Miracle-dz/UniFAR-FTUs huggingface dataset:
Adsorption and photocatalytic degradation of bisphenol A by low-cost carbon nanotubes synthesized using fallen leaves of poplar

Bisphenol A (BPA) is an endocrine disruptor and the removal of it from contaminated water is a primary environmental issue. Here, carbon nanotubes (CNTs) were synthesized by heating leaves of poplar to 450 °C in air. The outer and inner diameters of CNTs were about 77.5 nm and 40.1 nm, which contained C, O, Na, Zn, Cu, Ca, K, Cl, and S. The effects of adsorption and photocatalysis on BPA (initial concentration was 10 mg L−1) by CNTs (10 mg CNTs were added into 1 L BPA solution) synthesized using leaves of poplar were studied. The results show that without CNTs, the degradation of BPA was about 24.5 ± 4.7% in 180 min with solar light irradiation. The complete equilibration time of adsorption/desorption of BPA onto the surface of CNTs was within 30 min, and approximately 19.7 ± 0.9% BPA was adsorbed by CNTs. Afterwards, the photodegradation efficiency of BPA was increased to 74.8 ± 3.9% with the combination of CNTs and solar light irradiation for 180 min. The photocatalytic property on BPA was caused by CNTs and the metal oxide on the surface of them. The methods have positive effects on solving environmental and ecological problems as well as the problem of high cost for preparation of photocatalyst.
Adaptive Model Predictive Control for Frequency Control for Two Area Interconnected Power System

Reliability in of importance in load frequency control (LFC) in modern power generation, In LFC scheme, local load disturbance, inter-area ties power fluctuation, frequency deviation, and generation rate constraints (GRC), are the major nonlinear factors on the control scheme that affect the dynamic response of the system. In this paper, two-area hydro-thermal interconnected power system (IPS) is used to develop an adaptive model predictive control (AMPC) as a controller scheme for LFC scheme and compared with both robust distributed model predictive control (RDMPC) and proportional integral derivative (PID) controller, using MATLAB/Simulink, result shows that AMPC shows robustness over RDMPC and PID when compared in frequency deviation, area control error (ACE) and tie-line active power deviations.
A Substantial Modelling and Analysis of Solar Powered e-Rickshaw Drive System

The current environmental apprehensions are fascinating the world to swift towards green technology. The development of renewable energy-based equipment and machinery are increasing the approaches towards a pollution free world. In this paper, the substantial modelling and performance analysis of a solar powered electric rickshaw (e-Rickshaw) for different drive cycles is presented. The modelling of the e-Rickshaw is done as per design methodology of solar charger, battery bank, electric motor, e-Rickshaw drive train suitable for different drive cycles and different components to provide the required traction. The e-Rickshaw is having onboard solar charging system with provision of grid charging through suitable DC power plug. The battery bank supplies the power to a dedicated electric motor to generate required mechanical torque and power to provide required speed to the e-Rickshaw through suitable converter. This paper also emphases on the performance analysis of various components of the e-Rickshaw, namely: Battery bank, Drive system, DC motor and Battery Management System (BMS) during solar charging and without solar charging for different drive cycles. The simulation is done for various loads and speed profiles using Simulink of MATLAB.
A Quad-Band Stacked Hybrid Ambient RF-Solar Energy Harvester With Higher RF-to-DC Rectification Efficiency

This article addresses the design and implementation of a novel quad-band electromagnetic (EM) and solar energy scavenging system, ensuring energy harvesting from ambient RF environment with excellent “cold start” power level. The proposed scavenger consists of a single port quad-band rectangular slot antenna, power film solar cell, a quad-band RF- to-DC converter, a microcell power management module, and a battery. The harmonic balance of EM solver is used to design and maximize the RF- to - DC rectification efficiency with the combination of the antenna and the solar cell. The power film solar cell is placed in the middle of the antenna with positive and negative edges connected to the top and bottom layer of the antenna so that total harvested energy passes through the rectifier and forms an ambient hybrid energy harvesting system. One significant benefit of this method is the utilization of the antenna free space for the effective area of the power film. Another important contribution is the employment of multiband antennas for increasing the total ambient RF scavenged energy. Besides, a cost-effective and flexible FR4 substrate and a micropower film solar cell are used to make it conformal and cheaper. The prototype of hybrid harvester demonstrated that with 360 lux ambient light intensity, at the solar cell can generate 0.109 V energy while the harvester can attain an extra 5% - 48% energy with ambient RF input level variation from −15 to −20 dBm. The rectifier circuit achieves 74.5% RF-to-DC rectification efficiency for the value of load resistance 2.7 $\text{k}\Omega $ . These performances depict that the proposed multiband ambient hybrid RF-solar power scavenger can raise the scavenged power level and offer energy multiplicity.
Optimum Illumination for Real-time Wiper Arm Defect Detection

The importance of optimum illumination has been undervalued in machine vision system over the years. Most of the research work emphasized on developing advanced and complex algorithms with little attention on lighting design and configurations. Using the right lighting configuration can significantly improve the efficiency and effectiveness of an inspection system. This paper describes the design of a novel illumination system that reduces the complexity of the image processing algorithm in wiper arm surface defect detection. The lighting system increases the contrast between flawless area and defective area in the image captured. Thus, it facilitates a more accurate, reliable and productive machine vision system. The processing time has improved to 0.21 seconds per wiper arm.
Deep Learning Localization with 2D Range Scanner

In recent years, the use of 2D laser range scanners is increasing in industrial products, thanks to decreasing cost of this kind of devices and increasing accuracy. Nevertheless, the localization estimation of the moving objects (vehicles, robots, drones and so on) between consecutive laser range scans is still a challenging problem. In this paper, we explore different neural network approaches, using only a 2D laser scanner to address this problem. The proposed neural network shows promising results in terms of average accuracy (about 1cm in translation and 1° in rotation of Mean Absolute Error (MAE)) and in terms of overall used parameters (less than one hundred thousand), being an interesting method that could complement or integrate traditional localization approaches. The proposed neural network processes about 8000 pairs of compacted scans per second on Nvidia Titan X (Pascal) GPU.
On Optimal Grid Partitioning for Distributed Optimization of Reactive Power Dispatch

Distributed optimization has been shown to be one promising method for tackling reactive power dispatch, however the performance of distributed algorithms is known to be dependent on how the given problem is partitioned. The question of how to optimally partition a power grid for use in distributed optimization remains open in the literature. In the present paper, we test partitions generated by the graph partitioned KaFFPa, METIS, and spectral clustering using five edge-weighting metrics. The standard IEEE 14, 30, and 57 bus models are used as benchmark case studies and the Augmented Lagrangian Alternating Direction Inexact Newton algorithm is used as the distributed optimization algorithm. It is shown that performance varies drastically depending on which partitioner and weighting is used. Overall, KaFFPa with weightings given by the Y-bus matrix yields the best results.
Molecular dynamics simulations of liquid water using various long-range electrostatics techniques

Water is one of the most extensively studied molecules, owing to its crucial role in biological processes. The water molecule is both highly polar and highly polarizable. Properties of water computed from molecular simulations are therefore critically dependent on both the intermolecular potential and the method for computing long-range electrostatic corrections. In this paper, the effects of the potential and the long-range electrostatic corrections are quantified for liquid water from 260 to 400 K. Simulations were carried out for a system of 256 molecules in the NVT ensemble. Thermodynamic, structural, dynamical, hydrogen bonding and dielectric properties have been computed for the flexible SPC and rigid SPC, SPC/E, TIP4P, TIP4P-Ew and TIP4P-FQ potentials, using the Lekner, Ewald and reaction field techniques to handle long-range electrostatics. The Lekner method gave the best overall agreement with experimental data, while the reaction field approach produced poorer results. Some measurable differences were found between the Lekner and Ewald techniques. For dielectric properties, the performance of the TIP4P-FQ model was superior relative to other potentials. For 256 molecules, the computational speeds of the Ewald and reaction field methods were found to be 2.5 to 3 times and 3.5 to 5 times faster than the Lekner technique, respectively.
Modelling and Simulation of a DC-excited Vernier Reluctance Machine as a Synchronous Condenser

The paper presents techniques of analysing and predicting performance for a DC-excited vernier reluctance machine as a synchronous condenser, to ensure accuracy in experimental measurements. The techniques involves finite element based models on which machine performance parameters can be calculated. The models can be used to determine the field current performance characteristics of the machine. It is shown that the presented techniques give a good performance prediction of the machine operated as a synchronous condenser when compared to experimental measured results. Furthermore, the techniques can be used in the synthesis of different types of machines when operated as synchronous condensers.
Toward Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques

Nowadays, point clouds acquired through laser scanning and stereo matching have deemed to be one of the best sources for mapping urban scenes. Spatial coordinates of 3-D points directly reflect the geometry of object surfaces, which significantly streamlining the 3-D reconstruction and modeling of objects. The construction industry has utilized point clouds in various tasks, including but not limited to, building reconstruction, field inspection, and construction progress tracking. However, it is mandatory to generate a high-level (i.e., geometrically accurate, semantically rich, and simply described) representation of 3-D objects from those 3-D measurements (i.e., points), so that the acquired information can be fully utilized. The reconstruction of 3-D objects in a scene of man-made infrastructure and buildings is one of the core tasks using point clouds, which involves both the 3-D data acquisition and processing. There are few systematic reviews summarizing the ways of acquiring 3-D points and the techniques for reconstructing 3-D objects from point clouds for application scenarios in a built environment or construction site. This article therefore intends to provide a thorough review of the state-of-the-art acquisition and processing techniques for building reconstruction using point clouds. It places particular focus on data acquisition and on the strengths and weaknesses of key processing techniques. This review work will discuss the limitations of current data acquisition and processing techniques, as well as the current research gap, ultimately providing recommendations on future research directions in order to fulfill the pressing needs of the intended construction applications in the foreseeable future.
Eigen-Structure Assignment-Based Differential Evolution Algorithm for T-S Fuzzy Control Tuning Applied to Water-Turbine Governing System

In this paper, a novel Takagi-Sugeno (T-S) fuzzy controller based on eigenstructure assignment (EA) and optimized by differential evolution algorithm (DE) is proposed, and the application of this control strategy in the hydro-turbine governing system (HTGS) is studied. Based on the non-linear model of HTGS, the corresponding state-space equations (SSE) are obtained by linearization through multiple equilibrium points. Combining with the principle of T-S fuzzy control, a T-S fuzzy model of HTGS integrating multiple SSE with generator power angle as a prerequisite is established. This paper adopts the EA method to design the fitness function in the optimization process and uses DE to complete the optimization operation to improve the performance of T-S fuzzy control in HTGS. Which makes each Linear-Quadratic-Regulator (LQR) controller gain in the fuzzy control reach the optimal under the constraint conditions. The simulation results show that compared with the standard fitness functions IAE (integral absolute error), ITAE (integral time absolute error) and ISE (integral square error), the fitness function designed using the EA method can expand the angle between the left and right eigenvectors of the T-S closed-loop system, and push the closed-loop pole from the imaginary axis to the left. That will make the adjustment time of the system shorter and the robustness against external interferences enhanced. The results also show that the proposed control strategy has better dynamic performance during the non-linear motion of HTGS and is superior to the existing control strategy.
Quantized Dynamic Output Feedback Control and $L_2$-Gain Analysis for Networked Control Systems: A Hybrid Approach

In this article, the problems of stabilization and <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula>-gain performance analysis for continuous-time networked control systems (NCSs) with quantization effects are addressed. The plant output and control input are quantized respectively by two different dynamic quantizers before being transmitted over networks. In order to deal with the dynamic behavior resulting from the dynamic quantizers, a hybrid system framework is adopted to remodel the NCSs. The hybrid model is flexible to the joint design of dynamic output feedback controller, nonequidistant samplers and dynamic quantizers. Furthermore, our method can effectively prevent the Zeno phenomenon on the instants of sampling and the updates of quantizer variables. By constructing a novel Lyapunov function, new sufficient conditions guaranteeing the uniformly globally asymptotic stability (UGAS) and <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula>-gain performance are presented in terms of matrix inequalities, respectively. Finally, two numerical examples illustrate the effectiveness of our proposed method.
Recommendation System with Reasoning Path Based on DQN and Knowledge Graph

Recommendation system is a popular research field. In the age of information explosion, a reliable recommendation system is necessary for users. There are a certain number of approaches to do recommendation work. Reinforcement learning is one of the methods used in recommendation system. In this paper, we use reinforcement learning to recommend items to target users, and achieved a rather good result. To give a better user experience, we have added explanations for recommended items. The explanation is realized by Knowledge Graph. We use TransE to embed target users and items, and it helps manage the information of users and items. Our method KGDQN combines Knowledge Graph and reinforcement learning, which can decide the proper recommendation items, and find the reasoning paths from target users to recommended items. Redundant edges are pruned and the DQN model renders a reward function which gives back the result of recommended items, and the explanation paths of the recommendation. Experiments are conducted on Amazon datasets which show the superior performance of KGDQN
Learn to Walk Across Ages: Motion Augmented Multi-Age Group Gait Video Translation

We propose a framework for multi-age group gait video translation in which, for the first time, individuality-preserving aging patterns in walking style are learnt. More specifically, we build our framework on an existing multi-domain image translation model. Because the existing multi-domain image translation model was originally designed for a still image, we extend it to gait video by introducing a motion-augmented network architecture with three streams, where gait period, period-normalized phase-synchronized gait video, and its frame difference sequence are each input to one stream. We then train the network to ensure three aspects: aging effect (using an age group classification loss), individuality preservation (using a reconstruction loss), and gait realism (using an adversarial loss). Our framework quantitatively and qualitatively outperforms state-of-the-art age progression/regression methods on the largest gait database, OULP-Age, with respect to both age group classification and identity recognition.
Stability Analysis for T-S Fuzzy Semi-Markovian Switching CVNs with Mixed Delays and General Uncertain Transition Rates

This paper concerns with the stochastic stability problem for Takagi-Sugeno (T-S) fuzzy semi-Markovian switching complex-valued networks (CVNs) with mixed delays, where the transition rates of the semi-Markovian process are in the general uncertain form which contain two cases: completely unknown or unknown but with known upper/lower bounds. Based on the Lyapunov stability theory and the stochastic analysis technique, several mode-dependent stability criteria are established to guarantee the considered T-S fuzzy CVN to be asymptotically stable in the mean-square sense. Finally, one numerical example is provided to demonstrate feasibility of the obtained theoretical results.
Two-Layer Stackelberg Game-Based Offloading Strategy for Mobile Edge Computing Enhanced FiWi Access Networks

Carbon footprints of both computation and communications infrastructures attract growing public attention, raising concerns about global warming and motivating research efforts in developing energy-efficient solutions. Since Fiber-Wireless (FiWi) access networks have succeeded in interconnecting our digitalized world and mobile/multi-access edge computing (MEC) further removes unnecessary data transmission from optical backbone networks, MEC enhanced FiWi emerges as the win-win architecture for resource-hungry and delay-sensitive mobile apps. However, the assumption of unlimited and free resources offered by FiWi infrastructure is impractical, and energy-efficient offloading mechanisms for MEC enhanced FiWi still remain underexplored, conflicting with globally advocated green communications. This motivates us to design the Two-layer Stackelberg Game based Offloading (TSGO) strategy, including the FiWi layer and mobile edge computation offloading (MeCo) layer subgames capturing bandwidth allocation and offloading decision respectively. Then the backward induction and an iterative algorithm are exploited to acquire the Nash equilibrium of the two-layer Stackelberg game. Furthermore, we propose three energy efficiency benchmarks from both user-side and system-wide perspectives to evaluate mechanisms proposed for, but not limited to, FiWi, MEC, or a combination thereof. Simulation results validate the proposed TSGO strategy, which echoes the profound notion of green communications.
The Mobile Learning Adoption Model Tailored to the Needs of a
 Private University

The mission of today’s universities is to prepare the students properly to live and work in the 21st century. International research demonstrates the positive impact of using iPads in teaching. Successful deploying of mobile learning (m‑learning) is not a matter of accident, but depends on users’ acceptance of the technology. The purpose of this article is to create a mobile learning adoption pre‑model tailored to the needs of a private university. To achieve this goal, valued adoption models were analyzed and, on their basis, the new model was created. The pre‑model has been tested with a questionnaire. A paper‑based survey was conducted among 640 students representing 25 countries. The questionnaire was grouped into sections: specifications of mobile devices owned by students and mobile internet access, expectation from mobile education application and attitudes towards current and future use of mobile devices in education. We computed all data with The Statistical Package for the Social Sciences – IBM SPSS Statistics version 25. The results of the research showed that the students from a central‑eastern European University are technologically ready for mobile learning. Moreover, there appears to be no cause for concern regarding students from post‑Soviet countries with respect to their competency to meet the demands of modern teaching in the form of mobile learning. Students have shown that they have clearly defined expectations for educational materials for mobile devices, which will be a challenge for the university when creating m‑learning materials. Furthermore, students must feel the university's support in using mobile applications. That is why the teachers face a serious task: teachers must be prepared to show students the benefits of mobile learning, so they not only need to be trained but also convinced that it is worth using mobile learning.
Illustrating Computational Solvent Screening : Prediction of Standard Gibbs Energies of Reaction in Solution

Prediction of thermodynamic properties of new compounds or mixtures is industrially important, especially when processes are established or alternatives for existing processes are evaluated. Of particular importance is the equilibrium constant, derived from the standard Gibbs energy of reaction in solution. Up to date, experimental determination is challenging, mirrored by a lack of data available. A straightforward computational way for the prediction using quantum mechanics and statistical thermodynamics is presented. The use of three standard quantum chemical methods for the determination of the ground state energies enables a comparison in terms of accuracy and computational cost. The results show that the two most laborious methods coupled cluster with single and double excitations and perturbative triple excitations and MP2 lead to comparable accuracy, whereas the fastest method density functional theory leads to considerable deviations. Hence, the method of choice for the prediction of standard Gibbs energies of reaction in solution with good accuracy at medium computational cost is MP2 combined with conductor-like screening model for real solvents COSMO-RS. © 2008 American Institute of Chemical Engineers AIChE J, 2008