Miracle-dz/UniFAR-FTUs huggingface dataset:
A Load-Independent Multi-Output Wireless Power Transfer System Based on LCC-S Cascaded TResonant Circuit

In order to solve the practical application of the need for a wireless power transfer system to provide power to multiple loads, a new topology of constant-current multi-output wireless power transfer system is designed by connecting multiple T-type resonant circuits in parallel at the common port of the receiver by using T-type circuit characteristics. Simulation and analysis of the T-type multi-output wireless power transfer system are carried out and our results show that the proposed T-type resonant constant-current multi-output wireless power transfer system has the following characteristics which read (1) a load-independent constant-current output can be obtained due to the characteristics of the T-resonant circuit, (2) the entire system is purely resistive, which means that the inverter output voltage and output current can achieve zero phase angle (ZPA), (3) the current of the different output channels can be adjusted by configuring the component parameters, and (4) each output channel is independent of each other, when one of the output channels is disturbed, the other output channels are not affected. Our analysis indicates the feasibility and validity of the proposed wireless power transfer system.
Nonsmooth analysis on stochastic controls: A survey

A survey of recent results on the maximum principle, dynamic programming and their connection in stochastic controls via nonsmooth analysis is presented. The emphasis is placed upon the viscosity solution approach and the particular role of adjoint equations. Some results on the relationship between the viscosity solution and F. H. Clarke's (1983) generalized gradient are considered. <<ETX>>
An Intelligent Music Genre Classification Method with Feature Extraction based on Deep Learning Techniques

The music genre is a standard category that identifies some piece of music that belongs to a shared set of conventions. The physical musical albums are further replaced by digital music due to the varied interest. In order to enhance the efficiency and accuracy in digital music genre, deep learning technique is used. Deep learning is a type of machine learning and artificial intelligence which makes the process faster and earlier through training and learning process. This is an unsupervised learning technique that uses the way of automatic predictive analysis. It enhances to learn high level features from data in a sequential manner.
Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning

We present a systematic MEMS structural design approach via a "trial-and-error" learning process by using the deep reinforcement learning framework. This scheme incorporates the feedback from each "trial" to obtain sophisticated strategies for MEMS design optimizations. Disk-shaped MEMS resonators are selected as case studies and three remarkable advancements have been realized: 1) accurate overall performance predictions (97.9%) via supervised learning models; 2) efficient MEMS structural optimizations to guarantee targeted structural properties with an excellent generation accuracy of 97.7%; and 3) superior design explorations to achieve one order of magnitude performance enhancement than the training dataset. As such, the proposed scheme could facilitate a wide spectrum of MEMS applications with this data-driven inverse design methodology.
Stock Market Prediction using Machine Learning Techniques: Literature Review Analysis

Stock market prediction is an attractive research area for many researchers. Accurate predictions of stock prices enable investors to make more informed decisions about buying and selling stocks at the right time. In the past years, many researchers examined the predictability of stock prices utilizing machine learning algorithms. The primary objective of this study is to highlight the proper machine learning techniques that can be used for predicting stock prices. The methodology used to achieve this objective is reviewing 12 research papers that surround the use of machine learning and deep learning algorithms to predict stock prices. To distinguish this study from the existing research, it focuses on two main sectors which are the banking and healthcare sectors rather than selecting random papers. The findings of this study indicate that long short-term memory and gated recurrent unit techniques provide the best results in most of the selected papers regardless of the sector they are being applied in. The number of examined papers and the number of studied sectors are the main limitations of the study.
A New Analytical Approach to Optimally Allocate D-STATCOMs in Electric Distribution Networks

Distribution static compensators (D-STATCOMs) can effectively compensate for reactive power and mitigate steady-state voltage sag in electric distribution network (EDN). The utility of the D-STATCOM can be maximized by accurate modelling and efficient application. This article proposes a new analytical method to optimally allocate D-STATCOM using a new model and to maximize profit for distribution network operators (DNOs). The model of a D-STATCOM in power control mode is first curated to offer reactive power compensation for all loads connected to the point of common coupling (PCC) and its downstream. Due to the high cost of D-STATCOM, the best allocation approach for D-STATCOM model is proposed to optimize the EDN’s technical performance while maximizing the profits earned by the DNOs. On a 69-bus test system, the proposed methodology is assessed to demonstrate that the modeling and allocation procedures are both economically viable and effective at improving the performance of EDN.
Research on Identification Algorithm of Pollutant Excessive Emission Users Based on Power Big Data and Artificial Intelligence

With the rapid development of industrial technology, environmental pollution has attracted social attention. Traditional pollution prevention and control work mainly depends on on-site inspection, treatment and prevention, with high human and material costs and low efficiency. In order to solve this problem, we propose a scheme to identify pollutant excessive emission users using power big data. The algorithm combining machine learning and deep learning is exploited to deeply excavate the characteristic data such as daily energy consumption data and peak valley characteristics of users in recent one year, so as to identify and screen out the users with pollutant excessive emission behavior. Specifically, we first preprocesses different types of data, uses feature engineering to divide the data into different subsets, selects features from classification variables such as user information through machine learning algorithm, extracts features from energy consumption data through deep learning algorithm, and finally fuses different algorithms to generate the final user identification result of pollutant excessive emission. The scheme is tested on the test set and shows high prediction accuracy and good generalization ability.
Evaluation of Local and Internal Elasticity of Hydrogel Materials by Using Light-Driven Gel Actuator

We propose a method for evaluating local and internal elasticity of hydrogel materials. In this method, we deform inside of a sample by using a light-driven gel actuator and evaluate internal elasticity from strain of the material. Since the actuator can be driven by irradiating light through a microscope without wiring, this method enables us to evaluate local and internal elasticity which is difficult to be measured with conventional methods. In this study, we compared strains of two samples with different elasticities. It was observed that strain of a softer sample was larger than that of a harder sample. Furthermore, we can estimate the harder sample is approximately 1.3 times harder than the softer sample. Therefore, we succeeded in evaluating local and internal elasticities of samples with the proposed method.
A Study of Mental Health Self-Monitoring Based on the Combination of BERT and Low-Code Platform

This paper proposes a technical route for mental health self-monitoring based on BERT. This route can facilitate models that achieve good results on many tasks of natural language processing and can excellent in analyzing the emotions of the recorders. At the same time, the low-code platform, as an auxiliary system tool for software engineering, is able to deploy some machine learning tasks, including data preparation, storage, model building, etc., therefore, it used in this experiment to assist language models for sentiment analysis. With the combination of the two techniques, the accuracy of this technological route to facilitate sentiment analysis can reach up to 88.32%. And by reminding the changes in the emotions of the recorder, it can initially achieve the purpose of achieving mental health self-monitoring.
Piezoresistive Pressure Sensor with Monolithically Integrated Amplifier Based on Metal-Oxide Transistors

Presently described is a low-temperature technology for the implementation of circuits based on indium-gallium-zinc oxide (IGZO) thin-film transistors (TFTs) and their post-MEMS, monolithic integration with a micro-fabricated sensor. The feasibility of the technology is demonstrated with the design, implementation, and characterization of a bulk-type, dual-cavity piezoresistive pressure sensor integrated with a differential amplifier consisting of the IGZO TFTs. The resulting sensor-amplifier system exhibits an increase in sensitivity by more than 20 times and stable performance up to a hydrostatic pressure loading of 100 MPa.
Unique Dispersion Relation for Plasmonic Photodetectors with Submicron Grating

We proposed a plasmonic photodetector with a submicron grating pitch for accurate and robust near-infrared reconstructive spectroscopy. Miniaturized spectrometers have been intensively studied, and one promising candidate is the use of wavelength-specific plasmonic photodetector, which presents a correspondence between the detectable wavelength and the angle of incidence under measurements. However, the method fails to accurately reconstruct the spectrum because multiple wavelengths to be detected correspond to one angle. We, therefore, propose a plasmonic photodetector that has a one-to-one correspondence by shrinking the grating pitch to submicron, which was ~3 µm in previous reports. We compared reconstructed spectra with those with conventional photodetectors in the near-infrared range from 1150 to 1500 nm and confirmed the effectiveness of this method.
Study on IOT based Smart Disease Detection Model on Machine Learning Techniques for Healthcare Applications

The wearable medical devices are integrated with the internet of things (IoT) for smart healthcare applications to improve the quality of service. The information regarding the patient’s details updated through the IoT devices helps to provide the medication based on the present condition and hence the severity can be reduced instantly. Machine learning (ML) is utilized for making better decisions through clustering and classification methods that efficiently deal with the quality of information. Hence, this research introduces an analysis of smart disease prediction techniques for healthcare applications. The analysis of diseases such as heart disease, Alzheimer’s disease, and diabetic retinopathy disease based on machine learning is employed by reviewing the conventional detection methods along with the achievements and research gaps. In addition, the analysis based on the performance metrics, dataset, and tools utilized is also devised for the development of the novel smart disease detection technique with more efficient accuracy of detection.
A CNN Architecture for Detection and Segmentation of Colorectal Polyps from CCE Images

Colon capsule endoscopy (CCE) as a novel 2D biomedical image modality based on visible light provides a higher perspective of the potential gastrointestinal lesions like polyps within the small and large intestines than the conventional colonoscopy. As the quality of images acquired via CCE imagery is low, so the artificial intelligence methods are proposed to help detect and localize polyps within an acceptable level of efficiency and performance. In this paper, a new deep neural network architecture known as AID-U-Net is proposed. AID-U-Net consists of two distinct types of paths: a) Two main contracting/expansive paths, and b) Two sub-contracting/expansive paths. The playing role of the main paths is to localize polyps as the target objectives in high resolution and multi-scale manner, while the two sub paths are responsible for preserving and conveying the information of low resolution and low-scale target objects. Furthermore, the proposed network architecture provides simplicity so that the model can be deployed for real time processing. AID-U-Net with an implementation of a VGG19 backbone shows better performance to detect polyps in CCE images in comparison with the other state-of-the-art U-Net models like conventional U-Net, U-Net++, and U-Net3+ with different pre-trained backbones like ImageNet, VGG19, ResNeXt50, Resnet50, InceptionV3 and InceptionResNetV2.
The Experience of Women With Opioid Use Disorder Accessing Methadone Treatment

Abstract The number of women experiencing opioid use disorder (OUD) in Canada has increased exponentially. In Canada, healthcare is socialized and free for all citizens and, often, medications like methadone are free as well, yet few individuals with OUD access treatment services. The purpose of this study was to describe the lived experiences of Canadian women with OUD who were receiving methadone treatment. Interpretive phenomenology was used to investigate the treatment experiences of seven women with OUD. The conceptual framework of self-care of chronic illness was used to examine this phenomenon. Data were analyzed using a seven-step process of interpretive phenomenological analysis. Four major themes emerged: learning how to be you again, reaching out for help, finding your way to methadone, and going down the path of methadone. Women's experiences were influenced by family, friends, and healthcare providers. Accessibility and self-determination were important factors in entering and sustaining treatment. This study contributes to the discipline of nursing by providing accurate information regarding women's experiences with OUD and uncovering practice changes that can attract and retain women in treatment.
Stabilization in probability of nonlinear stochastic systems with guaranteed cost

We deal with nonlinear dynamical systems, consisting of a linear nominal part perturbed by model uncertainties, nonlinearities and both additive and multiplicative random noise, modeled as a Wiener process. In particular, we study the problem of finding suitable measurement feedback control laws such that the resulting closed-loop system is stable in some probabilistic sense and a given cost functional is minimized. We give a Lyapunov-based separation result which splits the control design into a state feedback problem and a filtering problem.
Compact XFEL and AMO sciences: SACLA and SCSS

The concept, design and performance of Japan's compact free-electron laser (FEL) facilities, the SPring-8 Compact SASE Source test accelerator (SCSS) and SPring-8 Angstrom Compact free electron LAser (SACLA), and their applications in mainly atomic, molecular and optical science are reviewed. At SCSS, intense, ultrafast FEL pulses at extreme ultraviolet (EUV) wavelengths have been utilized for investigating various multi-photon processes in atoms, molecules and clusters by means of ion and electron spectroscopy. The quantum optical effect superfluorescence has been observed with EUV excitation. A pump–probe technique combining FEL pulses with near infrared laser pulses has been realized to study the ultrafast dynamics of atoms, molecules and clusters in the sub-picosecond regime. At SACLA, deep inner-shell multi-photon ionization by intense x-ray FEL pulses has been investigated. The development of seeded FEL sources for producing transversely and temporally coherent light, as well as the expected impact on advanced science are discussed.
Study of the electrochemical properties of magnetite, maghemite and hematite nanoparticles for their applications in lithium ion batteries

Iron oxide nanoparticles, including magnetite, maghemite and hematite, are promising electrode active materials for lithium ion batteries due to their low cost, high capacity and environmental friendliness. Though the electrochemical properties of each kind of iron oxide nanoparticles have been intensively studied, systematic comparison of the three kinds of iron oxides is hardly reported. This paper reports the study and comparison of the electrochemical properties of magnetite, maghemite and hematite nanoparticles with the same shape and size. In this work, hematite and maghemite nanoparticles were obtained from commercial magnetite nanoparticles by thermal treatments at different conditions. Their crystalline structures were characterized by X-ray diffraction (XRD), their magnetic properties were measured by a vibration sample magnetometer (VSM), and their particle morphologies were analyzed by scanning electron microscopy (SEM). Composite electrodes were made from iron oxide nanoparticles with carbon black as the conducting material and PVDF as the binding material (iron oxide : carbon black : PVDF = 70 : 15 : 15). Prototype lithium ion batteries (CR2032 button cells) were assembled with iron oxide composite electrodes as cathodes, metal lithium as anodes, and Celgard 2400 porous membrane as separators. The impedance and discharge-charge behaviors were characterized by a Solartron electrochemical workstation and an Arbin battery tester, respectively. It was found that at the same shape and size, hematite nanoparticles has higher specific discharge and charge capacities than magnetite and maghemite nanoparticles.
Surface Crack Detection using Deep Convolutional Neural Network in Concrete Structures

Regular safety inspections of concrete and steel structures during their serviceability are essential since they directly affect the reliability and structural health. Early detection of cracks helps prevent further damage. Traditional methods involve the detection of cracks by human visual inspection. However, it is difficult to visually find cracks and other defects for extremely large structures because of time and cost constraints. Therefore, the development of smart inspection systems has been given utmost importance. We provide a deep convolutional neural network (DCNN) with transfer learning (TF) technique for crack detection. To reduce false detection rates, the images used to train in the TF technique come from two different datasets (CCIC and SDNET). Moreover, the designed CNN is trained on 3200 images of $256 \times 256$ pixel resolutions. Different deep learning networks are considered and the experiments on test images show that the accuracy of the damage detection is more than 99%. Results illustrate the viability of the suggested approach for crack observation and classification.
Facial Expression Recognition Robust to Occlusion and to Intra-Similarity Problem Using Relevant Subsampling

This paper proposes facial expression recognition (FER) with the wild data set. In particular, this paper chiefly deals with two issues, occlusion and intra-similarity problems. The attention mechanism enables one to use the most relevant areas of facial images for specific expressions, and the triplet loss function solves the intra-similarity problem that sometimes fails to aggregate the same expression from different faces and vice versa. The proposed approach for the FER is robust to occlusion, and it uses a spatial transformer network (STN) with an attention mechanism to utilize specific facial region that dominantly contributes (or that is the most relevant) to particular facial expressions, e.g., anger, contempt, disgust, fear, joy, sadness, and surprise. In addition, the STN model is connected to the triplet loss function to improve the recognition rate which outperforms the existing approaches that employ cross-entropy or other approaches using only deep neural networks or classical methods. The triplet loss module alleviates limitations of the intra-similarity problem, leading to further improvement of the classification. Experimental results are provided to substantiate the proposed approach for FER, and the result outperforms the recognition rate in more practical cases, e.g., occlusion. The quantitative result provides FER results with more than 2.09% higher accuracy compared to the existing FER results in CK+ data sets and 0.48% higher than the accuracy of the results with the modified ResNet model in the FER2013 data set.
A cell counting/sorting system incorporated with a microfabricated flow cytometer chip

Flow cytometry is a popular technique for counting and sorting individual cells. This study presents and demonstrates a new cell counting/sorting system integrated with several essential components including a micromachined flow cytometer chip device, an optical detection system and a data analysis and control system to achieve the functions of cell sample injection, optical signal detection and cell collection. By using MEMS technology, we have integrated several microfluidic components such as micro pneumatic pumps/valves onto a polymer-based chip device. Three pneumatic micropumps are used to provide the hydrodynamic driving force for both sample and sheath flows such that hydrodynamic flow focusing can be achieved, and a micro flow switch device comprising three pneumatic microvalves located downstream of the micro sample flow channel is used for cell collection. Cell samples of human lung cancer cells labelled with commercially available fluorescent dyes have been detected and collected successfully utilizing the developed device. The real-time image of dye-labelled cell samples being excited and detected can be monitored and observed through the LCD panel by a custom designed CCD/APD holder and moving stage. Finally, micro flow switch devices were used to successfully sort the cells into the desired outlet channel, and the counting results of the specific cell samples were monitored through the counting panel. The current study focuses on the setup of the overall system. The proposed flow cytometer system has several advantages such as portability, low cost and easy operation process. The size of the system is 37 cm × 16 cm × 18 cm and the weight is 3.5 kg. The error rate of counting and sorting was 1.5% and 2%, respectively. The sorting frequency of the microvalve device is calculated to be 120 cells min−1. The developed microfluidic chip device could be a promising tool for cell-based application fields such as profiling, counting and sorting.
Ultraviolet and optical variations in active galactic nuclei - The Seyfert 1 galaxy NGC 5548

The results of an extensive coordinated UV and optical spectroscopic study of the Seyfert 1 galaxy NGC 5548 are presented. The very broad component has a blueshift of 2000 km/s, possibly indicating obscuration in the innermost parts of an accretion disk. Unlike Ly-alpha, the very broad C IV 1549 A does not continue to increase with the UV continuum at large flux levels. For the broad component, the ratio semiforbidden C III 1909 A/C IV 1549 A is independent of the continuum luminosity, suggesting a large ionization parameter. The Balmer decrement in the broad component is smaller when the continuum is brighter. Ly-alpha and H-gamma increase more than H-alpha and H-beta with increasing UV flux. The He II lines are very broad and change by a factor of up to four. They seem to react quickly to UV continuum changes, and there is no need to invoke accretion events to explain their variability. 67 refs.