Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal sizing of fuel cell electric vehicle powertrain considering multiple objectives

In this paper, we propose a multi-objective optimization problem of power components parameters for a pre-defined driving cycle and energy management algorithm regarding fuel economy, system durability, power components cost and battery volume. We eliminate the parameter vectors that can’t meet the vehicle power demand. Then applying the algorithm to the model of a fuel cell city bus gives the opportunity to choose the best values for battery capacity Qbat and the maximal net output power of the FCS Pfcsmax according to the contour diagram. Simulation results show that for a "New Europe Driving Cycle", a battery capacity of 96 Ah and an FCS maximal net output power of 100 kW are optimal for the fuel economy, system durability, power components cost and battery volume limitation of a fuel cell city bus.
Gripping Force Modeling of a Variable Inclined Air Pillow Soft Pneumatic Actuator

Soft pneumatic actuators grasping tasks is one of the essential rules in robot manipulation methods. The grasping forces can be adapted to handle delicate and hard objects without leaving any damages on the object surfaces. This paper investigates the influence of the inclination angle of the soft pneumatic actuator (SPA) on its gripping force at its end tip. A range of inclination angles for SPA is analyzed using Finite Element Analysis (FEA) to estimate the gripping force at the end tip regarding SPA inner faces pressure. FEA study is conducted based on Hyperelastic material modeling employing non-linear analysis. Experimental validation is performed for three different air pillow inclined angle of SPAs fabricated by Fused Deposition Modeling (FDM) 3D printing. Experimental and FEA simulated data are compared to each other. Finally, a regression-based mathematical model is developed to obtain a direct correlation between the SPAs gripping force behavior concerning the inclination angle and applied pressure based on FEA data.
Suitability of AFM cantilevers as wideband acoustic point receivers for the characterization of acoustic sources

In Subsurface Scanning Probe Microscopy (SSPM), Atomic Force Microscopy (AFM) is combined with ultrasound. The AFM cantilever is used as a receiver. At low frequencies (O(MHz)) the method can be used to measure the stiffness contrast in a sample and at high frequencies (O(GHz)) to measure scattering based contrast. Both variants use modulated excitation signals in combination with the nonlinear tip-sample interaction to downmix the sample top surface displacement close to the resonance frequency of the cantilever. This concept has three advantages: 1) the resonance of the cantilever is used to boost the sensitivity, 2) the downmixing allows the system to record signals in an extremely wide range of carrier frequencies (kHz - GHz), and 3) the cantilever tip-sample contact diameter is usually much smaller than 10 nm. The latter implies that spatial averaging effects are negligible up to 100 GHz. Said advantages mean that AFM cantilevers could be useful to characterize acoustic sources. Here, we investigate the suitability of AFM cantilevers as acoustic point receivers to characterize acoustic sources. Investigation with an AFM setup and two sources - one O(MHz) and one O(GHz) - show a good match between the measured response, KLM simulations and the transducers bandwidth, which are promising results for the use of a cantilever as a wideband receiver for high resolution acoustic (transducer) characterization.
Adaptive Stator Current Disturbance Observer based on the Predictive Current Control for PMSM

This paper presents an adaptive stator current disturbance observer based on the model predictive current control (PCC) algorithm. For the various disturbances, the disturbance observer (DO) algorithm estimates the lumped disturbance and makes a feedforward compensation to correct the output. However, the discrete characteristic of current prediction error is neglected in conventional stator current disturbance observer (SCDO). Based on the detailed analysis of the stator current prediction error, the proposed method structures a parallel strategy to observe the discrete disturbance related to each voltage vector separately, and designs an adaptive compensation strategy to eliminate the prediction error. The effectiveness of proposed method is verified using experimental tests in the permanent-magnet synchronous motor (PMSM) system.
Evidence Theory Based Data Fusion for Centralized Cooperative Spectrum Sensing in Mobile Ad-hoc Networks

This paper presents an efficient method of cooperative spectrum sensing for mobile radio networks with the hierarchical management structure. The network is divided into clusters controlled by cluster heads. All cluster members perform spectrum sensing using energy detectors. Sensing results are then transmitted to fusion centers in cluster heads, where they are combined using evidence theory based on Dempster-Shafer theory. The achieved simulation results show that the proposed method outperforms commonly used rules and gives a high probability of proper signal detection and low probability of false alarms.
A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm

As a renewable and efficient power source, proton exchange membrane fuel cells (PEMFCs) are receiving more and more attention from the world, but it still has shortcomings with poor durability and insufficient reliability, so the purpose of this paper is to effectively improve the reliability and durability of PEMFCs by a data-driven fault diagnosis method. In this method, principal component analysis (PCA) is first adopted to reduce the dimensionality of fault data. Then, a classification method named eXtreme Gradient Boosting (XGBoost) which based on boosting algorithm is used to classify these data. In the end, the experiment is proposed to verify the diagnostic performance of this method. The result shows that the method can effectively identify four healthy states of membrane drying failure, hydrogen leakage failure, normal state as well as unknown state, the diagnostic accuracy can reach 99.72%, and the diagnosis period is 0.17751 s, which is suitable for online implementation.
Analysis on the use of non-resonant inverters in single-phase induction heating applications

Induction heating plays an important role in industrial processes and is becoming increasingly important for household appliances such as induction cookers. Most commonly, series or parallel resonant circuits based on IGBT technology are used due to their low cost. In contrast to these, non-resonant converter types are described in this paper for the first time regarding their use in single-phase induction heating applications up to 2 kW. The analysis is based on simulation and measurement results. The proposed non-resonant converter designs offer a very small size and allow full control of the output power. Different pulse pattern for controlling the induction power are investigated. It is found that the full-bridge inverter using block or PSM pulse pattern delivers the best performance and enough power capability. For the test system, the measured average electrical efficiency is 96.8 % over the input power range of 500 W to 1750 W.
Parallelization of Global Sequence Alignment on Graphics Processing Unit

In this paper, we propose a work distribution strategy to implement DNA global sequence alignment (GSA). The proposed strategy is implemented on Compute Unified Device Architecture (CUDA) for large biological sequences. The main objective of this work is to minimize the execution time required for DNA global alignment of large biological sequences. The proposed approach covers both the memory optimizations and minimization of execution time. We considered the biological sequences of different size to fit into the global memory under CUDA. The efficient use of global memory and memory optimization dominate the results of execution time. The results demonstrate the significant higher speedup using CUDA.
Hybrid models based on mode decomposition and recurrent neural networks for streamflow forecasting in the Chira river in Peru

Streamflow forecasting at short horizons is vital for the management of water resources. However, the streamflow behaviour is non-linear and not stationary. To address this challenge, artificial intelligence techniques have been used to increase accuracy. Additionally, signal decomposition techniques such as empirical mode decomposition, ensemble empirical mode decomposition, and variational mode decomposition, have been applied in different fields as a pre-processing stage prior to modelling to improve forecasting. This study evaluates the effect of the aforementioned decomposition techniques used with a recurrent neural network called long short-term memory to increase the precision of the daily prediction of the Chira river streamflow in northern Peru, characterized by a special dynamic due to a strong seasonal behavior and the influence of the El Niño–Southern Oscillation (ENSO).
Ambulance Dispatch via Deep Reinforcement Learning

In this paper, we solve the ambulance dispatch problem with a reinforcement learning oriented strategy. The ambulance dispatch problem is defined as deciding which ambulance to pick up which patient. Traditional studies on ambulance dispatch mainly focus on predefined protocols and are verified on simple simulation data, which are not flexible enough when facing the dynamically changing real-world cases. In this paper, we propose an efficient ambulance dispatch method based on the reinforcement learning framework, i.e., Multi-Agent Q-Network with Experience Replay(MAQR). Specifically, we firstly reformulate the ambulance dispatch problem with a multi-agent reinforcement learning framework, and then design the state, action, and reward function correspondingly for the framework. Thirdly, we design a simulator that controls ambulance status, generates patient requests and interacts with ambulances. Finally, we design extensive experiments to demonstrate the superiority of the proposed method.
An Adaptive GaoFen-3 SAR Wind Field Retrieval Algorithm Based on Information Entropy

Chinese GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery acquired in standard strip mode can capture streak features caused by wind over the sea surface, which are commonly known as wind streaks. To retrieve the sea surface wind field from a GF-3 SAR image, an adaptive wind retrieval algorithm based on information entropy is proposed in this paper. This algorithm can adaptively extract the optimal wavenumber range of each subimage based on maximum information entropy. After all subimages are processed for possible wind streak direction solutions, a circle median filter is used to determine the unique wind streak direction solution for each subimage. Twelve GF-3 standard strip SAR images are inverted to validate the proposed algorithm. The experiments show that when the GF-3 image data are compared to the European Centre for Medium-Range Weather Forecasts reanalysis wind field data, the root mean square error of wind speed and direction retrieved by the algorithm are 1.29 m/s and 15.33°, respectively, and when they are compared to the National Data Buoy Center buoy wind data, the mean absolute deviation errors of wind speed and direction are 0.57 m/s and 9.95°, respectively. These comparisons demonstrate that the proposed algorithm can effectively retrieve wind field information from GF-3 SAR images.
Single tree aboveground biomass models for native birch in Iceland

In Iceland, mountain birch dominates indigenous woodlands and scrub communities. For use in inventories of the natural birch population, we derived single parameter aboveground biomass functions from a stratified random sample encompassing the entire native birch population. We evaluated the accuracy of these models on independent data from the same population and used regressions of log-transformed predicted versus observed values and compared slope and intercept parameters against the 1:1 line. We propose that the level of accuracy of allometric models might be quantified by the size of Theil’s random error component (Ue) and the normality of residual variances might be a decisive test of acceptable functions. The commonly used allometric power function without intercept proved highly accurate for diameters at ground level but was biased for diameters measured at 0.5 m up the stem. We compared both non-linear regressions and log-transformed linear regression techniques. The latter produced more accurate models especially for applications to small diameter trees. Power functions with intercept and diameters measured 0.5 m above ground produced accurate estimates, except for trees with diameters less than 50 mm. We suggest allometric models for general use in Iceland for inventories of native birch woodlands and scrub.
Leaning Impedance Distribution of Object from Images Using Fully Convolutional Neural Networks

Robots have been introduced into industrial factory automation. It is necessary to consider interactions between the robots and environments to expand executable tasks of the robots. In the interaction, impedance is an essential factor for the robot to contact with the environment, whereas the impedance is unobservable without contact. In this study, we introduce a concept of affordance for impedance estimation without contact. We propose the impedance estimation method from an RGB image input using deep learning. In this paper, we show that the proposed method can extract pixels corresponding to sponges with its impedance composed of stiffness and viscosity, including the distribution of the impedance. We conducted the experiments to validate the proposed method.
A Novel Method for the Fast Calculation of the Characteristics of PM Synchronous Machines

This paper presents a novel approach for reducing the computation time for the determination of the characteristics of PM synchronous machines. The proposed method makes use of the a priori knowledge of the spatial harmonic spectrum of the air-gap field, which determines the harmonic spectrum of parameters like flux linkages, inductances or torque. When the harmonics with the most influence on the operating behaviour are known, systems of equations can be formulated and only a rather small number of magnetostatic FEM calculations needs to be performed to solve the systems. Hence, the calculation time for a simulation can be lowered drastically without a loss in accuracy.
Health supplement products use among patients with chronic illnesses: A multicenter study in rural areas of Sarawak, Malaysia

Health supplement products (HSPs) are gaining public acceptance in Malaysia. However, little is known about the practices and perceptions of HSPs among patients in rural areas. This study aims to explore the prevalence of concomitant use of HSPs with prescribed medicine, practices, perceptions, and factors influencing the consumption of HSPs among patients with chronic illnesses in rural areas of Sarawak, Malaysia. A multicenter cross-sectional study was conducted in the outpatient pharmacy department of seven districts’ hospitals from June to August 2018. A data collection form and questionnaire were developed and pilot tested prior to the data collection. Bivariate logistic regression was used to assess the association between patients’ sociodemographic characteristics and the use of HSPs. A total of 350 patients participated in the study. Only 84 (24.0%) patients were active HSP users and 54 (64.3%) of them always used HSPs concomitantly with prescribed medicines. Approximately half (52.0%) of the patients never consult healthcare providers about the use of HSPs. However, the majority of them expressed agreement upon the necessity to consult healthcare providers prior to HSP use (82.0%) or during the concomitant use of HSPs with prescribed medicines (80.3%). Patients with monthly household incomes of more than RM 2000 (USD 480.59) (n = 84) were 4.23 times significantly (p < 0.001) more likely to consume HSPs than those with an income of RM 830 (USD 199.44) or less (n = 173). In conclusion, the concomitantly used HSPs with prescribed medicines is prevalent among patients and educational interventions are needed to enhance the safe use of such products.
An Integrated Model Based on O-GAN and Density Estimation for Anomaly Detection

Anomaly detection is a classic and crucial problem in the field of artificial intelligence, which aims to find instances that deviate so much from the main distribution of data or differ from known instances. This paper explores how to combine advanced deep learning techniques with traditional probabilistic statistical methods for anomaly detection. We propose a very effective and concise semi-supervised anomaly detection method named “ORGAN-KDE” based on the orthogonal generative adversarial network (O-GAN) and kernel density estimation. In the training phase, we use the encoder of O-GAN to learn the latent representation of normal data, namely the code of normal data, and then use the kernel density estimation to solve the probability density function of code. The code of normal sample obtained through the trained encoder can get a larger probability value when passing through the trained kernel density estimator, while the code of anomalous sample can get a smaller probability value, so as to achieve the purpose of anomaly detection. Compared with other anomaly detection methods based on GAN, our method has a very simple network structure, and experiments have proved that it performs well on both structured datasets and image datasets.
Radio Distance Measurements Errors Estimation in an Indoor Environment

In the article the analysis of the distance errors observed in the radio distance measurements (RDMs) is presented. Distance measurements were performed by using the DWM1000 modules, which are wireless transceiver modules, compliant with IEEE 802.15. 4 UWB interface, which enable estimation of the object position in the real time. The data were collected during the static and dynamic measurement campaigns, which were carried out in an indoor environment for the (Line-Of-Sight) and the NLOS (Non-Line-Of-Sight) conditions. In the obtained results, a strong relation between the proximity of the nodes and the precision of the RDMs was shown. In a distance below 5 m the distance errors achieved values in the range from 11 cm to 40 cm. Additionally, the measurements results showed that the distance errors are LOS/NLOS condition dependent.
Analog Vector-Matrix Multiplier Based on Programmable Current Mirrors for Neural Network Integrated Circuits

We propose a CMOS Analog Vector-Matrix Multiplier for Deep Neural Networks, implemented in a standard single-poly 180 nm CMOS technology. The learning weights are stored in analog floating-gate memory cells embedded in current mirrors implementing the multiplication operations. We experimentally verify the analog storage capability of designed single-poly floating-gate cells, the accuracy of the multiplying function of proposed tunable current mirrors, and the effective number of bits of the analog operation. We perform system-level simulations to show that an analog deep neural network based on the proposed vector-matrix multiplier can achieve an inference accuracy comparable to digital solutions with an energy efficiency of 26.4 TOPs/J, a layer latency close to $100~\mu \text{s}$ and an intrinsically high degree of parallelism. Our proposed design has also a cost advantage, considering that it can be implemented in a standard single-poly CMOS process flow.
Finite Control Set Model Predictive Control of Isolated DC/DC Modular Multilevel Converter

Isolated DC/DC modular multilevel converters (MMC) consisting of two MMCs isolated by a transformer are one of the converter topologies being considered used for DC/DC conversion in HVDC transmission. Two level modulation can be used to obtain different voltage levels by inserting/bypassing a specific number of sub-modules (SM) in the converter circuit. However, it does not take into account the SM’s voltage when elevation factors need to be readjusted, which results in either higher or lower output voltage than the reference. This paper proposes a finite control set model predictive control (FCS-MPC) for output voltage regulation to the reference value. A cost function comprising the output voltages is defined and a mathematical equation is formed to calculate the one step ahead value of output voltage. The number of SMs which minimize the cost function is applied to the converter circuit. Voltage balancing amongst the sub-module cells has been achieved by using a sorting algorithm. The performance of the proposed control strategy is evaluated and verified.
Forming an Annular Array from 2D Capacitive Micromachined Ultrasonic Transducer (CMUT) Array Elements by Using a Metal Redistribution Layer

Different array geometries are desired for a variety of applications in diagnostic imaging as well as therapeutic interventions. One of the advantages of CMUT technology is the ability to develop transducers with complex geometries for diverse applications. In this paper, using a sacrificial release-based fabrication process, we implemented 8-concentric rings to form an annular array by connecting elements in a $32\times 32$ 2D CMUT array canvas. The $32\times 32$-element 2D arrays were developed on a glass substrate to form the 2D array canvas, and with an additional metal layer (Metal-3 layer), we formed an 8-element annular array. Preliminary results show the successful development of the fabrication process. This concept offers the advantages of developing different CMUT geometries on a glass substrate by using a 2D CMUT array canvas.
A Survey of Research on Lipreading Technology

Although automatic speech recognition (ASR) technology is mature, there are still some unsolved problems, such as how to accurately identify what the speaker is saying in a noisy environment. Lipreading is a visual speech recognition technology that recognizes the speech content based on the motion characteristics of the speaker’s lips without speech signals. Therefore, lipreading can detect the speaker’s content in a noisy environment, even without a voice signal. This article summarizes the main research from traditional methods to deep learning methods on lipreading. Traditional lipreading methods are mainly discussed from three aspects: lip detection and extraction, lip feature extraction, and classification. Traditional feature extraction methods focus on handmade features, which are, however, not very reliable under unconstrained conditions. In recent years, traditional lipreading methods have been gradually replaced by deep learning methods. The advantage of deep learning methods is that they can learn the best features from large databases. This article analyzes typical deep learning methods in detail according to their structural characteristics, and lists existing lipreading databases, including their detailed information and the methods applied to these databases. Finally, the problems and challenges of current lipreading methods are discussed, and the future research direction has prospected.