practice. Cervical cancer is one of the most frequent diseases among women, and early diagnosis could be a possible solution for preventing this cancer. Thus, this study presents an astute way to predict cervical cancer with ML algorithms. Research dataset, data pre-processing, predictive model selection (PMS), and pseudo-code are the four phases of the proposed research technique. The PMS section reports experiments with a range of classic machine learning methods, including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors algorithm (KNN), adaptive boosting, gradient boosting, random forest, and XGBoost. In terms of cervical cancer prediction, the highest classification score of 100% is achieved with random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The computational complexity of classic machine learning techniques is computed to assess the efficacy of the models. In addition, 132 Saudi Arabian volunteers were polled as part of this study to learn their thoughts about computer-assisted cervical cancer prediction, to focus attention on the human papillomavirus (HPV).
Analysis of Radon Measurements in Relation to Daily Seismic Activity Rates in the Vrancea Region, Romania
Authors: Galiana-Merino Juan José , Molina Sergio , Kharazian Alireza , Toader Victorin-Emilian , Moldovan Iren-Adelina , Gómez Igor
Published: May 2022

Many previous research studies have shown how local and even regional earthquakes can significantly affect the release of radon in the soil. The aim of this work is to investigate the relationship between radon measurements and the daily seismic activity rate and develop a methodology that allows estimating the seismic activity rate using only radon measurements. To carry out this study, the earthquake catalogue of the Vrancea region (Romania) has been used to estimate the daily seismic activity rate during a given time period, in which radon measurements were also recorded, from January 2016 to September 2020. The Vrancea zone represents the most active seismic zone in Europe and is located on the eastern edge of the strongly bent Carpathian arc. In the case of the radon measurements, seasonal behaviours and linear trends due to non-seismic factors have been identified and subsequently removed. The discrete wavelet transform has been used to analyse the radon signal at two different scales: long and short periods. From the analysis carried out on a long-period scale, an approximate linear relationship has been obtained between the radon series and the daily seismic activity rate, which provides insights into the behaviour of the seismic activity in the study region with only the radon information. In addition, the study reveals certain characteristics that could be used as precursors of earthquakes at different scales: weeks in the case of the estimated daily seismic activity rate, and days in the case of the short-period signal obtained by the wavelet analysis. The results obtained for this region allow us to hope that the analysis of the radon time series can become an effective complement to the conventional seismic analysis used in operational earthquake forecasting.
Demonstration of Coherent Interference between Acoustic Waves Using a Fiber Ring Resonator
Authors: Kim Jee Myung , Wee Junghyun , Peters Kara
Published: May 2022

Optical fibers were previously demonstrated to propagate and detect acoustic modes that were converted from Lamb waves for structural health-monitoring applications; typically, a fiber Bragg grating sensor in the optical fiber is used to detect acoustic modes. Acoustic modes can transfer from one fiber to another through a simple adhesive bond coupler, preserving the waveform of the acoustic mode. This paper experimentally investigates the coherence of acoustic waves through the adhesive coupler, using a fiber ring resonator (FRR) configuration. This configuration was chosen because the wave coupled to the second fiber interferes with the original wave after it encircles the fiber ring. We performed this experiment using different geometries of optical fibers in the ring, including a standard single-mode optical fiber, a hollow silica capillary tube, and a large-diameter multi-mode fiber. The results demonstrate that the acoustic wave, when transferring through an adhesive coupler, interferes coherently even when the main and ring fibers are of different types. Finally, we demonstrate that the FRR can be applied for sensing applications by measuring the mode attenuations in the ring due to a changing external environment (water-level sensing) and measuring the optical-path length change in the ring (temperature sensing).
Model Predictive Control of a Novel Wheeled–Legged Planetary Rover for Trajectory Tracking
Authors: He Jun , Sun Yanlong , Yang Limin , Gao Feng
Published: May 2022

Amid increasing demands for planetary exploration, wide-range autonomous exploration is still a great challenge for existing planetary rovers, which calls for new planetary rovers with novel locomotive mechanisms and corresponding control strategies. This paper proposes a novel wheeled–legged mechanism for the design of planetary rovers. The leg suspension utilizes a rigid–flexible coupling mechanism with a hybrid serial–parallel topology. First, the kinematic model is derived. Then, a control strategy for the wheeled–legged rover that includes a trajectory tracking module based on the model predictive control, the steering strategy, and the wheel speed allocation algorithm is proposed. After that, three groups of cosimulations with different trajectories and speeds, and experiments are carried out. Results of both the simulations and experiments validate the proposed control method.
Urban Greening Effect on Land Surface Temperature
Authors: Zaitunah Anita , Samsuri Samsuri , Silitonga Angelia Frecella , Syaufina Lailan
Published: May 2022

Urbanization has accelerated the conversion of vegetated land to built-up regions. The purpose of this study was to evaluate the effects of urban park configuration on the Land Surface Temperature of the park and adjacent areas. In urban parks, the study analyzed the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and the Land Surface Temperature (LST). The NDVI categorization process resulted in the development of a vegetation density distribution. The majority of Medan’s urban areas were categorized as low density, as seen by their low NDVI values. The NDBI values were significantly higher in the majority of the area. This shows that the majority of places are experiencing a decline in vegetation cover. The density of vegetation varies according to the placement of park components such as trees, mixed plants, recreation, and sports areas. According to LST data, the temperature in the urban park was cooler than in the surrounding areas. Although the surrounding areas are densely populated, urban parks are dominated by trees. Additionally, there is a green space adjacent to the park, which is a green lane that runs alongside the main roadways.
Single Diode Solar Cells—Improved Model and Exact Current–Voltage Analytical Solution Based on Lambert’s W Function
Authors: Rawa Muhyaddin , Calasan Martin , Abusorrah Abdullah , Alhussainy Abdullah Ali , Al-Turki Yusuf , Ali Ziad M. , Sindi Hatem , Mekhilef Saad , Aleem Shady H. E. Abdel , Bassi Hussain
Published: May 2022

There are three standard equivalent circuit models of solar cells in the literature—single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current–voltage relation of this circuit is derived in terms of Lambert’s W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model’s accuracy and the proposed algorithm’s efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current–voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed.
A Procedure for Precise Determination and Compensation of Lead-Wire Resistance of a Two-Wire Resistance Temperature Detector
Authors: Rerkratn Apinai , Prombut Supatsorn , Kamsri Thawatchai , Riewruja Vanchai , Petchmaneelumka Wandee
Published: May 2022

A procedure for the precise determination and compensation of the lead-wire resistance of a resistance transducer is presented. The proposed technique is suitable for a two-wire resistance transducer, especially the resistance temperature detector (RTD). The proposed procedure provides a technique to compensate for the lead-wire resistance using a three-level pulse signal to excite the RTD via the long lead wire. In addition, the variation in the lead-wire resistance disturbed by the change in the ambient temperature can also be compensated by using the proposed technique. The determination of the lead-wire resistance from the proposed procedure requires a simple computation method performed by a digital signal processing unit. Therefore, the calculation of the RTD resistance and the lead-wire resistance can be achieved without the requirement of a high-speed digital signal processing unit. The proposed procedure is implemented on two platforms to confirm its effectiveness: the LabVIEW computer program and the microcontroller board. Experimental results show that the RTD resistance was accurately acquired, where the measured temperature varied from 0 °C to 300 °C and the lead-wire resistance varied from 0.2 Ω to 20 Ω, corresponding to the length of the 26 American wire gauge (AWG) lead wire from 1.5 m to 150 m. The average power dissipation to the RTD was very low and the self-heating of the RTD was minimized. The measurement error of the RTD resistance observed for pt100 was within ±0.98 Ω or ±0.27 °C when the lead wire of 30 m was placed in an environment with the ambient temperature varying from 30 °C to 70 °C. It is evident that the proposed procedure provided a performance that agreed with the theoretical expectation.
A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems
Authors: Rahimi Masoumeh , Liu Haochen , Cardenas Isidro Durazo , Starr Andrew , Hall Amanda , Anderson Robert
Published: May 2022

Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. Ineffective job execution or failure in preventive maintenance can lead to railway service disruption and unsafe operations. The deployment of robotic and autonomous systems was proposed to conduct these maintenance tasks with higher accuracy and reliability. In order for these systems to be capable of detecting rail flaws along millions of mileages they must register their location with higher accuracy. A prerequisite of an autonomous vehicle is its possessing a high degree of accuracy in terms of its positional awareness. This paper first reviews the importance and demands of preventive maintenance in railway networks and the related techniques. Furthermore, this paper investigates the strategies, techniques, architecture, and references used by different systems to resolve the location along the railway network. Additionally, this paper discusses the advantages and applicability of on-board-based and infrastructure-based sensing, respectively. Finally, this paper analyses the uncertainties which contribute to a vehicle’s position error and influence on positioning accuracy and reliability with corresponding technique solutions. This study therefore provides an overall direction for the development of further autonomous track-based system designs and methods to deal with the challenges faced in the railway network.
Pear Recognition in an Orchard from 3D Stereo Camera Datasets to Develop a Fruit Picking Mechanism Using Mask R-CNN
Authors: Pan Siyu , Ahamed Tofael
Published: May 2022

In orchard fruit picking systems for pears, the challenge is to identify the full shape of the soft fruit to avoid injuries while using robotic or automatic picking systems. Advancements in computer vision have brought the potential to train for different shapes and sizes of fruit using deep learning algorithms. In this research, a fruit recognition method for robotic systems was developed to identify pears in a complex orchard environment using a 3D stereo camera combined with Mask Region-Convolutional Neural Networks (Mask R-CNN) deep learning technology to obtain targets. This experiment used 9054 RGBA original images (3018 original images and 6036 augmented images) to create a dataset divided into a training, validation, and testing sets. Furthermore, we collected the dataset under different lighting conditions at different times which were high-light (9–10 am) and low-light (6–7 pm) conditions at JST, Tokyo Time, August 2021 (summertime) to prepare training, validation, and test datasets at a ratio of 6:3:1. All the images were taken by a 3D stereo camera which included PERFORMANCE, QUALITY, and ULTRA models. We used the PERFORMANCE model to capture images to make the datasets; the camera on the left generated depth images and the camera on the right generated the original images. In this research, we also compared the performance of different types with the R-CNN model (Mask R-CNN and Faster R-CNN); the mean Average Precisions (mAP) of Mask R-CNN and Faster R-CNN were compared in the same datasets with the same ratio. Each epoch in Mask R-CNN was set at 500 steps with total 80 epochs. And Faster R-CNN was set at 40,000 steps for training. For the recognition of pears, the Mask R-CNN, had the mAPs of 95.22% for validation set and 99.45% was observed for the testing set. On the other hand, mAPs were observed 87.9% in the validation set and 87.52% in the testing set using Faster R-CNN. The different models using the same dataset had differences in performance in gathering clustered pears and individual pear situations. Mask R-CNN outperformed Faster R-CNN when the pears are densely clustered at the complex orchard. Therefore, the 3D stereo camera-based dataset combined with the Mask R-CNN vision algorithm had high accuracy in detecting the individual pears from gathered pears in a complex orchard environment.
Ultrasound Sensing Using Packaged Microsphere Cavity in the Underwater Environment
Authors: Wang Kai , Wang Heng , Wu Xing-Yu , Zhang Yong , Yang Daquan , Jiao Rongzhen , Wang Chuan
Published: May 2022

The technologies of ultrasound detection have a wide range of applications in marine science and industrial manufacturing. With the variation of the environment, the requirements of anti-interference, miniaturization, and ultra-sensitivity are put forward. Optical microcavities are often carefully designed for a variety of ultra-sensitive detections. Using the packaged microsphere cavity, we fabricated an ultrasound sensor that can work in an underwater environment. During practical detection, the optical resonance mode of the cavity can work with real-time response accordingly. The designed structure can work in various complex environments and has advantages in the fields of precision measurement and nano-particle detection.