bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Simulation Approach for a Dynamically Operated Sorption-Enhanced Water-Gas Shift Reactor
Authors: Stadler Tabea , Knoop Jan-Hendrik , Decker Simon , Pfeifer Peter
Published: June 2022

A dynamically operated sorption-enhanced water–gas shift reactor is modelled to leverage its performance by means of model-based process design. This reactor shall provide CO2-free synthesis gas for e-fuel production from pure CO. The nonlinear model equations describing simultaneous adsorption and reaction are solved with three numerical approaches in MATLAB: a built-in solver for partial differential equations, a semi-discretization method in combination with an ordinary differential equation solver, and an advanced graphic implementation of the latter method in Simulink. The novel implementation in Simulink offers various advantages for dynamic simulations and is expanded to a process model with six reaction chambers. The continuous conditions in the reaction chambers and the discrete states of the valves, which enable switching between reactive adsorption and regeneration, lead to a hybrid system. Controlling the discrete states in a finite-state machine in Stateflow enables automated switching between reactive adsorption and regeneration depending on predefined conditions, such as a time span or a concentration threshold in the product gas. The established chemical reactor simulation approach features unique possibilities in terms of simulation-driven development of operating procedures for intensified reactor operation. In a base case simulation, the sorbent usage for serial operation with adjusted switching times is increased by almost 15%.
Experimental Test and Feasibility Analysis of Hydraulic Cylinder Position Control Based on Pressure Detection
Authors: Zhou Rulin , Meng Lingyu , Yuan Xiaoming
Published: June 2022

This paper studies hydraulic cylinder position adjustment controlled by an on–off valve. The aim of this paper is to develop a method of position control for a hydraulic cylinder based on input and output pressure under the mutual coupling feedback of the load and flow, especially in multi-actuator coupling control scenarios. This method can solve the problem of position evaluation and hydraulic cylinder tracking in relation to position detection without a displacement sensor and provide the possibility of automatic adjustment of hydraulic support in the process of intelligent mining. First of all, according to the flow continuity equation and Navier–Stokes equation, a flow model with inlet and outlet pressure is derived. Secondly, the effectiveness of the flow resistance characteristic curve of differential valve is verified by experimental and theoretical analysis. Finally, through experimental verification, when the system pressure is larger than 10 MPa, the error between the actual experimental data and the data calculated by the fitting algorithm is within 5%, which is consistent with the derived formula and proves the validity of the simulation model.
Integrated Reservoir Model and Differential Stimulation Modes of Low Permeability Porous Carbonate Reservoir: A Case Study of S Reservoir in X Oilfield in Iraq
Authors: Yang Jing , Zhu Guangya , An Yichen , Li Nan , Xu Wei , Wan Li , Jin Rongrong
Published: June 2022

The S reservoir in the X Oilfield in Iraq has great development potential due to its rich geological reserves. However, the low permeability and strong heterogeneity of the reservoir lead to great differences in reservoir stimulation performance. In this study, an integrated reservoir model and differential stimulation mode are put forward to solve the above problems. First, the feasibility of fracturing is evaluated by laboratory experiments. Second, an integrated reservoir model is established, which mainly includes a rock mechanics model, fracturing simulation model, and numerical simulation model, and correct the integrated model by fracturing operation curves and production dynamic curves. Third, three types of stimulation areas are classified according to the combination of sweet spot types, and three different stimulation modes are proposed. In conclusion, a small-scale stimulation mode should be applied in the Type I area to maximize economic benefits. In the Type II area, the medium-scale stimulation mode should be performed to ensure certain productivity while achieving certain economic benefits. In the Type III area, the large-scale stimulation mode should be employed to obtain certain productivity while economic benefits must be above a limit. The differential stimulation model proposed in this paper has made a great reference for the efficient development of low-permeability carbonate rocks.
Humans and the Olfactory Environment: A Case of Gene-Culture Coevolution?
Authors: Frost Peter
Published: June 2022

As hunter-gatherers, humans used their sense of smell to identify plants and animals, to find their way within a foraging area, or to distinguish each other by gender, age, kinship, or social dominance. Because women gathered while men hunted, the sexes evolved different sensitivities to plant and animal odors. They also ended up emitting different odors. Male odors served to intimidate rival males or assert dominance. With the rise of farming and sedentism, humans no longer needed their sense of smell to find elusive food sources or to orient themselves within a large area. Odors now came from a narrower range of plants and animals. Meanwhile, body odor was removed through bathing to facilitate interactions in enclosed spaces. This new phenotype became the template for the evolution of a new genotype: less sensitivity to odors of wild plants and animals, lower emissions of male odors, and a more negative response to them. Further change came with the development of fragrances to reodorize the body and the home. This new olfactory environment coevolved with the ability to represent odors in the mind, notably for storage in memory, for vicarious re-experiencing, or for sharing with other people through speech and writing.
Waste Management for Green Concrete Solutions: A Concise Critical Review
Authors: Osial Magdalena , Pregowska Agnieszka , Wilczewski Sławomir , Urbańska Weronika , Giersig Michael
Published: June 2022

Reinforced concrete based on ordinary Portland cement (OPC) is one of the most commonly used materials in modern buildings. Due to the global growth of the building industry, concrete components have been partially or completely replaced with waste materials that can be used as binders or aggregates. Besides the ecological aspects, modern architecture widely needs materials to make the concrete durable, resisting large loads and various detrimental forces in the environment. This opens the possibilities of managing waste materials and applying them in practice. This paper presents a concise review of the green solutions for ecofriendly materials in the building industry that deal with the practical application of materials commonly treated as waste. The main emphasis was placed on their influence on the properties of the building material, optimal composition of mixtures, and discussion of the advantages and disadvantages of each of the “green” additives. It turned out that some solutions are far from being ecofriendly materials, as they leech and release numerous harmful chemicals into the environment during their presence in concrete. Finally, the paper suggests a research direction for the development of an ecofriendly structural material for a sustainable future.
Neoclassical Theism as Inherently Dialogical
Authors: Dombrowski Daniel A.
Published: June 2022

The position usually called “process theism” is seldom called this by one of its most important defenders, Charles Hartshorne. The label he typically uses is “neoclassical theism”. It is important to notice that these two designations are not equivalent. To speak of process theism is to accentuate the differences between this metaphysical view and an opposing metaphysical stance, that of traditional or substantialist theism. By way of contrast, to speak of neoclassical theism is not to accentuate differences but rather the inclusion of one metaphysical tradition within another. That is, the neoclassical theism of Hartshorne (along with that of A.N. Whitehead, John Cobb, and David Ray Griffin, et al.) is both “neo” and “classical”. The compatibility between the best insights of classical theism and the best in neoclassical theism is evidenced in Hartshorne’s startling claim that he learned almost as much from St. Thomas Aquinas as he did from Whitehead! Although Hartshorne spent a good deal of his career pointing out that classical theism was shipwrecked on certain rocks of contradiction (neo), Thomas, more than anyone else, has provided us with an admirable chart showing the location of the rocks (classical). Three different topics will be emphasized in my defense of the thesis that “process theism” tends to be a polemical designation, in contrast to the more irenic “neoclassical theism”. The first of these is the contrast between monopolar and dipolar metaphysics. In the divine case, the neoclassical theist emphasizes the claim that, in partial contrast to the classical theistic God who does not in any way change, God always changes, and both of these words are important. The second topic is the commonplace in “process” thought that one of the most important passages in the history of metaphysical writing is in Plato’s Sophist (247e), where it is suggested that being is power or dynamis, specifically the power, however slight, both to affect other beings and to be affected by them. The third topic is Whiteheadian prehension, wherein a metaphysical thinker in the present can literally grasp and include the best insights from previous metaphysical traditions and partially transform them by bringing them into a larger whole.
Biomass Energy Resources: Feedstock Quality and Bioenergy Sustainability
Authors: Duca Daniele , Toscano Giuseppe
Published: June 2022

The fossil fuel society is facing environmental, socio-economic, and geopolitical issues [...]
A Managed Volatility Investment Strategy for Pooled Annuity Products
Authors: Li Shuanglan , Labit Hardy Héloïse , Sherris Michael , Villegas Andrés M.
Published: June 2022

Pooled annuity products, where the participants share systematic and idiosyncratic mortality risks as well as investment returns and risk, provide an attractive and effective alternative to traditional guaranteed life annuity products. While longevity risk sharing in pooled annuities has received recent attention, incorporating investment risk beyond fixed interest returns is relatively unexplored. Incorporating equity investments has the potential to increase expected annuity payments at the expense of higher variability. We propose and assess a strategy for incorporating equity investments along with managed-volatility for pooled annuity funds. We show how the managed volatility strategy improves investment performance, while reducing pooled annuity income volatility and downside risk, as well as an investment strategy that reduces exposure to investment risk over time. We quantify the impact of pool size when equity investments are included, showing how these products are viable with relatively small pool sizes.
Expectations of Macroeconomic News Announcements: Bitcoin vs. Traditional Assets
Authors: Mužić Ivan , Gržeta Ivan
Published: June 2022

Research on cryptocurrencies has proliferated in recent years. Our research objective was to answer the question of whether macroeconomic news from the U.S. affects Bitcoin in the same way it affects other common investment assets such as gold, the S&P 500, 2-year Treasury bills, and 10-year Treasury bills. Following previous research, seven macroeconomic news announcements from the U.S. were selected, and an empirical analysis of the daily returns, volatility, and volume of the selected assets was conducted. The results show that while Bitcoin is the most volatile (i.e., riskiest) of all the assets, the expected direction of movement is visible after the official announcement of the macroeconomic news on that day, and is comparable to that of the 2-year Treasury bills. It is also evident that the trading volume of Bitcoin does not change, unlike other assets, suggesting that the price of Bitcoin is always moved by the same players, indicating the closed and, therefore, riskier nature of cryptocurrency markets. Finally, we found evidence that the impact of macroeconomic announcements on Bitcoin returns is stronger when the announcements are negative but, interestingly, the returns of Bitcoin, unlike those of other assets, are more volatile after positive announcements.
An Unsupervised Condition Monitoring System for Electrode Milling Problems in the Resistance Welding Process
Authors: Ibáñez Daniel , Garcia Eduardo , Soret Jesús , Martos Julio
Published: June 2022

Resistance spot welding is one of the most widely used metal joining processes in the manufacturing industry, used for structural body manufacturing, railway vehicle construction, electronics manufacturing, battery manufacturing, etc. Due to its wide use, the quality of welded joints is of great importance to the manufacturing industry, as it is critical for ensuring the integrity of finished products, such as car bodies, that withstand high levels of stress. The quality of the welding is influenced both by the programming of the welding and by the good condition of the mechanical part that carries out the welding. These mechanical factors, such as electrode geometry and wear, degrade over time. As the welding points are made, the geometry and properties of the electrodes change, so they undergo a milling process to remove impurities and return them to their initial geometry. Sometimes the milling is deficient, and the electrode continues to wear, causing welding problems such as loose spots and metal spatter. This article presents a method for condition monitoring of the milling process and weld wear based on existing data in real production lines. The use of unsupervised clustering methods is proposed to perform a check by which, using current and resistance data, the electrode wear is grouped. Specifically, a method using multidimensional k-means for the condition monitoring of electrode wear is established. This research gives a real and applicable solution for reducing the quality problems caused by milling defects and electrode wear in the production lines of high-production manufacturing industries, presenting a system for sending alarms based on the behavior of welding variables.
Part-Based Obstacle Detection Using a Multiple Output Neural Network
Authors: Itu Razvan , Danescu Radu
Published: June 2022

Detecting the objects surrounding a moving vehicle is essential for autonomous driving and for any kind of advanced driving assistance system; such a system can also be used for analyzing the surrounding traffic as the vehicle moves. The most popular techniques for object detection are based on image processing; in recent years, they have become increasingly focused on artificial intelligence. Systems using monocular vision are increasingly popular for driving assistance, as they do not require complex calibration and setup. The lack of three-dimensional data is compensated for by the efficient and accurate classification of the input image pixels. The detected objects are usually identified as cuboids in the 3D space, or as rectangles in the image space. Recently, instance segmentation techniques have been developed that are able to identify the freeform set of pixels that form an individual object, using complex convolutional neural networks (CNNs). This paper presents an alternative to these instance segmentation networks, combining much simpler semantic segmentation networks with light, geometrical post-processing techniques, to achieve instance segmentation results. The semantic segmentation network produces four semantic labels that identify the quarters of the individual objects: top left, top right, bottom left, and bottom right. These pixels are grouped into connected regions, based on their proximity and their position with respect to the whole object. Each quarter is used to generate a complete object hypothesis, which is then scored according to object pixel fitness. The individual homogeneous regions extracted from the labeled pixels are then assigned to the best-fitted rectangles, leading to complete and freeform identification of the pixels of individual objects. The accuracy is similar to instance segmentation-based methods but with reduced complexity in terms of trainable parameters, which leads to a reduced demand for computational resources.
A Displacement Sensing Method Based on Permanent Magnet and Magnetic Flux Measurement
Authors: Zhang Jikai , Shi Yicheng , Huang Yuewen , Liang Cheng , Dong Yantong , Kang Yihua , Feng Bo
Published: June 2022

This paper proposes a displacement sensing method based on magnetic flux measurement. A bridge-structured magnetic circuit, formed by permanent magnets and two ferromagnetic cores, is designed and discussed. The analyses of the equivalent magnetic circuit and three-dimensional finite element simulations showed that the magnetic flux density changes linearly with the reciprocal of the sum of a constant and the displacement. A prototype sensor of the bridge structure is developed that consists of four permanent magnets as excitation, a Hall sensor as reception, and two ferromagnetic cores as the connection. Experiments have validated the feasibility of this method. The measured results show a good linearity between the sensor’s output and the reciprocal of the sum of a constant and the displacement, with a correlation coefficient greater than 0.9995 across different measurement ranges. Additionally, the measured results significantly indicate that the proposed sensor is compatible with different ferromagnetic materials with a worst-case error of less than 5%. The proposed sensor has the advantages of low cost and good linearity; however, the test object is limited to ferromagnetic materials.
An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems
Authors: Tu Yonghui , Tang Haoye , Hu Wenyou
Published: June 2022

In recent years, the Internet of Things (IoT), based on low-power wide-area network (LPWAN) wireless communication technology, has developed rapidly. On the one hand, the IoT makes it possible to conduct low-cost, low-power, wide-coverage, and real-time soil monitoring in fields. On the other hand, many proximal soil sensor devices designed based on conventional communication methods that are stored in an inventory face elimination. Considering the idea of saving resources and costs, this paper applied LPWAN technology to an inventoried proximal soil sensor device, by designing an attachment hardware system (AHS) and realizing technical upgrades. The results of the experimental tests proved that the sensor device, after upgrading, could work for several years with only a battery power supply, and the effective wireless communication coverage was nearly 1 km in a typical suburban farming environment. Therefore, the new device not only retained the original mature sensing technology of the sensor device, but also exhibited ultralow power consumption and long-distance transmission, which are advantages of the LPWAN; gave full play to the application value and economic value of the devices stored in inventory; and saved resources and costs. The proposed approach also provides a reference for applying LPWAN technology to a wider range of inventoried sensor devices for technical upgrading.
Output Stabilization of Wavelength-Swept Laser Based on Closed-Loop Control of Fabry–Pérot Tunable Wavelength Filter for Fiber-Optic Sensors
Authors: Choi Byeong Kwon , Ahn Soyeon , Kim Ji Su , Pagidi Srinivas , Jeon Min Yong
Published: June 2022

The output of a wavelength-swept laser (WSL) based on a fiber Fabry–Pérot tunable filter (FFP-TF) tends to shift the peak wavelength due to external temperature or heat generated by the FFP-TF itself. Therefore, when measuring the output of WSL for a long time, it is very difficult to accurately measure a signal in the temporal domain corresponding to a specific wavelength of the output of the WSL. If the wavelength variation of the WSL output can be predicted through the peak time information of the forward scan or the backward scan from the WSL, the variation of the peak wavelength can be compensated for by adjusting the offset voltage applied to the FFP-TF. This study presents a successful stabilization method for peak wavelength variation in WSLs by adjusting the offset voltage of the FFP-TF with closed-loop control. The closed-loop control is implemented by measuring the deviation in the WSL peak position in the temporal domain using the trigger signal of the function generator. The feedback repetition rate for WSL stabilization was approximately 0.2 s, confirming that the WSL output and the peak position for the fiber Bragg grating (FBG) reflection spectrum were kept constant within ±7 μs at the maximum when the stabilization loop was applied. The standard deviations of WSL output and reflection peak positions were 1.52 μs and 1.59 μs, respectively. The temporal and spectral domains have a linear relationship; the ±7 μs maximum variation of the peak position corresponded to ±0.035 nm of the maximum wavelength variation in the spectral domain. The proposed WSL system can be used as a light source for temperature or strain-dependent sensors as it compensates for the WSL wavelength variation in applications that do not require a fast scanning rate.
Development and Automation of a Bacterial Biosensor to the Targeting of the Pollutants Toxic Effects by Portable Raman Spectrometer
Authors: Bandeliuk Oleksandra , Assaf Ali , Bittel Marine , Durand Marie-Jose , Thouand Gérald
Published: June 2022

Water quality monitoring requires a rapid and sensitive method that can detect multiple hazardous pollutants at trace levels. This study aims to develop a new generation of biosensors using a low-cost fiber-optic Raman device. An automatic measurement system was thus conceived, built and successfully tested with toxic substances of three different types: antibiotics, heavy metals and herbicides. Raman spectroscopy provides a multiparametric view of metabolic responses of biological organisms to these toxic agents through their spectral fingerprints. Spectral analysis identified the most susceptible macromolecules in an E. coli model strain, providing a way to determine specific toxic effects in microorganisms. The automation of Raman analysis reduces the number of spectra required per sample and the measurement time: for four samples, time was cut from 3 h to 35 min by using a multi-well sample holder without intervention from an operator. The correct classifications were, respectively, 99%, 82% and 93% for the different concentrations of norfloxacin, while the results were 85%, 93% and 81% for copper and 92%, 90% and 96% for 3,5-dichlorophenol at the three tested concentrations. The work initiated here advances the technology needed to use Raman spectroscopy coupled with bioassays so that together, they can advance field toxicological testing.
Design and Implementation of Real-Time Localization System (RTLS) Based on UWB and TDoA Algorithm
Authors: Zhang Fengyun , Yang Li , Liu Yuhuan , Ding Yulong , Yang Shuang-Hua , Li Hao
Published: June 2022

Nowadays, accurate localization plays an essential role in many fields, such as target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So, the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive ultra wide-band (UWB)-based real-time localization system (RTLS) is presented in this paper. We introduce the architecture of a real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, and finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and we analyze the relationship between anchor deployment and positioning accuracy. The extended Kalman filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy on moving targets.
Fast Transmitarray Synthesis with Far-Field and Near-Field Constraints
Authors: Loredo Susana , Plaza Enrique G. , León Germán
Published: June 2022

Millimeter-wave communications can potentially provide high-data-rate transmission. In addition, in the case of indoor small cells, new needs related to the radiation pattern of the antennas are emerging. In this work, a technique for the synthesis of planar transmitarray antennas with simultaneous near-field and far-field requirements is proposed. It is based on an iterative process, going from synthesized sources to generated field and back, through three operations: near-field computation as the sum of far-field contributions from the array elements, and inverse and direct fast Fourier transforms. As a result, the technique is very efficient from the point of view of computing time. In order to demonstrate the ability of the method, two examples are studied: one of them with a null in the near-field region and the other with a focal point, both pointing simultaneously in a specific far-field direction. The results are validated by manufacturing two dielectric “quasi-planar” prototypes at 26 GHz. The measure of the prototypes is in good agreement with the results advanced by the algorithm. These preliminary results suggest that the method can be extended to more complex scenarios.
In-Cabin Monitoring System for Autonomous Vehicles
Authors: Mishra Ashutosh , Lee Sangho , Kim Dohyun , Kim Shiho
Published: June 2022

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.
A Novel Auto-Synthesis Dataset Approach for Fitting Recognition Using Prior Series Data
Authors: Zhang Jie , Qin Xinyan , Lei Jin , Jia Bo , Li Bo , Li Zhaojun , Li Huidong , Zeng Yujie , Song Jie
Published: June 2022

To address power transmission line (PTL) traversing complex environments leading to data collection being difficult and costly, we propose a novel auto-synthesis dataset approach for fitting recognition using prior series data. The approach mainly includes three steps: (1) formulates synthesis rules by the prior series data; (2) renders 2D images based on the synthesis rules utilizing advanced virtual 3D techniques; (3) generates the synthetic dataset with images and annotations obtained by processing images using the OpenCV. The trained model using the synthetic dataset was tested by the real dataset (including images and annotations) with a mean average precision (mAP) of 0.98, verifying the feasibility and effectiveness of the proposed approach. The recognition accuracy by the test is comparable with training by real samples and the cost is greatly reduced to generate synthetic datasets. The proposed approach improves the efficiency of establishing a dataset, providing a training data basis for deep learning (DL) of fitting recognition.
Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints
Authors: Syed Abbas Shah , Sierra-Sosa Daniel , Kumar Anup , Elmaghraby Adel
Published: June 2022

One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.
Employing an Energy Harvesting Strategy to Enhance the Performance of a Wireless Emergency Network
Authors: Elshrkasi Ahmed , Dimyati Kaharudin , Ahmad Khairol Amali Bin , Abdullah Ezmin
Published: June 2022

Establishing a wireless communication network (WCN) is critical to saving people’s lives during disasters. Since the user equipment (UE) must transfer their information to the functioning area, their batteries will be significantly drained. Thus, technologies that can compensate for battery power consumption, such as the energy harvesting (EH) strategy, are highly required. This paper proposes a framework that employs EH at the main cluster head (MCH) selected by the enhanced clustering technique (CFT) and simultaneously transmits information and power wirelessly to prolong the lifetime of the energy-constrained network. MCH harvests energy from the radio frequency signal via the relay station (RS) and uses the harvested energy for D2D communications. The suggested framework was evaluated by analyzing the EH outage probability and estimating the energy efficiency performance, which is expected to improve the stability of the network. Compared to the UAV scenario, the simulation findings show that when RS is in its optimal location, it enhances the network EH outage probability performance by 26.3%. Finally, integrating CFT with wireless communications links into cellular networks is an effective technique for maintaining communication services for mission-critical applications.
Aerogel-Lined Capillaries for Raman Signal Gain of Aqueous Mixtures
Authors: Spiske Felix , Dirauf Martin Peter , Braeuer Andreas Siegfried
Published: June 2022

We report an experimental study on the gain of the Raman signal of aqueous mixtures and liquid water when confined in aerogel-lined capillaries of various lengths of up to 20 cm and various internal diameters between 530 and 1000 µm. The lining was made of hydrophobised silica aerogel, and the carrier capillary body consisted of fused silica or borosilicate glass. Compared to the Raman signal detected from bulk liquid water with the same Raman probe, a Raman signal 27 times as large was detected when the liquid water was confined in a 20 cm-long capillary with an internal diameter of 700 µm. In comparison with silver-lined capillaries of the same length and same internal diameter, the aerogel-lined capillaries featured a superior Raman signal gain and a longer gain stability when exposed to mixtures of water, sugar, ethanol and acetic acid.
An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm
Authors: Rathnayake Namal , Rathnayake Upaka , Dang Tuan Linh , Hoshino Yukinobu
Published: June 2022

Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.
Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving
Authors: Oh Geesung , Jeong Euiseok , Kim Rak Chul , Yang Ji Hyun , Hwang Sungwook , Lee Sangho , Lim Sejoon
Published: June 2022

As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub.
Ultra-Wideband (UWB) Systems in Biomedical Sensing
Authors: Tiberi Gianluigi , Ghavami Mohammad
Published: June 2022

The extremely low power transmission levels of ultra-wideband (UWB) technology, alongside its advantageously large bandwidth, make it a prime candidate for being used in numerous healthcare scenarios, which require short-range high-data-rate communications and safe radar-based applications [...]
Fiber-Optic Hydrophone Based on Michelson’s Interferometer with Active Stabilization for Liquid Volume Measurement
Authors: Duque Welton Sthel , Rodríguez Díaz Camilo Arturo , Leal-Junior Arnaldo Gomes , Frizera Anselmo
Published: June 2022

Sensing technologies using optical fibers have been studied and applied since the 1970s in oil and gas, industrial, medical, aerospace, and civil areas. Detecting ultrasound acoustic waves through fiber-optic hydrophone (FOH) sensors can be one solution for continuous measurement of volumes inside production tanks used by these industries. This work presents an FOH system composed of two optical fiber coils made with commercial single mode fiber (SMF) working in the sensor head of a Michelson’s interferometer (MI) supported by an active stabilization mechanism that drives another optical coil wound around a piezoelectric actuator (PZT) in the reference arm to mitigate external mechanical and thermal noise from the environment. A 1000 mL glass graduated cylinder filled with water is used as a test tank, inside which the sensor head and an ultrasound source are placed. For detection, amplitudes and phases are measured, and machine learning algorithms predict their respective liquid volumes. The acoustic waves create patterns electronically detected with resolution of 1 mL and sensitivity of 340 mrad/mL and 70 mvolts/mL. The nonlinear behavior of both measurands requires classification, distance metrics, and regression algorithms to define an adequate model. The results show the system can determine liquid volumes with an accuracy of 99.4% using a k-nearest neighbors (k-NN) classification with one neighbor and Manhattan’s distance. Moreover, Gaussian process regression using rational quadratic metrics presented a root mean squared error (RMSE) of 0.211 mL.
Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review
Authors: Amirpourabasi Arezoo , Lamb Sallie E. , Chow Jia Yi , Williams Geneviève K. R.
Published: June 2022

Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on their movement patterns. However, there is a range of NDA measures reported in the literature. The aim of this review was to summarise the variety, characteristics and range of the nonlinear dynamic measurements used to distinguish the gait kinematics of healthy older adults and older adults at risk of falling. Methods: Medline Ovid and Web of Science databases were searched. Forty-six papers were included for full-text review. Data extracted included participant and study design characteristics, fall risk assessment tools, analytical protocols and key results. Results: Among all nonlinear dynamic measures, Lyapunov Exponent (LyE) was most common, followed by entropy and then Fouquet Multipliers (FMs) measures. LyE and Multiscale Entropy (MSE) measures distinguished between older and younger adults and fall-prone versus non-fall-prone older adults. FMs were a less sensitive measure for studying changes in older adults’ gait. Methodology and data analysis procedures for estimating nonlinear dynamic measures differed greatly between studies and are a potential source of variability in cross-study comparisons and in generating reference values. Conclusion: Future studies should develop a standard procedure to apply and estimate LyE and entropy to quantify gait characteristics. This will enable the development of reference values in estimating the risk of falling.
Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model
Authors: Xu Ningke , Wang Xiangqian , Meng Xiangrui , Chang Haoqian
Published: June 2022

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm’s (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.
Denoising Ocean Turbulence Microstructure Signals for Application in Estimating Turbulence Kinetic Energy Dissipation Rates Based on EMD-PCA
Authors: Chen Xue , Zhao Xiangbin , Liang Yongquan , Luan Xin
Published: June 2022

When ocean turbulence signals are collected using turbulence observation instruments in real marine environments, the effective signals in the acquired data set are often polluted by noise. In order to eliminate the noise component contained in the non-stationary and nonlinear ocean turbulence signals, a new multi-scale turbulence signal denoising method is proposed by combining the empirical mode decomposition (EMD) and principle component analysis (PCA). First, the time series of turbulence signals are decomposed into a couple of components by EMD algorithm and approximately calculate the noise energy in each intrinsic mode function (IMF). Then, PCA is implemented on each IMF. The appropriate principal components are selected according to the decomposition characteristics of PCA and the noise energy proportion in IMF. Each IMF is reconstructed by the selected principle components. At last, the effective ocean turbulence signals are reconstructed by the corrected IMFs and the residue. Ocean turbulence signals collected in the South China Sea (SCS) are used to evaluate the effectiveness of the proposed method. The results show that the proposed method can effectively eliminate the noise and maintain the characteristics of the effective turbulence signals under high noise. Turbulence kinetic energy (TKE) is also estimated from the denoised signals, which provide a reliable data basis for the analysis of the turbulent characteristics in later stage.
Few-Shot Text Classification with Global–Local Feature Information
Authors: Wang Depei , Wang Zhuowei , Cheng Lianglun , Zhang Weiwen
Published: June 2022

Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose SumFS to find global top-ranked sentences by extractive summary and improve the local vocabulary category features. The SumFS consists of three modules: (1) an unsupervised text summarizer that removes redundant information; (2) a weighting generator that associates feature words with attention scores to weight the lexical representations of words; (3) a regular meta-learning framework that trains with limited labeled data using a ridge regression classifier. In addition, a marine news dataset was established with limited label data. The performance of the algorithm was tested on THUCnews, Fudan, and marine news datasets. Experiments show that the SumFS can maintain or even improve accuracy while reducing input features. Moreover, the training time of each epoch is reduced by more than 50%.
Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers
Authors: Miñón Raúl , Diaz-de-Arcaya Josu , Torre-Bastida Ana I. , Hartlieb Philipp
Published: June 2022

Development and operations (DevOps), artificial intelligence (AI), big data and edge–fog–cloud are disruptive technologies that may produce a radical transformation of the industry. Nevertheless, there are still major challenges to efficiently applying them in order to optimise productivity. Some of them are addressed in this article, concretely, with respect to the adequate management of information technology (IT) infrastructures for automated analysis processes in critical fields such as the mining industry. In this area, this paper presents a tool called Pangea aimed at automatically generating suitable execution environments for deploying analytic pipelines. These pipelines are decomposed into various steps to execute each one in the most suitable environment (edge, fog, cloud or on-premise) minimising latency and optimising the use of both hardware and software resources. Pangea is focused in three distinct objectives: (1) generating the required infrastructure if it does not previously exist; (2) provisioning it with the necessary requirements to run the pipelines (i.e., configuring each host operative system and software, install dependencies and download the code to execute); and (3) deploying the pipelines. In order to facilitate the use of the architecture, a representational state transfer application programming interface (REST API) is defined to interact with it. Therefore, in turn, a web client is proposed. Finally, it is worth noting that in addition to the production mode, a local development environment can be generated for testing and benchmarking purposes.
A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine
Authors: Iqbal Yasir , Tahir Shahzaib , Tahir Hasan , Khan Fawad , Saeed Saqib , Almuhaideb Abdullah M. , Syed Adeel M.
Published: June 2022

Globally, the surge in disease and urgency in maintaining social distancing has reawakened the use of telemedicine/telehealth. Amid the global health crisis, the world adopted the culture of online consultancy. Thus, there is a need to revamp the conventional model of the telemedicine system as per the current challenges and requirements. Security and privacy of data are main aspects to be considered in this era. Data-driven organizations also require compliance with regulatory bodies, such as HIPAA, PHI, and GDPR. These regulatory compliance bodies must ensure user data privacy by implementing necessary security measures. Patients and doctors are now connected to the cloud to access medical records, e.g., voice recordings of clinical sessions. Voice data reside in the cloud and can be compromised. While searching voice data, a patient’s critical data can be leaked, exposed to cloud service providers, and spoofed by hackers. Secure, searchable encryption is a requirement for telemedicine systems for secure voice and phoneme searching. This research proposes the secure searching of phonemes from audio recordings using fully homomorphic encryption over the cloud. It utilizes IBM’s homomorphic encryption library (HElib) and achieves indistinguishability. Testing and implementation were done on audio datasets of different sizes while varying the security parameters. The analysis includes a thorough security analysis along with leakage profiling. The proposed scheme achieved higher levels of security and privacy, especially when the security parameters increased. However, in use cases where higher levels of security were not desirous, one may rely on a reduction in the security parameters.