bluuebunny/crossref_metadata_2025 huggingface dataset:
Tribological Performance of PVD Film Systems Against Plastic Counterparts for Adhesion-Reducing Application in Injection Molds
Authors: Tillmann Wolfgang , Lopes Dias Nelson Filipe , Stangier Dominic , Gelinski Nikolai
Published: September 2019

The deposition of physical vapor deposition (PVD) hard films is a promising approach to enhance the tribological properties of injection molds in plastic processing. However, the adhesion is influenced by the pairing of PVD film and processed plastic. For this reason, the friction behavior of different PVD films against polyamide, polypropylene, and polystyrene was investigated in tribometer tests by correlating the relation between the roughness and the adhesion. It was shown that the dispersive and polar surface energy have an impact on the work of adhesion. In particular, Cr-based nitrides with a low polar component exhibit the lowest values ranging from 65.5 to 69.4 mN/m when paired with the polar polyamide. An increased roughness leads to a lower friction due to a reduction of the adhesive friction component, whereas a higher work of adhesion results in higher friction for polyamide and polypropylene. Within this context, most Cr-based nitrides exhibited coefficients of friction below 0.4. In contrast, polystyrene leads to a friction-reducing material transfer. Therefore, a customized deposition of the injection molds with an appropriated PVD film system should be carried out according to the processed plastic.
Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform
Authors: Anbu Srivani , Thangavelu Arunkumar , Ashok S. Denis
Published: September 2019

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.
Font Design—Shape Processing of Text Information Structures in the Process of Non-Invasive Data Acquisition
Authors: Kubiak Ireneusz
Published: September 2019

Computer fonts can be a solution that supports the protection of information against electromagnetic penetration; however, not every font has features that counteract this process. The distinctive features of a font’s characters define the font. This article presents two new sets of computer fonts. These fonts are fully usable in everyday work. Additionally, they make it impossible to obtain information using non-invasive methods. The names of these fonts are directly related to the shapes of their characters. Each character in these fonts is built using only vertical and horizontal lines. The differences between the fonts lie in the widths of the vertical lines. The Safe Symmetrical font is built from vertical lines with the same width. The Safe Asymmetrical font is built from vertical lines with two different line widths. However, the appropriate proportions of the widths of the lines and clearances of each character need to be met for the safe fonts. The structures of the characters of the safe fonts ensure a high level of similarity between the characters. Additionally, these fonts do not make it difficult to read text in its primary form. However, sensitive transmissions are free from distinctive features, and the recognition of each character in reconstructed images is very difficult in contrast to traditional fonts, such as the Sang Mun font and Null Pointer font, which have many distinctive features. The usefulness of the computer fonts was assessed by the character error rate (CER); an analysis of this parameter was conducted in this work. The CER obtained very high values for the safe fonts; the values for traditional fonts were much lower. This article aims to presentat of a new solution in the area of protecting information against electromagnetic penetration. This is a new approach that could replace old solutions by incorporating heavy shielding, power and signal filters, and electromagnetic gaskets. Additionally, the application of these new fonts is very easy, as a user only needs to ensure that either the Safe Asymmetrical font or the Safe Symmetrical font is installed on the computer station that processes the text data.
Active Eye-in-Hand Data Management to Improve the Robotic Object Detection Performance
Authors: Hoseini Pourya , Blankenburg Janelle , Nicolescu Mircea , Nicolescu Monica , Feil-Seifer David
Published: September 2019

Adding to the number of sources of sensory information can be efficacious in enhancing the object detection capability of robots. In the realm of vision-based object detection, in addition to improving the general detection performance, observing objects of interest from different points of view can be central to handling occlusions. In this paper, a robotic vision system is proposed that constantly uses a 3D camera, while actively switching to make use of a second RGB camera in cases where it is necessary. The proposed system detects objects in the view seen by the 3D camera, which is mounted on a humanoid robot’s head, and computes a confidence measure for its recognitions. In the event of low confidence regarding the correctness of the detection, the secondary camera, which is installed on the robot’s arm, is moved toward the object to obtain another perspective of the object. With the objects detected in the scene viewed by the hand camera, they are matched to the detections of the head camera, and subsequently, their recognition decisions are fused together. The decision fusion method is a novel approach based on the Dempster–Shafer evidence theory. Significant improvements in object detection performance are observed after employing the proposed active vision system.
Polaronic Emergent Phases in Manganite-based Heterostructures
Authors: Moshnyaga Vasily , Samwer Konrad
Published: September 2019

Transition metal functional oxides, e.g., perovskite manganites, with strong electron, spin and lattice correlations, are well-known for different phase transitions and field-induced colossal effects at the phase transition. Recently, the interfaces between dissimilar perovskites were shown to be a promising concept for the search of emerging phases with novel functionalities. We demonstrate that the properties of manganite films are effectively controlled by low dimensional emerging phases at intrinsic and extrinsic interfaces and appeared as a result of symmetry breaking. The examples include correlated Jahn–Teller polarons in the phase-separated (La1−yPry)0.7Ca0.3MnO3, electron-rich Jahn–Teller-distorted surface or “dead” layer in La0.7Sr0.3MnO3, electric-field-induced healing of “dead” layer as an origin of resistance switching effect, and high-TC ferromagnetic emerging phase at the SrMnO3/LaMnO3 interface in superlattices. These 2D polaronic phases with short-range electron, spin, and lattice reconstructions could be extremely sensitive to external fields, thus, providing a rational explanation of colossal effects in perovskite manganites.
Mitochondrial Genome Diversity in Collembola: Phylogeny, Dating and Gene Order
Authors: Leo Chiara , Carapelli Antonio , Cicconardi Francesco , Frati Francesco , Nardi Francesco
Published: September 2019

Collembola (springtails) are an early diverging class of apterygotes, and mark the first substantial radiation of hexapods on land. Despite extensive work, the relationships between major collembolan lineages are still debated and, apart from the Early Devonian fossil Rhyniella praecursor, which demonstrates their antiquity, the time frame of springtail evolution is unknown. In this study, we sequence two new mitochondrial genomes and reanalyze all known Collembola mt-genomes, including selected metagenomic data, to produce an improved phylogenetic hypothesis for the group, develop a tentative time frame for their differentiation, and provide a comprehensive overview of gene order diversity. Our analyses support most taxonomically recognized entities. We find support for an Entomobryomorpha + Symphypleona clade, while the position of Neelipleona could not be assessed with confidence. A Silurian time frame for their basal diversification is recovered, with an indication that divergence times may be fairly old overall. The distribution of mitochondrial gene order indicates the pancrustacean arrangement as plesiomorphic and dominant in the group, with the exception of the family Onychiuridae. We distinguished multiple instances of different arrangements in individual genomes or small clusters. We further discuss the opportunities and drawbacks associated with the inclusion of metagenomic data in a classic study on mitochondrial genome diversity.
First Survey of Heterobranch Sea Slugs (Mollusca, Gastropoda) from the Island Sangihe, North Sulawesi, Indonesia
Authors: Undap Nani , Papu Adelfia , Schillo Dorothee , Ijong Frans Gruber , Kaligis Fontje , Lepar Meita , Hertzer Cora , Böhringer Nils , König Gabriele M. , Schäberle Till F. , Wägele Heike
Published: September 2019

Indonesia is famous for its underwater biodiversity, which attracts many tourists, especially divers. This is also true for Sangihe Islands Regency, an area composed of several islands in the northern part of North Sulawesi. However, Sangihe Islands Regency is much less known than, e.g., Bunaken National Park (BNP, North Sulawesi). The main island, Sangihe, has recently experienced an increase in tourism and mining activities with potentially high impact on the environment. Recently, monitoring projects began around BNP using marine Heterobranchia as indicators for coral reef health. No information about this taxon exists from the remote islands in North Sulawesi. The present study represents the first monitoring study ever and focuses on marine Heterobranchia around Sangihe. In total, 250 specimens were collected, which could be assigned to Sacoglossa (3), Anthobranchia (19), and Cladobranchia (1). Despite the low number (23 versus 172 in BNP), at least eight species (35%) are not recorded from BNP, probably indicating differences in habitat, but also influence of a strong El Niño year in 2016. Here we also report for the first time a Chromodoris annae specimen mimicking C. elisabethina, and the discovery of a new Phyllidia species.
Zooxanthellate, Sclerite-Free, and Pseudopinnuled Octocoral Hadaka nudidomus gen. nov. et sp. nov. (Anthozoa, Octocorallia) from Mesophotic Reefs of the Southern Ryukyus Islands
Authors: Lau Yee , Reimer James
Published: September 2019

Shallow water coral reefs are the most diverse marine ecosystems, but there is an immense gap in knowledge when it comes to understanding the diversity of the vast majority of marine biota in these ecosystems. This is especially true when it comes to understudied small and cryptic coral reef taxa in understudied ecosystems, such as mesophotic coral reef ecosystems (MCEs). MCEs were reported in Japan almost fifty years ago, although only in recent years has there been an increase in research concerning the diversity of these reefs. In this study we describe the first stoloniferous octocoral from MCEs, Hadaka nudidomus gen. nov. et sp. nov., from Iriomote and Okinawa Islands in the southern Ryukyus Islands. The species is zooxanthellate; both specimens host Cladocopium LaJeunesse & H.J.Jeong, 2018 (formerly Symbiodinium ‘Clade C’) and were collected from depths of ~33 to 40 m. Additionally, H. nudidomus gen. nov. et sp. nov. is both sclerite-free and lacks free pinnules, and both of these characteristics are typically diagnostic for octocorals. The discovery and morphology of H. nudidomus gen. nov. et sp. nov. indicate that we still know very little about stoloniferous octocoral diversity in MCEs, their genetic relationships with shallower reef species, and octocoral–symbiont associations. Continued research on these subjects will improve our understanding of octocoral diversity in both shallow and deeper reefs.
Horsing Around—A Dataset Comprising Horse Movement
Authors: Kamminga Jacob W. , Janßen Lara M. , Meratnia Nirvana , Havinga Paul J. M.
Published: September 2019

Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz.
Association Between VEGF Expression and Diffusion Weighted Imaging in Several Tumors—A Systematic Review and Meta-Analysis
Authors: Meyer Hans-Jonas , Wienke Andreas , Surov Alexey
Published: September 2019

To date, only a few studies have investigated relationships between Diffusion-weighted imaging (DWI) and Vascular endothelial growth factor (VEGF) expression in tumors. The reported results are contradictory. The aim of the present analysis was to review the published results and to perform a meta-analysis regarding associations between apparent diffusion coefficients (ADC) derived from DWI and VEGF expression. MEDLINE library was screened for relationships between ADC and VEGF expression up to January 2019. Overall, 14 studies with 578 patients were identified. In 10 studies (71.4%) 3 T scanners were used and in four studies (28.6%) 1.5 T scanners. Furthermore, seven studies (50%) had a prospective design and seven studies (50%) had a retrospective design. Most frequently, prostate cancer, followed by rectal cancer, cervical cancer and esophageal cancer were identified. The pooled correlation coefficient of all tumors was r = −0.02 [95% CI −0.26–0.21]. ADC values derived from routinely acquired DWI do not correlate with VEGF expression in various tumors. Therefore, DWI is not sensitive enough to reflect angiogenesis-related microstructure of tumors.
Improved Adaptive Successive Cancellation List Decoding of Polar Codes
Authors: Wang Xiumin , He Jinlong , Li Jun , Wu Zhuoting , Shan Liang , Hong Bo
Published: September 2019

Although the adaptive successive cancellation list (AD-SCL) algorithm and the segmented-CRC adaptive successive cancellation list (SCAD-SCL) algorithm based on the cyclic redundancy check (CRC) can greatly reduce the computational complexity of the successive cancellation list (SCL) algorithm, these two algorithms discard the previous decoding result and re-decode by increasing L, where L is the size of list. When CRC fails, these two algorithms waste useful information from the previous decoding. In this paper, a simplified adaptive successive cancellation list (SAD-SCL) is proposed. Before the re-decoding of updating value L each time, SAD-SCL uses the existing log likelihood ratio (LLR) information to locate the range of burst error bits, and then re-decoding starts at the incorrect bit with the smallest index in this range. Moreover, when the segmented information sequence cannot get the correct result of decoding, the SAD-SCL algorithm uses SC decoding to complete the decoding of the subsequent segmentation information sequence. Furthermore, its decoding performance is almost the same as that of the subsequent segmentation information sequence using the AD-SCL algorithm. The simulation results show that the SAD-SCL algorithm has lower computational complexity than AD-SCL and SCAD-SCL with negligible loss of performance.
What Is the Entropy of a Social Organization?
Authors: Zingg Christian , Casiraghi Giona , Vaccario Giacomo , Schweitzer Frank
Published: September 2019

We quantify a social organization’s potentiality, that is, its ability to attain different configurations. The organization is represented as a network in which nodes correspond to individuals and (multi-)edges to their multiple interactions. Attainable configurations are treated as realizations from a network ensemble. To have the ability to encode interaction preferences, we choose the generalized hypergeometric ensemble of random graphs, which is described by a closed-form probability distribution. From this distribution we calculate Shannon entropy as a measure of potentiality. This allows us to compare different organizations as well as different stages in the development of a given organization. The feasibility of the approach is demonstrated using data from three empirical and two synthetic systems.
A Formal Model for Semantic Computing Based on Generalized Probabilistic Automata
Authors: Huang Guangjian , Wasti Shahbaz Hassan , Wei Lina , Jiang Yuncheng
Published: September 2019

In most previous research, “semantic computing” refers to computational implementations of semantic reasoning. It lacks support from the formal theory of computation. To provide solid foundations for semantic computing, researchers propose a different understanding of semantic computing based on finite automata. This approach provides a computer theoretical approach to semantic computing. But finite automata are not capable enough to deal with imprecise knowledge. Therefore, in this paper, we provide foundations for semantic computing based on probabilistic automata. Even though traditional probabilistic automata can handle imprecise knowledge, their limitation resides in their being defined on a fixed finite input alphabet. This deeply restricts the abilities of automata. In this paper, we rebuild traditional probabilistic automata for semantic computing. Furthermore, our new probabilistic automata are robust enough to handle any alphabet as input. They have better performances in many applications. We provide an application for weather forecasting, a domain for which traditional probabilistic automata are not effective due to their finite input alphabet. Our new probabilistic automata can overcome these limitations.
Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy
Authors: Song Enzhe , Ke Yun , Yao Chong , Dong Quan , Yang Liping
Published: September 2019

The normal operation of high-pressure common rail injector is one of the important prerequisites for the healthy and reliable operation of diesel engines. Therefore, this paper studies the high-precision fault diagnosis method for injectors. Firstly, this paper chooses VMD to adaptively decompose the common rail fuel pressure wave. The biggest difficulty in VMD decomposition is the need to manually set the internal combination parameters K and α. In order to overcome this shortcoming, this paper proposes an improved fruit fly search. The variational mode decomposition method of the algorithm, with the energy growth factor e as the objective function, can adaptively decompose the multi-component signal into superimposed sub-signals. In addition, based on the analytic hierarchy process and dispersion entropy, hierarchical dispersion entropy is proposed to obtain a comprehensive and accurate complexity estimation of time series. Then, a fault diagnosis scheme for high-pressure common rail injector based on IFOA-VMD and HDE is proposed. Finally, using the engineering test data, the method is compared with other methods. The proposed method appears, based on the numerical examples, to be better from both a computational and classification accuracy point of view.
Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems
Authors: Guth Stephen , Sapsis Themistoklis P.
Published: September 2019

The ability to characterize and predict extreme events is a vital topic in fields ranging from finance to ocean engineering. Typically, the most-extreme events are also the most-rare, and it is this property that makes data collection and direct simulation challenging. We consider the problem of deriving optimal predictors of extremes directly from data characterizing a complex system, by formulating the problem in the context of binary classification. Specifically, we assume that a training dataset consists of: (i) indicator time series specifying on whether or not an extreme event occurs; and (ii) observables time series, which are employed to formulate efficient predictors. We employ and assess standard binary classification criteria for the selection of optimal predictors, such as total and balanced error and area under the curve, in the context of extreme event prediction. For physical systems for which there is sufficient separation between the extreme and regular events, i.e., extremes are distinguishably larger compared with regular events, we prove the existence of optimal extreme event thresholds that lead to efficient predictors. Moreover, motivated by the special character of extreme events, i.e., the very low rate of occurrence, we formulate a new objective function for the selection of predictors. This objective is constructed from the same principles as receiver operating characteristic curves, and exhibits a geometric connection to the regime separation property. We demonstrate the application of the new selection criterion to the advance prediction of intermittent extreme events in two challenging complex systems: the Majda–McLaughlin–Tabak model, a 1D nonlinear, dispersive wave model, and the 2D Kolmogorov flow model, which exhibits extreme dissipation events.
A Novel Address Scheme for Continuous-Flow Parallel Memory-Based Real-Valued FFT Processor
Authors: Yuan Min , Ma Zhenguo , Yu Feng , Xing Qianjian
Published: September 2019

In this article, we present a modified constant-geometry based signal flow graph for memory-based real-valued fast Fourier transform architecture. Without an extra permutation, the corresponding address scheme solves the memory conflict and achieves continuous-flow operation with the minimal memory and computation cycles requirement when compared to the state-of-the-art designs. Besides, the address scheme meets the constraint of in-place operation, concurrent I/O, normal-order I/O, variable size, and parallel processing. The experimental results demonstrate the resource and frequency efficiency of the proposed address scheme.
A Study on the Improved Capacitor Voltage Balancing Method for Modular Multilevel Converter Based on Hardware-In-the-Loop Simulation
Authors: Lee Junmin , Kang Daewook , Lee Jangmyung
Published: September 2019

In the power industry, hardware in-the-loop simulation (HILS) based on a real-time digital simulator (RTDS) is important technology for modular multilevel converter (MMC)-based high-voltage direct current (HVDC) power transmission. It is possible in real time to verify various fault situations that cannot be predicted by the software-in-the-loop simulation (SILS). This paper introduces the implementation methodology of sub-module (SM) capacitor voltage balancing for a MMC-HVDC physical control system based on field-programmable gate array (FPGA), which has the advantages of high-speed parallel operation and validates the reliability and accuracy of MMC-HVDC control when this control system is operated with RTDS. The characteristics of conventional capacitor voltage balancing methods, such as the nearest level control (NLC) with full sorting method, the NLC with reduced switching frequency method, and the tolerance band (TB) method, implemented on a physical control system based on this implementation methodology, are compared and analyzed. This paper proposes the improved capacitor voltage balancing method for MMC-HVDC transmission. Finally, the proposed capacitor voltage balancing method is compared with conventional methods to analyze performance in real-time to demonstrate that the proposed method is better than the conventional methods.
Improvement of Digestate Stability Using Dark Fermentation and Anaerobic Digestion Processes
Authors: Albini Elena , Pecorini Isabella , Ferrara Giovanni
Published: September 2019

This paper assessed the effect of dark fermentation, the fermentative phase in a two-stage anaerobic digestion system, in terms of digestate biostabilization efficiency. The digestates analyzed in this study were obtained from a pilot-scale system in which two different substrates were used in order to simulate both the digestion and co-digestion process. Biostabilization performances were evaluated by measuring the specific oxygen uptake rate (SOUR) of the outgoing digestates. This index allowed us to define the degree of effectiveness in terms of stabilization of organic matter, between the traditional anaerobic digestion process and the two-stage configuration. Considering the traditional process as a reference scenario, the results highlighted an increase in biological stability for the two-stage co-digestion process, consisting of a dark fermentation stage, followed by an anaerobic digestion one. Digestates biostabilization efficiency increased up from 6.5% to 40.6% from the traditional one-stage configuration to the two-stage one by improving the anaerobic digestion process through a preliminary fermentative stage. The advantages of the two-stage process were due to the role of dark fermentation as a biological pre-treatment. Considering the partial stability results related to the second stage, biological stability was improved in comparison to a single-stage process, reaching an efficiency of 42.2% and 55.8% for the digestion and co-digestion scenario respectively. The dark fermentation phase allowed for a higher hydrolysis of the substrate, making it more easily degradable in the second phase. Results demonstrated better biostabilization performances of the outgoing digestates with the introduction of dark fermentation, resulting in more stable digestates for both the digestion and co-digestion process.
Development of Renewable Energy Sources in the Context of Threats Resulting from Low-Altitude Emissions in Rural Areas in Poland: A Review
Authors: Piwowar Arkadiusz , Dzikuć Maciej
Published: September 2019

The process of transformation of the Polish economy, traditionally based on coal, into an economy that uses low-carbon technologies, faces a problem associated with the diversification of energy sources, especially in rural areas. The scale of the use of conventional energy carriers in households located in rural areas in Poland has a very negative impact on the natural environment. The aim of the paper is to indicate possibilities of reducing low-altitude emissions (with emitters not exceeding 40 m in height) in rural areas in Poland, through the development of renewable energy sources. This paper provides an overview of the specific character of rural areas in Poland and the development challenges faced in these areas in the investigated scope. In order to reduce greenhouse gas emissions and improve energy efficiency, it is necessary to dynamize pro-ecological activities in agriculture and in rural areas, including the development of agricultural biogas plants, wind and photovoltaic farms. The use of renewable energy sources can be an important factor in the development and sustainable growth of rural areas in Poland.
Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet
Authors: Kunicki Michał , Borucki Sebastian , Cichoń Andrzej , Frymus Jerzy
Published: September 2019

A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models.
The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment
Authors: Miguel José V. P. , Fadigas Eliane A. , Sauer Ildo L.
Published: September 2019

Driven by the energy auctions system, wind power in Brazil is undergoing a phase of expansion within its electric energy mix. Due to wind’s stochastic nature and variability, the wind measurement campaign duration of a wind farm project is required to last for a minimum of 36 months in order for it to partake in energy auctions. In this respect, the influence of such duration on a measure-correlate-predict (MCP) based wind resource assessment was studied to assess the accuracy of generation forecasts. For this purpose, three databases containing time series of wind speed belonging to a site were considered. Campaigns with durations varying from 2 to 6 years were simulated to evaluate the behavior of the uncertainty in the long-term wind resource and to analyze how it impacts a wind farm power output estimation. As the wind measurement campaign length is increased, the uncertainty in the long-term wind resource diminished, thereby reducing the overall uncertainty that pervades the wind power harnessing. Larger monitoring campaigns implied larger quantities of data, thus enabling a better assessment of wind speed variability within that target location. Consequently, the energy production estimation decreased, allowing an improvement in the accuracy of the energy generation prediction by not overestimating it, which could benefit the reliability of the Brazilian electric system.
Electric Vehicle Inverter Electro-Thermal Models Oriented to Simulation Speed and Accuracy Multi-Objective Targets
Authors: Urkizu June , Mazuela Mikel , Alacano Argiñe , Aizpuru Iosu , Chakraborty Sajib , Hegazy Omar , Vetten Marco , Klink Roberto
Published: September 2019

With the increasing demand for electric vehicles, the requirements of the market are changing ever faster. Therefore, there is a need to improve the electric car’s design time, where simulations could be an appropriate tool for this task. In this paper, the modeling and simulation of an inverter for an electric vehicle are presented. Four different modeling approaches are proposed, depending on the required simulation speed and accuracy in each case. In addition, these models can provide up to 150 different electric modeling and three different thermal modeling variants. Therefore, in total, there were 450 different electrical and thermal variants. These variants are easily selectable and usable and offer different options to calculate the electrical parameters of the inverter. Finally, the speed and accuracy of the different models were compared and the obtained results presented.
Research on Carbon Emissions of Electric Vehicles throughout the Life Cycle Assessment Taking into Vehicle Weight and Grid Mix Composition
Authors: Li Yanmei , Ha Ningning , Li Tingting
Published: September 2019

To study the impact of the promotion of electric vehicles on carbon emissions in China, the full life carbon emissions of electric vehicles are studied on the basis of considering such factors as vehicle weight and grid mix composition, and fuel vehicles are added for comparison. In this paper, we collect data for 34 domestic electric vehicles, and linear regression analysis is used to model the relationship between vehicle weight and energy consumption. Then, a Hybrid Life Cycle Assessment method is used to establish the life cycle carbon emission calculation model for electric vehicles and fuel vehicles. Finally, the life cycle carbon emissions of electric vehicles and fuel vehicles under different electrical energy structures are discussed using scenario analysis. The results show that under the current grid mix composition in China, the carbon emissions of electric vehicles of the same vehicle weight class are 24% to 31% higher than that of fuel vehicles. As the proportion of clean energy in the grid mix composition increases, the advantages of electric vehicles to reduce carbon emissions will gradually emerge.
Energiewende @ Risk: On the Continuation of Renewable Power Generation at the End of Public Policy Support
Authors: Glensk Barbara , Madlener Reinhard
Published: September 2019

This paper aims to analyze what happens with renewable energy power plants, such as onshore wind, photovoltaics and biomass, when the public policy support based on the Renewable Energy Law expires. With its expiration, the first renewable energy (and especially onshore wind) power plants will have to be scrutinized as to whether they can economically continue operation, whether they have to be repowered, or whether they need to be decommissioned. The relative merits of these three alternatives are evaluated by applying real options analysis. In contrast to traditional project evaluation techniques, the real options approach takes advantage of the use of uncertain parameters included in the model, such as the development of the electricity price or electricity output. The results obtained suggest that parameters such as the level of future operation and maintenance costs, the expected development of the electricity price at the spot market, and the interrelations between these, as well as the development of the electricity output from renewables can significantly affect the profitability of these power plants and thus impact the decision about their further optimal operation.
Bumpless Optimal Control over Multi-Objective Microgrids with Mode-Dependent Controllers
Authors: Wu Ying , Guerrero Josep M. , Vasquez Juan C. , Wu Yanpeng
Published: September 2019

To avoid transient jumps at the switching time between two operating modes in microgrids, this paper proposes a linear quadratic-based optimal bumpless controller with two degrees of freedom (DOF) to suppress the transient disturbance and realize seamless switching between mode-dependent controllers. By minimizing the transient performance criteria, which contains both the reference tracking error and the controller tracking error, this bumpless algorithm not only effectively forces the latent controller to track the active controller, but also guarantees the plant output track the reference as close as possible. For the different control objectives of the two modes, a current-based networked PI controller is proposed in islanded mode to achieve power sharing, as well as suppressing circulating current, and a power-based PI controller is designed in grid connected mode to supply required P and Q, as well as effectively synchronize f and v safely with the main grid. A microgrid test system with two operation modes was built in Matlab/Simulink. Several operating cases were executed to verity the feasibility and effectiveness of this optimal bumpless control strategy.
Wind Farm NWP Data Preprocessing Method Based on t-SNE
Authors: Gu Jiu , Wang Yining , Xie Da , Zhang Yu
Published: September 2019

The operation prediction of wind farms will be accompanied by the need for massive data processing, especially the preprocessing of wind farm meteorological data or numerical weather prediction (NWP). Because NWP data are strongly correlated with wind farm operation, proper processing of NWP data could not only reduce data volume but also improve the correlations of wind farm operation predictions. For this purpose, this paper proposes a data preprocessing algorithm based on t-distributed stochastic neighbor embedding (t-SNE). Firstly, the data collected were normalized to eliminate the influence caused by different dimensions. The t-SNE algorithm is then used to reduce the dimensionality of the NWP data related to wind farm operation. Finally, the wind farm data visualization platform is established. In this paper, 22 index variables in NWP data were taken as objects. The t-SNE method was used to preprocess the NWP historical data of a wind farm, and the results were compared with the results of the principal component analysis (PCA) algorithm. It outperformed PCA in error precision; in addition, t-SNE dimension reduction preprocessing also had a visual effect, which could be applied to big data visualization platforms. A long short-term memory network (LSTM) was used to predict the operation of the wind farm by combining the preprocessed NWP data and the operation data. The simulation results proved that the effect of the preprocessed NWP data based on t-SNE on the wind power prediction was significantly improved.
Turbulent Flow Fields Over a 3D Hill Covered by Vegetation Canopy Through Large Eddy Simulations
Authors: Liu Zhenqing , Hu Yiran , Fan Yichen , Wang Wei , Zhou Qingsong
Published: September 2019

The flow fields over a simplified 3D hill covered by vegetation have been examined by many researchers. However, there is scarce research giving the three-dimensional characteristics of the flow fields over a rough 3D hill. In this study, large eddy simulations were performed to examine the coherent turbulence structures of the flow fields over a vegetation-covered 3D hill. The numerical simulations were validated by the comparison with the wind-tunnel experiments. Besides, the flow fields were systematically investigated, including the examinations of the mean velocities and root means square of the fluctuating velocities. The distributions of the parameters are shown in a three-dimensional way, i.e., plotting the parameters on a series of spanwise slices. Some noteworthy three-dimensional features were found, and the mechanisms were further revealed by assessing the turbulence kinetic energy budget and the spectrum energy. Subsequently, the instantaneous flow fields were illustrated, from which the coherent turbulence structures were clearly identified. Ejection-sweep motion was intensified just behind the hill crest, leading to a spanwise rotation. A group of vertical rotations were generated by the shedding of the vortex from the lateral sides of the hill.
A Two-Layer Interactive Mechanism for Peer-to-Peer Energy Trading Among Virtual Power Plants
Authors: Lyu Xiaoyu , Xu Zhiyu , Wang Ning , Fu Min , Xu Weisheng
Published: September 2019

This paper addresses decentralized energy trading among virtual power plants (VPPs) and proposes a peer-to-peer (P2P) mechanism, including two interactive layers: on the bottom layer, each VPP schedules/reschedules its internal distributed energy resources (DERs); and on the top layer, VPPs negotiate with each other on the trade price and quantity. The bottom-layer scheduling provides initial conditions for the top-layer negotiation, and the feedback of top-layer negotiation affects the bottom-layer rescheduling. The local scheduling/rescheduling of a VPP is formulated as a stochastic optimization problem, which takes into account the uncertainties of wind and photovoltaic power by using the scenarios-based method. In order to describe the capability of a seller VPP to generate more energy than the scheduled result, the concept of power generation potential is introduced and then considered during order initialization. The multidimensional willingness bidding strategy (MWBS) is modified and applied to the price bidding process of P2P negotiation. A 14-VPP case is studied by performing numerous computational experiments. The optimal scheduling model is effective and flexible to deal with VPPs with various configurations of DERs. The parallel price bidding with MWBS is adaptive to market situations and efficient due to its rapid convergence. It is revealed that VPPs can obtain higher profit by participating in P2P energy trading than from traditional centralized trading, and the proposed mechanism of two-layer “interactivity” can further increase VPPs’ benefits compared to its “forward” counterpart. The impacts of VPP configuration and VPP number are also studied. It is demonstrated that the proposed mechanism is applicable to most cases where VPPs manage some controllable DERs.
The Effect of Tree Crown Allometry on Community Dynamics in Mixed-Species Stands versus Monocultures. A Review and Perspectives for Modeling and Silvicultural Regulation
Authors: Pretzsch Hans
Published: September 2019

Many recent studies have shown that the structure, density, and productivity of mixed-species stands can differ from the weighted mean of monospecific stands of the respective species. The tree and stand properties emerging by inter-specific neighborhood should be considered in models for understanding and practical management. A promising approach for this is a more realistic representation of the individual tree allometry in models and management concepts, as tree allometry determines many structural and functional aspects at the tree and stand level. Therefore, this paper is focused on the crown allometry in mixed and mono-specific stands. Firstly, we review species-specific differences in the crown allometry in monospecific stands. Secondly, we show how species-specific differences and complementarities in crown allometry can emerge in mixed-species stands. Thirdly, the consequences of allometric complementarity for the canopy packing density will be analyzed. Fourthly, we trace the crown allometry from the tree level to the stand density and show the relevance for the self-thinning in mixed versus monospecific stands. Fifth, the consequence of the findings for modeling and regulating tree and stand growth will be discussed. The review deals mainly with widespread even-aged, mono-layered stands, but the main results apply for more heterogeneous stands analogously.
Use of MODIS NDVI Products to Map Tree Mortality Levels in Forests Affected by Mountain Pine Beetle Outbreaks
Authors: Spruce Joseph P. , Hicke Jeffrey A. , Hargrove William W. , Grulke Nancy E. , Meddens Arjan J. H.
Published: September 2019

Extensive bark beetle outbreaks have recently occurred in western North American forests, resulting in overstory tree mortality across millions of hectares. Annual aerial surveys are currently used to operationally monitor bark beetle induced tree mortality, though this method is subjective and can exclude some forest areas. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data offer a potential alternative means to develop regional tree mortality maps. Accurate methods using such data could aid natural resource managers in surveys of forests with frequent overstory mortality, helping to prioritize forest treatment and restoration activities. This paper discusses a study to test the potential of using MODIS data to detect tree mortality. We developed and tested an approach to use 250-m resolution MODIS Normalized Difference Vegetation Index (NDVI) data products collected during a mountain pine beetle (MPB) outbreak and related tree mortality event in the northern Rocky Mountains of Colorado, USA. The 94 km2 study area is predominantly lodgepole pine forest with most of the MPB-caused mortality occurring between 2003 and 2008. We used a 2.4-m forest conditions map from 2008 aerial multispectral imagery to calculate percentage of mortality within 240-m pixels for use as reference data. Using either daily or 16-day products, MODIS NDVI change products were calculated for 2008 versus either 2000 or 2003 baselines. MODIS change products were used as predictors in linear regression analysis to assess correlation between MODIS data and the aerial percent forest mortality map. Depending on the MODIS product, linear regression analyses yielded r2 values ranging from 0.362 to 0.544 without outliers removed and from 0.406 to 0.570 with extreme outliers removed. Daily MODIS NDVI products from 2003 and 2008 were used with exponential regression to improve the r2 to 0.593. The project showed some MODIS NDVI data potential for mapping percent tree mortality in forests subjected to regional bark beetle outbreaks and severe drought.
Necromass Carbon Stock in a Secondary Atlantic Forest Fragment in Brazil
Authors: Villanova Paulo Henrique , Torres Carlos Moreira Miquelino Eleto , Jacovine Laércio Antônio Gonçalves , Soares Carlos Pedro Boechat , da Silva Liniker Fernandes , Schettini Bruno Leão Said , da Rocha Samuel José Silva Soares , Zanuncio José Cola
Published: September 2019

Necromass has a relevant role to play in the carbon stock of forest ecosystems, especially with the increase of tree mortality due to climate change. Despite this importance, its quantification is often neglected in tropical forests. The objective of this study was to quantify the carbon storage in a secondary Atlantic Forest fragment in Viçosa, Minas Gerais, Brazil. Coarse Woody Debris (CWD), standing dead trees (snags), and litter were quantified in twenty 10 m x 50 m plots randomly positioned throughout the forest area (simple random sampling). Data were collected during 2015, from July to December. The CWD and snags volumes were determined by the Smalian method and by allometric equations, respectively. The necromass of these components was estimated by multiplying the volume by the apparent density at each decomposition classes. The litter necromass was estimated by the proportionality method and the average of the extrapolated estimates per hectare. The carbon stock of the three components was quantified by multiplying the necromass and the carbon wood content. The total volume of dead wood, including CWD and snag, was 23.6 ± 0.9 m3 ha−1, being produced mainly by the competition for resources, senescence, and anthropic and climatic disturbances. The total necromass was 16.3 ± 0.4 Mg ha−1. The total carbon stock in necromass was 7.3 ± 0.2 MgC ha−1. The CWD, snag and litter stocked 3.0 ± 0.1, 1.8 ± 0.1, and 2.5 ± 0.1 MgC ha−1, respectively. These results demonstrate that although necromass has a lower carbon stock compared to biomass, neglecting its quantification may lead to underestimation of the carbon balance of forest ecosystems and their potential to mitigate climate change.