Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Approach for Age and RUL Estimation of Lithium-Ion Batteries in EVs based on Capacitance

Electric Vehicles (EVs) have garnered tremendous popularity due to their eco-friendly characteristics. Nevertheless, the performance and durability of EV batteries can be influenced by factors such as usage patterns, charging behaviors, and temperature. Timely monitoring of Lithium-ion battery (LIB) health is vital to ensure reliability, safety, and longevity, thereby necessitating timely replacement. This paper presents a secure and efficient approach by leveraging capacitance measurement at 1 kHz as an alternative to Electrochemical Impedance Spectroscopy (EIS). This method provides valuable insights into the battery’s internal processes and enables accurate estimation of its remaining lifespan. By monitoring capacitance, potential issues that contribute to premature deterioration can be identified, enabling proactive replacement after a specific number of charge and discharge cycles. Such proactive maintenance reduces the risk of sudden battery failures and optimizes overall lifespan. Through the use of a non-destructive method to predict the battery’s age and Remaining Useful Life (RUL), this paper makes a significant contribution to the accurate assessment of LIBs’ age and RUL, benefiting battery management and energy storage systems. The experimental findings serve as evidence for the effectiveness of the proposed methodology.
Effect of Machine Reliability on the Cognitive Processes of the Task Performance

Brain machine interfaces (BMI) are becoming increasingly prevalent in diverse applications including motor rehabilitation, virtual reality training, etc. Two critical aspects of an effective BMI are machine reliability and cognitive workload (CWL). Previous studies have reported a notable effect of machine reliability on the 6 factors of the CWL. However, it remains unclear whether this effect can be detected in cognitive processes. Electroencephalography (EEG) is a widely used technique to explore cognitive processes by recording brain activities as signals. Therefore, we utilized the event-related spectral power (ERSP) feature of EEG signals to determine the cognitive processes regarding the effect of machine reliability. The results revealed that machine reliability affected the CWL factor of performance which was reflected in the $y$ band activities of the right prefrontal cortex. The findings indicate the potential of cognitive processes in detecting the effect of machine reliability. The detection could pave the way for designing adaptive BMI to balance the machine reliability and the CWL.
Advancing Epilepsy Disease Classification through Machine Learning and Deep Learning Models Utilizing EEG Data.

Epilepsy disease is a neurological condition marked by recurring seizures that has a big effect on people’s life. Effective management and therapy depend on a prompt and correct diagnosis. The traditional methods, including medical history checks and MRI-based diagnoses, are costly, time-consuming, and often create incorrect results. In order to overcome the drawbacks of conventional and expensive diagnostic techniques, this work offers a machine-learning and deep learning-based method for the classification of epilepsy disease utilizing EEG signal data. The proposed study uses the EEG signal dataset collected from the UCI machine learning repository. This study shows the potential for automating the classification of epilepsy disease by using EEG signal data, opening up a potentially new path for the early detection of disease. Proposed CNN model achieved the highest accuracy of 98.4% among all classifiers, which is quite good.
An Improved Iterative Reduction FISTA-Barzilai-Borwein Algorithm for Large-Scale LASSO

The iterative reduction FISTA algorithm (IR-FISTA-B) is an extremely excellent algorithm for solving large-scale LASSO problems. It utilizes the sparsity and gradient of the solution to transform large-scale problems into small-scale subproblems, and uses the FISTA algorithm to solve subproblems. In this paper, we improve the algorithm by using the Barzilai-Borwein step size as the step factor in the inner loop algorithm, and provide appropriate parameter update criteria to accelerate the convergence rate of the algorithm. Under the ideal termination criterion, we prove that the algorithm converges to a minimum with a locally linear rate. In numerical experiments, the performance of the proposed algorithm for solving large-scale LASSO problems was demonstrated by testing on synthetic data and LIBSVM datasets comparing with the original algorithm.
Deep Knowledge Tracing with Multiple Features

Knowledge tracing is an important task in online education platform. It aims to evaluate students' knowledge status and predict their future performance based on students' historical learning data. The existing methods only use interaction (exercise-response pair) sequence as model input and do not make full use of rich data collected by the platform. This paper proposes a Deep Knowledge Tracing model with Multiple Features (DKTMF), which considers three types of data that are easy to collect in online education platform: response time, exercise text and heterogeneous data. First, the negative exponential function about the interval time between responses is used to simulate students' forgetting behavior according to Ebbinghaus forgetting curve. Second, BERT and SimCSE are used to obtain the semantic features of the exercise text, and then the semantic similarity of the exercise is further calculated. Then, XGBoost is used to conduct feature engineering for heterogeneous data. Finally, the above three features are introduced into the deep knowledge tracing model based on Transformer. The experimental results show the effectiveness of DKTMF. Compared with the existing model, the average increase in AUC and ACC is 7.51% and 4.92% respectively, which can more accurately predict students' performance.
Clinical Pathway Clustering Using Surrogate Likelihoods and Replayability Validation

Modelling clinical pathways from Electronic Health Records (EHRs) can optimize resources and improve patient care, but current methods for generating pathway models using clustering have limitations including scalability and fidelity of the clusters. We propose a novel pathway modelling approach using Maximum Likelihood (ML) data clustering on Markov chain representations of clinical pathways. Our method is calibrated to produce clusters with low inter-cluster variability across the pathways. We use machine learning with Stochastic Radial Basis Functions (SRBF) kernels for surrogate optimization to handle non-convexity and propose an incremental optimization method to improve scalability. We also define a methodology based on novel replayability scores to help analysts compare the fidelity of alternative clustering results. Results show that our ML method produces clusters that have higher fidelity in terms of replayability scores than k-means based clustering and in capturing queueing contention, which is important for bottleneck identification in healthcare.
A Variable-Step LMS-Based Adaptive Filtering Algorithm Using Standard Deviation

Adaptive filters are used in digital signal processing for a variety of applications including echo cancellation, noise suppression, channel equalization, source separation, and channel estimation. Their main function is to automatically and dynamically adjust their coefficients to adapt to the changing characteristics of the input signal. In this work it is presented a new adaptive filtering algorithm that is aimed at reducing the estimation error and improving the speed of convergence with respect to existing algorithms. The proposed algorithm is variable step and adjusts the coefficients of the digital filter based on the standard deviation of the estimation error and its derivative. This is a new approach for the adaptation of the coefficients that had not been explored until now. The results obtained from the performance evaluation show that the algorithm shows a wide advantage over the well-known Variable Step Size (VSS) in terms of convergence speed and error reduction.
MvLog: Mining Invariants by Recovering Workflow on Log for Anomaly Detection

Abnormal features and normal features are the key to log anomaly detection. There are various features, but software workflow features are undoubtedly the most fundamental ones. So, this paper presents a novel method to restore workflow and construct the invariants, which we call it as MvLog. MvLog first uses modified Drain to obtain log events and corresponding parameters, then uses the improved Apriori to find related event sequences, and finally analyses the relationship between the event sequences to recover workflow fragment which are considered as the invariants for log anomaly detection. We had developed the MvLog prototype system and tested it with 11 kinds of log provided by LogHub. Experiments show that MvLog is capable of recovering the workflow fragment as invariants with an acceptable overhead.
Quantum K-Means Model based on Optimization Framework

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally.
Audibility and Preference of Musical Instrument by People with Hearing Loss

Some deaf and hard-of-hearing (DHH) individuals like to “listen to” music. For them to access a wider range of music, both the preference for and the audibility of music are important. As a starting point for understanding music accessibility, we investigated the audibility and preference for 19 musical instruments using five pitches totaling 95 timbres. To examine how people listen to music daily, owing to the difficulty of having participants come to the laboratory due to the spread of COVID-19, we conducted the experiment online. Participants listened to short clips of 95 sounds and subjectively answered questions on their audibility and preferences. An analysis of their subjective answers revealed the audibility and preference differences among instruments, specifically that the recorder lacks the same audibility and preference as some of the other instruments at certain pitches. However, there was little correlation between audibility and timbre preferences. The study findings will be used in music classes in which DHH students participate and indicate that timbre audibility is necessary for building music recommendation systems for DHH individuals. We also discuss the limitations and possibilities of online experiments using sound. Music, timbre, deaf and hard of hearing, audibility, preference, accessibility
The Identification of Device Status Indicator in Hydropower Station Based on YOLOv5s

In order to timely detect abnormal status indicators of hydropower stations when no one is on duty, it becomes very important to develop a real-time detection system for achieving remote alarms. To achieve this goal, this paper studies the detection and identification of facility status indicators by utilizing the surveillance camera in hydropower station. We firstly collect many camera based images from various station indicators and construct the training datasets. Then we choose to use the lightweight YOLOv5s model for detecting images captured by the camera. Finally, the detection effectiveness is evaluated by the average accuracy. The test results show that the model can effectively identify changes of device status indicators in hydropower station.
Design of a Tunable Astrocyte Neuromorphic Circuitry with Adaptable Fault Tolerance

Astrocytes are the star-shaped glial cells in a mammalian brain that facilitate self-repair by restoring the spike firing frequency of failed neurons. We design an astrocyte neuromorphic circuitry in 90nm CMOS. Our astrocyte parameters can be tuned to achieve different degrees of fault tolerance in the design. First, we implement a Leaky-Integrate-and-Fire (LIF) neuron with analog synapses enclosed with an astrocyte circuitry that provides a closed-loop retrograde feedback signal to the neuron. This feedback is responsible for reinstating the activity level at a faulty synaptic site. Next, we propose two adaptable fault tolerance approaches for designing a neuromorphic core - a partitioned design with multiple astrocytes and a single-astrocyte design with adjustable fault tolerance. Through extensive circuit-level simulations, we show the adaptive fault-tolerant behavior of our proposed design to randomly-injected faults.
Transmission Line Overload Relieving in Pool Based Electricity Market using TCSC

Congestion management in the power system is essential for ensuring the reliable and efficient distribution of electricity in competitive markets. In this work, particle swarm optimization (PSO) is utilized for managing congestion through generator rescheduling and optimal placement and sizing of thyristor-controlled series capacitor (TCSC) in a poolco environment. Real power outputs of generators (except at slack bus), TCSC location, and degree of compensation provided by TCSC are chosen as control variables. A constraint handling method that does not rely on penalty parameters is utilized to treat the inequality constraints. The proposed method is validated on a modified IEEE 30 bus system for two different contingency cases, and comparative analysis is performed. The simulation results manifest the efficacy of the proposed approach in obtaining stable power system operation.
State Feedback Controller for Switched Linear Systems

This paper introduces a novel state feedback control law specifically designed to stabilize switched systems with finite time stability (FTS) under restricted switching. The proposed control strategy utilizes the concept of multiple Lyapunov functions in conjunction with average dwell time(ADT) conditions, ensuring the achievement of FTS in the system. By incorporating average dwell time, the control law effectively regulates the switching rate, thereby improving overall stability and performance of the switched system. To validate the effectiveness of the proposed approach, a numerical example is presented, and simulation results are analyzed. The numerical example demonstrates how the control law successfully achieves FTS in the switched linear system, providing insights into its stability properties and performance under restricted switching.
Data-Driven Production Planning Formulations with Inventory Considerations

Data-driven (DD) production planning formulations for semiconductor wafer fabrication facilities (wafer fabs) are studied in this paper. These formulations are based on a set of system states representing the congestion behavior of the wafer fab with work in process and resulting output levels. We establish two DD formulations with inventory considerations. The first variant is a shortfall-based chance-constrained formulation that considers safety stocks at the finished goods inventory level. The second variant is a simple scenario-based stochastic program where the objective function reflects the expected inventory holding and backlog cost under uncertainty. The two variants are compared with the conventional DD formulation in a rolling horizon environment using a simulation model of a large-scaled wafer fab. The simulation experiments demonstrate that the stochastic program achieves the largest profit under all experimental conditions.
Malware Detection Based on Periodic Communication Behavior

Malicious software attacks are gradually becoming more prevalent, posing a serious threat to the information security of nations and large enterprises. Currently, widely applied methods for malicious software detection include signature-based detection, behavior-based detection, and heuristic-based detection. However, malicious software has been optimized to evade these detection methods by encrypting traffic and attempting to conceal communication traces with noise. While signature-based and heuristic-based detection methods offer fast and efficient detection of known malicious software, they exhibit poorer performance in detecting unknown malicious software. This paper proposes the use of periodic communication behavior to detect the presence of malicious software. The prevalent methods for detecting periodic traffic are mostly statistically based, facing challenges in accuracy due to fluctuations in real-world traffic. To address the shortcomings of existing methods for detecting malicious software using periodic traffic, this paper designs and implements a malicious software periodic traffic detection system based on Fourier Transform. The system introduces the Zeek tool to convert initial traffic pcap data tables into log files. It then employs a time series extraction algorithm to extract communication time series for each internal and external IP address pair. Finally, it utilizes Fourier Transform to calculate the periodicity of malicious software traffic and employs malicious software detection algorithms to determine whether the IP address generating this periodic traffic is a remote control endpoint for malicious software. The paper evaluates the system's detection performance using three publicly available datasets, with experimental results demonstrating an 77.3% detection accuracy on the IoT dataset. Furthermore, to address the limitations of existing publicly available datasets, such as a limited variety of attack scenarios and insufficiently diverse data, we collect a new dataset from a university campus network. The system is further evaluated on this campus network dataset, showing good detection efficiency and discovering several malicious software remote IP addresses, thus proving the value of this malicious software detection system.
SpectroNet: A Low Complexity CNN-LSTM Architecture for Keyword Spotting Application

Keyword spotting is a problem in Natural Language Processing which aim is to classify spoken words from a dataset. With the recent emergence of CNN, researchers have attempted to perform keyword spotting using CNN. However, CNN architectures are complex, thus making them not compatible with real-time applications. In this paper, SpectroNet, a low complexity hybrid CNN-LSTM approach is proposed to perform keyword spotting with less complexity. This architecture reaches 94.07% accuracy, with only 142,877 trainable parameters needed. The trainable parameters needed are significantly less than CNN networks while achieving comparable results.
A Time-domain Packet Loss Concealment Method by Designing Transformer-based Convolutional Recurrent Network

Due to the network constrain, the packet loss is inevitable in the real-time speech communication. The packet loss often leads to the short interruption of the voice communication, which seriously affects the quality of speech communication. In this paper, a transformer-based convolutional recurrent network (CRN-Trans) is designed for packet loss concealment (PLC) in time domain. In the CRN-Tans, the convolutional layers are used to extract the high-level feature of speech signal in each frame. The transformer and long short-term memory (LSTM) are combined to model the sequential information along the time dimension. Furthermore, using the self-attention mechanism, the transformer is used to compute the similarities between different positions in the long sequence and dynamically generate the weights for different positions. Thus, the lost packets can be better predicted by explicitly providing attention weights for the previous frames. The experimental results show that the proposed method has better performance in perceptual evaluation of speech quality (PESQ) and short-term objective intelligibility (STOI).
Low-Cost MEMS IMU Calibration Using Deep Learning and Visual-Inertial Odometry

In the past decade, machine learning algorithms have shown remarkable potential in comprehending and interpreting errors and their origins in Inertial Measurement Units (IMUs), Various strategies have been developed for IMU calibration and error compensation, assuming reliable ground truth data for training. The main challenge lies in creating a suitable training dataset, which often requires precise equipment that contradicts cost-effective calibration, especially for commercial use. This study introduces a pragmatic calibration framework for low-cost MEMS inertial measurement units, utilizing deep convolutional neural networks (CNNs). The lack of ground truth during learning is addressed by incorporating preliminary visual-inertial odometry results through a loosely coupled fusion algorithm based on the error-state extended Kalman Alter (ES- EKF). The proposed framework is validated for stationary, dynamic errors, and path dependency. With a precisely designed CNN and ample training and validation data, the simulation results confirm the reliability of the initial 6 seconds of ES-EKF flight navigation as a dependable CNN training reference. The scheme significantly reduces errors in a low-grade IMU attached to a quadcopter, coupled with a pre-calibrated camera, by an impressive 93.22%. The aim is to establish a highly accurate and robust offline calibration process, harnessing only quadcopter components. Moreover, the loosely coupled fusion approach empowers the trained calibration module to provide real-time online functionality within the Visual-Inertial Navigation System (VINS) by optimizing memory, CPU, and GPU usage.
OUSC-Net: Optimization-Based Unfolding Network for Super-Resolution Reconstruction of CEST-MRI

Chemical exchange saturation transfer magnetic resonance imaging (CEST-MRI) is a new approach to detect tissue metabolic changes. However, constrained by its low signal-to-noise ratio and long scan time, CEST-MRI is usually only able to get low-resolution (LR) images in clinical examination, which is disadvantageous for the diagnosis of small lesions. But simple interpolation to improve the CEST-MRI resolution will cause a severe decrease in the accuracy of CEST-MRI metabolic analysis. Many deep-learning methods are also not satisfying for CEST-MRI super-resolution reconstruction (SRR). In this work, we formulate the CEST-MRI SRR as a hyperspectral image recovery problem. We adopt the Multispectral/hyperspectral-fusion (MS/HS fusion) method to solve this problem. And the optimization process is unrolled to a deep neural network. This method is named as OUSC-Net. We also propose a novel CEST-based Normalization loss (CBN loss) to improve the quantitative accuracy of the high-resolution (HR) CEST-MRI image series. The proposed method is evaluated on an ischemia rat dataset and a human dataset. The results show that the proposed method reconstructs high-quality HR CEST-MRI series even at high down-sampling rate and outperforms all other comparative SRR methods in metabolic quantification.