Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear observer-based optimal control using the state-dependent Riccati equation for a class of non-affine control systems

The present study uses a combination of a state-dependent Riccati equation (SDRE) controller and SDRE observer to control a class of nonlinear non-affine control systems. This type of nonlinear system features control nonlinearities that change the formulation of the SDRE and its solution techniques. Control law is obtained using the exact solution method for low-order and uncomplicated systems; numerical approaches are available for complex and high order systems. Both solution methods must use integral control formulation or solve the system equation using control law simultaneously to treat control nonlinearities. The closed loop feedback of the system is partially available, providing an observer for estimation of full-state feedback. The efficacy, advantages and compatibility of the controller with the observer guides the choices to the SDRE observer. The stability proof of the SDRE controller in the non-affine structure is presented and four case studies are simulated and discussed. Two were selected from previous studies to verify the proposed algorithm. The third, more complicated model, shows the ability of the controller to solve complex and high order systems. A brief discussion of the suboptimal cheap control problem and an example are also presented.
ON THE NATURE OF FLUORESCENT SPECTRUM OF A TWO ATOM DICKE MODEL IN A NARROW BANDWIDTH SQUEEZED BATH

We discuss the spectrum of resonance fluorescence from a system of two identical coherently driven two-level atoms interacting with a finite bandwidth squeezed bath, including the atomic dipole-dipole interaction between the atoms. The characteristics of the spectral features are studied by solving the master equation analytically in the limit of strong driving field. The comparison of these features with respect to an ordinary bath as well as broadband squeezed bath are also presented.
TOWARDS TOTAL KALMAN FILTERING FOR MOBILE MAPPING

A dynamic model is the usual modus operandi of a Mobile Mapping System. The model solution, after linearization and discretization, is achieved using the Weighted Least-Squares (WLS) approach, which results in one of the various Kalman filter algorithms. However, implicit in the formulation is that neither the observation equation matrices nor the transition matrices at any epoch contain random entries. As such an assumption cannot always be guaranteed, we here allow random observational errors to enter the respective matrices. We replace the WLS by the Total-Least-Squares (TLS) principle - with or without weights - and apply it to this novel Dynamic Errors-in-Variables (DEIV) model, which results in what we call Total Kalman Filter (TKF). It promises to offer more representative solutions to the dynamic models of Mobile Mapping Systems over existing versions of Kalman filtering.
Linear gyrokinetic simulations using particles for small perpendicular wavelength perturbations

Note: Proc. Joint Varenna-Lausanne International Workshop Theory of Fusion Plasmas, Varenna, Italy, August 28 - September 1, 2000, ISPP-19, ISBN88-7794-248-7, 327 - 332 (2000) Reference CRPP-CONF-2000-009 Record created on 2008-05-13, modified on 2017-05-12
On convergence properties of variational constitutive updates for elasto‐visco‐plasticity

This paper deals with J2 elasto‐visco‐plasticity and analyzes in details a variational formulation of associated constitutive updates. The variational formulation is briefly presented and compared with the traditional radial return algorithm. Differences are highlighted in the case of combined hardening and rate‐dependency models. In that case, the variational formulation introduces an algorithmic parameter, which effect is analyzed on precision and convergence behavior. A practical rule is proposed for choosing an optimal value for this parameter (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Intelligent recognition method of indication of substation pointer instrument based on deformable convolution neural network

Intelligent identification method of indicator number of substation instrument based on deformable convolutional neural network there are many equipment in substation, and the value and scale of oil thermometer, oil level gauge, pressure gauge and other instrument equipment reflect the operation state of most instruments and meters, which is particularly important. Therefore, the research on value reading of instrument equipment in substation is particularly key. At present, for the instrument recognition of substation, most studies use traditional image processing and machine learning methods. However, in the recognition process, due to the influence of uneven illumination, complex background, rotation angle, image blur, shooting angle, proportion change and other factors, the recognition accuracy of pointer instrument is low and its usability is poor. In order to solve the above problems, this paper combines the traditional image processing technology with the deep learning method, and proposes an automatic recognition method of substation pointer instrument based on deformable convolutional neural network. The idea of deformable volume is introduced to enhance the modeling ability of convolutional neural network, so as to improve the accuracy of instrument recognition. The main idea is that firstly, the deformable convolution neural network method is used to detect the instrument image in the image, then the residual neural network is used to extract the key points of the instrument dial and pointer, then the detected key points are used to fit the dial circle and pointer, and finally the readout value is calculated according to the deflection angle of the pointer relative to the scale. The experimental results show that this method is very effective for the identification of pointer instruments, and has high accuracy and practicability, which is conducive to promoting the realization of intelligent operation and maintenance of substation.
Reliability Function for Streaming Over a DMC With Feedback

Conventionally, posterior matching is investigated in channel coding and block encoding contexts – the source symbols are equiprobably distributed and are entirely known by the encoder before the transmission. In this paper, we consider a streaming source, whose symbols progressively arrive at the encoder at a sequence of deterministic times. We derive the joint source-channel coding (JSCC) reliability function for streaming over a discrete memoryless channel (DMC) with feedback. We propose a novel instantaneous encoding phase that operates during the symbol arriving period and achieves the JSCC reliability function for streaming when followed by a block encoding scheme that achieves the JSCC reliability function for a classical source whose symbols are fully accessible before the transmission. During the instantaneous encoding phase, the evolving message alphabet is partitioned into groups whose priors are close to the capacity-achieving distribution, and the encoder determines the group index of the actual sequence of symbols arrived so far and applies randomization to exactly match the distribution of the transmitted index to the capacity-achieving one. Surprisingly, the JSCC reliability function for streaming is equal to that for a fully accessible source, implying that the knowledge of the entire symbol sequence before the transmission offers no advantage in terms of the reliability function. For streaming over a symmetric binary-input DMC, we propose a one-phase instantaneous small-enough difference (SED) code that not only achieves the JSCC reliability function, but also, thanks to its single-phase time-invariant coding rule, can be used to stabilize an unstable linear system over a noisy channel. For equiprobably distributed source symbols, we design low complexity algorithms to implement both the instantaneous encoding phase and the instantaneous SED code. The algorithms group the source sequences into sets we call types, which enable the encoder and the decoder to track the priors and the posteriors of source sequences jointly, leading to a log-linear complexity in time. While the reliability function is derived for non-degenerate DMCs, i.e., DMCs whose transition probability matrix has all positive entries, for degenerate DMCs, we design a code with instantaneous encoding that achieves zero error for all rates below Shannon’s joint source-channel coding limit.
The case study of bura of 1st and 3rd February 2007

Two cases when the operational forecast seriously underestimated the wind speed maxima are analysed. The first one in the night between 1 st and 2 nd February 2007 and the second one in the evening of 3 rd February 2007. The two cases are analyzed using measured data from Split and Makarska automatic stations as well as vertical soundings from Zagreb and Zadar and ALADIN model simulations. For the purpose of this study, ALADIN 72 hour forecast was run on 2 km resolution using the complete set of physics parametrizations, hydrostatic and nonhydrostatic dynamics. Results show the potential benefit of nonhydrostatic dynamics for operational forecast does not lie in improvement of the 10 m wind forecast as much as in forecasting clear air turbulence associated with the lee waves.
Determination of a coefficient in the wave equation with a single measurement

We consider an inverse problem of finding the coefficient of the second-order derivatives in a second-order hyperbolic equation with variable coefficients. Under a weak regularity assumption and a geometrical condition on the metric, we prove the uniqueness in a multidimensional hyperbolic inverse problem with a single measurement of Neumann data on a suitable sub-boundary. Moreover we show that our uniqueness yields the Lipschitz stability estimate in L 2 space for solution to the inverse problem. The key is a Carleman estimate for a hyperbolic operator with variable coefficients.
Monitoring river discharge with remotely sensed imagery using river island area as an indicator

Abstract. River discharge is an important parameter in understanding water cycles, and consistent long-term discharge records are necessary for related research. In practice, discharge records based on in situ measurement are often limited because of technological, economic, and institutional obstacles. Satellite remote sensing provides an attractive alternative way to measure river discharge by constructing an empirical rating curve between the parameter provided by remote sensing techniques and simultaneous ground discharge data. River width is a popular parameter for constructing the empirical curve, since change in river discharge can be represented by a change in river width. In some rectangular channels, however, river width does not change significantly with river discharge, so an alternative parameter is necessary. We analyze a novel technique using river island area as an indicator of discharge. A river island often has a flat terrain, and its area decreases with higher discharge. This technique is validated by three river islands in the Yangtze River in China. All 61 remotely sensed images acquired by the HuanJing (HJ) satellites from 2009 to 2010 were correlated with corresponding in situ discharge of the nearby Zhicheng hydrological station. The performance of fitted curves for inferring river discharge is validated using 36 HJ images taken in 2011, and the influence of remotely sensed imagery and river islands is discussed. All three river islands can be used as indicators of river discharge, although their performances are much different. For the river island with the best result, the mean accuracy of the estimates is less than 10% of the observed discharge, and all relative errors are within 20%, validating the effectiveness of the proposed method.
An Adaptive Slotted-Contention-Based MAC Protocol for Ad-hoc Networks

Due to flexibility, ad-hoc networking is an attractive structure for future emerging networks, such as Internet of Things (IoT), wireless sensor networks and vehicular networks. However, due to infrastructure-less, optimizing transmission control to avoid collision in ad-hoc networks is challenging. In this paper, we proposed an adaptive slotted-contention-based media access control (A-SCMAC) protocol which can adapt to network dynamics so that it can reduce collision and improve throughput. With A-SCMAC, each time slot consists of two periods: reservation period (RP) and transmission period (TP). By observing the channel outcomes in the RP the nodes learn the network status such as the number of nodes who have packets to transmit and control the transmission optimally for reserving the TP. Simulation results show that the proposed A-SCMAC can achieve the maximum throughput even the network traffic load changes.
Development of a System to Detect Stress Using Electrocardiographic Signals and Machine Learning Models

Due to the modern lifestyle, environment, and global events of the century, mental health has become a significant concern worldwide, increasing people's awareness about emotions that can evoke psychological and physiological diseases, such as stress. Stress can lead to severe diseases; therefore, detecting and predicting it is vital to avoid health-related problems. This study develops a monitoring system using physiological signals and machine learning models to detect stress responses. Electrocardiographic data has been obtained from datasets recorded during different stress conditions. The signals are filtered, and the heart rate variability has been analyzed to get the time and frequency domain features. Processed data feed the different models and perform stress binary classification. After comparing results on five machine learning models, the random forest classifier obtained an accuracy of 82.4%. Additional signals were recorded in a no laboratory environment and processed, and the stress detection protocol suggested could successfully determine the presence of stress.
Demystifying the use of Machine Learning in Fog Computing

The concept of cloud computing was a major milestone in technology development and brought along a lot of new concepts and ideas. However, cloud computing also introduced certain challenges and problems like an increase in computation cost, high latency, etc. To counter these issues, the notion of fog computing came along. It presented a new decentralized architecture and the idea of a fog layer. Even though fog computing solves the shortcomings of cloud computing, it harbors its own challenges. We have discussed the employment of ML for overcoming the limitations of Fog-based networks.
LAS: Locality-Aware Scheduling for GEMM-Accelerated Convolutions in GPUs

This article presents a graphics processing unit (GPU) scheduling scheme that maximizes the exploitation of data locality in deep neural networks (DNNs). Convolution is one of the fundamental operations used in DNNs and accounts for more than 90% of the total execution time. To leverage massive thread-level parallelism (TLP) in a GPU, deeply nested convolution loops are lowered (or unrolled) into large matrix multiplication, which trades memory capacity and bandwidth for TLP augmentation. A large workspace matrix is split into tiles of general matrix multiplication (GEMM) and concurrently executed by many thread blocks. Notably, the workspace is filled with a number of duplicate data that originate from the same sources in the input feature map during the lowering process. However, conventional GPU scheduling is oblivious to data duplication patterns in the workspace, and thread blocks are assigned to streaming multiprocessors (SMs) irrespective of data similarity between GEMM tiles. Such scheduling misses a significant opportunity to exploit data locality manifested in the DNN convolution. This article proposes a GPU scheduling technique called Locality-Aware Scheduling (LAS) that i) identifies which thread blocks share the largest amount of identical data based on the lowered patterns of a DNN convolution and ii) allocates such thread blocks showing the greatest data similarity to the same SM. In this way, small caches in SMs can efficiently utilize the data locality of the DNN convolution. Experimental results show that LAS with tensor cores achieves 20.1% performance improvements on average with 14.8% increases in L1 cache hit rates.
Accelerating Convolution Neural Networks in Embedded Intel GPUs

In recent years, there is a high demand for real-time operation of Convolutional Neural Networks on small computers in the IoT. Many Intel SoC models, which are useful as small computers, are equipped with GPUs, and OpenVINO software can be used to perform CNN inference. However, the GPU implementation of OpenVINO is not optimized for the low-end embedded GPUs used in the IoT, and there is room for improvement. In this study, we considered the features of Intel GPUs for embedded systems, implemented Resnet50 using C for Metal, and succeeded in increasing the speed up to 1.4 times faster than OpenVINO by optimizing the matrix multiplication and implementing Winograd.
Deep-Learning Based Hybrid Model For The Classification of Lung Diseases

Lung diseases are one of the deadliest diseases which affects many people worldwide. These diseases have become more prevalent due to increase in air pollution. In this paper, we use the Shenzhen and Montgomery small volume datasets to solve the issue of classifying tuberculosis from chest X-ray pictures. The model that is proposed in this paper provides a classification model which employs two convolutional neural networks (Inception-V3 and Xception) that have been tested for lung disease classification using the ImageNet dataset. Furthermore, Squeeze and Excitation module are included in the suggested model for classifying X-ray of the chest into the following two categories: normal and Tuberculosis. To verify the effectiveness and efficiency of our suggested classification model, numerous experiments are conducted on the Shenzhen and Montgomery dataset. Further, the proposed model an accuracy of about 98% and 95% on Shenzhen and Montgomery datasets.
Impact Evaluation of Driving Style on Electric Vehicle Battery based on Field Testing Result

Monitoring electric vehicles' battery status and forecasting their state of health is still an open challenge. To determine how and why a battery degrades over time, we have extensively monitored a Nissan Leaf's battery pack for more than one year. Collecting more than 4.5 million samples via a custom monitoring connected device to investigate how different driving behaviors affect battery aging. In addition, the best driving behaviors based on the battery's optimal temperature are revealed, including speed, acceleration and brake pedal pressure, and horsepower.
Data-driven Model Development and Validation for Laboratory Scale OC-OTEC Plant

The growth of Renewable Energy (RE) resources is increasing rapidly to meet the energy and freshwater demand of the remote islands. Ocean Thermal Energy Conversion (OTEC) is identified as one of the promising renewable energy resources for tropical islands. Major challenges associated with OTEC are to improve the efficiency and reduce the production cost. Data-driven approach is one of the efficient techniques to develop models from Real time data to improve the efficiency of industrial processes and develop advanced control schemes. In this paper, linear data-driven models are developed for the Open Cycle (OC-OTEC) process by considering it as Single -Input Single Output (SISO) and Multi-Input Multi-Output (MIMO). Data is collected from OTEC Laboratory scale experimental setup established at National Institute of Ocean Technology (NIOT) and data driven models are developed using system identification techniques. To identify the proper inputs and outputs, sensitivity analysis is carried out based on the collected data. Then, models for OC-OTEC are developed to generate power and freshwater. The manipulated variables for model development are considered as sea surface warm water flow rate and deep sea cold water flow rate and disturbance as sea surface temperature variation due to climate changes. Finally, the developed models are validated by evaluating their performance metrics.
A theorem on near equicontinuity of transformation groups

A transformation group is nearly equicontinuous if the set of nonequicontinuous points is zero dimensional and compact. It has been shown that if a transformation group is nearly equicontinuous with locally compact, locally connected metric phase space and if the set of equicontinuous points is connected, then the set N of nonequicontinuous points can contain at most two minima! sets. In this paper we will show that if in addition the phase space is not compact, then N contains exactly one minimal set. The purpose of this paper is to prove an additional theorem related to results in [3]. This theorem shows what happens when the phase space of [3] is assumed to be not compact. This result, in particular Corollary 3, is related to the results in [5] and [ó]. Notation and definitions are as in the book [2 ]. All topological spaces considered are assumed to be Hausdorff. Theorem. Let (A, T, it) be a transformation group such that X is a locally compact, locally connected metric space which is not compact, the set E of all points of X at which T is equicontinuous is connected, and the set N = X — E is zero dimensional and compact. Then N contains exactly one minimal set ithat is, a nonempty set which is the orbit closure of each of its points). This theorem will be proved by applying the theorem in [3] to the one-point compactification of X. The following remarks, which are known for the most part, show that the hypothesis of the theorem in [3] are satisfied. Remark 1. Let (A, T, w) be a transformation group where X is locally compact. Then (X, T, t) is a transformation group where X is the one-point compactification of X and (x, t)i= x for each t in T and (x, 1)0= (x, t)ir for each x in X and / in T. Here x is the one point added to make X compact. Proof. The first two axioms are trivial. That r is continuous follows from 1.18 (4) of [2]. Received by the editors October 20, 1969. AMS 1969 subject classifications. Primary 5482, 5480.
BloodHound: Early Detection and Identification of Jamming at the PHY-layer

Traditional jamming detection techniques, adopted in static networks, require the receiver (under jamming) to infer the presence of the jammer by measuring the effects of the jamming activity (packet loss and received signal strength), thus resulting only in a-posteriori analysis. However, in mobile scenarios, receivers (e.g., drones, vehicles, etc.) typically experience an increasing jamming effect while moving toward the jamming source. This phenomenon allows, in principle, an early detection of the jamming activity—being the communication not yet affected by the jamming (no packet loss). Under such an assumption, the mobile receiver can take an informed decision before losing the radio connection with the other party. To the best of our knowledge, this paper represents the first attempt toward the detection of a jammer before the radio link is fully affected by its activity. The proposed solution, namely, BloodHound, can early detect the approach to a jammer in a mobile scenario, i.e., before losing the capability of communicating, thus enhancing situational awareness and robustness. We performed an extensive measurement campaign, and we proved our solution to be able to detect the presence of a jammer with an accuracy higher than 0.99 even when the bit error rate is less than 0.01 (early detection), by varying several configuration parameters of the scenario.