bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting LoRaWAN Behavior: How Machine Learning Can Help
Authors: Cuomo Francesca , Garlisi Domenico , Martino Alessio , Martino Antonio
Published: August 2020

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases.
Single-Shot Measurement of Post-Pulse-Generated Pre-Pulse in High-Power Laser Systems
Authors: Kon Akira , Nishiuchi Mamiko , Kiriyama Hiromitsu , Kando Masaki , Bock Stefan , Ziegler Tim , Pueschel Thomas , Zeil Karl , Schramm Ulrich , Kondo Kiminori
Published: August 2020

In this study, a detailed investigation of the dynamics of the generation of pre-pulse by post-pulses is presented, using single-shot self-referenced spectral interferometry (SRSI). The capability of SRSI in terms of the single-shot measurement of the temporal contrast of high-power laser systems has been experimentally demonstrated. The results confirm that the energy levels of the pre-pulses increase proportional to the square of the B-integral parametrizing the nonlinearity of the amplifier chain.
Three-Dimensional CA-LBM Model of Silicon Facet Formation during Directional Solidification
Authors: Ma Wang , Li Ri , Chen Hongjian
Published: August 2020

A new 3D cellular automata-lattice Boltzmann method (CA-LBM) coupling model is proposed to simulate the formation of facet and facet dendrites in directional solidification. In this model, the CA method is used to simulate the crystal growth process and the LBM method is used to simulate the physical field in the calculation area. A new three-dimensional anisotropic function is introduced, and the model is modified by interpolation and neighborhood restriction. We add the remelting calculation model. The interaction between interface energy anisotropy and dynamic anisotropy is solved reasonably. The growth process and morphology of small plane and small plane dendrites were simulated.
Annihilation of Highly-Charged Topological Defects
Authors: Klemenčič Eva , Kurioz Pavlo , Ambrožič Milan , Rosenblatt Charles , Kralj Samo
Published: August 2020

We studied numerically external stimuli enforced annihilation of a pair of daughter nematic topological defect (TD) assemblies bearing a relatively strong topological charge |m|=3/2. A Landau- de Gennes phenomenological approach in terms of tensor nematic order parameter was used in an effectively two-dimensional Cartesian coordinate system, where spatial variations along the z-axis were neglected. A pair of {m=3/2,m=−3/2} was enforced by an appropriate surface anchoring field, mimicking an experimental sample realization using the atomic force microscope (AFM) scribing method. Furthermore, defects were confined within a rectangular boundary that imposes strong tangential anchoring. This setup enabled complex and counter-intuitive annihilation processes on varying relevant parameters. We present two qualitatively different annihilation paths, where we either gradually reduced the relative surface anchoring field importance or increased an external in-plane spatially homogeneous electric field E. The creation and depinning of additional defect pairs {12,−12} mediated the annihilation in such a geometry. Furthermore, we illustrate the absorption of TDs by sharp edges of the confining boundary, accompanied by m=±1/4↔∓1/4 winding reversal of edge singularities, and also E-driven zero-dimensional to one-dimensional defect core transformation.
Towing Test Data Set of the Kyushu University Kite System
Authors: Rushdi Mostafa A. , Dief Tarek N. , Yoshida Shigeo , Schmehl Roland
Published: August 2020

Kites can be used to harvest wind energy with substantially lower material and environmental footprints and a higher capacity factor than conventional wind turbines. In this paper, we present measurement data from seven individual tow tests with the kite system developed by Kyushu University. This system was designed for 7 kW traction power and comprises an inflatable wing of 6 m2 surface area with a suspended kite control unit that is towed on a relatively short tether of 0.4 m by a truck driving at constant speed along a straight runway. To produce a controlled relative flow environment, the experiment was conducted only when the background wind speed was negligible. We recorded the time-series of 11 different sensor values acquired on the kite, the control unit and the truck. The measured data can be used to assess the effects of the towing speed, the flight mode and the lengths of the control lines on the tether force.
An Enriched Customer Journey Map: How to Construct and Visualize a Global Portrait of Both Lived and Perceived Users’ Experiences?
Authors: Alvarez Juliana , Léger Pierre-Majorique , Fredette Marc , Chen Shang-Lin , Maunier Benjamin , Senecal Sylvain
Published: August 2020

Design is about understanding the system and its users. Although User Experience (UX) research methodologies aim to explain the benefits of a holistic measurement approach including explicit (e.g., self-reported) and implicit (e.g., automatic and unconscious biophysiological reactions) data to better understand the global user experience, most of the personas and customer journey maps (CJM) seen in the literature and practice are mainly based on perceived and self-reported users’ responses. This paper aims to answer a call for research by proposing an experimental design based on the collection of both explicit and implicit data in the context of an authentic user experience. Using an inductive clustering approach, we develop a data driven CJM that helps understand, visualize, and communicate insights based on both data typologies. This novel tool enables the design development team the possibility of acquiring a broad portrait of both experienced (implicit) and perceived (explicit) users’ experiences.
An Update on the Prognostic and Predictive Serum Biomarkers in Metastatic Prostate Cancer
Authors: Saxby Helen , Mikropoulos Christos , Boussios Stergios
Published: August 2020

Serum biomarkers are molecules produced by normal and abnormal cells. Prostate specific antigen (PSA) is an example of a serum biomarker used widely in the diagnosis and prognostication of prostate cancer. PSA has its limitations as it is organ- but not cancer-specific. The aim of this review is to summarize the current published data on the potential prognostic and predictive biomarkers in metastatic prostate cancer (mPC) that can be used in conjunction with PSA. These biomarkers include microRNAs, androgen receptor variants, bone metabolism, neuroendocrine and metabolite biomarkers, and could guide treatment selection and sequence in an era where we strive to personalized therapy.
Personalized Treatment Selection and Disease Monitoring Using Circulating Tumor DNA Profiling in Real-World Cancer Patient Management
Authors: Wehrle Julius , Philipp Ulrike , Jolic Martina , Follo Marie , Hussung Saskia , Waldeck Silvia , Deuter Max , Rassner Michael , Braune Jan , Rawluk Justyna , Greil Christine , Waller Cornelius F. , Becker Heiko , Duque-Afonso Jesús , Illert Anna L. , Fritsch Ralph M. , Meiss Frank , Duyster Justus , von Bubnoff Nikolas , Scherer Florian
Published: August 2020

Background: Circulating tumor DNA (ctDNA) in the blood plasma of cancer patients is an emerging biomarker used across oncology, facilitating noninvasive disease monitoring and genetic profiling at various disease milestones. Digital droplet PCR (ddPCR) technologies have demonstrated high sensitivity and specificity for robust ctDNA detection at relatively low costs. Yet, their value for ctDNA-based management of a broad population of cancer patients beyond clinical trials remains elusive. Methods: We developed mutation-specific ddPCR assays that were optimized for their use in real-world cancer management, covering 12 genetic aberrations in common cancer genes, such as EGFR, BRAF, KIT, KRAS, and NRAS. We assessed the limit of detection (LOD) and the limit of blank (LOB) for each assay and validated their performance for ctDNA detection using matched tumor sequencing. Results: We applied our custom ddPCR assays to 352 plasma samples from 96 patients with solid tumors. Mutation detection in plasma was highly concordant with tumor sequencing, demonstrating high sensitivity and specificity across all assays. In 20 cases, radiographic cancer progression was mirrored by an increase of ctDNA concentrations or the occurrence of novel mutations in plasma. Moreover, ctDNA profiling at diagnosis and during disease progression reflected personalized treatment selection through the identification of actionable gene targets in 20 cases. Conclusion: Collectively, our work highlights the potential of ctDNA assessment by sensitive ddPCR for accurate disease monitoring, robust identification of resistance mutations, and upfront treatment selection in patients with solid tumors. We envision an increasing future role for ctDNA profiling within personalized cancer management in daily clinical routine.
Phase-Coherent Dynamics of Quantum Devices with Local Interactions
Authors: Filippone Michele , Marguerite Arthur , Le Hur Karyn , Fève Gwendal , Mora Christophe
Published: August 2020

This review illustrates how Local Fermi Liquid (LFL) theories describe the strongly correlated and coherent low-energy dynamics of quantum dot devices. This approach consists in an effective elastic scattering theory, accounting exactly for strong correlations. Here, we focus on the mesoscopic capacitor and recent experiments achieving a Coulomb-induced quantum state transfer. Extending to out-of-equilibrium regimes, aimed at triggered single electron emission, we illustrate how inelastic effects become crucial, requiring approaches beyond LFLs, shedding new light on past experimental data by showing clear interaction effects in the dynamics of mesoscopic capacitors.
A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy
Authors: Chen Xuegong , Zhou Jie , Liao Zhifang , Liu Shengzong , Zhang Yan
Published: August 2020

With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node’s Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.
Application of Imbalanced Data Classification Quality Metrics as Weighting Methods of the Ensemble Data Stream Classification Algorithms
Authors: Wegier Weronika , Ksieniewicz Pawel
Published: August 2020

In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important. This task is hindered by changing the distribution of data over time, called the concept drift, and the emergence of a problem of disproportion between classes—such as in the detection of network attacks or fraud detection problems. In the following work, we propose methods to modify existing stream processing solutions—Accuracy Weighted Ensemble (AWE) and Accuracy Updated Ensemble (AUE), which have demonstrated their effectiveness in adapting to time-varying class distribution. The introduced changes are aimed at increasing their quality on binary classification of imbalanced data. The proposed modifications contain the inclusion of aggregate metrics, such as F1-score, G-mean and balanced accuracy score in calculation of the member classifiers weights, which affects their composition and final prediction. Moreover, the impact of data sampling on the algorithm’s effectiveness was also checked. Complex experiments were conducted to define the most promising modification type, as well as to compare proposed methods with existing solutions. Experimental evaluation shows an improvement in the quality of classification compared to the underlying algorithms and other solutions for processing imbalanced data streams.
Blind Tone-Mapped Image Quality Assessment Based on Regional Sparse Response and Aesthetics
Authors: He Zhouyan , Yu Mei , Chen Fen , Peng Zongju , Xu Haiyong , Song Yang
Published: August 2020

High dynamic range (HDR) images give a strong disposition to capture all parts of natural scene information due to their wider brightness range than traditional low dynamic range (LDR) images. However, to visualize HDR images on common LDR displays, tone mapping operations (TMOs) are extra required, which inevitably lead to visual quality degradation, especially in the bright and dark regions. To evaluate the performance of different TMOs accurately, this paper proposes a blind tone-mapped image quality assessment method based on regional sparse response and aesthetics (RSRA-BTMI) by considering the influences of detail information and color on the human visual system. Specifically, for the detail loss in a tone-mapped image (TMI), multi-dictionaries are first designed for different brightness regions and whole TMI. Then regional sparse atoms aggregated by local entropy and global reconstruction residuals are presented to characterize the regional and global detail distortion in TMI, respectively. Besides, a few efficient aesthetic features are extracted to measure the color unnaturalness of TMI. Finally, all extracted features are linked with relevant subjective scores to conduct quality regression via random forest. Experimental results on the ESPL-LIVE HDR database demonstrate that the proposed RSRA-BTMI method is superior to the existing state-of-the-art blind TMI quality assessment methods.
Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal
Authors: Qin Pengjie , Shi Xin
Published: August 2020

The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.
Direct and Indirect Effects—An Information Theoretic Perspective
Authors: Schamberg Gabriel , Chapman William , Xie Shang-Ping , Coleman Todd
Published: August 2020

Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g., neuroscience and climate science) domains. While these causal measures are desirable in that they are model agnostic and can capture non-linear interactions, they are fundamentally different from common statistical notions of causal influence in that they (1) compare distributions over the effect rather than values of the effect and (2) are defined with respect to random variables representing a cause rather than specific values of a cause. We here present IT measures of direct, indirect, and total causal effects. The proposed measures are unlike existing IT techniques in that they enable measuring causal effects that are defined with respect to specific values of a cause while still offering the flexibility and general applicability of IT techniques. We provide an identifiability result and demonstrate application of the proposed measures in estimating the causal effect of the El Niño–Southern Oscillation on temperature anomalies in the North American Pacific Northwest.
A Comprehensive, Three-Dimensional Analysis of a Large-Scale, Multi-Fuel, CFB Boiler Burning Coal and Syngas. Part 2. Numerical Simulations of Coal and Syngas Co-Combustion
Authors: Krzywanski Jaroslaw , Sztekler Karol , Szubel Mateusz , Siwek Tomasz , Nowak Wojciech , Mika Łukasz
Published: August 2020

This paper presents the results of numerical computations for a large-scale OFz-425 CFB (circulating fluidized bed) boiler utilizing coal and syngas. Four different operating scenarios are considered, including the reference variant, corresponding to the conventional, mono-combustion of bituminous coal, and three tests involving replacement of secondary air and part of the coal stream with syngas fed by start-up burners. Pressure, gas velocity, temperature, and carbon dioxide distribution in the combustion chamber are discussed in the paper. The results indicate that the syngas supply leads to an increase in local temperature and carbon dioxide concentrations. The proposed concept is not advisable as it may lead to frequent emergency stops of the CFB boiler.
Multiplicative Decomposition of Heterogeneity in Mixtures of Continuous Distributions
Authors: Nunes Abraham , Alda Martin , Trappenberg Thomas
Published: August 2020

A system’s heterogeneity (diversity) is the effective size of its event space, and can be quantified using the Rényi family of indices (also known as Hill numbers in ecology or Hannah–Kay indices in economics), which are indexed by an elasticity parameter q≥0. Under these indices, the heterogeneity of a composite system (the γ-heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the α- and β-heterogeneity, respectively). Since the average heterogeneity of a component subsystem should not be greater than that of the pooled system, we require that γ≥α. There exists a multiplicative decomposition for Rényi heterogeneity of composite systems with discrete event spaces, but less attention has been paid to decomposition in the continuous setting. We therefore describe multiplicative decomposition of the Rényi heterogeneity for continuous mixture distributions under parametric and non-parametric pooling assumptions. Under non-parametric pooling, the γ-heterogeneity must often be estimated numerically, but the multiplicative decomposition holds such that γ≥α for q>0. Conversely, under parametric pooling, γ-heterogeneity can be computed efficiently in closed-form, but the γ≥α condition holds reliably only at q=1. Our findings will further contribute to heterogeneity measurement in continuous systems.
Assessing the Potential Earthquake Precursory Information in ULF Magnetic Data Recorded in Kanto, Japan during 2000–2010: Distance and Magnitude Dependences
Authors: Han Peng , Zhuang Jiancang , Hattori Katsumi , Chen Chieh-Hung , Febriani Febty , Chen Hongyan , Yoshino Chie , Yoshida Shuji
Published: August 2020

In order to clarify ultra-low-frequency (ULF) seismomagnetic phenomena, a sensitive geomagnetic network was installed in Kanto, Japan since 2000. In previous studies, we have verified the correlation between ULF magnetic anomalies and local sizeable earthquakes. In this study, we use Molchan’s error diagram to evaluate the potential earthquake precursory information in the magnetic data recorded in Kanto, Japan during 2000–2010. We introduce the probability gain (PG′) and the probability difference (D′) to quantify the forecasting performance and to explore the optimal prediction parameters for a given ULF magnetic station. The results show that the earthquake predictions based on magnetic anomalies are significantly better than random guesses, indicating the magnetic data contain potential useful precursory information. Further investigations suggest that the prediction performance depends on the choices of the distance (R) and size of the target earthquake events (Es). Optimal R and Es are about (100 km, 108.75) and (180 km, 108.75) for Seikoshi (SKS) station in Izu and Kiyosumi (KYS) station in Boso, respectively.
Economics of Disagreement—Financial Intuition for the Rényi Divergence
Authors: Soklakov Andrei N.
Published: August 2020

Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap, which is often perceived as a lack of practical intuition regarding probabilistic and statistical concepts. Here, we propose to address disagreements using the methods of financial economics. In particular, we show how a large class of disagreements can be transformed into investment opportunities. The expected financial performance of such investments quantifies the amount of disagreement in a tangible way. This provides intuition for statistical concepts such as the Rényi divergence, which becomes connected to the financial performance of optimized investments. Investment optimization takes into account individual opinions as well as attitudes towards risk. The result is a market-like social mechanism by which funds flow naturally to support a more accurate view. Such social mechanisms can help us with difficult disagreements (e.g., financial arguments concerning the future climate). In terms of scientific validation, we used the findings of independent neurophysiological experiments as well as our own research on the equity premium.
The Discovery of Long-Run Causal Order: A Preliminary Investigation
Authors: Hoover Kevin D.
Published: August 2020

The relation between causal structure and cointegration and long-run weak exogeneity is explored using some ideas drawn from the literature on graphical causal modeling. It is assumed that the fundamental source of trending behavior is transmitted from exogenous (and typically latent) trending variables to a set of causally ordered variables that would not themselves display nonstationary behavior if the nonstationary exogenous causes were absent. The possibility of inferring the long-run causal structure among a set of time-series variables from an exhaustive examination of weak exogeneity in irreducibly cointegrated subsets of variables is explored and illustrated.
Low-Current Design of GaAs Active Inductor for Active Filters Applications
Authors: Pantoli Leonardo , Stornelli Vincenzo , Leuzzi Giorgio , Li Hongjun , Hu Zhifu
Published: August 2020

Active inductors are suitable for MMIC integration, especially for filters applications, and the definition of strategies for an efficient design of these circuits is becoming mandatory. In this work we present design considerations for the reduction of DC current in the case of an active filter design based on the use of active inductors and for high-power handling. As an example of applications, the approach is demonstrated on a two-cell, integrated active filter realized with p-HEMT technology. The filter design is based on high-Q active inductors, whose equivalent inductance and resistance can be tuned by means of varactors. The prototype was realized and tested. It operates between 1800 and 2100 MHz with a 3 dB bandwidth of 30 MHz and a rejection ratio of 30 dB at 30 MHz from the center frequency. This solution allows to obtain a P1 dB compression point of about −8 dBm and a dynamic range of 75 dB considering a bias current of 15 mA per stage.
TL-Net: A Novel Network for Transmission Line Scenes Classification
Authors: Li Hongchen , Yang Zhong , Han Jiaming , Lai Shangxiang , Zhang Qiuyan , Zhang Chi , Fang Qianhui , Hu Guoxiong
Published: August 2020

With the development of unmanned aerial vehicle (UAV) control technology, one of the recent trends in this research domain is to utilize UAVs to perform non-contact transmission line inspection. The RGB camera mounted on UAVs collects large numbers of images during the transmission line inspection, but most of them contain no critical components of transmission lines. Hence, it is a momentous task to adopt image classification algorithms to distinguish key images from all aerial images. In this work, we propose a novel classification method to remove redundant data and retain informative images. A novel transmission line scene dataset, namely TLS_dataset, is built to evaluate the classification performance of networks. Then, we propose a novel convolutional neural network (CNN), namely TL-Net, to classify transmission line scenes. In comparison to other typical deep learning networks, TL-Nets gain better classification accuracy and less memory consumption. The experimental results show that TL-Net101 gains 99.68% test accuracy on the TLS_dataset.
The Sustainable Development of Aged Coal Mine Achieved by Recovering Pillar-Blocked Coal Resources
Authors: Gao Huadong , An Baifu , Han Zhen , Guo Yachao , Ruan Zeyu , Li Wei , Zayzay Samuel
Published: August 2020

China faces the problem of depletion of its coal resources, and a large number of mines are becoming aged mines. Demand for coal, however, still increases due to the growth of China’s economy. Energy shortage might restrict the sustainability of China’s national economy. As one contribution to a solution, this paper proposes the innovative exploitation method of solid backfill coal mining (SBCM) technology to exploit parts of pillar-blocked (residual coal pillar resources under industrial square, RCPRIS) that protect industrial facilities. Thus, blocked coal resources may be converted into mineable reserves that improve the recovery ratio of mine resources. Also, waste would be removed from the surface reducing hazards of environmental pollution. Based on the case of the Baishan Coal Mine in Anhui, China, numerical simulation is used to study the size of shaft-protecting coal pillars (SPCP) required at different backfill ratios. Results show that the disturbance to a shaft caused by exploitation decreases with the increase of the backfill ratio. When using SBCM to exploit RCPRIS under the condition of 80% backfill ratio, compared with the caving method, a lot of pillar-blocked coal resources would be freed. The life of Baishan Coal Mine would be prolonged, resulting in appreciable social, environmental, and economic benefits.
An Algorithm for Circuit Parameter Identification in Lightning Impulse Voltage Generation for Low-Inductance Loads
Authors: Tuethong Piyapon , Kitwattana Krit , Yutthagowith Peerawut , Kunakorn Anantawat
Published: August 2020

This paper presents an effective technique based on an artificial neural network algorithm utilized for circuit parameter identification in lightning impulse generation for low inductance loads such as low voltage windings of a power transformer, a large distribution transformer and an air core reactor. The limitation of the combination between Glaninger’s circuit and the circuit parameter selection from Feser’s suggestions in term of producing an impulse waveform to be compliant with standard requirements when working with a low inductance load is discussed. In Feser’s approach, the circuit parameters of the generation circuit need to be further adjusted to obtain the waveform compliant with the standard requirement. In this process, trial and error approaches based on test engineers’ experience are employed in the circuit parameter selection. To avoid the unintentional damage from electrical field stress during the voltage waveform adjustment process, circuit simulators, such as Pspice and EMTP/ATP, are very useful to examine the generated voltage waveform before the experiments on the test object are carried out. In this paper, a system parameter identification based on an artificial neural network algorithm is applied to determine the appropriate circuit parameters in the test circuit. This impulse voltage generation with the selected circuit parameters was verified by simulations and an experiment. It was found that the generation circuit gives satisfactory impulse voltage waveforms in accordance with the standard requirement for the maximum charging capacitance of 10 µF and the load inductance from 400 µH to 4 mH. From the simulation and experimental results of all cases, the approach proposed in this paper is useful for test engineers in selection of appropriate circuit components for impulse voltage tests with low inductance loads instead of employing conventional trial and error in circuit component selection.
Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units
Authors: Hosseini Majid , Katragadda Satya , Wojtkiewicz Jessica , Gottumukkala Raju , Maida Anthony , Chambers Terrence Lynn
Published: August 2020

Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with solar power. Meteorological factors such as cloud cover, wind direction, and wind speed affect irradiance and are associated with a high degree of variability and uncertainty. Statistical models fail to accurately capture the dependence between these factors and irradiance. In this paper, we introduce the idea of applying multivariate Gated Recurrent Units (GRU) to forecast Direct Normal Irradiance (DNI) hourly. The proposed GRU-based forecasting method is evaluated against traditional Long Short-Term Memory (LSTM) using historical irradiance data (i.e., weather variables that include cloud cover, wind direction, and wind speed) to forecast irradiance forecasting over intra-hour and inter-hour intervals. Our evaluation on one of the sites from Measurement and Instrumentation Data Center indicate that both GRU and LSTM improved DNI forecasting performance when evaluated under different conditions. Moreover, including wind direction and wind speed can have substantial improvement in the accuracy of DNI forecasts. Besides, the forecasting model can accurately forecast irradiance values over multiple forecasting horizons.
Identification of the Efficiency Gap by Coupling a Fundamental Electricity Market Model and an Agent-Based Simulation Model
Authors: Torralba-Díaz Laura , Schimeczek Christoph , Reeg Matthias , Savvidis Georgios , Deissenroth-Uhrig Marc , Guthoff Felix , Fleischer Benjamin , Hufendiek Kai
Published: August 2020

A reliable and cost-effective electricity system transition requires both the identification of optimal target states and the definition of political and regulatory frameworks that enable these target states to be achieved. Fundamental optimization models are frequently used for the determination of cost-optimal system configurations. They represent a normative approach and typically assume markets with perfect competition. However, it is well known that real systems do not behave in such an optimal way, as decision-makers do not have perfect information at their disposal and real market actors do not take decisions in a purely rational way. These deficiencies lead to increased costs or missed targets, often referred to as an “efficiency gap”. For making rational political decisions, it might be valuable to know which factors influence this efficiency gap and to what extent. In this paper, we identify and quantify this gap by soft-linking a fundamental electricity market model and an agent-based simulation model, which allows the consideration of these effects. In order to distinguish between model-inherent differences and non-ideal market behavior, a rigorous harmonization of the models was conducted first. The results of the comparative analysis show that the efficiency gap increases with higher renewable energy shares and that information deficits and policy instruments affect operational decisions of power market participants and resulting overall costs significantly.
Optimal Scheduling for Energy Storage Systems in Distribution Networks
Authors: Escoto Miquel , Montagud Mario , González Noemi , Belinchón Alejandro , Trujillo Adriana Valentina , Romero Julián , Díaz-Cabrera Julio César , Pellicer García Marta , Quijano López Alfredo
Published: August 2020

Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper’s results demonstrate numerically the good performance of the developed methodology.
Modular Permanent Magnet Synchronous Machine with Low Space Harmonic Content
Authors: Wang Keyi , Lin Heyun
Published: August 2020

Modularity technique is desirable in large permanent magnet synchronous machines (PMSMs) because it facilitates manufacture, assembly, and maintenance. Although the PMSMs with fractional-slot concentrated windings (FSCWs) allow their stators to be modularized, they usually suffer from high nonworking space harmonic content. The PMSMs with various reported two-slot pitch windings (TSPWs) show much lower nonworking space harmonic content, but they do not support stator modularity. This paper proposes a modular PMSM with a special dual three-phase (DTP) TSPW, which exhibits quite low nonworking space harmonic content. First, the topology of the proposed machine is described in detail. Then, the mechanism of reducing the nonworking space harmonic content of the machine is expounded through winding magnetomotive force (MMF) analysis. Finally, the electromagnetic characteristics of a specific proposed modular PMSM and a conventional modular PMSM with DTP-FSCW are compared by finite element method (FEM), in terms of electromotive force (EMF), armature reaction field, torque performance, efficiency and power factor. The FEM results demonstrate that the proposed machine can realize low space harmonic content while retaining stator modularity.
Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules
Authors: Napole Cristian , Barambones Oscar , Calvo Isidro , Velasco Javier
Published: August 2020

This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.
Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach
Authors: Hussain Ayaz , Draz Umar , Ali Tariq , Tariq Saman , Irfan Muhammad , Glowacz Adam , Antonino Daviu Jose Alfonso , Yasin Sana , Rahman Saifur
Published: August 2020

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.
Life-Cycle Carbon Emissions and Energy Return on Investment for 80% Domestic Renewable Electricity with Battery Storage in California (U.S.A.)
Authors: Raugei Marco , Peluso Alessio , Leccisi Enrica , Fthenakis Vasilis
Published: August 2020

This paper presents a detailed life-cycle assessment of the greenhouse gas emissions, cumulative demand for total and non-renewable primary energy, and energy return on investment (EROI) for the domestic electricity grid mix in the U.S. state of California, using hourly historical data for 2018, and future projections of increased solar photovoltaic (PV) installed capacity with lithium-ion battery energy storage, so as to achieve 80% net renewable electricity generation in 2030, while ensuring the hourly matching of the supply and demand profiles at all times. Specifically—in line with California’s plans that aim to increase the renewable energy share into the electric grid—in this study, PV installed capacity is assumed to reach 43.7 GW in 2030, resulting of 52% of the 2030 domestic electricity generation. In the modelled 2030 scenario, single-cycle gas turbines and nuclear plants are completely phased out, while combined-cycle gas turbine output is reduced by 30% compared to 2018. Results indicate that 25% of renewable electricity ends up being routed into storage, while 2.8% is curtailed. Results also show that such energy transition strategy would be effective at curbing California’s domestic electricity grid mix carbon emissions by 50%, and reducing demand for non-renewable primary energy by 66%, while also achieving a 10% increase in overall EROI (in terms of electricity output per unit of investment).
Investigation of the Formation of Coherent Ash Residues during Fluidized Bed Gasification of Wheat Straw Lignin
Authors: Priscak Juraj , Fürsatz Katharina , Kuba Matthias , Skoglund Nils , Benedikt Florian , Hofbauer Hermann
Published: August 2020

Thermal conversion of ash-rich fuels in fluidized bed systems is often associated with extensive operation problems caused by the high amount of reactive inorganics. This paper investigates the behavior of wheat straw lignin—a potential renewable fuel for dual fluidized bed gasification. The formation of coherent ash residues and its impact on the operation performance has been investigated and was supported by thermochemical equilibrium calculations in FactSage 7.3. The formation of those ash residues, and their subsequent accumulation on the surface of the fluidized bed, causes temperature and pressure fluctuations, which negatively influence the steady-state operation of the fluidized bed process. This paper presents a detailed characterization of the coherent ash residues, which consists mostly of silica and partially molten alkali silicates. Furthermore, the paper gives insights into the formation of these ash residues, dependent on the fuel pretreatment (pelletizing) of the wheat straw lignin, which increases their stability compared to the utilization of non-pelletized fuel.
Dissolution Process Observation of Methane Bubbles in the Deep Ocean Simulator Facility
Authors: Uchida Tsutomu , Nagamine Ike , Yabe Itsuka , Fukumaki Tatsunori , Oyama Ai , Yoza Brandon , Tenma Norio , Masutani Stephen M.
Published: August 2020

To investigate the temperature dependency of the methane bubble dissolution rate, buoyant single methane bubbles were held stationary in a countercurrent water flow at a pressure of 6.9 MPa and temperatures ranging from 288 K to 303 K. The 1 to 3 mm diameter bubbles were analyzed by observation through the pressure chamber viewport using a bi-telecentric CCD camera. The dissolution rate in artificial seawater was approximately two times smaller than that in pure water. Furthermore, it was observed that the methane bubble dissolution rate increased with temperature, suggesting that bubble dissolution is a thermal activation process (the activation energy is estimated to be 9.0 kJ/mol). The results were different from the expected values calculated using the governing equation for methane dissolution in water. The dissolution modeling of methane bubbles in the mid-to-shallow depth of seawater was revised based on the current results.
Using a Crop Model to Benchmark Miscanthus and Switchgrass
Authors: El Akkari Monia , Ferchaud Fabien , Strullu Loïc , Shield Ian , Perrin Aurélie , Drouet Jean Louis , Jayet Pierre Alain , Gabrielle Benoît
Published: August 2020

Crop yields are important items in the economic performance and the environmental impacts of second-generation biofuels. Since they strongly depend on crop management and pedoclimatic conditions, it is important to compare candidate feedstocks to select the most appropriate crops in a given context. Agro-ecosystem models offer a prime route to benchmark crops, but have been little tested from this perspective thus far. Here, we tested whether an agro-ecosystem model (CERES-EGC) was specific enough to capture the differences between miscanthus and switchgrass in northern Europe. The model was compared to field observations obtained in seven long-term trials in France and the UK, involving different fertilizer input rates and harvesting dates. At the calibration site (Estrées-Mons), the mean deviations between simulated and observed crop biomass yields for miscanthus varied between −0.3 t DM ha−1 and 4.2 t DM ha−1. For switchgrass, simulated yields were within 1.0 t DM ha−1 of the experimental data. Observed miscanthus yields were higher than switchgrass yields in most sites and for all treatments, with one exception. Overall, the model captured the differences between both crops adequately, with a mean deviation of 0.46 t DM ha−1, and could be used to guide feedstock selections over larger biomass supply areas.
A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms
Authors: Fahad Muhammad , Shahid Arsalan , Manumachu Ravi Reddy , Lastovetsky Alexey
Published: August 2020

Accurate energy profiles are essential to the optimization of parallel applications for energy through workload distribution. Since there are many model-based methods available for efficient construction of energy profiles, we need an approach to measure the goodness of the profiles compared with the ground-truth profile, which is usually built by a time-consuming but reliable method. Correlation coefficient and relative error are two such popular statistical approaches, but they assume that profiles be linear or at least very smooth functions of workload size. This assumption does not hold true in the multicore era. Due to the complex shapes of energy profiles of applications on modern multicore platforms, the statistical methods can often rank inaccurate energy profiles higher than more accurate ones and employing such profiles in the energy optimization loop of an application leads to significant energy losses (up to 54% in our case). In this work, we present the first method specifically designed for goodness measurement of energy profiles. First, it analyses the underlying energy consumption trend of each energy profile and removes the profiles that exhibit a trend different from that of the ground truth. Then, it ranks the remaining energy profiles using the Euclidean distances as a metric. We demonstrate that the proposed method is more accurate than the statistical approaches and can save a significant amount of energy.