Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-service Teacher Conceptions of Integrated STEM Using the Engineering Design Approach

The engineering design approach is intensively incorporated into STEM instruction in response to the demand for intentional integration of different subject areas. Concerns have arisen about the need to prepare effective educators within integrated STEM curricula. This research aims to investigate pre-service teachers’ conceptions of the engineering design approach to integration of the STEM disciplines. An exploratory study was carried out adopting a framework based on constructivist theories, to investigate how participants incorporate new knowledge. It surveyed selected students of a teacher education programme to explore their understanding of and conceptions about integrated STEM and engineering design processes in curricula. The study revealed that the pre-service teachers struggled to describe integration across the STEM disciplines. Furthermore, they had limited exposure to engineering-design-related activities during their pedagogical coursework. By understanding pre-service teachers’ conceptualizations around engineering design in integrated STEM curricula, new insights may be obtained for designing appropriate programmes and instruction to help them incorporate engineering into their future classrooms.
Resolving Place Recognition Inconsistencies Using Intra-Set Similarities

Place recognition is the problem of finding associations between a query set of place descriptions and a database. It is an important means for loop closure detection in SLAM. The primary source of information to decide about associations is the pairwise similarity of descriptors between the query and the database items (e.g., image descriptor similarities). Beyond better descriptors, significant improvements were achieved by exploiting additional structural information, in particular by comparing sequences instead of individual items. In this letter, we propose to use another systematic source of information: intra-set similarities between items within the query or the database sets. They can be used to detect inconsistencies of groups of associations between database and query items, e.g. to inhibit matchings of multiple query descriptors to the same database descriptor if the query descriptors are mutually different. The underlying idea is a heuristic tightening of the triangle inequality of groups of descriptors. Based on a definition of matching inconsistencies, we propose an Inconsistency Resolution Procedure (IRP) to modify the inter-set similarities between database and query in a way that resolves existing inconsistencies with intra-set similarities. Our experiments show an average place recognition performance gain of $>$30% in a general place recognition setup with 21 datasets and two state of the art image processing front-ends. The proposed approach does not require additional information beyond descriptor similarities, makes no assumptions of sequences, does not require training, and has no parameter that needs adjustment. It can be combined with other established techniques like descriptor standardization and sequence processing.
Design of Wireless Power Transfer Coils for High Efficiency and Low Leakage Magnetic Fields

Electromagnetic fields (EMFs) around the wireless power transfer (WPT) coils should be lower than the limits set by standards. In this paper, a design of the WPT coils for a reduced level of leakage magnetic fields (LMFs) and constant efficiency is proposed. A hemicylinder-cylinder joint structure is given. Transmitting coil (TX-C) was wound on a hemicylindrical structure and receiving Rx coil (RX-C) was wound on a cylindrical structure. Thus, the RX-C can rotate around its axis between 0° and 85°. Design procedures are discussed in detail. EMF analysis is investigated under different rotation angles. The chosen design reduced the variation of the mutual inductance (M). Therefore, it maintained a constant power transfer efficiency (PTE) while rotating the RX-C. In addition, LMFs near the WPT windings of the selected design were reduced by 63.6% compared with other coil methods that have similar values of PTE.
Humanoid Loco-Manipulation Planning Based on Graph Search and Reachability Maps

In this letter, we propose an efficient and highly versatile loco-manipulation planning for humanoid robots. Loco-manipulation planning is a key technological brick enabling humanoid robots to autonomously perform object transportation by manipulating them. We formulate planning of the alternation and sequencing of footsteps and grasps as a graph search problem with a new transition model that allows for a flexible representation of loco-manipulation. Our transition model is quickly evaluated by relocating and switching the reachability maps depending on the motion of both the robot and object. We evaluate our approach by applying it to loco-manipulation use-cases, such as a bobbin rolling operation with regrasping, where the motion is automatically planned by our framework.
Application of learning algorithm for Diabetic Retinopathy Diagnosis

One of the causes of blindness among working-age adults is the diabetic retinopathy (DR). The DR raise the damage of the retina if it is not early diagnosed and treated. In the present paper, we are focusing on the detection of the primary signs of the diabetic retinopathy which is the exudate. The exudate appeared in white or yellow color with different shapes, sizes and positions. More its position is close to the optic disk, more the percentage of blindness is increased. That's why the position and the size are two important information for an ophthalmologist to distinguish or diagnose the severity of the disease. For those reasons, we aim in this paper to detect and segment the exudate using Random Forest classifier. A DR database used with the application of a Random Forest classifier to segment these regions. The impact of varying the main parameter of the random forest algorithm is studied. We achieved the highest value of the specificity and the accuracy with 99.5% and 99.50% respectively. An area under curve (AUC) of 0.952 is obtained.
Detection and Characterization of Network Anomalies in Large-Scale RTT Time Series

Network anomalies, such as wide-area congestion and packet loss, can seriously degrade network performance. To this end, it is critical to accurately identify network anomalies on end-to-end paths for high quality network services in practice. In this work, we propose an unsupervised two-step method for the detection and characterization of general network anomalies. It first finds the change-points in large-scale RTT time series by formalizing an optimization problem in terms of data series segmentation. Then we mark the segments as normal or abnormal on different sides of a change-point through exploitation of their distribution statistics. After detecting an anomaly, a further step is introduced to analyze the relations between links with state changes and localize the entities (nodes or links) that most likely cause the corresponding event. We believe such unsupervised and light-weighed method can provide valuable insights on anomaly mining in large-scale time series data. Extensive experiments on both simulated (artificial time series with ground truth) and real-network (RIPE Atlas traceroute measurements) datasets are performed. The results demonstrate that the proposed method can achieve better performance, w.r. t. accuracy and efficiency, than existing solutions.
CMOS-Compatible Photonic Phase Shifters With Extremely Low Thermal Crosstalk Performance

We report a thorough analysis of the thermal crosstalk issue for silicon photonic circuits using metal and doped silicon heaters for the two waveguide configurations, rib and strip waveguides. Full-wave thermal and optical simulations are performed and it is observed that integrated doped heaters with strip waveguides have the best thermal crosstalk performance. Also, the effect of metal contacts on the heater power consumption is investigated. Furthermore, to alleviate the phase crosstalk and improve the phase tuning efficiency, a deep trench is implemented to thermally shield an adjacent waveguide. Two well-known photonic components, namely, integrated Mach—Zehnder modulators and PN phase shifters are designed and the thermal crosstalk performance is analyzed rigorously. The proposed photonic phase shifter is outperformed in terms of thermal crosstalk over the existing designs. Such phase shifters are viable for compact silicon photonic circuits operating at a very high temperature.
Online Handwritten Mathematical Expression Recognition and Applications: A Survey

Handwritten mathematical expressions are an essential part of many domains, including education, engineering, and science. The pervasive availability of computationally powerful touch-screen devices, similar to the recent emergence of deep neural networks as high-quality sequence recognition models, result in the widespread adoption of online recognition of handwritten mathematical expressions. Also, a deeper study and improvement of such technologies is necessary to address the current challenges posed by the extensive usage of distance learning, and remote work due to the world pandemic. This paper delineates the state-of-the-art recognition methods along with the user’s experience in pen-centric applications for operating with handwritten mathematical expressions. Recognition methods have been categorized into classes, with a description of their merits and limitations. Particular attention is paid to end-to-end approaches based on encoder-decoder architecture and multi-modal input. Evaluation protocols and open benchmark datasets are considered as well as the comparison of the recognition performance, based on open competition results. The use of handwritten math recognition is illustrated by examples of applications for various fields and platforms. A distinctive part of the survey is that we also considered how UI design relies on the use of different recognition approaches, which is aimed at helping potential researchers improve the performance of the introduced approaches toward the best responses in practical applications. Finally, this paper presents the prospective survey of future research directions in handwritten mathematical expression recognition and their applications.
Seamless Transitions for DAB Converters Working In Multi Working Modes

A modulation scheme for dual active bridge (DAB) converter is proposed to achieve ZVS and reduce the conduction loss in this paper. A modulation scheme employing closed-loop control by integrating different working modes is proposed. Furthermore, the seamless transition among the different working modes can be achieved, so the modulation scheme is very suitable to be used for wide battery voltage and wide load requirements. The conduction loss and peak current by using the proposed modulation scheme are compared with those of the previous modulation schemes. Finally, a 1.2kW experimental prototype has been built to verify the effectiveness of the proposed converter and the modulation scheme.
A Weighted Feature Extraction Technique Based on Temporal Accumulation of Learner Behavior Features for Early Prediction of Dropouts

Dropout prediction is an important task due to the high attrition rate commonly found on the massive open online course (MOOC) platforms. Performing accurate prediction leading to timely intervention is therefore important to reduce attrition. To address this challenge, we propose a feature generation approach to allow machine learning models exploit current learner behavior data to predict dropouts during their learning journey. The proposed feature generation approach analyzes the behaviors across time for each learner and determines appropriate weightings of the behavior for each time slice based on both recency and correlation, allowing existing machine learning models to extract patterns from the varying behaviors across learners. We evaluate the feasibility via various machine learning algorithms that employ the proposed generated features. Results show that the proposed techniques achieve higher accuracy in the early weeks compared to existing feature generation approaches. In addition, we demonstrate the feasibility of implementing the techniques in real-time machine learning pipelines for actual use.
A Bayesian Regularized Neural Network for Analyzing Bitcoin Trends

Bitcoin is a decentralized digital currency without a central bank or single administrator sent from user to user on the peer-to-peer bitcoin blockchain network without intermediaries’ need. In this Bitcoin trend analysis work, initial attributes are considered from five sectors based on financial, social, token, network, and that count to thirteen attributes. The thirteen attributes considered are price, volume, market cap, a mean dollar invested age, social volume, social dominance, development activity, transaction volume, token age consumed, token velocity, token circulation, market value to realized value, and realized cap. We apply the attribute selection and trend analysis mapped with potential seven attributes: Price, Volume, Market Cap, Social Dominance, Development Activity, Market Value to Realized Value & Realized Cap. We have conducted Nonlinear Autoregressive with External Input analysis considering seven attributes. The work employed three training algorithms to train a neural network as Levenberg-Marquard, Bayesian Regularization, and Scaled Conjugate Gradient algorithm. The Error histogram and regression plots results indicate that the Bayesian Regularized Neural Network is showing good performance and thus provides a better forecast.
Research on Triple-Port MMC-Based PET Scheme Based on High Frequency Links Interconnection of Horizontal Sub-Modules

Development of power electronic transformers (PET) has made DC power distribution a preferred solution for future electrical network expansion. This paper focuses on the PET solution connecting medium-voltage and low-voltage networks. A triple-port PET (TP-PET) topology based on modular multilevel converter (MMC) structure is proposed. Based on the traditional MMC, the horizontal sub-modules (SMs) are interconnected by high frequency links (HFL) of isolated four full-bridges (IFFB), which also forms a low-voltage DC bus. SM voltage fluctuation is an inherent problem of conventional MMC, which usually needs to be solved by large size capacitor. In this paper, the phase difference between SM fluctuating powers is used to achieve mutual cancellation, which significantly reduces the size of capacitor. Moreover, the cancellation process is completed automatically through interconnected channels, which simplifies the control of the system. This paper analyzes the structural characteristics of the proposed topology, HFL operating characteristics, triple-port power transmission characteristics and corresponding control strategy. Finally, the simulation results verify the correctness and effectiveness of the proposed topology scheme and control strategy.
A Robustness Enhanced ADR-based Inferential Control Method for Single-phase MC

Inferential control is one of the indirection control method for the control system with immeasurable primary variables or disturbances. The method is difficult to be applied in the realm of power electronic and power transmission because of the weaken robustness and plant model accuracy. Matrix converter (MC) is a kind of complex topology with multiple bidirectional switches and difficult operating principle. In order to simplify the plant model and enhance the robustness of the inferential control, a robustness enhanced active disturbance resistance (ADR-) based inferential control method is proposed in this paper, and applied into a single-phase MC system. An ADR-structure has been inserted into the inferential control to enhance the robustness, and divided the single-phase MC topology into two decoupled single-phase converters. The design processes of the proposed method have been analyzed, and the effectiveness and performances have been verified by simulations and relational comparisons.
An Improved Current Feedforward Control Strategy for Multi-port Power Electronic Transformer

Due to the high energy density and transfer efficiency of Multi-port power electronic transformer (MP-PET), it has attracted extensive application in the hybrid distribution systems. However, the coupling relationship between different ports which could impact the dc bus voltage fluctuation and port stability of the PET during load changes. Thus, this paper establishes a full-order small signal model of MP-PET to analyze the coupling mechanism between different ports. Moreover, according to the interaction mechanism, an improved current feedforward control strategy is proposed which can be used to reduce coupling in the stable region. Adopting this proposed method, the dc bus has a smaller voltage fluctuation compared to the traditional control strategies. Simulation and experimental results show the effectiveness of the proposed control strategy.
Improvement of the Delamination Strength of Yttrium Barium Copper Oxide (YBCO) Films by the Hamaker Method

Failure of yttrium barium copper oxide (YBCO) coated conductors (CC) tapes usually manifests as delamination occurring majorly in the YBCO films. Because the delamination strength of any material is greatly influenced by its microstructure, a good understanding of the intermolecular interactions in the YBCO films is essential to achieve the desired control of the delamination of YBCO CC tapes. Our previous works on the delamination behavior of multilayered YBCO CC tapes showed that the Hamaker based-models is viable for investigating the damage characteristics of YBCO films. Therefore, in this article, we investigated the intermolecular interactions of the constituent molecules of YBa2Cu3O7 films by Hamaker method. Then, used the deductions for the combining laws of the Hamaker coefficients for the interacting molecules to uncover and eliminate the interactions that cause the microstructural defects in the YBCO films. Our results show that increasing or decreasing the energy of the intervening medium between the interacting molecules to specific critical values eliminates the identified microstructural defects in the YBCO films. In particular, the elimination of the natural separation phenomenon among interacting molecules of the YBCO films improves its delamination strength significantly. Although based on our previous works, this technique had good agreement with the numerical, closed-form solution, and X-rays diffraction methods; further experimental validation is still under construction.
IoT devices in smart grids

In this article, we focus on smart power grids, which are critical in building smart city architectures, especially devices in Transmission Networks (TNs) and Distribution Networks (DN) which are becoming smarter, and therefore more dynamic. The security control of these networks requires the knowledge in real-time of their key parameters which are voltage and current magnitude, and angle differences in these networks. This identification is the main function of Phasor Measurement Units (PMUs) in TNs and Micro Phasor measurement units (PMUs) in DNs. These tools send and receive data automatically and with the advent of Global Positioning System (GPS) and the IEEE 1588 Precision Time Protocol (PTP) for time synchronization of these devices, they can be installed in any node in the grids, therefore, µPMUs and PMUs become inevitable IoT devices in power networks.
Bayesian Network and Semidefinite Programming Based Wireless Power Transfer Manufacturing System State Estimation and Regulation

In order to supply energy to the smart objects such as electric vehicles and smart phones using wireless power transfer (WPT) framework, the internet of things (IoT) embedded sensors and actuators can play critical roles for sensing, monitoring and regulating the WPT system states. Specifically, the smart sensors are used to transmit sensing state information to the control center, where the state estimation program runs to know the operating and health conditions of WPT system. This study proposes a Bayesian network based WPT system state estimation algorithm. This scheme can estimate the WPT system states in a distributed way using the Bayesian tree structure. Afterwards, the semidefinite programming based optimal feedback control scheme is proposed to regulate the system states. Simulations results show that the proposed approach provides significant performance improvement compared with the existing scheme.
Modular Heatsink Design for High Power Converters Considering Cooling Wind Speed Optimization

Nowadays, with modern society’s increasing demand for power conversion, there are increasing demands towards high power converters. Cascaded structure is frequently used to build high power converters. For example, MMC is adopted in flexible HVDC [1] and series connected SiC MOSFETs are used in DC/DC converters [2]. Among these applications, the common point is that cascaded structure raises working voltage, meanwhile the requirements for insulation are improved. In these applications, traditional integrated heatsink structure is not applicable any more, therefore this paper proposes a modular heatsink design, which reduces the insulation stress a lot. Besides, the analytical model between wind speed and heat dissipation effect is established which shows the heat dissipation effect does not increase linearly with wind speed, hence a trade-off design principle is proposed. Finally, the simulation and experimental results verify the validity of proposed methods and the accuracy of analysis.
Research on feature extraction method based on brain network and CSP for MI-EEG signals

At present, most of the classification studies on EEG signals of motor imagination focus on the classification of left and right hands. In this paper, a feature analysis method based on the combination of brain functional network and common spatial pattern is proposed to solve the dichotomy of unilateral (right) hand motor imagination. Firstly, the phase analysis between Mu rhythm and Beta rhythm signals is carried out, and then the brain functional network topology is drawn according to the phase synchronization information matrix. Through analysis, Mu rhythm of MI - EEG signals than Beta rhythm is more active, and phase synchronization oscillation is concentrated in the center of the brain area C, FC and CP. Then, the concept of graph theory is used to extract the network attribute features under two rhythms: clustering coefficient and shortest path length, and the common space mode method is used to extract the spatial domain features, and the two types of features form an 8-dimensional feature vector. Finally, the features are classified by SVM, and the average classification accuracy is 94.69%, which is higher than that of the two methods. The research content of this paper breaks through the traditional right-handed MI-EEG classification problem, and the method in this paper does not need to consider the ERD/ERS phenomenon, thus expanding the range of data that can be classified.
Modeling and Design of Switched-Capacitor Converters for Voltage Stacking

For the digital load power management, voltage stacking is a modern way of reducing converter losses without impacting the load computational power performance. This paper proposes a model and a design guide for a switched-capacitor converter topology, the load stacking topology. Firstly, it proposes accurate modeling of the regulator-load system, two performance criteria are then defined, used thereafter to compare the topologies and to build a design guide. The results show the higher performance of the load stacking topology over the ladder converter topology, a conventional voltage stacking topology.