bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies
Authors: Fedushko Solomia , Ustyianovych Taras , Gregus Michal
Published: April 2020

An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure KPIs and metrics. The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative study of data project demands. A requirements analysis for the implementation of an Operational Intelligence solution with Machine learning capabilities has been conducted and represented in the study. A model based on machine learning algorithms for transaction status code and output predictions, in order to execute system load testing, risks identification and, to avoid downtimes, is developed. Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational intelligence and Site reliability insights. It turned out that data mining among the set of Operational Big Data simplifies the task of getting an understanding of what is happening with requests within the data acquisition pipeline and helps identify errors before a user faces them. Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. Additionally, a step-by-step algorithm for applying the application monitoring solution in a data-based project, especially when it is dealing with Big Data is described and proposed within the study.
Practical Time-Release Blockchain
Authors: Chae Sang-Wuk , Kim Jae-Ik , Park Yongsu
Published: April 2020

Time-release cryptography is a special encryption technique that allows a message to be hidden for some time. The previous schemes have shortcomings in that the encryptor should predict the decryptor’s computing power precisely or the trusted agent should be always available. In this paper, we propose a new, practical time-release blockchain, and find the key to decrypt the content after a certain time. In order to verify the effectiveness of the blockchain system automatically, which uses the proof-of-work (PoW) and the consensus algorithm in the the proposed technique, we have implemented a prototype version of our blockchain system using Python. The proposed method has the following advantages. First, the decryption time is automatically adjusted, even if the miner’s computing power changes over time. Second, unlike previous time-lock puzzle schemes, our algorithm does not require additional computation work for solving the puzzle. Third, our scheme does not need any trusted agents (third parties). Fourth, the proposed method uses standard cryptographic algorithms.
Capacitive Coupling Wireless Power Transfer with Quasi-LLC Resonant Converter Using Electric Vehicles’ Windows
Authors: Yi KangHyun
Published: April 2020

This paper proposes a new capacitive coupling wireless power transfer method for charging electric vehicles. Capacitive coupling wireless power transfer can replace conventional inductive coupling wireless power transfer because it has negligible eddy-current loss, relatively low cost and weight, and good misalignment performance. However, capacitive coupling wireless power transfer has a limitation in charging electric vehicles due to too small coupling capacitance via air with a very high frequency operation. The new capacitive wireless power transfer uses glass as a dielectric layer in a vehicle. The area and dielectric permittivity of a vehicle’s glass is large; hence, a high capacity coupling capacitor can be obtained. In addition, switching losses of a power conversion circuit are reduced by quasi-LLC resonant operation with two transformers. As a result, the proposed system can transfer large power and has high efficiency. A 1.6 kW prototype was designed to verify the operation and features of the proposed system, and it has a high efficiency of 96%.
Reducing Energy Requirements in the Production of Acrylic Acid: Simulation and Design of a Multitubular Reactor Train
Authors: Premlall Kiara Capreece , Lokhat David
Published: April 2020

Acrylic acid, a versatile chemical intermediate, is typically manufactured via a two-step process involving the selective oxidation of propylene and acrolein. This work presents an optimized simulation on Aspen Plus® (AspenTech, Bedford, MA, USA) of the production of acrylic acid, with focus on the optimum design and operation of the reactor train, and modification for reduction in energy usage. In the propylene oxidation reactor, an inert pre-heating zone was designed to make use of the excess energy present in the exothermic process fluid and carried within the molten salt cooling fluid circuit. In the acrolein oxidation reactor, injection of cold air was used as a means to absorb the reaction heat. The optimization methodology employed aimed to minimize the total capital expenditure (i.e., equipment sizing) and operating costs (heat exchange network) for the required acrylic acid production and design constraints. The design constraints were based on the selected reaction kinetics and design heuristics for an optimum reactor design in this service from literature. Sensitivity analyses conducted on Aspen Plus® (AspenTech, Bedford, MA, USA) were used to determine both the interactions between and grouping of the critical parameters for the optimization. Elimination of the conventional fired preheater from the propylene oxidation reactor resulted in energy savings of almost 7 MW, with the reduction in cooling fluid pumping power for the acrolein oxidation reactor amounting to 5 kW.
When Is Blockchain Worth It? A Case Study of Carbon Trading
Authors: Zhao Fangyuan , Chan Wai Kin (Victor)
Published: April 2020

Blockchain, as an emerging technology and a disruptive innovation, has attracted attention from both academia and industry. However, there are many potential risks associated with it, such as the technical risk, the legal risk and the privacy risk. A comprehensive risk analysis is crucial for cost-effective deployment of blockchain technology. Important adoption decisions, including when to deploy blockchain, how to plan the investment, how to transfer current businesses onto blockchain, and how to price the blockchain service depend on this risk analysis. Yet very little study exists concerning the blockchain adoption planning with risks analysis. This research presents a cost-and-risk analysis framework and an adoption planning method for the case of blockchain application in carbon trading. Design requirements implied by the analysis are inferred and the architecture of a novel hybrid blockchain system is proposed. The system leverages the advantages of blockchain technology and incorporates institutional risk control framework. The optimal adoption strategy of this system is derived through modelling of users’ and the organizer’s behavior.
Modeling and Forecasting End-Use Energy Consumption for Residential Buildings in Kuwait Using a Bottom-Up Approach
Authors: Alajmi Turki , Phelan Patrick
Published: April 2020

To meet the rapid-growing demand for electricity in Kuwait, utility planners need to be informed on the energy consumption to implement energy efficiency measures to manage sustainable load growth and avoid the high costs of increasing generation capacities. The first step of forecasting the future energy profile is to establish a baseline for Kuwait (i.e., a business-as-usual reference scenario where no energy efficiency incentives were given and the adoption of energy efficient equipment is purely market-driven). This paper presents an investigation of creating a baseline end-use energy profile until 2040 for the residential sector in Kuwait by using a bottom-up approach. The forecast consists of mainly two steps: (1) Forecasting the quantity of the residential energy-consuming equipment in the entire sector until 2040 where this paper used a stock-and-flow model that accounted for the income level, electrification, and urbanization rate to predict the quantify of the equipment over the years until 2040, and (2) calculate the unit energy consumption (UEC) for all equipment types using a variety of methods including EnergyPlus simulation models for cooling equipment. By combining the unit energy consumption and quantity of the equipment over the years, this paper established a baseline energy use profile for different end-use equipment for Kuwait until 2040. The results showed that the air conditioning loads accounted for 67% of residential electrical consumption and 72% of residential peak demand in Kuwait. The highest energy consuming appliances were refrigerators and freezers. Additionally, the air conditioning loads are expected to rise in the future, with an average annual growth rate of 2.9%, whereas the lighting and water heating loads are expected to rise at a much lower rate.
A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems
Authors: Sui Yu , Song Shiming
Published: April 2020

This paper presents a reinforcement learning framework for solving battery scheduling problems in order to extend the lifetime of batteries used in electrical vehicles (EVs), cellular phones, and embedded systems. Battery pack lifetime has often been the limiting factor in many of today’s smart systems, from mobile devices and wireless sensor networks to EVs. Smart charge-discharge scheduling of battery packs is essential to obtain super linear gain of overall system lifetime, due to the recovery effect and nonlinearity in the battery characteristics. Additionally, smart scheduling has also been shown to be beneficial for optimizing the system’s thermal profile and minimizing chances of irreversible battery damage. The recent rapidly-growing community and development infrastructure have added deep reinforcement learning (DRL) to the available tools for designing battery management systems. Through leveraging the representation powers of deep neural networks and the flexibility and versatility of reinforcement learning, DRL offers a powerful solution to both roofline analysis and real-world deployment on complicated use cases. This work presents a DRL-based battery scheduling framework to solve battery scheduling problems, with high flexibility to fit various battery models and application scenarios. Through the discussion of this framework, comparisons have also been made between conventional heuristics-based methods and DRL. The experiments demonstrate that DRL-based scheduling framework achieves battery lifetime comparable to the best weighted-k round-robin (kRR) heuristic scheduling algorithm. In the meantime, the framework offers much greater flexibility in accommodating a wide range of battery models and use cases, including thermal control and imbalanced battery.
Comparative Analysis of Probabilistic Analysis and Deterministic Analysis by RESRAD Code
Authors: Park Sang June , Byon Jihyang , Ahn Seokyoung
Published: April 2020

The decommissioning of nuclear facilities indicates that the site is finally released according to a limited or unlimited site reuse purpose. In this process, the assessment of exposure dose to decommissioning workers and nearby residents is essential. Based on MARSSIM, a widely used decommissioning guideline in the United States, derivation of the exposure dose and derived concentration guideline level (DCGL) is mandatory using the probabilistic analysis of the RESRAD code. Here, DCGL is the radionuclide-specific concentration that satisfies the site release criteria. By applying the priority 1 parameter, which has the greatest effect on the dose, the dose is derived through deterministic and probabilistic analyses. The results were compared and analyzed. The purpose of this study was to provide a basic database that can be applied to the development of parameter lists and distributions suitable for the characteristics of nuclear facilities in South Korea. In addition, the process of deriving the dose by applying the deterministic and probabilistic analyses of RESRAD was assessed.
Analysis of Energy Consumption of the Reduction of Fe2O3 by Hydrogen and Carbon Monoxide Mixtures
Authors: Sun Guanyong , Li Bin , Yang Wensheng , Guo Jing , Guo Hanjie
Published: April 2020

Energy consumption is directly related to the energy supply and production costs of gas-based direct reduction ironmaking, which is an effective choice to reduce the energy consumption of iron making. In this paper, the minimum Gibbs free energy principle was used to calculate the equilibrium composition under the conditions of reduction gas consisting of hydrogen and carbon monoxide (hydrogen concentration of 0–100%, reduction gas amount of 0–6.0 mol, reduction temperature of 790–1100 °C, and 0.5 mol Fe2O3). According to the enthalpy change, a simplified energy consumption model of a gas-based direct reduction ironmaking process was established, and the energy consumption per mole of metallic iron produced was calculated in detail. The following conclusions were drawn: at the stage when the reduction reaction occurred, the utilization rate of hydrogen or carbon monoxide remained unchanged with the increase in the amount of reduction gas or the increase in the hydrogen concentration of initial gas. The direct energy consumption increased with the increase in the hydrogen concentration at 790–980 °C and the opposite was true at 980–1100 °C. At 790–980 °C, the total energy consumption per ton of iron was greater than 0 and increased with the increase in initial hydrogen concentration from 40% to 100%, and it was less than 0 and increased with the increase in initial hydrogen concentration from 0% to 30%. It was possible to achieve zero total energy consumption with a hydrogen concentration of 30% and a 973 °C reduction.
Shading, Dusting and Incorrect Positioning of Photovoltaic Modules as Important Factors in Performance Reduction
Authors: Klugmann-Radziemska Ewa
Published: April 2020

The amount of solar radiation reaching the front cover of a photovoltaic module is crucial for its performance. A number of factors must be taken into account at the design stage of the solar installation, which will ensure maximum utilization of the potential arising from the location. During the operation of a photovoltaic installation, it is necessary to limit the shading of the modules caused by both dust and shadowing by trees or other objects. The article presents an analysis of the impact of the radiation reaching the surface of the radiation module on the efficiency obtained. Each of the analyzed aspects is important for obtaining the greatest amount of energy in specific geographical conditions. Modules contaminated by settling dust will be less efficient than those without deposits. The results of experimental studies of this effect are presented, depending on the amount of impurities, including their origins and morphologies. In practice, it is impossible to completely eliminate shadowing caused by trees, uneven terrain, other buildings, chimneys, or satellite dishes, and so on, which limits the energy of solar radiation reaching the modules. An analysis of partial shading for the generated power was also carried out. An important way for maximizing the incoming radiation is the correct positioning of the modules relative to the sun. It is considered optimal to position the modules relative to the light source, that is, the sun, so that the rays fall perpendicular to the surfaces of the modules. Any deviation in the direction of the rays results in a loss in the form of a decrease in the available power of the module. The most beneficial option would be to use sun-tracking systems, but they represent an additional investment cost, and their installations require additional space and maintenance. Therefore, the principle was adopted that stationary systems should be oriented to the south, using the optimal angle of inclination of the module surface appropriate for the location. This article presents the dependence of the decrease in obtained power on the angle of deviation from the optimal one.
On Distributional Effects in Local Electricity Market Designs—Evidence from a German Case Study
Authors: Lüth Alexandra , Weibezahn Jens , Zepter Jan Martin
Published: April 2020

The European Commission’s call for energy communities has motivated academia to focus research on design and trading concepts of local electricity markets. The literature provides a wide range of conceptual ideas and analyses on the technical and economic framework of single market features such as peer-to-peer trading. The feasible, system-wide integration of energy communities into existing market structures requires, however, a set of legal adjustments to national regulation. In this paper, we test the implications of recently proposed market designs under the current rules in the context of the German market. The analysis is facilitated by a simplistic equilibrium model representing heterogeneous market participants in an energy community with their respective objectives. We find that, on the one hand, these proposed designs are financially unattractive to prosumers and consumers under the current regulatory framework. On the other hand, they even cause distributional effects within the community when local trade and self-consumption are exempt from taxes. To this end, we introduce a novel market design—Tech4all—that counterbalances these effects. With only few legal amendments, it allows for ownership and participation of renewable technologies for all community members independent of their property structure and affluence. Our presented analysis shows that this design has the potential to mitigate both distributional effects and the avoidance of system service charges, while simultaneously increasing end-user participation.
Energy Fluctuations in the Homogenized Hyper-Elastic Particulate Composites with Stochastic Interface Defects
Authors: Sokołowski Damian , Kamiński Marcin , Wirowski Artur
Published: April 2020

The principle aim of this study is to analyze deformation energy of hyper-elastic particulate composites, which is the basis for their further probabilistic homogenization. These composites have some uncertain interface defects, which are modelled as small semi-spheres with random radius and with bases positioned on the particle-matrix interface. These defects are smeared into thin layer of the interphase surrounding the reinforcing particle introduced as the third component of this composite. Matrix properties are determined from the experimental tests of Laripur LPR 5020 High Density Polyurethane (HDPU). It is strengthened with the Carbon Black particles of spherical shape. The Arruda–Boyce potential has been selected for numerical experiments as fitting the best stress-strain curves for the matrix behavior. A homogenization procedure is numerically implemented using the cubic Representative Volume Element (RVE). Spherical particle is located centrally, and computations of deformation energy probabilistic characteristics are carried out using the Iterative Stochastic Finite Element Method (ISFEM). This ISFEM is implemented in the algebra system MAPLE 2019 as dual approach based upon the stochastic perturbation method and, independently, upon a classical Monte-Carlo simulation, and uniform uniaxial deformations of this RVE are determined in the system ABAQUS and its 20-noded solid hexahedral finite elements. Computational experiments include initial deterministic numerical error analysis and the basic probabilistic characteristics, i.e., expectations, deviations, skewness and kurtosis of the deformation energy. They are performed for various expected values of the defects volume fraction. We analyze numerically (1) if randomness of homogenized deformation energy can correspond to the normal distribution, (2) how variability of the interface defects volume fraction affects the deterministic and stochastic characteristics of composite deformation energy and (3) whether the stochastic perturbation method is efficient in deformation energy computations (and in FEM analysis) of hyper-elastic media.
Energy Efficiency of Novel Interior Surface Layer with Improved Thermal Characteristics and Its Effect on Hygrothermal Performance of Contemporary Building Envelopes
Authors: Fořt Jan , Šál Jiří , Kočí Jan , Černý Robert
Published: April 2020

Facing the consequences of climate change and fuel price rises, the achievement of the requirements for low-energy consumption of buildings has become a challenging issue. On top of that, increased demands on indoor hygrothermal conditions usually require the utilization of additional heating, ventilation, and air-conditioning (HVAC) systems to maintain a comfortable environment. On this account, several advanced and modern materials are widely investigated as a promising way for reduction of the buildings’ energy consumption including utilization of passive heating/cooling energy. However, the efficiency and suitability of passive strategies depending on several aspects including the influence of location, exterior climatic conditions, load-bearing materials used, and insulation materials applied. The main objective of this study consists of the investigation of the energy performance benefits gained by the utilization of advanced materials in plasters by computational modeling. Results obtained from a computational simulation reveal the capability of the studied passive cooling/heating methods on the moderation of indoor air quality together with the reduction of the diurnal temperature fluctuation. Achieved results disclose differences in terms of energy savings for even small variation in outdoor climate conditions. Additionally, the effectivity of passive cooling/heating alters considerably during the summer and winter periods. Based on the analysis of simulated heat fluxes, the potential energy savings related to improved thermal properties of the applied plaster layer reached up to 12.08% and thus represent an interesting passive solution towards energy sustainability to meet the criteria on modern buildings.
Effect of Existing Façade’s Construction and Orientation on the Performance of Low-E-Based Retrofit Double Glazing in Tropical Climate
Authors: Somasundaram Sivanand , Thangavelu Sundar Raj , Chong Alex
Published: April 2020

This paper investigates the effect of an existing façade’s construction (viz. clear/grey/solar film, with and without external shade) and orientation on the performance of low-e (hard coat)-based retrofit double glazing in a tropical climate. The performance of double-glazed façades is characterized by the ability to reduce solar heat gain and the consequent reduction in power consumption of air-conditioning systems. This study involves a real-life test-bedding of a low-e (hard coat)-based retrofit double-glazing façade for a few specific cases—clear glass southeast façade without shade, clear glass southwest façade with external shade, and northwest façade with solar film and external shade. Subsequently, energy modelling simulations were done to analyze other scenarios involving various combinations of façade orientation (north, south, west, and east) and façade material (clear glass, tinted grey glass, clear glass with solar film) with and without external sunshades. The east/west-facing façades had a higher impact on the retrofit solution, and more so when the existing façade was of tinted glass or with solar film. For the case analyzed, with a window-to-wall ratio of 8% (based on overall building envelope), a grey tinted east-facing façade could benefit from annual average HVAC energy savings of up to 5.9%.
Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production
Authors: Matyjaszek Marta , Fidalgo Valverde Gregorio , Krzemień Alicja , Wodarski Krzysztof , Riesgo Fernández Pedro
Published: April 2020

This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the optimum number of time-delayed terms or past values forming the lagged variables and (2) to improve the forecast accuracy by adding intrinsic signals to the lagged variables. The conclusions clearly are in opposition to the actual scientific literature: when addressing the lagged variable size, the results do not confirm relationships among their size, representativeness and estimation accuracy. It is also possible to verify an important effect of the results on the lagged variable size. Finally, adding the order in the time series of the lagged variables to form the predictor variables improves the forecast accuracy in most cases.
Annual Energy Consumption Cut-Off with Cooling System Design Parameter Changes in Large Office Buildings
Authors: Ha Ju-wan , Cho Soolyeon , Kim Hwan-yong , Song Young-hak
Published: April 2020

A variety of greenhouse gas reduction scenarios have been proposed around the world to ensure sustainable developments and strengthen the global response to the climate change. To cope with this, it is urgently needed to reduce the amount of energy used for the heating, ventilating, air conditioning, and refrigerating (HVAC&R) systems in large buildings. This study discusses the reduction of cooling energy in large office buildings through the minimization of changes in components and equipment, such as heat source equipment and pumps, changes in the layout and operating methods of chilled water circulation pumps, and changes in the temperatures of chilled and condenser water. To do this, this study targeted an entire cooling system consisting of a hydronic system, a chiller, and a cooling tower, and conducted a quantitative analysis of the energy consumption and of the reduction achieved through a change in the pumping system type in the cooling system and a change in the Korean standard design and temperature of chiller and cooling tower via EnergyPlus simulations. The simulation results showed a cooling energy reduction of 103.2 MWh/yr, around 15.7%, where the primary constant-speed system (Case A) was changed to a primary variable-speed pump (Case B) in the configuration with a chilled water circulation pump. To reduce the cooling energy further, annually 142.3 MWh, around 21.7%, Case C in this study changed the outlet temperature of the chiller and temperature difference from 7 °C, 5 K to 9 °C, 9 K. Finally, when applying a change in the condenser water production temperature from 32 to 23.9 °C in accordance with ASHRAE Standard 90.1 for Case D, a cooling energy saving of 182.4 MWh/yr was observed, which is about 27.8%.
A Multi-Class Multi-Movement Vehicle Counting Framework for Traffic Analysis in Complex Areas Using CCTV Systems
Authors: Bui Khac-Hoai Nam , Yi Hongsuk , Cho Jiho
Published: April 2020

Traffic analysis using computer vision techniques is attracting more attention for the development of intelligent transportation systems. Consequently, counting traffic volume based on the CCTV system is one of the main applications. However, this issue is still a challenging task, especially in the case of complex areas that involve many vehicle movements. This study performs an investigation of how to improve video-based vehicle counting for traffic analysis. Specifically, we propose a comprehensive framework with multiple classes and movements for vehicle counting. In particular, we first adopt state-of-the-art deep learning methods for vehicle detection and tracking. Then, an appropriate trajectory approach for monitoring the movements of vehicles using distinguished regions tracking is presented in order to improve the performance of the counting. Regarding the experiment, we collect and pre-process the CCTV data at a complex intersection to evaluate our proposed framework. In particular, the implementation indicates the promising results of our proposed method, which achieve accuracy around 80% to 98% for different movements for a very complex scenario with only a single view of the camera.
Adaptive Damping Design of PMSG Integrated Power System with Virtual Synchronous Generator Control
Authors: Deng Jun , Wang Jianbo , Li Shupeng , Zhang Haijing , Peng Shutao , Wang Tong
Published: April 2020

With the continuous development of wind power capacity, a large number of wind turbines connected by power electronic devices make the system inertia lower, which leads to the problem of system frequency stability degradation. The virtual synchronous generator (VSG) control can make wind turbines possess inertia and damping. However, the stochastic dynamic behavior of wind generation results in the stochastic changing of operating condition; this paper presents an adaptive subsynchronous oscillation (SSO) damping control method for the wind generation with VSG control. Firstly, the small signal model of the permanent magnet synchronous generator (PMSG) with VSG is built, and the model of state space is derived and built. The active power of PMSG is selected as the variable parameter vector to establish a polytopic linear variable parameter system model. Then, based on the hybrid H2/H∞ control method, each vertex state feedback matrix is solved by linear matrix inequality, and a subsynchronous oscillation adaptive damping controller with polytope is obtained. Finally, the 4-machine 2-area system connected to two PMSGs with VSG control is used as the test system for time domain simulation. The simulation results demonstrate that the LPV based adaptive damping controller could provide enough damping under the circumstances of wider changes of wind power outputs.
A Deep-Learning-Based Oil-Well-Testing Stage Interpretation Model Integrating Multi-Feature Extraction Methods
Authors: Feng Xin , Feng Qiang , Li Shaohui , Hou Xingwei , Liu Shugui
Published: April 2020

The interpretation of well-testing data is a key means of decision-making support for oil and gas field development. However, conventional processing methods have many problems, such as the stochastic nature of the data, feature redundancies, the randomness of the initial weights or thresholds, and fluctuations in the generalization ability with slight changes in the network parameters. These result in a poor ability to characterize data features and a low generalization ability of the interpretation models. We propose a new integrated well-testing interpretation model based on a multi-feature extraction method and deep mutual information classifiers (MFE-DMIC). This model can avoid the low model classification accuracy caused by the simple training structures, lacking of redundancy elimination, and the non-optimal classifier configuration parameters. First, we obtained the initial features according to four classical feature extraction methods. Then, we eliminated feature redundancies using a deep belief network and united the maximum information coefficient method to achieve feature purification. Finally, we calculated the interpretation results using a hybrid particle swarm optimization–support vector machine classification system. We used 572 well-testing field samples, including five working stages, for model training and testing. The results show that the MFE-DMIC model had the highest total stage classification accuracy of 98.18% as well as the least of features (nine) compared with the classical feature extraction and classification methods and their combinations. The proposed model can reduce the efforts of oil analysts and allow accurate labeling of samples to be predicted.
Replacement Strategy of Insulators Established by Probability of Failure
Authors: Sanyal Simpy , Kim Taeyong , Seok Chang-Sung , Yi Junsin , Koo Ja-Bin , Son Ju-Am , Choi In-Hyuk
Published: April 2020

Insulators comprise only 5% of the capital cost of transmission lines; they are accountable for 70% of line interruptions and 50% of maintenance costs of transmission lines. Major transmission lines situated in different parts of the world were mostly all constructed 30 years ago. These lines have either completed or are approaching the active life at 30 years. It is not possible to replace all insulators at a time in any utility. From a standpoint of consistency, it is quite important to locate insulators that require replacement prior to the occurrence of failure. Recalling these issues, a replacement strategy was modeled on insulator samples, operated at 154 kV, mechanical and electrical rating (M+E) 25,000 lbs and within the 10–50 years (Y) age group, collected in bulk for laboratory evaluation, based on the probability of mechanical failure (P(F)) of insulators. For conducting these studies, tensile load test such as combined electrical and mechanical failing load test was performed on selected 30 new and aged porcelain insulator samples from bulk to access recent condition. It was observed that insulators under service for 50 years manifested a decrease of 89.3% in quality factor (K), as compared to insulators within 10 years of service. A micro-structural study was carried out by using an optical microscope (OM) and a scanning electron microscope (SEM) for the further confirmation of previous evaluations. P(F) was derived by implementing Weibull distribution on the experimental observations. It was observed that insulators with an age of 50 years depicted a 2.7% increase in P(F), as compared to insulators with an age of 10 years. This article discussed a strategy for accessing the recent condition of new, aged bulk samples and the criteria of the replacement of the insulator string based on P(F).
Microgrid Controller Testing Using Power Hardware-in-the-Loop
Authors: Kikusato Hiroshi , Ustun Taha Selim , Suzuki Masaichi , Sugahara Shuichi , Hashimoto Jun , Otani Kenji , Shirakawa Kenji , Yabuki Rina , Watanabe Ken , Shimizu Tatsuaki
Published: April 2020

Required functions of a microgrid become divers because there are many possible configurations that depend on the location. In order to effectively implement the microgrid system, which consists of a microgrid controller and components with distributed energy resources (DERs), thorough tests should be run to validate controller operation for possible operating conditions. Power-hardware-in-the-loop (PHIL) simulation is a validation method that allows different configurations and yields reliable results. However, PHIL configuration for testing the microgrid controller that can evaluate the communication between a microgrid controller and components as well as the power interaction among microgrid components has not been discussed. Additionally, the difference of the power rating of microgrid components between the deployment site and the test lab needs to be adjusted. In this paper, we configured the PHIL environment, which integrates various equipment in the laboratory with a digital real-time simulation (DRTS), to address these two issues of microgrid controller testing. The test in the configured PHIL environment validated two main functions of the microgrid controller, which supports the diesel generator set operations by controlling the DER, regarding single function and simultaneously activated multiple functions.
Regional Sustainable Development with Environmental Performance: Measuring Growth Indexes on Chinese Provinces
Authors: Sueyoshi Toshiyuki , Du Zemin , Wang Derek
Published: April 2020

The rapid economic growth of China in the past decades has been accompanied by serious environmental problems. In the country, both economic development and environmental pollution show a geographically uneven pattern, with some regions displaying significantly better performance than others in economic and/or environmental performance. To understand the regional pattern of economic and environmental performance, this article analyzes sustainable development at the Chinese provincial level. Three sustainability indices are defined and computed by combining economic and environmental factors based on data envelopment analysis. The three indices correspond to the concepts of natural disposability, managerial disposability and null-joint relationship, respectively. Natural (managerial) disposability prioritizes economic (environmental) outcome in measuring sustainability. Furthermore, the assumption of a null-joint relationship implies that undesirable outputs are by-products of desirable outputs. We derive the three indices for the data on Chinese provinces over 2004–2017. We find that, in all indices, a small group of provinces have been maintaining very stable performance improvement over time, whereas a few provinces exhibit drastic swings in performance. Moreover, the fast-growing economies of some provinces contrast sharply with their poor sustainable development. Among the pollutants under study, carbon emissions play an important role in benchmarking the sustainability level for certain provinces. Further, provincial-level performance can be attributed to geographical and economic factors. Policy implications and future research are discussed based on the empirical results.
Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation
Authors: Jasiński Michał , Sikorski Tomasz , Kostyła Paweł , Leonowicz Zbigniew , Borkowski Klaudiusz
Published: April 2020

This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points.
A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint
Authors: Szabat Krzysztof , Wróbel Karol , Dróżdż Krzysztof , Janiszewski Dariusz , Pajchrowski Tomasz , Wójcik Adrian
Published: April 2020

This paper presents an application of an Unscented- and a Fuzzy Unscented- Kalman Filter (UKF and FUKF) to the estimation of mechanical state variables and parameters in a drive system with an elastic connection. The cascade control structure incorporating an IP controller supported by two additional feedbacks and suitable adaptation mechanism is investigated in this study. The coefficients of the control structure are retuned on the basis of the value of mechanical parameters estimated by filter. The effectiveness of the proposed approaches (classical and fuzzy) is researched through simulation and experimental tests.
On Obtaining Energy-Optimal Trajectories for Landing of UAVs
Authors: Horla Dariusz , Cieślak Jacek
Published: April 2020

The optimization issues connected to a landing task of an unmanned aerial vehicle are discussed in the paper, based on a model of a mini-class drone. Three landing scenarios are considered, including minimum-time landing, landing with minimum energy consumption, and planned landing. With the use of classical dynamic programming techniques, including the minimum principle of Pontryagin, as well as the calculus of variations, the optimal altitude reference trajectories are found, to form the altitude control system in such a way as to mimic the profile of the reference trajectory by the actual altitude of the UAV. The simulation results conducted with the use of the Simulink Support Package for Parrot Minidrones verify the correctness and effectiveness of the method, and open the research directions for further analysis, especially to tune altitude controller in a way, as to track the reference profile. Up to this point, optimization tasks considered in the literature, with respect to the drones, were connected to swarm formation optimization, optimization of the take-off process or landing process limited to optimal path planning. This paper thus considers a new topic in the field.
Ectomycorrhizal Fungi: Participation in Nutrient Turnover and Community Assembly Pattern in Forest Ecosystems
Authors: Liu Yanjiao , Li Xiangzhen , Kou Yongping
Published: April 2020

Ectomycorrhizal fungi (EcMF) are involved in soil nutrient cycling in forest ecosystems. These fungi can promote the uptake of nutrients (e.g., nitrogen (N) and phosphorus (P)) and water by host plants, as well as facilitate host plant growth and resistance to stresses and diseases, thereby maintaining the aboveground primary productivity of forest ecosystems. Moreover, EcMF can acquire the carbon (C) sources needed for their growth from the host plants. The nutrient regulation mechanisms of EcMF mainly include the decay of soil organic matter via enzymatic degradation, nonenzymatic mechanism (Fenton chemistry), and priming effects, which in turn promote C and N cycling. At the same time, EcMF can secrete organic acids and phosphatases to improve the availability of soil P, or increase mycelium inputs to facilitate plant acquisition of P. The spatiotemporal distribution of EcMF is influenced by a combination of historical factors and contemporary environmental factors. The community of EcMF is associated with various factors, such as climate change, soil conditions, and host distribution. Under global climate change, investigating the relationships between the nutrient cycling functions of EcMF communities and their distribution patterns under various spatiotemporal scales is conducive to more accurate assessments of the ecological effects of EcMF on the sustainable development of forest.
The Reformulation of a Beef Patty Enriched with n-3 Fatty Acids and Vitamin D3 Influences Consumers’ Response under Different Information Scenarios
Authors: Beriain Maria José , Gómez Inmaculada , Sánchez Mercedes , Insausti Kizkitza , Sarriés María Victoria , Ibañez Francisco C
Published: April 2020

The objective of this study was to investigate the sensory acceptability and willingness to pay (WTP) for a beef patty elaborated with beef from a local breed that was enriched with nutritional ingredients (vegetable oil mixture and vitamin D3). The experiment was conducted under two information scenarios (blind; full: ingredients used to enrich the patties in n-3 PUFA and vitamin D3). An in-home use test was carried out by 180 consumers to study consumer liking of two low-fat beef patties (C: conventional, M: modified). There were no differences in color and odor for the raw patties (p > 0.05). The sensory parameters of the cooked patties were assessed as being similar (p > 0.05) regardless of the information scenario. The sensory parameters remained crucial criteria for product acceptance and repeat purchase. Consumers positively evaluated the M patty. The information provided to consumers on the composition of the product influenced the response of consumers. It was also observed that the higher the education level of the consumer, the higher their scores for M beef patties in the blind scenario. It is thus necessary to implement appropriate marketing strategies in order to highlight the nutritional properties of the modified patties, making them competitive ahead of conventional patties.
Effect of Reconstituted Broth on the Taste-Active Metabolites and Sensory Quality of Stewed and Roasted Pork-Hock
Authors: Yang Yi , Pan Daodong , Wang Ying , He Jun , Yue Yi , Xia Qiang , Zhou Guanghong , Cao Jinxuan
Published: April 2020

Stewed pork-hock in soy sauce (SPHSS) is a cuisine that is stewed in broth with abundant taste-active compounds. Broth plays an important role in determining the meat taste. In order to promote the comprehensive utilization of the broth we treated it by spray drying, and secondary processed it into reconstituted broth. Two new products: SPH (stewed pork-hock with reconstituted broth) and MRPH (marinated and roasted pork-hock with reconstituted broth) were processed. Their metabolome consisted of amino acids, sugars, organic acids, nucleic acids and their derivatives. PC1 and PC2 explained a total of 63.07% and 35.31% of the variation, respectively. All the metabolite levels in SPH were higher than those in SPHSS, except for histidine and phosphorylcholine. SPH kept the highest levels of total FAAs and total sugars, which corresponded to the highest score of overall taste in the three products. These results demonstrated that reconstituted broth can promote the metabolite concentration in and improve the taste of pork-hock. Compared with marinating and roasting, reconstituted broth was more suitable for stewing pork-hock. This study preliminarily explored a feasible method to comprehensively utilize the surplus broth in food processing. SPH with a shortened processing time by a reconstituted broth have potential application in the industry due to the high concentrations of taste metabolites.
Table Olives: An Overview on Effects of Processing on Nutritional and Sensory Quality
Authors: Conte Paola , Fadda Costantino , Del Caro Alessandra , Urgeghe Pietro Paolo , Piga Antonio
Published: April 2020

Table olives are a pickled food product obtained by a partial/total debittering and subsequent fermentation of drupes. Their peculiar sensory properties have led to a their widespread use, especially in Europe, as an appetizer or an ingredient for culinary use. The most relevant literature of the last twenty years has been analyzed in this review with the aim of giving an up-to-date overview of the processing and storage effects on the nutritional and sensory properties of table olives. Analysis of the literature has revealed that the nutritional properties of table olives are mainly influenced by the processing method used, even if preharvest-factors such as irrigation and fruit ripening stage may have a certain weight. Data revealed that the nutritional value of table olives depends mostly on the balanced profile of polyunsaturated and monounsaturated fatty acids and the contents of health-promoting phenolic compounds, which are best retained in natural table olives. Studies on the use of low salt brines and of selected starter cultures have shown the possibility of producing table olives with an improved nutritional profile. Sensory characteristics are mostly process-dependent, and a relevant contribute is achieved by starters, not only for reducing the bitterness of fruits, but also for imparting new and typical taste to table olives. Findings reported in this review confirm, in conclusion, that table olives surely constitute an important food source for their balanced nutritional profile and unique sensory characteristics.