Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-arm Object Transportation via Model Predictive Control and External Disturbance Estimation

This paper addresses the problem of transporting a rigid box filled with unknown objects with a dual-arm robotic system. Enforcing non-sliding contact behavior, which guarantees the transportation of the box despite the unknown load's action, is the main difficulty in this setting. To solve this problem, we propose a high-level model-predictive controller, which uses a nonlinear extended state observer to estimate the external disturbance, and determine the wrench required to the box for tracking a trajectory. A quadratic program transforms the calculated wrench into optimal desired contact forces on the two end-effectors. Finally, a low-level admittance control framework with an inner velocity loop is established to indirectly control the actual contact forces. We verify the effectiveness of the proposed control method with experiments carried out on a real dual-arm robotic system.
Criteria for retrograde rotation of accreting black holes

Rotating supermassive black holes produce jets and their origin is connected to magnetic field that is generated by accreting matter flow. There is a point of view that electromagnetic fields around rotating black holes are brought to the hole by accretion. In this situation the prograde accreting disks produce weaker large-scale black hole threading magnetic fields, implying weaker jets that in retrograde regimes. The basic goal of this paper is to find the best candidates for retrograde accreting systems in observed active galactic nuclei. We show that active galactic nuclei with low Eddington ratio are really the best candidates for retrograde systems. This conclusion is obtained for kinetically dominated FRII radio galaxies, flat spectrum radio loud narrow line Seyfert I galaxies and a number of nearby galaxies. Our conclusion is that the best candidates for retrograde systems are the noticeable population of active galactic nuclei in the Universe. This result corresponds to the conclusion that in the merging process the interaction of merging black holes with a retrograde circumbinary disk is considerably more effective for shrinking the binary system.
Weaker Assumptions for the Short Path Optimization Algorithm

The short path algorithm gives a super-Grover speedup for various optimization problems under the assumption of a unique ground state and under an assumption on the density of low-energy states. Here, we remove the assumption of a unique ground state; this uses the same algorithm but a slightly different analysis and holds for arbitrary MAX-$D$-LIN-$2$ problems. Then, specializing to the case $D=2$, we show that for certain values of the objective function we can always achieve a super-Grover speedup (albeit a very slight one) without any assumptions on the density of states. Finally, for random instances, we give a heuristic treatment suggesting a more significant improvement.
Heterogeneous Multi-Robot Task Scheduling Heuristics for Garment Mass Customization

Industrial environments that rely on Mass Customization are characterized by a high variety of product models and reduced batch sizes, demanding prompt adaptation of resources to a new product model. In such environment, it is important to schedule tasks that require manual procedures with different levels of complexity and repetitiveness. In a garment mass customization scenario, task scheduling needs to take into consideration the dependency of the tasks, meaning that in order to initiate a certain task, materials from previous tasks may be required. In order to carry out a smooth scheduling process within a garment mass customization factory, not only the tasks but also the transportation of materials to perform such tasks need to be scheduled to static and mobile robots, respectively. To tackle this problem, we propose a set of heuristics that are able to schedule both the task work and transportation of materials. We analyze these heuristics theoretically with respect to computational complexity. Subsequently, the performance of each algorithm is evaluated using a synthetic testset. The comparative analysis shows that the extended algorithms have close results among themselves, whereas for the heuristics, Minimum Transportation Cost (MTC) outperforms all of the other algorithms. Moreover, the combination of Predict Earliest Finish Time (PEFT) and MTC is more efficient compared to other algorithm combinations.
Starburst99: Synthesis Models for Galaxies with Active Star Formation

Starburst99 is a comprehensive set of model predictions for spectrophotometric and related properties of galaxies with active star formation. The models are an improved and extended version of the data set previously published by Leitherer & Heckman. We have upgraded our code by implementing the latest set of stellar evolution models of the Geneva group and the model atmosphere grid compiled by Lejeune et al. Several predictions which were not included in the previous publication are shown here for the first time. The models are presented in a homogeneous way for five metallicities between Z = 0.040 and 0.001 and three choices of the initial mass function. The age coverage is 106—109 yr. We also show the spectral energy distributions which are used to compute colors and other quantities. The full data set is available for retrieval at a Web site, which allows users to run specific models with nonstandard parameters as well. We also make the source code available to the community.
Deep learning-based AMP for massive MIMO detection

Low-complexity detectors play an essential role in massive multiple-input multiple-output (MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate message passing (AMP) algorithm to the detection of massive MIMO transmission. To this end, we need to efficiently reduce the divergence occurrence in AMP iterations and bridge the performance gap that AMP has from the optimum detector while making use of its advantage of low computational load. Our solution is to build a neural network to learn and optimize AMP detection with four groups of specifically designed learn-able coefficients such that divergence rate and detection mean squared error (MSE) can be significantly reduced. Moreover, the proposed deep learning-based AMP has a much faster converging rate, and thus a much lower computational complexity than conventional AMP, providing an alternative solution for the massive MIMO detection. Extensive simulation experiments are provided to validate the advantages of the proposed deep learning-based AMP.
A Multifeatured Time–Frequency Neural Network System for Classifying sEMG

Human gesture-related electromyographic signal in intelligent recognition systems has attracted widespread attention. The EMG signal is the temporal and spatial superposition of motor unit neural potentials and it contains nonlinear and complex characteristics. Despite the previous efforts, recognizing more different actions and building an online recognition system remain challenging. To address these issues, we construct a multi-feature time-frequency neural network system and a dataset which contains twenty kinds of hand movements. In a multi-featured time-frequency neural network system, the multi-layer CNN structure is used to obtain faster recognition and better classification results. The experimental results demonstrate that equipment and algorithmic model can reach a 94.66% accuracy. Our approach focuses on processing multiple time and frequency domain features of sEMG using CNN. Importantly, we also perform real-time validation. The system can accurately recognize hand movements to control UAV motion in real-time, in which case system classification takes 0.05s. This demonstrates the practicality and effectiveness of the sEMG acquisition system based on the multifeatured time-frequency neural network.
Identifying Root Cause of 5G Radio Quality Deterioration Using Machine Learning

It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.
A restricted b‐spline basis for S‐shaped calibration curves

S‐shaped curves arise in many calibration assays. They are typically modeled using an empirically chosen nonlinear function. Nonparametric alternatives usually perform poorly because there are often insufficient data points and there are difficulties in enforcing the required asymptotic behavior. We propose an alternative approach using a b‐spline basis adapted to produce lower and upper asymptotes in the fitted curves. Its main advantage is that, with pre‐determined knot positions, the model is linear in the parameters and so is more numerically stable than a nonlinear model; this has advantages in, for example, fitting a mixed model to multiple curves. We explore the performance of this approach using data from a single radioimmunoassay for cortisol and from a set of 96 ELISA assays for the herbicide atrazine. Copyright © 2012 John Wiley & Sons, Ltd.
Resilience Analysis of a Distribution System During Major Events

The proliferation of climate change makes extreme weather events increasingly more common and more severe. With this motivation, this paper analyzes and compares the performance of an electric distribution system owner and operator during two category 5 hurricanes with different characteristics, using historical data like customers affected, outage numbers and hours on outage. The path of the first hurricane covered only part of the utility territory but still had a significant impact, while the second one, which happened merely days later, had a much more devastating effect as its path was directly through the utility territory, affecting a wider region of the already debilitated system. The historical data provide insight on the impact these events had on the electric utility and its customers, as well as it is an indicator of an overall system condition and performance. Also, the paper uses the data to create resilience curves and compare their shapes to the literature suggested patterns. The financial impacts were also extrapolated using quantifications of the value of lost load (VOLL). We conclude that the system health prior to an event plays a major role in its fragility.
A Demand-Responsive Green Ammonia Plant and its Impact on the Electricity Distribution System

Most large-scale ammonia production typically relies on natural gas or coal, which causes harmful carbon pollution to enter the atmosphere. The viability of a small-scale “green” ammonia plant is investigated where renewable electricity is used to provide hydrogen and nitrogen via electrolysis and air liquefaction, respectively, to a Haber-Bosch system to synthesize ammonia. A green ammonia plant can serve as a demand-responsive load to the electricity distribution system and provide long-term energy storage through chemical energy storage in ammonia. A coordinated operational model of an electricity distribution system and an electricity-run ammonia plant is proposed in this paper. Case studies are performed on a modified PG&E 69-node electricity distribution system coupled with a small-scale ammonia plant. Results indicate the ammonia plant can adequately serve as a demand response resource and positively impact the distribution locational marginal price (DLMP).
Keyless Entry System and Campus Office Management System

The project is a keyless entry and campus office management system to be used in campus offices. The problem addressed in the project is that it is difficult and costly to provide a suitable environment for study and meetings. It is aimed to provide an environment where home-office employees can conduct business meetings in isolation and students and other people can work individually or in groups. Users will do their transactions through the mobile application to be designed without entering into a dialog with anyone to use this environment. In this determined environment, the smart door lock system at the entrance doors and the mobile application are connected to the same database. The user does not need any key because he will use his own phone instead of the key. Another advantage is that office managers using the application will be able to view their employees' office entry/exit information and follow-up with this system.
Quantum Distribution System Reliability Assessment

The reliability assessment is becoming an inevitable part of distribution systems in lights of uncertainty output of integrated distributed energy resources (DERs). The Monte Carlo Simulation (MCS) approach is a broadly used method in replacing the analytical methods in reliability evaluation. The accuracy of this method, however, highly depends on the sampling size, and hence, a complicated system with large amount of components requires a large sampling size and thereby computational effort. In this paper, for the first time, the idea of quantum computing (QC) is introduced in power system reliability evaluation to achieve the convergence speed-up compared to the conventional methods with the same number of samples. The main contributions of this paper include: 1) the innovative development of a quantum model for reliability assessment; 2) achieving the quadratic speed up using QC compared to the classical MCS method; 3) utilization of quantum amplitude estimation (QAE) algorithm to calculate the reliability indices. Different scenarios are tested in a radial distribution system to show the efficacy of QC.
Simultaneous Parameter Estimation and Tracking Control without Persistence of Excitation with Application in Ink-Jet Deposition

In the field of ink-jet deposition, there is a lack of specific knowledge to detect and change drop volume to regulate fluid placement in real time. We first derive the model of line width as a function of nozzle velocity, valve duty cycle, and physical properties of fluid and surface. We then propose a novel approach for simultaneous parameter estimation and tracking control to estimate unknown parameters while regulating line width in ink-jet deposition. Stability of both the estimator and control are established using Lyapunov stability theory. Simulations and experimental results confirm the stability and performance of the approach.
Frequency-dependent current noise through quantum-dot spin valves

We study frequency-dependent current noise through a single-level quantum dot connected to ferromagnetic leads with noncollinear magnetization. We propose to use the frequency-dependent Fano factor as a tool to detect single-spin dynamics in the quantum dot. Spin precession due to an external magnetic and/or a many-body exchange field affects the Fano factor of the system in two ways. First, the tendency towards spin-selective bunching of the transmitted electrons is suppressed, which gives rise to a reduction of the low-frequency noise. Second, the noise spectrum displays a resonance at the Larmor frequency, whose line shape depends on the relative angle of the leads' magnetizations.
Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application

The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. Therefore, TSVR not only has the advantages of fast computation and low computational complexity, but also has better regression performance. Classic SVR, TSVR is assuming that accords with mean zero, variance of noise with the variance of the gaussian distribution or not to consider the effects of noise, but in some practical applications, such as wind speed forecasting, noise characteristic is more in line with mean zero, variance for heteroscedasticity of gaussian distribution, therefore, the return of the existing technology is not the best. In this paper, the characteristics of heteroscedasticity Gaussian noise are introduced into the model TSVR, and the twin support vector regression model based on heteroscedasticity Gaussian noise (TSVR-HGN) is constructed. The Lagrange multiplier method is used to solve the problem, and the optimization algorithm is used to find the global optimization. The artificial data set, UCI data set and wind speed data set were selected for experimental comparison. The experimental results show that TSVR-HGN has better prediction accuracy.
Simulation Aided Anticipatory Congestion Avoidance for Warehouses*

There is a sudden surge in the number of people ordering items online that are facilitated through warehouses. Modern-day warehouses use robots for most of the tasks including picking and sorting. Efficiency is of prime concern in any warehouse, while current efforts in the literature are restricted to solving the optimization of warehouse processes as an operational research problem using fixed travel costs. As the number of orders and robots increases, congestion is witnessed in the warehouse that invalidates fixed travel costs assumptions. To facilitate research in warehousing we first propose a modular simulator for the warehouse that simulates the business operations of order generation, order fulfillment scheduling, item picking, and sorting. The simulator also models the robots for traveling within the warehouse network, scheduling charging, intersection management, and congestion management. With the increasing demand, the warehouses increase the number of robots making the transportation network operate beyond capacity. In this direction, we analyze the performance of the warehouse from a transportation perspective using fundamental diagrams. The warehouses contain only controllable entities (robots) that enable predicting the congestion levels for solving the planning problem. The results show improvements of around 5% in the order fulfillment time and the number of picks that can significantly increase the profitability of the warehouse.
Development of Video-Based Emotion Recognition System using Transfer Learning

Due to the complexity of its system and the numerous advantages of its implementation, video emotion identification is a popular area of study today. There have been multiple ways implemented. In this project, emotion recognition in videos will be performed using deep learning. The model will include initialization, feature extraction, emotion categorization, and prediction. LeNet and AlexNet, two distinct neural networks, are used to extract features and classify emotions. There will be some parameter tuning to determine if it enhances the employed architecture’s performance, including optimizers and batch size. Each architecture for deep learning will generate a final forecast of four fundamental emotions: disgust, happiness, sadness, and surprise. AlexNet’s performance is enhanced by the SGD optimizer, whereas RMSprop improves LeNet’s performance. Results showed that AlexNet with SGD optimizer provides 93.00% recognition accuracy.
Koopman Kalman Particle Filter for Dynamic State Estimation of Distribution System

Dynamic state estimation (DSE) plays an important role in the real-time control and monitoring of distribution systems, which are high-dimensional space–time systems. The degree of nonlinearity of distribution system increases drastically with the availability of sustainable energy and the diversification of load types, thereby reducing the accuracy of the standard linear model. Under the existing schemes, the measurement noise may not follow a Gaussian distribution. Therefore, a Koopman operator-based Kalman particle filter (KKPF) is proposed herein for estimating the dynamic states of a distribution system. The KKPF performs data-driven dynamic state estimation based on the Koopman operator theory, which does not rely on the distribution system model and can be applied to high-dimensional systems. Furthermore, this method can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions. The KKPF method provides accurate estimation results when only a small number of measurements are available. IEEE 141 system and an improved 141 system were used to test the performance of the proposed KKPF compared to the EKF, CKF, and PF. The test revealed that the proposed KKPF can obtain accurate results under high-dimensional and non-Gaussian noise environments.
An Anomaly Detection Study for the Smart Home Environment

Unusual sensor data in smart homes may herald different problems based on sensor errors, security vulnera-bilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.
Activity-Monitoring System for Harmful Wildlife Using WuR Multi Stage Sensor Array

Wildlife has been damaging crops in mountainous areas where it is difficult to access commercial power sources. Therefore, a power-saving system for monitoring harmful wildlife has been proposed that combines multiple sensor nodes with different levels of power consumption. A sensor with higher power consumption capable of obtaining more detailed information is activated step by step after a sensor with low power consumption detects approaching harmful wildlife. However, such a method does not consider the coordination among multiple sensor nodes deployed in a region. An autonomous recording unit (ARU) uses sound to monitor wildlife intermittently for long-term operation to reduce power consumption. However, events are not captured when the ARU is in standby mode. Therefore, we propose a wildlife monitoring system that improves the probability of event acquisition while reducing power consumption by only activating sensor nodes in a target area. Other sensors are activated in standby mode by receiving wake-up calls sent by those sensor nodes when they detect events. We also evaluate the ability of event detection and power consumption of the proposed method.