Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of a frame structure using the Coulomb force search strategy-based dragonfly algorithm

ABSTRACT This article proposes an adaptive dragonfly algorithm (DA) for the structural optimization of frame structures. This algorithm, named the Coulomb force search strategy-based dragonfly algorithm (CFSSDA), combines the Coulomb force search strategy (CFSS) with the DA method. The CFSS is applied to automatically determine an adaptive search step in each iteration, thus enhancing the exploration capability of the CFSSDA. The performance of the CFSSDA is investigated on the basis of three widely used benchmark numerical examples. The results indicate that the proposed CFSSDA achieves a markedly improved convergence rate while maintaining a high accuracy. Furthermore, the CFSSDA is utilized to find the optimal parameters for an engineering frame structure problem with discrete variables under stress constraints. These results also show that the CFSSDA can rapidly generate an optimal structure with a weight that is reduced by 27.97% compared with the initial design.
Improved Functional Enrichment Analysis of Biological Networks using Scalable Modularity Based Clustering

The past decade has seen a rapid growth in the application of mathematical and computational tools for extracting insight from biological networks, and of particular interest here, visualising the community structure within such networks. Clustering approaches have proven useful methods to uncover structural and functional sub-groups from within protein interaction networks. However many commonly used clustering methods for identifying functionally relevant substructures within molecular networks do not perform well with increasing network sizes. We tested the performance of algorithms in terms of their ability to identify functionally relevant sub-clusters within networks of varying size as well as computational performance. Our studies suggest many algorithms perform well on smaller networks but fail to scale with network size. A Spectral based Modularity clustering algorithm, with a fine-tuning step, provided both scalability and improved identification of clusters enriched for functional annotation (e.g. disease) in real proteomic interaction datasets.
Research on System of Power Transformer Fault Diagnosis Based on WSN and Data Fusion Technology

The article discusses a method of power transformer fault diagnosis based on data fusion and extension theory,for this method a power transformer fault diagnosis system is constructed and developed by the application of wireless sensor network technology and VS.NET platform.The system applies WSN technology in wireless data acquisition of gases dissolved in power transformer oil,and can achieve real time data transmission.At the same time information fusion technology which is used to optimize the data,not only can reduce network energy consumption and ensure the accurate and objective data.For the coding rules of gas ratio method currently existed is incomplete and single,the extension method,combining quantitative and qualitative,it can make transformer fault diagnosis more refinement.Especially,the visual system can achieve the acquisition,displaying,analysis,storage and playback of gas parameters,and can be used in real-time analyzing and automatic diagnosis of transformer fault.The system of power transformer fault diagnosis can not only resolve the problem of the real-time and remote monitoring of operating personnel,but also greatly improve the performance of the transformer fault diagnosis.It provides a new direction for transformer on-line monitoring and fault diagnosis technology.
Analysis of a Haze Event over Nanjing, China Based on Multi-Source Data

We analyzed a June 2018 Nanjing, China haze event using ground-based and spaceborne sensors, combined with sounding and HYSPLIT backward trajectory data, with the ground-based and spaceborne sensor data exhibiting good consistency. Water vapor content showed significant positive correlation with AOD (aerosol optical depth), and AOD measured in urban and industrial areas was much higher compared to other similar zones. The afternoon convection caused the aerosol uplift during the haze event. Higher aerosol concentration was detected below 2 km. Due to the summer afternoon convective movement, pollutants at high altitude were dominated by small particles, while the overall pollutant mix was dominated by mixed aerosols. During a stable period over June 11–18, a single, near-surface inversion layer, and occasional two inversion layers, stopped pollutant dispersal, with only very stable ocean air mass transport in the southeast direction available. The Air Quality Index drop which took place during June 28–30 was caused by two inversion layers, combined with the immigration of pollutants from inland air masses.
Low power input/output port design using clock gating technique

Clock gating is a well-known technique to reduce chip dynamic power. This paper propose a modified clock gating techniques based on ACG(Adaptive Clock Gating) and instruction level clock gating. The proposed clock gating technique reduces not only switching activity of functional blocks in IDLE state but also dynamic power in running state. Our modified ACG can automatically enable or disable the clock of the functional block. The experimental results on some I/O port core in SoC show an average of 19.45% dynamic power reduction comparing to previous ACG technique.
Deep convolutional neural networks for land-cover classification with Sentinel-2 images

Abstract. Currently, analyzing satellite images requires an unsustainable amount of manual labor. Semiautomatic solutions for land-cover classification of satellite images entail the incorporation of expert knowledge. To increase the scalability of the built solutions, methods that automate image processing and analysis pipelines are required. Recently, deep learning (DL) models have been applied to challenging vision problems with great success. We expect that the use of DL models will soon outperform shallow networks and other classification algorithms, as recently achieved in multiple domains. Here, we consider the task of land-cover classification of satellite images. This seems particularly appropriate for deep classifiers due to the combined high dimensionality of the data with the presence of compositional dependencies between pixels, which can be used to characterize a particular class. We develop a pipeline for analyzing satellite images using a deep convolutional neural network for practical applications. We present its successful application for land-cover classification, where it achieves 86% classification accuracy on unseen raw images.
Impact of stellar evolution on planetary habitability

With the ever growing number of detected and confirmed exoplanets, the probability to find a planet that looks like the Earth increases continuously. While it is clear that being in the habitable zone does not imply being habitable, a systematic study of the evolution of the habitable zone is required to account for its dependence upon stellar parameters. In this study, we aim to provide the community with the dependence of the habitable zone upon the stellar mass, age, metallicity, and rotation. We use stellar evolution models computed with the STAREVOL code to study the evolution of the habitable zone. Mass and metallicity are the stellar parameters that have the most dramatic effects on the habitable zone limits. The evolution of the habitable zone limits is also linked to the evolution of the stellar activity (through the Rossby number) that depends on the considered stellar mass and rotation. Using observed trends of stellar magnetic field strength we also constrain the planetary magnetic field (at the zero order) required for a sufficient magnetospheric protection during the whole stellar evolution. We explicit for the first time the systematic dependence of planet habitability on stellar metallicity and rotation along the full evolution of low- and intermediate-mass stars. These results can be used as physical inputs for a first order estimation of exoplanetary habitability.
A Novel Approach for Big Data Analytics in Future Grids Based on Free Probability

Based on the random matrix model, we can build statistical models using massive datasets across the power grid, and employ hypothesis testing for anomaly detection. First, the aim of this paper is to make the first attempt to apply the recent free probability result in extracting big data analytics, in particular data fusion. The nature of this work is basic in that new algorithms and analytics tools are proposed to pave the way for the future's research. Second, using the new analytic tool, we are able to make some discovery related to anomaly detection that is very difficult for other approaches. To our best knowledge, there is no similar report in the literature. Third, both linear and nonlinear polynomials of large random matrices can be handled in this new framework. Simulations demonstrate the following: Compared with the linearity, nonlinearity is more flexible in problem modeling and closer to the nature of the reality. In some sense, some other nonlinear matrix polynomials may be more effective for the power grid
Interactive neural network – Differential evolution framework for haptic feedback retrieval system

We propose a haptic database retrieval system based on user’s perception and media converter that translates it into a physical haptic feedback. We have developed a three-dimensional psychological space that expresses human perception haptic stimuli by using semantic differential pairs. Differential Evolution (DE) algorithm searches and retrieves haptic stimuli within a haptic physical feature space. Moreover, Neural Networks (NN) maps the searched physical feature vectors into the psychological space. The distance between the mapped positions and target position in the psychological space is used for the GA search, and when the distance becomes less than the threshold value, the retrieval search ends. In this study, we have conducted a preliminary experiment to construct psychological space and the NN part of the proposed system. The result of the preliminary experiment and the configuration of the NN will be discussed in detail.
Modeling and Analysis of SOI Gratings-Based Opto-Fluidic Biosensor for Lab-on-a-Chip Applications

The design, modeling, and analysis of a silicon-on-insulator (SOI) grating coupler integrated with a microfluidic channel for lab-on-a-chip applications are presented. The grating coupler was designed to operate at 1310 nm. The simulated SOI structure consisted of a 220 nm top-Si device layer with an integrated waveguide, grating coupler, and a buried oxide layer of 2 µm. A rectangular microfluidic channel was deposited on the SOI optical grating structure for light and fluid interaction. The fluidic flow through the device was driven by centrifugal and Coriolis forces. The grating structure was designed to achieve a maximum coupling efficiency at the optimized injection angle of the light source. The sensitivity of the grating structure could be analyzed and evaluated using the change in coupled power as a function of the effective refractive index and was found to be 0.928 × 10−6 RIU. The SOI optical grating structure along with the micro fluidic channel on top could be effectively used as an absorbance-based lab-on-a-chip biosensor.
Characteristic Frequency of the Orbital Fluctuation in the Unconventional Iron-Based Superconductor Bafe2as2: a TDDFT Investigation of the Electron Pairing Mechanism

It is proposed that the electron-pairing medium of the iron-based superconductors may be the orbital fluctuation of the transition metal ions. But the characteristic frequency of the orbital fluctuation has not been given. For the first time, the author has calculated the real-time evolution of the electron clouds of transition metal ions in BaFe2As2 under excitations by the time-dependent density functional theory (TDDFT). There are different modes of fluctuations. The characteristic frequencies are 150 meV, 160 meV, 250 meV, and 200 meV, respectively, for the modes the author observed. The results are unexpected, because the general view is that the change of the electron density is very quick, and the frequency is much higher than the lattice vibration. The frequencies the author obtained are close to that of the lattice vibration in conventional superconductors at normal and high pressures, indicating the orbital (or electron cloud) fluctuation can by the electron pairing medium. Based on the calculation results, the author proposed a new electron pairing mechanism.
Mining Words and Targets Using Alignment Model

Opinion target is defined as the object about which user expresses their opinions, typically as nouns or noun phrases. Opinion words are the words that are used to express user’s opinions. Constructing an opinion words lexicon is also important because the lexicon is beneficial for identifying opinion expressions. Opinion mining is also called sentiment analysis, involves building a system to collect and categorize opinions about a product. Existing system uses Sentence-level extraction and corpus –level extraction. Sentence level extraction is the task of opinion target/word extraction is to identify the opinion target mentions or opinion expressions in sentences. These tasks are usually regarded as sequence-labeling problems. Sequence labeling models are used to build the extractor, such HMM. Corpus-level extraction did not identify the opinion target/word mentions in sentences, but aimed to extract a list of opinion targets or generate a sentiment word lexicon from texts. To overcome these problems by using alignment models.
Co-Integration Testing of the Relationship between Electricity Consumption and Investment in Senegal

The study investigates the relationship between natural logarithm of electricity consumption and natural logarithm of investment (proxy gross capital formation in the short and long run ARDL model. The data was obtained from world Development Indicator (WDI). The Phillips-Perron test and ADF test were applied to test stationary of the data in the level and in trend in their first difference between electricity consumption and investment in Senegal. The ARDL model was applied. As the results generated, the trend is statistionary and the intercept is not stationary. The first difference of investment is not a statistically significant and negative sign, while the first difference in electricity consumption is significant and negative coefficient. In the long run, the natural logarithm of electricity consumption as the dependent variable and natural logarithm of investment as the independent variable are significant and positive. The future researchers could also apply co-integration testing when there is a structural break or break point in the data set and error correction model.
An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk. https://osf.io/preprints/socarxiv/3yjk2/download
Timing Characterization of an SRAM Generated from OpenRAM: An Open-Source Memory Compiler

As process technology continues to shrink, Integrated Circuits (ICs) can hold more memories on the chip to improve overall system performance, efficiency, and cost. Most academic IC design methodologies are inhibited by the availability of memory designs and timing characterizers. Many standard-cell Process Design Kits (PDKs) are available from vendors and foundries, but these PDKs do not come with any memory compilers or characterizers, while more expensive solutions only provide memory models with limited configurations and restrictive licenses. This thesis showcases OpenRAM, a characterization methodology and the author’s contributions. The author added the characterization methodology to OpenRAM to be able to generate timing and power characterizations through SPICE simulations. He also reorganized the memory modules and rewrote the low-level parameterized modules such as transistors and inverters. In addition, he ported and managed the unit and regression tests from SVN to git. Lastly, he improved and implemented multiple dynamically generated memory modules. The goal of OpenRAM is to promote academic research by providing a portable and flexible platform for the generation and verification of memory designs across various technologies, sizes, and components.
Propagation of the Measurement Error and the Measured Mean of a Physical Quantity for Elementary Functions ax and loga x

Rules have been obtained for the propagation of the error and the mean value for a measured physical quantity onto another one with a functional relation of the type ax or loga x between them. In essence, these rules are inherently based on the Gaussian weight scheme. Therefore, they should be valid in the framework of a real Gaussian weight scheme applied to discrete data of a real physical experiment (a sample). An analytical form that was used to present the rules concerned (“analytical propagation rules”) and their character allow the processing and the analysis of experimental data to be simplified and accelerated.
An Intelligent Power Management Investigation for Stand-Alone Hybrid System Using Short-Time Energy Storage

Stand-alone Hybrid systems become appreciating issues that ensure the required electricity to consumers. The development of a stand-alone Hybrid system becomes a necessity for multiple applications The enhance energy security. To achieve this objective, we have proposed an accurate dynamic model using Multi-Agent System (MAS) in which a solar energy System (SES) serves as the main load supply, an energy Backup System (ERS) is based on a fuel cell and Electrolyzer for long-term energy storage and an Ultra Capacitor (UCap) storage system deployed as a short-time storage. To cooperate with all systems, an Intelligent Power Management (IPM) based on a specific MAS is included. Thus, to prove the performance of the system, we tested and simulated it using the Matlab/Simulink environment. Full Text: PDF DOI: http://dx.doi. org/10.11591/ijpeds.v8. i1.pp367-375
On the relation between bandwidth and robustness of indoor UWB communication

The paper focuses on the effect of small-scale fading on the throughput of communication systems that operate with different bandwidth within an indoor environment. The small-scale fading of the indoor radio channel has been measured within an office environment. From these measurements, a single intuitive scalar is derived, which characterizes the small-scale fading encountered by a certain signal. This signal has been characterized by its lower frequency and bandwidth. This measure allows for an objective, quantitative analysis of the relation between small-scale fading and signal bandwidth. The paper is complemented by a link-budget analysis to illuminate the relationship between signal bandwidth, fading margin and throughput. The bandwidth has been varied step-wise from 10 MHz to 5 GHz.
Learning one-to-many stylised Chinese character transformation and generation by generative adversarial networks

Owing to the complex structure of Chinese characters and the huge number of Chinese characters, it is very challenging and time consuming for artists to design a new font of Chinese characters. Therefore, the generation of Chinese characters and the transformation of font styles have become research hotspots. At present, most of the models on Chinese character transformation cannot generate multiple fonts, and they are not doing well in faking fonts. In this article, the authors propose a novel method of Chinese character fonts transformation and generation based on generative adversarial networks. The authors’ model is able to generate multiple fonts at once through font style-specifying mechanism and it can generate a new font at the same time if the authors combine the characteristics of existing fonts.
Energy and charge transfer during electron beam melting

Discovered nearly 150 years ago, electron beams contribute to nearly every discipline of science and technology. Recently, this mature field has found new purpose in the domain of Additive Manufacturing, resulting in significant industrial and academic interests. In nearly all cases, the process is examined through the lens of a single commercial supplier with limited access to fundamental process parameters. The objective of this thesis is to examine the fundamental science and technology underpinning electron beam powder bed fusion, beginning with thermionic emission and ending with single layers of consolidated powder.Between these points, the generation and control of an electron beam is explained, highlighting the specific opportunities and challenges compared to laser-based processes. Slotted Faraday Cup measurements experimentally demonstrate many of the issues associated with steering and focusing a beam of negatively charged particles. Using a novel calorimetric measurement technique, the fraction of absorbed energy is directly measured and compared to Monte-Carlo simulations over a range of process conditions. Next, the combined effects of beam and geometric parameters are examined with respect to dissimilar electron beam welding of titanium to niobium. The differences in material properties magnify how small variations to the process inputs induce large variations in melt pool stability via convective and evaporative heat transfer. These concepts of energy absorption and melt pool stability are extended in the final chapter, which directly measures the absorbed current during pulsed electron beam melting of solid and powder titanium. Our results show, that above the melting temperature, energy and charge transfer are coupled, which we attribute to the ionization of the Ti vapor. To the best of our knowledge, these measurements are the first of their kind, and suggest a new domain of investigation for electron beam powder bed fusion. %%%%Decouvert a pres de 150 ans, les faisceaux d'electrons ont contribue a presque toutes les disciplines de la science et technologie. Recemment, ce domaine mature a trouve un nouvel objectif dans le domaine de la fabrication additive, resultant en des interets industriels et academiques significatifs. Dans presque tous les cas, le processus est examine a travers la lentille d'un seul fournisseur commercial avec un acces restreint aux parametres de processus fondamentaux. L'objectif de cette these est d'examiner la science et la technologie fondamentale qui sous-tendent la fusion du lit de poudre du faisceau d'electrons, en commencant par l'emission thermionique et en terminant par des couches uniques de poudre consolidee.Entre ces points, la generation et le controle d'un faisceau d'electrons est explique, mettant en evidence les opportunits et les defis specifiques par rapport au processus base sur le laser. Les mesures de la Coupe Faraday a fentes demontrent experimentalement plusieurs des problemes associes a la direction et a la focalisation d'un faisceau de…