Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive automation of accelerated step start of engines in Autonomous power supply systems for own needs of nuclear power plants

At nuclear power plants, there are particularly responsible consumers, whose accident-free operation generally affects the safety of nuclear power plants (NPPs). Some of these electric receivers operate in the normal technological cycle of power generation, while others that are particularly responsible are switched on when an emergency occurs. To ensure the reliability and safety of NPP operation in these operating modes, especially responsible consumers are started in Autonomous power supply schemes with a power source (diesel generator) of limited and comparable power. To ensure the success of the start-up of highly critical electric motors for their own needs, the NPP uses automatic step-start. The speed of a typical, standard automation algorithm is about 50–60 seconds. However, in particularly critical conditions, for example, for the power supply of mechanisms for cooling systems and accident localization, greater speed of this type of automation is required. With this requirement in mind, this article proposes and tests an adaptive algorithm for automatic accelerated step start-up (USP) of specially responsible electric motors for the NPP's own needs. The use of adaptive automatic can reduce the time of successful start-up of all engines, while excluding overloading of the diesel generator and over-regulation of the voltage in the Autonomous power supply scheme for the NPP's own needs.
Research on Key Technologies of Smart Grid Signal Processing Sensors

This paper proposes a method of target source location combined with compressed sensing (CS) routing algorithm. The routing algorithm clusters the network nodes and concentrates the information of the nodes in the cluster on the cluster head. At the same time, based on the shortcomings of the traditional high-voltage switchgear temperature acquisition method, temperature sensor technology is used to collect passive wireless temperature of the high-voltage switchgear. The system has functions such as temperature measurement, alarm function, parameter setting, data storage, etc., and remote real-time online monitoring of temperature.
Intelligent Scheduling in Fog Environment based on Improved Hybrid Heuristics

The need for real-time decisions and the use of smart devices have increased to a great extent thereby increasing the use of the Fog computing paradigm. The major concern in task scheduling among the edge/fog nodes lie in the efficient choice of nodes with the least possible latency. This involves the design of an efficient scheduling algorithm with the least possible latency and increased energy efficiency. Our proposed work deals with an efficient algorithm of task scheduling amidst the fog nodes with an improvement in the energy utility. The paper provides an improved hybrid heuristics approach that helps in the proper utilization of the fog nodes that are limited in terms of computational resources and energy. The paper also compares the proposed work with the existing heuristic algorithms and is proved to be superior and a mathematical analysis of the objectives has also been presented. The proposed algorithm shows a reduction in service time by 18% as compared to neural networks and improvement in energy efficiency of about 29% as compared to the existing heuristics with a significant 40% reduction in the average delay of the system as compared to the use of Deep neural networks.
Risk Assessment of High-voltage Cable System Based on Cable Failure Probability and Failure loss

With the rapid development of urbanization and power grids, the scale of power cable assets continues to increase. By the end of 2019, 110kV and above XLPE cables in operation have exceeded 30,000 kilometers in China. At present, some XLPE cables in operation have entered the late stage of aging or have reached the design life. How to effectively evaluate the operation risks of XLPE cable lines and arrange cable asset management strategies has become an urgent problem in the field of high-voltage cables. In this paper, a high voltage cable system risk assessment model based on cable fault probability and fault loss is proposed from two aspects of cable state health and importance. Cable state health is characterized by the probability of cable failure. By combining the Weibull distribution and the cox risk ratio model, a high-voltage cable failure probability model is established. The importance of the cable is characterized by a failure loss model that considers both economic and noneconomic factors. Based on the risk assessment model of high voltage cable systems, this paper selects relevant information of multiple high voltage cable lines, obtains the key parameters of the model, and gives a practical case analysis. The cable risk assessment model proposed in this article can provide a certain reference for asset condition assessment and strategy planning of high-voltage cables.t.
Designing Wireless Sensor Network Routing on Agriculture Area Using The LEACH Protocol

Microelectronic technology that supports the establishment of wireless sensor networks (WSN) has brought hope to the ease of Internet of Things (IoT) technology that can generate smart environments. A WSN consists of a collection of sensor nodes that are small, intelligent, and inadequate in storage, energy, and processing power. One of the well-known routing techniques in WSN is LEACH. There have been many LEACH studies and their development with random node distribution. Here we designed several scenarios in WSN routing with the position of the sensor nodes specified in the mapping area. MATLAB simulation results show network lifetime can be extended by reducing data packet size. In five experiments, the number of 40 nodes was the optimal number to extend network lifetime. Each scenario has advantages and disadvantages that can be referenced in developing the LEACH protocol.
Communication-Efficient Distributed Algorithms for Solving Linear Algebraic Equations over Directed Graphs

This paper presents two communication-efficient distributed algorithms for solving linear algebraic equations of the form Ax = b, which has at least one solution, among a network of m> 1 agents. Each agent knows only a subset of the rows of the partitioned matrix [A b] and recursively updates its estimate of a solution by utilizing information received only from its neighbors. Neighbor relations are characterized by a fixed directed graph. The first algorithm aims to reduce communication costs at each iteration, in which each agent broadcasts the entries of its estimate in a cyclic manner, instead of broadcasting the entire vector of its estimate. It is shown that for any strongly connected neighbor graph, the algorithm causes all agents’ estimates to converge to the same solution to Ax = b exponentially fast. The second algorithm reduces the vector size of each agent’s estimate by exploiting the sparsity of the matrix A. It is shown that the algorithm causes each agent’s estimate to converge to a specific part of the same solution to Ax = b corresponding to its own interest exponentially fast for a certain class of directed graphs.
Development of micro energy harvest circuit using RF signal

This paper presents the design of the micro energy harvesting circuit (MEH) for sensor network systems by using radio wireless waves for wireless telecommunication system. A series L and C resonance circuit is connected in front of the RF-to-DC converting circuit in our proposed circuit in order to enhance the radio frequency (RF) signal. RF-to-DC converting circuit is based on the Dickson voltage multiplier and designed the cascode and cascade connection to obtain the high DC output voltage. Surface mount type devices such as diode, capacitor etc. are placed on the differential strip line. In the simulation, cascade structure has more than 2V DC output from 900MHz to 2.4GHz.
Convolutional Neural Network Customization for Parking Occupancy Detection

A lot of Research has been developed to improve the reliability of smart parking systems. The utilization of computer vision is more beneficial than using sensors for the detection of parking spaces on smart parking systems. One smart camera can monitor multiple parking spaces according to camera sensing. The use of cameras is more efficient than using sensors, because sensors require expensive installation and maintenance costs. The accuracy and computational time are challenges that must be resolved by applying computer vision to classify the parking spaces. We perform several comparisons of computer vision classifications by utilizing the pre-trained Convolutional Neural Network (CNN). The mAlexnet customization is called by CmAlexnet to improve accuracy in classification. Parking space classification for CNRPark Camera A and B dataset was done. GoogleNet, Alexnet, VGGNet, mAlexnet, and CmAlexnet had varied results. From the testing done states CmAlexnet is better in the accuracy of parking space classification. The average accuracy of CmAlexnet outperforms all pre-trained with almost the same training time as mAlexnet. The test result stated that the performance of mAlexnet can be improved.
Personal Health Mention Identification from Tweets Using Convolutional Neural Network

The past decade witnesses the unprecedent growth of social media users worldwide. People express health related outcomes, information, and views on social media platforms. This provides many opportunities to utilize the data source for health monitoring and surveillance, and digital epidemiology in real time. Personal health mention (PHM) is among one of the critical tasks for such purpose. It tries to identify whether a person’s health condition is mentioned in a sentence. However, social media texts contain noises, many creative and novel phrases, sarcastic Emoji expressions, and misspellings. This poses challenges to detect PHM from social media text. This paper explores the PHM identification task for six diseases from twitter using convolutional neural network (CNN). Specifically, word embeddings are used to encode the twitter text. Then they are fed into CNN structure to train the classifier for PHM identification. We also explore how the performance of different methods are affected by data imbalance issue and training sample size.
On Parameter Estimation of Stochastic Delay Difference Equation using the Two $m$-delay Autoregressive Coefficients

This paper aims to present how to estimate a model parameter, namely the fixed rate of the investment return in the stochastic delay difference equation in financial time series using the two m-delay autoregressive coefficients. The autoregressive coefficients (ARC) algorithm is proposed and compares with the classical differential evolution (DE) algorithm. For a Monte-Carlo simulation tool, the results obtained from the model with the estimated parameter are validated with historical financial data of IBEX 35, JPM and GOOG from Thomson Reuters database in the period between 2008 and 2010. The numerical results confirm that the two $m$-delay autoregressive coefficients perform well to estimate the fixed rate of the investment return and reduce the computation time for the matching process.
A relaxed small-gain theorem for discrete-time infinite networks

This paper provides a small-gain theorem for a so-called infinite network, i.e. a network composed of infinitely many finite-dimensional systems. Such a network is mainly motivated by addressing the scalability issue in large-but-finite networks. We develop a so-called relaxed small-gain theorem for input-to-state stability (ISS) with respect to a closed set. It is shown that every exponentially ISS network necessarily satisfies the proposed small-gain condition. Finally, we truncate the infinite network to obtain a large-but-finite network for all stability properties and performance indices obtained for its infinite counterpart are preserved, if each subsystem is individually ISS. The effectiveness of our small-gain theorem is verified by application to an urban traffic network.
Perspective Preserving Style Transfer for Interior Portraits

One of the jarring limitations of existing style transfer techniques is their failure to capture the illusion of depth through perspective. These often result in flat-looking images that have their style elements simply distributed across the image. Though recent methods attempt to alleviate this by considering depth information for a distinct stylization between foreground and background, they still fail to capture an image’s perspective. When used on interior portraits where perspective is instinctively observed through its surfaces (walls, ceiling, floor), previous methods cause unwanted styling such as style elements distorting boundaries of surfaces and style elements not receding according to the perspective of the surfaces. In this paper, we developed a novel approach to effectively preserve interior portraits’ perspective during style transfer, yielding stylized images that distribute and warps style elements according to the interior surfaces’ perspective. Our method involves removing the perspective information from an interior portrait image, such that when performing style transfer the image can be considered as a flat perspective-neutral canvas. After that, we restore the perspective to the image leading to its style elements recede towards the vanishing point of its respective surface. We also observe that our approach was able to preserve depth information for some styles despite not extracting depth maps from the content.
Microwave Photonic Signal Processing and Sensing : Plenary Paper

Photonic signal processing offers a new powerful paradigm for processing high speed signals, due to its inherent advantages of wide bandwidth and immunity to electromagnetic interference. Recent trends are focused on the integration of photonics on platforms that co-exist with electronics. Microwave photonic advances including broadband tunable microwave photonic filters, dispersion-free microwave-optical links, multi-function processors, and high-resolution photonic sensing systems that provide new capabilities for high-performance signal processing, are presented.
A Sparse Branch and Bound Optimization of Noisy Weighted DAG Modification Under Constraints: A Method for Monocular Data Association to Multiple Laser Planes

The line segments of multiple (and nearly parallel) planar laser sources appearing in monocular video are objectively associated to their respective laser sources. Such association is a precursor to 3D surface digitization where each laser plane develops a 3D surface. In this work, the detected laser line segments are modeled as a weighted directed acyclic graph (wDAG), in which the graph nodes represent the line segments and their maximum path hopes from any source node represents the laser source label. However, due to noise and occlusions, the structure of the wDAG will normally vary from that expected in the ideal cases. Furthermore, it is essential to robustness to incorporate the association information of the previous frames in the new frame associations. The proposed work objectively modifies the current frame’s wDAG under these constraints so the resulting wDAG becomes in an appropriate structure for the labeling of the detected line segments. Branch and bound (BnB) optimization has been a good choice for global optimization of combinatorial problems. Rather than approaching the optimization as a pure (or full) combinatorial problem, the proposed prioritizations of the branch modification actions are nearly optimal. The high priority actions will very likely lead to an optimal modification. This aspect is exploited by the proposed sparse variant of BnB (SBnB) optimization. It significantly reduces the number of the branches to be considered, in addition to those contributed by the bounding part of the BnB optimization. The proposed approach was tested on numerous monocular videos and plausible association results were efficiently achieved.
The optimal location of the electric vehicle infrastructure utilizing wireless charging in a multipath network, round-trip

There are a number of great benefits to electric vehicles. For this reason, electric vehicle (EV) is a preferred option in the field of public transport and recharging the battery far from home is considered a serious problem, and it needs a long waiting time. Induction charging of electric vehicles is a new technology based on the wireless transfer technology, which transforms the electric energy without having any contact between two objects. This technology is very expensive for that it is necessary to look for an optimal installation in the network. For this, in this paper our objective is to propose an optimal strategic installation of the infrastructure in a network composed of several lines between two points (origin and destination), which allows each vehicle to go from the origin to the destination and to return to the origin. Our objective is to propose a balance between the number of the inverters and power segments installed in the network and the capacity of the battery installed in the vehicles while minimizing the cost of the infrastructure.
Synthesis of polypyrrole under confinement in aqueous environment

Conducting polymers are used in a wide variety of applications such as electronic devices, batteries, functional electrodes and optical switching devices. Polypyrrole in particular has attracted interest recently due to the versatility of its applications, high conductivity and capacity for energy storage. Density functional theory is used to characterise the polymerisation process of polypyrrole in aqueous environment. The theoretical characterisation of the synthesis and the number steps involved showed the essential role of water in this confined polymer template for the activation as well as the polymerisation of pyrrole. This study is expected to have an important impact on a novel, environmentally friendly synthesis of nanostructures.
Hysteretic Control Lyapunov Functions with Application to Global Asymptotic Tracking for Underwater Vehicles

This paper introduces hysteretic control Lyapunov functions (HCLFs) for hybrid feedback control of a class of continuous-time systems. A family of HCLFs consists of local control Lyapunov functions defined on open domains, and include finite collections of open and closed sets that cover the state-space, implicitly defining a hysteresis-based switching mechanism. Given an HCLF family, we derive sufficient conditions for the existence of globally asymptotically stabilizing hybrid control laws. Moreover, we provide a constructive design procedure for synthesis of optimization-based feedback laws under mild conditions on the objective functions. We construct HCLFs for an underwater vehicle and demonstrate their applicability to hybrid control design for global asymptotic trajectory tracking for underwater vehicles.
Hysteresis Current Control of Switched Reluctance Generator

The subject of this paper is a hysteresis current control method of a three-phase 6/4 Switched reluctance generator (SRG) based on the nonlinear inductance model and electromagnetic field finite element analysis (FEA) to be used in wind energy applications. This work is intended to show the nonlinear electromagnetic characteristics of SRG and the power converter circuit which is composed with asymmetric half-bridge topology. Two control strategies are revealed: current chopper and angle position controls. SRG and its behavior, modeling and Simulations results are presented. It was analyzed the characteristics of the SRG, simulation results prove that the generator can achieve different output characteristics under different rotational speeds, which confirms the effectiveness of the designed system.
Double Mold Antenna in Package for 77 GHz Automotive Radar

A Fan-out wafer level package (FO-WLP) based Antenna in Package (AiP) test vehicle designed for 77 GHz have been demonstrated in this work. The AiP test vehicle is based on a double mold structure with 4 layers of redistribution lines. The RF device chips are embedded in the bottom mold compound layer, and the antenna arrays are fabricated on the top mold compound layer. This double mold structure was designed to reduce the form factor of AiP as the antenna arrays are fabricated directly on the top mold compound. The antenna structures were designed to meet a design target values of ∼12dBi gain and electrical simulation shows good electrical performance and radiation patterns over 77 to 81 GHz. Finally, the AiP test vehicles were fabricated using the “chip-first” process flow.
Authenticated Computation of Control Signal from Dynamic Controllers

Significant concerns on networked control systems are modifications on the control signals caused by a compromise on the network or the controller, since it can cause a devastating behavior or even entire failure of the system. In this paper, we present a fundamental solution to this problem by proposing a new authenticated computation that checks the matrix-vector multiplications—the main arithmetic of a controller—and verifies the updates on the states of the controller. It enables the plant-side not only to check the computation of the controller with much less computational cost than that required for the computation itself, but also to detect any modifications on the control signals.
Broadband Radiation Source Reconstruction Based on Phaseless Magnetic Near-Field Scanning

An effective broadband electromagnetic interference (EMI) source reconstruction method is proposed in this letter. An equivalent dipole model incorporating the global optimization method is developed to reconstruct EMI sources on printed circuit boards (PCBs) based on the scanned magnetic near-field magnitudes of device under test (DUT). Different to conventional approaches, the locations and quantity of the equivalent dipoles are fixed for all frequencies in the interesting band. The moments and locations of these dipoles are acquired by deploying interpolation based on near-field data at three predefined frequencies only. The near-field patterns at all frequencies can thus be obtained by customizing the dipoles moments. The values of the coupled powers derived from these models are used for near-field prediction. Through numerical and physical examples, the effectiveness of this broadband modeling method is verified.
A Spatiotemporal Analysis of a Group-based Access Solution for Massive MTC Networks and the Stability-Scalability Trade-off

Machine-Type Communication (MTC) Systems play a critical role in providing ubiquitous Internet of Things within the emerging 5G technologies. We consider an MTC system with a massive number of devices and provide a hybrid scheduled and group-based random access solution for accommodating the massive traffic. We study the proposed solution through a spatiotemporal analytical framework, where we use stochastic geometry, probability theory, and iterative algorithms to derive the frequency of successful transmissions and the expected queue status at the devices. The analytical model is then used to study the stability-scalability trade-off in the proposed scheme.