Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Energy Preservability of Runge-Kutta Methods in Power System Simulation

Runge-Kutta methods have been widely used in power system time-domain simulations. However, conventional Runge-Kutta methods can not preserve the total energy of the simulated system because they are not symplectic integrators. For the explicit Euler method and fourth-order Runge-Kutta method, this paper first finds explicit formulae on how the total energy of the simulated system trajectory can change with the integration time step by using the Hamiltonian system formulation of a single-machine-infinite-bus system. The formulae discover the existence of a critical time step for energy-preserving simulation. Then, the formulae are used to evaluate the error in observed damping of the system as well as the correction if the simulation is conducted for an extended period with a time step different from the critical time step. Finally, the formulae are applied to Kundur's two-area four-generator system regarding its dominant mode.
A Convolutional Neural Network with Equal-Resolution Enhancement and Gradual Attention of Features for Tiny Target Detection

The detection of tiny targets on the surface with high efficiency and accuracy is significant for the current intelligent manufacturing. Visual inspection methods based on deep learning are widely utilized to detect tiny objects. However, the tiny objects appear less distinct, less wide, and less area occupied in the image. At the same time, there is a lot of object-like noise, which further increases the difficulty of detecting tiny objects. In response to the challenges brought by the complexity of the detection environment, this paper proposes a detection network architecture that combines the enhancement of pixel-level features at equal resolution and the introduction of full-scale features based on attention. The model utilizes the subtle differences between the tiny target and the background and the semantic information of the tiny target outline to enhance the features of the tiny target while significantly reducing its loss in the equal-resolution feature layer. Additionally, a gradual attention mechanism is proposed to guide the network to pay attention to tiny objects features on the full-scale feature layer. The performance of this network architecture is validated on a real dataset. Experiments show that the model exhibits superior performance and outperforms existing resNet50, DenseNet, Racki-Net, and SegDecNet in detecting tiny objects.
The Point Spread Function and Optical Dot Gain

The diffusion of light within paper can affect the tone characteristics of a printed halftone image. This effect is known as optical dot gain or the Yule–Nielsen effect. In this article, an expression for the average reflectance from a region of a halftone is derived, taking into consideration optical dot gain. Optical dot gain is accounted for using a point spread function (PSF). A PSF is derived by solving the radiative transfer equation in the diffusion approximation. The connection between the PSF approach and the probability approach is shown. Keywords: optical dot gain; halftone image reproduction; radiative transfer; turbid media; point spread function
Safe Online Gain Optimization for Cartesian Space Variable Impedance Control

Smooth behaviors are preferable for many contact-rich manipulation tasks. Impedance control provides an effective way to regulate robot movements by mimicking motions of a mass-spring-damping system. Consequently, the robot behavior can be determined by the impedance gains. However, tuning of the impedance gains for different tasks is not straightforward, especially for unstructured environments. Moreover, online adaption of the optimal gains to deal with the time-varying performance index is even more challenging. In this paper, we present Safe Online Gain Optimization for Cartesian space Variable Impedance Control (Safe OnGO-VIC) to overcome these challenges. By reformulating the dynamics of impedance control as a control-affine system, in which the impedance gains are the inputs, we provide a novel perspective to understand the relation between impedance gains and the robot behaviors. Furthermore, we innovatively formulate an optimization problem that utilizes force measurement collected online to obtain the optimal impedance gains in real-time. Safety constraints are also embedded in the proposed framework. We experimentally validated the proposed algorithm on three contact-rich manipulation tasks. Comparison results with a constant gain baseline and an adaptive control baseline prove that the proposed algorithm is effective and generalizable to different scenarios. Experiment videos are available at https://msc.berkeley. edu/research/safe-ongo-vic.html.
Estimation of Remaining Useful Life Based on Time Series Analysis

With the introduction of Industry 4.0 into our lives and the creation of smart factories, predictive maintenance becomes even more important. The concept of predictive maintenance basically predicts when some physical data from machinery and equipment is taken and processed, with high accuracy, with various artificial intelligence models. In predictive maintenance systems, downtime differs according to the types of malfunctions. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is a prediction of the time until failure for the machinery. Developing a prediction of RUL value increases the reliability and safety of the systems while reducing the cost of maintenance. Within the scope of this study, the effectiveness of the Machine Learning (ML) and Deep Learning (DL) models were investigated. The experiments were carried out with the NASA Turbofan Engine Corruption Simulation (C-MAPSS) dataset, which consists of 21 sensor data of the aircraft engine. In the results obtained, it has been observed that traditional ML models outperform LSTM, GRU, CNNLSTM algorithms.
Efficiency Analysis and A Random Forest Based Trading Strategy for Heteroscedastic Electricity Market Data

Electricity market behaves similar to a capital market. Buyers and sellers trade on the energy in the Day Ahead (DA) and Real Time (RT) markets based on the Locational Marginal Price (LMP). An efficient market is desirable, because market prices follow a random walk in an efficient market, leaving no arbitrage opportunity for market traders. Market efficiency analysis is critical for Independent System Operators (ISO)s in order to ensure that no market participator make significant profit in the long run using the available information of market driving forces. In this paper we have proposed a machine learning based market efficiency analysis technique, and demonstrated its effectiveness using ISO New England LMP data. Firstly, we have used Generalized Auto-Regressive Conditional Heteroschedasticity (GARCH) model to analyze the time-series electricity market data and the Ljung-Box statistical test to accept or reject Efficient Market Hypothesis (EMH). Secondly, we have proposed a Random Forest based learning model to predict the real time LMPs and subsequent profit approximation. Finally, the relation between net profit and market efficiency is investigated. From the results we can observe that market efficiencies vary over the 24-hour period, and also vary seasonally. Furthermore, we can conclude that the proposed Random Forest based learning model incurs a net profit when the market is inefficient. These observation confirms the EMH.
Assembly state detection based on deep learning and object matching

To improve assembly quality and efficiency, a method based on deep learning and object matching is proposed to detect missing and wrong parts. An improved YOLO V3 neural network is designed to solve the problem of missing assembly. A small target detection scale and attention module is added to the neural network. The size of prior anchor box is optimized by K-means++ clustering algorithm. For the problem of wrong assembly, the standard assembly state detection template is constructed according to the virtual assembly scene in CAD software, and the 2D detection box of the current assembly object in the scene image is matched with the 2D box in the standard state template based on IoU (Intersection over Union) calculation. The assembly model MONA (a 3D model for the evaluation of manual Assembly tasks), is used to test the proposed method. Experimental results show that this method can accurately locate and identify assembly parts, and effectively detect the missing and wrong parts in the assembly process.
Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

The convolutional neural networks (CNN) have shown promising results for various classification problems over the past years. However, selecting various CNN architectures is still challenging as each architecture performs differently with the same dataset. This research aims to evaluate the dependence of brain MRI on various predictive models of CNN based on the complexity of the data for Brain Tumor and Alzheimer’s Disease. Our proposed approach has three parts. First part is the pre-processing of the data which mainly focuses on class balancing and the estimation of data complexity. The second part uses stratified k-fold cross-validation for more reliable results. The last part corresponds to the implementation of four CNN models applying described methods. This paper compares the classification performance of rigorous experimentation on four CNN variants namely S-CNN (CNN trained from scratch), ResNet50, InceptionV3, and Xception over two brain MRI image datasets evaluated with and without the use of Principal Component Analysis (PCA). The work benchmarks CNN models by comparing the average scores of Accuracy, Precision, Recall, F1 score, and AUC score from the stratified five-fold cross-validation.
System Level ESD Testing with Capacitively Coupled Stress Pulses

A debugging test method is presented which helps to identify susceptible pins which failed after system level ESD testing in compliance with IEC 61000-4-2. It applies capacitive coupling between the test probe ground plane (GP) and a chip of an operated equipment under test (EUT) to provide a return path for a stress pulse generated by a transmission line pulsing (TLP) pulse generator. The voltage pulse is injected into a single pin of a tested device within an EUT, which allows to evaluate the susceptibility to ESD induced failures for each pin of an operated EUT in a time and cost-effective manner.
IoT Anomaly Detection and Attack Identification Using Smart Traffic Classification Techniques

Attacks in IoT networks are increasingly numerous and frequent. Therefore, knowing how to extract abnormal traffic in order to identify potential attacks seems to be a task of primary importance. Here, we first seek to detect anomalies in smart home IoT network traffic through binary classification. Then we try to identify the attacks contained in the anomalous traffic by using learning models based on multiclass classification. In each of these two cases, we used two different datasets to study and compare four learning models based on Decision-Tree (Adaboost), Random Forest, KNN (K-Nearest Neighbors) and a Neural Network model. The results show that our models based on Decision-Tree and Random Forest are more efficient both in the detection of anomalies and in the identification of attacks. This is also the case both in predictions and in behavior with new data.
Point cloud extraction of aircraft skin butt joint based on adaptive matching calibration algorithm

The assembly accuracy of aircraft skin directly affects the aerodynamics, air tightness and invisibility of aircraft. Extracting the butt joint feature area of aircraft skin can provide an effective basis for the assembly of aircraft skin. With the wide application of surface structured light measurement in many fields, based on surface structured light, we propose an aircraft skin seam point cloud extraction technology with adaptive matching calibration algorithm. Firstly, multi-stage high-precision calibration is carried out, and then the negative feedback of height difference is introduced to correct and calculate the calibration range of aircraft skin height by using the negative feedback of height difference to realize adaptive calibration. Finally, high-precision point cloud data of aircraft skin butt joint are obtained. The experimental results show that the adaptive matching calibration algorithm proposed in this paper can effectively extract the butt seam feature area of aircraft skin, and the scanning time and data processing time are less than that of single line structured light measurement. This study provides an efficient method for 3D point cloud data extraction of aircraft skin butt joint feature area.
Reducing Time in Active Visual Target Search with Bayesian Optimization for Robotic Manipulation

For robotic tasks in unknown and large environments, it is important to locate targets to be manipulated by the robot in the environment. If the workspace is too large to capture at a time within the limited measurement field of visual sensors, it is necessary to explore the workspace by moving sensors. However, previous research which models almost the whole environment conduct unnecessarily exhaustive exploration for task execution. The purpose of this research is to enable robots to locate targets in a large workspace in a short time for task execution. Target exploration is formulated as a variation of a 2D black-box optimization problem. A sample-efficient exploration algorithm is proposed based on Bayesian optimization(BO) with Gaussian Process(GP). To utilize the continuity of physical motion of the camera, the algorithm for continuous search is proposed. Furthermore, to utilize prior knowledge about target positions, if available, a method of incorporating prior distribution in GP is proposed, which enables even faster exploration. Experimental results in both simulation and real environments demonstrated the efficiency of the proposed method compared to hand-crafted or naive BO-based exploration algorithms.
Transcription of Audio to MIDI Using Deep Learning

We investigate automatic transcription of polyphonic piano music with recurrent neural networks directly from raw audio. Previous approaches to this task rely on spectrograms or other features extracted for training, which requires timely pre-processing. In our approach, we train directly on sequences of 64 milliseconds of samples from the waveform. We evaluate our models on the MAESTRO dataset and achieve promising levels of pitch and onset accuracy but difficulty detecting the correct offset of the notes. Our approach also enables learning the note pitches and velocities at the same time. Together these results demonstrate the potential of relatively simple deep learning architectures to learn music transcription directly from raw audio.
Naturalness and Dark Matter Properties of the BLSSM

In this report, we compare the naturalness and Dark Matter (DM) properties of the Minimal Supersymmetric Standard Model (MSSM) and the $B-L$ Supersymmetric Standard Model (BLSSM), with universality in both cases. We do this by adopting standard measures for the quantitative analysis of the Fine-Tuning (FT), at both low (i.e. supersymmetric (SUSY)) and high (i.e. unification) scales. We will see a similar level of FT for both models in these scenarios, with a slightly better FT for the BLSSM at SUSY scales and MSSM at Grand Unification Theory (GUT) scales. When including DM relic constraints, we drastically confine the MSSM's parameter space, whereas we still find a large parameter space available for the non-minimal scenario.
A Python Based Automatic Impedance Scan Tool for PSCAD Models

The growth of inverter-based resources (IBRs) in power systems leads to stability challenges on system operations that were not of major concerns in the past, such as control interactions among IBRs and between IBRs and other components within the transmission network. Control instability for the IBRs operating under weak grid conditions and sub-synchronous control interaction (SSCI) for IBRs connected near series compensated transmission lines are common phenomena considered by system operators. Electromagnetic transient (EMT) study incorporating high bandwidth EMT models are required to assess weak grid control interactions and SSCI issues because positive-sequence electromechanical dynamic models are generally considered inadequate for these types of studies. However, EMT studies are more time-consuming and demand more computational resources. In practice, an SSCI screening process is adopted by system operators during the interconnection process to shortlist the potential vulnerable IBRs for further assessment using detailed EMT simulations. A frequency domain analytical approach is emerging within industry research communities in recent years to evaluate the stability of an IBR dominated system. The impedance characteristic profiles of the IBRs are necessary to use a frequency domain study approach. A python-based impedance scan tool applicable to the PSCAD models has been developed at ERCOT to automatically conduct the frequency scan process. Considering the frequency-coupling effect, the developed toolset, namely ZSC, scans the IBR's impedance based on individual frequency perturbation signals and the tool has been incorporated into the PsCad Auto Run (PCAR) platform developed at ERCOT. This paper presents the schematic of the ZSC tool and some implementation notes on selected PSCAD models.
SWAP-IT: A Scalable and Lightweight Industry 4.0 Architecture for Cyber-Physical Production Systems

In recent years, various abstract and practical architectures have been proposed in the context of Industry 4.0. While these architectures focus on different aspects, their common goal is to facilitate the transformation of a static production into a flexible, resilient, interconnected cyber-physical production system (CPPS). However, reviewing those reveals that a general procedure for applying those architectures is missing. In this paper, we propose a modular, scalable and lightweight architecture utilizing simplified semantic information models. We also present an integration guide that helps factory owners and process engineers to apply the proposed architecture. Furthermore, we also show how the factory operators can make architectural decisions according to their needs. This approach will help speed up the application of the architecture for the realization of a modular and scalable CPPS.
Augmenting Vision-Based Grasp Plans for Soft Robotic Grippers using Reinforcement Learning

Vision-based techniques for grasp planning of robotic end-effectors have been successfully deployed in pick-and-place tasks. However, for computing the optimal grasp, they assume the gripper to be of a rigid parallel-jaw type or a single-point vacuum suction-based design. Planners for soft robotic grippers have used learning from demonstration or heuristics that rely on the compliance of the gripper to achieve a grasp. We demonstrate a model-free reinforcement learning (RL) approach that modifies vision-based grasp plans generated for parallel-jaw grippers and adapts them to grasping with a four-fingered soft gripper. The observed state of the RL model is comprised of the grasp plans from the vision module, the deformation of the fingers, and the pose of the end-effector. The RL model controls each finger separately, discovering grasp synergies during training. This approach is compared to a baseline grasp synergy where all four fingers simultaneously enclose the object. In simulation, we achieve a pick-and-place success rate of 58.4% with the RL model for top-down grasping, compared to 43.2% with the baseline grasp synergy.
An Enhanced Imputation Approach for Spatio-Temporal Clinical Data

Recently, clinical decision making processes largely benefit from electronic health records (EHR) to improve the diagnosis outcomes and quality of patient care. In particular, patients’ conditions in critical care are routinely monitored through essential vital signs such as respiratory rate, heart rate and blood pressure, and lab results. These patient variables in a spatio-temporal and irregular form provide valuable information to support time-dependent decision making. However, the absence of values in these data is almost inevitable due to various causes including irregular sampling rate, sensor dropoff, or manual interference during the data collection process. It, therefore, inhibits the extraction of meaningful information using data-driven machine-learning techniques. To this end, we propose a novel imputation method to enhance data quality. In our approach, we improve the performance of pre-filling methods including mean imputation and interpolations by 7%– 95% on both synthetic dataset and MIMIC III database. Also, our method improves the mean imputation and outperforms the 3D-MICE for 9 out of 13 laboratory variables by 25%–80% on 8267 MIMIC III patients.
Convolution Neural Network Model for Image Classification of Graphene Oxide Thin Films Sheet Resistance

The microscopic images indicate the presence of few layers of graphene sheet resistance with a morphology resembling a thin curtain, and it is hard to recognize manually without artificial intelligence. Therefore, the purpose of this project is to classify the Graphene Oxide (GO) thin films based on sheet resistance values through microscopic images by using a convolution neural network (CNN) model. The data was split into 80% for training and 20% for testing and validation. There were two datasets used for the classification; GO deposited from Atomizer 2 and GO deposited from Atomizer 3. The data were classified into three classifications based on their sheet resistance value using MATLAB programming with a deep learning toolbox model and pre-trained network Vgg-16. The proposed method has shown the efficiency of a deep learning convolution neural network in classifying the microscopic images of GO through sheet resistance value. The experimental results show that microscopic images of GO from Atomizer 2 has 72.59% validation accuracy while for microscopy images of GO from Atomizer 3 has SS.72% validation of accuracy.
A Novel TSO-DSO Ancillary Service Procurement Coordination Approach for Congestion Management

This paper proposes a novel TSO-DSO coordination approach which aims to optimally procure ancillary service (AS) for congestion management in day-ahead operation planning framework. The proposed three-step approach consists of: (i) provision of active power flexibility range along with the associated cost by the Distribution System Operators (DSOs) at TSO-DSO interface, (ii) procurement of active power by the Transmission System Operator (TSO) for relieving congestion in its system and (iii) optimal re-dispatch of existing flexible resources by DSOs in active distribution systems (ADSs) to maintain the committed active power schedule at their TSO-DSO interface and to ensure the reliable operation of ADSs. Furthermore, both real-world N-1 security criterion at TSO level and inter-temporal aspects of emerging flexible resources such as flexible loads (FLs) and energy storage systems (ESSs) at both TSO and DSO levels are considered. Accordingly, the proposed approach is based on the deterministic multi-period AC security-constrained optimal power flow (MP-SCOPF) at TSO level and multi-period AC optimal power flow (MP-OPF) at DSO level, formulated as non-linear programming (NLP) problems. The applicability of the proposed methodology is demonstrated on a power system connecting a 60-bus transmission system with five 34-bus ADSs.