Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-Term Wind Power Prediction Based on Wavelet Transform and Convolutional Neural Networks

Wind power prediction (WPP) is critical to the safe operation and economic dispatch of power systems, but it faces two challenges: 1) Information on different frequency bands is included in the numerical weather prediction (NWP) data, making it difficult to mine vital information if the original NWP data is directly applied as input data; 2) There is a strong nonlinear relationship between the input and output data in the WPP model, meaning that traditional models are difficult to fit accurately. This paper proposes a new short-term WPP model based on wavelet transform (WT) and convolutional neural networks (CNN). First, WT is applied to split the original NWP data and historical power data into multiple sets of different frequency components. Then, based on the NWP and historical power data sets of relevant frequencies, CNN models are established for prediction. Finally, based on the prediction results of different frequency components, inverse WT is applied to reconstruct the WPP results. This case study shows that 1) the WT method allows for effective mining of the information contained in the different frequency components of the NWP data; and 2) when the WT-CNN model is applied to predict the wind power in the next 12 hours, the NRMSE of the model is reduced by 1.55 percentage points compared with Back Propagation Neural Network.
Research on Fault Location Based on Improved Romp Algorithm for Compressed Sensing in Distribution Network

The structure of distribution network is complex and has many branches. At the same time, with the increasing proportion of distributed power in the distribution network, the traditional fault location method has been difficult to take into account the cost and accuracy of the method. In this paper, a fault location algorithm based on sparse measurement points is proposed. From the perspective of negative sequence injection current, the mapping relationship between compression sensing theory and distribution network fault location problem is established. The negative sequence node voltage after fault is calculated by fuzzy power flow calculation method, and the base matrix is constructed. The limited isometric property of the sensing matrix is analyzed, and an improved ROMP algorithm is proposed to reconstruct the current signal. A distribution system with multiple distributed power sources is built and simulated in the ieee-33 node distribution system. The simulation results show that compared with the traditional method, the algorithm proposed in this paper has stronger ability to withstand transition resistance and noise, higher accuracy of fault location and better real-time performance.
Parasitics of Orthocyclic Windings in Inductors and Transformers

Orthocyclic winding in magnetic components is common in both laboratory prototyping and mass production, especially for round wires with curved edges. However, its impact on winding parasitics, i.e., winding capacitance, ac resistance, and leakage inductance, has not been systematically investigated yet. Omission or inaccurate calculation of these parameters leads to current ringing, additional losses, and even abnormal operations. In this article, three modified formulas for parasitics of orthocyclic windings are proposed, and the impacts of orthocyclic windings are comprehensively studied. Magnetic field distortion and air-gap effect are analyzed and compensated. The formulas are verified by finite-element method (FEM) simulation and experimental results. Finally, normal and orthocyclic windings are compared, and conclusions are listed to illustrate the impact of orthocyclic windings on parasitics.
Sagacious Intrusion Detection Strategy in Sensor Network

Almost all smart appliances are operated through wireless sensor networks. With the passage of time, due to various applications, the WSN becomes prone to various external attacks. Preventing such attacks, Intrusion Detection strategy (IDS) is very crucial to secure the network from the malicious attackers. The proposed IDS methodology discovers the pattern in large data corpus which works for different types of algorithms to detect four types of Denial of service (DoS) attacks, namely, Grayhole, Blackhole, Flooding, and TDMA. The state-of-the-art detection algorithms, such as KNN, Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), and ANN are applied to the data corpus and analyze the performance in detecting the attacks. The analysis shows that these algorithms are applicable for the detection and prediction of unavoidable attacks and can be recommended for network experts and analysts.
Flexible, Self-Adaptive Sense-and-Compress SoC for Sub-microWatt Always-On Sensory Recording

We present a 5-sensor, fully integrated sensing system with interchangeable sensors and programmable configuration to create a sub-microWatt multisensor node that can tackle a wide range of sensing applications. Furthermore, the sensor node is capable of autonomously adapting its configuration to the application requirements hence minimizing system power. Such self-reconfiguration is enabled at low overhead by developing an automated offline optimization strategy, in combination with an autonomous embedded configuration controller, using the concept of behavioral trees (BTs). The resulting fully integrated platform consumes a maximum of 321 nW when sampling at 500 Hz and 3025 nW at 8 kHz. Furthermore, we demonstrate the end-to-end autonomous optimization flow for two different applications exploiting different sensors: 1) human activity recognition using accelerometers and 2) machine listening using a microphone. Both use cases demonstrate that the introduced system and methodology reduces the power by more than a factor 2 without losing significant application detection accuracy.
Fully Soft-Switched Multiport DC–DC Converter With High Integration

In this article, a soft-switched nonisolated high step-up multiport converter is proposed, which provides separate power flow paths between the input sources and the output. The energy storage device can be charged from an energy generation source and the output dc link, which causes this converter to be a good candidate for utilization in hybrid energy applications. In this converter, some components are shared in different operating modes; thus, this converter has a high integration. Four different soft-switching techniques are employed to provide soft-switching condition for all switches without affecting the converter functionality in any operating mode. In this article, the converter analysis is presented and design considerations are discussed. Finally, to validate the theoretical analysis, a converter prototype to supply a 200 W–400 V load is implemented.
Gallium-Erbium Fiber Amplifier

This paper reports the optical signal amplifying performance of a Gallium co-doped Erbium (Er/Ga) fiber amplifier. The performance was analyzed for various input power and Er/Ga fiber length. The amplifier was constructed in a singlepass arrangement and operated with 980 nm pumping. At input signal power of −30 dBm, a maximum gain of 22.45 dB was obtained with noise figure of 5.71 dB at 1535 nm.
Performance Evaluation of Dampers Under Dynamic Impact Based on Magnetic Field FE and CFD

It is necessary to install a damping energy absorbing structure for a certain type of naval gun test shell without projectile belt. Applicable high-performance dampers currently include viscoelastic colloidal damper and magnetorheological damper, and two kinds of special dampers for test shells have been designed and manufactured. In order to evaluate the mechanical properties of these two dampers under dynamic impact, a one-way coupling numerical method was proposed for simulation and analysis. Quasi-static experiments were used to verify the model based on magnetic field finite element (FE) and computational fluid dynamics (CFD). The model is also characterized by passive dynamic mesh and user-defined functions (UDF) based on C language. The results showed that the CFD models of both dampers were accurate. Though the colloidal damper is simpler in structure and lower in the cost, the MR damper has more significant damping and higher energy absorption ratio under impact. Compared with colloidal damper, MR damper is characterized by long working time, multiple vortices, chaotic streamlines and more uniform temperature distribution. When the power was continuously applied, the velocity of the impacted MR damper dropped to zero within 80ms, and the piston finally stopped at a stroke of 1.3mm. The colloidal damper reset within 8ms, and the reset velocity was 1.1m/s. In this study, the performance evaluation results of the two dampers were obtained, and the latest numerical methods for the mechanical performance analysis of colloidal dampers and MR dampers under dynamic impact were provided.
Fast Commutation Error Compensation for BLDC Motors Based on Virtual Neutral Voltage

Precise commutation signal is the prerequisite for the efficient operation of brushless direct current (BLDC) motors. However, the low-pass filter, nonideal current, circuit, and software retarding can bring about the commutation errors that will deteriorate the performance of BLDC motors, for example, resulting in larger current ripple and noise. To eliminate the commutation errors quickly and improve the performance of the motor, a novel compensation method is proposed in this letter. The compensation time is calculated by a counter. And the start–stop signals of the counter are obtained by the virtual neutral voltage. The proposed method requires none voltage or current sensor and has a faster compensation speed and higher precision than the traditional compensation method based on the integration of virtual neutral voltage. The effectiveness of the proposed method is verified by comparable exp eriments using an 80-W BLDC motor.
Travelers-Tracing and Mobility Profiling Using Machine Learning in Railway Systems

With the advent of Coronavirus Disease 2019 (COVID-19) throughout the world, safe transportation becomes critical while maintaining reasonable social distancing that requires a strategy in the mobility of daily travelers. Crowded train carriages, stations, and platforms are highly susceptible to spreading the disease, especially when infected travelers intermix with healthy travelers. Travelers-profiling is one of the essential interventions that railway network professionals rely on managing the disease outbreak while providing safe commute to staff and the public. In this plethora, a Machine Learning (ML) driven intelligent approach is proposed to manage daily train travelers that are in the age-group 16-59 years and over 60 years (vulnerable age-group) with the recommendations of certain times and routes of traveling, designated train carriages, stations, platforms, and special services using the London Underground and Overground (LUO) Network. LUO dataset has been compared with various ML algorithms to classify different agegroup travelers where Support Vector Machine (SVM) mobility prediction classification achieves up to 86.43% and 81.96% in age-group 16-59 years and over 60 years.
Hybrid Active Damping Combining Capacitor Current Feedback and Point of Common Coupling Voltage Feedforward for LCL-Type Grid-Connected Inverter

Both the capacitor-current-feedback (CCF) active damping and the point of common coupling (PCC) voltage feedforward can provide damping for the LCL-type grid-connected inverter. They are usually individually adopted, and negative damping will occur in a certain frequency range due to the digital control delay, leading to a nonminimum phase behavior. In this article, a hybrid active damping that combines the CCF and unit PCC voltage feedforward is studied. With properly designing the CCF gain, the positive damping range could sweep the entire frequency range with the variation of grid impedance. As a reward, the maximum profit of damping cooperation can be harvested, ensuring high robustness against both grid impedance variation and filter parameter fluctuation. The simulation and experimental results are provided to verify the effectiveness of the hybrid active damping.
Security Challenges in Cyber Systems

CPS (Cyber-Physical Systems) is proposed by the NSF (National Scientific Foundation) to describe a type of necessities which conglomerates hardware and software components and being the next step in development of embedded systems. CPS includes a wide range of research topics from signal processing to data analysis. This paper contains a brief review of the basic infrastructure for CPS including smart objects and network aspects in relation to TCP/IP stack. As CPS reflect the processes of the physical environment onto the cyber space, virtualisation as important tool for abstraction plays crucial role in CPS. In this context paper presents the challenges associated with mobility and vritualisation; accordingly, three main types of virtualisation, namely network, devices and applications virtualisation are presented in the paper. The main focus of the paper is made on security. Different threats, attack types and possible consequences are discussed as well as analysis of various approaches to cope with existing threats is introduced. Furthermore, needs and requirements for safety-critical CPS are reviewed.
Remote Synthesis of Computer Devices for FPGA-Based IoT Nodes

The need of data processing in IoT node is described and its data processing components are reviewed. The expediency of using a reconfigurable environment in IoT nodes is justified. An idea to place into the cloud the specialized processors automatic design and synthesis tools from high-level languages was proposed and evaluated. These cloud design and synthesis tools are laid at the base of new method for computing organization in reconfigurable IoT nodes by giving them possibility of self-configuration. The challenges which need such a solution are shown and the benefits of the self-configurable IoT nodes use are justified.
SD-seq2seq : A Deep Learning Model for Bus Bunching Prediction Based on Smart Card Data

Bus bunching, a phenomenon due to the failure of headway or timetable adherence, often causes low level of public transit service with poor bus on-time performance and excessive passenger waiting time. To mitigate bus bunching, an accurate and real-time prediction method plays an important role. In this paper, we propose a supply-demand seq2seq model called SD-seq2seq to predict bus bunching using smart card data. Features from both supply and demand sides of bus service are taken into account, like bus stop type, dwelling time, passenger demand and type, and so on. Extensive experiments on multiple bus routes in real world demonstrate that our method outperforms other baseline methods. The proposed method is expected to provide useful online information of bus operation to both bus operators and passengers.
Dynamic Filtered Path Tracking Control for a 3RRR Robot Using Optimal Recursive Path Planning and Vision-Based Pose Estimation

This study proposes a dynamic filtered path tracking control schema for a 3RRR planar parallel robot to reduce the positioning errors caused by various sources such as backlash in the mechanical system, system nonlinearities, uncertainties, and unknown disturbances. The optimal recursive algorithm is implemented absolutely in path planning based on the optimization problem. Compared to other methods, this algorithm not only helps the robot avoid singularities but also ensures the shortest movement distance of links thanks to the optimization in path planning. The vision system using the 3D Intel RealSense camera D435i is proposed to provide the visual measurement as feedback for the pose estimation of the 3RRR planar parallel robot. The unscented Kalman filter is designed to reduce the errors of the pose estimation due to the camera’s intrinsic parameters, the vibration of the mechanical system, and unknown noises. The establishment of important equations and parameters of the unscented Kalman filter is detailed in this study. The simulation, experiment, and comparison are conducted to verify the effectiveness and feasibility of the proposed approaches.
Photorealistic Object Reconstruction Using Perturbation Functions and Features of Passive Stereo Projection

An effective approach to the calculation of volumetric models based on images of silhouettes in the object space is described. A method of forming the contours of a real object from a sequence of images is proposed. The construction of a bounding pyramid using the projection of the center and silhouette is shown. The intersection of all bounding pyramids determines the geometric representation of the object. Methods of obtaining information about the depth of the scene are proposed. A method of binocular stereo reconstruction of three-dimensional objects based on perturbation functions is proposed. In the method, interference is linear and requires linear compensation methods that are performed using the GPU by means of standard OpenGL. Defines the portion of the frame buffer (channel) in the GPU in which the image is output. Passive stereo uses two channels that process images intended for the left and right eye. Both channels handle a single main scene graph representing a user-defined model and attached to a root node (scene-root). Channels differ in the settings of the projection transformation (view-transform), which is determined by known parameters, such as the size and distance to the screen, the position of the user’s point of view, stereo base and others, which are specified in the calibration file. The paper shows the advantages of the proposed method over the known approaches of reconstruction of three-dimensional models.
Multi-Lingual Optical Character Recognition System Using the Reinforcement Learning of Character Segmenter

In this article, we present a new multi-lingual Optical Character Recognition (OCR) system for scanned documents. In the case of Latin characters, current open source systems such as Tesseract provide very high accuracy. However, the accuracy of the multi-lingual documents, including Asian characters, is usually lower than that for Latin-only documents. For example, when the document is the mix of English, Chinese and/or Korean characters, the OCR accuracy is lowered than English-only because the character/text properties of Chinese and Korean are quite different from Latin-type characters. To tackle these problems, we propose a new framework using three neural blocks (a segmenter, a switcher, and multiple recognizers) and the reinforcement learning of the segmenter: The segmenter partitions a given word image into multiple character images, the switcher assigns a recognizer for each sub-image, and the recognizers perform the recognition of assigned sub-images. The training of recognizers and switcher can be considered traditional image classification tasks and we train them with a supervised learning method. However, the supervised learning of the segmenter has two critical drawbacks: Its objective function is sub-optimal and its training requires a large amount of annotation efforts. Thus, by adopting the REINFORCE algorithm, we train the segmenter so as to optimize the overall performance, i.e., we minimize the edit distance of final recognition results. Experimental results have shown that the proposed method significantly improves the performance for multi-lingual scripts and large character set languages without using character boundary labels.
Ways of Unauthorized Access to Medical Data and Approach to Organize Secure Access using Blockchain Technology

The introduction of digital technologies into medical institutions promises great prospects, allowing of improving the quality of treatment and radically changing the approach to early diagnosis of dangerous diseases. However, this progress has a dangerous side effect - the risk of information security. The system of differentiation of access in many systems of medical establishments is not reliable due to the lack of active ways of cryptographic protection of information. In addition, most systems do not provide mandatory access control, and accordingly do not allow you to differentiate computer resources by secrecy and category. In some cases, it is not possible to set passwords to access some of the most important computer resources. Cryptographic protection is the basis for protecting information from theft. The disadvantage of modern diagnostic center systems is the high speed of cryptographic transformations, which forces users to abandon the encryption function.
Tony Whitaker: life and achievements of a pioneer New Zealand lizard ecologist

ABSTRACT Tony Whitaker grew up in Pinehaven, north of Wellington, New Zealand. He soon became interested in natural history, with interests centred on lizard ecology. Newly developed nocturnal, mark–recapture and quadrat survey techniques enabled density and niche segregation studies, resulting in numerous scientific publications throughout his life. From 1966 to 1977 he worked as an ecologist at the Department of Scientific and Industrial Research (DSIR). His surveys of islands off northern New Zealand led to an important study of the impact of kiore (Polynesian rat; Rattus exulans) on lizard populations, showing that rat eradication was necessary for conservation. At DSIR he also researched birds and mammals. Later, Tony and his wife Viv established a farm near Motueka and began mainland studies of lizards, contracted to mining companies or the New Zealand Wildlife Service/Department of Conservation. Having built up a well-documented collection of New Zealand lizards (now at the Museum of New Zealand Te Papa Tongarewa), Tony then contributed to a bibliography, atlas and field guide. During the period when the taxonomy of lizards was revolutionised by the use of molecular techniques, Tony provided specimens, guidance and encouragement to many herpetologists. From 2000, his interests turned to the little-known and threatened lizards of New Caledonia, on which he contributed to 13 major papers. His life and work are here described under thematic, rather than strictly chronological, headings.
A Python Application for Hourly Electricity Prices Forecasting Using Neural Networks

The paper considers the problem of forecasting hourly market electricity prices using the artificial neural networks. The existing publications are analyzed to determine the most popular methods of electricity prices forecasting, the types of neural networks most commonly used in forecasting, and the existing tools of forecasting algorithms implementation based on neural networks. A Python application has been developed to perform the electricity prices forecasting and analysis, using NeuroLab and Keras libraries for creating neural networks. The paper also presents the results of numerical experiments on electricity prices forecasting using the developed application. The experiments tested the forecast quality dependence on the library used, the structure of neural network, and the training algorithms used.
A comparison study on the different SNR levels to the accuracy of two deep learning techniques in fault diagnosis of planetary gearbox

For most of the current deep learning models, the noise contaminated data are usually used in both model training and test. The ability of deep learning models to withstand different levels of noise becomes an important research topic. In this work, a recent developed deep learning model called DCAE-CNN is studied under different noise levels of training and test samples. The model combines a one-dimensional De-noising Convolutional Auto-encoder (DCAE-1D) and a one-dimensional Convolutional Neural Network (CNN-1D). The DCAE-1D network enables the noise reduction ability embedded in the model and may accommodate noisy training and test samples. The effectiveness of this model is studied by using different levels of SNR to planetary gearbox fault diagnosis. The DCAE-CNN and CNN model are compared by using different SNR noise contaminated training and test samples, the results demonstrated the effectiveness of the DCAE-CNN.