Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Tight Oil Recovery by Volume Fracturing in Chang 7 Reservoir: Experimental Study and Field Practice

The Chang 7 reservoir in Changqing oilfield is rich in tight oil. However, due to the low formation permeability, it is very difficult to obtain economical oil production without stimulation treatments. Volume fracturing seems to be a more efficient tight oil recovery enhancement (EOR) method in Chang 7 pilot tests compared with conventional hydraulic fracturing. In this study, Chang 7 tight oil reservoir was first characterized by its geological property, hydrocarbon source rock distribution, and formation physiochemical property. Tight core flooding tests were then conducted to experimentally investigate the EOR ability of the volume fracturing technique. The field-scale practice was also demonstrated and analyzed. The results show that Chang 7 reservoir is favorable for the generation of a large amount of tight oil. Fractures created in tight cores can significantly improve the fluid flow conductivity and enhance the imbibition of displacing water, resulting in a greater tight oil recovery increment. Volume fracturing is an effective way to generate a larger number of fractures. Field application indicates that volume fracturing treatment can form a much greater reservoir stimulation volume. Daily oil production in the volume-fracturing-treated wells can be more than twice as high as that in the conventional-fracturing-treated wells.
Life cycle assessment of facade solutions made of durable reactive powder concrete

Reactive powder concrete (RPC) has become an important material for durable building envelope solutions in form of curtain walls or façade panels. RPC, also known as ultra-high performance concrete (UHPC), has a very dense micro structure and reaches compressive strength beyond 120 MPa. In conjunction with fibers or textile reinforcement it shows a ‘quasi’ ductile behavior. As a drawback it is often regarded as not so environmentally sustainable due to the high amount of cement used. This study investigates therefore the environmental aspects of façade panels consisting of RPC. Solutions with different mix proportions and reinforcement types will be analyzed and compared with elements made of standard steel reinforced concrete. The analysis will not only consider the results of the bulk material but also view it in the context of the entire façade elements. The life cycle assessment (LCA) does also approach the durability related aspects of the reactive powder concrete and alternative reinforcement materials.
Apply Object-Oriented Technology to Construct Chinese News Ontology on the Internet

The Ontology is increasingly important for many information systems and Semantic Web, while the cost of constructing ontology is too much. In this paper, we propose an automatic approach for ontology construction to assist the knowledge engineers to construct the specific domain ontology from the documents, which are gathered from Internet. We hope to raise the automation level to make correctly and efficiently build the ontology while applying the method to different domains. For different domains, we propose an approach to extract new Chinese terms from the specific corpus automatically. And we use information retrieval, natural language processing, and soft computing techniques to find out the concepts of the ontology. In addition, we extend the concept of episode to construct an Episode Net. Using Episode Net, we can find out the static attributes, dynamic operations, and the associations between concepts of the ontology. Finally, we use object-oriented model to represent the ontology and then construct the ontology with four-layer object-oriented structure. The experimental results show that our approach can effectively assist ontology engineers to construct the domain ontology.
Flux-Focusing Permanent Magnet Machines With Modular Consequent-Pole Rotor

In this paper, the modular consequent-pole (MCP) rotor is adopted in the fractional-slot interior permanent magnet (IPM) machine to utilize the strong flux-focusing effect and reduce the leakage flux. Hence, the torque density and utilization ratio of permanent magnet (PM) material can be improved. However, the unacceptable unbalanced magnetic force (UMF) may arise due to asymmetric airgap flux density produced by consequent-pole topology. Thus, a pole-shaping method based on three-sectional arcs is proposed to suppress it. Due to the relatively complex rotor structure, the multiobjective optimization combined finite-element method with genetic algorithm is performed in this paper. Furthermore, the electromagnetic performance of the IPM machine with MCP rotor (namely IPM-MCP machine), including the open-circuit airgap flux density, phase back EMF, torque, PM utilization ratio, efficiency, flux-weakening capability and UMF, are compared with the conventional surface-mounted PM (SPM), IPM, and consequent-pole IPM (IPM-CP) machines. It is demonstrated that the IPM-MCP machine obtains the largest output torque in these machines. Meanwhile, the IPM-MCP and IPM-CP machines respectively have >30% and >20% higher PM utilization ratio than the conventional SPM and IPM machines while they obtain similar torque ripple and efficiency. Moreover, the IPM-MCP machine can obtain much lower UMF than the IPM-CP machine due to the effective suppression of asymmetric airgap flux density. Finally, a 12-slot/10-pole IPM-MCP machine is fabricated to verify the analyses.
Evidence-based Cybersecurity: Data-driven and Abstract Models

Achieving computer security requires both rigorous empirical measurement and models to understand cybersecurity phenomena and the effectiveness of defenses and interventions. To address the growing scale of cyber-insecurity, my approach to protecting users employs principled and rigorous measurements and models. In this dissertation, I examine four cybersecurity phenomena. I show that data-driven and abstract modeling can reveal surprising conclusions about longterm, persistent problems, like spam and malware, and growing threats like data-breaches and cyber conflict. I present two data-driven statistical models and two abstract models. Both of the data-driven models show that the presence of heavy-tailed distributions can make naive analysis of trends and interventions misleading. First, I examine ten years of
Real-Time Direct Measurement Method of Power System Frequency

Accurate measurement of grid frequency timely is essential to the stable operation of a grid network and the performance of a grid-connected converter. When harmonic pollution exists in the power system, indexes of timeliness, accuracy, and noise rejection performance are required to be considered synthetically. However, these indexes are usually difficult to be taken care of at the same time, and problems of convergence and stability should also be considered. In order to calculate grid frequency precisely and timely, this paper proposes a grid frequency measurement method. A three-level function of the grid voltage signal is defined step by step. On the basis of the function, equations with the signal's frequency and amplitude as two unknowns are established. Finally, a system of equations is obtained and solved in order to calculate grid frequency. The proposed method calculates grid frequency precisely through strict theoretical derivation, possesses a strong anti-disturbance ability against harmonics, and is simple for realization. In addition, because of the absence of iteration and feedback calculation, problems of convergence property as well as stability do not exist. Effectiveness of this method is proved through simulation and experiment.
Big Data Performance Evaluation Analysis Using Apache Pig

While companies' usage of big data products increases, the question of which big data architecture is the most suitable to the company's needs is rising. This study presents an approach of running multiple processes which simulates preliminary data processing of sale transactions input dataset using Apache Pig, in order to find the best performing big data environment in terms of decentralization level over the HDFS. The case study approach can provide companies an additional tool for understanding the required investment on hardware or cloud computing resources. We analyze which decentralization level achieves the best processing time, and explore the behavior of performance's change according to the change in decentralization level and performance change according to the change in the size of the input dataset. The case study's insights are: When processing the same data flow over the same input dataset, processing time performance is better as long as decentralization level increases; As long as decentralization level increases the change between performances decreases significantly; Processing the same Pig data flow under the same scale of decentralization level over large input dataset performs better then processing it over a smaller input dataset - in terms of processing time per volume unit; As blocks-data nodes ratio becomes higher, the processing time becomes longer, and vice versa.
Failure‐mode analysis of modular multilevel capacitor‐clamped converter for electric vehicle application

The bi-directional buck/boost converter is being widely used as bi-directional DC/DC converter (BDC) in an electric vehicle (EV) traction system owing to its simple topological configuration. The major drawback of this configuration is in the event of failure of any of the power semiconductor devices and the power supply to the load will be interrupted. To overcome the above drawback, this work proposes a modular multilevel capacitor clamped converter (MMCCC) for EV application employing battery and ultracapacitor (UC) as energy storage sources. The battery-ultracapacitor (UC) combination provides better acceleration capability during high power demands and efficient energy recovery during regenerative braking. The design, control, and failure-mode analysis of MMCCC are discussed in detail to showcase the merits in terms of conversion gain, modularity, and reconfiguring capability under fault conditions. The high conversion gain owed to the modularity of the topology ascertains optimal stacking of UC thus reducing the weight and cost. Simulation is performed in MATLAB/SIMULINK environment, and hardware prototype is fabricated to validate the simulation results.
Performance Analysis of a Hierarchical Inter-Domain Provisioning Framework for Multi-Domain Software-Defined Optical Transport Networks

This paper analyzes the performance of a hierarchical inter-domain provisioning (HIP) framework for multi-domain software-defined optical transport networks (SD-OTNs). The challenge of this paper is to evaluate the performance and scalability of the HIP framework so that the framework can account for the complexity of the inter-domain routing and wavelength assignment (RWA) problem of the underlying OTNs. The framework is implemented in an experimental testbed to show the effectiveness of the HIP framework while ensuring seamless integration of multiple OTNs in a hierarchical control plane architecture. In addition to the experimental evaluations, the throughput, latency, and power metric of the framework based on a closed-form queueing model are evaluated analytically by considering the RWA problem of the underlying OTNs. The analytic results show that the HIP framework significantly improves the performance of a large-scale OTN with respect to the provisioning delay and throughput. Despite the marginal benefits of the blocking probability, the HIP framework permits enhanced scalability up to 24, 000 times by using a three-layer control plane architecture for multi-domain SD-OTNs.
Hybrid neuromorphic system for automatic speech recognition

A multilayer neural network, equipped with a two-memristors synapse, for speech recognition is proposed. The discussed neuromorphic neural network is a hybrid system which uses a Gaussian-Bernoulli restricted Boltzmann Machine (RBM) to transform the speech data into sparse encoded binary data. The sparse data is used to train a standard RBM, and a two-memristors synapse using AI/Pr 0.7 Ca 0.3 MnO 3 memristor is used as a connection between the two layers of the RBM. The simulations are performed with the real memristor's behavioural data, for potentiation and depression, to adjust learn-able parameters of the neuromorphic RBM. Instead of hard coded representation of the data, memristive synapses follow the biological way of learning for plasticity by training with examples. Experimentation with the American common use vowels and its results assert the efficacy of the proposed architecture.
Conceptual Design Studies of Short Range Aircraft Configurations with Hybrid Electric Propulsion

In this paper, the activities at the German Aerospace Center (DLR) related to the conceptual design studies of aircraft configurations with hybrid electric propulsion for a typical short range commercial transport mission in the scope of the European Clean Sky 2 program in the project Advanced Engine and Aircraft Configurations (ADEC) are described. The configurations analyzed incorporate different hybrid powertrain architectures consisting of gas turbines, electric machines, and batteries. Technologies as distributed propulsion are utilized for the boundary layer ingestion at the fuselage and for the powered yaw control. The synergetic effects of a canard configuration and a BLI propulsor at the rear of the fuselage are investigated. The focus of this paper is on the quantitative evaluation of configurations utilizing a conceptual aircraft sizing workflow built in the DLRs Remote Component Environment incorporating semi-empirical and low level physics based methods. In order to depict the rationale behind the investigated aircraft configurations, a brief overview of the foregone downselection process is given. The concepts evaluated in the scope of this work are shown. The multi-disciplinary overall aircraft sizing workflow is described. An overview of reference configurations is given. The design space studies of configurations with hybrid electric propulsion are presented. The most promising configurations are shown in detail and compared to the advanced reference aircraft. Finally, a conclusion is drawn and an outlook is presented.
Research and Application of the Security Domain Division Technique in the E-Government System

In view of the problem of lacking interoperability in the process of security domain division,this paper presents a new method for the division of the E-government system. There establishes three level index system of asset classification and an assets value similarity index system of the e-government system based on the similarity value of the assets,and uses a fuzzy clustering method and Matlab to divide the security domain in a particular e-government system based on the three levels index system of asset classification and the assets value similarity index system of e-government system.
Power Allocation in Cognitive Radio in Energy Constrained Wireless Ad Hoc Networks

The acute scarcity of radio frequency spectrum has inspired to think of a new communication technology where the devices are expected to be able to sense and adapt to their spectral environment, thereby appearing as cognitive radios (CR) who can share opportunistically the bands assigned to primary users (PUs). At the same time, low cost, increased coverage, enhanced capacity, infrastructure-less configuration, and so forth, become the essence of future wireless networks. Although the two research fields came up independently, in due time it is observed that CR has a promising future and has excellent applications in wireless networks. To this aim, this chapter explores some scope of integration in CR and ad hoc networks (called here CRAHNETs) in some specific design perspective. First, a brief literature review on CR power allocation and energy aware routing in wireless ad hoc networks (WANETs) is done that highlights the importance for the scope of their integration. Then, power allocation in CRAHNETs with extended network lifetime is considered as an example problem. More specifically, the design problem is: given a set of paths (routes) between a pair of source (S) and destination (D) nodes in CRAHNETs, how to allocate optimal power to the source and relay nodes such that outage probability for data transmission is minimized and network lifetime is enhanced, while meeting the limits of total transmit power of CRs and interference threshold to PU simultaneously. A solution for the stated problem is proposed along with performance evaluation. A few related research problems are mentioned as future research directions.
A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are “hand-crafted”, which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency.
Character Recognition in Air-Writing Based on Network of Radars for Human-Machine Interface

Radar technology plays a vital role in contact-less detection of hand gestures or motions, which forms an alternate and intuitive form of human–computer interface. Air-writing refers to the writing of linguistic characters or words in free space by hand gesture movements. In this paper, we propose an air-writing system based on a network of millimeter wave radars. We propose a two-stage approach for extraction and recognition of handwriting gestures. The extraction processing stage uses a fine range estimate combined with the trilateration technique to detect and localize the hand marker, followed by a smoothening filter to create a trajectory of the character through the hand movement. For the recognition stage, we explore two approaches: one extracts the time-series trajectory data and recognizes the drawn character using long short term memory (LSTM), bi-directional LSTM (BLSTM), and convolutional LSTM (ConvLSTM) with connectionist temporal classification (CTC) loss function, and the other approach reconstructs a 2D image from the trajectory of drawn character and uses deep convolutional neural network (DCNN) to classify the alphabets drawn by the user. ConvLSTM-CTC performs best among LSTM variants on time-series trajectory data achieving 98.33% classification accuracy similar to DCNN over the chosen character set. This paper employs real data using a network of three 60-GHz millimeter wave radar sensor to demonstrate the success of the proposed setup and associated algorithm with design consideration.
Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks

Device-to-device communications refer to the emerging paradigm that permits direct communication between cellular users that are in close physical and social proximity. It is expected to become an essential aspect of the future 5G cellular communications system. As a prerequisite for device-to-device communications, a user has to select another user in its proximity that has the desired information/service and is willing to share it. In this paper, we propose a method for peer selection in dense small-cell device-to-device networks that uses multi-attribute decision modeling. The method exploits both the physical and social characteristics of the user equipment to find the most suitable peer for device-to-device communications. We assume hexagonal small-cell and macro-cell architectures with a small-cell/macro-cell base station with multiple user equipments in its coverage area to evaluate the proposed scheme’s performance. The small-/macro-cell base station exploits various social and physical attributes to rank peers and selects the best one for device-to-device communication with the requesting user. The numerical results demonstrate the proposed algorithm’s efficiency in terms of computational time, selection of the best peer, throughput, and energy efficiency of device-to-device communications.
Ensemble Learning Based Essay Automated Scoring Algorithm for Chinese English Learners

Nowaday,there are a large number of Chinese English learners in China,the substantial quantities and great difficulties in English writing assessment is now the bottleneck problem in English teaching and testing.So the effective automatic essay scoring algorithms are in great need of in China.In this paper,we first propose a feature selection method which can extract Chinese learners'writing characters effectively and automatically.And then we continue propose a resemble learning based essay automatic scoring algorithm for unbalanced essays data.The classification results on 1 115university CET4and CET6essays from CLEC shows that our algorithm has dramatically promotion in precision,recall,and F-meature value compared with classification for balanced data.
A Novel Classification of Supply Chain Risks: A Review

Every organization is a complex supply chain system. If any fragment of this supply chain is disturbed, it will directly affect the entire structure. Numerous studies have been conducted to categorize the supply chain risk sources, but very few available cover all types of risks. This study did an extensive literature review and content analysis on the subject. A supply chain is the flow of information, material, and money starting from suppliers and ending to end-users. To cover all types of risks, risk sources must be based on three perspectives: (i) internal to the firm, (ii) external to the firm but internal to the supply chain, and (iii) external to the supply chain. Risks can be categorized into seven types under these three perspectives. Regarding internal risks, three types of risks come from the supply side, process side, and demand side, while these members of the supply chain are connected with the supply affected by logistic side risks, finance side risks, and collaboration side risks. The external environment also affects the supply chain so the seventh type of risks comes from environmental side risks. This study extends the literature of supply chain risk management by identifying new risk sources. This study will help the managers to understand what kind of risks can affect their supply chain. Future study can be conducted to empirically verify these risks sources.