Miracle-dz/UniFAR-FTUs huggingface dataset:
RFID Hybrid Positioning Method of Phased Array Antenna Based on Neural Network

This paper presents a new type of radio frequency identification positioning method. This method uses a hybrid positioning method combining signal angle of arrival and RSSI (signal strength value positioning). First, the phased array antenna is used to measure the azimuth angle of the target tag, and then, the reader is read to read the signal strength of the target tag. After using the neural network model to fit the curve of signal strength and distance, when the signal strength is converted into a distance by the curve relationship, the positioning function of the target tag is realized. Experimental results show that compared with traditional positioning algorithms, not only the positioning accuracy is improved but also the working range is increased and the system anti-jamming capability is improved.
Low Cost Nano Satellite Communication System Using GNURadio, HackRF, and Raspberry Pi

In recent years, nano satellite has gained popularity among students and young researchers. The reason is that, nano satellite has the ability similar to larger satellite, but in tiny compact size. It is easy to made and can be made using low cost devices. However the main problem in making a nano satellite is in the its communication system. Nano satellite communication system has to be reliable to send information from ground station to nano satellite or vice versa. In this paper, we propose a nano satellite communication system using Raspberry Pi, and a HackRF SDR which is programmed using GNURadio. From the testing process, the text data can be transmitted properly from ground station to nano satellite using GMSK modulation scheme. This communication system also has ability to be reconfigured, which means many modulation scheme and algorithm can be implemented easily without changing the hardware.
Adaptive RBF Neural-Network-Based Design Strategy for Non-Strict-Feedback Nonlinear Systems by Using Integral Lyapunov Functions

This paper develops an adaptive radical basis function neural-network (NN)-based controller design strategy that uses integral Lyapunov functions for a class of non-strict-feedback nonlinear systems subject to perturbations. The design difficulty caused by the non-strict-feedback system structure is handled by using the inherent property of the square of neural network’s base vector. The design procedure of the adaptive NN tracking controller is presented by using backstepping technique, which can update the adaptive laws at any time and solve the design problem derived from the correlation degree of the controlled plant. The uniform ultimate boundedness and good tracking performance of the derived closed-loop system are ensured with the design controller. Finally, a comparative simulation example is carried out to prove the effectiveness of the proposed control method.
Supervised Learning via Unsupervised Sparse Autoencoder

Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can improve the efficiency of subsequent learning tasks. However, existing methods of dimensionality reduction mostly involve datasets with sufficient labels and fail to achieve effective feature vectors for datasets with insufficient labels. In this paper, an unsupervised multiple layered sparse autoencoder model is studied. Its advantage is that it reduces the reconstruction error as its optimization goal, with the resulting low-dimensional feature being reconstructed to the original dataset as much as possible. Therefore, the reduction of high-dimensional datasets to low-dimensional datasets is effective. First, the relationship among the reconstructed data, the number of iterations, and the number of hidden variables is explored. Second, the dimensionality reduction ability of the sparse autoencoder is proven. Several classical feature representation methods are compared with the sparse autoencoder on publicly available datasets, and the corresponding low-dimensional representations are placed into different supervised classifiers and the classification performances reported. Finally, by adjusting the parameters that might influence the classification performance, the parametric sensitivity of the sparse autoencoder is shown. The extensively low-dimensional feature classification experimental results demonstrated that the sparse autoencoder is more efficient and reliable than the other selected classical dimensional reduction algorithms.
A Capacity Planning Method for Wind Power Based on Cooperative Game Theory in Carbon Trading Process

In low-carbon economy, carbon trading process is introduced to quantify the effect by actual economic value. Based on the cooperative game theory, the improvement strategies and cost benefit for both wind farms and the grid company are analyzed. Furthermore, a reasonable capacity distribution model of the alliance of wind farms and grid company is built to achieve the fairness of profit distribution. Finally, a practical example is given to illustrate that the alliance of wind farms and grid company is effective in increasing grid initiative in accommodating wind power.
A Chinese Word Segmentation Model for Energy Literature Based on Conditional Random Fields

Chinese word segmentation is one of the foundation and core tasks for Chinese natural language processing. Although some achievements have been made for Chinese word segmentation system in general domains, it is far away to meet practical requirements in energy domain. We focus on Chinese word segmentation standard and segmentation technology in the energy domain which consists of 13283 energy basic terms. This paper firstly proposes a conditional random field segmentation model. Then, the character features, character type features and conditional entropy features which influence the word segmentation performance are chose and described. Finally, the proposed model is tested on the dataset of the State Grid energy literature and compared with current word segmentation tools, such as the Harbin Institute of Technology's Language Technology Platform and the Tsinghua's THU Lexical Analyzer for Chinese language processing tools. The F1 value of the best result of the proposed model is 0.8319.
Research on IGBT Series Optimization Method Based on Gate Voltage Control

IGBT devices are widely used in power devices such as photovoltaic inverters, wind power converters, flexible DC converter valves, and unified power flow controllers due to their advantages of easy drive control, fast switching speed and large capacity. The existing flexible DC converter valve, DC circuit breaker, unified power flow controller and other equipment voltage levels have reached 500kV, usually requires a large number of power electronic devices or series stacking of half-bridge components, which has met the needs of high voltage applications, so IGBT The series technology is still of great research significance for improving the compactness of the device and further improving the voltage level of the equipment. Based on the IGBT gate active voltage control method, this paper proposes a reference voltage optimization method, which can further reduce the IGBT switching loss and improve the voltage balance, and improve the applicability of the method. Finally, the correctness of the method is verified by IGBT series experiment.
Robotic Grasp Pose Detection Using Deep Learning

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. This paper proposes the use of a transfer learning technique with deep convolutional neural networks to learn how to visually identify the grasping configurations for a parallel plate gripper that will be used to grasp various household objects. The Red-Green-Blue-Depth (RGB-D) data from the Cornell Grasp Dataset is used to train the network model using an end-to-end learning method. With this method, we achieve a grasping configuration prediction accuracy of 93.91%.
VUZ: A Crowdsourced Framework for Scalable Interdisciplinary Curriculum Design

Higher education in traditional, highly centralized institutions is struggling to adequately prepare professionals in accordance to the current job environment, where demonstrating abilities to quickly adapt to changes and learn new skills is progressively gaining weight against solely holding a college degree, for example. Elevated tuition costs is another factor pushing people into short, internet-based specialized courses or directly into the job market, while an increasing number of people holding full degrees are underemployed in respect to their knowledge level. We propose VUZ, a domain-general framework capable of tackling these issues by designing personalized curricula from a high-level perspective (general courses, subjects and topics) down to specific how to's and step-by-steps, by means of crowdsourcing: delegating the curriculum development to a community of experts. The framework is scalable in the sense that individuals can choose the level of refinement of their curriculum depending on their prior knowledge; is decentralized in the sense that all decisions are taken by the users; and is collaborative because of the possibility of sharing knowledge with other users. We detailed the crowdsourcing algorithm used for curricula development and simulated a VUZ platform using a multiagent system, which yielded a series of cost and retribution results associated to potential users of a VUZ system.
A fW- and kHz-Class Feedforward Leakage Self-Suppression Logic Requiring No External Sleep Signal to Enter the Leakage Suppression Mode

In this letter, we present a novel logic family for nanowatt and subnanowatt always-on circuits. This logic family achieves the ultralow leakage of 5 fW per gate without requiring external sleep control signals to put the circuits in the leakage suppression mode while addressing the switching speed problem of such ultralow leakage logic families. It achieves the fan-out-of-4 delay of 10.2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, marking an improvement of <inline-formula> <tex-math notation="LaTeX">$148\times $ </tex-math></inline-formula> over the state-of-the-art. With the proposed logic family, we prototyped a finite impulse response filter for physical signal sensing systems, in which sampled signals are often sparse (predominantly constant or slowly changing) and their frequency varies with time in a burst fashion. During the sparse period, the filter consumes only 109 pW by autonomously suppressing leakage. It can still process signals whose bandwidth can be as high as half of the filter’s maximum operating frequency, 1.03 kHz. The proposed logic improves leakage-delay-product by <inline-formula> <tex-math notation="LaTeX">$200 \times $ </tex-math></inline-formula> over the prior art.
Noise Analysis and Modeling in Visible Light Communication Using Allan Variance

Visible Light Communication (VLC) is a rising communication technology which uses Light-Emitting Diode (LED) luminaries for high-speed data transferring; however, the optical signal is usually degraded by the noise in the practical VLC system. In this paper, Allan Variance is introduced for noise analysis and modeling in VLC for the first time, which provides an efficient method to measure different kinds of noise in the VLC systems. By applying Allan variance to analyze the signals collected from the real-world environment, both white noise and random walk are observed in the VLC systems. The observed white noise and random walk are also modeled by using the Allan variance. The noise analysis and modeling based on Allan variance provide a method to improve the performance of VLC.
A DPLL-Centric Bluetooth Low-Energy Transceiver With a 2.3-mW Interference-Tolerant Hybrid-Loop Receiver in 65-nm CMOS

This paper presents a Bluetooth low-energy (BLE) transceiver (TRX) achieving ultra-low-power operation for Internet-of-Things (IoT) applications. The proposed TRX utilizes a wide-bandwidth low-power fractional-N digital phase-locked loop (DPLL) as a central component to perform multiple roles: a direct frequency modulator for the transmitter (TX), an analog-to-digital converter (ADC) for the receiver (RX), a local oscillator for RX, and a frequency and phase synchronizer for the RX. The DPLL-based single-path downconversion method is adapted to halve the analog baseband circuit to further reduce the power consumption while maintaining a high interference rejection. A DPLL-based ADC with a digital-to-analog converter feedback greatly improves the ADC dynamic range, which improves the RX sensitivity and interference tolerance. By maximally reducing the required radio frequency and analog front-end components in RX, an RX power consumption of 2.3 mW is achieved with a −94 dBm sensitivity. The RX also satisfied all interference requirements with a certain margin. A 5.0-mW TX is achieved when delivering an output power of 0 dBm with a frequency-shift keying error of only 1.89%.
A Novel Fault-Reconfiguration Scheme for the Active Distribution System

Active distribution system(ADS) plays a crucial role in integrating distributed generators(DGs). Therefore, it is necessary to quickly and effectively reconfigure ADS when it suffers from serious faults. In this paper, a two stage strategy is introduced to solve this problem. In stage one, the DG islands are divided dynamically according to their characters to guarantee the power supply of important loads. In stage two, a fault-reconfiguration strategy based on the imperialist competitive algorithm(ICA) is proposed to obtain the optimal reconfiguration scheme, which takes the load balance, voltage and capacity constrains into account simultaneously. Switches of loop and inferior solution identifying techniques are employed to improve the computation efficiency. The introduced approach can maximize the protection for important loads and restore power supply for black areas rapidly compared to existing methods. Simulation results of the IEEE 69-bus test system verified the effectiveness and practicability of the proposed scheme.
Understanding Multimodal Popularity Prediction of Social Media Videos With Self-Attention

Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as well as the number of factors that play a part in this process. As solving this task provides tremendous help for media content creators, many successful methods were proposed to solve this problem with machine learning. In this work, we change the viewpoint and postulate that it is not only the predicted popularity that matters but also, maybe even more importantly, understanding of how individual parts influence the final popularity score. To that end, we propose to combine the Grad-CAM visualization method that allows to visualize spatial relevance to popularity with a soft self-attention mechanism to weight the relative importance of frames in time domain. Our preliminary results show that this approach allows for more intuitive interpretation of the content impact on video popularity while achieving competitive results in terms of prediction accuracy.
Ultralow Input–Output Capacitance PCB-Embedded Dual-Output Gate-Drive Power Supply for 650 V GaN-Based Half-Bridges

Wide-bandgap devices have been widely used to reduce the size and increase the efficiency of power converters by operating at a high switching frequency, at the expense of heightened radiated and conducted electromagnetic inference (EMI) emissions, of which the latter circulates through the power loop and ancillary circuitry. In effect, the parasitic isolation capacitance <inline-formula><tex-math notation="LaTeX">$C_{i}$</tex-math></inline-formula> of the gate-driver power supply represents a key EMI propagation path to be controlled in order to ensure the operational integrity of power converters. To this end, this paper proposes an integrated, dual-output gate-drive power supply for gallium-nitride (GaN) 650 V, 60 A, half-bridge phase legs, rated at 2 W (2 × 1 W), 15 to 2 × 7 V, featuring an ultralow <inline-formula><tex-math notation="LaTeX">$C_{i}$</tex-math></inline-formula> of 1.6 pF, an output-to-output parasitic capacitance of 1.6 pF, a power density of 72 W/in<sup>3</sup>, and an efficiency of 85%. All this is attained using an active-clamp flyback converter switching at 1 MHz using 65 V GaN high-electron-mobility transistor devices and Schottky output rectifiers, and a Pareto-optimized transformer design minimizing its interwinding capacitances, volume, and losses. Finally, the transformer is fully embedded in a printed circuit board (PCB) material, doubling as a substrate for the topside active layer of the power supply. The paper presents the complete design procedure, processing, and experimental demonstration of the proposed integrated power supply, evaluating as well the reliability impact of the magnetic-PCB material interface in high ambient temperature applications (>200 °C).
SDAE-Based Probabilistic Stability Analysis of Wind Integrated Power Systems

This paper proposes a method for investigating the stability of the power system in presence of significant amounts of wind generation using stochastic differential algebraic equations (SDAEs). Contrary to the traditional approach of modelling the stochasticity only in the initial conditions, the SDAE-based method (SDAEM) describes wind power variation as a continuous stochastic process. Next, the model of a doubly fed induction generator (DFIG) grid-connected power system is also expressed as a SDAE. Finally, the probabilistic small signal stability (PSSS) and the probabilistic transient stability (PTS) conditions are defined. A single machine infinite bus (SMIB) system and a 16-machine, 68-bus system are used as the test systems for this analysis. The results indicate that SDAEM is more accurate and reliable in describing the system conditions in presence of stochastic inputs, and thus can be used to decide the right course of controller action.
Word Beam Search: A Connectionist Temporal Classification Decoding Algorithm

Recurrent Neural Networks (RNNs) are used for sequence recognition tasks such as Handwritten Text Recognition (HTR) or speech recognition. If trained with the Connectionist Temporal Classification (CTC) loss function, the output of such a RNN is a matrix containing character probabilities for each time-step. A CTC decoding algorithm maps these character probabilities to the final text. Token passing is such an algorithm and is able to constrain the recognized text to a sequence of dictionary words. However, the running time of token passing depends quadratically on the dictionary size and it is not able to decode arbitrary character strings like numbers. This paper proposes word beam search decoding, which is able to tackle these problems. It constrains words to those contained in a dictionary, allows arbitrary non-word character strings between words, optionally integrates a word-level language model and has a better running time than token passing. The proposed algorithm outperforms best path decoding, vanilla beam search decoding and token passing on the IAM and Bentham HTR datasets. An open-source implementation is provided.
Comparative study of RGB-D sensors based on controlled displacements of a ground-truth model1

In the context of developing a pedagogical tool for teaching anatomy, the need for a comparative study between two RGB-D cameras has emerged. This paper addresses the assessment of the accuracy and precision of two RGB-D sensors (Carmine Primesense and Persee Orbbec) through two different experiments. The evaluation not only provides comparative results on the sensors performances but also aims at determining in which conditions they are the most efficient. The first experiment evaluates the variability of the output depth map data. The second experiment focuses on analyzing the influence of the distance in the positioning accuracy of an object submitted to controlled displacements. The results are summarized in a set of error heat maps and a table; they provide clues for using one sensor rather than another by describing their robustness both in a static scene and in a motion capture scenario.
ICFHR2018 Competition on Automated Text Recognition on a READ Dataset

We summarize the results of a competition on Automated Text Recognition targeting the effective adaptation of recognition engines to essentially new data. The task consists in achieving a minimum character error rate on a previously unknown text corpus from which only a few pages are available for adjusting an already pre-trained recognition engine. This issue addresses a frequent application scenario where only a small amount of task-specific training data is available, because producing this data usually requires much effort. We present the results of five submission. They show that the task is a challenging issue but for certain documents 16 pages of transcription are sufficient to adapt a pre-trained recognition system.
A Feeder Investment Model for Distribution System Planning Including Battery Energy Storage

The distribution system asset (DSA) planning process for a new or existing subdivision, which entails new loads and/or distributed generation, involves a comparison of a variety of plans followed by the selection of the least costly one. The costs comprise the cost of new assets (feeders and transformers), their replacements, operation and maintenance costs, as well as customer interruption costs. For a fair comparison, all costs reflect the current year and are annualized over a specific study period. The optimal plan must be capable of addressing the connection of new loads and/or generation while satisfying performance standards at the lowest annualized cost. Current DSA planning algorithms, however, fail to take battery energy storage systems (BESSs) into account, hence impeding their inclusion as a solution in DSA plans. This paper proposes a new DSA planning algorithm that includes the installation and optimal sizing of BESS units in addition to traditional assets, such as feeders and transformers. It incorporates plan and asset lifetimes as a means of establishing the minimum total annualized costs of new and replacement assets, operation and maintenance, and customer interruptions. The algorithm is successfully validated on a 3-bus system and a 33-bus distribution system.
A New Outlier Detection Model Using Random Walk on Local Information Graph

Large number of outlier detection methods have emerged in recent years due to their importance in many real-world applications. The graph-based methods, which can effectively capture the inter-dependencies of related objects, is one of the most powerful methods in this area. However, most of the graph-based methods ignore the local information around each node, which leads to a high false-positive rate for outlier detection. In this study, we present a new outlier detection model, which combines the graph representation with the local information around each object to construct a local information graph, and calculates the outlier score by performing a random walk process on the graph. Local information graph is constructed to capture the asymmetric inter-dependencies relationship between various types of data objects. Based on two different types of restart vectors to solve the dangling link problem, we propose two distinct algorithms for outlier detection. Experiments on synthetic datasets indicate that the proposed algorithms could efficiently distinguish outlier objects in different distributed datasets. Furthermore, the results on a number of real-world datasets also show that our approaches outperform the state-of-the-art outlier detection methods.