Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Conductivity Classification Technique for Nonmagnetic Metal Immune to Tilt Variations Using Eddy Current Testing

The widespread promotion of the concept of circular economy has placed higher demands on the sorting and recycling of metals. Eddy current testing as a non-contact, economical and fast response non-destructive testing technology provides a new development direction for the laborious task of classifying non-magnetic metal. The inevitable inclined surfaces in the metal classification process increase the difficulty of classification. In this paper, we present a novel conductivity classification technique for nonmagnetic metal, which uses the method of minimum error of characteristic slope to identify the metal property. The characteristic slope is extracted by fitting the line obtained from the endpoint of the maximum magnitude of mutual inductance trajectory under different tilt angles. The characteristic slope exists due to the consistency of the phase of the magnitude peak endpoints The test results show that the technique performs accurately in classifying five non-magnetic metals, copper, aluminum, zinc, tin, and titanium, within the tilt angle of 16.7°. This method benefits to achieve real-time metal classification.
A Method for Measuring Three-Dimensional Human Joint Movements in Walking

A method was developed for converting a stereo-pair two-dimensional human key-points recognized by OpenPose to three-dimensional ones. Severe noise included in them due to fluctuation in OpenPose recognition and/or missing values were surprisingly reduced by four procedures; missing value interpolation, low rank filter, puppet model, and spatial AR model. The method gave us enough performance to analyze personal character in walking.
A Comparison of Cepstral Features in the Detection of Pathological Voices by Varying the Input and Filterbank of the Cepstrum Computation

Automatic voice pathology detection enables objective assessment of pathologies that affect the voice production mechanism. Detection systems have been developed using the traditional pipeline approach (consisting of the feature extraction part and the detection part) and using the modern deep learning -based end-to-end approach. Due to the lack of vast amounts of training data in the study area of pathological voice, the former approach is still a valid choice. In the existing detection systems based on the traditional pipeline approach, the mel-frequency cepstral coefficient (MFCC) features can be regarded as the defacto standard feature set. In this study, automatic voice pathology detection is investigated by comparing the performance of various MFCC variants derived by considering two factors: the input and the filterbank in the cepstrum computation. For the first factor, three inputs (the voice signal, the glottal source and the vocal tract) are compared. The glottal source and the vocal tract are estimated using the quasi-closed phase glottal inverse filtering method. For the second factor, the mel-frequency and linear-frequency filterbanks are compared. Experiments were conducted separately using six databases consisting of voices produced by speakers suffering from one of four disorders (dysphonia, Parkinson’s disease, laryngitis, or heart failure) and by healthy speakers. Support vector machine (SVM) was used as the classifier. The results show that by combining mel- and linear-frequency cepstral coefficients derived from the glottal source and vocal tract, better overall detection accuracy was obtained compared to the defacto MFCC features derived from the voice signal. Furthermore, this combination provided comparable or better performance than four existing cepstral feature extraction techniques in clean and high signal-to-noise ratio (SNR) conditions.
Hybrid Particle Filter trained Neural Network for Prognosis of Lithium-ion Batteries

Prognostics and Health Management (PHM) plays a key role in Industry 4.0 revolution by providing smart predictive maintenance solutions. Early failure detection and prediction of remaining useful life (RUL) of critical industrial machines/components are the main challenges addressed by PHM methodologies. In literature, model-based and data-driven methods are widely used for RUL estimation. Model-based methods rely on empirical/phenomenological degradation models for RUL prediction using Bayesian formulations. In many cases, the lack of accurate physics-based models emphasizes the need to resort to machine learning based prognostic algorithms. However, data-driven methods require extensive machine failure data incorporating all possible operating conditions along with all possible failure modes pertaining to that particular machine / component, which are seldom available in their entirety. In this work, we propose a three-stage hybrid prognostic algorithm (HyA) combining model-based (Particle Filters-PF) and data-driven (Neural Networks-NN) methods in a unique way. The proposed method aims to overcome the need for accurate degradation modeling or extensive failure data sets. In the first stage, a feedforward neural network is used to formulate lithium-ion battery’s degradation trends and the corresponding NN model parameters are used to define the initial prior distribution of PF algorithm. In the second stage, the PF algorithm optimizes the model parameters and the posterior model parameter distributions are utilized to ‘warm-start’ the neural network used for prognosis and the third/final stages focuses on prognosis and RUL estimation using the trained NN model leveraging on the posterior distributions of the PF fine-tuned weights and biases. The proposed method is demonstrated on CALCE and NASA lithium-ion battery capacity degradation datasets. The efficacy of the proposed hybrid algorithm is evaluated using root mean square error (RMSE) values and alpha-lambda prognostic metrics. Also, the impact of the NN architecture on the prediction accuracy and computational load are analyzed.
Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

Automatic image annotation is a key technology in image understanding and pattern recognition, and is becoming increasingly important in order to annotate large-scale images. In the past decade, the nearest neighbor model-based AIA (Automatic image annotation) methods have been proved to be the most successful in all classical models. This model has four major challenges including semantic gap, label-imbalance, wider range labels, and weak-labeling. In this paper, we propose a novel annotation model based on three-pass KNN (k-Nearest Neighbor) to address the aforementioned challenges. The key idea is to identify appropriate neighbors at each pass KNN. In the first pass KNN, we identify the several most relevant categories based on label feature rather than visual feature as traditional models. In the second pass KNN, we determine the relevant images based on multi-modal (visual and textual label) embedding features. As the test image has not been annotated with any label, we propose a pre-annotation strategory before image annotation to improve the semantic level. In the third pass KNN, we capture relevant labels from semantically and visually similar images and propagate them to the given unlabeled image. In contrast with traditional nearest neighbor based methods, our method can inherently alleviate the problems of semantic gap, label-imbalance, and wider range labels. In addition, to alleviate the issue of weak-labeling, we propose label refinement for training images. Extensive experiments on three classical benchmark datasets and MS-COCO demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of per-label and per-image metrics.
Motion Cue-Based Sudden Pedestrian Behavior Prediction Using Fuzzy Inference

As the motion of pedestrians is largely unpredictable, situational awareness presents a challenge for safe autonomous driving in urban areas. In particular, conventional sensor information about the dynamic states involved in determining and predicting pedestrian motion, including the walking speed, is significantly affected by latency when pedestrians suddenly increase their pace. In this paper, we propose a framework for predicting the steady-state walking speed of sudden pedestrian movement at the early stage of walking after heel-off. Based on the analysis that some motion cues during gait initiation are related to the steady-state walking speed, a fuzzy inference framework for predicting the steady-state walking speed, where the related motion cues are input to the inference model, is developed. The proposed framework can accurately predict the steady-state walking speed, even at the end of the first gait cycle. Moreover, the future trajectory of the pedestrian can be predicted using the piecewise linear speed model. Using the proposed framework, installed on the edge server of the cooperative-intelligent transportation system (C-ITS), this study aims to ensure the safety of autonomous vehicles by enabling them to successfully navigate the danger caused by sudden pedestrian movement. Experimental results obtained from testing the system at a real urban intersection verify the value offered by the proposed framework.
Dual System Representation and Prediction Method for Data-Driven Estimation

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for a prediction of states and a calculation of an observer gain. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represents the original system. Linear matrix inequality that depends on data of the dual system provides the observer gains.
An efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets

This study proposes an efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. Most real datasets include an irregular subset with higher variance than most data, and predictive models do not learn well from these datasets. While most existing prediction models learned from the whole or randomly sampled training datasets, our suggested method draws up training datasets by separating regular and highly biased subsets to build accurate prediction models. We use a two-step approach to prepare the training dataset: (1) divide the initial training dataset into two groups, commonly distributed and highly biased using Principal Component Analysis, (2) enrich the highly biased group by Variational Autoencoders. Then, two deep neural network classifiers learn from the isolated training groups separately. The well-organized training groups enable a chance to build more accurate prediction models. When predicting the risk of coronary heart disease from the given input, only one appropriate model is selected based on the reconstruction error on the Principal Component Analysis model. Dataset used in this study was collected from the Korean National Health and Nutritional Examination Survey. We have conducted two types of experiments on the dataset. The first one proved how Principal Component Analysis and Variational Autoencoder models of the proposed method improves the performance of a single deep neural network. The second experiment compared the proposed method with existing machine learning algorithms, including Naïve Bayes, Random Forest, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Adaptive Boosting. The experimental results show that the proposed method outperformed conventional machine learning algorithms by giving the accuracy of 0.892, specificity of 0.840, precision of 0.911, recall of 0.920, f-measure of 0.915, and AUC of 0.882.
Fusing Multiple Deep Models for In Vivo Human Brain Hyperspectral Image Classification to Identify Glioblastoma Tumor

Glioblastoma (GBM) tumor is the most common primary brain malignant tumor. The precise identification of GBM tumors is very important for diagnosis and treatment. Hyperspectral imaging is a fast, noncontact, accurate, and safe modern medical detection technology, which is expected to be a new tool of intraoperative diagnosis. In order to make full use of the spectral and spatial information of hyperspectral images (HSIs) to achieve accurate GBM tumor identification, a method based on the fusion of multiple deep models is proposed for in vivo human brain HSI classification. The proposed method includes the following major steps: 1) spectral phasor analysis and data oversampling; 2) 1-D deep neural network (1D-DNN)-based spectral HSI feature extraction and classification; 3) 2-D convolution neural network (2D-CNN)-based spectral–spatial HSI feature extraction and classification; 4) edge-preserving filtering-based classification result fusion and optimization; and 5) fully convolutional network (FCN)-based background segmentation. To verify the capabilities of the proposed method, experiments are performed on two real human brain hyperspectral datasets, including 36 in vivo HSIs captured from 16 different patients. The proposed method can achieve an overall accuracy of 96.69% for four-class classification and overall accuracy of 96.34% for GBM tumor identification. Experimental results demonstrate that the proposed method exhibits competitive classification performance and can generate satisfactory thematic maps of the location of the GBM tumor, which can provide the surgeon with guidance on successful and precise tumor resection.
A Convolution Residual Network for Heating-Invariant Defect Segmentation in Composite Materials Inspected by Lock-in Thermography

This article proposes an automatic approach for segmenting inclusion defects in composite materials inspected by lock-in thermography (LT) in a heat source invariant way. In the proposed pipeline, temperature maps are preliminarily processed to enhance the contrast between sound and defective areas, reduce the acquisition noise, and mitigate the effect of nonuniform heat excitation. Then, the enhanced sequences are grouped in highly local sets, the input of a convolution residual neural network, which segments the defects focusing on the temporal evolution of the temperature. Experiments have been run on a dataset acquired from a CFRP specimen tested under five different heat source configurations. Here, a brief study of signal variations associated with the heat source configuration is presented. Then, an ablation study has been presented to compare different architectures of increasing complexity. The best results are achieved by using the proposed preliminary processing, the residual layers, and an input set of temperature signals of size $3 \times 3$ . This network can reach a global intersection over union (IOU) of 0.78, while defects are segmented with an IOU value of 0.58. In addition, the balanced segmentation accuracy of the test reaches 94.78%, with a maximum recall of the defective class of 91.41%. Further cross validation changing the source configurations of the training and test sets produces comparable results of IOU and balanced accuracy, which are on average equal to 0.71 and 84.02%, respectively. A final evaluation of the trained network is performed on a new dataset augmented through a vertical stretch. Results are still comparable with an average IOU and balanced accuracy of 0.74 and 87.15%, respectively. These results prove the capability of the method to segment inclusion defects of any shape without being affected by the configuration of the heating sources used in the LT inspections.
Prioritizing Relevant Information: Decentralized V2X Resource Allocation for Cooperative Driving

Cooperative driving is a promising approach to increase traffic efficiency and safety. However, cooperative driving requires high communication performance from the application perspective when coordinating and executing a cooperative maneuver, especially in scenarios with high vehicle densities under congested channel conditions. Recent studies identified that content-agnostic congestion control mechanisms deployed in decentralized vehicular networks, e. g. , from the European Telecommunications Standard Institute, maintain the radio communication performance under congested channel conditions but can severely degrade the communication performance from the application perspective. In this paper, we propose a collaborative resource allocation mechanism with content-aware congestion control for decentralized vehicular networks that prioritizes vehicles with relevant information under congested channel conditions. Our evaluation results show that our proposed approach maintains the radio communication performance and significantly increases the communication performance from the application perspective compared to content-agnostic congestion control mechanisms.
Time Series Regulation Model of Aggregate Load of Distribution Network Based on Characteristic Analysis

Considering the influence of factors like new energy access, flexible load regulation, virtual electrical field, and demand response on the operation of the distribution network, the ‘bottom-up’ modeling method is adopted to propose a quasi-steady-state mechanism model including flexible load, virtual electrical field, and demand response resource. Based on the mechanism model, we proposed the response timing characteristics, load characteristics, and complementary characteristics of different resources. Taking into account the uncertainty of distributed new energy output, we proposed a time sequence adjustment model by Monte Carlo, scene reduction, and other methods for the aggregated load of the distribution network, according to the analysis of the characteristics of the flexible load, virtual electrical field, demand response, and other resources.
Wave Scattering by a Planar Junction Between Anomalously Reflecting Metasurface and Impedance Sheets

Metasurface technology has become one of the promising structures for controlling and manipulating of the phase and amplitude characteristics of the electromagnetic waves in recent years. For this reason, the determination of the exact diffracted fields is also crucial especially in the edge or discontinuity points of these types of materials. In the paper, the discontinuity problem by the junction between the layers of an anomalously reflecting metasurface and an impedance half-screen is taken into consideration. These two planar layers have a common edge discontinuity where the diffracted waves occur. The scattered waves from the structure are obtained by the novel technique of the method of transition boundary, which especially offers the relation between the diffracted and scattered geometrical optical waves at the transition boundaries. The diffracted wave expressions are derived by the technique of the method of transition boundary and the uniform wave expressions are obtained by the method of uniform geometrical theory of diffraction. The total wave result of the method of transition boundary is also compared and verified by the well-known theory of physical optics. The behaviors of the total wave and its components are analyzed numerically.
A fault prediction method of distribution network based on the improved RelieF-Softmax algorithm

A fault risk prediction method for distribution network based on improved RelieF-Softmax algorithm is proposed. Firstly, the fault data of distribution network is obtained and 14 fault characteristics are extracted through data investigation. The improved relief filtering feature extraction method is adopted to screen the initial fault feature set and select key fault features. Finally, a distribution network fault warning method based on the cross entropy loss function of focal loss is proposed, and the results of calculation examples demonstrate the effectiveness of the method proposed in this paper.
Decoding Imagined Speech from EEG using Transfer Learning

We present a transfer learning based approach for decoding imagined speech from electroencephalogram (EEG). Features are extracted simultaneously from multiple EEG channels, rather than separately from individual channels. This helps in capturing the interrelationships between the cortical regions. To alleviate the problem of lack of enough data for training deep networks, sliding window based data augmentation is performed. Mean phase coherence and magnitude-squared coherence, two popular measures used in EEG connectivity analysis, are used as features. These features are compactly arranged, exploiting their symmetry, to obtain a three dimensional “image-like” representation. The three dimensions of this matrix correspond to the alpha, beta and gamma EEG frequency bands. A deep network with ResNet50 as the base model is used for classifying the imagined prompts. The proposed method is tested on the publicly available ASU dataset of imagined speech EEG, comprising four different types of prompts. The accuracy of decoding the imagined prompt varies from a minimum of 79.7% for vowels to a maximum of 95.5% for short-long words across the various subjects. The accuracies obtained are better than the state-of-the-art methods, and the technique is good in decoding prompts of different complexities.
Deep Reinforcement Learning Based Adaptive Modulation With Outdated CSI

The problem of adaptive modulation with outdated channel state information (CSI) is considered. Best existing approach to tackle this problem relies on using a (non-)linear auto-regressive moving average (ARMA) model to predict current CSI from outdated values. This approach is valid only if the wireless channel variations over time behave in a linear or smooth enough nonlinear fashion, which is not necessarily the case. We propose a deep reinforcement learning based adaptive modulation (DRL-AM) approach that can handle this limitation. While DRL-AM is more complex than (non-)linear AR(MA), it performs significantly better as corroborated via numerical results on real channel measurements. Furthermore, compared to capacity-achieving codes, complexity is moved from receiver to transmitter making this approach suitable for receiving nodes with limited resources such as internet of things (IoT) devices.
A Heuristic Parameter-dependent Open-loop Model Predictive Control

For the uncertain linear systems described by the linear parameter varying (LPV) model, a parameter-dependent open-loop model predictive control (MPC) is proposed. The controller applies a tree trajectory to generate the vertices of uncertainty state predictions. Based on the state prediction tree, the future free control moves are parameter-dependent, whose vertices correspond to those of state predictions. The cost function penalizes the deviations of all the vertices of state/input from their steady-state target values. It is shown that the offset-free property is achieved by this method. A simulation example is given to demonstrate effectiveness of the approach.
Update-Sensitive Structured Encryption with Backward Privacy

—Many recent studies focus on dynamic searchable encryption (DSE), which provides efﬁcient data-search and data-update services directly on outsourced private data. Most encryption schemes are not optimized for update-intensive cases, which say that the same data record is frequently added and deleted from the database. How to build an efﬁcient and secure DSE scheme for update-intensive data is still challenging. We propose UI-SE, the ﬁrst DSE scheme that achieves single-round-trip interaction, near-zero client storage, and backward privacy without any insertion patterns. UI-SE involves a new tree data structure, named OU-tree, which supports oblivious data updates without any access-pattern leakage. We formally prove that UI-SE is adaptively secure under Type-1 − backward privacy, which is stronger than Type-1 backward privacy proposed by Bost et al. in CCS 2017. Experimental data also demonstrate UI-SE has low computational overhead, low local disk usage, and high update performance on scalable datasets.