Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimode Operation of a PV-Battery System fed Brushless DC Motor Drive

Utilization of energy storage systems along with Photovoltaic systems, can provide an efficient solution to overcome the challenges associated with renewable energy sources. This paper proposes an algorithm for the multimode operation of a Brushless DC motor drive fed using a combination of Photovoltaic System and battery-based energy storage. Based on the solar irradiation and the state of charge of the battery, the proposed algorithm identifies a control scheme wherein the entire system operates in Four different modes. The working of the PV-Battery System fed Brushless DC Motor Drive under various operating modes is presented and is verified using simulation analysis performed in MATLAB/Simulink.
BEAM: An Anomaly-Based Threat Detection System for Enterprise Multi-Domain Data

Organizations are faced with the ever-increasing risk of security threats. Security threats are multifaceted and present different levels of challenges to the defenders. While traditional deterministic signature and correlation-based methods serve limited purpose, behavior-based anomaly detection methods are best suited for identifying signature-less threats such as those from external adversary or insider activities. Challenges in building an anomaly detection system for enterprise security are numerous. This paper introduces a new anomaly detection system that addresses the feature engineering process for multi-domain data in enterprises and provides a Bayes-based risk scoring method for information fusion. The system is adaptive to dynamic user and network behaviors and produces interpretable outcomes by design. To perform quantitative evaluation against a baseline system without known labeled attack data, we propose a method to synthesize the ground truth to demonstrate the improvement in detection performance.
A Dual-Layer-Stator Doubly Salient Permanent Magnet Machine

A new doubly salient permanent magnet (DSPM) machine is proposed in this paper. Different from the existing counterparts, the proposed machine employs a two-layer stator, in which the inner stator layer is used to wound armature windings and the outer stator layer is used to employ PM excitation. In this case, the armature flux will not pass through the PMs, so the reluctance of the armature magnetic path is reduced, and the armature current possesses no demagnetizing risk on the magnets. Meanwhile, the proposed machine can achieve stronger PM excitation since there is more space for PM employment. The stator can be designed with several modules and manufacturing cost can be reduced during mass production. Design optimization is conducted on a 12/8 prototype, and the electromagnetic performance is investigated. The simulation results show that this proposed machine can be designed with a high torque density, and the PMs are protected from being demagnetized.
Food Peeling Method for Dual-arm Cooking Robot

In this study, a method is proposed to carry out food peeling task using our dual-arm robot system with a peeler. First, the problem of peeling a food using robot arms is discussed. Then, a new method for food peeling is introduced, which contains two parts: “ One peeling motion “ in which the robot peels part of a rind or skin through a pre-generated trajectory, and “Rotation motion” where the robot calculates the pose of food for the next “One peeling motion”. The introduced peeling method was implemented into the dual-arm system and then put into test by peeling five types of food. The results prove that the proposed method can peel the skin of various types of food successfully.
The Role of Hearing Screening Using an Audiometry Application in the Education of Children with Hearing Impairment

This paper describes and discusses the process and the role of the hearing screening of children with hearing impairment in education using the interactive audiometry application that offers the easiest and most comfortable and attractive way for rapid daily (self)testing. The proposed audiometry application looks like a game that enables the effective measuring of hearing capabilities of child patients in home environment. Besides the description of the audiometry application itself, the paper is focused also on the selection of appropriate words and information about the processing of related sound stimuli. This work brings a possible scenario for daily testing using this audiometry application. It discuss also an alternative Ling 6 sound test that can effectively verify the working of a hearing aid. The information provided clearly indicates the need for regular hearing measurements to support permanent education and personal development.
Optimal Decision Tree for Cycle Time Prediction and Allowance Determination

The due-date quotation is a key performance indicator for managing customer orders which would influence customer acceptance and/or the future potential lateness penalty. The production cycle time and allowance time are added and used as the due date of order. The objective is to maximize the hit rate which is the percentage of the orders fulfilled within the time limit of quoted due date. Under the framework of supervised machine learning, we explore the new developments in feature selection and the optimal decision tree to predict cycle time by using mixed-integer optimization. Cycle time allowance could be added to the predicted cycle time or incorporated in an optimization problem as a managerial decision variable. Case studies are used to demonstrate the effectiveness of this approach, and their performances are comparable to the other popular ensemble tree approaches, such as random forests and gradient boosting.
Multi-Resonant Control in a PV-SEPIC Fed PEV Charger with Distorted Grid Conditions

The wide variation in the EV battery voltage proportionate to the charging mileage and the demand for fast charging certainly requires a versatile EV charger to meet various battery standards. This paper demonstrates an implicit wide range of plug-in electric vehicle (PEV) charger based upon a single-ended primary inductor converter (SEPIC) and PV connectivity for improvement of grid power quality in the presence of grid abnormality. The Level-I PEV charger is uniquely identified due to its suitability to adopt a wide range of battery voltage by incorporating a two-stage decoupled control action with a front-end multi-resonant control (MRC). The implemented parallel resonant controller successfully eliminates the low-frequency odd harmonics at a very poor grid condition and maintains the unity power factor (UPF) at the utility. In addition, the photo-voltaic (PV) connectivity improves the grid reliability by providing additional power to both utility and EV unit during peak hour charging demand. The controller is developed and verified in a MATLAB/SIMULINK platform for a 3.3 kW isolated EV charger. The control implementation and EV charging phenomenon with a multi-resonant controller are also verified with the Opal-RT hardware-in-loop (HIL) simulator in presence of grid disturbances.
A Model Stacking Approach for Ride-Hailing Demand Forecasting : a Case Study of Algiers

A good understanding of supply of taxis drivers and demand of passengers through the forecasting of future demands is important for an intelligent transportation system in smart cities. A good prediction allows a better allocation of taxi fleets, reduce passengers' waiting time and energy waste for taxis. In this paper, we harness the power of statistical and machine learning models in a joint ensemble model and propose a stacking approach which combines the predictions of ARIMA, SARIMA, MLP, LSTM and XGBoost. Our proposed approach consider also external factors such weather conditions and national holidays. We consider in this work the city of Algiers as a case study, using ride hailing data of an Algerian ride-hailing platform. Experimental results show that our model performed better than the selected baseline statistical and machine learning algorithms.
Scalable and Fast Optical Circuit Switch Based on Colorless Coherent Detection: Design Principle and Experimental Demonstration

We investigate a large-scale and fast optical circuit switch system that uses digital coherent technologies. The achievable port count is expanded by introducing colorless detection which eliminates channel selecting filters at receivers. Wavelength selection is performed using a shared local oscillator (LO) bank, which is configured by combining a multi-wavelength optical source with Silicon-photonic tunable filters (TFs). Microsecond switching times are realized with the thermo-optic effect available with the Silicon-photonic TF. Design optimization of the proposed system handles complex level of freedom, i.e., available wavelength number, space switch port count, optical amplifier location and gain, and device loss. In addition, colorless coherent detection induces penalties, which further complicates switch scale assessment. In this paper, we develop a simulator to clarify how those parameters affect switch performance; the switch throughput is maximized with consideration of the degradation stemming from colorless detection. The design performance accurately matches results measured in various system scenarios. A dual-carrier 256-Gb/s DP-QPSK experiment successfully demonstrates a 1,856 $ \times $ 1,856 optical switch system with switching time under 3.52 μs. The resulting total throughput is 441 Tb/s assuming 7%-overhead HD-FEC. To the best of our knowledge, this is the first demonstration of 400-Tbps class large bandwidth optical switches that use TFs as wavelength selective devices for coherent detection.
Force Control of a Hydraulic Actuator With a Neural Network Inverse Model

In this study, a learning-based force controller for a hydraulic actuator is presented. We propose a control method with an inverse model composed of a deep neural network, which accurately tracks a force trajectory. This learning-based controller can be trained offline using force and position data sets from the hydraulic actuator. The methodology for training the controller network and the experimental setup for data collection are proposed. The learning-based controller was implemented on a hydraulic actuator hardware platform. The proposed learning-based controller demonstrates improved tracking performance compared to that of conventional model-based adaptive control methods.
Disruptions of Academic Activities in Nigeria: University Lecturers’ Perceptions and Responses to the COVID-19

The outbreak of the COVID-19 pandemic has exposed the different layers of vulnerabilities within systems across different political, socio-economic, educational and cultural contexts, so much that even the most advanced economies are struggling to check the impact of the ravaging Corona virus. However, not much is known about the impact of the disruptions in higher educational systems in developing contexts particularly in Nigeria. This study adopted a qualitative research design using a validated semi-structured interview to provide evidence-based data on the impact of the pandemic on the university system from the university lecturers’ perspectives. Using purposive and snowballing techniques, seven lecturers in five universities located in three geographical zones of Nigeria were interviewed using the Zoom and Skype platforms. The results of this study show that COVID-19 had a huge negative impact on the university system in Nigeria. Nonetheless, there are a few exceptions as a few universities within the study switched to alternative teaching medium to mitigate the disruptions thus, underscoring the potentials for a more responsive higher education system. The paper discussed the limitations, and future directions that may be drawn from the study.
On Convolutional Autoencoders to Speed Up Similarity-Based Time Series Mining

Time series represent a type of data that is increasingly present in research and industry applications. The most commonly used approach to obtain knowledge from these data is similarity-based data mining algorithms. However, in large volumes of data, applying these algorithms may be infeasible. Therefore, several techniques are proposed in the literature to accelerate the distance calculation between time series, such as algorithms' adaptations, indexing, and approximations. Recent results show that the union of techniques in different categories usually improves efficiency in time series mining tasks by similarity. In this way, we evaluate the dimensionality reduction through convolutional autoencoders to speed up time series distance calculation. To this end, we propose an offline training phase, with data previously observed in other domains, before applying the autoencoders. Our proposal is orthogonal to state-of-the-art tools so that we can use it as a complementary technique. We show that our proposal can lead similarity-based algorithms to execute up to two orders of magnitude faster than these tools alone, without loss of quality in the results obtained through all-pairwise distance calculation, similarity search, and motif discovery.
64-GHz Datapath Demonstration for Bit-Parallel SFQ Microprocessors Based on a Gate-Level-Pipeline Structure

We successfully demonstrated an 8-bit-wide, bit-parallel datapath composed of an arithmetic logic unit and register files for high-throughput oriented SFQ microprocessors based on a gate-level-pipeline structure. Achieving high-speed operation in the bit-parallel datapath is difficult because of feedback paths. We used concurrent-flow clocking and counter-flow clocking in combination to solve the timing problem at the feedback path in the datapath, and we optimized the number of JJs and pipeline stages in the register file for solving the timing issue. We designed the datapath with the cell library for the AIST 10 kA/cm$^2$ Advanced Process. The total number of pipeline stages, Josephson junctions, and circuit area of the designed datapath were 52, 18448, and 3.81 mm × 4.05 mm, respectively. We obtained a relatively wide bias margin of the designed datapath at the target clock frequency of 50 GHz, and it operated up to 64 GHz in on-chip high-speed testing.
Variational Autoencoders for Polyphonic Music Interpolation

This paper aims to use machine learning techniques to solve the novel problem of music interpolation composition. Two models based on Variational Autoencoders (VAEs) are proposed to generate a suitable polyphonic harmonic bridge between two given songs, smoothly changing the pitches and dynamics of the interpolation. The interpolations generated by the first model surpass a Random data baseline and a bidirectional LSTM approach and its performance is comparable to the current state-of-the-art. The novel architecture of the second model outperforms the state-of-the-art interpolation approaches in terms of reconstruction loss by using an additional neural network for direct estimation of the interpolation encoded vector. Furthermore, the Hsinchu Interpolation MIDI Dataset was created, making both models proposed in this paper more efficient than previous approaches in the literature in terms of computational and time requirements during training. Finally, a subjective evaluation was done in order to ensure the validity of the metric-based results.
Pencil Drawing Generation Algorithm Based on GMED

The automatic generation of pencil drawings from natural images has been a hot spot in the field of nonphotorealistic rendering in recent years and has attracted the attention of many scholars. Aiming at the characteristics of single line drawings generated by the existing pencil drawing algorithm and stiff lines, this paper proposes a new edge generation Gradlent Morphological Edge Detector (GMED) algorithm, which uses the generated edge convolution to generate pencil line drawings, making stylized pictures more natural. First, the gradient map of the input image is extracted according to the feature of the tone rate of change around the line at the boundary. Second, to make up for the shortcomings of too thin and discontinuous lines in the gradient map, morphological operations are performed on the gradient map and convolved to generate a hand-drawn pencil line drawing. Finally, texture filling and tone mapping are performed, and the resulting line drawing is fused to obtain the final pencil drawing effect. Compared with the existing pencil drawing algorithm, the proposed GMED edge generation method is applied to the line drawing generation process to generate a hand-drawn style, fine and concise line drawing, and the texture simulation process is simple and efficient, combined with histogram matching. In the process, a more suitable GML method is used, so that the final pencil drawing not only has natural lines but also colors closer to the input image.
A Resource Allocation Scheme of D2D Energy Harvesting Networks Based on Stochastic Learning

As one of the key technologies in the 5th generation mobile networks, device-to-device (D2D) can significantly improve spectrum efficiency (SE), throughput, energy efficiency, and reduce congestion in cellular networks. However, due to the spectrum sharing between D2D users (DUs) and cellular users (CUs), unreasonable resource allocation may cause serious interferences. Meanwhile, considering the energy limitation of D2D equipment and the requirement of green communication, this paper studies the resource allocation scheme under D2D energy harvesting (EH) networks based on stochastic learning and Stackelberg game. We formulate the joint channel and EH time slot allocation problem as a distributed learning problem. The DUs should pay for reusing the spectrum resource to base station if joining the networks reduce the SE of CUs severely. The proposed resource allocation scheme can converge to a Nash Equilibrium which maximizes the SE of the D2D networks and protects the quality of service of CUs. Finally, numerical results validate that the proposed scheme achieves an advanced performance relatively.
A Simple Method to Create Corners for the Lookup Table-Based MOSFET Models Through Inputs and Outputs Mapping

This article presents a simple method to create corner models to capture process variations for the lookup table-based MOSFET models through inputs and outputs mapping. The electrical specifications, including the threshold voltage, peak transconductance, ON-state resistance, saturation current, and gate oxide thickness, can be fitted through five model parameters. The effectiveness of the method is demonstrated by both the FinFET model BSIM-CMG and the planar MOSFET measurement data.
An Error Rate Comparison of Power Domain Non-Orthogonal Multiple Access and Sparse Code Multiple Access

Non-orthogonal Multiple Access (NOMA) has been envisioned as one of the key enabling techniques to fulfill the requirements of future wireless networks. The primary benefit of NOMA is higher spectrum efficiency compared to Orthogonal Multiple Access (OMA). This paper presents an error rate comparison of two distinct NOMA schemes, i.e., power domain NOMA (PD-NOMA) and Sparse Code Multiple Access (SCMA). In a typical PD-NOMA system, successive interference cancellation (SIC) is utilized at the receiver, which however may lead to error propagation. In comparison, message passing decoding is employed in SCMA. To attain the best error rate performance of PD-NOMA, we optimize the power allocation with the aid of pairwise error probability and then carry out the decoding using generalized sphere decoder (GSD). Our extensive simulation results show that SCMA system with “<inline-formula> <tex-math notation="LaTeX">$5\times 10$ </tex-math></inline-formula>” setting (i.e., ten users communicate over five subcarriers, each active over two subcarriers) achieves better uncoded BER and coded BER performance than both typical “<inline-formula> <tex-math notation="LaTeX">$1\times 2$ </tex-math></inline-formula>” and “<inline-formula> <tex-math notation="LaTeX">$2\times 4$ </tex-math></inline-formula>” PD-NOMA systems in uplink Rayleigh fading channel. Finally, the impacts of channel estimation error on SCMA, SIC and GSD based PD-NOMA and the complexity of multiuser detection schemes are also discussed.
Global Data Distribution Weighted Synthetic Oversampling Technique for Imbalanced Learning

Imbalanced learning is a common problem in data mining. There is a different distribution of data samples among other classes in the imbalanced datasets. It’s a challenge for standard algorithms designed for balanced class distributions. Although there are various strategies to solve this problem, generating artificial data to achieve a relatively balanced class distribution is universal rather than directly modifying specific classification algorithms. The oversampled data can be combined with any user-specified algorithm without any restrictions. In this paper, we present a novel oversampling method, Global Data Distribution Weighted Synthetic Oversampling Technique (GDDSYN). By applying clustering, optimizing the selection criteria of the minority class samples that are used to generate synthetic samples, avoiding generating more noise samples. GDDSYN assigns weights for the number of synthetic samples to tackle the within-class imbalance and between-class imbalance simultaneously, according to the informative level of the sample and the sparsity of the cluster to which the sample belongs. The use of scores with Silhouette Coefficient and Mutual Information helps the k-means algorithm set a reasonable number of clusters for the minority and majority classes respectively so that the clustering effect can be guaranteed. Next, by using clustering information, synthetic samples’ generation path is improved to avoid class overlap. Additionally, GDDSYN has been evaluated extensively on 10 artificial and 10 real-world data sets. The empirical results show that our method is outperforms or comparable with some other existing methods in terms of assessment metrics when artificial data generated by GDDSYN are used.
EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction

Air quality affects social activities and human health. Air quality prediction, especially for extreme events such as severe haze pollution, plays an essential guiding role in government decision-making and outdoor activity scheduling. Established prediction models face the challenges of forecasting extreme values and long-term tendency. In this paper, we propose an extreme value attention network (EvaNet) based on encoder and decoder framework to achieve long-term air quality prediction. This model designs an extreme value attention mechanism to alleviate the impact of sudden changes on prediction. In addition, to capture long-term dependence relationships, EvaNet introduces a temporal attention mechanism. Integrating the dual attention mechanisms, the extracted features are fed into a decoder to yield the final prediction. The experiments evaluated on two real-world air quality datasets show the superiority of our method against other state-of-the-art baselines.