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Series arc fault (SAF) is the main cause of the electrical fire and other safety accidents. In the industrial field, now, it is still a lack of effective solutions to accurately identify the SAF that occurred in the three-phase motor with the frequency converter load (TPMFCL) circuit. The transient SAF experiments and two-phase SAF experiments in the TPMFCL circuit were carried out. A novel SAF identification method based on variational mode decomposition (VMD) of a single-phase current signal and entropy-based features was proposed. First, the current signal is decomposed by VMD. The intrinsic mode function (IMF) components whose central frequency is within the frequency band between 0 and 650 Hz are obtained. Second, the sample entropy and the energy entropy of those IMFs are calculated and used as the fault features. Finally, the SAF is identified by using a support vector machine optimized by firefly algorithm (FA-SVM). Both the identification accuracy and calculation time of the method were systematically tested by changing several different test conditions. When the sampling frequency is 2 kHz and the sample length is four current cycles, the method can accurately and quickly identify the SAF that occurred in the TPMFCL circuit even under complicated harmonic power supply conditions. The conclusions can be applied to develop the industrial arc fault circuit interrupter (AFCI) used in the TPMFCL circuit. [ABSTRACT FROM AUTHOR]

Detection of mild cognitive impairment (MCI) and dementia (DEM) is an important topic because, unless it is treated early, MCI can progress to DEM, which is an untreatable disease. This article proposes a timed-up-and-go (TUG) task features analysis and classification of MCI and DEM using inertial measurement units (IMUs) in wearable devices. Our goal is to create a generalized model that can be used for preclinical screening. As a result, rather than classifying only one subtype of DEM, such as Alzheimer’s disease (AD) or Parkinson’s disease (PD), we classify all subtypes as DEM. We also utilize feature selection methods on features from TUG tasks to optimize the MCI and DEM classification performance. From the results, our generalized model can outperform other works in normal control (NC)-MCI&DEM classification with an accuracy of 86.94% and sensitivity of 97.40%. For NC-DEM classification, the performance of our generalized model is slightly lower than that of specific-subtype models (e.g., NC versus AD). However, our generalized model can outperform the specific-subtype models when using a diverse variety of subtypes. It is a reasonable tradeoff, and it can be a good first step toward a future where the patient can preclinically self-screening the cognitive impairments using wearable devices in free-living environments. This could allow patients to notice the cognitive impairment early on and seek a comprehensive diagnosis from a doctor. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have achieved remarkable performance in driver drowsiness detection based on the extraction of deep features of drivers’ faces. However, the performance of driver drowsiness detection methods decreases sharply when complications, such as illumination changes in the cab, occlusions and shadows on the driver’s face, and variations in the driver’s head pose, occur. In addition, current driver drowsiness detection methods are not capable of distinguishing between driver states, such as talking versus yawning or blinking versus closing eyes. Therefore, technical challenges remain in driver drowsiness detection. In this article, we propose a novel and robust two-stream spatial–temporal graph convolutional network (2s-STGCN) for driver drowsiness detection to solve the above-mentioned challenges. To take advantage of the spatial and temporal features of the input data, we use a facial landmark detection method to extract the driver’s facial landmarks from real-time videos and then obtain the driver drowsiness detection result by 2s-STGCN. Unlike existing methods, our proposed method uses videos rather than consecutive video frames as processing units. This is the first effort to exploit these processing units in the field of driver drowsiness detection. Moreover, the two-stream framework not only models both the spatial and temporal features but also models both the first-order and second-order information simultaneously, thereby notably improving driver drowsiness detection. Extensive experiments have been performed on the yawn detection dataset (YawDD) and the National TsingHua University drowsy driver detection (NTHU-DDD) dataset. The experimental results validate the feasibility of the proposed method. This method achieves an average accuracy of 93.4% on the YawDD dataset and an average accuracy of 92.7% on the evaluation set of the NTHU-DDD dataset. [ABSTRACT FROM AUTHOR]

Neuroimaging experiments in general, and EEG experiments in particular, must take care to avoid confounds. A recent TPAMI paper uses data that suffers from a serious previously reported confound. We demonstrate that their new model and analysis methods do not remedy this confound, and therefore that their claims of high accuracy and neuroscience relevance are invalid. [ABSTRACT FROM AUTHOR]

Transistor scaling steadily approaches fundamental limits. Sustaining circuit reliability becomes an overwhelming challenge for foundries and their manufacturing processes. Therefore, early and rapid characterization of degradation effects impacting the circuits’ transistors becomes essential. Such degradation effects are caused by design-time variation due to manufacturing variability and/or run-time variation due to transistor aging. In this work, we are the first to employ brain-inspired HDC for circuit reliability. HDC is quickly emerging as an attractive light-weight machine-learning solution. Nowadays, it is mainly applied to bio-signal processing or language recognition. We bring the research of HDC to the next level by demonstrating how it can be applied to address the challenges in circuit reliability. This has far-reaching consequences due to the large savings achieved by 1) reducing the amount of training data and hence the development cycle, 2) removing the need to send the data to the cloud for model training, and 3) speeding up significantly the characterization and classification tasks due to the fast edge-inference. We demonstrate the viability of HDC using SRAM and other circuits as examples. HDC outperforms traditional machine learning methods, such as support vector machine or random forest, in accuracy and requires up to 20x fewer training samples. For a given budget of samples, HDC achieves a 4x smaller error. Our implementation and analysis are based on industrial $14 \;\mathrm{n}\mathrm{m}$ 14 n m FinFET fully calibrated with Intel measurements with respect to both transistor electrical characteristics as well as manufacturing variability. Open Source: Our framework including the algorithm implementation is available for the community to explore other algorithms and circuits at https://github.com/ML-CAD/HDC-Circuit-Reliability. [ABSTRACT FROM AUTHOR]

Impedance-source inverters provide a promising single-stage power conversion with boost-buck capabilities and inverter legs short-circuit immunity. To achieve superior thermal and output harmonic performance, two novel discontinuous modulation strategies for the three-phase quasi-Z source inverter, called the maximum-boost discontinuous space vector modulation (SVM) and the simple-boost discontinuous SVM respectively, are proposed in this article. By properly arranging the shoot-through states and clamping a certain switch to the positive or negative dc-link rail during one-third of the fundamental period, reduced thermal stresses and superior total harmonic distortion performance can be achieved. The proposed modulation strategies are analyzed and compared to the conventional ones in terms of current stresses, power losses, thermal stresses, and output harmonics. Finally, simulations and experimental tests are carried out to validate the performance of the proposed modulation strategies and verify the presented analysis. [ABSTRACT FROM AUTHOR]

In this article, a modified convolutional neural network (CNN) algorithm, namely 1D-GAPCNN-SVM, is proposed to address the early anomaly diagnosis problem. Considering the fact that traditional 2D-CNN based approaches contain too many model parameters and are not suitable for fast diagnosis applications using multisensor 1-D time-series measurements, 1D-CNN is introduced to deal with this problem. To reduce the number of parameters, a 1-D global average pooling layer is designed to substitute the fully connected layer with two or three layers. In order to further improve the diagnosis accuracy, a nonlinear multiclass support vector machine (SVM) is adopted to replace the traditional Softmax classifier as the final discriminator. Raw multisensor 1-D time-series data are directly fed into the diagnosis model, then the diagnosis result can be automatically generated. Two experiments, which are rolling bearing and GPS anomaly detection, have been conducted to demonstrate the effectiveness and the superior performance of the proposed method compared to the conventional SVM, K-nearest neighbor, deep neural network (DNN), and traditional 2D-CNN. [ABSTRACT FROM AUTHOR]

This article declares two control strategies, named “average-model based” (AMB) and “energy-based” (EB) control methods, to control a monoinverter dual parallel surface-mounted permanent magnet synchronous motor (MIDP-PMSM) with different parameters. Up to now, it has been assumed that two parallel PMSMs have the same parameters, so the controller has been designed based on this simplifier assumption. To extend the control subject to MIDP-PMSMs with different parameters, two new methods are proposed and compared in this article. The average value of both motors’ parameters is used in the AMB control design, while in the proposed EB control method, each motor’s parameters separately contribute to designing the control laws. The proposed methods are based on the “mean-difference” technique. These methods have been compared by mathematical analysis regarding the torque per total ampere ratio, the copper losses and the dynamic response of motors’ speed. The MATLAB simulation and experimental implementation have been carried out to verify the proposed methods. The results have shown that the EB control method has better performance than the other. [ABSTRACT FROM AUTHOR]

In the decoupling control process of the outer rotor coreless bearingless permanent magnet synchronous motor using a least squares support vector machine, the kernel width σ2 and the penalty factor γ of the least squares support vector machine are difficult to tune, resulting in high kernel space complexity and low fitting accuracy. A new improved genetic algorithm is used to optimize the above parameters. First, the generalized inverse system model of the original nonlinear multiple-input multiple-output system is fitted online with the optimized least squares support vector machine, and the generalized inverse system model is connected in series with the original system to form a pseudolinear system. Then, a linear closed-loop controller is used for control. Simulations and experiments show that the robustness and wide applicability of the original system are guaranteed. The dynamic and static performances of the system are improved, verifying the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

Space vector modulation (SVM) is a mature modulation scheme, which is widely used for voltage source inverters. In this article, a flexible online SVM scheme is proposed for a cascaded H-bridge multilevel inverter (CHMI) under short-circuit and open-circuit switch as well as cell failure conditions. The proposed scheme directly calculates all the active redundant switching states and the dwelling duty cycles of the three space vectors enclosing the reference vector for a CHMI such that balanced line-to-line voltages and currents under failure conditions are generated. Accordingly, the resultant switching sequences are determined to meet any specific requirement, e.g., even-order harmonics elimination and minimization of switching losses. Furthermore, in order to reduce voltage stress on the semiconductors under short- and open-circuit switch conditions, an equal power distribution criterion is introduced. The proposed SVM scheme is fast and independent of the number of levels. Experimental results are presented to demonstrate performance and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

AC-type flying capacitor (FC) concept has been proposed for current source converter (CSC) topologies recently, which offers unique merits over the traditional CSCs, such as lower dv/dt with improved dc-link voltage quality as well as flexible common-mode voltage (CMV) control. To cope with the main-stream three-phase industrial applications, in this article, the three-phase flying-capacitor-clamped CSC is introduced and extensively investigated for the first time. A novel space vector modulation is proposed, featuring a modular design process, active FC voltage control, and CMV suppression. The detailed commutation principles and the capacitor clamping voltage influence under different switching states are comprehensively addressed. Then, the redundant switching states are optimized to fulfill the multiple targets simultaneously. Simulation and experimental results are performed to demonstrate the superior dc-link power quality and CMV performance compared with the traditional three-phase CSC topology. [ABSTRACT FROM AUTHOR]

Single-phaseauto-reclosing schemes depend on two main characteristics:(i) secondary arc extinction instant (ii) fault type (temporary or permanent). However, secondary arc quenching time greatly varies from no-arc to prolonged-arc generation depending on the secondary arc current and meteorological factors (wind, thermal buoyancy, etc.). Existing solutions face challenges in making reclosing decisions owing to variable secondary arc quenching instant, non-linear arc voltage characteristics, and varying recovery voltage waveform magnitude. To comprehensively address the discussed problems, an approach based on a particle filter algorithm along with a convolutional neural network is developed. Initially, harmonics of compromised phase are online estimated using a particle filter algorithm, and two indices are developed: (i) estimation of the precise moment of secondary arc extinction (ii) fault nature. Then, extensive simulations are carried out with variable arc resistance, line lengths, no-shunt, and shunt configurations to develop a database based on these two indices. Finally, support vector machine, fully connected deep neural network, and the convolutional neural network are applied to the dataset to predict the reclosing decisions. The comparative study of classifiers confirms the effectiveness of the proposed convolutional neural network in terms of overall model and inter-class accuracy under a noisy and noise-free environment. [ABSTRACT FROM AUTHOR]

The four-level hybrid-clamped converter (HCC) is a new high-efficiency dc–ac converter. However, its dc-link capacitors face challenging voltage fluctuation issues at low frequencies, resulting in the distorted ac current and oversize converter volume. This letter presents a simple and effective control method of the HCC using open-loop common-mode voltage (CMV) injection. The mathematical model of the CMV and the capacitor voltage fluctuations is derived. The optimal CMV algorithm with the maximum suppression effect of capacitor voltage fluctuations is proposed as well as the implementation method. The microlevel mechanism of the proposed method in suppressing voltage fluctuation is clarified. The capability of the proposed method in improving dc voltage utilization is explored. Compared with existing methods, the proposed one reduces dc-link voltage sensors and computation complexity and mitigates CMVs at the load side. The optimal CMV is approximately 25% of the output voltage magnitude, and enhances the dc-bus voltage utilization by 12%. Results demonstrate that voltage fluctuations of HCC capacitors are reduced by over 60%. The proposed method has a solid dynamic balancing capability and works well for the wide-range frequency and modulation index. [ABSTRACT FROM AUTHOR]

This article proposes a modified deadbeat predictive current control (M-DPCC) method to improve the dynamic performance of permanent magnet synchronous motor (PMSM). Classical DPCC only predicts one control cycle of the PMSM, which restricts the dynamic performance since multiple control cycles are required for the transient state under heavy load and rated speed working condition. The proposed M-DPCC extends the single-step prediction to the entire dynamic interval in the transient state, by which the comprehensive optimization on the angle of the stator voltage vector can be implemented. Moreover, M-DPCC performed multi-step deadbeat calculation to keep the computation modest. To verify the effectiveness of the proposed M-DPCC, transient-state performance of PMSM using the M-DPCC method, the modulated model predictive control and classical DPCC are explored under a wide range of working condition. Comparative simulation and experimental results indicate that dynamic performance of the PMSM can be improved, especially under rated speed and heavy load conditions. Besides, the parameter sensitivity to inductance mismatch of the three methods is carried out. The stable operation range of the M-DPCC under inductance mismatch is larger than the other two methods. Moreover, the computation complexity of the three methods were investigated. The M-DPCC acquired less computation time than the modulated model predictive control. [ABSTRACT FROM AUTHOR]

The existing fault diagnosis methods of rotating machinery constructed with both shallow learning and deep learning models are mostly based on vibration analysis under steady rotating speed. However, the rotating speed frequently changes to meet practical engineering needs. The shallow learning models largely depend on domain experience of feature extraction, and training a deep learning model requires large samples and a long time. In addition, vibration monitoring has the shortcomings of contact measurement, small coverage, and noise interference. To address these problems, this article proposes a new fault diagnosis method with the least square interactive support matrix machine (LSISMM) and infrared thermal images. In this method, a novel matrix-form classifier called LSISMM is constructed under the concept of nonparallel interactive hyperplanes to fully leverage the structure information of infrared thermal images. To improve the computation efficiency, a new least square loss constraint is designed for LSISMM. Besides, we derive an effective solution framework based on the alternating direction method of the multiplier (ADMM) framework. The constructed LSISMM is directly used to analyze the collected thermal images of rotating machinery under time-varying speeds. Experiment results demonstrate that the proposed method is superior to state-of-the-art methods in terms of diagnosis accuracy and efficiency, especially under small thermal image samples. [ABSTRACT FROM AUTHOR]

Android is a growing target for malicious software (malware) because of its popularity and functionality. Malware poses a serious threat to users’ privacy, money, equipment and file integrity. A series of data-driven malware detection methods were proposed. However, there exist two key challenges for these methods: (1) how to learn effective feature representation from raw data; (2) how to reduce the dependence on the prior knowledge or human labors in feature learning. Inspired by the success of deep learning methods in the feature representation learning community, we propose a malware detection framework which starts with learning rich-features by a novel unsupervised feature learning algorithm Merged Sparse Auto-Encoder (MSAE). In order to extract more compact and discriminative feature from the rich-features to further boost the malware detection capability, a hybrid deep network learning algorithm Stacked Hybrid Learning MSAE and SDAE (SHLMD) is established by further incorporating a classical deep learning method Stacked Denoising Auto-encoders (SDAE). After that, we feed the feature learned by MSAE and SHLMD respectively to classification algorithms, e.g., Support Vector Machine (SVM) or K-NearestNeighbor (KNN), to train a malware detection model. Evaluation results on two real-world datasets demonstrate that SHLMD achieves 94.46 and 90.57 percent accuracy respectively, which outperforms the classical unsupervised feature representation learning Sparse Auto-encoder (SAE). MSAE performs similarly to SAE. SHLMD can further improve the performance of MSAE and the supervised fine-tuned method SDAE. Besides, we compare the performance of our methods with that of state-of-the-art detection approaches, including classical deep-learning-based methods. Extensive experiments show that our proposed methods are effective enough to detect Android malware. [ABSTRACT FROM AUTHOR]

The natural gas tightly correlates with our everyday life. However, driven by gray incomes, some users are prone to stealing gas by refitting the equipment without permission. Especially for the boiler room users in winter, this phenomenon appears more rampant. Traditional gas-theft detection methods highly rely on the on-site inspection, where exists ineffective and randomness. With the rapidly deployed IoT sensors, we can collect real-time gas consumption data to analyze users’ behavior patterns, where the gas-theft suspects could be discovered early and accurately. In this paper, we propose a data-driven approach, named SVOC, to detect gas-theft suspects among boiler room users. Our approach consists of a scenario-based data quality detection algorithm, a deformation-based normality detection algorithm, and an One-Class Support Vector Machine (OCSVM) based anomaly detection algorithm. Specifically, considering the temporal proximity between the gas consumption and the outdoor temperature, the normality detection algorithm adopts a similarity-based deformation correlation to detect normal boiler room users out of abnormal ones. Then, we employ OCSVM as the anomaly detection algorithm to capture various features across multiple data sources, aiming to distinguish gas-theft suspects from the remaining irregular users. Here, the detected normal and abnormal users are fed into the OCSVM for training and prediction, respectively, which can overcome the label scarcity problem. We conduct extensive experiments on a real-world dataset during one heating season. The results demonstrate distinct advantages of our approach over various baselines. We have developed a real-time system on the cloud, providing daily gas-theft suspects for gas companies. [ABSTRACT FROM AUTHOR]

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models. To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled. [ABSTRACT FROM AUTHOR]

Anomaly detection plays an important role in industry, especially in ensuring system safety and product quality. Due to the unavailability of anomalous data in many practical cases, anomaly detection is usually solved by one-class classification (OCC) methods using only normal data. As a classical OCC method, one-class support vector machine (OCSVM) is a popular discriminative approach for anomaly detection, which detects abnormal data points by establishing a decision boundary in the kernel space. However, the performance of OCSVM heavily relies on kernel parameters, whose selection is not trivial for anomaly detection problems. Moreover, for some uneven and complex data distributions, different data regions may have quite different densities and shapes, making it difficult for OCSVM to obtain good boundaries in all regions using a global kernel parameter. To address the above two issues, in this article, we propose a hybrid algorithm incorporating vector quantization and OCSVM (VQ-OCSVM). Specifically, vector quantization is used to extract distribution information of normal data, and the results are used to construct an explicit mapping function to map data into a high-dimensional feature space. Then, OCSVM is performed in the feature space to build a classifier. By introducing the explicit mapping into OCSVM, the proposed method can effectively bypass the kernel parameter selection problem of the classical OCSVM method. Furthermore, the constructed mapping carries the data distribution information, and the VQ-OCSVM model can be regarded as an integration of generative learning and discriminative learning. The complementary properties of these two paradigms make the proposed VQ-OCSVM algorithm have better generalization capacity for complex data distribution. Both qualitative and quantitative experimental results demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches. [ABSTRACT FROM AUTHOR]

Human activity recognition has a significant impact on people’s daily lives. The need to infer human activities is prominent in many human-centric applications, such as healthcare and individual assistance. In this article, we introduce a noninvasive human activity recognition system that utilizes footstep-induced vibration and sound in an outdoor environment with the aim of achieving improved performance over a single source of information. We employ 1-D convolutional neural networks (1-D CNNs) for automated feature extraction, fusion, and activity recognition on a nine-class classification problem. The proposed framework reports an average F1 score of 92%, which corresponds to a 5.74% improvement over the best-performing state-of-the-art. Confusion matrix-based analysis demonstrates that audio-seismic fusion not only reduces misclassifications, but also reduces the impact of background noise on model performance. In addition, we demonstrate that a model trained on a balanced dataset has a higher F1 score than one trained on an imbalanced dataset. Activity-wise performance is reported to show the efficacy of the proposed fusion-based framework. We also contribute an audio-seismic dataset for human activity recognition in an outdoor environment. The dataset is collected in a variety of challenging environments, such as varying grass length, soil moisture content, and the passing of unwanted vehicles. [ABSTRACT FROM AUTHOR]

The online anomaly detection of an aeroengine gas path system is very important to ensure the safety of aircraft. The traditional online anomaly detection method based on a sliding window has many shortcomings, which leads to the lack of accuracy of anomaly detection. In addition, the extreme lack of abnormal samples is also a difficult problem for engine anomaly detection. Therefore, this article proposes an anomaly detection method based on piecewise linear representation (PLR) and support vector data description (SVDD). First, the monitoring data of the aeroengine gas path are segmented and linearly represented so that the data in each segment have basically consistent changing trends, which can make up for the defects of the traditional sliding window. And an improved online segmentation method is proposed that increases the efficiency of the segmentation algorithm. Then, segment features are extracted, and input samples for the anomaly detection model are constructed. The classification model employs SVDD that requires only normal samples. The SVDD model is trained on the offline normal samples and anomaly detection is performed on the online samples. Experimental results show the superiority of this method in aeroengine gas path anomaly detection. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the event-driven multilayer spiking convolutional neural network (SCNN) can reduce computational complexity largely while keeping accurate. To fully utilize the advantages of SCNN, this brief proposed an efficient multilayer SCNN processor for object recognition. The interconnection between spiking layers is implemented for the first time. The rank-order coding with mutual and lateral inhibitions enables sparse event transmission. By further combining the spike-centric membrane potential update, channel-level parallel operation, and the low bitwidths of synapse weights and potentials, the proposed design achieves 500 classifications/s, and 68 uJ/classification for recognizing images with $160\mathbf {\times }250$ resolution, which is superior to the recent works. [ABSTRACT FROM AUTHOR]

Multiview learning (MVL), which enhances the learners’ performance by coordinating complementarity and consistency among different views, has attracted much attention. The multiview generalized eigenvalue proximal support vector machine (MvGSVM) is a recently proposed effective binary classification method, which introduces the concept of MVL into the classical generalized eigenvalue proximal support vector machine (GEPSVM). However, this approach cannot guarantee good classification performance and robustness yet. In this article, we develop multiview robust double-sided twin SVM (MvRDTSVM) with SVM-type problems, which introduces a set of double-sided constraints into the proposed model to promote classification performance. To improve the robustness of MvRDTSVM against outliers, we take L1-norm as the distance metric. Also, a fast version of MvRDTSVM (called MvFRDTSVM) is further presented. The reformulated problems are complex, and solving them are very challenging. As one of the main contributions of this article, we design two effective iterative algorithms to optimize the proposed nonconvex problems and then conduct theoretical analysis on the algorithms. The experimental results verify the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

Surrogate-assisted multiobjective evolutionary algorithms (MOEAs) have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Furthermore, multiple local classifiers can hedge the risk of overfitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines (SVMs) on a decomposition-based multiobjective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well. [ABSTRACT FROM AUTHOR]

This article proposes a human posture classification system to classify the four postures of standing, bending, sitting, and lying using three-dimensional (3-D) fuzzy body voxel features obtained from two cameras and hierarchical fuzzy classifiers (FCs). The system consists of four parts: human body segmentation, 3-D body voxel model construction, feature extraction, and classification. A region-dependent Gaussian mixture model with shadow removal is proposed for human body segmentation. The silhouette volume intersection method with computation speedup is then used to construct the 3-D body model using the segmented images from two cameras. After that, fuzzy 3-D body voxel features for posture classification are extracted from a minimum enclosed cube of the 3-D model. For classification, this article proposes an FC with soft margin minimization (FC-SMM) and its hierarchical structure to improve the classification performance. The consequent and antecedent parameters are optimized through a linear batch support vector machine and margin selective gradient descent algorithm, respectively. The FC-SMM also shows the advantage of model interpretability through visualization of the fuzzy rules. Experimental results of the comparison of different feature extraction methods and classifiers in different videos show the superiority of the proposed classification method. [ABSTRACT FROM AUTHOR]

Body Orientation Estimation (BOE) is important for a wide array of applications, including robotics, surveillance and consumer analysis. Although multi-sensor approaches are effective, they are not a viable option for in the wild scenarios; the usual approach in such cases is to use single camera images, with imprecise results. Some applications that deal with people benefit from obtaining 2D human skeletons for gesture recognition, and these skeletons bring valuable information about the person's pose. It is proposed to build a 2D skeleton via OpenPose and using its data as training data on XGBoost to detect BOE. To evaluate predictions considering real situations based on a single camera, the TUD Multiview Pedestrian dataset is used and extended considering that a single person is originally considered in images where more people were often identified. It is compared the proposed approach against various state-of-the-art methods and our results indicate better performance. Finally, it is proved that our method is viable for BOE in real-time scenarios by presenting case studies on simulated scenes. [ABSTRACT FROM AUTHOR]

We propose OneFlow – a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region. Contrary to density-based methods, OneFlow is constructed in such a way that its result typically does not depend on the structure of outliers. This is caused by the fact that during training the gradient of the cost function is propagated only over the points located near to the decision boundary (behavior similar to the support vectors in SVM). The combination of flow models and a Bernstein quantile estimator allows OneFlow to find a parametric form of bounding region, which can be useful in various applications including describing shapes from 3D point clouds. Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems. [ABSTRACT FROM AUTHOR]

Identity authentication based on Doppler radar respiration sensing is gaining attention as it requires neither contact nor line of sight and does not give rise to privacy concerns associated with video imaging. Prior research demonstrating the recognition of individuals has been limited to isolated single-subject scenarios. When two equidistant subjects are present, identification is more challenging due to the interference of respiration motion patterns in the reflected radar signal. In this research, respiratory signature separation techniques are functionally combined with machine learning (ML) classifiers for reliable subject identity authentication. An improved version of the dynamic segmentation algorithm (peak search and triangulation) was proposed, which can extract distinguishable airflow profile-related features (exhale area, inhale area, inhale/exhale speed, and breathing depth) for medium-scale experiments of 20 different participants to examine the feasibility of extraction of an individual’s respiratory features from a combined mixture of motions for subjects. Independent component analysis with the joint approximation of diagonalization of eigenmatrices (ICA-JADE) algorithm was employed to isolate individual respiratory signatures from combined mixtures of breathing patterns. The extracted hyperfeature sets were then evaluated by integrating two different popular ML classifiers, k-nearest neighbor (KNN) and support vector machine (SVM), for subject authentication. Accuracies of 97.5% for two-subject experiments and 98.33% for single-subject experiments were achieved, which supersedes the performance of prior reported methods. The proposed identity authentication approach has several potential applications, including security/surveillance, the Internet-of-Things (IoT) applications, virtual reality, and health monitoring. [ABSTRACT FROM AUTHOR]

Physical unclonable functions (PUFs) have been adopted in many resource-constrained Internet of Things (IoT) applications to provide effective and lightweight solutions for device authentication. However, an attacker can collect challenge–response pairs (CRPs) of a strong PUF, to build a machine learning (ML) model and mimic its behavior, i.e., predicting the responses of unseen challenges with high accuracy. Although several PUFs have been proposed to resist such modeling attacks, they incur high hardware overhead. Developing a PUF primitive with low hardware cost and high resistance to ML attacks is thus a crucial task. In this article, we propose the first response–feedback-based lightweight anti-ML-attack PUF (FLAM-PUF). It is only composed of one arbiter PUF (APUF) and one Galois linear-feedback shift register (LFSR), with some basic logic gates, reducing more than 62% hardware cost compared with the state-of-the-art robust strong PUFs. Specifically, FLAM-PUF leverages a cost-effective feedback loop structure to dynamically control and update the LFSR configuration. FLAM-PUF has two main characteristics: 1) it feeds back a 1-bit response in every cycle to intentionally poison the data of the CRP set for training. To resist ML-based modeling attacks, the 1-bit response can randomly update one coefficient of the feedback polynomial to implant more complex correlations into the model built by attackers and 2) it takes advantage of an $n-$ bit response feedback-controlled reconfigurable Galois LFSR to enlarge the original challenge space of the APUF. Extensive experimental results show that the proposed FLAM-PUF achieves near-optimal uniformity, uniqueness, and reliability. Our scheme works well under standard attack models with public crucial initial information. In particular, the prediction accuracy of modeling attacks against FLAM-PUF is nearly 50% under the four widely used ML algorithms, i.e., support vector machines (SVMs), logistic regression (LR), covariance matrix adaptation evolution strategy (CMA-ES), and deep neural networks (DNNs), indicating excellent resistance against these ML attacks. [ABSTRACT FROM AUTHOR]

With the rise of the Internet of Things (IoT), resource-constrained and power-constrained devices attract more attention. The need for lightweight solutions as alternatives to resource-intensive applications became more urgent. Moreover, as the number of connected devices grew, authenticating them became more challenging. Traditionally, this would be performed by using hash functions and secure memory to store a key, which both come at a high cost. physical unclonable functions (PUFs) emerged as a suitable lightweight alternative to hash functions to authenticate the devices. Using the inherent minute differences between integrated circuits (ICs), they can generate IC-specific responses for input challenges coming from a so-called verifier. Through the years, machine learning (ML) has been used to attack PUFs by modeling them and accurately predicting their response to a given challenge. This stimulated research on ML-resilient PUFs. This resilience came with the significant area and challenge-to-response delay overheads. In this work, we introduce the novel cascaded PUF (CaPUF) and show that it is resilient against state-of-the-art ML-based attacks, i.e., logistic regression (LR) and support vector machines (SVMs). These attacks could not achieve accuracy better than 52% against our CaPUF, which is only as good as flipping a coin. Additionally, our CaPUF requires 89% less area compared to state-of-the-art ML-resilient PUFs. [ABSTRACT FROM AUTHOR]

In this article, a new zero-sequence current suppression technique for electrical machine emulators with reduced component count is proposed. The proposed control scheme is implemented by properly regulating the zero states in space vector modulation (SVM). It is well known that a machine emulator allows fast experimental validation of the control and design of a drive system without having a physical electrical machine, since the machine is replaced by another voltage-source converter and a three-phase inductor. However, in emulators with a coupled dc link, circulating zero-sequence current is freely developed creating additional load for the power switches. The currently available control methods require additional hardware common-mode filters to effectively reduce this current. Contrarily, the proposed SVM algorithm suppresses the zero-sequence current more effectively via direct compensation of common-mode voltage, and thus, no additional hardware filters are needed. Thus, an electrical machine emulator with less hardware requirements can be developed with the proposed control technique for being utilized in several laboratory test-bench applications. Experimental results on a 60-kW system validate the effectiveness of the proposed SVM algorithm, since the zero-sequence current amplitude has been measured to be 3.7% of the phase current or even less than this. [ABSTRACT FROM AUTHOR]

Inadequate drying and overdrying of grains due to the absence of timely and reliable information on moisture content (MC) aggravates grain postharvest loss. In this study, we investigate the real-time acquisition of MC using microwave free-space transmission measurements. A multifrequency swept signal is used to acquire the attenuation and phase-shift spectrum (2.00–10.00 GHz) of the paddy samples (9.553%–29.633% w.b.) for six thicknesses (1–6 cm). Based on the four rules that are followed in the phase-shift measurement, a phase-shift correction algorithm without any restrictions on the sample thickness is proposed to solve the phase-shift ambiguity that occurs when the sample thickness exceeds the wavelength. To choose the most effective frequencies, 17 candidate frequency subsets are generated by the recursive feature elimination algorithm, and the optimal frequency set, containing eight individual frequencies, is selected by using the Friedman and Nemenyi post hoc tests. The sample thickness and microwave characteristics are used as the input variables to establish the prediction models to achieve a thickness-independent measurement of the MC, and the support vector machine model yielded the best performance ($R^2$ = 0.992, RMSE = 0.555%, and MAE = 0.398%). The results of this study should encourage future research on the real-time acquisition of reliable MC information in food processing and agriculture-related industries. [ABSTRACT FROM AUTHOR]

Defect pattern detection and classification are challenging for thin-film-transistor liquid-crystal display (TFT-LCD) manufacturing. Limitations of the existing solutions for automatic optical inspection can be traced in part to the lack of a framework within which different existing and new defect patterns can be analyzed, while integrating domain knowledge and effective technologies. This study aims to develop a framework for image-based defect classification that employs the convolution neural networks without using complex and time-consuming image-processing processes in advance. An empirical study was conducted in a leading TFT-LCD manufacturing in Taiwan for validation. The results have shown that the defect patterns can be effectively classified by the proposed convolutional neural networks that outperform the existing approaches such as Support Vector Machine and Random Forest. The developed solution is implemented to effectively support the engineers. [ABSTRACT FROM AUTHOR]

As the network slicing is one of the critical enablers in communication networks, one anomalous physical node (PN) or physical link (PL) in substrate networks that carries multiple virtual network elements can cause significant performance degradation of multiple network slices. To recover the substrate networks from anomaly within a short time, rapid and accurate identification of whether or not the anomaly exists in PNs and PLs is vital. Online anomaly detection methods that can analyze system data in real-time are preferred. Besides, as virtual nodes and links mapped to PNs and PLs are scattered in multiple slices, the distributed detection modes are required to adapt to the virtualized environment. According to those requirements, in this paper, we first propose a distributed online PN anomaly detection algorithm based on a decentralized one-class support vector machine (OCSVM), which is realized through analyzing real-time measurements of virtual nodes mapped to PNs in a distributed manner. Specifically, to decouple the OCSVM objective function, we transform the original problem to a group of decentralized quadratic programming problems by introducing the consensus constraints. The alternating direction method of multipliers is adopted to achieve the solution for the distributed online PN anomaly detection. Next, by utilizing the correlation of measurements between neighbor virtual nodes, another distributed online PL anomaly detection algorithm based on the canonical correlation analysis is proposed. The network only needs to store covariance matrices and mean vectors of current data to calculate the canonical correlation vectors for real-time PL anomaly analysis. The simulation results on both synthetic and real-world network datasets show the effectiveness and robustness of the proposed distributed online anomaly detection algorithms. [ABSTRACT FROM AUTHOR]

Wi-Fi fingerprint-based indoor localization has recently attracted significant research interest since Wi-Fi devices are widely deployed and practical, and no additional infrastructure is required. However, the Received Signal Strength (RSS) is significantly different on heterogeneous devices, and this difference has a negative impact on localization results. In this paper, we propose a multi-fingerprint and multi-classifier fusion (MFMCF) localization method to improve the localization accuracy and solve the problem of heterogeneous hardware. First, the individual feature set of original RSS fingerprint, signal strength difference (SSD) fingerprint and hyperbolic location fingerprint (HLF) are fused as a composite fingerprint set (CFS), and then the data dimension is reduced by linear discriminant analysis (LDA). Second, three representative machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) are selected and trained to construct an integrated fusion model. Finally, in order to get more accurate predictions, in the online phase, a selective strategy based on entropy is proposed by calculating the entropy of each classifier’s prediction result. Experiments show that MFMCF is an effective scheme to solve the localization problem of heterogeneous devices and improve the localization accuracy. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries offer excellent advantages of high efficiency, small size, and low cost, but their instability and inconformity remain challenging. Sudden failure of batteries may cause serious accidents, endangering the safety of people’s lives and properties. Advanced remaining useful life prediction methods for batteries can effectively avoid those accidents. In this article, we proposed a novel hybrid method with the purpose of enhancing battery remaining useful life prediction precision and robustness. Based on improved complete ensemble empirical mode decomposition with adaptive noise algorithm, utilizing a special-designed interpolation reconstruction mechanism, the battery capacity degradation series was decomposed into a trend subseries and several fluctuation subseries. Weighted least square support vector machine and long short-term memory network are then established to perform prediction for the trend subseries and the fluctuation ones, respectively. A complementary series of experiments is designed to verify the effectiveness of the proposed method. The simulation results represent that the proposed method achieves higher prediction accuracy and robustness over other comparison models. The proposed approach provides a promising and effective alternate for lithium-ion batteries remaining useful life prediction without relying on the cell dynamic process, which is meaningful for cases with limited measurement parameters or limited computational power. [ABSTRACT FROM AUTHOR]

As the data size in Machine Learning fields grows exponentially, it is inevitable to accelerate the computation by utilizing the ever-growing large number of available cores provided by high-performance computing hardware. However, existing parallel methods for clustering or regression often suffer from problems of low accuracy, slow convergence, and complex hyperparameter-tuning. Furthermore, the parallel efficiency is usually difficult to improve while striking a balance between preserving model properties and partitioning computing workloads on distributed systems. In this article, we propose a novel and simple data structure capturing the most important information among data samples. It has several advantageous properties supporting a hierarchical clustering strategy that contains well-defined metrics for determining optimal hierarchy, balanced partition for maintaining the clustering property, and efficient parallelization for accelerating computation phases. Then we combine the clustering with regression techniques as a parallel library and utilize a hybrid structure of data and model parallelism to make predictions. Experiments illustrate that our library obtains remarkable performance on convergence, accuracy, and scalability. [ABSTRACT FROM AUTHOR]

Consider the compressed sensing setup where the support $\mathbf {s}^{\ast}$ of an $m$ -sparse $d$ -dimensional signal $\mathbf {x}$ is to be recovered from $n$ linear measurements with a given algorithm. Suppose that the measurements are such that the algorithm does not guarantee perfect support recovery and that true features may be missed. Can they efficiently be retrieved? We address this question through a simple error-correction module referred to as LiRE. LiRE takes as input an estimate $\mathbf {s}_{\text {in}}$ of the true support $\mathbf {s}^{\ast}$ , and outputs a refined support estimate $\mathbf {s}_{\text {out}}$. We establish sufficient conditions under which LiRE is guaranteed to recover the entire support, that is $\mathbf {s}_{\text {out}}\supseteq \mathbf {s}^{\ast} $. These conditions imply, for instance, that in high dimension LiRE can correct a sublinear in $m$ number of errors made by Orthogonal Matching Pursuit (OMP). The computational complexity of LiRE is ${\mathcal{O}}(m n d)$. Experimental results with random Gaussian design matrices show that LiRE substantially reduces the number of measurements needed for perfect support recovery via Compressive Sampling Matching Pursuit, Basis Pursuit (BP), and OMP. Interestingly, adding LiRE to OMP yields a support recovery procedure that is more accurate and significantly faster than BP. This observation carries over in the noisy measurement setup where the combination of LiRE and OMP is faster and more accurate than LASSO. Finally, as a standalone support recovery algorithm with a random initialization, experiments show that LiRE’s support recovery performance lies between OMP and BP. These results show that LiRE can be used generically, on top of any suboptimal baseline support recovery algorithm, to improve support recovery or to operate with a smaller number of measurements, at the cost of a relatively small computational overhead. Alternatively, LiRE may be used as a standalone support recovery algorithm that is competitive with respect to OMP. [ABSTRACT FROM AUTHOR]

Road surface condition can significantly impact the interaction between vehicles and pavement structure, which may even cause high fuel consumption and safety issues of drivers and vehicles. Distributed fiber optic sensing (DFOS) technology is a useful tool to perform continuous and real-time monitoring of traffic and road surface condition. However, it is challenging to process the data for the purpose of road anomaly detection. The study proposed two approaches to detect the road anomaly using DFOS. In the first method, local binary pattern (LBP) histograms were used to extract the features of the images with and without road anomaly, and support vector machine (SVM) combined with principal component analysis (PCA) was adopted as the classifier. The convolutional neural network (CNN) was applied on the binary classification data to analyze the images in the second method. The accuracy and benefits of two methodologies were compared. The vehicle speed was estimated by detecting lines using Hough transform. The feasibility of road anomaly detection using DFOS is proved. [ABSTRACT FROM AUTHOR]

The growing availability of maritime IoT traffic data and continuous expansion of the maritime traffic volume, serving as the driving fuel, propel the latest Artificial Intelligence (AI) studies in the maritime domain. Among the most recent advancements, vessel trajectory prediction is one of the most essential topics for assuring maritime transportation safety, intelligence, and efficiency. This paper presents an up-to-date review of existing approaches, including state-of-the-art deep learning, for vessel trajectory prediction. We provide a detailed explanation of data sources and methodologies used in the vessel trajectory prediction studies, highlight a discussion regarding the auxiliary techniques, complexity analysis, benchmarking, performance evaluation, and performance improvement for vessel trajectory prediction research, and finally summarize the current challenges and future research directions in this field. [ABSTRACT FROM AUTHOR]

Several deep learning techniques have been used to detect pavement cracks for the partial replacement of inefficient traditional inspections. However, the extensively varying real-world situations limit the detection accuracy. While many existing studies utilized the attention modules in pavement crack detection to improve model performance, few studies considered the impact of “how” and “where” to add attention modules on model performance, namely the module optimization. Combined with the attention mechanism, a new pavement crack detection method was proposed based on the You Only Look Once 5th version (YOLOv5) in this paper. Considering two adding ways and three adding positions, the spatial and channel squeeze and excitation (SCSE) module and convolutional block attention module (CBAM) were used to build a total of 12 different attention models for the cracking detection. Each model was trained on 3248 images, and the weight with the best performance on the validation set was saved for testing. The test results show that the mAP@0.5:0.95 of the best attention model is improved by nearly 6.7% compared to the original model without the attention mechanism. In addition, it can process images at 13.15ms/pic while maintaining 94.4% precision, fully meeting the needs of real-time detection. Compared with the existing pavement crack detection methods, the advantages of the proposed method include a great detection speed, high accuracy, and good robustness. [ABSTRACT FROM AUTHOR]

In this paper, we present a deep learning model for high-accuracy, high-speed detection of vehicle taillights in traffic. The model consists of three major modules: the lane detector, the car detector, and the taillight detector. Unlike most previously proposed algorithms where hand-coded schemes are used, we have adopted a data-driven approach. This data-driven scheme was implemented in both the car and taillight detection modules. First, we used an intricately designed lane detection module, then we adopted the Recurrent Rolling Convolution (RRC) architecture and tracking mechanism for detecting car boundaries. Subsequently, we used the same RRC architecture to extract the taillight regions of the detected cars. The lane detection and car detection modules improve both the speed and detection rate of the final taillight detection. The robustness of our model was verified using datasets from Sungkyunkwan University (SKKU) as well as the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI). Our model works well even in hostile conditions. It achieves detection rates as high as 99% in testing with the SKKU dataset. When using the KITTI 2D Object dataset, the model achieves a taillight detection rate of 86%. The model achieves 100% taillight detection rate on a certain, small subset of the KITTI Tracking dataset. [ABSTRACT FROM AUTHOR]

Automatic assessment of sleep apnea/ hypopnea syndrome (SAHS) based on fewer physiological signals is critical for the success of healthcare at home. However, previous studies that use such settings only achieve a lower assessment accuracy, causing fewer syndromes to be separated for effective diagnosis. This paper presents a 3-stage support vector machines (SVM)-based algorithm for SAHS assessment using a single-channel nasal pressure (NP) signal. In this work, NP signal is utilized for feature extraction. Amplitude features, as well as those extracted using discrete Fourier transform and discrete wavelet transform, are used for machine learning. A total of 58 sets of polysomnography recordings, each with approximately 7 h in duration, were analyzed. This work achieves a sensitivity of 95.7% and a positive predictive value of 90.9%, outperforming previous works using NP signal. Compared with prior studies using only SpO2 signal, this work still achieves better performance and supports more classification levels. Thanks to the low-complexity settings based only on the NP signal, the proposed approach provides a promising solution to SAHS assessment for remote healthcare. [ABSTRACT FROM AUTHOR]

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power. [ABSTRACT FROM AUTHOR]

Twin support vector machines (TSVMs) have been successfully employed for binary classification problems. With the advent of machine learning algorithms, data have proliferated and there is a need to handle or process large-scale data. TSVMs are not successful in handling large-scale data due to the following: 1) the optimization problem solved in the TSVM needs to calculate large matrix inverses, which makes it an ineffective choice for large-scale problems; 2) the empirical risk minimization principle is employed in the TSVM and, hence, may suffer due to overfitting; and 3) the Wolfe dual of TSVM formulation involves positive-semidefinite matrices, and hence, singularity issues need to be resolved manually. Keeping in view the aforementioned shortcomings, in this article, we propose a novel large-scale fuzzy least squares TSVM for class imbalance learning (LS-FLSTSVM-CIL). We formulate the LS-FLSTSVM-CIL such that the proposed optimization problem ensures that: 1) no matrix inversion is involved in the proposed LS-FLSTSVM-CIL formulation, which makes it an efficient choice for large-scale problems; 2) the structural risk minimization principle is implemented, which avoids the issues of overfitting and results in better performance; and 3) the Wolfe dual formulation of the proposed LS-FLSTSVM-CIL model involves positive-definite matrices. In addition, to resolve the issues of class imbalance, we assign fuzzy weights in the proposed LS-FLSTSVM-CIL to avoid bias in dominating the samples of class imbalance problems. To make it more feasible for large-scale problems, we use an iterative procedure known as the sequential minimization principle to solve the objective function of the proposed LS-FLSTSVM-CIL model. From the experimental results, one can see that the proposed LS-FLSTSVM-CIL demonstrates superior performance in comparison to baseline classifiers. To demonstrate the feasibility of the proposed LS-FLSTSVM-CIL on large-scale classification problems, we evaluate the classification models on the large-scale normally distributed clustered (NDC) dataset. To demonstrate the practical applications of the proposed LS-FLSTSVM-CIL model, we evaluate it for the diagnosis of Alzheimer’s disease and breast cancer disease. Evaluation on NDC datasets shows that the proposed LS-FLSTSVM-CIL has feasibility in large-scale problems as it is fast in comparison to the baseline classifiers. [ABSTRACT FROM AUTHOR]

DNA methylation is an epigenetic marker that plays an important role in the biological processes of regulating gene expression, maintaining chromatin structure, imprinting genes, inactivating X chromosomes, and developing embryos. The traditional detection method is time-consuming. Currently, researchers have used effective computational methods to improve the efficiency of methylation detection. This study proposes a fuzzy model with correntropy induced loss (C-loss) function to identify DNA N4-methylcytosine (4 mC) sites. To improve the robustness and performance of the model, we use kernel method and the C-loss function to build a higher order fuzzy inference systems. To test performance, our model is implemented on six 4 mC and eight University of California Irvine (UCI) datasets. The experimental results show that our model achieves better prediction performance. [ABSTRACT FROM AUTHOR]

Fighting against climate change and global warming is one of the biggest challenges faced by the Maritime Industry nowadays to make the supply chain greener and environmentally sustainable. Cutting greenhouse gases (GHG) emissions and decarbonizing the international shipping has been a paramount activity for the International Maritime Organization (IMO) since the first set of international mandatory measures to improve ships' energy efficiency and reduce CO2 emissions per transport work, as part of the International Convention for the Prevention of Pollution from Ships (MARPOL) released in 2011. Besides that, changes in consumption habits around the globe (i.e., digitalization and growth of e-commerce) plus disruptive events like the COVID-19 or the blocking of the Suez Canal, to name only a few, have also highlighted the need for building more resilient maritime transport networks. In this work, a pragmatical analysis of the principal machine learning algorithms has been carried out to provide a qualitative prediction of the Estimate Time of Arrival (ETA) of container vessels applied to short-sea shipping where the distance between ports is reduced. By exploiting both, the Automatic Identification System (AIS) and meteorological data gathered over a desired area of interest, the developed approach delivers a model capable of predicting the ETA of ships where the reaction time of the stakeholders involved in the management of the Port Call is very reduced (i.e., less than two hours of sailing between ports) and therefore, tolerance for error is low. Very positive results were obtained for the training dataset collected under real conditions for more than a year. The best results were obtained by the RF model with a Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 11.31 and 19.56 minutes respectively. [ABSTRACT FROM AUTHOR]

We present a novel framework for designing multiplierless kernel machines that can be used on resource-constrained platforms such as intelligent edge devices. The framework uses a piecewise linear (PWL) approximation based on a margin propagation (MP) technique and uses only addition/subtraction, shift, comparison, and register underflow/overflow operations. We propose a hardware-friendly MP-based inference and online training algorithm that has been optimized for a field-programmable gate array (FPGA) platform. Our FPGA implementation eliminates the need for digital signal processor (DSP) units and reduces the number of Look-Up Tables (LUTs). By reusing the same hardware for inference and training, we show that the platform can overcome classification errors and local minima artifacts that result from MP approximation. The implementation of this proposed multiplierless MP-kernel machine on FPGA results in an estimated energy consumption of 13.4 pJ and power consumption of 107 mW with ~9 k LUTs and Flip Flops (FFs) each for a 256 $\times $ 32 sized kernel making it superior in terms of power, performance, and area compared with other comparable implementations. [ABSTRACT FROM AUTHOR]

Terahertz (THz) communication is promising as it can enable ultra-wide-band and ultra-high-rate for various emerging communication services. In this letter, we propose to exploit the extreme learning machine (ELM) network based regressor for simple and low-complexity joint channel estimation (CE) and signal detection (SD) for THz-band spatial modulation (THz-SM) communications impaired by hardware imperfections. Computer simulations show the performance superiority of the proposed joint CE/SD scheme when compared with the state-of-the-art schemes, and other machine learning-based ones, including the support vector machine (SVM), deep neural network (DNN) and some variants of ELM. Specifically, we show that its bit error rate (BER) performance approaches to that of the recently derived maximal likelihood (ML) SD. In addition, the robustness of the proposed scheme is validated by considering two types of background impulsive noises. [ABSTRACT FROM AUTHOR]