Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Forward Simulation Scheme for Eddy Current Testing of Crack in a Structure of Carbon Fiber Reinforced Polymer Laminate

High frequency Eddy Current Testing (ECT) is one of the key non-destructive testing techniques for ensuring integrity of a structure of Carbon Fiber Reinforced Polymer (CFRP) material. An efficient numerical simulator is indispensable to enhance the performance of quantitative ECT for CFRP structures from both point of view of probe optimization and defect sizing. In this paper, a fast forward simulation scheme based on the A- $\phi $ formulation and databases approach is proposed, implemented and experimentally validated for the rapid and high precision simulation of ECT signals due to defects in a CFRP plate by updating an FEM-BEM hybrid code for ECT problem. Comparison of numerical results of the present method with those of the conventional full FEM-BEM code and the experimental results for artificial cracks in CFRP laminate plates indicates that the proposed novel fast forward scheme can predict ECT signals over 300 times faster but without worsening numerical accuracy, which enables it to be applied to efficient reconstruction of cracks in CFRP plates and for probe optimization.
Dynamic beamforming using moving phased arrays with integrated 3D scanners

Throughout the past 25 years Sound Source Localization was a matter of two dimensional sound maps and acoustic images. Since 2001 Beamforming is also used inside a cavity such as cars and trains. Results are mapped onto given CAD models created by the 3D designers. The visualization of the sound sources was finally moving to the third dimension. Very often a 3D model of the measurement object is required, but not at hand. Including a 3D scanning device into a microphone array is consequential step that seems logical, but has been an extremely costly matter. When looking at the size of scanning data even its handling is a challenging issue until today, but computers get faster by the year and the real-time reconstruction of a 3D model of a measurement object is now a feasible approach. This paper presents a technique that shows the integration of a 3D scanning device into a microphone array to measure an object. This new way of using a microphone array with a 3D scanner allows a user to create a 3D model of a measurement scene and record and map the entire sound field instantly. Furthermore by moving the array around a measurement object one gets a much higher spatial sampling for the sound field and therefore much better understanding of the objects acoustic behavior. The aim of this paper is to present the first results of this approach and discuss its chances and challenges. Session: Acoustical Holography / Beamforming / Microphone Array. Section T12.2
The relationship between aortic calcification and osteoporosis in postmenopausal women

s and Publications Appendix – Abstracts and publications 198 Edwards S, Fogelman I, Frost M; The prevalence of abdominal aortic calcification assessed using lateral vertebral fracture assessment DXA scans in postmenopausal women with and without osteoporosis. Osteoporos Int (2009) 20 (Suppl 4):S257–S314 S307 Edwards S, Fogelman I, Frost M; The relationship between aortic stiffness and abdominal aortic calcification measured on spine DXA VFA scans in postmenopausal women. Osteoporos Int (2012) 23 (Suppl 5): S520 – S551 P6 Frost M, Edwards S; Osteoporosis and vascular calcification. Osteoporosis Review (2012) 20:1; 10-12 Hampson G, Edwards S, Conroy S, Blake G M, Fogelman I, Frost M L: The relationship between inhibitors of the Wnt signalling pathway (Dickkopf-1(DKK1) and sclerostin), bone mineral density, vascular calcification and arterial stiffness in postmenopausal women. Bone (2013); 56:42-47 Edwards S, Fogelman I, Hampson G, Blake GM, Moore A, Frost ML: Aortic calcification and bone mineral density; A cross-sectional study of healthy postmenopausal women. Osteoporos Int (2014) Ishtiaq S, Edwards S, Sankaralingam A, Evans BAJ, Frost M, Fogelman I, and Hampson G: The effect of nitrogen containing bisphosphonates, zoledronate and alendronate, on the production of pro-angiogenic factors by osteoblastic cells. Cytokine 71 (2015) 154–160. Hampson G, Edwards S, Sankaralingam A, Harrington D J, Voong K, Fogelman I, and Frost ML: Circulating concentrations of vitamin E isomers: Association with bone turnover in post-menopausal women. Osteoporos Int (2014) 25 (Suppl 6):S659–S726. Cecelja M, Edwards S F, Moore A E, Fogelman I, Chowienczyk P, Frost M L. A pilot study to assess effects of alendronic acid on aortic calcification and stiffness in postmenopausal women. (2015) European Society of Hypertension, Poster
A Novel Approach for Maintaining Consistency in Distributed File System

Maintaining the consistency in a distributed file system has always been an important issue. In recent time, many researchers have addressed the issue. The existing solutions primarily use variations of read-write lock on the file for updating operations, or make explicit assumptions on the appearance of the particular mode of files. In this paper a novel mechanism has been proposed that neither uses a lock nor uses any such explicit assumption. The proposed solution uses Write Preference Heap Tree to ensure lock-free file consistency. This Write Preference Heap Tree instantly updates multiple copies of data spread over a distributed system. We have established the efficiency of the new approach in case of interactive system where many read and write requests are simultaneously processed.
Discrete Implementation of Sensorless IM Drive with MRAS-type Speed Estimator

In this paper the discrete implementation of a sensorless IM drive with stator current error based MRAS-type speed estimator is analyzed. The system is discretized using analogue emulation method. Two numerical integration methods, Forward Euler and Modified Euler, are compared in simulation and experimental tests. The comparison is confirmed by ITAE criterion. Moreover, the stability improvement method of the tested speed estimator, developed in continuous time domain, is used and tested in the discrete modeling and implementation. This approach also validate the stable operation of the tested speed estimation method in full operating range of the drive system.
Taxi Passenger Travel Spatial and Temporal Characteristics Analysis and Application Based on Ridesourcing Data

Nowadays, with the development of humanistic consciousness, the residents' travel behavior is becoming more and more important to be considered in urban planning, and has become an important reference for urban traffic construction. The ridesourcing softwares like Uber and Didi have been widely accepted and used. As a model of travel, the ridesourcing has many features, like convenience and flexibility, and the origin and destination of every trip are completely determined by passengers, the running track of vehicles can directly reflect the travel behavior of urban residents. So this paper will study the passengers’ travel behaviour by data mining based on the Didi order data, try to reveal the travel characteristics of urban residents from a macro perspective. This paper focuses on these issues: to analyze the order data from ridesourcing company Didi; to explore the temporal characteristics of passengers’ travel from different angles like the temporal distribution of travel, the distribution of peak hour, time consumption; to analyze the spatial characteristics from the different aspects including the distribution of travel distance, travel hot spots; to propose an optimization model of the taxi stand location by analyzing travel characteristics, which can improve user’s experience of residents by setting up reasonable site layout. Finally we found that the ridesourcing track data can well reveal travel characteristics of urban residents, which could be helpful for the urban planning and road network optimization.
Cross-Configuration Substrate Integrated Waveguide Beamforming Network for 1D and 2D Beam Patterns

This paper presents the simulated and measured results of a dual-layer substrate integrated waveguide (SIW) beamforming network utilizing an <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> cross-configuration Butler matrix over a frequency range of 28.5 to 31.5 GHz. By arranging the input ports on the bottom SIW layer and employing dual-layer passband filters as a detachment point, the top SIW layer can be interchanged with the purpose of exploring one-dimensional and two-dimensional broadside beam patterns. Although Butler matrices are not typically utilized for beamforming in this configuration, two examples of interchangeable top layer arrays are demonstrated; the first being a <inline-formula> <tex-math notation="LaTeX">$2\times8$ </tex-math></inline-formula> slot antenna array for 1-D scanning, and the second being a <inline-formula> <tex-math notation="LaTeX">$2\times4$ </tex-math></inline-formula> center-slot array for 2-D scanning. Each of the beamforming network’s simulated and measured 10 dB bandwidth is demonstrated over a range of 28.5 to 31.5 GHz. Additional design details and dimensions are specified for the aforementioned passband filter transitions, as well as for each of the slot antenna arrays.
What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing—partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r. t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges.
Modelling Social Networks Using Modified Hopfield Neural Network and Identify Leadership Traits

This paper aims at studying human behaviour in a group using centrality measures applying Modified Hopfield Neural Network, and analyse the change in group dynamics when chronological event sequences are altered. Further, to understand the personality traits unique to leaders and how to influence people with the right marketing strategy. The simulation considers the intragroup dynamics of centrality where every individual is considered to have a unique personality. Individuals are represented as a node, and the effect of the change in intragroup dynamics are simulated using modified Hopfield Neural Network and Hebb's rule. Our results show that every chronological change in the events can affect a groups opinion and influence the choice of leaders. Also, that the leaders (node with the highest centrality measures) have unique personality trait which comprises a rare combination of adaptability $\alpha$, retention $\rho$, and forgetfulness $\gamma$.
Epileptic EEG Detection Using a Multi-View Fuzzy Clustering Algorithm with Multi-Medoid

Using clustering algorithms to automatically analyze EEGs of patients and to identify the characteristic waves of epilepsy is of high clinical value. Traditional clustering algorithms mostly use a calculated virtual single representative medoid point to describe the cluster structure, but this single representative medoid point has insufficient information. To accurately capture more accurate intracluster structural information, a representative multi-medoid points strategy is adopted, which describes the cluster structure by assigning representative weights to each sample in the cluster. Considering that the multi-view learning mechanism combines information from each view to improve the algorithm’s clustering performance, a multi-view fuzzy clustering algorithm with multi-medoid (MvFMMdd) is proposed. This algorithm discards the approach of the traditional fuzzy clustering algorithm, which uses a single virtual representative point to characterize the cluster structure, and uses several real representative points to describe the cluster structure. Experiments verify the medical significance of the proposed algorithm.
LQI-DCP: A LQI based Multihop Clustering Protocol for Wireless Sensor Networks

The Link Quality Indicator (LQI) is defined in the 802.15. 4 standard, but its context of use is not specified in this standard. Some works on the LQI, few of which are field experiments, have shown that the LQI decreases as the distan ce increases. In WSN, it is not often desirable to use the GPS technology. Indeed, the use of GPS is expensive and may reduc e the overall network performance. Moreover, indoor reception of the GPS signal is not possible. However, in cluster formatio n, it is quite important to sufficiently outspread the clusterheads (or caryommes) in order to improve the network efficiency. Therefore we propose a LQI based d-clustering protocol (LQI DCP) which produces clusters of which each clusterhead has a better positioning regarding the locations of other clusterheads. Simulation results show that the caryommes resulting from LQIDCP are sufficiently outspread. LQI-DCP also reduces the den sity of clusterheads and then improves the WSN energy efficiency, while each sensor remains still at less than d-hops from a caryomme.
Extraction of Regions Related to Cardiac Sarcoidosis in Polar Map Images

This paper presents a method for extracting important regions for deep learning models in the identification of cardiac sarcoidosis using polar map images. Although deep learning-based detection methods have widely studied, they are still often called black boxes. Since high reliability for provided results from computer-aided diagnosis systems is important toward clinical applications, this problem should be solved. In this paper, we try to visualize important regions for deep learning-based models for improvement of understanding to clinicians. We monitor the variance of confidence of a model constructed with a deep learning-based feature and define it as a contribution value toward the estimated label. We visualize important regions for models based on the contribution value.
Safe Inputs Approximation for Black-Box Systems

Given a family of independent and identically distributed samples extracted from the input region and their corresponding outputs, in this paper we propose a method to under-approximate the set of safe inputs that lead the black-box system to respect a given safety specification. Our method falls within the framework of probably approximately correct (PAC) learning. The computed under-approximation comes with statistical soundness provided by the underlying PAC learning process. Such a set, which we call a PAC under-approximation, is obtained by computing a PAC model of the black-box system with respect to the specified safety specification. In our method, the PAC model is computed based on the scenario approach, which encodes as a linear program. The linear program is constructed based on the given family of input samples and their corresponding outputs. The size of the linear program does not depend on the dimensions of the state space of the black-box system, thus providing scalability. Moreover, the linear program does not depend on the internal mechanism of the black-box system, thus being applicable to systems that existing methods are not capable of dealing with. Some case studies demonstrate these properties, general performance and usefulness of our approach.
AGC of a two area nonlinear power system using BOA optimized FOPID+PI multistage controller

The work presents automatic generation control (AGC) of a two region interconnected thermal power system with nonlinearities, for example, generation rate constraint (GRC), generation dead band (GDB) and time delays. The system pinned to step load perturbation (SLP) of 0.1 per unit in area-1 which need priority to design the optimal controller by using novel butterfly optimization algorithm (BOA). The results show that BOA is more efficient than others. Recently, fractional order (FO) controllers are gaining popularity. These controllers provide wider control flexibility for system dynamics and have good disturbance rejection capability than PID controller. The present work proposes a new FOPID+PI multistage controller for AGC. The execution of the proposed controller is compared with that of PID and FOPID controllers.
College Choice in the Philippines

This descriptive and correlational study examined the applicability of major U.S. college choice factors to Philippine high school seniors. A sample of 226 students from a private school in Manila completed the College Choice Survey for High School Seniors. Cronbach's alpha for the survey composite index was 0.933. The purposes of this nonexperimental, quantitative study were (1) to describe the relative importance of major college choice factors (as identified in U.S. research) to Philippine high school seniors, and (2) to determine whether there were statistically significant differences in the importance ascribed to these factors, according to students' demographic attributes. For all statistical analyses, SPSS 16.0 software was used. To address the first purpose, the mean and standard deviation were calculated for each college choice factor addressed in the survey. To address the second purpose, ANOVAs, Mann-Whitney U tests, and Kruskal-Wallis tests were run, in order to study the relationship between each of the major college choice factors and students' demographic attributes. This study found that all of the major U.S. college choice factors were important, to some degree, in the Philippine context. Other factors were added based on pilot studies. This study also found that some of the U.S.-literature-generated demographic choice attributes functioned similarly in the Philippine setting (e.g. academic ability, gender), while others did not (e.g. educational level of fathers and of mothers). Moreover, students' academic ability was the primary demographic attribute, accounting for statistically significant differences in assessment of the importance of college choice factors for most (12 out of 13) of the factors. The major U.S. college choice factors appear to be important to Philippine private high school students. Two choice attributes (academic ability, gender) appear to apply to private high school students in the Philippines, while the attributes of father's and mother's education levels do not appear to apply. Among Philippine private high school students, academic ability may account for differences in assessment of the importance of college choice factors. Using a survey method alone to study college choice is limiting. Future studies should utilize a variety of methods to collect data and should involve several schools.
Magnetic Anisotropy of Chiral Magnet Yb(Ni1−xCux)3Al9 at High Magnetic Fields

We have measured the magnetization of rare-earth chiral magnets Yb(Ni$_{1-x}$Cu$_x$)$_{3}$Al$_{9}$ for $x$=0 and 0.06 up to 14.5 T. Magnetization for the field direction $H \parallel c$, which is the hard axis at low temperatures and low fields, overtakes the easy axis magnetization for $H \perp c$ above $\sim$4 T. We analyzed this overtaking of the magnetization curves by considering a crystalline electric field model. It is shown that the $B_{66}$ term, which connects the $|\pm5/2 \rangle$ and the $|\mp7/2\rangle$ states through the off-diagonal matrix element, is responsible for the characteristic magnetization process in this compound. We also introduce orbital dependent exchange interactions to explain the temperature dependence of magnetic susceptibility.
Toward Edge-Assisted Video Content Intelligent Caching With Long Short-Term Memory Learning

Nowadays video content has contributed to the majority of Internet traffic, which brings great challenge to the network infrastructure. Fortunately, the emergence of edge computing has provided a promising way to reduce the video load on the network by caching contents closer to users.But caching replacement algorithm is essential for the cache efficiency considering the limited cache space under existing edge-assisted network architecture. To investigate the challenges and opportunities inside, we first measure the performance of five state-of-the-art caching algorithms based on three real-world datasets. Our observation shows that state-of-the-art caching replacement algorithms suffer from following weaknesses: 1) the rule-based replacement approachs (e.g., LFU,LRU) cannot adapt under different scenarios; 2) data-driven forecast approaches only work efficiently on specific scenarios or datasets, as the extracted features working on one dataset may not work on another one. Motivated by these observations and edge-assisted computation capacity, we then propose an edge-assisted intelligent caching replacement framework LSTM-C based on deep Long Short-Term Memory network, which contains two types of modules: 1) four basic modules manage the coordination among content requests, content replace, cache space, service management; 2) three learning-based modules enable the online deep learning to provide intelligent caching strategy. Supported by this design, LSTM-C learns the pattern of content popularity at long and short time scales as well as determines the cache replacement policy. Most important, LSTM-C represents the request pattern with built-in memory cells, thus requires no data pre-processing, pre-programmed model or additional information. Our experiment results show that LSTM-C outperforms state-of-the-art methods in cache hit rate on three real-traces of video requests. When the cache size is limited, LSTM-C outperforms baselines by 20%~32% in cache hit rate. We also show that the training and predicting time of one iteration are $8.6~ms$ and $300~\mu s$ on average respectively, which are fast enough for online operations.
The Empirical Study of IPO in Hong Kong China: with the Initial Offer Price to the IPO Reflected its Fundamental Information Significantly

Initial Public Offer (IPO) under-pricing is the “traditional world-wide phenomenon” for raising capital in the market. It used to enhance the attractiveness to the investors as well as ensuring the sufficient subscription for listing successively. The issue of IPO pricing is depending on different determinants to decide in under-pricing or over-pricing as offer. In this study, it examines how influences to the offer price of IPO by those factors of determinants. This paper tackles the issue of under-pricing IPO and the relevant mechanism from the perspectives of behavioral biases and agency conflicts. In addition, it evaluates between the under-pricing mechanism and any information asymmetry involvement for generating abnormal return in IPO investment. We examine the IPO listed to help to investigate the influence in the offer price from the various factors of determinants. A methodology of multiple regression technique used to define the null hypothesis as “With the Initial Offer Price to the IPO reflected its fundamental information significantly. ” is adapted or not. It showed that initial offer price is influenced in earnings per share, retained ownership, growth prospects, leverage and under-pricing level significantly.