Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-database Hybrid Storage Method for Big Data of Power Dispatching and Control

At present, the service scale of the power grid dispatching and control system is rapidly expanding and the multi-level power grid dispatching and control services possess features, including a wide variety of data and scattered storage. In order to manage the data of power grid more efficiently and meet the more efficient, flexible and accurate information acquisition requirements of multi-type users, we apply intelligent search technology to multi-level power grid dispatching and control system. Facing with the drawbacks of single database in storage and search of massive data, including model data historical data and real-time data, a multi database hybrid storage based intelligent search method and service has been proposed to unified management and application service for multiple types of power dispatching and control big data. On this basis, performance of collection, storage, search, utilization and display of dispatching and control big data for different services has been able to be enhanced.
Wideband Millimeter-Wave End-Fire Magnetoelectric Dipole Antenna With Microstrip-Line Feed

A novel wideband microstrip-fed end-fire magnetoelectric (ME) dipole antenna element is proposed and exploited. The antenna yields advantages of small size, wide operating bandwidth, low cost, and stable gain. A prototype of the element has a bandwidth (SWR < 2) of 56.7% covering from 24 to 43 GHz with a gain of 4–6.7 dBi. An integrated balun covering the whole operating bandwidth is designed for the feed. A four-element linear antenna array of the antenna elements is implemented using a one-to-four microstrip power divider. The antenna array exhibits an operating bandwidth of 55.4% from 23.5 to 41.5 GHz with a maximum gain of 11.9 dBi. The antenna element and the array can be used for wireless equipment of millimeter-wave communications covering the 28, 37, and 39 GHz frequency band.
Improvement in fuel economy of hybrid hydraulic powertrain by conducting a comparative study of two different optimization strategies

This contribution investigates two different power management optimization techniques to optimally split the power between the engine and accumulator of a parallel hybrid hydraulic vehicle (HHV). The goal is to operate the engine at its most efficient region, keep the accumulator charge within bounds, and reduce the fuel consumption while maintaining the vehicle performance. After deriving the mathematical model of the HHV, a local optimization technique is used to solve the problem in each time step for an urban European drive cycle. Then for the same cycle, the results are compared with a global optimization technique. The global optimization shows a distinct improvement in terms of fuel consumption.
NMT Enhancement based on Knowledge Graph Mining with Pre-trained Language Model

Pre-trained language models like Bert, RoBERTa, GPT, etc. have achieved SOTA effects on multiple NLP tasks (e.g. sentiment classification, information extraction, event extraction, etc.). We propose a simple method based on knowledge graph to improve the quality of machine translation. First, we propose a multi-task learning model that learns subjects, objects, and predicates at the same time. Second, we treat different predicates as different fields, and improve the recognition ability of NMT models in different fields through classification labels. Finally, beam search combined with L2R, R2L rearranges results through entities. Based on the CWMT2018 experimental data, using the predicate's domain classification identifier, the BLUE score increased from 33.58% to 37.63%, and through L2R, R2L rearrangement, the BLEU score increased to 39.25%, overall improvement is more than 5 percentage
An upgrade solution for Belle II DAQ system

The Belle II experiment has just started data acquisition in May 2018 which pushes the upgrade of the DAQ system into action. The Present system is based on the Belle2link system which collects the digitized data in each of the Front End electronics of detector systems and moves them to the COPPER( Common readout Platform) readout. Data from COPPER system will be sent to readout PC via network switch for data packing and processing. To meet the further requirement in the future higher luminosity SuperKEKB running, a discussion for a DAQ upgrade is underway to keep the Belle2link functionalities but replace the COPPER readout by new technology. This talk presents a solution for this upgrade based on a MicroTCA compatible CPPF+ module developed for CMS trigger phase I upgrade. In this solution the FEE part of Belle2link will be kept so that there is no need to change the FEE electronics hardware and firmware at all for all functionalities including the slow control.The back-end part of Belle2link implemented in the HSLB(High Speed receiver Link Board) daughter card of COPPER board will be reimplemented in the CPPF+ board, which provides powerful data concentration and processing, providing output capabilities of up to 10Gbps network bandwidth. The slow control will be done via another PC which is connected to MCH shelf manager. The advantage of this solution is that the transition from present to the upgrade system is smooth as we can share most of the Belle2link back-end firmware in HSLB, and the xTCA platform provides much higher scalability, re-configurability, and reliabilities.
VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

Indoor environments have abundant presence of high-level semantic information which can provide a better understanding of the environment for robots to improve the uncertainty in their pose estimate. Although semantic information has proved to be useful, there are several challenges faced by the research community to accurately perceive, extract and utilize such semantic information from the environment. In order to address these challenges, in this paper we present a lightweight and real-time visual semantic SLAM framework running on board aerial robotic platforms. This novel method combines low-level visual/visual-inertial odometry (VO/VIO) along with geometrical information corresponding to planar surfaces extracted from detected semantic objects. Extracting the planar surfaces from selected semantic objects provides enhanced robustness and makes it possible to precisely improve the metric estimates rapidly, simultaneously generalizing to several object instances irrespective of their shape and size. Our graph-based approach can integrate several state of the art VO/VIO algorithms along with the state of the art object detectors in order to estimate the complete 6DoF pose of the robot while simultaneously creating a sparse semantic map of the environment. No prior knowledge of the objects is required, which is a significant advantage over other works. We test our approach on a standard RGB-D dataset comparing its performance with the state of the art SLAM algorithms. We also perform several challenging indoor experiments validating our approach in presence of distinct environmental conditions and furthermore test it on board an aerial robot. Video: https://vimeo.com/368217703 Released Code: https://bitbucket.org/hridaybavle/semantic_slam. git
Rainfall Prediction Using Data Visualisation Techniques

The volume of big data has opened up great opportunities for prediction and analysis of different aspects of weather. Data Visualisation is common in day to day life. Various charts and graphs are used to illustrate the practical approach towards the classification of rainfall with the help of data visualisation methods. Since it was impossible to analyze the large datasets earlier, the data visualisation techniques has made easier to plot the graphs for the better understanding of the weather. With the help of data visualisation patterns such as the highest, lowest and average rainfall in the States/Union Territories the weather of India has been visualised. In this paper, the rainfall pattern in the States/Union Territories of India was successfully visualised. The pattern identifies drought prone region in India, decrease in the annual rainfall over the century and heavy rainfall in the coastal regions of India.
Erasure Code and Edge Computing for Providing an Optimal Platform for Storage of IoT Data

IoT data tends to be small in size however huge in quantities generated. Cloud storage has been designed to store large files in huge quantities. The storage of IoT data on the cloud has become an issue. To this end, Object Storage was introduced. It involves the grouping files of IoT Data into a single object and then store it. Cloud Environment is prone to failure, therefore storing data in cloud, would include the storage of redundant data aka Replication. However, Replication is too resource greedy and Erasure code is proposed as the next best alternative. Our implementation combines those technologies together in order to minimise storage capacity and gain on speed transfer. We use edge computing to aggregate IoT data from different IoT modules and forward it to the Object Storage System, which has Erasure Coding embedded, to provide fault tolerance with less storage space. The results of our implementation shows the effectiveness of such a platform.
High-Linearity 19-dB Power Amplifier for 140–220 GHz, Saturated at 15 dBm, in 130-nm SiGe

This letter presents an integrated power amplifier (PA), with high linearity and efficiency, which uses a passive four-way power distribution network to increase the maximum output levels. The gain unit consists of a differential preamplifier and power stage, interfaced by passive baluns at the input and output. The circuit is intended for ultrawideband mm-wave communication systems operating around 180 GHz. It exhibits a gain of 19 dB, with low ripple and group delay variation, over a large bandwidth of 80 GHz. The 1-dB compression point and the saturated output power occur around 13 and 15 dBm, respectively. The maximum dc power consumption is 830 mW and results in a power-added efficiency of up to 3.5%. The final chip occupies an area of 0.92 mm2 and is implemented in a 130-nm SiGe BiCMOS process, which offers a maximum oscillation frequency $f_{\text {max}}$ of 450 GHz. To the best of authors’ knowledge, this circuit demonstrates one of the largest bandwidths, in combination with the highest output power and best linearity, of any silicon-based PA operating above 200 GHz.
BikeNet: Accurate Bike Demand Prediction Using Graph Neural Networks for Station Rebalancing

Bike sharing systems are widely operated in many cities as a green transportation means to solve the last mile problem and to reduce traffic congestion. One of the key challenges in operating high quality bike sharing systems is rebalancing bike stations from being full or empty. To this end, operators usually need to foresee the bike demands and schedule trucks to reposition bikes among stations. However, an accurate prediction of city-wide bike demands is not trivial due to the spatial correlation and temporal dependency of user mobility dynamics. Moreover, finding an optimal station rebalancing strategy from potentially enormous candidates is challenging given resource optimization objectives. In this work, we propose a two-phase framework to accurately predict city-wide bike demands and effectively rebalance bikes stations leveraging state-of-the-art deep learning techniques. First, we build a spatiotemporal graph neural network (ST-GNN) to model and predict city-wide bike demands, simultaneously capturing the spatial correlation by Graph Convolutional Networks (GCN) and the temporal dependency by Gated Recurrent Units (GRU). Then, we formulate the truck-based station rebalancing problem as an optimization problem with transportation cost objectives, and effectively solve the problem with Integer Linear Programming (ILP) algorithm. Experiments on real-world datasets from New York City validate the performance of the proposed framework, reducing 13% of prediction error and 5% of transportation cost compared with the baseline methods.
A Cable Partial Discharge Signal Extraction Method Based on Improved Wavelet-Laplace Function

In order to extract the partial discharge pluses accurately in power cables, an improved wavelet Laplace function method is proposed. The method is based on the characteristics of partial discharge pulse propagation in power cables. Firstly, the Laplace wavelet oscillation frequency and damping rate at different frequencies are determined by simulation of cable transmission characteristics. Then, the occurrence time and center frequency of partial discharge signal are extracted by Laplace wavelet matching pursuit algorithm. The extraction results of experiment data reveal that the effect of extracting PD pulses is satisfied with the proposed algorithm.
Early Diagnoses of Alzheimer using EEG data and Deep Neural Networks classification

Alzheimer's disease has always been a challenge to be detected at early stages as it has always been mistaken as normal aging. It can be recognized when the patient starts to have Mild Cognitive Impairment (MCI) and by that stage, only little can be done as no treatment can reverse its effect but only to delay its progression. In this work, we present a method for early diagnoses of AD by creating a low-cost EEG device along with using a deep neural network that can classify the suspected patients into three classes: MCI patients, AD patients, and healthy patients. This is done using collecting brain wave signals using Electroencephalography (EEG) device during a 3 level N-Back working memory test. This is called event-related potential (ERP), later the data will be cut into small frames, FFT transformed to extract brainwave subbands (theta, alpha, and beta) and then projected to 2D images where it will be used in training convolution neural network for classification. Early detection of Alzheimer will reduce the progression of AD at early stages. We implement and evaluate our hardware and classification with an accuracy of 90.36% for MCI and 92.52% for Alzheimer's disease detection.
Learning And Predicting Diabetes Data Sets Using Semi-Supervised Learning

Now these days, many tools have been developed by the researchers to analyze the impact of diabetes disease on common people within a definite period. However, all these tools have predicted the results based on the labeled dataset or smaller dataset. But in a recent environment, we have collected a large amount of data using both online and offline media. Consequently, data are generated from heterogeneous sources, are in unstructured form and voluminous, etc. As a result, it is not possible to use huge data by using traditional prediction algorithms because they work only on the structured dataset. In this paper, we have used the semi-supervised learning approach that works on a partially labeled dataset for predicting diabetes disease. The partial dataset is the combination of a labeled and unlabelled dataset. For prediction, we have considered 80% unlabelled datasets and 20% labeled datasets. We developed a user based interface for the user to build their prediction model using labeled and unlabeled datasets and analyze the data according to their requirements and interest. Our main objective is to develop a diabetes prediction system that can be used by the researcher and the common people using with minimal labelled datasets.
Redefining Micro-Moments for Improving Energy Behaviour: The SIT4Energy Approach

The critical deterioration of the environment in recent years has led to an increasing urgency of minimizing energy consumption. As residential and tertiary buildings hold a significant potential in terms of energy savings a lot of different approaches have been followed to not only reduce energy consumption but also change energy-related occupant behaviour for long-term results. Taking into account recommendation services through mobile applications, this paper proposes a more dynamic and occupant-centered solution, which not only offers useful energy related recommendations, but also adjusts the moment the recommendation is sent to maximize the receptiveness of the user in order to achieve the optimal energy behavior change. These moments are redefined micro-moments in terms of energy behavior change and occur when three factors' conditions coincide, the location, the physical and the mobile activity of the user. We develop a mobile application which can monitor them and detect a potential micro-moment, when a notification could be pushed to the user. The location is periodically collected from the device's sensors, the physical activity is inferred with the use of Google Activity Recognition API, while the mobile activity is inferred utilizing mobile event triggers and usage statistics. The experimental results show that our dynamic approach is justified, since the micro-moments are highly unpredictable and user-specific.
A Probabilistic Decision Engine for Navigation of Autonomous Vehicles under Uncertainty

Navigation or route planning under uncertainty is a very challenging but important task for autonomous vehicles such as self-driving cars, drones, unmanned aerial systems (UAS), etc. Probabilistic methods to the modeling and optimization of these systems are attractive in quantitatively capturing the uncertainty present in their dynamic environment. This paper will present a novel probabilistic decision engine that can serve as the core for the navigation of autonomous vehicles under uncertain conditions. This probabilistic decision engine takes in a network connection matrix (based on maps and graph theory) and a cost matrix (with entries of the cost’s mean values and probability distributions) as its input and generates the probability distributions of the optimal routes as its output. The proposed probabilistic decision engine consists primarily of a stochastic network standardization module, a stochastic network decomposition module and a probabilistic solver (i.e., decision kernel). A deterministic network reduction method based upon Dijkstra’s algorithm is first used to derive a standard, reduced network, augmented by a stochastic network reduction process. The standard network is then decomposed into a series of stochastic subnetworks by using sequential convolution, PDF (probability distribution function) shifting and reshaping techniques. A purely-analytical probabilistic solver is finally used to solve the stochastic decision-making problem. In this paper, the principle of operation and implementation methods of the entire probabilistic decision engine will be discussed in detail. Some representative simulation results will be provided to demonstrate the effectiveness of the proposed computational methodology and compared with the traditional Monte-Carlo simulation method to validate the analytical results. This study suggests that the time needed to find the solution using the proposed decision engine can be reduced by three to four orders of magnitude, compared with the Monte-Carlo simulation method.
A Summary of Palmprint Recognition Technology

Palmprint recognition, as a new biometrics technology, has attracted wide attention and research in recent years. Compared with other biometrics, palmprint has many unique advantages, including high recognition rate, low cost of acquisition equipment and good user acceptance. Firstly, this paper introduces the method of extracting the region of interest of palmprint image, the evaluation index of recognition system, the method of obtaining palmprint image by oneself and the commonly used palmprint databases. Then it introduces the method of identifying by extracting single palmprint features, and the method of identifying by fusing palmprint features and other biological features. What's more, it introduces the methods of feature matching, such as Fisher Linear Discriminant and Neural Network. In addition, it also introduces the application of palmprint recognition in life and the Android system developed in this paper. Finally, it makes the summary and prospect.
Precise Analysis of Interruption Response Rate and Interruptible Capacity of Large Users

The research of interruptible features of large users is the basis to make dis-patching plan of interruptible loads. Interruption response rate and interruptible capacity are the critical characteristics of interruptible loads. In order to analyze the characteristics of the maximal interruption rate and maximal interruption capacity of large users, a quadratic classification model is used to cluster the daily load data of users. Besides, the fuzzy C-means clustering method is adopted for each classification. The first classification is used to separate the interruption response data from the conventional data. The second classification aims to obtain the refined interruption patterns. Then, the response rate and interruptible capacity are analyzed from two dimensions which are time dimension and industry dimension. The results show that the quadratic classification model can effectively excavate the users’ maximal response rate and maximal interruptible capacity.
A Compact High-Efficiency Broadband Rectifier With a Wide Dynamic Range of Input Power for Energy Harvesting

In this letter, a compact high-efficiency broadband microwave rectifier with an extended range of input power is proposed for energy harvesting. In the proposed structure, a novel broadband impedance-matching network is employed to reach high radio frequency (RF) to dc conversion efficiency. The reduction in mismatching loss over a wide bandwidth and input power range is achieved by impedance transformation from three segments of microstrip lines, which leads to a compact design to realize broadband impedance matching. A theoretical analysis and simulations of the proposed rectifier are presented. For validation, a broadband rectifier operating between 2.1 and 3.3 GHz is implemented and tested. The proposed rectifier shows a bandwidth of 44.4% for efficiency over 70% at an input power of 14 dBm. The measured efficiency remains above 50% from 2 to 3.3 GHz with an input power from 4 to 16 dBm. Moreover, the proposed rectifier has a compact size of 31 mm $\times18$ mm.
Handwritten Devanagari Character Recognition using Convolutional Neural Network

In any pattern recognition task, feature extraction and classification stages handle the responsibility of recognizing the patterns accurately. Deep learning relives the task of feature extraction and extracts them automatically, thus reducing the programmer's burden. Deep learning is replacing other pattern recognition techniques recently. In applications like character recognition, which involves large amount of database and variability in the data, deep learning is the right choice to handle the challenges involved. In this paper, a system for handwritten Devanagari character recognition using Convolutional Neural Network is discussed. The recognition accuracy obtained is 91.23% for Devanagari characters and 100% for Devanagari numerals.
Detection of Birds in the Wild using Deep Learning Methods

Object detection and localization is one of the prominent applications of the computer vision. The paper presents a comparative study of state of the art deep learning methods-YOLOv2, YOLOv3 and Mask R-CNN, for detection of birds in the wild. Detection of birds is an important problem across multiple applications including the aviation safety, avian protection and ecological science of migrant bird species. Deep learning based methods are very pre-eminent at detecting and localizing the birds in the image as it can tackle the conditions wherein the birds shown are diverse in shapes and sizes and most importantly the complex backgrounds they are in. We used the training and testing dataset provided by the NCVPRIG (BROID) conference which contained 325 and 275 images respectively. For training, we used the pre-trained models on the VOC 2012 and COCO dataset and trained them on the 325 images. We used F-score as one of the performance metrics, and F-Scores were 0.8140, 0.8721, 0.8688 for the YOLOv2, YOLOv3 and Mask R-CNN respectively. The results show that YOLOv3 outperforms YOLOv2 and is a marginal improvement over Mask R-CNN.