Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-Term Cross-border Electricity Trading Considering the Uncertainty of Wind Power

The improvement of wind power technology and the promotion of the Global Energy Internet Project, provide technical supports and platform for cross-border trading. However, the uncertainty of wind power and the specific feature of cross-border power market are the key points of trading mechanism design. Based on this, this paper proposes a long-term cross-border electricity trading model considering the uncertainty of wind power. First, annual transaction results among countries considering political factors are obtained according to the declared data of bidders; Scenario reduction algorithm is applied to deal with wind power data, and then trading contract of multi-stage and multi scenario is obtained. Finally, the case study results of multinational power market in Northeast Asia show that the trading model designed in this paper is feasible to the cross-border electricity trading with wind power.
Brain Network Analysis of Compressive Sensed High-Density EEG Signals in AD and MCI Subjects

Alzheimer's disease (AD) is a neurodegenerative disorder that causes a loss of connections between neurons. The goal of this paper is to construct a complex network model of the brain-electrical activity, using high-density EEG (HD-EEG) recordings, and to compare the network organization in AD, mild cognitive impaired (MCI), and healthy control (CNT) subjects. The HD-EEG of 16 AD, 16 MCI, and 12 CNT was recorded during an eye-closed resting state. The permutation disalignment index (PDI) was used to describe the dissimilarity between EEG signals and to construct the connection matrices of the network model. The three groups were found to have significantly different (<italic>p</italic> <inline-formula><tex-math notation="LaTeX">$ <$</tex-math></inline-formula> 0.001) characteristic path length (<inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula>), average clustering coefficient (CC), and the global efficiency (GE). This is the first time that HD-EEG signals of AD, MCI, and CNT have been compared and that PDI has been used to discriminate between the three groups. Considering the large amount of data originating from HD-EEG acquisition, compared to standard EEG, the aim of this paper is also to assess that compression did not alter the results of the complex network analysis. Compressive sensing was adopted to compress and reconstruct the HD-EEG signals with minimal information loss, achieving an average structural similarity index of 0.954 (AD), 0.957 (MCI), and 0.959 (CNT). When applied to the reconstructed HD-EEG, complex network analysis provided a substantially unaltered performance, compared to the analysis of the original signals: <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula>, CC, and GE of the three groups were indeed still significantly different (<italic>p</italic> <inline-formula><tex-math notation="LaTeX">$ <$</tex-math></inline-formula> 0.001).
Distribution of Virtual Machines in Cloud Infrastructure with the Account of Various Posibilities of Access to Data Storage Systems

the article addresses the well-known "knapsack problem" in the context of the distribution of virtual servers within the cloud infrastructure. At the same time, the main focus in solving this problem is the distribution of virtual machines within the cloud, which includes resources with different access capabilities. The mathematical model proposed in the article allows conducting optimal allocation of resources not only taking into account such parameters as random access memory and processor power, but also parameters of data storage systems related to memory capacity and access speed. The resulting model was tested on a real network infrastructure. From a practical perspective, the method of virtual machines distribution under question allows using the low-power devices available at the data center.
Comprehensive Study of Multiple CNNs Fusion for Fine-Grained Dog Breed Categorization

Fine-grained visual categorization aims to distinguish objects in subordinate classes instead of basic class, and is a challenge visual task due to the high correlation between subordinated classes and large intra-class variation (e.g. different object poses). Although, deep convolutional neural network (DCNN) has brought dramatic success on generic object classification, detection and segmentation with the availability of the large-scale training samples, direct application of DCNN on fine-grained visual categorization, where only decades or at most hundreds of training samples for each subordinate class are available in most public finegrained image datasets, cannot lead to satisfactory classification results due to small number of training samples. This study explores the transfer learning strategy for finegrained dog breed categorization based on the learned CNN models with the large-scale image dataset: ImageNet, and prove promising performance with two DCNN models: AlexNet and VGG-16. Furthermore, we argue that different DCNN architecture may extract the representation of different image aspects due to the previously defined CNN kernel sizes, number and various operations in the model learning procedure, and thus result in different performance for visual categorization. This study proposes to fusion multiple CNN architectures for combining different aspect representations to give more accurate performance. We compressively study the fusion of different layers such as Fc6 and Fc7 in AlexNet and VGG-16, and manifest 2.88% improvement of the fusion architecture over the best performance of the only one DCNN model: VGG-16 from 81.2% to 84.08%.
Hands and Faces, Fast: Mono-Camera User Detection Robust Enough to Directly Control a UAV in Flight

We present a robust real-time system for simultaneous detection of hands and faces in RGB and gray-scale images, and a novel dataset used for training. Our goal is to provide a robust sensor front-end suitable for real-time human-robot interaction using face-engagement and gestures. Using hand-labelled videos obtained from real human-UAV interaction experiments, we re-trained the YOLOv2 Deep Convolutional Neural Network to detect only hands and faces. This model was then used to automatically label several much larger third-party datasets. After manual correction of these results, we modified and re-trained the model on all this labelled data. We obtain qualitatively good detection results at 60Hz on a commodity GPU: our simultaneous hand-and-face detector gives state of the art accuracy and speed in a hand detection benchmark and competitive results in a face detection benchmark. To demonstrate its effectiveness for human-robot interaction we describe its use as the input to a simple but practical gestural human-Uavinterface for entertainment or industrial applications. All software, training and test data are freely available.
Mobile Continuum Robot with Unlimited Extensible Sections

Typical continuum robots, such as pneumatic and tendon-driven robots, have a restricted section length and require a large external component for pulleys and a compressor, making them unsuitable for locomotion. This paper presents a new mobile continuum robot design with virtually unlimited extensible sections. A driving unit, which has a mechanism similar to the rack-and-pinion, consists of three DC motors with gears, each of which moves each flexible tube. The rotation of the motor translates the flexible tube, which has a helical groove on the surface that meshes with the gear. The long flexible tube provides a large traveling distance as long as it does not buckle. The elongation and bending motion of each section may be controlled during operation by varying the speed of each flexible tube. This design not only allows the expansion of the robot to otherwise unreachable work areas but also improves the locomotion velocity by generating a large traveling distance of the flexible tubes. The most important point in this paper is to use multiple driving units for locomotion. Since all the driving units can be mounted on the same tubes, by increasing the number of them, the robot can take various forms without expanding its diameter. A preliminary prototype was built, and its crawling locomotion performance was tested using two operating sequences. The results indicate that earthworm-like locomotion can be achieved with good performance by elongating the sections even when the ground is slippery. The proposed design can be easily be rebuilt by anyone with access to a basic 3D printer.
Learning Hardware Dynamics Model from Experiments for Locomotion Optimization

The hardware compatibility of legged locomotion is often illustrated by Zero Moment Point (ZMP) that has been extensively studied for decades. One of the most popular models for computing the ZMP is the linear inverted pendulum (LIP) model that expresses ZMP as a linear function of the center of mass(COM) and its acceleration. In the real world, however, it may not accurately predict the true ZMP of hardware due to various reasons such as unmodeled dynamics and differences between simulation model and hardware. In this paper, we aim to improve the theoretical ZMP model by learning the real hardware dynamics from experimental data. We first optimize the motion plan using the theoretical ZMP model and collect COP data by executing the motion on a force plate. We then train a new ZMP model that maps the motion plan variable to the actual ZMP and use the learned model for finding a new hardware-compatible motion plan. Through various locomotion tasks of a quadruped, we demonstrate that motions planned for the learned ZMP model are compatible on hardware when those for the theoretical ZMP model are not. Furthermore, experiments using ZMP models with different complexities reveal that overly complex models may suffer from over-fitting even though they can potentially represent more complex, unmodeled dynamics.
Reconfigurable Smart Factory for Drug Packing in Healthcare Industry 4.0

Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.
Omnidirectional Robot Undercarriages with Standard Wheels - A Survey

In the last decades, omnidirectional mobile robot platforms have entered many areas of industrial automation and daily life. While platforms with universal and Mecanum wheels are quite common, standard wheels have the advantage of robustness against uneven ground and slippage. This paper gives an overview on recent developments in the design of omnidirectional undercarriages with standard wheels and categorizes mechanical designs according to their holonomy. Furthermore, requirements for different applications are discussed and simple evaluation criteria are proposed that can be used to compare designs.
Novel Multilayered Ultra-Broadband Bandpass Filters on High-Impedance Slotline Resonators

This paper presents a novel class of multilayered ultra-broadband bandpass filters based on high-impedance slotline resonators. The equivalent circuit and electrical properties of the proposed high-impedance slotline resonator realized on a low-permittivity substrate are first discussed. To largely remove the radiation losses of the devised high-impedance slotline resonator, two identical shunt-connected folded low-impedance slotline sections are utilized to replace one short-circuited high-impedance slotline section. Through the synthesis design of an ultra-broadband microstrip-to-microstrip vertical transition, the effective impedance of the conceived shunt-connected low-impedance slotline sections is confirmed and the impedance variations of this proposed high-impedance slotline resonator are quantitatively determined by the means of the extracted turns ratios of the employed transformers. To further demonstrate the effectiveness of the engineered slotline resonator in terms of radiation reduction and verify its equivalent circuit model, a new type of multilayered high-order bandpass filters with ultra-broadband Chebyshev equiripple filtering response is theoretically presented. Furthermore, for experimental validation purposes, a 2-GHz two-layered bandpass filter prototype is fabricated and measured. It exhibits a seven-pole ultra-broadband filtering response, high in-band amplitude/group-delay flatness, highly reduced radiation losses, and sharp-rejection upper-stopband characteristics.
Admittance Spectroscopy of Nanoheterostructures: Computer-Controlled Data Acquisition and Modeling of Emission Processes

A computer-controlled complex of admittance spectroscopy has been developed, which allows one to perform a non-destructive diagnostics of key electronic parameters of bulk semiconductors and nanoheterostructures. The complex is essential both for research of nanoobjects (quantum wells, quantum dots, wetting layers etc.), and at the stage of device production for control of growth parameters and concentrations during doping processes. The created computer-controlled complex realizes both data acquisition and processing.
Application of Variational Auto-Encoder in Mechanical Fault Early Warning

Due to the non-stationary characteristic of the vibration signals of complex machinery, such as reciprocating compressors and aeroengines, the single feature threshold alarm method that is currently frequently utilized suffers from a shortage of inaccurate alarm. Therefore, a variational auto-encoder-based method for fault early warning is proposed according to the characteristics of the response signals of the mechanical response point. The method is based on high-dimensional feature parameters of mechanical vibration signals. It is used to compress the feature parameters and to self-learn the statistical distribution model from this compressed data via variational auto-encoder. The normal working condition model is used as benchmark model. The fault early warning is realized by comparing the difference between the real-time working condition model and the benchmark model with the adaptive early warning threshold. The advantages of the proposed method are verified via comparison between the single feature threshold alarm method and the state subspace-based early warning method. The results indicate that the method can improve the accuracy of mechanical fault early warning and can greatly advance the alarm time point of the fault. This method is highly timely and strongly adaptable.
Prescribed performance control of one-DOF link manipulator with uncertainties and input saturation constraint

In this paper, we mainly address the position control problem for one-degree of freedom ( DOF ) link manipulator despite uncertainties and the input saturation via the backstepping technique, active disturbance rejection control ( ADRC ) as well as predefined tracking performance functions. The extended state observer ( ESO ) is employed to compensate uncertain dynamics and disturbances, and it does not rely on the accurate model of systems. The tracking differentiator ( TD ) is utilized to substitute the derivative of the virtual control signals, and the explosion of complexity caused by repeated differentiations of nonlinear functions is removed. The auxiliary system is used to deal with the control input limitation, and the tracking accuracy and speed are improved by predefined tracking performance functions. With the help of the input-to-state stability ( ISS ) and Lyapunov stability theories, it is proven that the tracking error can be gradually converged into arbitrarily small neighborhood of the origin, and the tracking error is adjusted by suitable choice of control parameters. The simulation results are presented for the verification of the theoretical claims.
Research and Development of the Algorithm of Mixing Audio Data Streams for Heterogeneous Computer Systems in Telecommunications

This paper presents an algorithm enhanced mixing of audio streams for computation on GPUs, which combines multiple stages of mixing by using two-pass rendering, which significantly reduces the switching time between buffers. Methods of computer experimental comparative studies were carried out evaluating the performance of the developed algorithm. The purpose of the present paper is development of an efficient algorithm for mixing audio streams for processing on GPUs. The method of transfer operations computing on graphics processors with the use of Shader programs was developed. Novel features presented solutions is using a two-pass rendering. The results showed that the application of the developed algorithm leads to an increase in computational performance up to 6 times. Presented solution can be implemented as software in the telecommunications multiprocessor systems.
Periodic Data Eviction Algorithm of SCM/NAND Flash Hybrid SSD with SCM Retention Time Constraint Capabilities at Extremely High Temperature

Storage class memory (SCM)/NAND flash hybrid SSD has been proposed to improve SSD performance. Evict operation is one of the SSD functions to remove data from SCM to NAND flash when SCM blank capacity becomes small. With conventional LRU order-based eviction, data written to SCM stays over acceptable data-retention time because the conventional algorithm evicts only data exceeding the SCM capacity. To reduce errors by data-retention failure, Periodic eviction algorithm are proposed. With proposed eviction, written data in SCM is evicted periodically to avoid staying data in SCM over data-retention limit. From the evaluation results, Periodic eviction moves all data in SCM to NAND flash before the data exceed data-retention limit. On the other hand, I/O performance decreases due to increasing number of data movement from SCM to NAND flash. The SCM capacity is limited to have no error in SCM because the number of error sector depends on SCM capacity ratio with conventional eviction. However, Periodic eviction removes written data before occurring error completely regardless of SCM capacity ratio. Therefore, more SCM capacity can be added to hybrid SSD. As a result, the additional SCM enhances I/O performance 2.6 times compared with the performance with conventional eviction.
A Coordinated Operation Approach for Multi-microgrid with Flexible Interconnection

This paper focuses on coordinated operation of the multiple grid-connected microgrids (MGs) to achieve both operation economy and higher power quality to distribution network. To accurately control of power flow and transfer the renewable energy between different MGs, flexible interconnect device (FID) is used. The interconnection structures of multiple MGs with FID in different transformers are discussed, and the model of FID is established. The multi-objective operation optimization model is established, including operation cost and nodal voltages these two targets. In order to solve the complicated multi-objective model, NSGA-II is used to search the Pareto frontier. A study case which is based on six MGs in three distribution transformer areas is used to carry out the coordinated operation approach, and simulation results show that the proposed model and method are effective.
Hybrid sEMG, NIRS and MMG Sensor System

In recent years, surface electromyography (sEMG) is widely used in human-computer interface (HCI). For example, it is used for prosthetic manipulation to improve the quality of amputees’ life. However, sEMG approaches has some drawbacks such as poor robustness of the electrode-skin interface. Near-infrared spectroscopy (NIRS) and mechanomyography (MMG) can also monitor muscle motion. Comparing with approaches that only use sEMG signal, hybrid sEMG, NIRS and MMG sensor system will have better system performance. Investigations about the fusion of sEMG, NIRS and MMG are scant. This paper presents a hybrid sEMG, NIRS and MMG sensor system and puts it into practice. Fuse surface electromyography, near-infrared spectroscopy and mechanomyography acquisition circuits into a compact sensor, which can measure the muscle motion from the modalities of electrophysiology, optics and acoustics. Using the hybrid sensor system, incremental grip force experiment is carried out to explore the relationship between the three signals, blood oxygen metabolism and grip force. And muscle fatigue is carried out in order to explain the phenomenon of muscle fatigue from the perspective of electrophysiology, blood oxygen metabolism and mechanomyography.
An Improved Binary Wolf Pack Algorithm for Solving Optimal Sensor Selection Problems

The problem of selecting optimal sensors is a typical discrete combinatorial optimization problem, which is proved to be NP-hard. In this paper, an improved binary wolf pack algorithm (IBWPA) by adaptive step length, information interaction and difference evolution update strategy is proposed to choose the optimal sensors with high computational accuracy and robustness. Firstly, the wolves' position, motion operator, intelligent behaviors and rules of basic binary wolf algorithm for optimum in discrete state space are introduced. Then, tentative direction of scouting behavior based on information interaction, adaptive steps length of scouting and summoning behavior, as well as the differential evolution updates strategy are used to improve the traditional BWPA and the schemes and procedures of IBWPA are also presented. Finally, experiments show that compared with BPSO, BWPA and other sensor selection methods, the proposed IBWPA method has a better accuracy, robustness and global searching ability and the problem can be solved in reasonable computational time. Moreover, the effects of algorithm parameters on the performance of selecting optimum are also analyzed through simulation and theoretical analysis, which indicate that IBWPA can achieve a better tradeoff between the global searching ability and computational time by choosing reasonable parameters.
The Comparison of STANAG 4559 NSILI Compliant Tactical Intelligence Database Implementation in Relational Database and NoSQL Database

The goal of the article is to evaluate the benefits of the NoSQL approach for implementation of Intelligence, Surveillance and Reconnaissance database compliant to STANAG 4559 NATO Standard ISR Library Interface. The first part of the article introduces the intelligence cycle, the key standard for the interoperable sharing of tactical intelligence products and issues of relational databases which led to the introduction of NoSQL approach. In the second part of the article is discussed implementation of STANAG 4559 compliant database both in relational and NoSQL database. We discuss limitations of both solutions, and we propose hybrid approach based on metadata based enrichment of the stored document to enable efficient and flexible querying.