Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids

Non-intrusive load monitoring (NILM) objective is to disaggregate the total power consumption of a building into individual appliance-level profiles. This gives insights to consumers to efficiently use energy and realizes smart grid efficiency outcomes. While many studies focus on achieving accurate models, few of them address the models generalizability. This paper proposes two approaches based on generative adversarial networks to achieve high-accuracy load disaggregation. Concurrently, the paper addresses the model generalizability in two ways, the first is by transfer learning by parameter sharing and the other is by learning compact common representations between source and target domains. This paper also quantitatively evaluate the worth of these transfer learning approaches based on the similarity between the source and target domains. The models are evaluated on three open-access datasets and outperformed recent machine-learning methods.
AADGen: Automatic Annotated Data Generation for Training Text Detection and Recognition Models

_ One of the biggest challenges faced in deep learning community is the image data annotation, we know how data is essential for any machine learning problems but when it comes to object detection and segmentation (ODS) problems the data preparation process becomes extremely difficult and time-consuming. The data preparation becomes two steps process, first the data collection itself and second the data annotation. In this paper I present AADGen, a novel way of automatic data collection and annotation (DCA) for text detection and segmentation (TDS) problems. This method reduces the time taken for DCA to almost zero and provides error-free data annotation process. The DCA process will help us in building online learning model training by providing on demand continuous stream of training data. I have done extensive experiments on the data annotation process and achieved more than 99% annotation accuracy. I present a fast, scalable, customizable and error free data annotation approach.
Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on DWPT and Bidirectional LSTM Network

Accurate wind speed forecasting is essential for the reliability and security of the power system, and optimal operation and management of wind integrated smart grids. However, it is still a challenging task due to the highly uncertain and volatile nature of wind speed. Accordingly, in this work, a novel deep learning-based model integrating the discrete wavelet packet transform (DWPT) and bidirectional long short-term memory (BLSTM) is developed to precisely capture deep temporal features and learn the time-varying relationship of wind speed time series. In the proposed method, by applying the DWPT, both approximations and details parts are decomposed by passing through the filters to choose the frequency band related to the features of the original signal more adaptively. The BLSTM networks are incorporated to deal with the uncertainties more effectively as they have bidirectional memory capability (feedforward and feedback loops) to investigate both previous and future hidden layers data. To simultaneously improve the forecasting performance and decrease the learning complexity, the reconstructed state space of historical wind data is employed to reflect the evolution laws of wind speed. Two case studies using real-world wind speed datasets gathered from Flatirons campus (M2) of National Renewable Energy Laboratory (NREL) located in Colorado, USA and weather station of Edmonton, Canada are implemented to demonstrate the effectiveness and superiority of the proposed hybrid method compared to the shallow architectures and state-of-the-art deep learning models in the recent literature.
Lossless EEG Compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation

In this paper, a hardware-oriented lossless EEG compression algorithm including a two-stage prediction, voting prediction and tri-entropy coding is proposed. In two stages prediction, 27 conditions and 6 functions are used to decide how to predict the current data from previous data. Then, voting prediction finds optimal function according to 27 conditions for best function to produce best Error (the difference of predicted data and current data). Moreover, a tri-entropy coding technique is developed based on normal distribution. The two-stage Huffman coding and Golomb-Rice coding was used to generate the binary code of Error value. In CHB-MIT Scalp EEG Database, the novel EEG compression algorithm achieves average compression rate to 2.37. The proposed hardware-oriented algorithm is suitable for VLSI implementation due to its low complexity.
Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis

Understanding and alleviating I/O bottlenecks in HPC system workloads is difficult due to the complex, multilayered nature of HPC I/O subsystems. Even with full visibility into the jobs executed on the system, the lack of tooling makes debugging I/O problems difficult. In this work, we introduce Gauge, an interactive, data-driven, web-based visualization tool for HPC I/O performance analysis. Gauge aids in the process of visualizing and analyzing, in an interactive fashion, large sets of HPC application execution logs. It performs a number of functions met to significantly reduce the cognitive load of navigating these sets - some worth many years of HPC logs. For instance, as its first step in many processing chains, it arranges unordered sets of collected HPC logs into a hierarchy of clusters for later analysis. This clustering step allows application developers to quickly navigate logs, find how their jobs compare to those of their peers in terms of I/O utilization, as well as how to improve their future runs. Similarly, facility operators can use Gauge to ‘get a pulse’ on the workloads running on their HPC systems, find clusters of under performing applications, and diagnose the reason for poor I/O throughput. In this work, we describe how Gauge arrives at the HPC jobs clustering, how it presents data about the jobs, and how it can be used to further narrow down and understand behavior of sets of jobs. We also provide a case study on using Gauge from the perspective of a facility operator.
Properties of Carbon Nanotube Fibers Spun from DNA‐Stabilized Dispersions

The fabrication of single‐walled carbon nanotube (CNT) fibers containing (salmon) DNA has been demonstrated. The DNA material has been found to be adequate for dispersing relatively large concentrations (up to 1 % by weight) of carbon nanotubes. These dispersions are better suited for fiber spinning than previously studied dispersions based on conventional surfactants, such as sodium dodecyl sulfate (SDS). The DNA‐containing fibers were less conductive than the fibers based on SDS, but they were significantly stronger. Considerably increased conductivity was obtained by thermally annealing the CNT/DNA fibers, a process accompanied by a loss in mechanical strength. Smaller improvements in conductivity could be introduced by annealing the carbon nanotubes before fiber production, with no alteration of the fiber mechanical properties. Those CNT/DNA fibers that were mechanically strong and conductive also exhibited good electrochemical behavior and useful capacitance values (up to 7.2 F g–1).
Design and Implementation of Close-loop Detection for Coupled Core Fluxgate Magnetic Sensors

This paper presents a closed-loop detection strategy with significant potential to solve the detection defects existed in the residence-times difference (RTD) method of the coupled core fluxgate magnetic sensors. The external magnetic field (e.g. Earth's magnetic field), as the main source of these detection defects, has been analyzed by the theoretical investigation. To overcome such deficiencies, the close-loop detection method based on the analog-type RTD readout method and magnetic flux-balance detection is utilized to implement external magnetic field real-time compensation and achieve a near-zero-field working state of the magnetic material. The fluxgate with the optimized detection strategy and vertical arrangement structure has been designed, fabricated and examined, and the preliminary results showed steady operation and a detection resolution of ±0.10 nT. The optimized detection method based on the balance detection concept proposed in this manuscript demonstrated a great potential to obtain the optimal detection performances which are embedded in the coupling-induced oscillation of nonlinear dynamics.
Analysis of Capacitor Voltage Balancing Method of Modular Multilevel Converter (MMC) for the Application of SPWM, NLC & SHE-PWM Modulation Techniques

This paper presents the analysis of the capacitor voltage balancing of Modular multilevel converter (MMC) for the application of various modulation techniques. The modulation schemes namely SPWM, SHEPWM and the NLC technique are used here for generation of pulses for the MMC switches. The generalized capacitor balancing method based on sorting of submodule (SM) capacitor voltages is employed for balancing of SM capacitor voltages. The simulation was performed in the MATLAB/Simulink. The three-phase seven-level MMC converter is simulated to investigate its performance as well as to analyze the capacitor balancing approach. The analysis and performance of MMC for specific load, and for the mentioned modulation techniques is presented as well as validated using the simulated results.
Improved Temperature Estimation Model of 4H-SiC MOSFET under Avalanche Condition

Silicon Carbide (SiC) Metallic Oxide Semiconductor Field Effect Transistor (MOSFET) has fast switching speed and under unclamped inductive switching (UIS) environment, the device may suffer from avalanche breakdown. In this occasion, it is necessary to estimate the temperature inside SiC MOSFET accurately. However, current models rarely consider the effect of temperature-dependent parameters, leading to inaccuracy estimation. In this paper, a Cauer thermal network considering the temperature-dependent property of material thermal characteristics under avalanche state is proposed. First, the relationship between temperature and material parameters is illustrated. Then, according to the scanning electron microscope (SEM) analysis, the geometric parameters of the chip are obtained, and the cell model of SiC MOSFET is built using Technology Computer Aided Design (TCAD). Next, a transient Cauer thermal network is proposed with temperature-dependent parameters considered. Finally, the junction temperature of the proposed model is extracted and verified. Compared with the thermal network using constant parameters, the accuracy is significantly improved. Also, the model saves computation time and resource in contrast with the TCAD model.
Performance Analysis of Modified Hybrid 2-qZS dc/DC Boost Converter for PV System

This paper presents the performance analysis of modified hybrid 2-qZS dc/DC boost converter for grid connected PV power system. Since PV array has low dc output voltage therefore a high step up dc-dc converter is required. The proposed converter is modified form of conventional hybrid 2-quasi ZS boost converter. In this converter an additional switch and a diode is connected at the output terminal of conventional hybrid 2-quasi ZS boost converter. A comparison of proposed modified hybrid 2-qZS dc/DC boost converter is carried out with conventional hybrid 2-quasi ZS boost converter and other family of Z-source converters. The proposed converter has boost factor about twice of conventional hybrid 2-quasi ZS boost converter at very small duty cycle 0 < D < 0.166. The performance of proposed converter is analyzed in Matlab 2016a.
A Hybrid, Encompassing, Three-Fingered Robotic Gripper Combining Pneumatic Telescopic Mechanisms and Rigid Claws

Unstructured and uncertain environments that are encountered in search and rescue applications, require complex interactions that involve a wide range of grasps. These grasps are needed in order to handle unpredictable types of objects of various shapes, stiffnesses, and dimensions. Traditionally, the robotic end-effectors used in such situations are rigid and their operation requires sophisticated sensing elements and complicated control algorithms to manipulate delicate and fragile objects. Over the last decade, considerable research effort has been put into the development of adaptive, underactuated, and soft robots that facilitate robust interactions with dynamic environments. In this paper, we propose a three-fingered robotic gripper that combines pneumatic telescopic mechanisms and rigid claws. The gripper uses three large, rigid fingers to accomplish the execution of all the tasks required by a traditional robot gripper, while three inflatable, telescopic fingers provide soft interaction with objects. This synergistic combination of soft and rigid structures allows the gripper to cage / trap and firmly hold heavy and irregular objects. The experiments demonstrate that the gripper can successfully grasp a plethora of objects exerting up to 60 N of clench force.
Fully Neural Network Mode Based Intra Prediction of Variable Block Size

Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarded as a regression from the neighboring reference blocks to the current coding block. (1) For variable block size, we utilize different network structures. For small blocks 4×4 and 8×8, fully connected networks are used, while for large blocks 16×16 and 32×32, convolutional neural networks are exploited. (2) For each prediction mode, we develop a specific pre-trained network to boost the regression accuracy. When integrating into HEVC test model, we can save 3.55%, 3.03% and 3.27% BD-rate for Y, U, V components compared with the anchor. As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.
An Implicitly Parallel Meshfree Solver in Regent

This paper presents the development of a Regent based implicitly parallel meshfree solver for inviscid compressible fluid flows. The meshfree solver is based on the Least Squares Kinetic Upwind Method (LSKUM). The performance of the Regent parallel solver is assessed by comparing with the explicitly parallel versions of the same solver written in Fortran 90 and Julia. The Fortran code uses MPI with PETSc libraries, while the Julia code uses an MPI + X alternative parallel library. Numerical results are shown to assess the performance of these solvers on single and multiple CPU nodes.
BTI Aging Monitoring based on SRAM Start-up Behavior

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.
Comparison of Incremental Current Based MPPT Algorithms for Wind Fed DC Microgrid

This paper presents the comparison of incremental current based maximum power point tracking algorithms (MPPT) for micro wind turbine (MWT) fed DC microgrid. Incremental MPPT algorithms are popular in MWT as it avoids the measurement of turbine/generator speed measurement. It reduces the sensor requirements and curtails overall system cost. However, the selection of suitable step size can be tedious and system will subject to oscillation at maximum power point or sluggish in maximum power point (MPP) tracking depending on high or low step size. These algorithms control the DC link current according to the variation in DC link power/voltage at different wind speeds. The simulation results demonstrate the tracking ability of the proposed algorithm at different wind speeds. A comparison is made between the proposed algorithm and other incremental current based MPPT algorithms.
Soil Study of Coastal Hyperspectral Data using K-means and LDA (Latent Dirichlet Allocation)

Recent advances in Hyperspectral imaging have induced the growth of various paradigms for advanced systems in remote sensing and Geospatial Data Analysis. Various hyperspectral sensors have been laden on artificial satellites or UAV flights, to capture granular data regarding the topologies of the land. Unsupervised learning is generally used to classify and identify portions of an image. In this paper, the effectiveness of the K-Means Clustering Algorithm and Latent Dirichlet Allocation (LDA) in clustering the soil type present on a beach is checked. The dataset used is from high signal-to-noise ratio imaging spectroscopy (AVIRIS-NG). The data was visualized using the ENVI toolkit present in Python’s Spectral library. Clustering was performed, and an analysis was conducted about the similarity in the same. The soil types are classified, and the two algorithms are contrasted and effectiveness is generalized.
A 72-nW 440-mV Time Register Using Stacked-NMOS-Switched Gated Delay Cell in Biomedical Applications

This paper presents a time register that uses a gated delay pipeline, to hold or propagate time information through the line by a stacked NMOS switch. The minimum supply voltage can be down to 440 mV, which is much lower than previous state-of-the-arts whose supply voltage is 1 V or higher, making this design beneficial for wearable/implantable devices in biomedical applications. The post-layout simulation performed in 65-nm CMOS technology confirms the function of the proposed time register at a conversion rate of 10 MSamples/s. The stacked-NMOS-switched architecture contributes to low leakage current, realizing low power consumption of 72 nW. In addition, the coefficient of determination, denoted R2, is 0.9956 in the linear input range of 4–11 ns, indicating good linearity.
Autonomous Control of Combat Unmanned Aerial Vehicles to Evade Surface-to-Air Missiles Using Deep Reinforcement Learning

This paper proposes a new reinforcement learning approach for executing combat unmanned aerial vehicle (CUAV) missions. We consider missions with the following goals: guided missile avoidance, shortest-path flight and formation flight. For reinforcement learning, the representation of the current agent state is important. We propose a novel method of using the coordinates and angle of a CUAV to effectively represent its state. Furthermore, we develop a reinforcement learning algorithm with enhanced exploration through amplification of the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We assert that these two algorithms complement each other and that combining them amplifies the imitation effect for exploration. Empirical results show that the proposed AIE approach is highly effective at finding a CUAV’s shortest-flight path while avoiding enemy missiles. Test results confirm that with our method, a single CUAV reaches its target from its starting point 95% of the time and a squadron of four simultaneously operating CUAVs reaches the target 70% of the time.
IoT based Smart Baby Cradle System using Raspberry Pi B+

This paper presents IoT based smart system that act as baby cradle monitoring system for engaged or working parent so that they can manage properly, and also for proper care and safety of the infant. Parent can recognize baby’s movement, sound like crying and video output of baby’s present position and motion will be visible on a screen monitor so the parent or any person can watch the infant even while away from baby. This cradle system is useful for monitoring or detecting movement and crying condition of the child automatically. The Raspberry pi B + module is used to have control on the entire hardware, condenser MIC is implemented for baby cry detection, PIR motion sensor is designed to identify baby’s motion and pi camera is capturing the infant condition of motion and the display is used to show video output of sleeping baby. This smart baby monitoring system have n number of parameters such as live video and sound, set down audio and leisure movement of infant, measuring the room temperature and the humidity indicates if the baby is sleepless, and the most important characteristic is the ability to listen to the baby noise with cry detection feature.
Quality of Service Measurement and Prediction through AI Technology

With the development of Information Technology (IT) and Software-Defined Networking (SDN), Communications Service Providers (CSPs) can collect much information from telecommunication circuits. However, some of the existing circuit measurement has not been used well. For CSPs, it is important to find more efficient ways of utilizing the circuit measurement, e.g., delay, jitter, packet loss, and speed-test results for customer satisfaction. One of the most popular ways is to use speed-test tools (such as Speedtest online) to measure the service rate of the application layer. However, it is difficult to justify whether the telecommunication circuit is normal or not. For example, when the speed-test result of a specific circuit is 90 Mbps, the physical line rate may be 100Mbps. To address the above issues, we first investigate the measurement and management mechanisms of the existing telecommunications networks, including the core components and protocols. In this paper, we leverage artificial intelligence (AI) technologies to predict whether customers complain or not. We evaluate the proposed AI model by using the real data from telecommunication circuits and analyze the key performance metrics.
Penalty Adjustment-Based Sizing Method for Flexible Resources in Isolated Microgrids

The fixed and subjective setting of the penalty coefficient is a significant drawback of existing penalty-based programming models used to determine the size of flexible resources (FRs), such as microturbines and battery energy storage systems, for isolated microgrids (IMs). Generally, most researchers set the penalty coefficient to a sufficiently large value to ensure a satisfactory operational performance of IMs, which often leads to redundant and uneconomical configurations. How to balance the operational performance and economic configuration is an intractable problem confronted by many researchers. In this paper, a penalty adjustment-based sizing method is proposed to solve the aforementioned problems. Specifically, four indicators are defined to evaluate the size of FRs. Based on the assessment metrics, a trade-off sizing method is proposed to attain the FR size with the maximum marginal benefit. This method is combined with mixed integer programming to propose a penalty adjustment-based sizing algorithm to solve the optimal penalty coefficient and size of FRs. Simulations verify that the proposed method can avoid subjectivity and improve the traditional conservative configuration.