Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospect theory in the health domain : a quantitative assessment

1 We are grateful to Laurens Niëns and Matthijs Versteegh for advice on the experimental design. gave helpful comments on a previous version of this manuscript. The usual disclaimer applies. 2 ABSTRACT It is well-known that expected utility (EU) has empirical deficiencies. Prospect theory (PT) has developed as an alternative with more descriptive validity. However, PT's full function had not yet been quantified in the health domain. This paper is the first to simultaneously measure utility of life duration, probability weighting, and loss aversion for health. We observe loss aversion and risk aversion, which for gains can be explained by probabilistic pessimism. Utility for gains is almost linear. For losses, we find less weighting of probability 1/2 and concave utility. This contrasts with the common finding of convex utility for monetary losses. However, PT was proposed to explain choices among lotteries involving small outcomes. Life years are arguably not 'small' and need not generate convex utility for losses. Moreover, utility of life duration reflects discounting, causing concave utility. These results are a first step in fitting non-EU models for health-related decisions.
A Chinese Knowledge Graph Q&A System Based on Dense Relationship Retrieval

Currently, most knowledge graph question answering (KGQA) systems need to retrieve all entities when dealing with complex problems involving multiple entities and relationships, which results in high time complexity and resource consumption. The response time of KGQA systems is an important indicator for evaluating their performance. To address this issue, this paper proposes a Chinese KGQA system based on dense relationship retrieval. The system uses the Faiss index mechanism for vector similarity retrieval, quickly extracts the top K relationships from the pre-built vector relationship library, and constructs paths to reduce a large number of irrelevant semantic paths in most KGQA tasks, thereby improving the time cost and computational resource overhead caused by the exponential growth of path numbers in second-order and higher-order questions. By using this method, the response time of the QA system can be shortened to within 1 second with minimal loss of accuracy. This paper combines pre-training models to complete tasks such as text semantic similarity and entity mention recognition, and achieves an average F1 value of 71.3% on the CCKS2019-CKBQA test set. Comparing with other systems demonstrates the superiority of our method in terms of accuracy and efficiency.
Stabilization of T-S Fuzzy Nonlinear Systems by Intermittent Control

As an extremely significant class of system, the stability analysis problem of nonlinear systems has always been a research hotspot. This paper studies the intermittent control problem of nonlinear systems, and represents the system as a Takagi Sugeno (T-S) fuzzy model system. In order to stabilize the nonlinear systems, the intermittent control with variable switching rate control strategy is put out. In the proposed method, the sequence of switching rate is both variable and monotonically diminishing. In addition, this method can also be used to estimate the range of control gain K. Finally, numerical simulation outcomes validate the method’s viability and efficiency.
A Grid Frequency and Voltage Agnostic Startup Technique for Cascaded H-bridge Converters

This paper presents a decentralized startup technique for bidirectional series-connected inverters with grid-forming capability. We control each converter module as a virtual resistor that absorbs power from the grid and transfers energy to the dc side. Owing to the passive nature of the virtual resistors, the series ac-side current automatically synchronizes the cascaded ac-side module voltages to the grid voltage. The virtual resistors not only prevent over-currents, but they are also agnostic to changes in grid frequency and voltage. This is extremely useful for startup during off-nominal grid conditions. Furthermore, unlike existing grid-connected startup methods, this technique does not require external charging resistors or a centralized phase-locked loop (PLL) to synchronize the series-connected modules. We demonstrate that stable rectifier operation is achieved if the emulated resistances are lower than a critical value. Once steady-state operation is reached, a local decentralized PLL within each module is able to decipher the frequency, phase angle, and magnitude of the grid voltage. This information is then passed to the primary control (droop, virtual oscillator, or virtual synchronous machine) and we flip the direction of power flow as the system begins normal operation. We verify the proposed startup technique for a 1.5 kVA system with three cascaded converters connected across a grid.
Closed-loop Control of MOSFET Gate Voltage for Charge Balance in a Smart Li-ion Battery Cell

A smart battery (SB) cell integrates a half-bridge topology with Li-ion cells using very low on-resistance MOSFETs. The half-bridge provides additional degrees of freedom such as bypassing or inserting the cell in a pack depending on the cell state-of-charge (SoC) and state-of-health (SoH). In this paper, a new operation mode is proposed where the gate voltage of the bottom MOSFET is controlled in closed-loop to provide additional current path, which can be made independent of the charge/discharge current magnitude. This controllable additional current through the bottom MOSFET provides a new approach to balance the SoCs of the batteries without the requirement of a full-scale dc-dc converter. The current magnitude can be controlled based on the amount of SoC unbalance and the time required to balance. The parameters of the analog controller to get the desired transient response are determined. Since this operation mode introduces additional power loss, a thermal analysis is performed to optimize the balancing current and the temperature rise. The results are verified using the real device models in a spice simulation environment.
High-Order Harmonic Currents Analysis for Accurate Coupling Coefficient Extraction in Multi-Transmitter Wireless Power Transfer System

This paper proposes an improved coupling coefficient extraction method considering the high-order harmonic component of the input current in a multi-transmitter wireless power transfer(WPT) system. Coupling coefficients between each transmitter coil and receiver coil are necessary to operate the transmitter with high power transfer efficiency according to the characteristics of the receiver in the multi-transmitter WPT system. Some previous studies have analyzed circuits using the first harmonic approximation method and derived coupling coefficient extraction methods. However, in the case of the LCC-S topology, which is widely used in WPT system, the proportion of the high-order harmonic component in the input current is significant, and the proportion increases as the size of the coupling coefficient decreases. Therefore, a method for extracting the coupling coefficient that analyzes the harmonic component of the input current and considers it is required to accurately extract the coupling coefficient.
An empirical study on utilizing online k-means clustering for intrusion detection purposes

K-means clustering is widely used in data mining applications. The k-means algorithm is built to pass over the data to be classified in multiple iterations assuming that the whole data is reachable in every iteration. While the pleasure of having the complete data at a time is not available for online data, the online versions of the k-means clustering have to be used when needed. Online data is a notable pattern extensively used in cybersecurity applications such as intrusion detection systems (IDS). In this work, we develop an unsupervised learning-based IDS using an online k-means clustering algorithm. We also measure the IDS efficiency of clustering highly unbalanced online data generated from an IoT network environment attacked by diverse intrusions and using various cluster centers. Besides, the evaluation process was performed for raw (unnormalized) and normalized data records. The performance of online k-means clustering was compared to offline k-means clustering. The results showed that the online clustering method could operate adequately as the offline k-means clustering, especially when used with normalized data traffic scoring an overall clustering purity of 99% for normal packets and 93% for anomaly packets. Besides, the model peaked at an overall F1 score of 99% for normal packet prediction and 94% for anomaly packet prediction.
Gear Fault Diagnosis Based on Multi-scale Convolutional Neural Network

Convolutional neural network (CNN) has been widely used in fault diagnosis of mechanical equipment such as gears. However, when using traditional CNN for fault diagnosis, there are still difficulties such as taking a long time to train model and feature extraction interfered by noise. Therefore, while ensuring the accuracy of the model, in response to the above problems, this paper proposes a new CNN model - full vector multi-scale CNN (FVMCNN). Firstly, the full vector spectrum algorithm is used to fuse the multi-channel vibration signals of a single sensor in order to retain the original information to the greatest extent and input it from the data source of simplified model to improve the efficiency of model training; Secondly, FVMCNN adaptively extracts multi-scale features of sensor through branch network and the feature fusion strategy based on attention mechanism and the scaling module (DRM) adaptively fuses the features extracted by the branch network to suppress the influence of background noise. Experimental verification is carried out on the parallel shaft gear dataset, and the results show that FVMCNN can achieve more accurate, fast and robust gear fault diagnosis compared with other traditional gear fault diagnosis algorithms.
Magnetics Design Optimization for LLC Converter employing Machine Learning

This paper proposes an intelligent and machine-learning based optimization method that targets to optimal windings layer setup for LLC converter transformer with small number of optimizing iterations. The research utilizes Bayesian optimization (BO) to guide designers toward winding layers setup that achieves the maximum efficiency at nominal operating point of LLC converters. Furthermore, a new multiphysics simulation platform is proposed for feeding efficiencies to optimizing iterations. This simulation platform is able to provide entire losses estimation, high simulation accuracy is verified by measurement. An LLC converter prototype with 33V input and 400V/250W output is implemented to demonstrate effectiveness of proposed optimization method.
Understanding Drought Awareness from Web Data

16 We used computer vision (U-Net) model to leverage Standardized Precipita-17 tion Evapotranspiration Index (SPEI), Google Trends Search Interest (SI), 18 and Twitter data to understand patterns with which people in Continental 19 United States (CONUS) indicate awareness of and interest in droughts. We 20 found significant statistical relationships between the occurrence of meteoro-21 logical droughts (MD), as measured by SPEI, and SI on drought topics over 22 CONUS. SI tends to lag MD by a period of 2-3 months, however relationships 23 between MD and corresponding SI varies significantly over the CONUS in 24 both space and time. People in states with increasingly dry conditions have 25 become increasingly interested in drought topics. However, with worsening 26 drought conditions in California, public SI on drought topics in the state 27 has not increased significantly between 2016 and 2020, despite the overall SI 28
Comparison of MM-Wave WPT With Single and Multiple Fresnel Zone Lens Using High Density Polyethylene

The fresnel zone lens (FZL) in this research work is designed using high-density polyethylene (HDPE). HDPE is characterized for the 28 GHz frequency by making it a super-substrate of the micro-strip patch antenna. The use of the HDPE as the FZP is proposed due to its lower density (950 Kg/m3) and also lower dielectric loss tangent (0.6e-4). A comparison between the single and multiple FZL having the same focal length (L) is shown. The multiple FZL help to increase the power transfer to larger distance efficiently compared to single or no FZL. In this study, the F/D = 0.5 with the F = 100 mm, and D is the diameter of the lens. The measurements of the received power without FZL, with single FZL, and two FZL is done. An improvement of 4 and 36 times in received power is achieved for one and two FZL lenses, respectively, at a 515.05 mm distance.
Modelling and Control of Upper Limb Rehabilitation System Based on Variable Stiffness Joints

With the aggravation of population aging, the incidence of stroke is increasing exponentially, the number of professional rehabilitation doctors is limited, and the application of upper limb rehabilitation robots is becoming more and more extensive. In this paper, a variable-stiffness elbow rehabilitation robot is simulated, and its dynamics are modelling and analysis, and the kinematics and dynamic performance of the robot are explored. Meanwhile, the control method of the robot is optimized to achieve adaptive position control. Finally, through the optimization and improvement of the kinematic parameters of the robot, a good rehabilitation effect is achieved. In this paper, the superior performance of variable stiffness elbow rehabilitation robot is verified by simulation, which provides an effective reference for the application of robot in elbow rehabilitation treatment.
Reactance Compensation Control for Multiple-Receiver Wireless Power Transfer System with Coil Inductance Variations

In practical multiple-receiver wireless power transfer systems, the parameters of the coils and capacitors may vary from their designed values, thus preventing the reactance of the transmitter and receiver coils from being completely compensated. This paper proposes a control scheme for pulsewidth modulation controlled switched capacitors to compensate for the self-inductance variations of the coils and maintain the transmission efficiency. The proposed control scheme is implemented by completely separate closed-loops on each circuit to avoid wireless communication, which has been a major challenge in previous methods. The effectiveness of the proposed control scheme is verified by an experimental prototype consisting of a single transmitter coil and two receiver coils.
Preview Control-based Jumping and Spot-Jogging Trajectory Generation for Quadruped Robots

This work presents a vertical center of mass (CoM) trajectory generation approach based on preview control for quadruped robots to accomplish agile locomotion tasks such as jumping and jogging. Conventionally, preview control is implemented to walking planning with constant CoM height, since this condition renders the pendulum equation linear. In this matter, we show that the same implementation can be done for varying CoM height, while the pendulum frequency is constant. To this end, we compactly derived the infinite horizon and finite horizon time-invariant linear quadratic regulator solutions for potential real-time applicability. The proposed trajectory generation approach was tested and compared with conventional approaches via a set of simulation experiments, where we used a realistic model of our quadruped robot Kara. As a result, we obtained dynamically-balanced, coherent, and continuous jumping and spot-jogging behavior, adequately confirming our approach.
Influence of Current Measurement Error to Split Capacitor Voltage in Single-Phase Half-Bridge Grid Connected Inverter

For a single-phase half-bridge grid connected inverter with a split capacitor configuration, the adoption of the Proportional-Integral (PI) will generally suffice the need of current control action in normal inverter operation. However, the presence of measurement error in the current feedback signal can cause distortion in the grid current. Since the grid and the midpoint of split capacitors are connected through a neutral line, the unwanted dc component will flow to both capacitors and drift their voltages. This article aims to formulate mathematical equations to elucidate this phenomenon, and to recommend suitable gain ratio of the controller to maintain the safety of the inverter and to collect the voltage deviation profile before specific additional control action can be incorporated. Computer simulations under MATLAB/SIMULINK environment of 1 kVA inverter rating were performed to corroborate the theoretical analysis and the proposed method.
A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics.
Using differential equation inspired machine learning for valve faults prediction

In an industrial plant it is necessary to monitor the operation of the equipment. Deviations from normal operation should be detected as early as possible to avoid production failures or to keep their impact as small as possible. Valves are core components of plants and critical damage can occur if they are set to switch but in reality do not switch. Therefore, plant operators would like to detect such failures as early as possible. Valves with electrodynamic actuators offer a unique diagnostics option that allows for the early detection of valve malfunctions by their intrinsic properties alone. In particular, we measure the inrush current of the valve as a function of time. From the resulting signal, we derive the information, if the valve has switched or not. Machine learning (ML) methods can be used to accurately predict these valve errors. In this paper, a long-short term memory (LSTM)-convolutional neural network (CNN) and two differential equation-inspired neural networks are presented for encoding valves function anomalies to ensure the production capability of an industrial plant. For this purpose, derivatives of the inrush currents are considered and integrated into the deep neural networks (DNNs). The implementation of this diagnostics option is cheap and robust as no extra sensor on the valve is required.
Time Domain Modelling of LLC Converter

This paper presents the time domain model of an LLC converter. Reducing switching losses at increased frequency has become one of the essential requirements for designing converters. There are several techniques that are used for controlling and designing the LLC converter. Fundamental harmonic analysis (FHA) is a frequency-domain analysis with a low accuracy. Other methods are time-domain analysis and state-plane methods that use the exact model of the converter to describe the accurate behavior of the system. The time-domain analysis (TDA) has a higher frequency than the FHA and provides optimized parameters and results. TDA is performed by dividing the time duration into different stages, and the proposed model discusses the four stages that are P, Px, Ox, and Nx. The proposed mathematical model is implemented and analyzed through MATLAB using different conditions to understand the working modes of the converter. The system efficiency was observed to be up to 97% and the model was stable and effective.
Switching Frequency is Not the Limit: Multiphase Coupled Inductor FCML Converter Tracking Signals Above the Switching Frequency

Switching frequency is usually considered the main limiting factor for reducing the passive component size and improving the control bandwidth of power converters. The switching frequencies of semiconductor devices and passive components are usually the same in typical power electronics systems. Multi-level, multi-phase, and magnetically coupled circuits utilize a large number of low-frequency semiconductor devices to drive a few high-frequency passive components, enabling reduced ripple, reduced dc energy storage, miniaturized volume, and improved transient performance. This paper presents a 1 MHz four-phase, five-level coupled inductor flying capacitor multilevel (FCML) converter that tracks signals at and above the switching frequency. Combining multiple voltage levels, multiple phases, and coupled inductors, the effective switching frequency and achievable control bandwidth are multiplied by 16× to 16 MHz. A printed circuit board (PCB) embedded planar coupled inductor is designed to intrinsically balance the flying capacitor voltages, minimize output distortion, minimize output voltage/current ripple, and improve the transient performance. Spread spectrum pulse-width modulation (PWM) is implemented to mitigate high frequency harmonics.
Motivational Exploration of Explanations in Industrial Analytics*

Explainable AI (XAI) provides approaches and techniques for building trust in AI models. This paper presents and explores XAI approaches focusing on user interface concepts in predictive maintenance. The underlying AI model is based on an open dataset for wind turbines. An enhanced multi-class self-conceived labeling strategy improves the model and, thus, supports the XAI approaches. Previous research in user-centered XAI shows that users do not exploit the possibilities of XAI methods and instead rely on their intuition. To counter this tendency, we present user interfaces incorporating gamification elements to enhance understanding of AI outputs. We highlight our approach via two examples, demonstrating a local and a global XAI technique respectively. A preliminary user study was conducted to assess the value added by these gamification aspects. While the findings were inconclusive, they provided an initial insight into the potential of these design elements to foster user engagement in the realm of XAI.
Usability of Industrial HMI Testing Method

The article deals with methods of usability testing of industrial SCADA/HMI interfaces such as graphical operator panels or control room visualization screens. Analysis of the designed HMI screens and quantitative and qualitative testing methods are used. Usability is estimated by measuring the reaction times of the defined test tasks and by the Eye Tracking methods. The proposed method for testing and evaluating the industrial HMI is presented as a case study. The method allows for to improvement the quality of industrial HMI. Especially its usability, reliability, and efficiency.
Influence of Different PWM Methods on Thermal Loadings of Power Devices and DC-link Capacitors of Single-Phase Five-Level T-type NPC Inverter

The pulse width modulation (PWM) method significantly affects the performance of inverters such as efficiency, power quality, and reliability. In previous research, the efficiency and power quality are typically considered when Discontinuous PWM methods are applied, whereas study about the reliability of the power devices and DC-link capacitors is insufficient. In this paper, the impact of PWM methods on the reliability of power devices and DC-link capacitors of a single-phase five-level T-type NPC inverter is investigated by considering their power losses and thermal loadings based on simulations and experiments.