bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geothermal resources in southern Thailand – part of a renewable energy mix
Authors: Duerrast Helmut 
Published: April 2020

Abstract
Electrical energy demand for Southern Thailand is continuously increasing partly due to an increase in tourism. Around 3,000 MW of coal fired power plants are planned by the government, but the proposal was put on hold due to the resistance of local communities and civil society groups. Besides this, coal fired power plants are not only large CO2 emitters, thus intensifying the on-going climate change processes, but also require lengthy environmental impact assessments, and their technology costs remain stagnant at comparable high levels. Solar and wind energy can be produced at far lower costs than coal, however, their shares on the renewable energy mix are comparable small in Thailand, but with a steady increase. A disadvantage of solar and wind energy production is that the production is not constant due to day/night and weather, respectively. These disadvantages can be compensated by adding geothermal energy, as this energy from deeper geological sources is continuously reaching the Earth’s surface. This allows the continuous feed to geothermal power plants which by this can act as a backbone of a renewable energy mix. In Southern Thailand hot springs from the north to the south are the surface expressions of active geothermal systems at depth. Surface exit temperatures can reach up to 80 degree Celsius, thus considered as low enthalpy resources, which can be utilized applying state-of-the-art binary power plant technology. In the current renewable power plan of the government geothermal energy is not considered due to the low availability of this resource. However, recent research has shown that Southern Thailand holds promising quantities of geothermal resources; with further scientific investigations needed. Finally, the only current geothermal power plant in Thailand located in Fang, Northern Thailand, is situated close to a national park in a beautiful landscape and environment, thus acting as a positive example for Southern Thailand.
Network reconfiguration and optimal allocation of distributed generations for Mayangone distribution system
Authors: Mon Khine Khine , Htay Tin Tin , Lin Ohn Zin 
Published: April 2020

Abstract
This paper focuses on reconfiguration of radial distribution system incorporated with optimal allocation of Distributed Generation units (DGs). The network reconfiguration is to optimize the structure of radial feeders by changing the status of normally closed sectionalizing and normally open tie switches. The optimal allocation of DGs in distribution system has to determine the DGs location and their correspondent sizing for enhancing the system effectively. There are many investigations with both two researches either separately or incorporated simultaneously. But network reconfiguration by using Particle Swarm Optimization (PSO) incorporated with optimal allocation of DGs based on Voltage Stability Index (VSI) and analytical approach of Forward Backward Sweep (FBS) method in radial power flow is the novelty of this research. The main objective is to achieve the minimum losses and improvement of voltage profile while all distribution constraints are satisfied. In proposed algorithm, optimal allocation is determined by finding the best sizing and location of DGs based on VSI and FBS load flow, and then PSO reconfiguration finds out the optimal location of tie and sectionalizing switches simultaneously. The study of test system is carried out on Mayangone Distribution System (MDS) and the obtained results are inefficient for this research.
Geothermal characteristics of two deep U-shaped downhole heat exchangers in the Weihe Basin, China
Authors: Zhang Y. P. , Huang S. P. , Meng X. Z. , Jia G.S. , Yang F. , Zhou C. 
Published: April 2020

Abstract
Geothermal resources are abundant in the Weihe Basin, which provides great potential for winter district heating in the Guanzhong area in western China. As a demonstration project for deep geothermal exploitation and utilization, four boreholes of over 2,000 m were drilled and paired to form two U-shaped downhole heat exchangers (DHEs) in the Xi’an Depression of the Weihe Basin. In this paper, the borehole equilibrium temperatures of these two DHEs, measured two years after the systems had been completed, are presented. The analysis of the borehole temperatures show that: (1) the thermal and hydrological regimes of the study area are stable, as indicated by the highly consistent borehole temperatures among the four boreholes, both horizontally and vertically. (2) the geothermal gradient range down to the depth of 2,000 m in this area is 3.45 ˜ 3.47°C/hm with an average of 3.46 ± 0.01°C/hm, substantially higher than the regional mean and implying a great geothermal energy exploitation potential; the high geothermal gradient anomaly can be attributed to the rather thin crust in the Weihe Basin with a relatively high mantle heat flow and hydrothermal convection systems involving fluid circulation in nearby discordogenic fault zones. (3) the Lantian-Bahe Formation has the features of stable distribution, good aquifer yield, high transmissivity, high permeability and high thermal conductivity, and hence, it is a good hosting stratum to the horizontal section of U-shaped DHEs in this area to maximize heat exchange efficiency and sustainability.
The use of artificial neural networks (ANN) in the prediction of energy consumption of air-source heat pump in retrofit residential housing
Authors: Ye K.K. , Demirezen Gulsun , Fung A.S. , Janssen E. 
Published: April 2020

Abstract
Machine learning algorithms using Artificial Neural Network (ANN) were developed to predict the performance of heat pump systems in retrofit residential housing. The study attempts to address the research gap in the application of machine learning algorithms to real-life field measurements as a case study. Rowhouse units with electric resistance baseboard heating were retrofitted with Ductless Air Source Heat Pumps (DASHPs). Sensors were installed to collect the energy consumption data during the baseboard and DASHP monitoring periods. Linear and quadratic regression methods following the International Performance Measurement and Verification Protocol (IPMVP) were applied to predict energy consumption based on outdoor temperature and heating degree days. These predictions were compared against results from ANN models based on Levenberg-Marquardt algorithms using the hour of the day, day of the week, outdoor temperature, wind speed and direction, relative humidity, condition and indoor temperature as inputs. Preliminary results indicate that predictions from ANN models produced higher correlation of determination than those from IPMVP regression analysis.
Performance of graphene-based hydrogel in oil removal using graphene oxide derived from powder and flake graphite
Authors: Ng L.Y. , Lee J.J. , Ng C.Y. , Mahmoudi E. , Hairom N.H.H. , Ong C.B. 
Published: April 2020

Abstract
Graphene-based hydrogel had been formed by chemical reduction process of graphene oxide using ascorbic acid as a reducing agent in this study. Oil adsorption capacity of the hydrogels that were synthesized using different parameters, including type of graphene oxide feedstocks (powder or flake graphite), concentration of graphene oxide used (2 mg/mL or 5 mg/mL), as well as the amount of ascorbic acid added into the graphene oxide (5 grams or 8 grams), had been evaluated in this work. Fourier Transform Infrared Spectroscopy (FTIR) results showed that the oxygen-containing functional groups which were initially present in the graphene oxide could be removed or reduced to form the 3-dimensional π-π interactions hydrogel. In overall, hydrogel produced from flake graphene oxide with the use of higher concentration of graphene oxide (5 mg/mL) and the lower amount of ascorbic acid (5 g), showed the best performance in oil adsorption capacity. The hydrogel produced from these parameters showed the highest adsorption capacity of 7.90 g of oil adsorb per g of hydrogel. This study has provided useful information on the functional groups of graphene-based hydrogels in addition to their oil adsorption capability.
Particle Swarm Optimization based Power Control Algorithms for SWIPT-Assisted D2D Communications Underlaying Cellular Networks
Authors: Xia Xueyao , Kang Guixia 
Published: April 2020

Abstract
We studied power control algorithms for SWIPT-assisted D2D communications underlaying cellular networks in this paper. The particle swarm optimization (PSO) based power control algorithm is put forward to mitigate the interference and enhance the sum throughput of D2D networks. In order to avoid falling into the local optimum, we combined simulated annealing with particle swarm optimization(SA-PSO) to improve performance of the algorithm. The main idea is to allocate proper transmitting powers to D2D links while maximizing the total throughput. Unlike other related works, the transmitting power and the power splitting ratio are both optimized in this paper. Simulation results demonstrate our algorithms are conductive to boosting the total throughput of D2D networks. The PSO and SA-PSO based algorithms outperform the game-theory based algorithm in terms of the sum throughput of D2D networks.
Situation Projection Based on Multi-time-scale Recursive Dynamic State Estimation
Authors: Li Liangzi , Li KeJun , Sun Kaiqi , Liu Zhijie , Liu Wentao 
Published: April 2020

Abstract
In order to solve the problem of lacking of situation awareness in smart distribution network, this paper proposed a real-time situation projection method for distribution network based on multi-time-scale dynamic state estimation. Based on hybrid measurement including micro PMU, a multi-time-scale recursive dynamic state estimation was realized through recursive transformation between state projection and pseudo measurement. A pseudo measurement Correction algorithm is implemented in the proposed state estimation loop to increase the estimation accuracy. Utilizing the historic states data calculated from the proposed state-measurement recursive dynamic state estimation, a novel real-time situation projection method is proposed in this paper. With the modelling of partitioned multivariate time series analysis, the proposed situation projection method is achieved by predicting the situation indexes for the distribution network operator in real time. Simulation validates the feasibility of the proposed method to improve the situation awareness level in the distribution network.
Research on Three-phase Ground Fault Diagnosis Technology Based on Two Terminal Measurement Points
Authors: Yao Zheng , Hu Yutao , Yu Xin , Mei Suzhen , Si Hongxiao , Yu Xiaochen 
Published: April 2020

Abstract
Because the distribution power network is very complex, and easy to cause trouble, The signal is too small to be submerged in the noise, So it is hard to find where the fault is from the branches. based on the distributed parameter model of transmission lines, this paper proposes a fault location method of multi branch distribution lines based on the distributed parameter model of transmission lines. It will affect the whole network to generate faults. Find the fault line through network analysis, and then find the fault point on that line through calculation The simulation results prove the correctness and accuracy of this method, and provide a theoretical basis for the fault location of distribution lines.
Research on ADRC of Double Buck Inverter
Authors: Zhang Zixi , Xiong Hejin , Tang Rongjiang 
Published: April 2020

Abstract
The traditional double buck full bridge inverter adopts the hysteresis current control mode, and the current is in the half cycle. Because of its no bridge arm through, high voltage utilization and high efficiency, it is widely used. Based on the disturbance caused by power supply fluctuation, external noise and system parameter change, this paper proposes an auto disturbance rejection control (ADRC) strategy. The simulation results show that the ADRC strategy can make the double buck full bridge inverter have good waveform tracking performance, good robustness, stability and high accuracy.
Design of Multi-band Circularly Polarized Microstrip Antenna
Authors: Han Qinghua , Shi Mingdeng 
Published: April 2020

Abstract
Design a multi-band circularly polarized microstrip antenna for ISM (2.4GHz, 5.8GHz) and WiMAX (3.3GHz-3.6GHz).Using Ansoft HFSS15.0 for the simulation analysis, The size of the resonance point and the resonance bandwidth are adjusted by opening four triangular grooves on four sides of the radiation patch. The simulation results show that the frequency band of the antenna’s return loss below -10dB is 2.38GHz-2.52GHz,3.37GHz-3.45GHz, and 5.6GHz-5.9GHz. The antenna meets the design requirements of multi-frequency and miniaturization.
Research on Multi-target Signal Separation and Processing Method
Authors: Lv Hong , Li Jun , Xu Hongfeng , Sun Quanling 
Published: April 2020

Abstract
Nowadays, intelligent cities and transportation are being implemented in major cities around the world. In unmanned vehicles, autonomous identification and separation of moving targets is the essential and important part of intelligent transportation. This paper proposes a multi-object separation algorithm in order to solve the security problems that can promote smart transportation. This paper deals with multi-target signals based on MATLAB. In terms of distance, between the cars (targets) that can be separated, multiple targets are separated by matched filter and FFT algorithm, then the performance of the research is expressed by distance resolution. The separable target distance is up to 200 m, and the resolution is up to 0.6 m; in the terms of speed, multiple targets are separated by the Doppler transform, then the performance of the research is reflected in the form of speed resolution. The speed at which the separated target can be identified is up to 200km/h, and the resolution is up to 1.8km/h, otherwise, both stationary and moving targets can be identified. Finally, all the results are obtained by MTALAB simulation.
Trajectory Prediction Based on Machine Learning
Authors: Su Lumin , Li Li 
Published: April 2020

Abstract
With the rapid development of the Internet and positioning technology, trajectory prediction has become a popular research direction because of its wide range of application scenarios. However, the trajectory points are represented only by a series of incomprehensible numerical labels in current studies, and the extraction of the stay points are also lack of time consistency. On the other hand, conventional trajectory prediction models do not make full use of the trajectory context information, as well as only predict the position of the next stay points, which limits the prediction accuracy. To fulfill the above problems, the trajectory prediction method based on machine learning is studied. In this paper, a spatial information extraction method based on multistage cluster is proposed, as well as the LSTM model and the bidirectional LSTM model in the deep learning are used to predict the next position. Experimental results show that the novel method proposed can better represent the semantic information of trajectory and improve the prediction performance greatly.
Dilated Residual Spatial Attentive Generative Adversarial Network for Single Image De-raining
Authors: Xing Junyao , Bi Xiaojun 
Published: April 2020

Abstract
This paper propose a dilated residual spatial attentive generative adversarial network for single image de-raining. The improved spatial attentive mechanism is combined with the dilated convolution residual module to optimize the Condition Generative Adversarial Network(CGAN) structure, to solve two problems still exist in the task of removing rain from a single image: First, the rain streaks contained in the dataset we can use are limited, and in the case of real rainy days, the rain streak density is diverse, it is impossible to simulate them completely and accurately. Then, the existing rain removal models cannot remove rain streaks properly for images with different rain streak density which attend to over or under rain removal. In our methods, Firstly, the dilated convolution module is used to enhance the feature extraction of rain streaks, and then an attention map is generated by the spatial attention mechanism module to accurately locate the position of rain streaks, and the rain streaks are extracted as the foreground information by combining the two parts of the network to guide the subsequent rain removal operation; Then the rain streaks is removed through the Contextual auto-encoder, combining with PatchGAN discriminator. The experimental results on two synthetic dataset and real-world rain image show that the network model proposed in this paper has high generalization ability under different rainfall conditions. it improves the rain removal effect of the model on the most realistic public dataset, and improves the recovery ability of image details while effectively removing rain on other public datasets, which verifies the effectiveness of the model in improving the generalization ability and improving the image quality after rain removal. At the same time, the effect of rain removal is better than other methods on the real rain images, which proves that the method proposed in this paper has high practical application value.
Development of Networked NC Practical Training System
Authors: Fan Hongmei , Zhang Chuncheng 
Published: April 2020

Abstract
With the development of information technology, networked education has become the trend. It is one of the effective measures to solve many problems in higher education to make full use of the modern information technology educational resources and to carry out the professional teaching based on network widely. To meet the needs of networked teaching and experiments of NC(Numeric Control) principle course, this paper develops a web-based NC practical training system based on WINDOWS operating system, which uses Tomcat to build a WEB server, and uses JAVA language to manage ORACLE database. The networked NC practical training system provides a good platform for the students majoring in numeric control. This platform can not only deepen their understanding and mastery of numerical control expertise, but also cultivate their ability of system design and development.
Comprehensive Evaluation Method of Office Building Energy Consumption Based on Improved Multi-index
Authors: Chai Kang , Yu Junqi , Tian Ying , Zhao Anjun , Gao Jiaojiao , Nie Jikai 
Published: April 2020

Abstract
In view of the imperfection in the selection of energy consumption characteristics and energy conservation evaluation methods, this paper proposes an energy consumption analysis and energy conservation evaluation method suitable for office buildings. Through the analysis of office building energy consumption monitoring layer data, establishing standard of building energy consumption data sets, including office building day by day, month by month, year after year way of energy consumption and heating and cooling is analyzed, and aiming at the problem of building energy efficiency evaluation index weights are difficult to determine the, combined with the feature of energy consumption was proposed to build multiple indexes comprehensive evaluation of energy consumption. Through the example of office building, the evaluation results are verified, which proves that this method can evaluate the energy consumption of buildings reasonably and feasible, and provides a new idea for the comprehensive evaluation of building energy conservation.
Graph-Theoretic Models for the Holistic Analysis of Actor-Networks with Emergent Properties
Authors: Tselykh Alexey , Denisov Boris 
Published: April 2020

Abstract
This paper presents the results of a research in modeling a concept of a black box and a mechanism of translation in sociotechnical actor-networks with emergent properties based on the extended and powerful formalisms of hypergraphs, directed hypergraphs, hypernetworks, metagraphs, and nested metagraphs. We introduce graph-theoretical models that allow for systemic studies and holistic analysis of actor-networks using rich mathematical apparatus of a set theory. The main object of the research are actor-networks in Science and Technology Studies. We hope that our contribution to the arsenal of graphical presentation tools and rigorous mathematical models for actor-network analysis will encourage the transition from qualitative to quantitative studies and the wider practical application of actor-network theory.
Independency of Knowledge Diffusion Analyzed by Inverse Citation Networks
Authors: Chi Qiaozheng , Ma Yinghong 
Published: April 2020

Abstract
With the development of worldwide knowledge-based economy, structures of knowledge diffusion in scientific research have become extremely complex and dynamic. Properly evaluating the knowledge diffusion would encourage authors to pursue high quality researches. Hence, this paper presents a novel metric of independency of knowledge diffusion (IKD) on the published paper v, defined as the ratio of citation counts of v without its references’ to citation counts of v and its references’ minus their commons’. Utilizing the inverse citation network formed by published papers in American Physical Society (APS) from 1997 and 2016, the experimental results show that the distributions of IKD are following power law behaviors and the values of IKD are affected by citation counts and involved cooperative institutions. It is reasonable to assess the performances of knowledge diffusion by the metric of IKD.
Research on Financial Software Selection Decisions Based on a Fuzzy Evaluation Model
Authors: Shao Changyan , Xu Sheng , Wang Xiaojia 
Published: April 2020

Abstract
Appropriate financial software is the foundation of strengthening the enterprise information construction. For enterprises, choosing appropriate financial software is not only conducive to improving the efficiency and profitability of business processing, but also to ensuring the authenticity of financial information and the accuracy of decision-making. In this paper, we first established an evaluation index system of financial software evaluation, and then combined two-dimensional uncertain linguistic variables (2DULV) and grey relational analysis (GRA) to evaluate and select the financial software. Finally, the effectiveness of the proposed 2DULV-GRA model is verified by an example of an enterprise. The fuzzy evaluation model proposed in this paper allows decision-makers to express uncertainty, which provides a reference for the enterprise decision-making.
Taguchi-fuzzy Theory Applied to Optimization of Teaching Module
Authors: Wu Bo-Wen , Huang Chou-Chun , Lin Wen-Chieh 
Published: April 2020

Abstract
In this study, a case-reference teaching method was selected, and Taguchi L9 orthogonal table combined with fuzzy control system was adopted to minimize the experimental design, maintain the high precision of the experimental data, and successfully develop the optimal analysis of teaching design mode. This model can effectively improve the quality of teaching. The research results have made a significant contribution to the optimization of teaching design, and can be replicated and followed up.
Research on Airline Route Optimization Based on Ant Colony Optimization Algorithm
Authors: Feng Shemiao 
Published: April 2020

Abstract
International route network planning is an important issue in airline management decision. To solve the problem, in this paper the author introduces the concept of optimal air transport route, proposes the ant colony algorithm to solve the network optimization, then the new method for this problem is presented in the paper. At last, a typical simulation study was carried out to illustrate its validity. It is founded that ACO algorithms is reliable and effective for aviation network optimization problems.