Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Formulation of Economic Model Predictive Control for Periodic Operations

This paper proposes a novel formulation of economic model predictive control (MPC) for linear systems with periodic operations. In this economic MPC design, the optimal periodic trajectory from an economic point of view is unknown, hence it is not possible to follow a standard control strategy in which the MPC uses this trajectory to define a terminal constraint to guarantee closed-loop convergence. The economic cost function is optimized with a periodicity constraint at each time step considering all periodic trajectories in a period including the current state. The recursive feasibility and closed-loop convergence to the optimal periodic trajectory are analyzed using the Karush-Kuhn-Tucker conditions. Finally, two simulations are provided to demonstrate the main results.
The Saturation Effect Analysis of Oriented Silicon Steel Permanent Magnet Linear Synchronous Motor

Due to the nonlinear influence of ferromagnetic materials, the saturation effect of the magnetic circuit becomes one of the main factors affecting the improvement of motor performance. Magnetic saturation is one of the major challenges for all electrical motors and makes it much more difficult to design high performance electrical motors. The magnetic permeability of the oriented silicon steel, which is often used in transformers, is significantly higher than that of the traditional silicon steel, and can provide a higher magnetic flux density for the motor. Firstly, the influence of the saturation effect of the magnetic circuit on the inductance parameters is analyzed. Secondly, using the finite element software Ansoft, the inductance parameters of the traditional silicon steel and oriented silicon steel PMLSMs are calculated with different loads. Simulation results show that the inductance of the surface-mounted PMLSM is no longer a constant due to the influence of the armature reaction with load. The magnetic saturation of the oriented silicon steel PMLSM decreases by 4.4% and 19% respectively at rated load and 5 times rated load, and its overload capacity is stronger.
Design of Quadrature Voltage Controlled Oscillator at 60GHz in 65nm CMOS

In this paper, we present an analog design of tunable quadrature voltage-controlled oscillator (QVCO). The QVCO uses a cross coupled CMOS core with differential inductor with a capacitor bank to tune it around 60GHz range for 5G wireless technology solutions. It incorporates an in-phase injection circuit for coupling between the two oscillators. The proposed circuit is designed and simulated in UMC 65nm technology. Simulation results showed a tuning range of 55.5 to 62.9GHz and a worst-case phase noise of −99.26 to −97.29 dBc/Hz at 1MHz offset. The resulting figure-of-merit (FOM) comes to be −190.34 to −189.46 dBc/Hz at 1MHz offset and the corresponding FOM for tuning (FOMT) is −192.28 to −191.4 dBc/Hz at 1MHz offset.
Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition

A key task in Bayesian machine learning is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). One prevalent example of this is sampling posteriors in parametric distributions, such as latent-variable generative models. However sampling (even very approximately) can be #P-hard. Classical results going back to Bakry and Emery (1985) on sampling focus on log-concave distributions, and show a natural Markov chain called Langevin diffusion mixes in polynomial time. However, all log-concave distributions are uni-modal, while in practice it is very common for the distribution of interest to have multiple modes. In this case, Langevin diffusion suffers from torpid mixing. We address this problem by combining Langevin diffusion with simulated tempering. The result is a Markov chain that mixes more rapidly by transitioning between different temperatures of the distribution. We analyze this Markov chain for a mixture of (strongly) log-concave distributions of the same shape. In particular, our technique applies to the canonical multi-modal distribution: a mixture of gaussians (of equal variance). Our algorithm efficiently samples from these distributions given only access to the gradient of the log-pdf. For the analysis, we introduce novel techniques for proving spectral gaps based on decomposing the action of the generator of the diffusion. Previous approaches rely on decomposing the state space as a partition of sets, while our approach can be thought of as decomposing the stationary measure as a mixture of distributions (a "soft partition"). Additional materials for the paper can be found at this http URL. The proof and results have been improved and generalized from the precursor at www.arxiv. org/abs/1710.02736.
Chest X-Rays Image Inpainting with Context Encoders

Chest X-rays are one of the most commonly used technologies for medical diagnosis. Many deep learning models have been proposed to improve and automate the abnormality detection task on this type of data. In this paper, we propose a different approach based on image inpainting under adversarial training first introduced by Goodfellow et al. We configure the context encoder model for this task and train it over 1.1M 128x128 images from healthy X-rays. The goal of our model is to reconstruct the missing central 64x64 patch. Once the model has learned how to inpaint healthy tissue, we test its performance on images with and without abnormalities. We discuss and motivate our results considering PSNR, MSE and SSIM scores as evaluation metrics. In addition, we conduct a 2AFC observer study showing that in half of the times an expert is unable to distinguish real images from the ones reconstructed using our model. By computing and visualizing the pixel-wise difference between source and reconstructed images, we can highlight abnormalities to simplify further detection and classification tasks.
Non-iterative multiple data registration method based on the motion screw theory and trackable features

Registration of 3D point clouds is an important issue in the field of 3D reconstruction. In this work, we proposed a non-iterative registration method based on the motion screw theory and trackable features. The screw theory is derived from the theory of rigid body mechanics, which holds the idea that the motion of rigid body can be regarded as a kind of spiral motion and can be effectively represented by an angular velocity vector and a linear velocity vector. It has not been utilized in 3D data registration before as far as we know. In this paper, 3D data registration based on the motion screw theory is specifically introduced, and a searching strategy based on the trackable features in image sequences is presented to improve the accuracy of 3D registration. The proposed method has been successfully tested on real multi-view data. Experimental results showed that it could simplify the computational process, accelerate the speed of registration, and achieve higher precision than other methods.
Online Hate Interpretation Varies by Country, But More by Individual: A Statistical Analysis Using Crowdsourced Ratings

Hate is prevalent in online social media. This has resulted in a considerable amount of research in detecting and scoring it. Most computational efforts involve machine learning with crowdsourced ratings as training data. A prominent example of this is the Perspective API. , a tool by Google to score toxicity of online comments. However. , a major issue in the existing approaches is the lack of consideration for the subjective nature of online hate. While there is research that shows the intensity of hate varies and the hate depends on the context. , there is no research that systematically investigates how hate interpretation varies by country or individual. In this exploratory research, we undertake this challenge. We sample crowd workers from 50 countries, have them score the same social media comments for toxicity and then evaluate the differences in the scores. , altogether 18. ,125 ratings. We find that the interpretation score differences among countries are highly significant. However. , the hate interpretations vary more by the individual raters than by countries. These findings suggest that hate scoring systems should consider user-level features when scoring and automating the processing of online hate.
The Using of Object-Oriented Method to Developing Android Application for Mapping Distribution of MSMEs (Micro, Small Medium Enterprises)

The objective of this research is to develop a Mobile Geographic Information System based on android mobile from user and customer requirements, which it will help the business owner in marketing their products. MSMEs has a big contribution not only to economic growth but also become a private income source for most of the people in Indonesia. Therefore, to increase the independence, efficiency and effectivity of MSMEs, utilization of information technology should be improved. The reason why a mobile application was chosen because mobile technology is one of technology that often used by the community. So, it's easy to use. An object-oriented approach was chosen in this system development because it can give a clear and understandable information to the stakeholders and also it can describe the task and function of each actor in the system. We expect that this model will help the developer to see the system and application comprehensively. And also help the owner of MSMEs to increase their effectiveness and efficiency in promoting their product
Possible Detection of Multirotor UAVs Based on Disturbances in Magnetic Field

The number of unmanned aerial vehicles (UAVs) incidents in the world is increasing, because it is not difficult to buy multirotor UAVs that are inexpensive and technologically well equipped. This is the reason why the development of detection systems gains importance. Radar systems and specific frequencies detectors have their limitations, so it is necessary to develop different sensing methods. The presented paper deals with the possible lowrange method to detect electric-powered multirotor UAVs based on the low frequency magnetic fields generated around them. The results of the realized experiments with magnetic field sensors show that the UAV creates measurable magnetic fields disturbances that could be used as an intrusion indicator.
Optimized Arrival Time Determination of UHF Pulses for Localization of Partial Discharges in Power Transformers

Location of a partial discharge (PD) fault inside a transformer can be determined using the Ultrahigh Frequency (UHF) PD measurement technique. Hereby, the electromagnetic (EM) waves that propagate due to the PD are captured using specially designed probes that are inserted into the transformer tank through its oil drain valves. The difference in the arrival time (AT) of these signals is the base of an equation system, where the PD location and the PD inception time are unknown. In this contribution, the influence of erroneous determined ATs on the localization error is investigated using a two-dimensional analogy model. Furthermore, a novel method based on detection of abrupt changes in a certain statistical property of the signal for the AT determination is proposed. A test setup is utilized to evaluate the localization algorithm. Finally, the results are presented and the precision of the proposed method for PD localization using the UHF measurement technique is discussed.
Three Strategies for Information Systems Research in the Presence of an Efficient Knowledge Market

The value of research can be measured by different criteria such as their aesthetic, scholarly, and practical utility. In this conceptual paper, we argue that generating outcomes that are of utility to practitioners is a challenging task: In a fast-changing digital economy there is an efficient knowledge market where memes about technologies and methods that are of competitive value spread quickly, and where IS research is often relegated to explaining what practice already knows. Understanding how an efficient knowledge market works can help us identify research problems that can generate results of practical utility. We suggest three strategies for the IS field. First, to develop grand theory that is applicable to emergent phenomena across contexts and time and can thus transcend particular innovations. Second, to move into interesting territory by studying the impact of new memes beyond the defined boundary conditions in both intended and unintended ways. Third, to more often be a meme generator, rather than a meme analyzer or reporter.
Enhanced Upconversion Performance in Alloyed CdSe(Te)/CdS1–xSex/CdSe Core/Rod/Emitter Nanostructures

Colloidal quantum dot (QD) nanostructures have been shown to convert multiple low-energy photons into a single, high-energy photon through a process called photon upconversion. QD nanostructures could replace conventional upconverter materials in solar cell applications and can be tuned to absorb photons transparent to the host cell and emit usable photons, increasing the short circuit current of the host cell while maintaining open circuit voltage. Double QDs (coupled via a nanorod) have previously demonstrated 0.1% upconversion efficiency and a peak-to-peak energy gain of 380meV under pulsed excitation equivalent to 105 times solar concentration. However, for upconverters to be viable for solar energy harvesting, they must have high efficiency in low-light (1-sun) conditions. Engineering improved performance under device-relevant conditions requires a better understanding of the upconversion mechanisms. We synthesize core/rod/emitter complexes and demonstrate near-infrared (NIR)-to-visible upconversion photoluminescence (UCPL) in these structures with continuous wave (CW) illumination near low-light conditions. We further observe photon energy gains of 700meV (from 850nm to 575nm). To improve upconversion efficiency, we grow homogeneous absorber QDs and alloyed rods and systematically study the quantum yield using an integrating sphere to measure upconversion photoluminescence relative to a rhodamine 101 standard. We observe an order of magnitude increase in upconversion quantum efficiency. Further understanding the effect of morphology and composition on upconversion and carrier transfer mechanisms is critical to realizing improved optical performance and upconversion quantum efficiencies in these nanostructures.
A Blockchain-Based Privacy-Preserving Payment Mechanism for Vehicle-to-Grid Networks

As an integral part of V2G networks, EVs receive electricity from not only the grid but also other EVs and may frequently feed the power back to the grid. Payment records in V2G networks are useful for extracting user behaviors and facilitating decision-making for optimized power supply, scheduling, pricing, and consumption. Sharing payment and user information, however, raises serious privacy concerns in addition to the existing challenge of secure and reliable transaction processing. In this article, we propose a blockchain-based privacy preserving payment mechanism for V2G networks, which enables data sharing while securing sensitive user information. The mechanism introduces a registration and data maintenance process that is based on a blockchain technique, which ensures the anonymity of user payment data while enabling payment auditing by privileged users. Our design is implemented based on Hyperledger to carefully evaluate its feasibility and effectiveness.
A Hierarchical Game for Joint Wireless and Cloud Resource Allocation in Mobile Edge Computing System

In this paper, a game theoretic approach is proposed for the joint wireless and cloud resource allocation problem in Mobile Edge Computing (MEC) system. We put forward a hierarchical game based algorithm (HGBA) to address the problem, which includes the lower-level evolutionary game (LEG) and the upper-level exact potential game (UEPG) to jointly allocate wireless and cloud resources for mobile users, aiming at minimizing the cost of mobile terminals (MTs) and maximizing the utility of MEC servers. The existence of Nash equilibrium (NE) is proved. At the lower-level, the dynamic replicator method is applied to obtain the NE so as to minimize the cost of MTs, including momentary cost, energy consumption and delay cost. At the upper-level, to solve the resource sharing problem, we use the convex optimization method and game theory to maximize the servers' utility under MTs' QoE constraints, and NE is obtained through solving Karush-Kuhn-Tucker (KKT) condition. The simulation results show that the average utility of MEC servers is higher compared with existing algorithms, and the cost of MTs is significantly lower than other algorithms with the increasing size of input data.
Composite Robust $H_\infty$ Control for Uncertain Stochastic Nonlinear Systems With State Delay via a Disturbance Observer

In this note, the robust stochastic stabilization and robust $H_\infty$ control problems are investigated for uncertain stochastic time-delay systems with nonlinearity and multiple disturbances. By estimating the disturbance, which can be described by an exogenous model, a composite hierarchical control scheme is proposed that integrates the output of the disturbance observer with the state feedback control law. Sufficient conditions for the existence of the disturbance observer and composite hierarchical controller are established in terms of linear matrix inequalities, which ensure the mean-square asymptotic stability of the resulting closed-loop system and the disturbance attenuation. It has been shown that the disturbance rejection performance can also be achieved. A numerical example is provided to show the potential of the proposed techniques and encouraging results have been obtained.
An Automated Point Set Registration Framework for Multimodal Retinal Image

Multimodal retinal image registration plays an important role in medical image analysis. In this field, retinal images from different modalities are aligned together to achieve a more evaluable fusion image for diagnoses. One of the challenging problem solved in this paper is the low success rate in multimodal retinal image registration. An automated point set registration framework is proposed to solve the problem. The framework includes three parts: feature point extraction and robust initial point matching, matching postprocessing, adaptive mismatches removing and transformation estimation. The experimental results show that our proposed framework is robust to outliers and repeated pattern and it obtains a more stable and accurate result than state-of-the-art methods.
On the possibility to achieve 6-DoF for 360 video using divergent multi- view content

With the rapid emergence of various 360 video capturing devices and head-mounted displays, providing immersive experience using 360 videos is becoming a topic of paramount interest. The current techniques face motion sickness issues, resulting in low quality of experience. One of the reason is that they do not take advantage of the parallax between the divergent views, which may be helpful to provide the correct view according to the user's head motion. In this paper, we propose to get rid of the classical ERP representation, and to synthesize arbitrary views using different divergent views together with their corresponding depths. Thus, we can exploit the parallax between the divergent views. In this context, we assess the feasibility of the depth estimation and the view synthesis using state-of-the-art techniques. Simulation results confirmed the feasibility of such a proposal, in addition to possibility to achieve sufficient visual quality for a head motion up to 0.1m from the rig, when using generated depth map for view synthesis.
Deep Age Estimation Model Stabilization from Images to Videos

Deep learning models for age estimation from a single image have significantly improved the state-of-the-art. However, when deploying a deep age estimation model from images directly to videos, it often suffers from the fluctuation issue, i.e., the estimated age varies a lot for face frames from the same person. To deal with this problem, this work presents a new deep age estimation model specifically designed for video facial age estimation, which produces very stable and accurate age estimation results. The proposed deep architecture for video facial age estimation incorporates a convolutional neural network with an attention mechanism, where the convolutional neural network extracts the facial features, and an attention block aggregates the facial feature vectors into a single feature representation for final age estimation. The whole model is trained by a novel loss function to guarantee both the accuracy of each frame and the stabilization of age estimation results of all the frames. To evaluate the proposed model for video facial age estimation, a new dataset is collected and annotated. Extensive experimental analyses and comparisons demonstrate the effectiveness of the proposed model and the state-of-the-art performances compared to many competing methods.
Simple all-fiber optical-microwave phase detector for subfemtosecond synchronization

We demonstrate a passively phase-biased simple all-fiber optical-microwave phase detector based on intrinsic phase shift of multiport fiber coupler. It is composed of fiber coupler, phase modulator and balanced photodetector, hence free of additional phase bias and magneto-optic components. As a result of optical-microwave synchronization between mode-locked laser and voltage controlled microwave oscillator, 875-as integrated jitter [1 Hz-1 MHz] and 860-as rms timing drift over 1 hour is achieved.
Assessing Variability and Uncertainty in Green Infrastructure Planning Using a High-Resolution Surface-Subsurface Hydrological Model and Site-Monitored Flow Data

Green infrastructure (GI) is increasingly being used in urban areas to supplement the function of conventional drainage infrastructure. GI relies on the “natural” hydrological processes of infiltration and evapotranspiration to treat surface runoff close to where it is generated, alleviating loading on the conventional infrastructure systems. This research addresses growing interest in identification and quantification of uncertainties with distributed, infiltration-based stormwater control measures, retrofitted on private and public properties and in right-of-ways in existing urban areas. We identify four major sources of variability and uncertainty in cumulative performance of systems that rely on extensive implementation of distributed GI: non-additive effects of individual best management practices (BMPs) at the catchment scale; the spatial configuration of fine-scale land use and land cover changes; performance changes due to climate change; and noise levels present in urban flow monitoring programs. Using a three-dimensional coupled surface-subsurface hydrological model of a residential sewershed in Washington DC, we find that prolonged, large-magnitude rain events affect various spatial configurations of GI networks differently. Runoff peaks and volumes can both be influenced by the spatial permutations of infiltration opportunities in addition to the absolute magnitude of treated area. However, the magnitude of the last source of uncertainty—noise levels in urban flow monitoring programs—may be larger than sources of variability associated with spatial changes in fine-scale land use and land cover. Changes associated with climate change-- more frequent and larger rainfall events-- will likely intensify performance differences between spatial configurations of GI but also increase noise levels in urban flow monitoring programs.