Miracle-dz/UniFAR-FTUs huggingface dataset:
A new algorithm based on bipartite graph networks for improving aggregate recommendation diversity

Most of the traditional recommendation algorithms focus on the accuracy of recommendation results; however, the diversity of recommendation results is also important, which can be used to avoid the long-tail phenomenon. In this paper, a new algorithm for improving aggregate recommendation diversity is proposed. Firstly, a candidate recommendation list based on predictive scores is constructed; and then a bipartite graph network model is constructed. Secondly, item capacity is set to limit the number of recommendations of popular items. Finally, the final recommendation result is generated by combining the recommendation augmenting path. Based on the real world movie rating datasets, experiment results show that the proposed algorithm can effectively guarantee the accuracy of the recommendation results as well as improved the aggregate diversity of the recommendation.
Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning

To address the problems of defective pixels and strips in hyperspectral images affecting subsequent processing and applications, we modeled the hyperspectral image (HSI) inpainting problem as a sparse signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning. First, we studied the HSI observation model under the assumption of additive noise. We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an online learning optimization method and performing dictionary learning using a robust function. Afterwards, the image was sparsely encoded by applying the variable decomposition and augmented Lagrangian sparse regression method. Finally, the inpainted HSI was obtained by sparse reconstruction. The experimental results showed that compared with the existing algorithms, the algorithm proposed herein could effectively inpaint the defective HSI under different noise conditions with a shorter calculation time than those of existing methods and other dictionary learning inpainting algorithms.
Robust Geometric Navigation of a Quadrotor UAV on SE(3)

SUMMARY In this paper, a robust geometric navigation algorithm, designed on the special Euclidean group SE(3), of a quadrotor is proposed. The equations of motion for the quadrotor are obtained using the Newton–Euler formulation. The geometric navigation considers a guidance frame which is designed to perform autonomous flights with a convergence to the contour of the task with small normal velocity. For this purpose, a super twisting algorithm controls the nonlinear rotational and translational dynamics as a cascade structure in order to establish the fast and yet smooth tracking with the typical robustness of sliding modes. In this sense, the controller provides robustness against parameter uncertainty, disturbances, convergence to the sliding manifold in finite time, and asymptotic convergence of the trajectory tracking. The algorithm validation is presented through experimental results showing the feasibility of the proposed approach and illustrating that the tracking errors converge asymptotically to the origin.
A 40-Pulse Multiphase Staggering Modular Transformer With Power Quality Improvement in Multilevel Inverter Fed Medium-Voltage Induction Motor Drives

In this article, a new configuration of the adjustable speed drive is proposed for the medium-voltage industrial induction motor drives (IMDs). Here, a new configuration of a 40-pulse multiphase staggering modular transformer is proposed for power quality improvement in the vector-controlled induction motor drive (VCIMD) at the utility end. In it the two transformers (T1 and T2) are utilized and both have extended-delta configured input windings with +9° and −9° phase shift and their secondaries have two sets of five-phase ac supplies to achieve a 20-pulse ac–dc conversion. Because of these primary angles, these two transformers are reconfigured as a 40-pulse ac–dc converter. A new cascaded multilevel inverter (C-MLI) is proposed at the drive end. This C-MLI is cascading four two-level voltage source inverters in a unique manner to get nine levels in its line voltage. A modified sinusoidal pulsewidth modulation strategy is utilized, which offers very low switching frequency, low switching losses, enhanced fundamental magnitude, and low total harmonic distortion. The performance of the proposed VCIMD, is evaluated by simulating it in MATLAB Simulink. The performance of the proposed VCIMD is also verified using a scaled down laboratory prototype under various operating conditions such as steady state, starting, load perturbation, speed control, varying load conditions, and over a wide speed range of IMD.
Center Concentrated X-Torus Topology

The topologies are the very important part of the interconnection network. The topologies once decided cannot be further modified in some cases, so we have to design best topology before its use. The regular topologies have been used in various massively parallel computers. In this paper, we have proposed a new variant of X-torus topology which the objective gets the better on the various qualities of service parameter like latency and throughput. The performance the proposed topology has been tested on the four traffic patterns and have been found that the topology is either better or same in the terms of performance. However, it has been found that we were able to get improvement of 85.24% in the terms of average latency than the other topologies similarly the throughput of the topology has improved by 17.86%. The Hop count is also another factor to study as if we can reduce the hop count in a particular topology we will be able to improve the performance and average hop count of our topology has been improved by 9.58%.
Optimization of a Kitting Line: A Case Study

Assembly kitting lines typically deal with components that present different quantities in the final kit. Re-configurable feeders are not advisable for low quantity components since this solution leads to high inefficiency, due to the time required for retooling and for the feeding rate unbalance between the components. On the other hand a fully flexible feeder can increase the flexibility of the system at the cost of reducing the throughput. An assembly kitting problem was therefore addressed in different ways for low quantity components and high quantity components, studying the two different subsystems that compose a hybrid flexible assembly system. To optimize the re-configurable feeders, which supply the high quantity components, the opening sequence of a system composed by several hoppers was analyzed. We propose a solution which replaces the weighing device with a vision inspection system, showing its impact on the productivity of the line. A model coded into a Matlab script was developed to perform the optimization of the system and understand its behavior. Furthermore a fully flexible assembly system was developed in the laboratory in order to test the kitting of the low quantity components with the proposed subsystem. Finally the Overall Equipment Effectiveness of the line was calculated to evaluate the possible improvements obtained by the proposed solution.
An Analysis of the Learning Behavior of Cultural and Creative Industries Employing Structural Equation Modeling

Occupational training is a term has long been an important directive for government administration, because it is necessary to have better understanding of the various factors influencing Trainee learning behavior in occupational training, and Promote enterprises to pay attention to the development of human capital,Invest in improving the quality of workers. This research uses(TAM) and (TPB) two theories and Structure Equation Modeling (SEM). to learners who participated in the laborer autonomy learning project and aborigine specialized occupational training course held by the Tainan Training Center, Bureau of Employment and Vocational Training. A total of 300 questionnaires were released. After eliminating 17 invalid samples that were filled out incompletely, there were sample 283 valid questionnaires. The research results showed that: (1) perceived usefulness and perceived ease of use have a significant positive influence on the Attitude of participating learners; (2) perceived usefulness and perceived ease of use have a significant positive influence on the training intention of participating learners; (3) Perceived usefulness and perceived ease of use have no significant direct positive impact on the learning behavior of participating learners; (4) Perceived usefulness and perceived ease of use have a complete mediating effect on learning behaviors of participating learners through attitudes and training intentions.
The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction

A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent class imbalance between dropout and non-dropout students could pose difficulty in building accurate predictive modeling for a dropout early warning system. The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and the ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) and precision–recall (PR) curves. To that end, we trained random forest, boosted decision tree, random forest with SMOTE, and boosted decision tree with SMOTE using the big data samples of the 165,715 high school students from the National Education Information System (NEIS) in South Korea. According to our ROC and PR curve analysis, boosted decision tree showed the optimal performance.
Control of VSC for enhancement of power quality in off‐grid distributed power generation

In this study, a control algorithm based on non-linear adaptive second-order Volterra filter (NLVF) is utilised to operate the voltage-source converter (VSC). The main role of VSC is to supply compensating current to improve the power quality in wind-based off-grid distributed power generation system. This off-grid system consists of three-phase induction generator, VSC and non-linear loads. The proposed control algorithm is adaptive as well as non-linear in nature. The characteristic of Volterra series is exploited to generate the reference source current for VSC operation. The NLVF estimates the active and reactive weights of fundamental load current for reference current generation. It mitigates the power quality problems created by disturbances in non-linear load or wind oscillations on off-grid system. The solution of power quality problems such as the presence of harmonics in supply current, load unbalance and reactive power compensation is obtained. Nevertheless, voltage and frequency control are also achieved. The VSC is used as a shunt compensator in the system. The entire off-grid system is designed and validated through the laboratory prototype development. The objective of the system is achieved and the steady state and dynamic performance is found satisfactory.
Evaluación de algoritmos de seguimiento de objetos

The main objective of this master thesis is the evaluation of video tracking algorithms. A protocol is designed for such evaluation task including the following aspects: accuracy, robustness to initialization errors, computational cost and optimum parametrization. In order to support the evaluation, a dataset is de ned considering the most common problems in video tracking. Finally, the proposed protocol is tested on representative approaches of the state-of-the-art.
Design and Control of a Series Elastic Actuator With Clutch for Hip Exoskeleton for Precise Assistive Magnitude and Timing Control and Improved Mechanical Safety

Transparency and guaranteed safety are important requirements in the design of wearable exoskeleton actuators for individuals who have lower limb deficits but still maintain a certain level of voluntary motor control. Specifically, precision in torque delivery timing and magnitude, robustness, disturbance rejection, and repeatability are desired in the actuator design and control. Motivated by these needs, this study aims to develop a series of elastic actuators with clutch (SEAC) that can precisely generate the desired assistance in terms of both timing and torque magnitude for a wearable hip exoskeleton and guarantee the wearer's safety at the same time. The proposed mechanical design improves actuator transparency and safety by a mechanical clutch that automatically disengages the transmission when needed. A new torque control for the SEAC, based on singular perturbation theory with flexible compensation techniques, is proposed to precisely control the assistive torque by rejecting the undesired human motion disturbance. The mechanical design of the proposed device and the design of a singular perturbation control algorithm are discussed, and the SEAC performance is verified by experiments. Experimental results, derived from a test with a human subject, are presented to demonstrate the precision of the assistive torque and timing control of the SEAC while interacting with a human wearer.
Nonlocal and Size-Dependent Dielectric Function for Plasmonic Nanoparticles

We develop a theoretical approach to investigate the impact that nonlocal and finite-size effects have on the dielectric response of plasmonic nanostructures. Through simulations, comprehensive comparisons of the electron energy loss spectroscopy (EELS) and the optical performance are discussed for a gold spherical dimer system in terms of different dielectric models. Our study offers a paradigm of high efficiency compatible dielectric theoretical framework for accounting the metallic nanoparticles behavior combining local, nonlocal and size-dependent effects in broader energy and size ranges. The results of accurate analysis and simulation for these effects unveil the weight and the evolution of both surface and bulk plasmons vibrational mechanisms, which are important for further understanding the electrodynamics properties of structures at the nanoscale. Particularly, our method can be extended to other plasmonic nanostructures where quantum-size or strongly interacting effects are likely to play an important role.
Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics

Landmark generation is an essential component in landmark-based visual place recognition. In this paper, we present a simple yet effective method, called multi-scale sliding window (MSW), for landmark generation in order to improve the performance of place recognition. In our method, we generate landmarks that form a uniform distribution in multiple landmark scales (sizes) within an appropriate range by a process that samples an image with a sliding window. This is in contrast to conventional methods of landmark generation that typically depend on detecting objects whose size distributions are uneven and, as a result, may not be effective in achieving shift invariance and viewpoint invariance, two important properties in visual place recognition. We conducted experiments on four challenging datasets to demonstrate that the recognition performance can be significantly improved by our method in a standard landmark-based visual place recognition system. Our method is simple with a single input parameter, the scales of landmarks required, and it is efficient as it does not involve detecting objects.
Energy Efficiency Optimization for Massive MIMO Non-Orthogonal Unicast and Multicast Transmission with Statistical CSI

We study the energy efficiency (EE) optimization problem in non-orthogonal unicast and multicast transmission for massive multiple-input multiple-output (MIMO) systems with statistical channel state information of all receivers available at the transmitter. Firstly, we formulate the EE maximization problem. We reduce the number of variables to be solved and simplify this large-dimensional-matrix-valued problem into a real-vector-valued problem. Next, we lower the computational complexity significantly by replacing the objective with its deterministic equivalent to avoid the high-complex expectation operation. With guaranteed convergence, we propose an iterative algorithm on beam domain power allocation using the minorize maximize algorithm and Dinkelbach’s transform and derive the locally optimal power allocation strategy to achieve the optimal EE. Finally, we illustrate the significant EE performance gain of our EE maximization algorithm compared with the conventional approach through conducting numerical simulations.
Evidential-Based Approach for Trajectory Planning With Tentacles, for Autonomous Vehicles

Driving is a combination of continuous mental risk assessment, sensory awareness, and judgment, all adapting to extremely variable surrounding conditions. Several research projects are working on autonomous vehicle to robotize this complex task. The work presented in this paper focuses on reactive local trajectory planning in uncertain environment. The environment uncertainty is one of the challenges that we face in trajectory planning. For autonomous vehicle, to be efficient, they need to be able to deal with this kind of uncertainty. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages in the context of trajectory planning. Using the belief functions, we build evidential grid that represents the surrounding environment. To plan a local trajectory, we generate a set of clothoid tentacles in the egocentred reference frame related to the ego-vehicle. Those tentacles represent possible trajectories that consider the current dynamical state of the vehicle and make a smooth variation in the vehicle dynamic variables. Once the representation of the environment and the possible trajectories are generated, an evaluation of each trajectory is carried out according to several criteria and the choice of the trajectory is made using the decision formalism of Markov decision process. To demonstrate the effectiveness of our evidential approach, we apply it to scenarios where ego-vehicle has to make decision in uncertain dynamical environments, using a driving simulator (SCANeR™ Studio).
Measure of Design M&As: Exploratory investigations of IP analysis in design

Despite active participation of design firms in M&A markets, there has been little research measuring the value of design M&As. It is partially due that design has been seen to be an intangible asset. This paper seeks to the understanding of the value of design M&As and provides a possible metric for measuring the value using patent analysis. The value of design M&A was investigated at two levels: Design as differentiator (i.e., new product development) and Design as coordinator or integrator (i.e., organizational growth). The evolution of patenting quantity (e.g., the number of design patent applications, Locarno classes) and quality (e.g., forward citation, coinventor networks) in pre- and post-acquisition deals was suggested. We conducted a case study using the design and utility patents of Adobe Systems Inc. The results show the dynamics of innovation area and the presence of the high values of inventors holding design-tech linkage, which could be a potential intangible source of company growth. This study further provides implications for companies which might consider design M&As as new ways of design investment.
Real-Time Downward View Generation of a Vehicle Using Around View Monitor System

In this paper, a method to generate the downward image of current vehicle location using a commercial around view monitor (AVM) is proposed. The proposed system consists of three stages, namely feature tracking, obstacle filtering, and downward view generation. In the feature tracking stage, the Shi–Tomasi corner detection is used and feature tracking is performed with a Kanade–Lucas–Tomasi (KLT) tracker to determine the transformation between AVM images. In the obstacle filtering stage, features on obstacles are filtered by using the difference in a tracking distance between features detected on the ground and features detected on the obstacle. This is performed by using a histogram based on the tracking distance of features. Finally, in the downward view generation stage, transformation between the current AVM image and the previous AVM image is obtained by using the tracked feature pair refined via the aforementioned steps. Specifically, the random sample consensus (RANSAC) method is used to obtain transformations from which the influence of outliers is removed. The downward image of the current vehicle is generated by the obtained transformation and is synthesized to the existing AVM image. The results indicate that the proposed system synthesizes the existing AVM image and the generated downward image of a vehicle in a seamless way by determining the exact pair of matching points between current and the previous AVM images. We believe the proposed system can be utilized in efficient wireless charging system and safe driving system.
A General Description of the Performance of Surface Plasmon Sensors Using a Transmission Line Resonant Circuit Model

We analyze the response of surface plasmon (SP) sensors using a transmission line model. We illustrate this analysis with particular reference to a layered structure in which plasmon hybridization occurs. By applying the appropriate resonant condition to the system, we derive a circuit model which predicts the responsivity of different modes. This gives new physical insight into the sensing process. We discuss how the change in the sample region may be modeled as a change in the reactance in the equivalent circuit and from this, it follows that a single parameter can determine the change in resonance position with reactance. This approach is used to predict the response of a generic sensor to binding of an analyte and the bulk change of refractive index. This parameter arises naturally from the circuit representation in a way not readily accessible with the transfer matrix approach. The parameters can be expressed in terms of the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> of a resonant circuit and confirms the intuition that a high <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> is associated with poor responsivity, however, we demonstrate that there is another circuit parameter, the resistance at resonance, that can mitigate this effect, providing a route for optimization of the sensor properties.