Miracle-dz/UniFAR-FTUs huggingface dataset:
Proactive Run-Time Mitigation for Time-Critical Applications Using Dynamic Scenario Methodology

Energy saving is important for both high-end processors and battery-powered devices. However, for time-critical application such as car auto-driving systems and multimedia streaming, saving energy by slowing down speed poses a threat to timing guarantee of the applications. The worst-case execution time (WCET) is a widespread solution to this problem, but its static execution time model is not sufficient anymore for highly dynamic hardware and applications nowadays. In this work, a fully proactive run-time mitigation methodology is proposed for energy saving while ensuring timing guarantee. This methodology introduces heterogeneous datapath options, a fast fine-grained knob which enables processors to switch between datapaths of different speed and energy levels with a switching time of only tens of clock cycles. In addition, a run-time controller using a dynamic scenario methodology is developed. This methodology incorporates execution time prediction and timing guarantee criteria calculation, so it can dynamically switch knobs for energy saving while rigorously still ensuring all timing guarantees. Simulation shows that the proposed methodology can mitigate a dynamic workload without any deadline misses, and at the same time energy can be saved.
A Bidirectional Three-Level Converter with Single Point Sensing Technique for Flying Capacitor Balance

In this paper, a novel bidirectional three-level (TL) converter control scheme is presented with a flying capacitor (FC) control loop and adaptive constant on-time (ACOT) control. Due to the FC balance issue in TL converters, the FC control with single point sensing (SPS) formed by only two pairs of sample and holds (S/Hs) and operational transconductance amplifiers (OTAs) is proposed not only to balance FC at half of high-side voltage, but also to have simpler design. The sensing method additionally minimizes inductor ripple. Moreover, the proposed ACOT design solves the switching frequency variation issue, and the variation is only 5kHz, which is much smaller than previous works especially in boost converter. Besides, both buck mode and boost mode share the same control elements except for compensator. Consequently, the controller die area can be minimized. The proposed scheme is realized with a 1.5 MHz switching frequency bidirectional TL converter IC in TSMC 0.18um CMOS process. Due to the characteristics of TL converter, the stress of switch is chosen with half of high-side voltage. Full transistor simulation result verifies that the functions are worked well on both directions and there are no FC voltage runaway and mismatch issues. The efficiency up to 96% at load 1A in high conversion ratio.
Development of Applicable Reinforcement Learning Compensator Using Ranking Information for AUV

In nonlinear multi input multi output systems such as Autonomous Underwater Vehicle (AUV), a physical dynamical model is usually used to design a control system. To satisfy trade-off between plural control targets, design and adjustment of the control system are complicated, and which lead to struggle by control design expert. In recent years, applying data-driven techniques such as deep reinforcement learning have been tried to improve control performance by automatic adjustment in the deep neural network. However, we have difficulty to applying data-driven methods in sight of designing reward functions, and how to combine learning results into a controller so as not to lose prior information such as models. Then, in this study, we developed compensation control technology by deep reinforcement learning (DRL) for efficient control under multi-purpose trade-off, by using selector with ranking to several options of control target trajectory, and to verify its effectiveness by simulation using obstacle avoidance as an example.
Magnetic Circuit Analysis of Tubular-Direct Drive Linear Generator for Wave Energy Conversion

The rise in conventional energy use leads to a global energy crisis and environmental pollution. These problems have encouraged research on renewable energy harvesting technologies. Ocean energy has grown exponentially over the last three decades as a sustainable energy source. The current research focuses on wave energy benefits such as being eco-friendly, highly abundant and reliable, low cost effectiveness and predicted easily. This paper presents the design and performance of a 3 phase, Tubular-Direct Drive Linear Generator (TDDLG) for wave energy conversion using a three, four and six side short flux path. Also, it enhances the power density while reducing magnetic flux leakage produced by the unequal distribution of flux in the stator and translator’s core. The tubular direct drive linear generator consists of a long translator with short armature windings including an array of permanent magnets. For designing this linear generator, magnetic circuit analysis is incorporated into the static mode to analyse the magnetic flux and its leakage, torque, losses and voltage. The static response of these variables is calculated using finite element analysis. Our simulation result shows the designed four-side short flux path linear generator achieves a magnetic flux of 1.79 T with flux linkage of 0.06 Wb, with the torque of 0.56 Nm and output voltage of 65 V.
Drone-based solar panel inspection with 5G and AI Technologies

It’s been considered an incomplete task for years to maintain large solar power plants for years. Presented here is an Artificial Intelligence (AI) based defects detection of Photovoltaic(PV) modules using Thermal Images (TI) darknet YOLOV4 object detection, which can be processed in two ways: (1) Creating a huge number of high-resolution TI samples using a huge number of TI generation methods; and (2) using the generated TI’s, to develop an efficient method of defects classification. Convolution Neural Network (CNN) technology and traditional image processing technology are combined to result in the TI object detection method. This method has a capability of training a large number of high-resolution TI samples to give a good AI model output. Then, CNN is used to extract the deep feature of TI to show the defected cells. In other hand using enhanced 5G technology it is used to operate the drone for long range and by help of AI, can send the defected cells location to the ground station. Compared to other solutions, using it can improve PV module inspection and health management solutions significantly. It has been demonstrated experimentally that the proposed AI-based solution is efficient and accurate at detecting defects using TI and drones automatically.
Provision of Phase Balancing and Reactive Power Compensation with Junction Temperature Control by Photovoltaic Inverters

The provision of reactive power compensation and phase balancing by photovoltaic (PV) systems is considered as an essential component for enhancing the power quality and efficiency of distribution grids. However, phase balancing and reactive power compensation can increase the power losses of PV inverters in an asymmetric way. This can cause a different temperature rise in each switching device, leading to a reduction of the overall lifetime of the inverter. This paper investigates the potential improvement of PV inverters reliability with a junction temperature control while providing phase balancing and reactive power compensation. The PV inverters can provide either full compensation to asymmetric loading and reactive power or a limited compensation considering the thermal stresses at the switching component level. With the latter option for ancillary services provision, the junction temperature is always kept bellow a desired value, limiting the degradation of the inverter lifetime. Simulations and experimental tests are carried out to demonstrate the performance of the junction temperature control during ancillary services provision.
T-SKID: Predicting When to Prefetch Separately from Address Prediction

Prefetching is an important technique for reducing the number of cache misses and improving processor performance, and thus various prefetchers have been proposed. Many prefetchers are focused on issuing prefetches sufficiently earlier than demand accesses to hide miss latency. In contrast, we propose aT-SKID prefetcher, which focuses on delaying prefetching. If a prefetcher issues prefetches for demand accesses too early, the prefetched line will be evicted before it is referenced. We found that existing prefetchers often issue such too-early prefetches, and this observation offers new opportunities to improve performance. To tackle this issue, T-SKID performs timing prediction indepen-dently of address prediction. In addition to issuing prefetches sufficiently early as existing prefetchers do, T-SKID can delay the issue of prefetches until an appropriate time if necessary. We evaluated T-SKID by simulations using SPEC CPU 2017. The result shows that T-SKID achieves a 5.6 % performance improve-ment for multi-core environment, compared to Instruction Pointer Classifier based Prefetching, which is a state-of-the-art prefetcher.
On the Optimal OBDD Representation of 2-XOR Boolean Affine Spaces

A Reduced Ordered Binary Decision Diagram (ROBDD) is a data structure widely used in an increasing number of fields of Computer Science. In general, ROBDD representations of Boolean functions have a tractable size, polynomial in the number of input variables, for many practical applications. However, the size of a ROBDD, and consequently the complexity of its manipulation, strongly depends on the variable ordering: depending on the initial ordering of the input variables, the size of a ROBDD representation can grow from linear to exponential. In this paper, we study the ROBDD representation of Boolean functions that describe a special class of Boolean affine spaces, which play an important role in some logic synthesis applications. We first discuss how the ROBDD representations of these functions are very sensitive to variable ordering, and then provide an efficient linear time algorithm for computing an optimal variable ordering that always guarantees a ROBDD of size linear in the number of input variables.
Development of High-dimensional Data Sparse Modeling in Data Space and Its Application in Manufacturing Multi-value Chain Collaboration

The arrival of data space marks that all kinds of data applications can be recorded, stored and continuously expanded. The changes of data's source, volume and type have been increasing the difficulty of statistical analysis undoubtedly. In order to cope with the high-dimensional characteristics of data in data space, this paper discusses the challenges brought by high-dimensional data and high-dimensional models to traditional methods. The development of sparse modeling, the role of selection mechanism and the theoretical nature of penalty function method have also been combed in this paper. Finally, as an application, this paper discusses the feasibility of using high-dimensional sparse vector autoregressive model (HDS-VAR) to predict the profitability of manufacturing enterprises represented by electrical machinery and equipment manufacturing enterprises under the synergy of manufacturing value chain and service value chain.
An Inner-Loop Control Method for the Filter-less, Voltage Sensor-less, and PLL-less Grid-Following Inverter-Based Resource

This paper presents an inner-loop control method for the filter-less, voltage sensor-less, and PLL-less inverter-based resource (IBR) under grid-following operation mode. The innovative concepts of the proposed control structure incorporate the removal of the voltage sensors at the point of common coupling (PCC), the removal of the inverter output filter or interface inductance, and the removal of the traditional phase-locked loop (PLL) circuits. Furthermore, the virtual impedance control is applied to compensate for the inverter voltage reference change due to the removal of the interface inductance. With the proposed control method, the physical interface inductor and the bulky high-voltage sensors can be offloaded, but the inverter can still synchronize with the grid and respond to the control command with fast speed and small steady state error.
Minimizing DC-Link Capacitor RMS Current in Power Conversion Units Through Synchronous Operation

DC-Link filtering capacitors occupy a significant volume in DC-DC-AC power conversion unit (PCU) for electric vehicle (EV) applications, where manufacturing costs and power density are related metrics and are important. Typically, the DC-Link capacitor is sized electrically based on the desired voltage ripple and rms current capacity, with the latter being the ultimate deciding factor in the capacitor overall physical volume. This paper proposes a synchronous modulation strategy that matches the current of an interleaved DC-DC converter in discontinuous conduction mode (DCM) with that of the inverter-drawn current to significantly reduce the RMS current ripple in the DC-link capacitor, and thus enabling a volumetric reduction when considering system design and increasing capacitor lifetime. Simulation and experimental results are presented to validate the claims of this work.
A design of Underwater Wireless Optical Communication system based on OFDM technology

The complex seawater environment will cause the absorption and scattering of optical signals, and the light will be seriously attenuated within several hundred meters of transmission in seawater. Therefore, the development of underwater wireless optical communication (UWOC) has great limitations. Orthogonal frequency division multiplexing (OFDM) is suitable for promoting the development of optical communication in an underwater environment with the characteristics of high bandwidth and data rate. We propose and experimentally demonstrate an underwater wireless optical communication system employing 16-quadrature amplitude modulation OFDM (16QAM-OFDM) and cost-effective TO56 packaged blue-light laser diode (LD). Over a 10m tap water channel, we have achieved a 125Mbps transmission and the bit error rate (BER) is 1.71 × 10−3, which is lower than the forward error correction (FEC) criterion.
A High Efficiency and High Current LLC Resonant Converter with Improved Interleaving Winding Arrangements

This paper proposes a magnetic core geometry with merged winding layouts for a 30/5V 660W LLC resonant converter used in data centers. It enables only one turn for both primary and secondary windings to realize the 6:1 turns ratio. The resonant capacitors are integrated into primary windings to reduce the winding loss and the converter footprint. The merged winding is applied to secondary windings. Compared with the state-of-the-art, the proposed winding layout illustrates more effective proximity effect reduction, smaller footprint, lower DC and AC resistances. The experiment results show that the prototype achieves a peak efficiency of 97.7%.
MagNet: An Open-Source Database for Data-Driven Magnetic Core Loss Modeling

This paper introduces an open-source database - MagNet - for data-driven magnetic core loss modeling. MagNet aims to support data-driven magnetics research by hosting a large amount of experimentally measured excitation waveform data for many materials across a variety of operating conditions. This database in its current state contains over 150,000 excitation waveforms for six ferrite materials - TDK{N27, N49, N87}, Ferroxcube{3C90, 3C94}, Fair-Rite{78} - in the 50 kHz to 500 kHz, 10 mT to 300 mT range for sinusoidal, triangle, and trapezoidal waveforms. This paper presents the purposes of building MagNet, introduces the data acquisition system and data format, discusses the data quality, and presents a few examples of using this database with data driven methods.
3D Deep Residual Convolutional Neural Network for Underwater Acoustic Source Localization Using Local Acoustic Intensity Field

The development of underwater robot technology provides a powerful means to obtain the local spatial distribution of an acoustic source radiated field. The local acoustic field interference characteristics can be used to locate the target source. In this paper, a deep neural network was established by taking the local acoustic intensity field as input, and the interval prediction and confidence distribution of the target source position as output. To extract multiscale features and improve network degradation problems, a 3D deep residual convolutional neural network structure was constructed. The convolution kernel was specially designed to keep the 3D spatial detail features of the local acoustic intensity field. The training and validation data sets are generated by the BELLHOP model. The performance of the trained network was evaluated using the validation set. The results show that the designed deep neural network can accurately estimate the azimuth, distance and depth interval of the target source relative to the local acoustic intensity field, and provide the confidence distribution of each interval.
Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems

In-memory deep neural network (DNN) accelerators will be the key for energy-efficient autonomous edge systems. The resistive random access memory (ReRAM) is a potential solution for the non-CMOS-based in-memory computing platform for energy-efficient autonomous edge systems, thanks to its promising characteristics, such as near-zero leakage-power and non-volatility. However, due to the hardware instability of ReRAM, the weights of the DNN model may deviate from the originally trained weights, resulting in accuracy loss. To mitigate this undesirable accuracy loss, we propose two stochastic fault-tolerant training methods to generally improve the models' robustness without dealing with individual devices. Moreover, we propose Stability Score-a comprehensive metric that serves as an indicator to the instability problem. Extensive experiments demonstrate that the DNN models trained using our proposed stochastic fault-tolerant training method achieve superior performance, which provides better flexibility, scalability, and deployability of ReRAM on the autonomous edge systems.
Towards Low-Cost High-Accuracy Stochastic Computing Architecture for Univariate Functions: Design and Design Space Exploration

Univariate functions are widely used. Several recent works propose to implement them by an unconventional computing paradigm, stochastic computing (SC). However, existing SC designs either have a high hardware cost due to the area-consuming randomizer or a low accuracy. In this work, we propose a low-cost high-accuracy SC architecture for univariate functions. It consists of only a single stochastic number generator and a minimum number of D flip-flops. We also apply three methods, random number source (RNS) negating, RNS scrambling, and input scrambling, to improve the accuracy of the architecture. To efficiently configure the architecture to achieve a high accuracy, we further propose a design space exploration algorithm. The experimental results show that compared to the conventional architecture, the area of the proposed architecture is reduced by up to 76%, while its accuracy is close to or sometimes even higher than that of the conventional architecture.
Design and Optimization of a SiC-Based Versatile Bidirectional High-Voltage Waveform Generator

Versatile waveform generators are required for multiple load characterization and excitation in a wide variety of industrial and biomedical applications. In the past, these generators have been typically limited in voltage/current/frequency due mainly to limitations of the power devices. However, advances in semiconductor technology have enabled the design of high-performance versatile waveform generators with improved operational ranges. In this paper, to overcome these limitations, a bidirectional multi-level structure is proposed to obtain a versatile waveform generator. To achieve this aim, this paper deals with the design, optimization and experimental validation of a SiC-based bidirectional 13-kVpp versatile large-signal high-frequency arbitrary waveform generator. Unlike previous proposals, the proposed architecture enables bidirectional conversion, being able to deal with reactive and active loads.
Low Temperature Evaluation of Silicon Carbide (SiC) based Converter

Cryogenic power electronics is regarded as the next step to improve converter efficiency and power density. Thus, investigations on component and converter performances under low temperature operations are of great importance. Silicon carbide (SiC) devices have gradually replaced traditional Silicon (Si) devices in high voltage and high power applications. This is due to their fast switching speed and low switching loss. In this article, the feasibility and performance of SiC devices based power converters are investigated under low temperature operation. Firstly, the individual components performances are evaluated under low temperatures, including SiC MOSFET, gate driver, capacitor, magnetic components, and SiC Schottky diode. Then, a 600 W resonant converter based DC transformer was built and tested under low temperatures. The efficiency for the converter with ferrite cores drops from 97.86% at room temperature (298 K) to 93.44% at 143 K. Different magnetic components are tested and comparisons are made.
An AC-Coupling Parallel Hybrid Supply Modulator Achieving 100MHz Envelope Tracking Power Amplifiers for Wireless Communications

A wideband hybrid envelope tracking supply modulator (ETSM) utilizing a switching converter (SW) with fast VCAP tracking path and a bandwidth and efficiency enhanced linear amplifier (LA) is presented. In addition to smaller ripple and lower losses of SW with 4.7uH inductance and 5MHz switching frequency, the use of the recommended fast VCAP tracking path produce a higher speed loop and wider converter bandwidth. The SW provides a high percentage of power amplifier (PA) supply load current, reduces the LA low-frequency current and ripple cancellation current, improves the overall system efficiency, while maintaining the best operating point of LA. Through the two proposed compensation methods, the unity gain bandwidth of LA exceeds 670MHz, which meets the strict requirements of 5G New Radio (NR). The supply modulator is fabricated in a 180-nm CMOS process. The post-simulation result shows the tracking of 100MHz sine wave with 79% peak efficiency at 2-W output power and the simulated tone difference reaches 22.6dB.