Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Sliding Mode Control for Dual Three-phase PMSM System

The speed control problem of dual three-phase permanent magnet synchronous motor has the characteristics of nonlinearity, parameter perturbation and load disturbance. To solve this problem, a new sliding mode approach law is proposed. The saturation function is used to replace the sign function to suppress chattering, and the approach law is used to design a sliding mode speed controller for dual three-phase permanent magnet synchronous motor. According to the established mathematical model, the vector control model of the sliding mode speed controller based on the new approach law is established. Under three states of motor starting, steady state and sudden load, the new sliding mode approach law has less phase current impact , almost no buffeting, fast response of speed and torque, no overshoot of speed. The simulation results show that the speed controller can effectively improve the static and dynamic characteristics of the dual three-phase permanent magnet synchronous motor drive system.
3D Reconstruction Based on Dense Point Cloud Sampling and Filtering Optimization Algorithm

In order to solve the problems of large memory consumption and large reconstruction error in the process of dense point cloud 3D reconstruction, an optimal reconstruction method of dense point cloud sampling and filtering is proposed. First of all, the feature points are extracted and matched, the mismatching is removed, the camera pose is calculated, and the sparse point cloud is generated. Then, the sparse point cloud is densified, and the improved sampling algorithm and filtering algorithm are used to reduce the memory and optimize the dense point cloud. Finally, the optimized dense point cloud is reconstructed and smoothed. The experimental results show that after the sampling algorithm, the computational memory required in the reconstruction process decreases from 5.3GB to 3.2GB, and the relative error of the optimized point cloud reconstruction is less than 4%, which is significantly lower than that before optimization. By comparison, it is found that the relative error is reduced by 1% to 3%. Experimental results verify the effectiveness of the method and show that the dense point cloud reconstruction with higher accuracy can be achieved with lower computational memory.
A Review on Clustering in VANET: Algorithms, Phases, and Comparisons

A Vehicular Ad hoc Network (VANET) is a kind of mobile ad hoc network (MANET), where the nodes in VANET are vehicles. VANET is the main component for the development of the Intelligent Transportation System (ITS). VANET has a high dynamic topology due to continuous stopping and movement with different speeds of vehicles. These characteristics lead to untrustworthy information transmission in VANET. To enhance connection reliability and network scalability, clustering in VANET is introduced. It is a method of clustering network nodes into groups known as clusters. In this work, we provide a review of the most clustering algorithms presented between 1999 and 2020. Different clustering algorithms from the aspects of Cluster Head (CH) selection metrics, cluster formation according to hop distance, and cluster maintenance are explored. Also, different performance parameters used to evaluate the clustering approaches are summarized. Then, our proposed approach is summarized and compared with the most usable algorithms in literature to show its supremacy.
Reinforcement Learning-Based Impedance Learning for Robot Admittance Control in Industrial Assembly

Industrial assembly is essential for manufacturing, and robots have been broadly applied to assembly tasks. The conventional impedance control with fixed impedance parameters may restrict the compliance and interactivity of robots, posing a threat to the safety of robots and environments during assembly tasks. This paper presents an impedance learning method using reinforcement learning (RL) for robot admittance control in industrial assembly. A model-free RL method termed twin delayed deep deterministic policy gradient is utilized to tune stiffness parameters, guaranteeing the safety of robots and environments during the completion of assembly tasks. The proposed method is applied to a collaborative robot to complete the peg-in-hole task, a benchmark problem in industrial assembly. Simulation results show that the proposed method achieves a trade-off between force response and tracking accuracy, and performs better than the fixed admittance control in terms of safety and efficiency.
Robust IRIS Presentation Attack Detection Through Stochastic Filter Noise

The vulnerability of iris recognition algorithms against presentation attacks demands a robust defense mechanism. Much research has been done in the literature to create a robust attack detection algorithm; however, most of the algorithms suffer from generalizability, such as inter database testing or unseen attack type. The problem of attack detection can further be exacerbated if the images contain noise such as Gaussian or Salt-Pepper noise. In this research, we propose a multi-task deep learning model with a denoising convolutional skip autoencoder and a classifier to inbuilt robustness against noisy images. The Gaussian noise layer is introduced as a dropout between the encoder network’s hidden layers, which helps the model learn generalized features that are robust to data noise. The proposed algorithm is evaluated on multiple presentation attack databases and extensive experiments across different noise types and a comparison with other deep learning models show the generalizability and efficacy of the proposed model.
Chronicling the Development of a Growth Mindset Community of Practice for Computing Faculty: Lessons Learned and Looking Forward

This Innovative Practice Full Paper chronicles and reports on the innovative use of a Growth Mindset Faculty Community of Practice (GM-FCoP) to promote student success in a college of computing at a large private university in the northeast United States. Faculty communities of practice bring together faculty as a peer group to engage in learning about a specific topic. The experience is typically a structured approach where members with varying levels of knowledge and expertise about a topic provide encouragement and support to each other as they develop their knowledge and collaboratively generate shared resources. The community members in this GM-FCoP were experienced faculty specializing in computer programming, networking, web technologies, and computing career services, gathering to learn about embracing and developing a growth mindset to apply and promote in the courses they teach and in their interactions with computing students.Situated in theories of intelligence, growth mindset posits that intelligence is malleable and can be developed over time. A growth mindset views failures as a springboard for future success. This contrasts with a fixed mindset that views intelligence and talent as fixed, or in other words, if you are not good at something you will never be good at it. This encourages the notion that talent alone is responsible for success. While many faculty, including computing faculty, may innately and informally embrace and encourage growth mindset approaches in their classrooms and interactions with students, the integration of growth mindset approaches is not broadly recognized or mindfully applied in day-to-day computing education curriculum. As such, the GM-FCoP provided an innovative opportunity to develop faculty understandings of growth and fixed mindset with a goal of planning for more intentional growth mindset interactions with students. Using communities of practice as a framework for examination, this paper details the innovative practice of a semester-long GM-FCoP, including the planning of weekly meetings and activities. Lessons learned from initial evaluative reporting alongside faculty leadership reflections are examined with an eye toward presenting opportunities for faculty who may also be looking to instill a growth mindset in colleagues and students. This has potential implications for both faculty and student success in numerous domains.
Testing for Spatial Effects in Seemingly Unrelated Regressions

Abstract The paper focuses on the case of a panel data set, without unobserved individual effects, treated by means of an SUR specification. The problem raised is to test for the presence of spatial effects in these multivariate systems. Various useful tests are developed based on the principle of the Lagrange Multiplier in a maximum-likelihood framework. Also, we address the question of the time stability of the sequence of spatial dependence coefficients, as a maintained hypothesis that is not necessarily true in applied work. The second part of the paper presents the results of a Monte Carlo experiment. Essais sur les effets spatiaux dans des régressions apparemment sans rapport Resume Cette communication se concentre sur le cas de l'ensemble de données de panel, sans effets individuels non observés, traitées au moyen d'une spécification SUR. Le problème soulevé concerne l'examen de la présence d'effets spatiaux dans ces systèmes à multi-variables. On développe plusieurs essais utiles, basés sur le principe du multiplicateur d'Euler-Lagrange dans un cadre de probabilité maximale. En outre, nous nous penchons sur la question de la stabilité en fonction du temps des coefficients de dépendance spatiale, en tant qu'hypothèse maintenue qui n'est pas nécessairement vraie dans les applications pratiques. La deuxième partie de la communication présente les résultats d'une expérience Monte Carlo. Ensayando los efectos espaciales en ecuaciones aparentemente no relacionadas Extracto El trabajo se centra en el caso de un conjunto de datos de panel, donde no existen efectos individuales inobservados, tratado por medio de una especificación SUR. El problema que se plantea es contrastar la existencia de efectos espaciales en ese tipo de sistemas multivariados. Se desarrollan varios contrastes basados en el principio del Multiplicador de Lagrange en un contexto de máxima verosimilitud. Igualmente tratamos la cuestioen de la estabilidad temporal en la secuencia de coeficientes de dependencia espacial, como una hipótesis mantenida que no es necesariamente cierta en trabajos de tipo aplicados. La parte final del arti′culo presenta los resultados de un experimento de Monte Carlo.
Directional Total Variation Regularized Breast Tomosynthesis Image Reconstruction

Digital Breast Tomosynthesis (DBT) is an effective breast imaging modality that provides 3D cross-sectional images of the breast. However, reconstructed images from the DBT have some distortions due to the limited scan angle and number of projections. In this study, a 3D Directional Total Variation (DTV) algorithm was developed by changing partial gradient terms. Four neighboring pixels along the y axis and 4 partial gradient terms were used in DTV. The effect of strengthening the partial gradient terms according to the DBT geometry were investigated. Contrast to noise ratio (CNR), image intensity profiles and visual assessment were used to assess the performance of the proposed method. It can be clearly seen that proposed DTV algorithm successful to suppress background noise according to visual assessment and image intensity profiles. Besides, it can be said that contrast resolution of the masses and micro calcifications can be improved according to CNR measurements by using DTV algorithm. The obtained results clearly show that the proposed DTV algorithm is effective in decreasing the artefacts in reconstructed DBT images.
Modular Multilevel Converter Based Topology with Lower Number of High-Frequency Switches

This paper proposes a new configuration based on modular multilevel converter (MMC) with lower number of high-frequency switches. Compared with the conventional MMC, the proposed topology halves the number submodules to generate the same output voltage by adding four line-frequency switches in each phase. Using this technique, the number of high-frequency switches, and accordingly the overall switching losses are substantially reduced. Simulation and experimental results are provided to validate the performance of the proposed configuration.
Workshop: Using Interpretative Phenomenological Analysis to Study Psychological Experience within Engineering Education

In this workshop, we introduce participants to the tacit and often hidden skills of doing interpretative phenomenological analysis (IPA) to understand lived experience in engineering education. With the growth of IPA research in engineering education, this workshop will sharpen the skills of participants who come with experience in qualitative research and provide practical guidance to participants who may be novices to qualitative research. The workshop is characterized by an interactive style, in which participants collectively analyze a transcript excerpt from an interview with an engineering student regarding their experience of shame. To strengthen the translation of the workshop, the session is intentionally facilitated by both an expert in conducting IPA research and a highly trained engineer who is at the beginning stages of doing IPA.
Energy Redistribution as a Method for Mitigating Risk of Propagating Thermal Runaway

Propagating thermal runaway events are a significant threat to utility-scale storage installations. A propagating thermal runaway event is a cascading series of failures in which energy released from a failed cell triggers subsequent failures in nearby cells. Without intervention, propagation can turn an otherwise manageable single cell failure into a full system conflagration. This study presents a method of mitigating the severity of propagating thermal runaway events in utility-scale storage systems by leveraging the capabilities of a module-interfaced power conversion architecture. The method involves strategic depletion of storage modules to delay or arrest propagation, reducing the total thermal energy released in the failure event. The feasibility of the method is assessed through simulations of propagating thermal runaway events in a 160 kW/80 kWh energy storage system.
High Pressure Heater Fault Monitoring and Early Warning Based on PCA-XGBoost

In order to improve the accuracy and reliability of fault diagnosis of high-voltage heaters, a fault monitoring method based on historical data of thermal power units is proposed. Firstly, the principal component analysis method is used to extract the 6-dimensional features that have a great influence on the outlet water temperature of the high-pressure heater, and the data is normalized. Then the XGBoost algorithm is used to establish a fault monitoring model, and the method is compared with the random forest and GBDT algorithms. Evaluate the quality of the model by mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). Finally, the model is verified by the data of the high-pressure heater in the fault state. The results show that the PCA-XGBoost algorithm has the best effect in predicting the outlet water temperature of the high pressure heater.Through the comparison of the temperature prediction curve and the continuous fluctuation signal of the residual curve, the fault warning is realized. It shows that the fault monitoring model proposed in this paper can give early warning to the fault of the high-pressure heater, and can improve the reliability of the system operation.
Long-Term Energy Demand Forecasting in Thailand with Ensemble Prediction Model

This research has proposed to utilize the combination of Machine Learning models (ML models) to optimally forecast the energy demand in Thailand. The various ML models are explored in which the individual and the combination of ML models are each optimized and evaluated for their best achievable performances. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are utilized to compare models' performances. A total of 4 ML models are executed, which include Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF) Ensemble and proposed Vote Ensemble models. The results show that, by means of ensemble or model combination, the Vote Ensemble model could perform well with the lowest RMSE for training and testing of 613.63 and 666.52 and the lowest MAPE of 3.59% accordingly while also using less execution time of 3 minutes and 56 seconds.
Three Degree-of-freedom Optimization of High Efficiency High Power Density LLC Converter

Applications in the field of aviation power supply put forward higher requirements for efficiency and power density of power converters. LLC resonant converter has received extensive attention because it can realize soft switching. On the premise of ensuring the reliability of switching devices, improving the efficiency of LLC converter mainly depends on parameter optimization, thus it is necessary to accurately calculate the loss, especially for switching devices. Integrating the auxiliary power supply into the converter is beneficial to improve the power density of the module. Based on the Simplified Time Interval Analysis, this paper proposed a three degree-of-freedom parameter optimization method including inductance ratio, quality factor and dead time. A 270V/28V 800W power converter prototype is designed using the optimal results, which achieves 96.9% efficiency and 620W/inch3 power density with integrated auxiliary power supply.
Rectifier Circuits for RF Energy Harvesting and Wireless Power Transfer Applications: A Comprehensive Review Based on Operating Conditions

Low-power wireless sensors will play a big role in smart city applications. These sensors require a power supply to function. Batteries are not optimal for such numerous sensor networks because of their limited life and need for regular maintenance; hence, they are not a universal solution. RF energy harvesting (RFEH) and wireless power transfer (WPT) techniques have the potential to be a green and sustainable solution for supplying power to various low-power devices by capturing ambient RF energy. The ubiquitous nature of RF energy makes it available to exploit and reutilize in power wireless sensor nodes, wireless body area networks, wireless charging systems, RFID tags, and the Internet of Things <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>. <xref ref-type="fig" rid="fig1">Figure 1(a)</xref> displays a conceptual diagram of an RFEH/WPT system. To capture power from free space that is transmitted by the base station, the harvesting node needs a receiving antenna and rectifier to convert the power into dc signals. This dc output is then further utilized to feed a low-power device. <xref ref-type="fig" rid="fig1">Figure 1(b)</xref> shows that at 10-m distance from the cellular tower, a digital batteryless watch was powered by a rectenna operating at 1.8 GHz. This LCD watch (low-power device) required 1.5 V to function and was successfully powered up using the RFEH system <xref ref-type="bibr" rid="ref3">[3]</xref>.
Multi-Hierarchical Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Traffic forecasting is essential for transportation services such as traffic control and route planning. However, accurate traffic prediction is challenging due to complex characteristics of traffic data. Existing solutions may not adequately capture dynamic and nonlinear spatial-temporal correlations in traffic network. In this paper, we propose a novel Multi-Hierarchical Spatial-Temporal Graph Convolutional Networks (MH-GCN) to solve traffic flow forecasting problem. It adopts an attention-based encoder-decoder structure. Firstly, MH-GCN uses a spatial-temporal attention mechanism in encoder to model dynamic spatial and nonlinear temporal correlations. Then, a transformer attention layer is positioned between encoder and decoder, which is used to model the correlation of historical and future time. Finally, the decoder utilizes Convolution Group, Pooling Group, and Dilation Group to extract different hierarchical of characteristics from the already modeled features, and then the fused results are used for predicting future traffic conditions. Experiments on two real traffic datasets demonstrate that the proposed MH-GCN obtains improvements over the state-of-the-art baselines.
A Highly Integrated GaN Power Module with Low Parasitic Inductance and High Thermal Performance

This paper presents a highly integrated 650V/150A GaN power module with low parasitic inductance and high thermal performance. The bus decoupling capacitors, drive components and bootstrap components are successfully integrated through proper layout, thus reducing main parasitic parameters and the need for an external isolated power supply. The high thermal performance is achieved by sandwiching the GaN bare dies between two alumina substrates with high thermal conductivity. The characteristics of the driver, parasitic inductance, and thermal are analyzed. A double-pulse-test is carried out on the presented GaN power module. The result shows that the power loop inductance is 0.75nH. The maximum voltage overshoot across the drain-source is 19.9V with a bus voltage of 400V. 50W power loss is placed on two GaN bare dies through their reverse conduction and the maximum temperature of the GaN power module is 100°C. A 3kW buck converter is built based on the proposed module, the power density of the whole module is calculated as 2160W/in3.
An Interleaved Multi-Phase Boost Converter with Coupled Inductors for High Power Density

To reduce the size of passive components, especially the magnetics, is one of the major challenges of boost converters operating in continuous conduction mode. This can be addressed by increasing the switching frequency, which however is infeasible in high power applications. This paper presents an interleaved multi-phase boost converter utilizing the six-pack silicon carbide (SiC) power module as a single-phase building block to triple the ripple frequency of the inductor current at the same switching frequency, hence, reducing the size of passives by three-fold and achieving higher power density while improving performance and maintaining high efficiency. In addition, planar inversely coupled inductors were designed to further decrease the inductor size and to improve the steady-state and transient performance of the boost converter. The analysis and hardware design of the converter are presented in this paper with the experimental results at full load presented to validate the effectiveness of the proposed design.
Effect of Clustering in Federated Learning on Non-IID Electricity Consumption Prediction

When applied to short-term energy consumption forecasting, the federated learning framework allows for the creation of a predictive model without sharing raw data. There is a limit to the accuracy achieved by standard federated learning due to the heterogeneity of the individual clients' data, especially in the case of electricity data, where prediction of peak demand is a challenge. A set of clustering techniques has been explored in the literature to improve prediction quality while maintaining user privacy. These studies have mainly been conducted using sets of clients with similar attributes that may not reflect real-world consumer diversity. This paper explores, implements and compares these clustering techniques for privacy-preserving load forecasting on a representative electricity consumption dataset. The experimental results demonstrate the effects of electricity consumption heterogeneity on federated forecasting and a non-representative sample's impact on load forecasting.