Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stable and Admissible Explicit Solution to Fuzzy Predictive Control using polyhedral invariant sets

One of the main problems in Model Predictive control MPC is the lack of guaranteed feasibility and stability. In this paper we propose a strategy to resolve the optimization problem associated with fuzzy predictive control to find a solution that is both stable and admissible. The proposed explicit approach, combined with the theory of invariant sets, provides a secure and admissible solution for the optimization problem. The approach begins by applying the principles of decision making in a fuzzy environment, specifically the Bellman Zadeh approach. Next, we divide the state space into separate regions the objective of this separation is to identify the state region that contains the appropriate solutions. We start with the terminal region and then working down, developing a set of permissible control laws for each region. The simulation results confirm that our strategy effectively addresses the problems of feasibility and stability in Model Predictive Control.
SoftLexicon-BERT-BiLSTM-CRF-based Named Entity Recognition Model for Transportation Organization Design Domain

The traffic organization design field involves more disciplines and the same entity is expressed in a variety of ways, which causes the named entity recognition effect to be greatly reduced, and the existing named entity recognition method based on single character granularity is unable to solve the dissimilarity of characters in different traffic organization vocabularies. In order to solve this problem, an entity extraction method based on the enhancement of domain knowledge features is investigated, which incorporates the domain knowledge system and professional vocabularies as external information into the domain entity recognition process of intersection traffic organization design, so as to improve the accuracy of named entity recognition. A traffic organization design domain entity extraction model based on the SoftLexicon-BERT-BiLSTM-CRF framework is constructed, and experiments on textual named entity extraction in traffic organization design domain are carried out, and the results show that, relative to the Bi LSTM-CRF model, the accuracy of this paper's model is improved by about 7.5%, the recall rate is improved by about 11%, and the F1-score improved by about 9.4%; relative to the BERT-BiLSTM-CRF model, the model in this paper improves the accuracy by about 3%, the recall by about 4%, and the F1-score by about 4%.
An Optimized Method Based on Two-Layer PSO Algorithm for the Node Placement of the Wireless Ad Hoc Network in Train Autonomous Circumambulate System

Train autonomous circumambulate system (TACS) is a significant trend in urban rail transit system, which adopts both train-to-train and train-to-ground communication to transmit data in high throughput and low latency. A clustered Ad hoc network is proposed for TACS. Multiple paths of transmission can be established to increase the survivability and throughput of communication in TACS. The QoS of the communication is highly related to the placement of nodes in the ad hoc network. In this paper, a multi-object optimized node placement method based on the two-layer swarm optimization algorithm (TLPSO) is proposed. In the outer layer, the node interval in rows and the distance from the track are abstracted as particle positions to realize the regular placement of relay nodes. And in the inner layer, the gateway nodes are selected from the deployed relay nodes in the outer layer. The simulation results show that the network capacity of a cluster is 186Mbps, and the average latency is 14.53ms, which meets the Quality of Service (QoS) requirements of the TACS.
Music Genre Classification Using Attention-Based CNN-Feature Fusion Paradigm

In order to organise the enormous music collections available on the Internet, genre hierarchies are widely used. Automatic musical genre classification, which may supplement or even replace the human user in this process, would substantially assist music information retrieval systems. A framework for developing and evaluating features for any content-based analysis of musical signals is also provided by automatically categorising musical genres. The paper's primary goal is to demonstrate how audio signals may be automatically classified using two models into a hierarchy of musical genres. Two feature sets are recommended for expressing timbral texture and rhythmic content, respectively. The CNN model with attention, which uses the fusion of timbral and rhythmic features, has the highest classification accuracy of 85%.
A novel Conditional Generative Adversarial Networks for Automotive Radar Range-Doppler Targets Synthetic Generation

Radar target simulations are vital to automotive research as they make it possible to reproduce real scenarios with reasonable accuracy. However, this process comes with high computational costs and limitations. The generated targets are ideal and thus far from reality. Recent studies have led to an alternative to generated synthetic radar data: Generative Adversarial Networks (GAN), a neural network architecture designed to generate realistic data to synthesize images, texts, and more, which is being studied for radar data applications. Until now, only a few studies have explored the approach, especially when considering automotive radars, but showing promising results in its applications. This work proposes a Conditional GAN (CGAN) that synthesizes FMCW radar Range-Doppler targets with Micro-Doppler given a selected object input. The results show that the generated samples are realistic enough to be classified with an accuracy of 82% in a pre-trained classifier, proving that the synthetic data seems to be similar to the real ones but not representing ideal results, as simulation targets do, thus fulfilling an important gap of knowledge for simulation purposes.
Designing a High School Course on Machine Learning for Cyberthreat Analytics

Introducing secure computing through the integration of Machine Learning (ML) applications at the early education stage, particularly within high school, presents both benefits and challenges. This paper conceptualizes the imperative, hurdles, and the blueprint for implementing a hands-on machine learning curriculum within school-level computing education. The convergence of ML with secure computing necessitates the prior inclusion of lessons encompassing precomputing, ethics, and privacy to prepare students effectively. Consequently, this integration is recommended for high school education (9th to 12th grade) due to maturity and comprehension levels of students at this stage. Considering the foundational knowledge and backgrounds of school students, our proposal advocates the adoption of basic and user-friendly ML tool, such as Weka, tailored to the school environment. This article delves into several facets of the Weka tool, elucidating its relevance in the context of research directions and methodologies.
Introducing New Three Phase Fractional Slot Windings With Low Space Harmonic Contents

One of the most effective winding type for use in electric machines is fractional slot concentrated winding (FSCW). These winding type prepares the shortest end-winding length for electric machines. In addition, they can tolerate the fault condition more in compare to other windings type. Besides, higher copper fill factor, lower cogging torque, simple construction and field weakening capability are another merits of FSCWs. Unfortunately, in compare to conventional distributed-windings, FSCWs have high space Magnetomotive Force harmonics (MMF harmonics). These unwanted harmonics will cause core saturation (localized saturation), mechanical vibration, acoustic noise and iron losses. In order to develop winding benefits, many methods have been introduced. This manuscript introduces some new three-phase windings, by using the stator slot shifting concept. The novelty of this paper is introducing some new winding topologies for the first time. An analytical study and the 2-D FEM are applied to three sample-designed synchronous reluctance machines to evaluate the new winding's characteristics as case studies.
Adversarial Example Detection Techniques in Speech Recognition Systems: A review

Automatic Speech Recognition (ASR) is a crucial application of deep learning in today's world. However, ASR systems are vulnerable to attacks from malicious actors who can create adversarial examples to fool the system into producing incorrect outputs. Adversarial example detection techniques are being developed to mitigate the risk of such attacks. This review analyzes the effectiveness of various adversarial example detection techniques in the context of ASR systems. It begins by explaining the concept of adversarial examples and their potential impact on ASR systems as well as the most used evaluation metrics. Then, it provides an overview of the different types of adversarial attacks that can be launched against ASR systems. Upon reading this review, researchers are able to identify the existing weakness in ASR, and evade them in their future proposed schemes.
Evaluating the Efficacy of Feature Selection Methods in Cardiovascular Disease Prediction with Machine Learning

Cardiovascular disease (CVD) remains a primary contributor to global mortality, necessitating refined predictive models. Machine learning (ML) has emerged as a pivotal tool in augmenting clinical decisions, particularly in CVD prediction. Nonetheless, the performance of ML classifiers is substantially influenced by the judicious selection of relevant features. This manuscript presents a focused comparative analysis of feature selection methodologies in CVD prediction through ML classifiers. Utilizing a clinical dataset, the study assesses the efficacy of various classifiers based on distinct feature subsets. Empirical results indicate that the Random Forest classifier, with a mere five selected features, attains a remarkable 99.70% accuracy, thus qualifying as an optimal choice for clinical settings. The research underscores the necessity of scrupulously evaluating diverse combinations of feature selection and learning algorithms to derive the most effective model. Collectively, this work provides pivotal insights into optimal feature selection for precise CVD prediction, thereby contributing to the development of more efficient predictive strategies.
L-IDS: A Multi-Layered Approach to Ransomware Detection in IoT

Ransomware is increasingly targeting Internet of Things (IoT) devices, and the resource constraints of these devices make detecting and mitigating such attacks a significant challenge. Unlike traditional ransomware attacks, ransomware in IoT-based attacks aims to affect functionality rather than the availability of data, thus defeating traditional detection methods. To address this issue, this article introduces a lightweight intrusion detection system, L-IDS. It is designed based on the principle of defense in depth and combines multilayer controls, hardware-enhanced TEE such as TrustZone, with machine learning (ML) algorithms. L-IDS can effectively detect and mitigate ransomware attacks inside IoT systems with low resources compared to traditional security scanning methods. By integrating TEE, L-IDS will enhance the security and protection of IoT devices, while ML algorithms will help detect ransomware attacks more efficiently and accurately. Overall, the proposed approach provides a promising solution for protecting IoT systems against ransomware attacks, especially for resource-constrained devices.
A correlation-based stochastic model for massive MIMO channel

In this paper, the channel impulse response matrix (CIRM) can be expressed as a sum of couplings between the steering vectors at the base station (BS) and the eigenbases at the mobile station (MS). Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases. Extensive measurements were carried out to evaluate the performance of this proposed model. Furthermore, the physical implications of this model were illustrated and the capacities are analyzed. In addition, the azimuthal power spectrum(APS) of several models was analyzed. Finally, the channel hardening effect was simulated and discussed. Results showed that the proposed model provides a better fit to the measured results than the other CBSM, i.e., Weichselberger model. Moreover, the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis. This CIRM model can be used for massive MIMO design in the future communication system design.
A New Methodology for Design Optimization of Interior Permanent Magnet Motors for Electric Vehicle Applications

This paper proposes a new design optimization methodology for V-type interior permanent-magnet (IPM) motors used in electric vehicle (EV) applications. The proposed technique is able to find the optimum design through a wide range of potential design candidates. A heuristic optimization method implemented in MATLAB with the least number of function calls is used to facilitate the optimization process. Finite Element analysis is utilized as the model solver to improve the accuracy. Parallel processing using high performance computing resources is employed to make design process fast. The whole of design optimization process is implemented using MATLAB and Python programming language to make developed optimization algorithm automated. To examine the performance of developed method, a V-type IPM motor for electric bus (E-bus) application is optimized. The machine design with best fitness is selected as the best case in which overall weight is minimized and the motor efficiency is maximized. Implementation of this optimization procedure showed how an EV can be efficiently designed where accuracy is maintained and computational cost is improved.
An Improved Delay Time Of Optical Half-Adder Applying Nonlinear Photonic Crystals Technology

In the present investigation, we attempt to develop a new structure of optical half adder using nonlinear photonic crystal ring resonator. As a main advantage, the use of coupling between bus waveguide and ring resonator increases the power intensity of the output and opens a wider photonic band gap (PBG). In this design, in order to improve the optical characteristics of the designed half adder, the combination of interference-based defect and nonlinear Kerr effect is exploited, as well as logic function transmit power control, including radius and refractive index of coupling rods. As a result, the structure contrast ratio is valued between 12.74 dB and 16.33 dB, proportional to the output power, which varies between 1.7% to 5% and 88 % to 108% for logic level “0” and “1” respectively. Resulting a minimum threshold power around of $0.3\ \mathrm{W}/\mu\mathrm{m}^{2}$. Additionally, the suggested design can be taken into account for ultra-fast integrated circuits to assure their integration in optical communication networks. Plane wave expansion (PWE) and finite difference time domain (FDTD), two distinct numerical techniques, are applied to estimate the propagation modes and the output spectrum respectively.
Screening method for automotive suspension parts based on image 3D reconstruction technology

To address screening suspension spring and lateral stabilizer bar inefficiencies, a screening method for 3D modeling based on 2D images is proposed. This replaces the previous manual inspection or scanner-based 3D model reconstruction. The first step is to set up the drive and camera array to capture 2D images of the processed parts from multiple angles. Then the SFM and MVS algorithms are used to obtain the 3D model of the part through a series of steps such as feature point extraction and matching, sparse reconstruction, global optimization of the beam method, and dense reconstruction. Eventually, after measuring and comparing the modeling results, the purpose of distinguishing the advantages and disadvantages is achieved. Compared to traditional screening methods, the modeling results based on 2D image reconstruction meet the accuracy requirements of the screened parts needed for the assembly line. It also effectively improves screening efficiency and shortens the work cycle.
Evaluation of User Experience on Taspen otentikasi Application Using User Experience Questionnaire (Case Study of PT. TASPEN (Persero))

PT TASPEN (Persero) is one of the state-owned companies engaged in insurance, especially insurance for civil servants and state officials. In carrying out its business processes, PT TASPEN (Persero) collects updated data on pension recipients through the Taspen Otentikasi application. The purpose of this research is to evaluate the Taspen Otentikasi application, especially in terms of user experience. The evaluation was conducted online by distributing questionnaires and then the data was processed using the User Experience Questionnaire (UEQ). Analysis was carried out from the results of the questionnaire which was then assessed using UEQ tools which were grouped into 6 assessment categories, namely attractiveness, clarity, stimulation, novelty, efficiency, and accuracy. The results showed a good evaluation value in the aspects of Attractiveness (mean 1.74), Clarity (mean 1.91), Stimulation (mean 1.67), Novelty (mean 1.26) while for the Efficiency aspect (mean 1.93) and the Accuracy aspect (mean 1.80) received an Excellent score. UEQ tools can find parts or features that require improvement and development so as to further maximize the use and functionality of the Taspen Otentikasi application in digitizing services.
Attention ResNet-GRU model for Typhoon Prediction

Multimodal data analysis refers to the integration of different types of data (such as images and numerical features) to leverage the complementarity and interactive information between various data sources, aiming to enhance the accuracy and effectiveness of analytical tasks. The advantage of multi- modal methods lies in their ability to comprehensively utilize diverse data sources, thereby providing more comprehensive and accurate analysis results. Consequently, they have significant advantages in various prediction tasks. In this study, we investi- gate multimodal data analysis methods using a typhoon dataset obtained from the Chinese Academy of Sciences’ Ocean Science Data Center. Through preprocessing, intensity mapping, and data augmentation techniques, we construct a dataset containing features such as longitude, typhoon category, wind speed, and typhoon intensity. To improve prediction accuracy, we utilize the data from the past five days to predict the current typhoon longitude and combine every five samples into a complete ty- phoon record. Additionally, we transform numerical features into images and normalize them to enhance data representation. In terms of model design, we employ a multimodal neural network, where ResNet and a GRU network with multi-head attention mechanism handle image and data features, respectively. The typhoon longitude prediction is achieved through feature fusion and fully connected layers. Experimental results demonstrate that the multimodal approach outperforms single-modal methods in regression tasks. This research holds significance for typhoon path prediction and disaster prevention.
Performance Evaluation between Back-to-Back Converter and DC-DC Converter for Power Sharing Between Two Interconnected Microgrids

As the voltage and frequency fluctuate concerning load or source power variations, it is necessary to maintain a predetermined nominal voltage and frequency to ensure stability and reliability in a microgrid system, particularly one that is operating in islanded mode. Nevertheless, introducing interconnection between microgrids ensures system stability and reliability by producing precise power flow management among microgrids. Hence, an infrastructure of interconnected microgrids using a back-to-back (BTB) converter has been proposed in this paper to integrate the microgrids operating in an islanded mode to maintain the prescribed limits of the system parameters, optimum power flow, and resilient operation during load fluctuation. The interconnected system with a BTB converter is designed in MATLAB/Simulink software with one converter controlled by DC-link voltage and the other by an AC voltage controller from microgrid-2 based on droop control and the system's performance is further evaluated using the OP5600-based OPAL-RT simulator. The findings demonstrate that aside from some switching losses, the proposed system incorporating a BTB converter provides effective power flow with minimal voltage and frequency fluctuations, making it a preferable choice compared to the DC-DC converter, which introduces significant ripples in power sharing and the energy storage system’s output, negatively impacting battery life.
CApriori: A Parallel Association Rule Algorithm for Time Series Data

China ADS front-end demo linac (CAFe) uses a distributed control system based on experimental physics and industrial control system (EPICS), and the EPICS software system Alarms is used to monitor the alarm states of process variable(PV) in real time. Alarms stores a large amount of alarm data of time series representing alarm events, and the cause of failure can be determined by analyzing the correlation between alarm events. The traditional association rule algorithm is limited by the minimum support and can only get the association rules among frequent alarm events. Therefore, this paper proposes a parallel association rules algorithm, called CApriori, based on Spark, the big data computing engine, for processing the large amount of time series alarm data to find the association rules between low-support alarm events. In the second stage of the CApriori, distance correlation is introduced to remove candidate sets that of high frequency but low correlation. The proposed algorithm is applied to the data generated by the CAFe alarm system, and the results show that CApriori can find the association rules between the alarm events with high correlation and low support, which provides a basis for the intelligent fault diagnosis of the accelerator.
Torque Fluctuation Minimization in PMa-SynRM Using Optimized Current Harmonic Injection by Genetic Algorithm

This paper presents a comprehensive approach to significantly reduce torque fluctuation in permanent magnet-assisted synchronous reluctance motors (PMa-SynRM). This paper constructed a torque Fluctuation model utilizing a multi-layer perceptron (MLP) artificial neural network (ANN) based on the motor’s finite element method (FEM) simulation output. Subsequently, leveraging this ANN machine model, we propose an optimization strategy for harmonic injection utilizing a genetic algorithm (GA). The objective is to minimize torque Fluctuation through precise modulation of current harmonics. The study demonstrates the effectiveness of this integrated approach, showcasing notable reductions in torque Fluctuation and consequently enhancing the overall performance and efficiency of PMa-SynRM.
Safe Reinforcement Learning for Mixed-Autonomy Platoon Control

Deep reinforcement learning (DRL) has been demonstrated to be effective for the control of mixed-autonomy platoons. However, incorporating safety considerations into DRL typically relies on designing collision avoidance-based reward functions, which lack collision-free guarantees. To address this research gap, we introduce a differentiable safety layer to convert DRL actions to safe actions with collision-free guarantees, which relies on a differentiable quadratic programming problem featuring Control Barrier Function-based safety constraints. The proposed safety-informed DRL approach explicitly integrates safety constraints into the training process and enables our method to adapt to varying safety-critical scenarios. Simulation results demonstrate that our proposed method effectively ensures CAV safety in mixed platoon environments while simultaneously enhancing traffic capacity and string stability. Furthermore, the safety layer expedites training by reducing the range of exploration.