Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication Emitter Motion Behavior’s Cognition Based on Deep Reinforcement Learning

Considering the successful application of deep reinforcement learning (DRL) on tasks of moving objects, this paper innovatively applies deep deterministic policy gradient algorithm (DDPG) to complete the cognition task on multi-dimension and continuous communication emitter motion behavior. First, we propose a DDPG-based behavior cognition algorithm (DDPG-BC). It chooses direction, velocity, and communication frequency as state space, gains experience from interaction between network and environment and outputs deterministic cognition results. Second, under the condition of sufficient prior information such as geographic information, we further propose a novel algorithm named DDPG-based behavior cognition with Attention algorithm (DDPG+A-BC). It introduces attention mechanism into DDPG-BC which limits exploration scope and the randomness of initial state and improves the exploration efficiency and accuracy. The simulation experiments verify that DDPG-BC and DDPG+A-BC show good cognition ability on two different data set. And the algorithms are all superior to other DRL algorithm and existing cognition method with higher cognition accuracy and less time. In addition, we also discuss the influence of episode, reward function, and added attention mechanism on algorithm performance.
Exploring Dimensionality Reduction Techniques for Efficient Surrogate-Assisted optimization

Constructing surrogate models of high dimensional optimization problems is challenging due to the computational complexity involved. This paper empirically investigates the practicality of major dimensionality reduction techniques for encapsulating the high dimensional design space into compact representations. Such low dimensional representations of the design space can be utilized for constructing the surrogate models efficiently. Based on historical mainstays and recent developments in deep learning, we study four dimensionality reduction techniques in this paper, namely Principal Component Analysis, Kernel Principal Component Analysis, Autoencoders and Variational Autoencoders. We evaluate and compare these techniques based on quality assessments of the corresponding low dimensional surrogate models on a diverse range of test cases. These test cases are defined on combinations of three dimensionsalities, ten well-known benchmark problems from the continuous optimization domain and two surrogate modeling techniques, namely Kriging and Polynomials. Our results clearly demonstrate the superiority of Autoencoders and Principal Component Analysis on the criteria of modeling accuracy and global optimality respectively.
Partial Fingerprint Verification via Spatial Transformer Networks

Partial fingerprint verification is a challenging task because of the few features contained in small area as well as the large rotation angle and translation between query images and template images. In this paper, we propose a new framework of partial fingerprint verification based on spatial transformer networks (STN) model, where a transform model, i.e., AlignNet network, is proposed to estimate the alignment parameters, and the verification is modeled as a binary classification task. The experimental results on the simulated datasets created from FVC2004 and the real-world dataset FVC2006 DB1 show that our method is invariant to rotation, and also robust to different kinds of scanners, and dramatically outperforms the rank-1 entry of FVC2006 participants. The EER on FVC2006 DB1 of the proposed algorithm is 3.587% compared to that of 5.564%, the best of FVC2006 DB1 entries.
Removal of Motion Artifacts from ECG signals by Combination of Recurrent Neural Networks and Deep Neural Networks

Electrocardiogram (ECG) is the graphical portrayal of heart usefulness. The ECG signals holds its significance in the discovery of heart irregularities. These ECG signals are frequently tainted by antiques from various sources. It is basic to diminish these curios and improve the exactness just as dependability to show signs of improvement results identified with heart usefulness. The most commonly disturbed artifact in ECG signals is Motion Artifacts (MA). In this paper, we have proposed a new concept on how machine learning algorithms can be used for de-noising the ECG signals. Towards the goal, a unique combination of Recurrent Neural Network (RNN) and Deep Neural Network (DNN) is used to efficiently remove MA. The proposed algorithm is validated using ECG records obtained from the MIT-BIH Arrhythmia Database. To eliminate MA using the proposed method, we have used Adam optimization algorithm to train and fit the contaminated ECG data in RNN and DNN models. Performance evaluation results in terms of SNR and RRMSE show that the proposed algorithm outperforms other existing MA removal methods without significantly distorting the morphologies of ECG signals.
Human daily activity behavioural clustering from Time Use Survey

Identification of daily pattern behaviours of people from Time use Survey with the purpose of defining archetypes of persons is becoming a new rising research field. Identified patters are useful for developing more realistic models to simulate activities of citizens related to mobility and households energy consumption. These models are required to test and develop simulation scenarios of future smart grids and cities. In this work we apply the k-modes algorithm to clusterize the Italian TUS data-set. For the best of our knowledge this is the only study that applied unsupervised clusterization and classification of Italian TUS data and the only one that extended the analysis to mobility activities of the TUS data-sets. From experimental results we obtained different clusters for weekdays, saturdays and holidays, respectively.
Parallel LSTM Architectures for Non-Intrusive Load Monitoring in Smart Homes

Non-Intrusive Load Monitoring (NILM) is becoming popular as an appliance monitoring technique that can extract detailed power consumption information related to the different appliances used in a household. NILM utilises time series analysis methods to disaggregate signals of operating appliances from a single point in houses. Based on that it can help to provide energy consumption advice for household owners. NILM is known to be a challenging task from a computational aspect. This study proposes deep neural network models with a Parallel Long Short-Term Memory Topology (PLT) and evaluates them on the public REDD dataset. The new models’ experimental evaluation results exhibit a significant improvement in four main state-based and energy-based metrics in comparison to previous results obtained using deep denoising autoencoders.
Performance Enhancement of Ge/GaAs Heterostructure Tunnelling Field Effect Transistor

Tunnelling Field Effect Transistor (TFET) is an emerging alternative to Metal Oxide Semiconductor Field Effect Transistor (MOSFET) in device technology as the latter struggles with scaling down effects. But, low on-state current and abrupt doping profile are the limitations of TFET. However, Junctionless Tunnelling Field Effect Transistor (JLTFET) has solved this doping problem by uniformly doping all the regions. A higher tunneling barrier causes the low on-state current, and that is still a limitation of the JLTFET. Our study aims to reduce the tunneling barrier to have a higher on-sate current using Ge/GaAs Heterostructure Junctionless Tunnelling Field Effect Transistor (H-JLTFET) and enhance the overall performance of this H-JLTFET by analyzing different device parameters, such as channel thickness, oxide thickness, and doping concentration. The Non-Local Band to Band Tunnelling (BTBT) model was employed to achieve a realistic tunneling mechanism in our device using TCAD. Our proposed device provides a high on-state current of 7.68e-5 A/um, a low off-state current of 7.4e-17 A/um, nearly an ideal subthreshold swing of 5.73 mV/decade. Finally, this work concludes with a comparative study between our optimized device and other published similar TFET. The analysis shows that our device provides a significant improvement in performance, which could be very promising for the emerging low-power, high-speed VLSI technology.
Risk Assessment for Farmers in the Vicinity of Abandoned Nokdong Mine in South Korea

A risk assessment of environmental media was performed for the inhabitants in the area of the abandoned Nokdong metal mine. Soil, groundwater, and crop samples were collected from September to October 2008 around the mine. After pretreatment of these samples, metal concentrations were measured, and a risk assessment was performed using the Korean soil-contamination risk assessment guidelines. Lead (Pb) and arsenic (As) intake rates were the highest for inhalation of soil dust. The cancer risks from ingestion of As-contaminated groundwater, inhalation of As-, Cd-, and Pb-contaminated soils, and contact of As-contaminated soils exceeded the acceptable risk. The sum of all carcinogenic risks was 9.29 × 10 -3 . The non-carcinogenic risk was highest for ingestion of As-contaminated water (11.0), followed, in descending order, by inhalation of Hg-contaminated soil and ingestion of Pb-contaminated water. Most of the risks were associated with As, Cd, Pb, and Hg contamination, and therefore, these metals were considered to be potential toxic carcinogens and non-carcinogens for humans in this area. In this study, the non-carcinogenic risks of ingestion of contaminated water or crops, as well as those associated with the inhalation of soil dust were observed.
Preparation of Li4Ti5O12 nanoparticles by a simple sonochemical method

Homogeneous Li4Ti5O12 nanoparticles were prepared: First, LiOH was coated onto TiO2 nanoparticles via a sonochemical method, operated at 20 kHz and 220 W for 20 min. These particles have about 2–5 nm coating depth of LiOH on the TiO2 nanoparticle surface. Second, the resulting nanoparticles were thermally treated at 500 °C for 1 h, resulting in the formation of Li4Ti5O12 (LTO) nanoparticles. These LTO nanoparticles had an average grain size of about 30–40 nm with excellent phase purity in good stoichiometric ratios of Li4Ti5O12. These nanoparticles with a uniform size distribution were characterized by infrared spectroscopy, X-ray diffraction, and high resolution-transmission electron microscopy analyses.
The Effects Of Non-linear Operators In Voxel-Based Deep Neural Networks For 3D Style Reconstruction

Neural Style Transfer has been successfully applied for generating plausible novel 2D images by transferring the style of a painting to an existing content image and has been extended to image, video, motion and animation manipulation. Besides first works on 2D/3D hybrid approaches relying on 2D rendering operations, a consistent end-to-end 3D Neural Style Transfer is missing. For realizing such an end-to-end 3D Neural Style Transfer, we suggested a first 3D voxel-based neural network architecture in previous works. Here, we extend this architecture to a resolution of 2563 voxels to process vastly finer visual features, and we include our recently introduced standardized gram matrix approach, which reduces noise and visually improves style transfer results. We furthermore show that the type of activation function used in the network is crucial for the successful application in 3D Neural Style transfer. In particular, we demonstrate that the recently proposed bipolar exponential linear unit (belu) operator achieves a consistent style reconstruction independent of filter orientation relative to the shape surface. We compare the operator to the commonly used exponential linear units (elu) and provide insights for the shown behavior. In addition to a purely visual inspection, we evaluate the constructed shapes using local 3D shape descriptors and confirm the subjective visual improvements. We conclude that the utilization of belu operators in the deep neural network architecture is a crucial component for realizing high-resolution voxel-based 3D Neural Style Transfer.
Augmenting Telephony Audio Data using Robust Principal Component Analysis

Audio augmentation (e.g., corrupting audio data by noise) has been shown to improve the performance of Automatic Speech Recognition (ASR) systems for low-resource languages. In light of this, we are interested in understanding whether corrupting speech data with telephone channel characteristics (e.g., background music, artifact caused by down-sampling) improves the performance of ASR systems as well. In this work, we investigate the possibility of applying Sound Source Separation (SSS) approaches to capture the telephone channel characteristics. We are in particular interested in Robust Principal Component Analysis (RPCA), which is an unsupervised approach used for various SSS tasks. Our results show that augmenting clean speech data corpus with telephone channel characteristics yields a more robust ASR system, with 7.8% of Word Error Rate reduction. We also find that the characteristic, which has the lowest spectral features, improves ASR the most.
Does the Supervision Mechanism Promote the Incentive Effects of Government Innovation Support on the R&D Input of Agricultural Enterprises?

The stimulating issues of government innovation support to the market players’ R&D vitality has always been the focus of innovation. This article analyzes the impact of government innovation support’s two policies, namely, government subsidies and tax & fee returns, on the R&D input of agricultural enterprises from the perspective of government intervention. Moreover, this article is primarily focusing on the institutional investors, leading shareholders, and social audit institutions in the corporate governance to play a role in the improvement of shareholding and audit supervision mechanism. This article conducts unbalanced panel data regression tests based on the relevant data of government innovation support of A-share agricultural enterprises listed in Shanghai and Shenzhen from 2009 to 2019. The conclusions are as follows: Firstly, government subsidies have a significant crowding-out effect on R&D investment of agricultural enterprises, while tax & fee returns have a significant stimulating effect. Secondly, when the shareholding ratio of institutional investors and leading shareholders increases, the shareholding supervision mechanism strengthens the effect of government innovation support on enterprise R&D input. Finally, with the improvement of social audit institutions’ auditing opinions, the enhancement of auditing supervision mechanisms has weakened the effect of government innovation support on enterprise innovation investment. These research findings can provide some academic supports and policy references for formulating appropriate government innovation support and giving full play to the role of shareholding supervision and audit supervision in corporate governance.
PVCLN: Point-View Complementary Learning Network for 3D Shape Recognition

As an important topic in computer vision and multimedia analysis, 3D shape recognition has attracted much research attention in recent years. For point cloud data and multiview data, various approaches have been proposed with remarkable performance. However, few works simultaneously employ the point cloud data and multiview data to represent 3D shapes, which is complementary and beneficial in our consideration. Moreover, existing multimodal approaches mainly focus on the multimodal fusion strategy or on exploring the relation between them. However, the intra-modality characteristic information and inter-modality complementary information are ignored in these methods. In this paper, we tackle the above limitations by introducing a novel Point-View Complementary Learning Network (PVCLN) to explore the potential of both the complementary information and characteristic information for 3D shape recognition. Inspired by the successful application of graph neural networks in capturing relations between features, we introduce a novel multimodal fusion strategy. Concretely, we first separately extract the visual feature from multiview data and structural feature from point cloud data. We then project the visual feature and structural feature into the same feature space to learn the complementary information between two modalities by modeling the inter-modality affinities. The characteristic information in each modality is also preserved by considering the intra-modality affinities. The intra-modality and inter-modality affinities compensate for the lacking characteristic information and enhance the complementary information in the feature learning process. Finally, the updated visual and structural features are further combined to achieve a unified representation for a 3D shape. We conduct extensive experiments to validate the superiority of the overall network and the effectiveness of each component. The proposed method is evaluated on the ModelNet40 dataset and the experimental results demonstrate that our framework achieves competitive performance in the 3D shape recognition task.
Parameter Extraction Testbed to Optimize Interconnections of Non-Planar Photovoltaics

The performance of photovoltaic (PV) systems drops considerably due to partial shading. Partial shading is inherent to non-planar PV applications where the surface normal of various segments of the PV material is not uniformly oriented, which causes non-uniform insolation. A mitigation technique is to optimize the interconnections between the PV elements to yield the highest energy harvest by finding and grouping the homogeneous cells. This paper presents results from an experimental study using a new experimental testbed that was developed to extract PV parameters in-situ. The experimental testbed is flexible, scalable, and suitable for research and development of non-planar PV applications. The benefit of this testbed is to collect data considering factors like cell aging, partial shading, and temperature effects. Such factors are not accounted for in manufacturer datasheets. The non-planar experimental setup enables the testing of a variety of PV interconnections to gather the characteristic i-v and p-v curves for PV characterization. Extracted parameters can then be used to model, simulate, and emulate the operation of the non-planar PV systems. Developed simulation and emulation models are used for testing and tuning maximum power point tracking (MPPT).
An Index for Determination and Manipulation of Steady State Voltage Stability of Transmission and Distribution Infrastructures

This paper introduces the concept of static stability limit breakdown margin for circuit elements and demonstrates that it is linked with steady state voltage stability of unbalanced, multiphase T&D systems. It is shown that the proposed index can be effectively utilized to recognize and reinforce the system elements that limit the voltage stability. The reinforcement may come in the form of adding a new line, impedance modification or reactive compensation. Thus by changing stability margin, the voltage stability limits of system buses can be manipulated to increase the net load capability of the buses in events of system contingencies and/or load/generation fluctuations. The proposed concepts are implemented using Graph Trace Analysis (GTA), and demonstrated using standard IEEE Transmission, Distribution, and Integrated Transmission and Distribution models built from standard IEEE models.
THE PRECISION OF DETERMINATIONS OF THE NATURAL ABUNDANCE OF NITROGEN‐15 IN SOILS, FERTILIZERS, AND SHELF CHEMICALS

Data are presented which establish the reproducibility of 15N:14N mass spectrometer ratio measurements at approximately ± 1 unit of 816N, the per mill 16N enrichment. This degree of reproducibility has been obtained even when the concentration of large volumes of water is required or with nitrogen derived from soil samples. The procedures we follow to achieve this precision are also detailed. A series of shelf chemicals had a range of values of 816N, probably reflecting isotopic fractionation during their manufacture. Commercial fertilizers had values of 816N close to zero and uniformly lower than sources of nitrogen of recent biological origin. While the 816N value of the products of the mobilization of soil N (i.e., NH4+ and NO2-) will probably most often be of interest rather than the values of the total nitrogen, some values of the latter are reported. The 816N of the total nitrogen of soils sampled near Cerro Gordo in the Illinois Corn Belt had values higher than the common commercial fertilizers. Some soils sampled in other locations had significantly lower values than those found in central Illinois.
CO 2 Foam: Results From Four Developmental Field Trials

Four pattern-scale CO{sub 2}-foam field trials were conducted to determine the effectiveness of foam in reducing CO{sub 2} channeling, to evaluate the economic potential of the process, and to develop application criteria and procedures. The trials were conducted under various process and geologic conditions so that the resulting technology would be applicable in a number of different CO{sub 2} floods. Two different surfactants, Rhodapex (formerly Alipal) CD-128 and Chaser CD-1045, and two injection methods, alternating vs. coinjection of CO{sub 2} and surfactant, were tested in San Andres (west Texas) and platform carbonate (southeast Utah) reservoirs. In all, 161,000 lbm of active surfactant was injected in the four field trials, with one well undergoing foam treatment for as long as 18 months. The treatments resulted in a significant reduction in a gas production and indications of increased oil production. The total cost of the four treatments (excluding labor and overhead) was $700,000.
Advanced Controls to Improve Dynamic Stability Performance for Large Power Systems

This paper provides a very detail-oriented study to prepare a full pathway for having an advanced closed-loop control implemented on selected controllable assets of N ew York Power Authority (NYPA), including wind generation to improve dynamic stability performance. The detailed methodology to identify the most effective control devices is outlined. Coordinated tuning of MBPSS is performed. The results show that the advanced closed-loop control with NYPA assets improves the dynamic stability performance of New York power grid and even of Eastern Interconnection as a whole.
NileOS: A Distributed Asymmetric Core-Based Micro-Kernel for Big Data Processing

Big Data applications have demanding expectations on computational resources front. Thus, general purpose operating systems are not a good fit. In this paper, we present a new special purpose distributed micro-kernel designed with big data applications’ needs in mind. The new micro-kernel adopts a core-based Asymmetric Multiprocessing (AMP) approach. It optimizes interrupt management and I/O to suit the Map-Reduce model. The proposed micro-kernel design is based on Inter-processor Interrupts over Ethernet (IPIoE) frames and a BareMetal Operating System Markup Language (BOSML). A transparent deployment mechanism is presented to completely shield the developer of the micro-kernel service from the underlying distribution infrastructure and decouple the application implementation from its deployment perspective. Based on the initial prototype and the experiments presented, a considerable gain in performance of average 2.34 folds was achieved using the distributed TeraSort benchmark over Linux/Hadoop.