bluuebunny/crossref_metadata_2025 huggingface dataset:
How Does Neural Network Model Capacity Affect Photovoltaic Power Prediction? A Study Case
Authors: Andrade Carlos Henrique Torres de , Melo Gustavo Costa Gomes de , Vieira Tiago Figueiredo , Araújo Ícaro Bezzera Queiroz de , Medeiros Martins Allan de , Torres Igor Cavalcante , Brito Davi Bibiano , Santos Alana Kelly Xavier
Published: January 2023

The use of models capable of forecasting the production of photovoltaic (PV) energy is essential to guarantee the best possible integration of this energy source into traditional distribution grids. Long Short-Term Memory networks (LSTMs) are commonly used for this purpose, but their use may not be the better option due to their great computational complexity and slower inference and training time. Thus, in this work, we seek to evaluate the use of neural networks MLPs (Multilayer Perceptron), Recurrent Neural Networks (RNNs), and LSTMs, for the forecast of 5 min of photovoltaic energy production. Each iteration of the predictions uses the last 120 min of data collected from the PV system (power, irradiation, and PV cell temperature), measured from 2019 to mid-2022 in Maceió (Brazil). In addition, Bayesian hyperparameters optimization was used to obtain the best of each model and compare them on an equal footing. Results showed that the MLP performs satisfactorily, requiring much less time to train and forecast, indicating that they can be a better option when dealing with a very short-term forecast in specific contexts, for example, in systems with little computational resources.
SpaceWire-to-UWB Wireless Interface Units for Intra-spacecraft Communication Links
Authors: Buta Rares-Calin , Drobczyk Martin , Firchau Thomas , Luebken Andre , Palade Tudor Petru , Pastrav Andra , Puschita Emanuel
Published: January 2023

In the context of the Eu:CROPIS mission requirements, this paper aims to test and validate an intra-spacecraft wireless transmission carried between two SpW-to-UWB Wireless Interface Units (WIUs). The WIUs are designed to replace the on-board SpaceWire (SpW) connections of a spacecraft network. The novelty of this solution resides in prototyping and testing proprietary TRL6 WIUs for the implementation of both PDHU and CDHU units, which constitute a spacecraft network. The validation test scenarios employed in this paper were designed under the Eu:CROPIS mission system requirements as defined by the WiSAT-3 European Space Agency (ESA)-funded project. The SpW-to-UWB WIUs run a custom-built ISA100 over an IEEE 802.15.4 UWB PHY layer communication stack. The WIUs are evaluated based on four mission-specific performance test scenarios: (1) the link setup test, (2) the end-to-end delay test, (3) the maximum data rate test and (4) the housekeeping test. The validation test scenarios of the WIUs are carried out with the use of STAR-Dundee SpW-capable equipment. The test results demonstrate the reliability of the deployed SpW-to-UWB WIUs devices for UWB wireless communications carried out within a space shuttle. The SpW data were successfully transmitted across the intra-spacecraft wireless network in all experimental tests. The technology can be considered to be at the maturity level TRL6 (functionality demonstrated in relevant environment) for LEO missions.
MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking
Authors: Imran Muhammad , Din Muhammad Salah Ud , Rehman Muhammad Atif Ur , Kim Byung-Seo
Published: January 2023

The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead.
CH4, C2H6, and CO2 Multi-Gas Sensing Based on Portable Mid-Infrared Spectroscopy and PCA-BP Algorithm
Authors: Yang Yunting , Jiang Jiachen , Zeng Jiafu , Chen Zhangxiong , Zhu Xiaosong , Shi Yiwei
Published: January 2023

A multi-gas sensing system was developed based on the detection principle of the non-dispersive infrared (NDIR) method, which used a broad-spectra light source, a tunable Fabry–Pérot (FP) filter detector, and a flexible low-loss infrared waveguide as an absorption cell. CH4, C2H6, and CO2 gases were detected by the system. The concentration of CO2 could be detected directly, and the concentrations of CH4 and C2H6 were detected using a PCA-BP neural network algorithm because of the interference of CH4 and C2H6. The detection limits were achieved to be 2.59 ppm, 926 ppb, and 114 ppb for CH4, C2H6, and CO2 with an averaging time of 429 s, 462 s, and 297 s, respectively. The root mean square error of prediction (RMSEP) of CH4 and C2H6 were 10.97 ppm and 2.00 ppm, respectively. The proposed system and method take full advantage of the multi-component gas measurement capability of the mid-infrared broadband source and achieve a compromise between performance and system cost.
Overview of Digital Twin Platforms for EV Applications
Authors: Ibrahim Mahmoud , Rjabtšikov Viktor , Gilbert Rolando
Published: January 2023

Digital twin (DT) technology has been used in a wide range of applications, including electric vehicles. The DT platform provides a virtual representation or advanced simulation of a physical object in real-time. The implementation of DT on various aspects of EVs has recently transpired in different research studies. Generally, DT can emulate the actual vehicle on the road to predict/optimize its performance and improve vehicle safety. Additionally, DT can be used for the optimization of manufacturing processes, real-time condition monitoring (at all levels and in all powertrain components), energy management optimization, repurposing of the components, and even recycling processes. This paper presents an overview of different DT platforms that can be used in EV applications. A deductive comparison between model-based and data-driven DT was performed. EV main systems have been discussed regarding the usable DT platform. DT platforms used in the EV industry were addressed. Finally, the review showed the superiority of data-driven DTs over model-based DTs due to their ability to handle systems with high complexity.
A Low-Loss, 77 GHz, 8 × 8 Microstrip Butler Matrix on a High-Purity Fused-Silica (HPFS) Glass Substrate
Authors: Sakhiya Ronak , Chowdhury Sazzadur
Published: January 2023

A low-loss, compact, ultra-thin, passive, 77 GHz, 8 × 8 microstrip Butler matrix on a 200 μm thick high-purity fused-silica (HPFS) glass substrate embedded in 0.8 μm thick patterned gold conducting layers was developed for low power automotive radars. The first-of-its-kind, HPFS, glass-based Butler matrix comprised 12 hybrid couplers, 16 crossovers, and 8 phase shifters in a footprint area of 19.1 mm × 26.6 mm. The device and the corresponding building blocks were designed and optimized using 3D electromagnetic finite element method (FEM) simulations using the Advanced Design System (ADS) from Keysight™ Technologies. Due to the very-low-loss tangent of the HPFS glass substrate (0.0005 @77 GHz) compared to other common substrate materials and rigorous design optimization, the return loss and isolation of the input ports are both below −20 dB, respectively, as verified by 3D FEM simulations. Due to the absence of any published data on a 77 GHz 8 × 8 Butler matrix, the design was validated by developing a 4 × 4 version of the Butler matrix using the same building blocks and comparing the 3D simulation results in ADS with results published elsewhere that showed that the developed Butler matrix offers lower insertion loss in a 10% smaller footprint area. A low-cost microfabrication method has been developed to fabricate the devices using a standard lift-off process. A scaled version of the device can be used for 5G beamforming applications.
Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification
Authors: Kolappan Geetha Ganesh , Yang Hyun-Jung , Sim Sung-Han
Published: January 2023

Existing deep learning (DL) models can detect wider or thicker segments of cracks that occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow segment or propagating crack occupying fewer pixels. Therefore, in this study, we proposed a scheme for tracking missing thin/propagating crack segments during DL-based crack identification on concrete surfaces in a computationally efficient manner. The proposed scheme employs image processing as a preprocessor and a postprocessor for a 1D DL model. Image-processing-assisted DL as a precursor to DL eliminates labor-intensive labeling and the plane structural background without any distinguishable features during DL training and testing; the model identifies potential crack candidate regions. Iterative differential sliding-window-based local image processing as a postprocessor to DL tracks missing thin cracks on segments classified as cracks. The capability of the proposed method is demonstrated on low-resolution images with cracks of single-pixel width, captured using unmanned aerial vehicles on concrete structures with different surface textures, different scenes with complicated disturbances, and optical variability. Due to the multi-threshold-based image processing, the overall approach is invariant to the choice of initial sensitivity parameters, hyperparameters, and the sequence of neuron arrangement. Further, this technique is a computationally efficient alternative to semantic segmentation that results in pixelated mapping/classification of thin crack regimes, which requires labor-intensive and skilled labeling.
Anomalous Nernst Effect in Flexible Co-Based Amorphous Ribbons
Authors: Correa Marcio A. , Ferreira Armando , Souza Arthur L. R. , Dantas Neto João. M. , Bohn Felipe , Vaz Filipe , Kurlyandskaya Galina V.
Published: January 2023

Fe3Co67Cr3Si15B12 ribbons with a high degree of flexibility and excellent corrosion stability were produced by rapid quenching technique. Their structural, magnetic, and thermomagnetic (Anomalous Nernst Effect) properties were studied both in an as-quenched (NR) state and after stress annealing during 1 h at the temperature of 350 °C and a specific load of 230 MPa (AR). X-ray diffraction was used to verify the structural characteristics of our ribbons. Static magnetic properties were explored by inductive technique and vibrating sample magnetometry. The thermomagnetic curves investigated through the Anomalous Nernst Effect are consistent with the obtained magnetization results, presenting a linear response in the thermomagnetic signal, an interesting feature for sensor applications. Additionally, Anomalous Nernst Effect coefficient SANE values of 2.66μV/K and 1.93μV/K were estimated for the as-quenched and annealed ribbons, respectively. The interplay of the low magnetostrictive properties, soft magnetic behavior, linearity of the thermomagnetic response, and flexibility of these ribbons place them as promising systems to probe curved surfaces and propose multifunctional devices, including magnetic field-specialized sensors.
Acoustoelectric Effect for Rayleigh Wave in ZnO Produced by an Inhomogeneous In-Depth Electrical Conductivity Profile
Authors: Caliendo Cinzia
Published: January 2023

The acousto-electric (AE) effect associated with the propagation of the Rayleigh wave in ZnO half-space was theoretically investigated by studying the changes in wave velocity and propagation loss induced by in-depth inhomogeneous changes in the ZnO electrical conductivity. An exponentially decaying profile for the electrical conductivity was attributed to the ZnO half-space, for some values of the exponential decay constant (from 100 to 500 nm), in order to simulate the photoconductivity effect induced by ultra-violet illumination. The calculated Rayleigh wave velocity and attenuation vs. ZnO conductivity curves have the form of a double-relaxation response as opposed to the single-relaxation response which characterizes the well-known AE effect due to surface conductivity changes onto piezoelectric media. As to the author’s knowledge, this is the first time the double-relaxation AE effect has been theoretically predicted.
Dialogue Management and Language Generation for a Robust Conversational Virtual Coach: Validation and User Study
Authors: Vázquez Alain , López Zorrilla Asier , Olaso Javier Mikel , Torres María Inés
Published: January 2023

Designing human–machine interactive systems requires cooperation between different disciplines is required. In this work, we present a Dialogue Manager and a Language Generator that are the core modules of a Voice-based Spoken Dialogue System (SDS) capable of carrying out challenging, long and complex coaching conversations. We also develop an efficient integration procedure of the whole system that will act as an intelligent and robust Virtual Coach. The coaching task significantly differs from the classical applications of SDSs, resulting in a much higher degree of complexity and difficulty. The Virtual Coach has been successfully tested and validated in a user study with independent elderly, in three different countries with three different languages and cultures: Spain, France and Norway.
HERAS: A Modular Matlab Tool Using Physical Optics for the Analysis of Reflector Antennas
Authors: Baldominos Alejandro , Mercader-Pellicer Salvador , Goussetis George , Mengali Alberto , Fonseca Nelson J. G.
Published: January 2023

HERAS is a tool developed in Matlab for the analysis of reflector antennas using physical optics (PO) theory. Its graphical user interface (GUI) and source code are freely available for educational and research purposes. It has the necessity of being a flexible tool to provide adaptability to system engineering requirements and can also be of interest to antenna engineers working on the design of reflector antennas. Due to the increasing demand of broadband services, satellite communications systems are becoming highly complex in order to meet connectivity requirements. To fully exploit the benefits of these systems, multidimensional optimisations are crucial, which call for an efficient estimation of the coverage characteristics. Reflector-based solutions are one of the preferred architectures for very high-throughput satellite (VHTS) systems. A detailed description of HERAS is presented in this paper which has been validated with available commercial software packages. In addition, some examples are described in which the tool has been used for the efficient estimation of VHTS systems performance.
Suction Influence on Load–Settlement Curves Predicted by DMT in a Collapsible Sandy Soil
Authors: Saab Alfredo Lopes , Rodrigues André Luís de Carvalho , Rocha Breno Padovezi , Rodrigues Roger Augusto , Giacheti Heraldo Luiz
Published: January 2023

The plate load test (PLT) is the most reliable in situ testing for studying the load–settlement behaviour of footings on unsaturated collapsible soils. In these soils, the suction profile is not constant along the depth, and the scale effect between the prototype and footing leads to different suction averages and, consequently, different data. One method to eliminate the effect of soil suction on the test data is to fully saturate the soil prior to the test, which is also recommended at the design process for footing on collapsible soils. However, the inundation process on PLTs is expensive and time-consuming, which makes this procedure difficult to incorporate into engineering practice. This study presents a device that can be attached to flat dilatometer (DMT) to allow local inundation of the soil as part of the in situ test campaign and obtain the DMT-constrained modulus (MDMT) for both natural and inundated conditions. The MDMT presented an average reduction of 56% from natural to inundated condition. This parameter can be used in a model to predict load–settlement curves by DMT data considering the suction influence on this behaviour. The curves obtained from the prediction model were compared to curves determined by PLT conducted under the same in situ conditions. Good agreement was found between the curves predicted by DMT and those measured by PLT for both conditions. The proposed procedure, which uses a device attached to the DMT blade, provides an investigation method to obtain the load–settlement curve under different suction conditions, which can help in the selection and performance prediction of shallow foundations, taking into account suction and collapse phenomenon-related problems.
Tailoring the Performance of a Nafion 117 Humidity Chipless RFID Sensor: The Choice of the Substrate
Authors: Marchi Giada , Mulloni Viviana , Acerbi Fabio , Donelli Massimo , Lorenzelli Leandro
Published: January 2023

Chipless radio-frequency identification (RFID) sensors are not yet widespread in practical applications because of their limited sensitivity and selectivity when compared to more mature sensing technologies. The search for a suitable material to perform the sensing function has often been focused on the most common materials used in electrochemical sensing approaches, but little work has been done to directly relate the performances of chipless or microwave sensors to the characteristics of the materials used to fabricate them. In this work we are simulating the impact of the substrate material on the performances of a chipless RFID sensor for humidity detection. The dielectric parameters of the substrate material turn out to be very important to maximize the sensor performances, in relation to the operative range of the sensor (based on the desired application) and to the effective dielectric properties of the sensitive material used, we verify the simulated results with measurements of real chipless humidity cells with Nafion 117 sensitive material. We show which types of substrate are preferable for low-humidity detection and which substrates’ features are instead fundamental to operate in a wider humidity range.
Measurement System for Short-Pulsed Magnetic Fields
Authors: Stankevič Voitech , Keršulis Skirmantas , Dilys Justas , Bleizgys Vytautas , Viliūnas Mindaugas , Vertelis Vilius , Maneikis Andrius , Rudokas Vakaris , Plaušinaitienė Valentina , Žurauskienė Nerija
Published: January 2023

A measurement system based on the colossal magnetoresistance CMR-B-scalar sensor was developed for the measurement of short-duration high-amplitude magnetic fields. The system consists of a magnetic field sensor made from thin nanostructured manganite film with minimized memory effect, and a magnetic field recording module. The memory effect of the La1−xSrx(Mn1−yCoy)zO3 manganite films doped with different amounts of Co and Mn was investigated by measuring the magnetoresistance (MR) and resistance relaxation in pulsed magnetic fields up to 20 T in the temperature range of 80–365 K. It was found that for low-temperature applications, films doped with Co (LSMCO) are preferable due to the minimized magnetic memory effect at these temperatures, compared with LSMO films without Co. For applications at temperatures higher than room temperature, nanostructured manganite LSMO films with increased Mn content above the stoichiometric level have to be used. These films do not exhibit magnetic memory effects and have higher MR values. To avoid parasitic signal due to electromotive forces appearing in the transmission line of the sensor during measurement of short-pulsed magnetic fields, a bipolar-pulsed voltage supply for the sensor was used. For signal recording, a measurement module consisting of a pulsed voltage generator with a frequency up to 12.5 MHz, a 16-bit ADC with a sampling rate of 25 MHz, and a microprocessor was proposed. The circuit of the measurement module was shielded against low- and high-frequency electromagnetic noise, and the recorded signal was transmitted to a personal computer using a fiber optic link. The system was tested using magnetic field generators, generating magnetic fields with pulse durations ranging from 3 to 20 μs. The developed magnetic field measurement system can be used for the measurement of high-pulsed magnetic fields with pulse durations in the order of microseconds in different fields of science and industry.
sEMG-Based Hand Gesture Recognition Using Binarized Neural Network
Authors: Kang Soongyu , Kim Haechan , Park Chaewoon , Sim Yunseong , Lee Seongjoo , Jung Yunho
Published: January 2023

Recently, human–machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and intuitively control the device. Among the various sensors used in the HGR system, the surface electromyography (sEMG) sensor is independent of the acquisition environment, easy to wear, and requires a small amount of data. Focusing on these advantages, previous sEMG-based HGR systems used several sensors or complex deep-learning algorithms to achieve high classification accuracy. However, systems that use multiple sensors are bulky, and embedded platforms with complex deep-learning algorithms are difficult to implement. To overcome these limitations, we propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor, which is implemented on a field-programmable gate array (FPGA). The proposed HGR system classifies nine dynamic gestures that can be useful in real life rather than static gestures that can be classified relatively easily. Raw sEMG data collected from a dynamic gesture are converted into a spectrogram with information in the time-frequency domain and transferred to the classifier. As a result, the proposed HGR system achieved 95.4% classification accuracy, with a computation time of 14.1 ms and a power consumption of 91.81 mW.
Performance Optimization for a Class of Petri Nets
Authors: Shi Weijie , He Zhou , Gu Chan , Ran Ning , Ma Ziyue
Published: January 2023

Petri nets (PNs) are widely used to model flexible manufacturing systems (FMSs). This paper deals with the performance optimization of FMSs modeled by Petri nets that aim to maximize the system’s performance under a given budget by optimizing both quantities and types of resources, such as sensors and devices. Such an optimization problem is challenging since it is nonlinear; hence, a globally optimal solution is hard to achieve. Here, we developed a genetic algorithm combined with mixed-integer linear programming (MILP) to solve the problem. In this approach, a set of candidate resource allocation strategies, i.e., the choices of the number of resources, are first generated by using MILP. Then, the choices of the type and the cycle time of the resources are evaluated by MILP; the promising ones are used to spawn the next generation of candidate strategies. The effectiveness and efficiency of the developed methodology are illustrated by simulation studies.
Imitation Learning-Based Performance-Power Trade-Off Uncore Frequency Scaling Policy for Multicore System
Authors: Xiao Baonan , Yang Jianfeng , Qi Xianxian
Published: January 2023

As the importance of uncore components, such as shared cache slices and memory controllers, increases in processor architecture, the percentage of uncore power consumption in the overall power consumption of multicore processors rises significantly. To maximize the power efficiency of a multicore processor system, we investigate the uncore frequency scaling (UFS) policy and propose a novel imitation learning-based uncore frequency control policy. This policy performs online learning based on the DAgger algorithm and converts the annotation cost of online aggregation data into fine-tuning of the expert model. This design optimizes the online learning efficiency and improves the generality of the UFS policy on unseen loads. On the other hand, we shift our policy optimization target to Performance Per Watt (PPW), i.e., the power efficiency of the processor, to avoid saving a percentage of power while losing a larger percentage of performance. The experimental results show that our proposed policy outperforms the current advanced UFS policy in the benchmark test sequence of SPEC CPU2017. Our policy has a maximum improvement of about 10% relative to the performance-first policies. In the unseen processor load, the tuning decision made by our policy after collecting 50 aggregation data can maintain the processor stably near the optimal power efficiency state.
Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN
Authors: Chang Jiaxing , Hu Fei , Xu Huaxing , Mao Xiaobo , Zhao Yuping , Huang Luqi
Published: January 2023

For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.
Research on Comprehensive Evaluation and Early Warning of Transmission Lines’ Operation Status Based on Dynamic Cloud Computing
Authors: Wang Minzhen , Li Cheng , Wang Xinheng , Piao Zheyong , Yang Yongsheng , Dai Wentao , Zhang Qi
Published: January 2023

The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that influence dynamic key evaluation indices such as grounding resistance, sag, and wire corrosion, establishes the evaluation criteria of the ETL operation state, and proposes five ETL status levels and seven principles for selecting evaluation indices. Nine grade I evaluation indices and twenty-nine grade II evaluation indices, including passageway and meteorological environments, are determined. The cloud model theory is embedded and used to propose a warning technology for the operation state of ETLs based on inspection defect parameters and the cloud model. Combined with the inspection defect parameters of a line in the Baicheng district of Jilin Province and the critical evaluation index data such as grounding resistance, sag, and wire corrosion, which are used to calculate the timeliness of the data, the solid line is evaluated. The research shows that the dynamic evaluation model is correct and that the ETL status evaluation and early warning method have reasonable practicability.
Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter
Authors: Alidadi Mahdi , Rahimi Afshin
Published: January 2023

Reaction wheels (RW), the most common attitude control systems in satellites, are highly prone to failure. A satellite needs to be oriented in a particular direction to maneuver and accomplish its mission goals; losing the reaction wheel can lead to a complete or partial mission failure. Therefore, estimating the remaining useful life (RUL) over long and short spans can be extremely valuable. The short-period prediction allows the satellite’s operator to manage and prioritize mission tasks based on the RUL and increases the chances of a total mission failure becoming a partial one. Studies show that lack of proper bearing lubrication and uneven frictional torque distribution, which lead to variation in motor torque, are the leading causes of failure in RWs. Hence, this study aims to develop a three-step prognostic method for long-term RUL estimation of RWs based on the remaining lubricant for the bearing unit and a potential fault in the supplementary lubrication system. In the first step of this method, the temperature of the lubricants is estimated as the non-measurable state of the system using a proposed adjusted particle filter (APF) with angular velocity and motor current of RW as the available measurements. In the second step, the estimated lubricant’s temperature and amount of injected lubrication in the bearing, along with the lubrication degradation model, are fed to a two-step particle filter (PF) for online model parameter estimation. In the last step, the performance of the proposed prognostics method is evaluated by predicting the RW’s RUL under two fault scenarios, including excessive loss of lubrication and insufficient injection of lubrication. The results show promising performance for the proposed scheme, with accuracy in estimation of the degradation model’s parameters around 2–3% of root mean squared percentage error (RMSPE) and prediction of RUL around 0.1–4% error.
Nonlinear Frequency Offset Beam Design for FDA-MIMO Radar
Authors: Xu Yanjie , Wang Chunyang , Zheng Guimei , Tan Ming
Published: January 2023

The beam pattern of frequency diversity array (FDA) radar has a range–angle two-dimensional degree of freedom, which makes it possible to distinguish different targets from the same angle and brings a new approach to anti-jamming of radars. However, the beam pattern of conventional linearly frequency-biased FDA radar is range–angle-coupled and time-varying. The method of adding nonlinear frequency bias among the array elements of the FDA array has been shown to eliminate this coupling property while still allowing for better beam performance of the emitted beam. In this paper, we obtain a decoupled and time-invariant beam direction map using the FDA-multi-input–multi-output (FDA-MIMO) radar scheme and then obtain a sharp pencil-shaped main sphere beam pattern with range–angle dependence using a linear frequency offset scheme weighted by a Chebyshev window. Finally, the anti-interference performance of the proposed method is verified in an anti-interference experiment.
Personal Data Stores (PDS): A Review
Authors: Fallatah Khalid U. , Barhamgi Mahmoud , Perera Charith
Published: January 2023

Internet services have collected our personal data since their inception. In the beginning, the personal data collection was uncoordinated and was limited to a few selected data types such as names, ages, birthdays, etc. Due to the widespread use of social media, more and more personal data has been collected by different online services. We increasingly see that Internet of Things (IoT) devices are also being adopted by consumers, making it possible for companies to capture personal data (including very sensitive data) with much less effort and autonomously at a very low cost. Current systems architectures aim to collect, store, and process our personal data in the cloud with very limited control when it comes to giving back to citizens. However, Personal Data Stores (PDS) have been proposed as an alternative architecture where personal data will be stored within households, giving us complete control (self-sovereignty) over our data. This paper surveys the current literature on Personal Data Stores (PDS) that enable individuals to collect, control, store, and manage their data. In particular, we provide a comprehensive review of related concepts and the expected benefits of PDS platforms. Further, we compare and analyse existing PDS platforms in terms of their capabilities and core components. Subsequently, we summarise the major challenges and issues facing PDS platforms’ development and widespread adoption.
Energy Balance of Wireless Sensor Nodes Based on Bluetooth Low Energy and Thermoelectric Energy Harvesting
Authors: Liu Yuming , Riba Jordi-Roger , Moreno-Eguilaz Manuel
Published: January 2023

The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or an energy harvesting system charging a rechargeable battery typically powers IoT devices. The energy harvesting unit and rechargeable battery supply the sensors and wireless communications modules. Therefore, the energy harvesting unit must be correctly sized to optimize the availability and reliability of IoT devices. This paper applies a power balance of the entire IoT device, including the energy harvesting module that includes two thermoelectric generators and a DC–DC converter, the battery, and the sensors and communication modules. Due to the small currents typical of the different communication phases and their fast-switching nature, it is not trivial to measure the energy in each phase, requiring very specific instrumentation. This work shows that using conventional instrumentation it is possible to measure the energy involved in the different modes of communication. A detailed energy balance of the battery is also carried out during charge and discharge cycles, as well as communication modes, from which the maximum allowable data transfer rate is determined. The approach presented here can be generalized to many other smart grid IoT devices.
Text Sentiment Classification Based on BERT Embedding and Sliced Multi-Head Self-Attention Bi-GRU
Authors: Zhang Xiangsen , Wu Zhongqiang , Liu Ke , Zhao Zengshun , Wang Jinhao , Wu Chengqin
Published: January 2023

In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model.
QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems
Authors: Nandhini Ramesh Sneka , Lakshmanan Ramanathan
Published: January 2023

The rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have been carried out in the past to develop suitable computational models for short- and long-term traffic. This study developed an effective multi-dimensional dataset-based model in cyber–physical systems for more accurate traffic-volume prediction. The integration of quantum convolutional neural network and Bayesian optimization (QCNN_BaOpt) constituted the proposed model in this study. Furthermore, optimal tuning of hyperparameters was carried out using Bayesian optimization. The constructed model was evaluated using the US accident dataset records available in Kaggle, which comprise 1.5 million records. The dataset consists of 47 attributes describing spatial and temporal behavior, accidents, and weather characteristics. The efficiency of the proposed model was evaluated by calculating various metrics. The performance of the proposed model was assessed as having an accuracy of 99.3%. Furthermore, the proposed model was compared against the existing state-of-the-art models to demonstrate its superiority.
Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology
Authors: Martínez-Martín Iván , Hernández-Jiménez Miriam , Revilla Isabel , Vivar-Quintana Ana M.
Published: January 2023

Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.
A Financial Management Platform Based on the Integration of Blockchain and Supply Chain
Authors: Liu He , Yang Biao , Xiong Xuanrui , Zhu Shuaiqi , Chen Boyu , Tolba Amr , Zhang Xingguo
Published: January 2023

Internet of Things (IoT) finance extends financial services to the whole physical commodity society with the help of IoT technology to realize financial automation and intelligence. However, the security of IoT finance still needs to be improved. Blockchain has the characteristics of decentralization, immutability, faster settlement, etc., and has been gradually applied to the field of IoT finance. Blockchain is also considered to be an effective way to resolve the problems of the traditional supply chain finance industry, such as the inability to transmit core enterprise credit, the failure of full-chain business information connections and the difficulty of clearing and settlement. Supply chain finance allows the strongest enterprise in the supply chain to apply for credit guarantee from the bank to obtain bank loans, and use the funds for circulation in the supply chain to ensure that each enterprise in the whole supply chain can obtain working capital to realize profits, so as to maximize common interests. In this paper, a financial management platform based on the integration of blockchain and supply chain has been designed and implemented. Blockchain is used to integrate supply chain finance to synchronize the bank account payment system, realize the automatic flow of funds, process supervision and automatically settle account periods based on smart contracts. The four functional modules of the system are designed using unified modeling language (UML), and the model view controller (MVC) architecture is selected as the main architecture of the system. The results of the system test show that the proposed platform can effectively improve the system security, and can use the information in the blockchain to provide multi-level financing services for enterprises in supply chain finance.
Malaria Detection Using Advanced Deep Learning Architecture
Authors: Siłka Wojciech , Wieczorek Michał , Siłka Jakub , Woźniak Marcin
Published: January 2023

Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected mosquitoes. The early diagnosis and treatment of malaria are crucial for reducing morbidity and mortality rates, particularly in developing countries where the disease is prevalent. In this article, we present a novel convolutional neural network (CNN) architecture for detecting malaria from blood samples with a 99.68% accuracy. Our method outperforms the existing approaches in terms of both accuracy and speed, making it a promising tool for malaria diagnosis in resource-limited settings. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Additionally, we present an analysis of model performance on different subtypes of malaria and discuss the implications of our findings for the use of deep learning in infectious disease diagnosis.
Design and Fabrication of Embroidered Textile Strain Sensors: An Alternative to Stitch-Based Strain Sensors
Authors: Colli Alfaro Jose Guillermo , Trejos Ana Luisa
Published: January 2023

Smart textile sensors have been gaining popularity as alternative methods for the continuous monitoring of human motion. Multiple methods of fabrication for these textile sensors have been proposed, but the simpler ones include stitching or embroidering the conductive thread onto an elastic fabric to create a strain sensor. Although multiple studies have demonstrated the efficacy of textile sensors using the stitching technique, there is almost little to no information regarding the fabrication of textile strain sensors using the embroidery method. In this paper, a design guide for the fabrication of an embroidered resistive textile strain sensor is presented. All of the required design steps are explained, as well as the different embroidery design parameters and their optimal values. Finally, three embroidered textile strain sensors were created using these design steps. These sensors are based on the principle of superposition and were fabricated using a stainless-steel conductive thread embroidered onto a polyester–rubber elastic knit structure. The three sensors demonstrated an average gauge factor of 1.88±0.51 over a 26% working range, low hysteresis (8.54±2.66%), and good repeatability after being pre-stretched over a certain number of stretching cycles.
Indoor Positioning Design for Mobile Phones via Integrating a Single Microphone Sensor and an H2 Estimator
Authors: Chen Yung-Hsiang , Chang Pei-Yu , Chen Yung-Yue
Published: January 2023

An indoor positioning design developed for mobile phones by integrating a single microphone sensor, an H2 estimator, and tagged sound sources, all with distinct frequencies, is proposed in this investigation. From existing practical experiments, the results summarize a key point for achieving a satisfactory indoor positioning: The estimation accuracy of the instantaneous sound pressure level (SPL) that is inevitably affected by random variations of environmental corruptions dominates the indoor positioning performance. Following this guideline, the proposed H2 estimation design, accompanied by a sound pressure level model, is developed for effectively mitigating the influences of received signal strength (RSS) variations caused by reverberation, reflection, refraction, etc. From the simulation results and practical tests, the proposed design delivers a highly promising indoor positioning performance: an average positioning RMS error of 0.75 m can be obtained, even under the effects of heavy environmental corruptions.
An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach
Authors: Abdusalomov Akmalbek Bobomirzaevich , Islam Bappy MD Siful , Nasimov Rashid , Mukhiddinov Mukhriddin , Whangbo Taeg Keun
Published: January 2023

With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires. Fast detection with high accuracy is the key to controlling this unexpected event. To address this, we proposed an improved forest fire detection method to classify fires based on a new version of the Detectron2 platform (a ground-up rewrite of the Detectron library) using deep learning approaches. Furthermore, a custom dataset was created and labeled for the training model, and it achieved higher precision than the other models. This robust result was achieved by improving the Detectron2 model in various experimental scenarios with a custom dataset and 5200 images. The proposed model can detect small fires over long distances during the day and night. The advantage of using the Detectron2 algorithm is its long-distance detection of the object of interest. The experimental results proved that the proposed forest fire detection method successfully detected fires with an improved precision of 99.3%.