Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and voltage regulation of inductively coupled wireless power transfer circuits with an intermediate coil

An inductively coupled wireless power transfer (WPT) circuit consisting of the transmitter, the receiver, and an additional intermediate coil is studied. It is assumed that the intermediate coil is located coplanar with the transmitter coil. Although it has been known that the intermediate coil serves as a power repeater, its effect on the three-coil WPT circuit characteristics has not been thoroughly investigated. Accordingly, this study analyses how the intermediate coil affects the voltage transfer function and the input impedance of the WPT circuit. Based on the analysis result, design considerations are presented and output voltage regulation via operation frequency adjustment is discussed. A design example for the three-coil WPT circuit is presented and the simulation and experimental results are provided.
Effect of Gurney Flaps on the Aerodynamic Characteristics of NACA 0010 Cascades

Abstract Two dimensional computations were conducted to investigate the performance of NACA 0010 cascades with Gurney Flaps. The cascades with three different stagger angles (βB) viz. 45°, 90° and 135° were studied for the aerodynamic performance characteristics of the blades with and without Gurney Flaps and the results were compared. The Gurney Flap’s length is 2 % of the chord length and has a 45° inclination with respect to the chord line. A significant lift augmentation is observed in all the three blade angle configurations due to the Gurney Flap. In terms of aerodynamic efficiency, it was observed that the presence of Gurney Flap was detrimental when βB = 45°, while the same was highly advantageous when βB = 135°. The streamline visualizations indicated the existence of counter rotating vortex pairs behind the Gurney Flap and that their rate of decay with respect to the Angle of attack was affected by the stagger angle at which the blades were arranged.
Trust Management and Security Analysis of Opportunistic Routing Protocols in Multihop Wireless Networks

Routing in wireless sensor, mobile ad-hoc, and vehicular networks is a crucial operation having significantly attracted scientists’ attention during the past years. Opportunistic routing, however, is a novel and promising approach, which is still an ongoing research paradigm. The purpose of opportunistic routing protocols is to increase the reliability of delivering data packets to their destination by utilizing the broadcast nature of wireless networks and selecting a set of nodes, instead of only one, as potential next-hop candidates. Apart from the reliability, security and trustworthiness of communications is also a challenging task. More specifically, almost all traditional and opportunistic routing protocols require cooperation of all network nodes to complete the routing process. However, some nodes in the network may be compromised and avoid collaboration with others due to various selfish or malicious motivations. The focus of this thesis is on modeling security challenges and developing trust-based opportunistic routing protocols. This way, communication between nodes will not only benefit from the reliability of opportunistic routing methods, but also from the security of trust and reputation management schemes. For this purpose, a novel trust-based opportunistic routing protocol is proposed that introduces three different candidate selection metrics known as RTOR, TORDP, and GEOTOR. Such metrics have been designed to address specific characteristics of opportunistic routing protocols. In continue, a watchdog mechanism is proposed to assist nodes in monitoring their candidates, recalculating their trust value, and finally replacing malicious candidates with benign ones. Afterwards, an analytical approach is introduced using Discrete-Time Markov Chain to demonstrate the effect of malicious nodes on different parameters of a wireless network that uses opportunistic routing. In this model, a new method of calculating packet drop ratio is introduced to represent the effect of attackers. The model is then applied on different well-known opportunistic routing protocols and results of the model are verified by simulation. Finally, the proposed analytical model is extended to include a defensive mechanism against adversary nodes. In fact, a packet salvaging mechanism is developed through which backup candidates attempt to save some of the maliciously dropped data packets. Different related network parameters such as delivery ratio, salvage ratio, directdelivery ratio, etc. are then introduced, calculated, and reported using the introduced model.
Content-Based Instruction Approach In Instructional Multimedia For English Learning

Content-based Instruction (CBI) is an approach in English learning that integrates certain topic and English learning objectives. This approach focuses on using English competencies as a “bridge” to comprehend certain topic or theme in English. Nowadays, this approach can be used in instructional multimedia to support English learning by using computer. Instructional multimedia with computer system refers to the sequential or simultaneous use of variety of media formats in a given presentation of selfstudy program in a computer. Instructional media combines several aspects such as text, pictures, graphic, sound and video. There are many English materials that are presented in a computer display by combining all aspects of multimedia. This method can help learner to understand the material better by visualizing the concept. In integrating CBI approach in instructional multimedia by using computer, there are two important aspects in creating effective multimedia, such as displaying reading text and integrating all English skills. In displaying reading text, CBI approach play important role in presenting reading passages about certain topic based on students' interest and multimedia aspects also play significant role in visualizing all concepts and vocabularies from the reading passage. This paper will discuss the integration of CBI approach in instructional multimedia for English learning deeply by presenting the basic concept and providing some examples of instructional multimedia display.Keywords: Content-Based Instruction Approach, Instructional Multimedia, English Learning
Semi-supervised Bayesian learning

In traditional supervised learning, classifiers are learned from a number of training data examples where each data example has a label showing the category that the example falls into. Bayesian network classifiers, representing the joint probability and the conditional independencies among a set of random variables, have been widely used in traditional supervised classification. As a special case of Bayesian Networks, Naive Bayes has also been popularly used in many applications such as text classification. However, in many realworld machine learning applications, it may be expensive or time consuming to obtain sufficient labeled data because it needs human efforts to categorize them. On the other hand, it is easier to collect a large amount of unlabeled data. Learning from a small number of labeled data may not generate good classifiers. Semi-supervised learning is one method to deal with the problem by utilizing the unlabeled data to help learn better classifiers. Although various semi-supervised methods have been studied by researchers, there are still some problems remaining unknown in semi-supervised learning. One of the problems is the performance of different Bayesian network classifiers in
ESQLite:a relational database solution for JSON data with applications in mobile computing

This thesis concerns the problem of working with large amounts of partially non-relational or non-structured data on lightweight clients, such as mobile devices. The amount of devices and the data generated from them grows at an unprecedented speed, propelling the development of new database systems. Whereas the vast majority of data transfers on the web is standardized through for instance the JavaScript Object Notation (JSON)), storage and querying techniques for this type of data are still very limited and do not meet today’s needs. Although a lot of research is done in this field, when looking at (lightweight) client solutions it leaves much to be desired. We introduce and develop ESQLite, an extension on top of the relational SQLite database which is able to fill the void in JSON data handling. ESQLite can store and foremost query non-relational data represented as JSON. ESQLite offers both efficient storage, as well as an XPath like query language extension to perform path queries. Before storing, the JSON is shredded at insertion into key-value pairs, along their tree-structure meta data. When disk space is of concern, the shredding can also be done temporary at query time. This approach enables us to implement efficient path querying on JSON properties. Reconstructing the JSON can be done with a tree walk or self-join algorithm. Finally, ESQLite is able to extract relational parts from the JSON, and handle these as relational data, further boosting performance. Special effort is put into developing an easy to use, backwards compatible and open source available library, in the form of an Android specific wrapper. Android is the platform of choice since it is open source, has more than one billion users, built-in SQLite support and a lot of data intensive applications. Using two datasets, of one which is a real world medication database, the performance of ESQLite is benchmarked. A set of queries and dimensions of interest is chosen, with speed and disk space as the main focus. Whereas ESQLite insertion queries are on average an order of magnitude slower than normal SQLite, it can handle selection XPath queries on JSON data as fast as normal SQL SELECT queries. Therefore there is a trade off between insertion and query speed. Several optimizations and variations are discussed, boosting the performance on average 8% to 50%. While ESQLite as an open source library may be a useful tool for those interesting in working and querying JSON data, it may also form a starting point for further research. A lot remains to be done; for instance improvements in determining and extracting the relational base of the JSON, efficient reconstruction and extension of the query language. ESQLite: A relational database solution for JSON data with applications in mobile computingiii
A cell-based dynamic congestion pricing scheme considering travel distance and time delay

This study introduces the dynamic congestion pricing (DCP) problem with the consideration of the actual travel distance and time delay (i.e. a joint distance and time-delay toll, JDTDT) in a dynamic network, which is more equitable and effective compared with existing tolling scheme. The system dynamics can be reflected in two aspects: (a) travelers' path choice decisions follow the dynamic user equilibrium principle and (b) the joint distance and time-delay toll takes a time-varying pattern. A multiperiod demand scheme is adopted during the entire modeling horizon and a bi-level programming model for the DCP is formulated to obtain the optimal toll value. Numerical results indicate that the percentage reductions of the minimum total system travel time in the dynamic JDTDT scheme are 6.28%, 4.30% and 7.45% compared to that obtained by the static joint JDTDT, the dynamic joint distance and time toll, and the dynamic pure distance toll, respectively.
Dynamic virtual cluster cloud security using hybrid steganographic image authentication algorithm

ABSTRACT Storing data in a third party cloud system causes serious problems on data confidentiality. Generally, encryption techniques provide data confidentiality but with limited functionality, which occurs due to unsupported actions of encryption operation in cloud storage space. Hence, developing a decentralized secure storage system with multiple support functions like encryption, encoding, and forwarding tends to get complicated, when the storage system spreads. This paper aims mainly on hiding image information using specialized steganographic image authentication (SSIA) algorithm in clustered cloud systems. The SSIA algorithm is applied to virtual elastic clusters in a public cloud platform. Here, the SSIA algorithm embeds the image information using blowfish algorithm and genetic operators. Initially, the blowfish symmetric block encryption is applied over the image and then the genetic operator is applied to re-encrypt the image information. The proposed algorithm provides an improved security than conventional blowfish algorithm in a clustered cloud system.
Integrated Coordinated/Synchronized Contouring Control of a Dual-Linear-Motor-Driven Gantry

To realize high-performance contouring tracking of dual-linear-motor-driven (DLMD) gantry, both motion coordination of multiaxes and synchronization of redundant actuators are indispensable factors. However, earlier contouring methods of multiaxes systems only focus on motion coordination, leading to certain inherent limitations and uncertainties when applied to DLMD gantry. Especially, the usually ignored synchronization issue may directly affect the normal operation of system. To this end, this paper proposes a novel contouring control method by integrating both motion coordination between axes and synchronization of redundant actuators. With the knowledge of the complete system dynamics, corresponding modification and extension have been done within the framework of global task coordinate frame (GTCF). Subsequently, both associated issues are effectively dealt with in the task space to simultaneously obtain efficient motion coordination ability and high-level synchronization, along with more accurate contouring error calculation. The proposed method is compared with the earlier GTCF-based contouring control method without consideration of a synchronization issue. The experimental results fully indicate the effectiveness and superiority of the proposed method to indeed guarantee and further improve the contouring performance.
The Study on Comparative Analysis of the Same Data through Regression Analysis Model and Structural Equation Model

This study analyzed empirically the same data through SPSS statistic(regression analysis) and AMOS program(structural equation model) used for cause and effect analysis. The result of empirical analysis was as follows. The different outcome of coefficients and p-values were deducted. Especially, in the mediated effect testing, meanwhile, SPSS statistic(regression analysis) pictured mediated effect, AMOS program(structural equation model) did not picture mediated effect on the reject zone of null hypothesis(absolute t-value and C.R.-value were nearby 1.96). Eventually, this study showed that what program used determined the outcomes of coefficients and p-values(In particular, the outcomes were differentiated further in the increasing measurement error) though using the same data.
Materials, properties, manufacturing methods and cutting performance of innovative ceramic cutting tools − a review

For mechanical machining the quality of cutting-tool materials is one of the most significant issues that need to be addressed. Enhancement of cutting tool performance may be achieved through the use of modern composition ceramic cutting tools This may be enabled through surface treatment, and also hot pressing and spark plasma sintering – the two main processes used for manufacturing such tools. In this article the advantages and disadvantages of the technologies and processes involved are analyzed and compared to identify the most appropriate methods for creating ceramic cutting-tools. In parallel the latest improvements in ceramic cutting-tool materials are reviewed. The paper shows that the choice of ceramic cutting tools is a quite complex process with a number of important factors to be taken into account.
Comparison of applicable adjustment procedures for very small batches

The objective of this study is to propose and compare three methods of production process control that are voluntarily simple to implement on a machine. A mathematical formulation of the “sequence” type is used to model each method. An experimental validation is then carried out on the principle of launching a pre-production consisting of five parts machined and measured consecutively. A graphical analysis makes it possible to compare the different methods proposed and makes it possible to show the interest of the Bayesian weighted adjustment method.
Demagnetization Analysis of Interior Permanent Magnet Machines Under Integrated Charging Operation

Integrated battery charging (IBC)—multiplexing the motor windings as line inductors for charging electric vehicle battery packs—can improve the onboard charging capability without increasing the weight and cost. However, during the charging operation, currents flowing in the stator winding definitely generate a magnetic field, which may demagnetize the permanent magnets (PMs) in the PM traction motors. In this paper, the demagnetization effects of IBC current on magnets in interior PM machines are compressively investigated. In this paper, a recursive algorithm to update the PM magnetization states by using a parallelogram hysteresis model at different demagnetization levels is applied. The PM temperature is also considered for establishing this model. By coupling this algorithm to the finite element analysis, the demagnetization transit at different demagnetization levels can be assessed. By decomposing the flux density of each node within the magnets along its magnetization direction, irreversible demagnetization can be accurately evaluated. Impacts from both the pulsating and rotating demagnetization magnetic fields during the IBC operation are assessed and compared.
Urban rail transit passenger flow forecast based on LSTM with enhanced long‐term features

Outbreak passenger flow is the main cause of rail transit congestion. In this regard, the accurate forecast of passenger flow in advance will facilitate the traffic control department to redeploy infrastructures. Traffic sequence is a typical time series with long temporal dependence. For instance, an emergency may cause traffic congestion for the next several hours. Only a few studies focused on the way to capture long temporal dependence of passenger flow in the rail transit system. Here, an improved model enhanced long-term features based on long-short-term memory (ELF-LSTM) neural network is proposed. It takes full advantages of LSTM Neural Network (LSTM NN) models in processing time series and overcomes its limitations in insufficient learning of long temporal dependency due to time lag. The proposed network strengthens the long temporal dependency features embedded in passenger flow data and incorporates the short-term features to predict the origin destination (OD) flow in the next hour. The experiment results show that ELF-LSTM outperforms other state-of-the-art methods in terms of forecasting.
“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.
Opportunity for Improving Lead-Acid Battery Management of Photovoltaic-Genset-Battery Hybrid Power Systems Based on Measured Field Data

In this communication, the measured behaviour of a lead-acid battery bank within a stand-alone residential solar photovoltaic (PV)-genset-battery hybrid power system in Canada is presented and discussed. In order to capture rare field-based battery performance data, a newly commissioned lead-acid battery bank was equipped with a battery monitoring device capable of logging voltage, current, temperature and amp-hours every 30 s for the life of the battery. The measured data captures a severe loss of battery capacity due to a combination of short-term deep discharge and extended partial state of charge operation—conditions not unusual during winter season PV-genset-battery hybrid power system operation. Subsequent manual override of the system control set points to encourage gradual battery overcharge are shown to recover the lead-acid battery bank’s performance over the following three months. Limitations of the power conversion system’s battery management approach are discussed and a novel closed loop control system for improving lead-acid based PV-genset-battery hybrid system performance is rationalized and proposed for further research.
Infrastructures and bound for distributed entity resolution

Entity resolution (ER), deduplication or record linkage is a computationally hard problem with distributed implementations typically relying on shared memory architectures. We show simple reductions to communication complexity and data streaming lower bounds to illustrate the difficulties with a distributed implementation: If the data records are split among servers, then basically all data must be transferred. As a key result, we demonstrate that ER can be solved using algorithms with three different distributed computing paradigms: • Distributed key-value stores; • Map-Reduce; • Bulk Synchronous Parallel. We measure our algorithms in the real-world scenario of an insurance customer master data integration procedure. We show how the algorithms can be modified for non-Boolean fuzzy merge functions and similarity indexes.