bluuebunny/crossref_metadata_2025 huggingface dataset:
Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
Authors: Chiou Yih-Shyh , Chen Shih-Lun , Chen Wei-Ting
Published: February 2023

This article puts forward a low-complexity filtering algorithm to achieve a low-complexity filtering chip design for real-time location tracking. In order to meet the need for low-complexity and real-time, the positioning tracking algorithm based on Kalman Filter (KF) is proposed. The KF itself has the functions of tracking, predicting, etc., which can correct the positioning into more accurate results. However, in the calculation of KF algorithms, each iteration often requires tedious and complex calculations of Kalman Gain (KG). Both software and hardware are very resource-intensive. Therefore, use the feature of KG in alpha-beta (α-β) filtering algorithm which can gradually balance in each iteration. Proposed a filtering algorithm that is based on low-complexity, low cost, and high efficiency. This algorithm uses DKF (Difference Kalman Filter) and PKF (Percentage Kalman Filter) depending on different environments. In other words, DKF and PKF are the algorithms which are generated based on different judging conditions. This algorithm can not only significantly reduce the time and the complexity of computing, but also greatly shorten the circuit area of the original algorithm. This algorithm has a large number of matrix operation. In the hardware calculation process, it solves matrix problems about hardware and then developed chip design. Coefficients are used by a multiple of 2 for operation. Use shifters instead of multipliers and dividers, significantly reducing complexity and circuit area. At the same time, deal with the problem of a floating-point number, achieve circuit function verification on the FPGA, and finally tape-out. The design uses the TSMC 0.18μm CMOS cell library provided by the TSRI, uses EDA to implement VLSI with the Design Vision of SYNOPSYS, the operating frequency of the circuit is 83.33 MHz, the value of gate counts is 22.84 K, the power consumption is 3.86 mW, and chip area is 582.63 μm × 580.23 μm.
A New Noise Shaping Approach for Sigma-Delta Modulators Using Two-Stage Feed-Forward Delays and Hybrid MASH-EFM
Authors: Ijaz Khalid , Adnan Muhammad , Toor Waqas Tariq , Butt Muhammad Asim , Idrees Muhammad , Ali Usman , Hassan Izaz , Ghadi Yazeed Yasin , Awwad Fuad A. , Abonazel Mohamed R. , Ashraf Syed Rehan
Published: February 2023

Sigma-delta modulators use a noise-shaping technique to curtail the noise power in the band of interest during digital-to-analog conversion. Error feedback modulator employs an efficient noise transfer function for time varying inputs than any other sigma-delta modulators. However, the efficiency of the conventional noise transfer function degrades and the quantizer saturation issue provokes when the input signal reaches to full scale. This work proposes a new noise transfer function which is a combination of transfer functions of two-stage Feed-forward delays and a novel Hybrid multi-stage noise shaping-error feedback sigma-delta modulator. The noise transfer function of two-stage Feed-forward delays mitigates the concern of quantizer saturation. The noise transfer function offered by the Hybrid multi-stage noise shaping-error feedback architecture provides sustainable solutions to limit cycles and idle tones. The simulation concludes that the proposed noise-shaping approach obtains comparatively high signal-to-quantization noise ratio than the conventional error feedback modulators. Other performance parameters like spurious-free dynamic range, effective number of bits and signal-to-noise plus distortion ratio are also significantly improved.
Modified RPWM Strategy Based on Level-Shifted Random Carrier and Power Balance to Reduce the PWM Voltage Noise in Three-Phase CHB Inverters
Authors: Liu Jianfeng , Gong Renxi , Zhang Yuanyuan
Published: February 2023

Aimed at the pulse width modulation (PWM) voltage noise and power imbalance in three-phase cascaded H-Bridge (CHB) inverters, a modified random PWM (RPWM) strategy, named the power-balanced RPWM (PB-RPWM) strategy, is proposed in this paper. The PB-RPWM strategy mainly includes three steps: (1) random pulse signals are generated by compared modulation wave with level-shifted random carriers; (2) the random pulse signals are circularly distributed between CHB units by a logic operation method, and then the driving pulse signals of switching devices are produced; (3) the driving pulse signals are used to control the inverter. Under the PB-RPWM strategy, the spectra of the line voltage become uniform and continuous, that is, the PWM voltage noise of the line voltage can be effectively reduced. The output voltage of a single H-bridge unit can contain three basic voltages within 3/2 TA, that is, the power balance between CHB units can be realized. When compared with conventional non-random and random PWM strategies, the PB-RPWM strategy has a lower PWM voltage noise and smaller total harmonic distortion (THD). When compared with the level-shifted PWM (LS-PWM) strategy, the PB-RPWM strategy has a balanced power performance. The effectiveness and feasibility of the PB-RPWM strategy is verified by abundant simulations and experiments.
Increasing the Speed of Multiscale Signal Analysis in the Frequency Domain
Authors: Ďuriš Viliam , Chumarov Sergey G. , Semenov Vladimir I.
Published: February 2023

In the Mallat algorithm, calculations are performed in the time domain. To speed up the signal conversion at each level, the wavelet coefficients are sequentially halved. This paper presents an algorithm for increasing the speed of multiscale signal analysis using fast Fourier transform. In this algorithm, calculations are performed in the frequency domain, which is why the authors call this algorithm multiscale analysis in the frequency domain. For each level of decomposition, the wavelet coefficients are determined from the signal and can be calculated in parallel, which reduces the conversion time. In addition, the zoom factor can be less than two. The Mallat algorithm uses non-symmetric wavelets, and to increase the accuracy of the reconstruction, large-order wavelets are obtained, which increases the transformation time. On the contrary, in our algorithm, depending on the sample length, the wavelets are symmetric and the time of the inverse wavelet transform can be faster by 6–7 orders of magnitude compared to the direct numerical calculation of the convolution. At the same time, the quality of analysis and the accuracy of signal reconstruction increase because the wavelet transform is strictly orthogonal.
UAV Sensors Autonomous Integrity Monitoring—SAIM
Authors: Koukiou Georgia , Anastassopoulos Vassilis
Published: February 2023

For Unmanned Aerial Vehicles (UAVs), it is of crucial importance to develop a technically advanced Collision Avoidance System (CAS). Such a system must necessarily consist of many sensors of various types, each one having special characteristics and performance. The poor performance of one of the sensors can lead to a total failure in collision avoidance if there is no provision for the performance of each separate sensor to be continuously monitored. In this work, a Sensor Autonomous Integrity Monitoring (SAIM) methodology is proposed. The configuration of the sensors and their interaction is based on a fusion procedure that involves a total of five sensors. Accordingly, the performance of each one of the sensors is continuously checked against the combined (fused) operation of the other four. A complementary experiment with a total of four sensors, one of which had low performance, was also conducted. Experimental results reveal a reliable approach for Sensor Autonomous Integrity Monitoring (SAIM). The method can be easily extended to a larger number of sensors.
The Impact of Augmented Reality (AR) on Vocabulary Acquisition and Student Motivation
Authors: Belda-Medina Jose , Marrahi-Gomez Victor
Published: February 2023

Although there is a growing body of literature about the use of Augmented Reality (AR) in language learning, research regarding its effectiveness for vocabulary development among secondary students is scarce. This study aims to measure the perception toward AR technology (H1), the effect of its use on vocabulary development (H2), and its impact on student motivation (H3). In this mixed method research based on convenient sampling, 130 students aged 14 to 15 (9th-graders) from two secondary education schools were assigned to an experimental (n = 64) and a control (n = 66) group. Both groups needed to learn 30 geographic terms in English over four weeks. The control group used a traditional method based on a handbook, while the experimental group was exposed to an AR-based lesson containing the same lexical terms. The instruments involved an English pre-post-test about the target vocabulary, a pre-post-survey, and a class discussion. Quantitative and qualitative data were analyzed through SPSS 20 statistical software and a thematic analysis, respectively. The results evidenced positive attitudes and a strong interest in AR integration in language learning. However, no significant differences were observed regarding vocabulary learning performance between both groups of secondary students.
Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing)
Authors: Siripurapu Sridhar , Darimireddy Naresh K. , Chehri Abdellah , Sridhar B. , Paramkusam A. V.
Published: February 2023

In the realm of the emergence and spread of infectious diseases with pandemic potential throughout the history, plenty of pandemics (and epidemics), from the plague to AIDS (1981) and SARS (in 2003) to the bunch of COVID variants, have tormented mankind. Though plenty of technological innovations are overwhelmingly progressing to curb them—a significant number of such pandemics astounded the world, impacting billions of lives and posing uncovered challenges to healthcare organizations and clinical pathologists globally. In view of addressing these limitations, a critically exhaustive review is performed to signify the prospective role of technological advancements and highlight the implicit problems associated with rendering best quality lifesaving treatments to the patient community. The proposed review work is conducted in two parts. Part 1 is essentially focused upon discussion of advanced technologies akin to artificial intelligence, Big Data, block chain technology, open-source technology, cloud computing, etc. Research works governing applicability of these technologies in solving many uncovered healthcare issues prominently faced by doctors and surgeons in the fields of cardiology, medicine, neurology, orthopaedics, paediatrics, gynaecology, psychiatry, plastic surgery, etc., as well as their role in curtailing the spread of numerous infectious, pathological, neurotic maladies is thrown light off. Boundary conditions and implicitly associated challenges substantiated by remedies coupled with future directions are presented at the end.
Inertial Measurement Units’ Reliability for Measuring Knee Joint Angle during Road Cycling
Authors: Obradović Saša , Stančin Sara
Published: February 2023

We explore the reliability of joint angles in road cycling obtained using inertial measurement units. The considered method relies on 3D accelerometer and gyroscope measurements obtained from two such units, appropriately attached to two adjacent body parts, measuring the angle of the connecting joint. We investigate the effects of applying a simple drift compensation technique and an error-state Kalman filter. We consider the knee joint angle in particular, and conduct two measurement trials, a 5 and a 20 minute one, for seven subjects, in a closed, supervised laboratory environment and use optical motion tracking system measurements as reference. As expected from an adaptive solution, the Kalman filter gives more stable results. The root mean square errors per pedalling cycle are below 3.2°, for both trials and for all subjects, implying that inertial measurement units are not only reliable for short measurements, as is usually assumed, but can be reliably used for longer measurements as well. Considering the accuracy of the results, the presented method can be reasonably extended to open, unsupervised environments and other joint angles. Implementing the presented method supports the development of cheaper and more efficient monitoring equipment, as opposed to using expensive motion tracking systems. Consequently, cyclists can have an affordable way of position tracking, leading to not only better bicycle fitting, but to the avoidance and prevention of certain injuries as well.
A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines
Authors: Zheng Zhiqiang , Liu Minghao , Weng Zhi
Published: February 2023

With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models.
Modelling and Simulation of Quasi-Resonant Inverter for Induction Heating under Variable Load
Authors: Spateri Enrico , Ruiz Fredy , Gruosso Giambattista
Published: February 2023

Single-switch quasi-resonant DC inverters are preferred in low-power induction-heating applications for their cheapness. However, they pose difficulties in enforcing soft-switching and show limited controllability. A good design of these converters must proceed in parallel with the characterization of the load and the operating conditions. The control of the switching frequency has a critical relationship to the non-linear behavior of the load due to electro-thermal coupling and geometrical anisotropies. Finite element methods enable the analysis of this kind of multiphysics coupled systems, but the simulation of transient dynamics is computationally expensive. The goal of this article is to propose a time-domain simulation strategy to analyze the behavior of induction heating systems with a quasi-resonant single-ended DC inverter using pulse frequency modulation and variable load. The load behavior is estimated through frequency stationary analysis and integrated into the time-domain simulations as a non-linear equivalent impedance parametrized by look-up tables. The model considers variations in temperature dynamics, the presence of work-piece anisotropies, and current harmonic waveforms. The power regulation strategy based on the control of the switch turn-on time is tested in a case study with varying load and it is shown that it is able to maintain the converter in the safe operation region, handling variations up to of 22% in the equivalent load resistance.
Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training
Authors: Liu Yong , Zhan Weiwen , Li Yuan , Li Xingrui , Guo Jingkai , Chen Xiaoling
Published: February 2023

Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significance to sustainable power-grid operation and the personal safety of operators. In this paper, a joint algorithm of a spatio-temporal convolutional neural network and multidimensional cloud model (STCNN-MCM) is proposed to complete the segmentation of fine actions during power operation. Firstly, the spatio-temporal convolutional neural network (STCNN) is used to extract action features from the multi-sensor dataset of hand actions during power operation and to predict the next moment’s action to form a multi-outcome dataset; then, a multidimensional cloud model (MCM) is designed based on the motion features of the real power operation; finally, the corresponding probabilities are obtained from the distribution of the predicted data in the cloud model through the multi-outcome dataset for action-rsegmentation point determination. The results show that STCNN-MCM can choose the segmentation points of fine actions in power operation in a relatively efficient way, improve the accuracy of action division, and can be used to improve smart grid-training systems for the segmentation of continuous fine actions in power operation.
Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia
Authors: Nahiduzzaman Kh Md , Said Abdallah Abdullatif , Moradzadeh Arash , Mohammadpour Shotorbani Amin , Hewage Kasun , Sadiq Rehan
Published: February 2023

Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior.
Wind Turbine Blade Waste Circularity Coupled with Urban Regeneration: A Conceptual Framework
Authors: Karavida Spyridoula , Peponi Angeliki
Published: February 2023

With the vast majority of scientists agreeing that the only hope in mitigating the adverse effects of climate change is to drop our carbon emissions to net zero by 2050, the decarbonization of the electricity sector is an environmental emergency. Wind energy can be a leader in the energy transition to a carbon emission-free economy. However, the wind energy transition must be carefully implemented to mitigate the economic, environmental, and social consequences of this change. Blade waste from end-of-life wind turbines is the Achilles’ heel of this energy transition and the main impediment to its full acceptance. Aiming to support efficient blade waste management and therefore to ensure sustainable wind energy transition, we conduct a two-fold methodology. In the first part, we propose a novel conceptual framework of upcycling and downcycling end-of-life solutions in an urban regeneration setting. In the second part, we use the case study method to illustrate the aspects of our conceptual framework by analyzing real life case studies. This study suggests that end-of-life blades are used in the cement coprocessing of waste and in architectural projects under urban regeneration transformation processes, closing the material loop according to the circular economy and sustainability principles.
Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids
Authors: Salazar Eduardo J. , Jurado Mauro , Samper Mauricio E.
Published: February 2023

International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.
Moderating Effect of Financial Development on the Relationship between Renewable Energy and Carbon Emissions
Authors: Chiu Yi-Bin , Zhang Wenwen
Published: February 2023

This study investigates the moderating effect of financial development on the renewable energy–CO2 emissions nexus in OECD countries. We find that both composite financial development and banking sector development have an inverted U-shaped impact on CO2 emissions, while stock market development has a U-shaped impact on CO2 emissions. Further, an increase in renewable energy will reduce CO2 emissions, and this reducing impact is affected by different levels of financial development. When promoting financial development, policymakers should pay more attention to its role in enhancing renewable energy, which is related to emissions reduction.
A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods
Authors: Shao Liyuan , Zhang Yong , Zheng Xiujuan , He Xin , Zheng Yufeng , Liu Zhiwei
Published: February 2023

Lithium-ion batteries are a green and environmental energy storage component, which have become the first choice for energy storage due to their high energy density and good cycling performance. Lithium-ion batteries will experience an irreversible process during the charge and discharge cycles, which can cause continuous decay of battery capacity and eventually lead to battery failure. Accurate remaining useful life (RUL) prediction technology is important for the safe use and maintenance of energy storage components. This paper reviews the progress of domestic and international research on RUL prediction methods for energy storage components. Firstly, the failure mechanism of energy storage components is clarified, and then, RUL prediction method of the energy storage components represented by lithium-ion batteries are summarized. Next, the application of the data–model fusion-based method based on kalman filter and particle filter to RUL prediction of lithium-ion batteries are analyzed. The problems faced by RUL prediction of the energy storage components and the future research outlook are discussed.
An Assessment of the Operation and Emission Characteristics of a Diesel Engine Powered by a New Biofuel Prepared Using In Situ Transesterification of a Dry Spirogyra Algae–Jatropha Powder Mixture
Authors: Jain Siddharth
Published: February 2023

The present work deals with the optimization of the process parameters of in situ transesterification of dry spirogyra Algae–Jatropha powder along with engine efficiency and combustion analysis of the prepared biofuel. Three operational parameters, namely catalyst concentration (0–5 wt.%), methanol to dry algae–Jatropha curcas powder (v/v) (20–60%), and reaction time (60–180 min) at a constant reaction temperature of 50 °C, were selected. Response surface methodology (RSM) was used to design the experiments. The maximum biodiesel yield of 88.5% was obtained under the optimized conditions of a catalyst concentration of 3.396% (w/w), methanol/oil ratio of 19.86, and reaction time of 180 min. At varying loads, the performance and emissions of a diesel engine linked to a power source and fueled with various biodiesel mixes (Diesel, B5, B10, and B20) were tested. It was found that BSFC decreased as the applied load increased for all of the evaluated fuels. All of the biodiesel blends had greater BSFC than the diesel fuel. However, a substantial decrease in the emissions, including hydrocarbon (HC) and carbon monoxide (CO), was observed with the increase in NOx emissions. This method of preparing biodiesel will be beneficial in order to cater to the needs of the transportation sector because it has a lower energy consumption and less engine emissions.
Modeling of Inrush Current Surges—LED Strip Drivers Case Study
Authors: Smugala Dariusz , Bonk Michal
Published: February 2023

This paper is an investigation into the high inrush current effect that emerges during the energizing of drivers of different circuit types loaded by light-emitting-diode (LED) strips. Two different driver circuit types were analyzed—a voltage stabilization (VS) circuit utilizing a Zener diode, and a current stabilization (CS) circuit type in the form of an integrated circuit (IC). Inrush current waveforms were calculated for drivers loaded by various numbers of LEDs. In the frame of the study, analysis executed for drivers energized by diverse input voltage RMS values at different switching-on phases was performed. The analysis is comprised of experimental verification of LT SPICE simulation results of current waveforms and calculations of current peak values appearing while switching on the supply voltage. The developed models for simulation needs were elaborated concerning precise reflection of the real current and voltage waveforms obtained during oscilloscopic registration of the switching-on process. The assumed simulation model parameters were mainly attuned in view of steady-state current simulations, and the implemented method of high-frequency (HF) transient modeling allowed for precise current waveform reflection. Finally, the utilization effectiveness of the proposed simple inrush current limiting circuit was estimated.
Research on the Effect of an Air-Blown Interrupting Gap to Reduce the Rate of Lightning Tripping
Authors: Li Hao , Wang Jufeng , Huang Ping , Guo Kezhu , Wang Yanlei
Published: February 2023

The air-blown interrupting gap protection method is a self-energy interrupting method based on the concept of suppressing arc building by frequency continuation. In order to verify the effect of an air-blown interrupting gap to reduce the rate of line lightning tripping, in this paper, the protection principle of the air-blown interrupter gap is first described, and the action process simulated by COMSOL Multiphysics simulation software. Next, a frequency renewal test circuit is built for the test. Then, an arc-building rate calculation model and a lightning trip rate calculation model under the condition of an air-blown interrupting gap are established, and, finally, a 10 kV overhead line in Yunnan is selected for the verification of the calculation example. The results show the following: a gas blowing arc gap can be effectively extinguished in about 2.5 ms frequency arc and with no re-ignition phenomenon. Before and after the installation of the gas blowing arc gap line, the arc rate changed from 37.27% to 4.1%, respectively, and the lightning trip rate changed from 8.64 times/(40 thunderstorm days—100 km) to 0.337 times/(40 thunderstorm days—100 km), respectively. The decline rate was more than 95%, and in actual operation, it reduced the lightning trip rate enough to achieve good results.
Cross-Well Lithology Identification Based on Wavelet Transform and Adversarial Learning
Authors: Sun Longxiang , Li Zerui , Li Kun , Liu Haining , Liu Ge , Lv Wenjun
Published: February 2023

For geological analysis tasks such as reservoir characterization and petroleum exploration, lithology identification is a crucial and foundational task. The logging lithology identification tasks at this stage generally build a lithology identification model, assuming that the logging data share an independent and identical distribution. This assumption, however, does not hold among various wells due to the variations in depositional conditions, logging apparatus, etc. In addition, the current lithology identification model does not fully integrate the geological knowledge, meaning that the model is not geologically reliable and easy to interpret. Therefore, we propose a cross-domain lithology identification method that incorporates geological information and domain adaptation. This method consists of designing a named UAFN structure to better extract the semantic (depth) features of logging curves, introducing geological information via wavelet transform to improve the model’s interpretability, and using dynamic adversarial domain adaptation to solve the data-drift issue cross-wells. The experimental results show that, by combining the geological information in wavelet coefficients with semantic information, more lithological features can be extracted in the logging curve. Moreover, the model performance is further improved by dynamic domain adaptation and wavelet transform. The addition of wavelet transform improved the model performance by an average of 6.25%, indicating the value of the stratigraphic information contained in the wavelet coefficients for lithology prediction.
Condition Monitoring Accuracy in Inverter-Driven Permanent Magnet Synchronous Machines Based on Motor Voltage Signature Analysis
Authors: Allafi Ibrahim M. , Foster Shanelle N.
Published: February 2023

Condition monitoring and preventative maintenance are essential for reliable and efficient operation of permanent magnet synchronous machines driven by inverters. There are two types of industrial inverter drives available: field oriented control and direct torque control. Their compensation nature and control structure are distinct and, therefore, the condition monitoring approach designed for the former control may not be applicable to the latter one. In this paper, we investigate the Motor Voltage Signature Analysis approach for both inverter drives under healthy and faulty conditions. Four typical fault conditions are addressed: turn-to-turn short circuit, high resistance contact, static eccentricity, and local demagnetization. High fidelity cosimulation is developed by coupling the finite element machine model with both control drives. The spectral elements of the commanded stator voltage are utilized as indicators for supervised classification to identify, categorize, and estimate the severity of faults. Linear discriminate analysis, k-nearest neighbor, and support vector machines are the classification techniques used. Results indicate that the condition monitoring based on the Motor Voltage Signature Analysis performs adequately in field oriented control. Nevertheless, the utilized monitoring scheme does not exhibit satisfactory performance in direct torque control owing to the nonlinear characteristics and tolerance nature of this drive.
Responses of Fine Root Traits and Soil Nitrogen to Fertilization Methods and Nitrogen Application Amounts in a Poplar Plantation
Authors: Yan Xiaoli , Dai Tengfei , Gao Yuan , Di Nan , Jia Liming
Published: February 2023

Inappropriate fertilization management practices have led to low timber production in intensive plantation systems in China. Thus, optimized conventional or advanced fertilization management practices are needed. We aimed to quantify whether optimized furrow fertilization (FF) is comparable to advanced drip fertigation (DF) and to make recommendations regarding fertilization management strategies for poplar plantations. A completely randomized block design experiment with two fertilization methods (DF and FF) and four N application amounts (F0: 0, F1: 68, F2: 113, and F3: 158 kg N·ha−1·yr−1) was carried out on a Populus × euramericana cv. ‘Guariento’ plantation. Fine root biomass density (FRBD), fine root length density (FRLD), specific root length (SRL), soil total nitrogen (STN), soil inorganic nitrogen (SIN), soil ammonium (NH4+-N) and nitrate nitrogen (NO3−-N) were measured. The productivity increment was calculated based on tree surveys. The results showed that FRBD and FRLD decreased with the soil depth, and more than 86% was distributed within the 40 cm soil depth. FRBD, FRLD, productivity increment and soil N increased with an increasing amount of N application. DF treatments achieved 117%, 94% and 10% higher FRBD, FRLD and productivity increments, respectively, than did FF treatments. The averages of STN, SIN, NH4+-N and NO3−-N under FF were higher than those under DF, leading to higher concentrations of residual NO3−-N in deep soil. Beneficial management practices for fine root growth were evaluated in the following order: water coupled with N > only N ≥ only water > control. FRBD was positively correlated with the productivity increment. Therefore, fine root extension to increase soil resource absorption yields greater productivity under DF treatments. Drip fertilization is recommended as a better fertilization method to greatly promote the growth of fine roots, as well as productivity and residual lower soil N for poplar plantations.
A Novel Wood Log Measurement Combined Mask R-CNN and Stereo Vision Camera
Authors: Yu Chunjiang , Sun Yongke , Cao Yong , He Jie , Fu Yixing , Zhou Xiaotao
Published: February 2023

Wood logs need to be measured for size when passing through customs to verify their quantity and volume. Due to the large number of wood logs needs through customs, a fast and accurate measurement method is required. The traditional log measurement methods are inefficient, have significant errors in determining the long and short diameters of the wood, and are difficult to achieve fast measurements in complex wood stacking environments. We use a Mask R-CNN instance segmentation model to detect the contour of the wood log and employ a binocular stereo camera to measure the log diameter. A rotation search algorithm centered on the wood contour is proposed to find long and short diameters and to optimal log size according to the Chinese standard. The experiments show that the Mask R-CNN we trained obtains 0.796 average precision and 0.943 IOUmask, and the recognition rate of wood log ends reaches 98.2%. The average error of the short diameter of the measurement results is 5.7 mm, the average error of the long diameter is 7.19 mm, and the average error of the diameter of the wood is 5.3 mm.
Estimating Stem Diameter Distributions with Airborne Laser Scanning Metrics and Derived Canopy Surface Texture Metrics
Authors: Gallagher-Duval Xavier , van Lier Olivier R. , Fournier Richard A.
Published: February 2023

This study aimed to determine the optimal approach for estimating stem diameter distributions (SDD) from airborne laser scanning (ALS) data using point cloud metrics (Mals), a canopy height model (CHM) texture metrics (Mtex), and a combination thereof (Mcomb). We developed area-based models (i) to classify SDD modality and (ii) predict SDD function parameters, which we tested for 5 modelling techniques. Our results demonstrated little variability in the performance of SDD modality classification models (mean overall accuracy: 72%; SD: 2%). Our best SDD function parameter models were generally fitted with Mcomb, with R2 improvements up to 0.25. We found the variable Correlation, originating from Mtex, to be the most important predictor within Mcomb. Trends in the performance of the predictor groups were mostly consistent across the modelling techniques within each parameter. Using an Error Index (EI), we determined that differentiating modality prior to estimating SDD improved the accuracy of estimates for bimodal plots (~12% decrease in EI), which was trivially not the case for unimodal plots (<1% increase in EI). We concluded that (i) CHM texture metrics can be used to improve the estimate of SDD parameters and that (ii) differentiating for modality prior to estimating SSD is especially beneficial in stands with bimodal SDD.
Forest Insurance for Natural Events: An Overview by Economists
Authors: Brunette Marielle , Couture Stéphane
Published: February 2023

Forest insurance exists for more than a century in lots of countries around the world. Currently, it is put forward as a recommended tool to finance resilience and adaptation towards climate change. However, little synthetic knowledge exists on forest insurance, although this seems to be a prerequisite for using insurance as an adaptation tool. This article aims at providing an overview of the current economics literature on the topic of forest insurance. More precisely, the objectives of this study are to carry out a review of the literature on this topic, to produce a bibliometric overview of knowledge on this issue, and thus to highlight scientific fronts. For that purpose, we propose a literature review. We collected 38 articles published in English between 1928 and 2021. We provide the following bibliometric information: journals, evolution over time of the publications, authors and co-citations network and analysis of the keywords. We also propose to synthesize the methods used, the various issues of interest, the risks considered and the countries where the studies were conducted. We show that an article on forest insurance has a high probability of being recent (after 2000) and of being published in the journal Forest Policy and Economics. In addition, it is highly probable that it will identify some determinants of insurance demand and that it will deal with fire risk in the U.S. or storm risk in Europe. Noting a small scientific community and a low number of publications, we identified seven fronts of science related to methods and data, new risks and uncertainties, public policies and forest insurance, and openness and the international dimension.
Effects of Bee Pollen Derived from Acer mono Maxim. or Phellodendron amurense Rupr. on the Lipid Composition of Royal Jelly Secreted by Honeybees
Authors: Zhou Enning , Wang Qi , Li Xiangxin , Zhu Dan , Niu Qingsheng , Li Qiangqiang , Wu Liming
Published: February 2023

Royal jelly is a specific product secreted by honeybees, and has been sought after to maintain health because of its valuable bioactive substances, e.g., lipids and vitamins. The lipids in royal jelly come from the bee pollen consumed by honeybees, and different plant source of bee pollen affects the lipid composition of royal jelly. However, the effect of bee pollen consumption on the lipid composition of royal jelly remains unclear. Herein, we examined the influence of two factors on the lipid composition of royal jelly: first, two plant sources of bee pollen, i.e., Acer mono Maxim. (BP-Am) and Phellodendron amurense Rupr. (BP-Pa); secondly, different feeding times. Lipidomic analyses were conducted on the royal jelly produced by honeybees fed BP-Am or BP-Pa using ultra-high performance liquid chromatography (UPLC)-Q-Exactive Orbitrap mass spectrometry. The results showed that the phospholipid and fatty acid contents differed in royal jelly produced by honeybees fed BP-Am compared to those fed BP-Pa. There were also differences between timepoints, with many lipid compounds decreasing in abundance soon after single-pollen feeding began, slowly increasing over time, then decreasing again after 30 days of single-pollen feeding. The single bee pollen diet destroyed the nutritional balance of bee colonies and affected the development of hypopharyngeal and maxillary glands, resulting in differences in royal jelly quality. This study provides guidance for optimal selection of honeybee feed for the production of high-quality royal jelly.
Effects of Test Location and Sample Number on the Liking Ratings of Almond Beverage and Vegan Ramen Products
Authors: Yoon Jae-Yeon , Kwak Han-Sub , Kim Mi-Ran , Chung Seo-Jin
Published: February 2023

The present study investigated the effects of the evaluation environment and sample number on liking ratings within the same testing session. It comprised two experiments that determined consumer taste ratings of the following food products: (1) almond beverage and (2) vegan ramen, as rated by 322 and 287 Korean consumers, respectively. Consumers tasted each food product under either laboratory or home-used test conditions. Additionally, three levels of sample numbers were established for evaluation (almond beverage test: 1, 2, and 4; vegan ramen test: 1, 3, and 5) in each test condition. A target sample was selected for each of the two food products to directly ascertain the effects of the evaluation environment and sample number on the liking ratings. The results revealed that during the same evaluation session, the sample number affected the liking ratings of the target sample more than the testing location. Moreover, the sample number effect was product item dependent, that is, no significant change was noted in the liking ratings of the target almond beverage sample according to sample number, whereas significant differences were observed in the liking ratings of the target vegan ramen sample. Furthermore, the sample number effect was more prominent under laboratory test conditions than under home-used test conditions probably due to the serving order effect driven by hedonic contrast, carry over effect, and sensory specific satiety. The findings demonstrate that home-used tests should be recommended over laboratory tests when measuring the liking of a small number of multiple sample food items with high flavor complexity.
Preparation of Colorimetric Sensor Array System to Evaluate the Effects of Alginate Edible Coating on Boiled-Dried Anchovy
Authors: Cho Byungchan , Charoensri Korakot , Doh Hansol , Park Hyun jin
Published: February 2023

The colorimetric sensor array (CSA) is a simple, rapid, and cost-effective system widely used in food science to assess food quality by identifying undesirable volatile organic compounds. As a prospective alternative to conventional techniques such as total volatile basic nitrogen, peroxide value, and thiobarbituric acid reactive substance analysis, the CSA system has garnered significant attention. This study evaluated the quality of edible-coated food products using both conventional and CSA methods in order to demonstrate that the CSA approach is a feasible alternative to conventional methods. Boiled-dried anchovies (BDA) were selected as the model food product, and the sample’s quality was assessed as a function of storage temperature and incubation period using conventional techniques and the CSA system. The surface of BDA was coated with an edible alginate film to form the surface-modified food product. The conventional methods revealed that an increase in storage temperature and incubation time accelerated the lipid oxidation process, with the uncoated BDA undergoing lipid oxidation at a faster rate than the coated BDA. Utilizing multivariate statistical analysis, the CSA approach essentially yielded the same results. In addition, the partial least square regression technique revealed a strong correlation between the CSA system and conventional methods, indicating that the CSA system may be a feasible alternative to existing methods for evaluating the quality of food products with surface modifications.
Extraction, Structural Characterization, Biological Functions, and Application of Rice Bran Polysaccharides: A Review
Authors: Chen Bingjie , Qiao Yongjin , Wang Xiao , Zhang Yi , Fu Linglin
Published: February 2023

Rice bran is a “treasure house of natural nutrition”. Even so, utilization of rice bran is often ignored, and this has resulted in the wastage of nutrients. Polysaccharides are one of the active substances in rice bran that have gained widespread attention for their antioxidant, antitumor, immune-enhancing, antibacterial, and hypoglycemic properties. This review summarizes the extraction methods, structural characterization, bioactivity, and application of rice bran polysaccharides that have been developed and studied in recent years, laying a foundation for its development into foods and medicines. In addition, we also discuss the prospects for future research on rice bran polysaccharides.
Development of Fermented Rice Water to Improve the Quality of Garaetteok, a Traditional Korean Rice Cake
Authors: Lee Eun-Hyeong , Jeong Hyun-Mo , Kim Eun-A , Lee Ye-Rim , Shim Jae-Hoon
Published: February 2023

In the rice processing industry, wastewater is an inevitable by-product of rice washing. To increase the utilization of washed rice water (WRW), seven types of fermented washed rice water (FWRW) were prepared using lactic acid bacteria (LAB) and carbohydrate hydrolase. The total concentration of small maltooligosaccharides (MOSs) in the amyloglucosidase (AMG) treatment groups was about ten times higher than in the untreated groups. After 6 h of fermentation, six of the seven FWRW samples reached a pH of 4 due to the increased concentration of organic acids and could, therefore, be used as food acidity regulators. To confirm the applicability of FWRW, the traditional Korean rice cake garaetteok was prepared with FWRW and stored at 4 °C for 5 days. A texture profile analysis (TPA) revealed that the hardness of garaetteok treated with FWRW was significantly lower than that of untreated garaetteok following storage. Differential scanning calorimetry (DSC) showed that FWRW retarded the retrogradation of garaetteok during storage. The addition of FWRW using Lactobacillus reuteri with an AMG group was particularly effective for inhibiting microbial activity in garaetteok during storage. These results suggest that FWRW using AMG-added L. reuteri can be used as a novel food additive for improving the quality of traditional Korean starch foods and could also reduce the volume of waste WRW.