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This paper presents a novel concept of a universal solar converter suitable for application in both in the dc or single-phase ac grids using the same terminals. The idea lies in the utilization of the same semiconductors in the dc-dc and in the dc-ac configuration, resulting in minimal redundancy. Possible semiconductor stages are considered. The particular attention is focused on the output filter design along with proper protection circuit selection for dc and ac grids. The design example and comparative analysis between dc-dc, dc-ac and universal solutions are given. The experimental prototype of the universal solar converter that is rated for 3.6 kVA power in the ac mode and 5 kW in the dc mode is presented. The experimental results demonstrate the ability of operation in ac or dc grids with main correspondent modes. Possible fields of application along with main benefits are addressed in conclusions.

Deep neural networks (DNNs) have become a widely deployed model for numerous machine learning applications. However, their fixed architecture, substantial training cost, and significant model redundancy make it difficult to efficiently update them to accommodate previously unseen data. To solve these problems, we propose an incremental learning framework based on a grow-and-prune neural network synthesis paradigm. When new data arrive, the neural network first grows new connections based on the gradients to increase the network capacity to accommodate new data. Then, the framework iteratively prunes away connections based on the magnitude of weights to enhance network compactness, and hence recover efficiency. Finally, the model rests at a lightweight DNN that is both ready for inference and suitable for future grow-and-prune updates. The proposed framework improves accuracy, shrinks network size, and significantly reduces the additional training cost for incoming data compared to conventional approaches, such as training from scratch and network fine-tuning. For the LeNet-300-100 (LeNet-5) neural network architectures derived for the MNIST dataset, the framework reduces training cost by up to 64% (67%), 63% (63%), and 69% (73%) compared to training from scratch, network fine-tuning, and grow-and-prune from scratch, respectively. For the ResNet-18 architecture derived for the ImageNet dataset (DeepSpeech2 for the AN4 dataset), the corresponding training cost reductions against training from scratch, network fine-tunning, and grow-and-prune from scratch are 64% (67%), 60% (62%), and 72% (71%), respectively. Our derived models contain fewer network parameters but achieve higher accuracy relative to conventional baselines.

Through Silicon Via (TSV) is considered as the near-future solution to realize low-power and high-performance 3D-Integrated Circuits (3D-ICs) and 3D-Network-on-Chips (3D-NoCs). However, the lifetime reliability issue of TSV due to its fault sensitivity and the high operating temperature of 3D-ICs, which also accelerates the fault-rate, is one of the most critical challenges. Meanwhile, most current works focus on detecting and correcting TSV defects after manufacturing without considering high-temperature nodes’ impact on lifetime reliability. Besides, the recovery for defective clusters is also challenging because of costly redundancies. In this work, we present HotCluster: a hotspot-aware self-correction platform for clustering defects in 3D-NoCs to help understand and tackle this problem. We first give a method to predict normalized fault rates and place redundant TSV groups according to each region’s fault rate. In our particular medium fault-rate (normalized to the coolest area), HotCluster reduces about 60% of the redundancies in comparison to the uniformly distributed redundancies while having a higher ratio of router working in a normal state. Furthermore, HotCluster integrates both online (weight-based) and offline (max-flow min-cut offline method) mapping algorithms to help the system correct the faulty TSV clusters. The experimental results show that both the max-flow min-cut offline method and weight-based online mode with a redundancy of 0.25 exhibits less than 1% of routers disabled under 50% defect-rates.

Recently, data-driven industrial monitoring systems have been rapidly developed and significantly improved the performance on industrial monitoring tasks. However, the widely deployed data-driven models expose industrial data to more unsecured links, increasing the safety risk of safe-critical industrial systems. The research about adversarial attacks has shown that the potential attackers can utilize tiny crafted perturbations on the input data to mislead the machine learning model outputs. In this paper, a novel cross-domain data protection scheme named Data Guardian is proposed to authenticate and correct industrial data under potential attacks on data-driven monitoring systems. Data Guardian embeds designed redundancy information into the data least significant bits (LSB), based on q-ary low density parity-codes (LDPC) over Galois field (finite field). The data is encoded at the secured industrial sites, then decoded before being input into the data-driven monitoring systems, where the security risk is usually higher. The decoding capability of q-ary LDPC codes is improved by the data statistical characteristics, with a new proposed prior estimation method. In the experiments, the Data Guardian is tested under the attacks to the fault diagnosis models on Tennessee Eastman process (TEP) and rolling element bearing (REB) from Case Western Reserve University. The results show that Data Guardian can efficiently reduce the success rate of adversarial attacks, especially when the attack variable ratio is small.

OSS projects are being developed by globally distributed contributors, who often collaborate through the pull-based model today. While this model lowers the barrier to entry for OSS developers by synthesizing, automating and optimizing the contribution process, coordination among an increasing number of contributors remains as a challenge due to the asynchronous and self-organized nature of distributed development. In particular, duplicate contributions, where multiple different contributors unintentionally submit duplicate pull requests to achieve the same goal, are an elusive problem that may waste effort in automated testing, code review and software maintenance. While the issue of duplicate pull requests has been highlighted, to what extent duplicate pull requests affect the development in OSS communities has not been well investigated. In this paper, we conduct a mixed-approach study to bridge this gap. Based on a comprehensive dataset constructed from 26 popular GitHub projects, we obtain the following findings: (a) Duplicate pull requests result in redundant human and computing resources, exerting a significant impact on the contribution and evaluation process. (b) Contributors' inappropriate working patterns and the drawbacks of their collaborating environment might result in duplicate pull requests. (c) Compared to non-duplicate pull requests, duplicate pull requests have significantly different features, e.g., being submitted by inexperienced contributors, being fixing bugs, touching cold files, and solving tracked issues. (d) Integrators choosing between duplicate pull requests prefer to accept those with early submission time, accurate and high-quality implementation, broad coverage, test code, high maturity, deep discussion, and active response. Finally, actionable suggestions and implications are proposed for OSS practitioners.

Recent research shows that 4-bit data precision is sufficient for Deep Neural Network (DNN) inference without accuracy degradation. Due to the low bit-width, a large amount of data is repeated. In this paper, we propose a hardware architecture, named Rare Computing Architecture (RCA), that skips redundant computations due to the repeated data in the networks. By exploiting redundancy, RCA is not significantly affected by data-sparsity and maintains great improvements in performance and energy efficiency, while the improvements of existing DNN accelerators are vulnerable to variations in sparsity. In the RCA, repeated data in a window for censoring repetition are detected by a Redundancy Censoring Unit (RCU) and processed at a time, achieving high effective throughput. Additionally, we present a dataflow that exploits abundant data-reusability in DNNs, which enables the high-throughput computations to be ceaselessly performed without an increase of bandwidth for data-read. The proposed architecture is evaluated in two ways of exploiting weight- and activation-repetition. In the evaluation, RCA is compared to a value-agnostic computation and UCNN that is the state-of-the-art accelerator exploiting weight-repetition. Additionally, RCA is compared to Bit-pragmatic that exploits bit-level sparsity. Both evaluations demonstrate that the RCA shows steadily high improvements in performance and energy-efficiency.

The modular multilevel converter (MMC) has emerged as the preferred choice for voltage source converter (VSC)-based high voltage direct current (HVDC) systems due to its low losses, low harmonic distortion, modularity, and redundancy. These advantages come at the expense of a complex control system with the strong coupling among its control variables, which complicates the design procedure of both its individual control loops and the DC link controllers. In fact, the performance improvement of a control loop could lead to degraded performance of other loops. Hence, to deal with such a complex dynamic system, this article suggests adopting multivariable H optimal control techniques in order to ensure stability and optimized performance of a VSC-HVDC link. First, a full-order, centralized multi-input-multi-output (MIMO) controller is derived based on H optimization and used as a benchmark for the system performance level. Then, a fixed-structure, decentralized MIMO controller is synthesized to make a compromise between the optimal performance and feasibility of practical implementation. Finally, simulations are conducted to evaluate and compare the performance and robustness degradation due to the migration from a high-order, centralized controller towards a low-order, decentralized controller.

The pulse rectifier of a traction system in locomotives and electric multiple unit (EMUs) is usually vulnerable to performance degradation and faults due to uncertain factors, such as vibrations, aging, electromagnetic interferences. In order to ensure adequate redundancy and isolation measures in time to avoid fault propagation in traction systems, a real-time fault diagnosis method for sensors and IGBTs of the impulse rectifier is proposed, which lays a foundation for the redundant design of traction systems. Once a fault is detected in this paper, which can immediately act to the detected faults and take effective remedial measures to avoid the failure of the whole system. It is based on the structural analysis of the traction system whose structural model of interest will be established. Meanwhile, the structural model is evaluated and optimized according to the analytical relation model under various fault conditions, and the minimum structural overdetermined sets (MSOs) are obtained based on the optimized model, which can be used to isolate all faults. Using the MSOs, the redundancy relationship is deduced and the sequence residuals are generated. Afterward, the cumulative sum (CUSUM) algorithm is used for diagnosis decision making. The effectiveness of the proposed method is finally verified on a hardware-in-loop test platform, which can accurately simulate a traction system. It shows that the proposed method can achieve both good feasibility and high accuracy.

The plethora of ways of data representation and their applications to system modeling is inherently associated with dimensionality reduction. In a nutshell, the result of dimensionality reduction should support efficient ways of constructing ensuing models (classifiers, predictors) as well as an interpretation of the data themselves. Furthermore, there should be a suitable measure quantifying the quality of data positioned in the reduced space. We advocate that what makes the reduced data interpretable, goes hand in hand with revealing a logic fabric of the data, suppressing redundancy, and finally arriving at a logic description of data. The anticipation is that the reduced data can be described in the form of logic expressions formed over the original highly dimensional data. Evidently, having these above stated points in mind, the aim of this study is two-fold: (i) to develop a logic-oriented data representation, and (ii) to quantify the quality of results of dimensionality reduction by incorporating a facet of information granularity. In other words, we argue that the result of dimensionality reduction gives rise to information granules whose level of granularity associates with the quality of processing completed by the autoencoder. In light of the recent surge of architectures of deep learning, the study is focused on the construction and analysis of logic-oriented autoencoders. We propose a two-level architecture composed of the logic-oriented processing units (and processing carried out at the first layer of the autoencoder) followed by the or processing completed at the second layer. As data representation provided by the autoencoder is not ideal, we augment the original architecture by granular parameters which give rise to granular logic-oriented autoencoders. A suite of experiments is also reported.

Band selection is one of the most effective methods to reduce the band redundancy of hyperspectral images (HSIs). Most existing band selection methods tend to regard each band as a whole, and then explore the band redundancy with the pixel-wise features directly. However, since the regions of HSIs corresponding to different objects have diverse spectral properties and spatial structure, such above scheme limits the performance of hyperspectral band selection due to the lack of spatial information. To address above issues, a novel band selection method via region-aware latent features fusion based clustering (RLFFC) is proposed. Specifically, we employ the superpixel segmentation to segment HSIs into multiple regions so that the spatial information of HSIs can be fully preserved. In order to capture the priori information, we construct its corresponding Laplacian matrix from which a group of low dimensional latent features are generated to further enhance the separability among different bands. Then, a shared latent feature representation of HSIs is obtained by fusing region-aware latent features to effectively capture the band redundancy of HSIs. Finally, the k -means clustering algorithm is utilized to obtain the index of the selected bands from the shared latent feature representation. As a result, the spectral and spatial properties are well exploited in the proposed method. Extensive experiments on four public hyperspectral datasets show that the proposed method achieves superior performance when compared with other state-of-the-art ones.

Crowdsourcing is a hotspot research field which can facilitate machine learning by collecting labels to train models. Consequently, the state-of-the-art research efforts in crowdsourcing focus on truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, the integrated labels will be used to fine-tune machine learning models. Particularly, in this paper, we change the target of truth inference in crowdsourcing from discrete labels to multiple comments given by online participants, that is, the integration of the crowdsourced comments. For such a goal, we propose a Self-play and Sentiment-Emphasized Comment Integration Framework (SSECIF), based on deep Q-learning, with three unique features. First, our framework SSECIF can generate the comment integration in a totally self-play way, without relying on the ground truth generated by human effort. Second, the integrated comment generated by SSECIF can include salient content with low redundancy. Third, the proposed framework SSECIF has emphasized, with a higher intensity, the sentiment in the integrated comment, in order to reflect the attitude or opinion more obviously. Extensive evaluation on real-world datasets demonstrates that SSECIF has achieved the best overall performance in terms of both effectiveness and efficiency, compared with the state-of-the-art methods. Index Terms: Crowdsourcing; Comment Integration; Reinforcement Learning; Deep Q-Learning; Sentiment Analysis.

Visual grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. Generally, it requires the machine to first understand the query, identify the key concepts in the image, and then locate the target object by specifying its bounding box. However, in many real-world visual grounding applications, we have to face with ambiguous queries and images with complicated scene structures. Identifying the target based on highly redundant and correlated information can be very challenging, and often leading to unsatisfactory performance. To tackle this, in this paper, we exploit an attention module for each kind of information to reduce internal redundancies. We then propose an accumulated attention (A-ATT) mechanism to reason among all the attention modules jointly. In this way, the relation among different kinds of information can be explicitly captured. Moreover, to improve the performance and robustness of our VG models, we additionally introduce some noises into the training procedure to bridge the distribution gap between the human-labeled training data and the real-world poor quality data. With this "noised" training strategy, we can further learn a bounding box regressor, which can be used to refine the bounding box of the target object. We evaluate the proposed methods on four popular datasets (namely ReferCOCO, ReferCOCO+, ReferCOCOg, and GuessWhat?!). The experimental results show that our methods significantly outperform all previous works on every dataset in terms of accuracy.

Reduction of redundancy and improvement of error-correcting capability are essential research themes in the coding theory. The best codes constructed in various ways are recorded in a database maintained by Markus Grassl. In this paper, we propose an algorithm to construct the best code using punctured codes and a supporting method for constructing the best codes. First, we define a new evaluation function to determine deletion bits and propose an algorithm for constructing punctured linear codes. 36 best codes were constructed in the proposed algorithm, and 131 best codes were constructed by further modifying those best codes. Secondly, we evaluate the possibility of increasing the minimum distance based on the relationship between code length, information length, and minimum distance. We narrowed down the target (n,k) code to try the best code search based on the evaluation and found 30 other best codes.

Artificial Neural networks (ANNs) are widely used to solve classification problems for many machine learning applications. When errors occur in the computational units of an ANN implementation due to for example radiation effects, the result of an arithmetic operation can be changed, and therefore, the predicted classification class may be erroneously affected. This is not acceptable when ANNs are used in many safety-critical applications, because the incorrect classification may result in a system failure. Existing error-tolerant techniques usually rely on physically replicating parts of the ANN implementation or incurring in a significant computation overhead. Therefore, efficient protection schemes are needed for ANNs that are run on a processor and used in resource-limited platforms. A technique referred to as Selective Neuron Re-Computation (SNRC), is proposed in this paper. As per the ANN structure and algorithmic properties, SNRC can identify the cases in which the errors have no impact on the outcome; therefore, errors only need to be handled by re-computation when the classification result is detected as unreliable. Compared with existing temporal redundancy-based protection schemes, SNRC saves more than 60 percent of the re-computation (more than 90 percent in many cases) overhead to achieve complete error protection as assessed over a wide range of datasets. Different activation functions are also evaluated. This research was supported by the National Science Foundation Grants CCF-1953961 and 1812467, by the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and by the Madrid Community research project TAPIR-CM P2018/TCS-4496. Publicado

The redundancy allocation problem (RAP) is one of the most commonly used approaches for improving the reliability of systems. The present study investigates the most recent variant of RAP, namely, the RAP with heterogeneous components under the mixed redundancy strategy. An exact branch-and-price (BP) algorithm is proposed for the problem that solves in less than one CPU second all the benchmark instances reported in the literature. The algorithm is not only the first exact one proposed for the RAP with a mixed strategy but also the first application of the BP algorithm in the literature of reliability optimization problems. The proposed BP algorithm is also capable of solving other variants of RAP, including RAPs with either an active or a standby strategy, making it possible to investigate the advantages of the mixed strategy over the active and standby strategies. Finally, a new set of instances is generated to evaluate the performance of the proposed algorithm in dealing with more challenging instances.

In this paper, we improve the inter coding performance of surveillance videos by simultaneously investigating the distinct characteristics of background and foreground redundancy, and introduce two novel reference frames in a complementary manner. On one hand, a block level background reference frame (BRF) is proposed to reduce the background redundancy. The proposed scheme incorporates semantic information into the compression process, and makes use of instance segmentation to facilitate the background block decision, making the generated BRF free from foreground pollution. On the other hand, in order to handle foreground redundancy, a foreground reference frame (FRF) is generated based on Surveillance Prediction Generative Adversarial Network (SP-GAN), which utilizes previous reconstructed frames, optical flow based prediction, as well as BRF to infer the foreground objects of the to-be-coded frame. We integrate the proposed scheme into HM-16.6 software and append BRF and FRF to the reference pictures list (RPS). Simulation results demonstrate considerable superiority of the proposed scheme. In particular, by adding the proposed BRF to RPS, 3% coding gains are observed compared with the state-of-the-art BRF method. When both BRF and FRF are incorporated into RPS, 5.8% gains are achieved for surveillance video coding.

The gastrointestinal polyp (GIP) is the abnormal growth of tissues in digestive organs. Identifying these polyps from endoscopy video or image is a tremendous task to reduce the future risk of gastrointestinal cancer. This paper proposes a proper diagnosis method of polyp using a fusion of contourlet transform and fine-tuned VGG19 pre-trained model from enhanced endoscopic 224 × 224 patch images. This study has used different fine-tuned models (Alexnet, ResNet50, VGG16, VGG19) as well as a few scratch models while fine-tuned VGG19 works better. Also, this research has used Principal Component Analysis (PCA) and Minimum Redundancy Maximum Relevance (MRMR) dimensionality reduction methods to collect the intuitive features for classification. In Support Vector Machine (SVM) based polyp detection, the prior method (PCA) performs better. Besides, a proposed algorithm marks polyp region from identified polyp patches and uses a binning strategy to process video. A set of experiments are performed on standard public data sets and found comparative improved performance with an accuracy of 99.59%, sensitivity of 99.74% and specificity of 99.44%. This work can be instrumental for the radiologist for diagnosis of polyps during real-time endoscopy.

The emergence of 3D point clouds (3DPCs) is promoting the rapid development of immersive communication, autonomous driving, and so on. Due to the huge data volume, the compression of 3DPCs is becoming more and more attractive. We propose a novel and efficient color attribute compression method for static 3DPCs. First, a 3DPC is partitioned into several sub-point clouds by color distribution analysis. Each sub-point cloud is then decomposed into a lot of 3D blocks by an improved k-d tree-based decomposition algorithm. Afterwards, a novel virtual adaptive sampling-based sparse representation strategy is proposed for each 3D block to remove the redundancy among points, in which the bases of the graph transform (GT) and the discrete cosine transform (DCT) are used as candidates of the complete dictionary. Experimental results over 10 common 3DPCs demonstrate that the proposed method can achieve superior or comparable coding performance when compared with the current state-of-the-art methods.

In the presence of distributed energy resources (DERs), distribution networks (DNs) operate in multiple configurations. Maintaining complete observability is essential for the monitoring of the network during operation in any configuration. This article proposes a methodology for the optimal deployment of micro-phasor measurement units ( $\mu$ PMUs) in a DN operating in multiple configurations. A composite objective function has been formulated to minimize the number of $\mu$ PMUs while maximizing measurement redundancy. The optimal solution has been obtained considering the $\mu$ PMU with or without channel limits. The constraints have been formed simultaneously to cover all operational configuration of the network while eliminating repetition. The proposed methodology has been illustrated on an eight-node reconfigurable network. It has also been verified on several reconfigurable DNs, having 33, 34, 69, 118, 123, and 167 nodes. A comparison between the proposed methodology and recently published approaches has been made. The constraints for zero-injection nodes with various network configuration has also been considered to find optimal $\mu$ PMU deployment. The obtained optimal deployment of $\mu$ PMUs with or without channel limit satisfies the criteria of complete topological observability, topological in-dependency, and maximizes the sum of topology measurement redundancy.

The problem of detecting and identifying sensor faults is critical for efficient, safe, regulatory-compliant, and sustainable operations of modern industrial processing systems. The increasing complexity of such systems brings, however, new challenges for sensor fault detection and sensor fault isolation (SFD-SFI). One of the key enablers for any SFD-SFI method is analytical redundancy, which is provided by an analytical model of sensor observations derived from first principles or identified from historical data. As defective sensors generate measurements that are inconsistent with their expected behavior as defined by the model, SFD amounts to the generation and monitoring of residuals between sensor observations and model predictions. In this article, we introduce a disentangled recurrent neural network (RNN) with the objective to cope with the smearing-out effect, i.e., where the propagation of a sensor fault to nonfaulty sensor results in large and misleading residuals. The introduction of a probabilistic model for the residual generation allows us to develop a novel procedure for the identification of the faulty sensors. The computational complexity of the proposed algorithm is linear in the number of sensors as opposed to the combinatorial nature of the SFI problem. Finally, we empirically verify the performance of the proposed SFD-SFI architecture using a real data set collected at a petrochemical plant.

To adapt to the increasing storage demands and varying storage redundancy requirements, practical distributed storage systems need to support storage scaling by relocating currently stored data to different storage nodes. However, the scaling process inevitably transfers substantial data traffic over the network. Thus, minimizing the bandwidth cost of the scaling process is critical in distributed settings. In this paper, we show that optimal storage scaling is achievable in erasure-coded distributed storage based on network coding, by allowing storage nodes to send encoded data during scaling. We formally prove the information-theoretically minimum scaling bandwidth for both scale-out and scale-in cases. Based on our theoretical findings, we also build a distributed storage system prototype NCScale based on Hadoop Distributed File System, so as to realize network-coding-based scaling while preserving the necessary properties for practical deployment. Experiments on Amazon EC2 show that the scaling time can be reduced by up to 50% over the state-of-the-art.

The control of kinematically redundant robots is often approached using nullspace projection, which requires precise models and can be computationally challenging. Humans have many more degrees of freedom than are required to accomplish their tasks, but given neuromechanical limitations, it seems unlikely that biology relies on precise models or complex computation. An alternative biologically-inspired approach leverages the compositionality of mechanical impedance. In theory, nullspace projection eliminates any conflict between two tasks. In contrast, superposition of task-space impedance and a full-rank joint-space impedance may impose a task conflict. This work compared nullspace projection with impedance superposition during unconstrained motion and forceful physical interaction. In practice, despite their theoretical differences, we did not observe a substantial influence of the nullspace projector weighting matrix. We found that nullspace projection and impedance superposition both resulted in measurable task conflict. Remarkably, when the dimensionality of the nullspace was increased, impedance superposition was comparable to nullspace projection.

Dual-inverter fed open-end winding PMSM (OW-PMSM) drive topology can extend the speed range under limited DC bus voltage, but it suffers from the zero-sequence current (ZSC) problem when with common bus configuration. Predictive current control (PCC) is a potential high-performance control method for electric drive. However, the PCC algorithm for dual-inverter system may bring a huge computational burden because of more space vectors to traverse. In order to improve the steady-state current performance and suppress the ZSC, this paper proposes a dual-vector PCC (DV-PCC) method for OW-PMSM with ZSC hysteresis control. The reference voltage vector is predicted in advance according the model. A novel sector judgment method is used to judge the sector and sub-sector where the reference voltage vector is located, and determines the two candidate space vectors. Then the dwell time of the two candidate space vectors is calculated through the projection length of the reference voltage vector. Moreover, the switching states redundancy feature of dual-inverter system is employed to suppress ZSC. Since some space vectors correspond to multiple switching states of different zero-sequence voltages (ZSVs), the switching state whose ZSV polarity is opposite to the ZSC polarity is selected as the output to realize the hysteresis control of ZSC. When the ZSVs of the candidate space vectors contradict the hysteresis control demand, the zero-vector insertion method is used to guarantee the effective suppression of ZSC. The experiments are implemented in comparison with the existing dual-vector PCC method.

Quantitative ultrasound (QUS), which is commonly used to extract quantitative features from the ultrasound radiofrequency (RF) data or the RF envelope signals for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, to some extent limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machinelearning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for classifying liver fibrosis with the selected features, based on the results of which the optimal classifier was established and used for final classification. The performance of the proposed MPQUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82% and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24% and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects.

In this manuscript, we adopt a copula-based hierarchical dependent approach to analyze the redundancy allocation policy and statistical dependence effect on the reliability of two-parallel-series and series-parallel systems with interdependent subsystems from statistically dependent components and use a Schur-concave copula to model the statistical dependence between the outer subsystems, and an Archimedean copula to characterize the statistical dependence among the inner components. Firstly, we analyze and compare the reliability of different redundancy strategies for a two-parallel-series system in the sense of the usual stochastic order under the number of chosen components that are fixed and randomized. Secondly, we also provide sufficient conditions on the usual stochastic order of two different assembly policies for a two-series-parallel system when the number of chosen components is deterministic and randomized. These results can be conductive to allocate the two-parallel-series and two-series-parallel systems with interdependent subsystems from statistically dependent components in practical scenarios. Finally, some applications of the real-data and the potential managerial implications and future directions are also provided to enhance the system’s reliability.

A key challenge in the synthesis and subsequent analysis of supervisory controllers is the impact of state-space explosion caused by concurrency. The main bottleneck is often the memory needed to store the composition of plant and requirement automata and the resulting supervisor. Partial-order reduction (POR) is a well-established technique that alleviates this issue in the field of model checking. It does so by exploiting redundancy in the model with respect to the properties of interest. For controller synthesis, the functional properties of interest are nonblockingness, controllability, and least-restrictiveness, but also performance properties, such as throughput and latency are of interest. We propose an on-the-fly POR on the input model that preserves both functional and performance properties in the synthesized supervisory controller. This improves the scalability of the synthesis (and any subsequent performance analysis). Synthesis experiments show the effectiveness of the POR on a set of realistic manufacturing system models.

Industrial robot provides a promising alternative in free-surface milling. However, due to its low stiffness, it is difficult to guarantee the machining quality. While existing research considers mainly the influence of robot posture on stiffness, the workpiece setup's influence is equally important. In this study, to ensure the overall workpiece's robot stiffness meets the requirement of stiffness threshold in robotic milling, a method for simultaneously optimizing the robot posture and the workpiece setup is proposed. Firstly, to evaluate the robot stiffness during machining, this work presents a new stiffness index considering the robot's rotational deformation. And then, an optimization model is established to optimize both the robot redundancy and the workpiece setup, considering the constraints of joint limitation, singularity-free and collision-free. Moreover, for complex free-surface, to obtain the minimum number of posture changes of the robot and workpiece under the premise of meeting the limit of stiffness threshold, this work constructs a minimum set covering problem, which is solved by a clustering algorithm and a greedy algorithm. Finally, simulations and experimental studies are conducted to validate the effectiveness of the proposed robot stiffness index and the proposed optimization method, showing that the robot stiffness is improved during a milling process of the entire workpiece.

Single image dehazing, which aims at restoring a haze-free image from its correspondingly unconstrained hazy scene, is highly challenging and has gained immense popularity in recent years. However, the images generated using existing haze-removal methods often contain haze, artifacts, and color distortions, which severely degrade the visual quality of the final images. To this end, we propose a network combining multi-scale hierarchical feature fusion and mixed convolution attention to progressively and adaptively enhance the dehazing performance. The haze levels and image structure information are accurately estimated by fusing multi-scale hierarchical features, thus the model restores images with less remaining haze. The proposed attention mechanism is capable of reducing feature redundancy, learning compact internal representations, highlighting task-relevant features and further helping the model to estimate images with sharper textural details and more vivid colors. Therefore, with the application of multi-scale features extracted from both diverse layers and filters, the dehazing performance is significantly improved. Furthermore, a deep semantic loss function is proposed to highlight more semantic information in deep features. The experimental results show that the proposed method outperforms state-of-the-art haze removal algorithms.

Unlike theoretical distributed learning (DL), DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks (e.g., using mmW interfaces). This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and then design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes' straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.

For the existing repetitive motion generation (RMG) schemes for kinematic control of redundant manipulators, the position error always exists and fluctuates. This article gives an answer to this phenomenon and presents the theoretical analyses to reveal that the existing RMG schemes exist a theoretical position error related to the joint angle error. To remedy this weakness of existing solutions, an orthogonal projection RMG (OPRMG) scheme is proposed in this article by introducing an orthogonal projection method with the position error eliminated theoretically, which decouples the joint space error and Cartesian space error with joint constraints considered. The corresponding new recurrent neural networks (NRNNs) are structured by exploiting the gradient descent method with the assistance of velocity compensation with theoretical analyses provided to embody the stability and feasibility. In addition, simulation results on a fixed-based redundant manipulator, a mobile manipulator, and a multirobot system synthesized by the existing RMG schemes and the proposed one are presented to verify the superiority and precise performance of the OPRMG scheme for kinematic control of redundant manipulators. Moreover, via adjusting the coefficient, simulations on the position error and joint drift of the redundant manipulator are conducted for comparison to prove the high performance of the OPRMG scheme. To bring out the crucial point, different controllers for the redundancy resolution of redundant manipulators are compared to highlight the superiority and advantage of the proposed NRNN. This work greatly improves the existing RMG solutions in theoretically eliminating the position error and joint drift, which is of significant contributions to increasing the accuracy and efficiency of high-precision instruments in manufacturing production.

Inter prediction is a crucial part of hybrid video coding frameworks, utilized to exploit the temporal redundancy in video sequences and improve the coding performance. During inter prediction, a predicted block is typically derived from reference pictures using motion estimation and motion compensation. To improve the coding performance of inter prediction, a neural network based enhancement to inter prediction (NNIP) is proposed in this paper. NNIP is composed of three networks, namely residue estimation network, combination network, and deep refinement network. Specifically, first, a residue estimation network is designed to estimate the residue between current block and its predicted block using their available spatial neighbors. Second, the feature maps of the estimated residue and the predicted block are extracted and concatenated in a combination network. Finally, the concatenated feature maps are fed into a deep refinement network to generate a refined residue, which is added back to the predicted block to derive a more accurate predicted block. NNIP is integrated in HEVC to evaluate its efficiency. The experimental results demonstrate that NNIP can achieve 4.6%, 3.0%, and 2.7% BD-rate reduction on average under LDP, LDB, and RA configurations compared to HEVC.

In high-power industrial installations, instead of a single shunt active harmonic filter (SAHF), parallel-connected multiple modular units of SAHF having lower current rating are generally preferred. In order to ensure that these modules share the compensating current in proportion to their individual current ratings, an intermodular communication network is generally being employed. This intermodular communication network reduces the overall reliability of the system, and it can be avoided if every individual module can autonomously estimate the compensating current to be shared by it. In case of voltage-source inverters operating in parallel, a droop-based current control scheme is generally employed to achieve the aforesaid requirement. However, in the SAHF, the inverters are operated in the current control mode, and hence, the existing droop-based control scheme needs appropriate modification. In this article, a modified droop-based scheme is proposed for a multimodular SAHF operating in the current control mode. The principle of operation of the proposed control scheme and its detailed mathematical analysis are presented. The feasibility of the scheme is verified by performing detailed simulation studies. A laboratory prototype having two SAHF modules, each having a VA rating of 30 kVA, is fabricated. Detailed experimental studies are carried out utilizing the laboratory prototype to confirm the viability of the proposed scheme.

The Internet of Things (IoT) has significantly increased the number of terminals and network traffic. It is necessary to exploit the full capacity of the network and optimize content transfer. Despite the powerful processing and storage capabilities of base stations in 5G technology, edge caching effectively reduces content access time and duplicate traffic, thus optimizing content transfer for more storage resources. The limited memory resources and the dynamic nature of the requested content have necessitated the use of smart caching methods. Sending the required data to central servers can cause additional network overload and learning disabilities due to private data. For this reason, a hierarchical federated deep learning (HFDRL) is proposed in this paper that uses the FDRL method to predict the user’s future requests and to determine the appropriate content replacement strategy. In addition, to perform learning and collaborate caching, the method by which partner devices are determined plays a key role in edge caching performance. HFDRL categorizes edge devices hierarchically, thus avoids the disadvantages of very small or large clusters, takes advantage of both. By minimizing the redundancy of content storage and latency, HFDRL improves the performance for each local base station network individually and the global network. Simulation results of the proposed method show that the hit rate and delay have improved, respectively, by an average of 55% and 67% compared to traditional methods, 40% and 56% compared to the collaborative method, and 14% and 15% compared to one-level FDRL without using hierarchical edge devices clustering.

The use of deep learning methods in hyperspectral image (HSI) classification has been a promising approach due to its powerful ability to automatically extract features in recent years. This article proposes a novel deep framework for HSI classification problems, referred to as feature-grouped network based on spectral-spatial connected attention mechanism (FG-SSCA). Different from the existing deep learning methods, the proposed framework integrates the spectral attention module and spatial attention module continuously from the raw HSI input, which is embedded into convolutional neural networks and could enhance the distinguishing ability of spectral bands and learn the spatial relevance between the neighboring pixels together. Meanwhile, the generating feature maps are sliced into a series of small groups in sequence along the direction of spectral bands and each group sequentially extracts spatial-spectral features through multiple spectral and spatial residual blocks. This feature-grouped strategy could fully utilize the redundancy and difference of bands and obtain more available and valuable information. The proposed FG-SSCA method could greatly improve generalization performance and make tremendous successes in HSI classification. Experimental results on several HSI benchmark data sets verify the effectiveness and superiority of the proposed method in comparison with the state-of-the-art approaches for HSI classification.

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-the-art approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-the-art methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

Neuromorphic vision sensors (NVS) can enable energy savings due to their event-driven that exploits the temporal redundancy in video streams from a stationary camera. However, noise-driven events lead to the false triggering of the object recognition processor. Image denoise operations require memoryintensive processing leading to a bottleneck in energy and latency. In this paper, we present in-memory filtering (IMF), a 6TSRAM in-memory computing based image denoising for eventbased binary image (EBBI) frame from an NVS. We propose a non-overlap median filter (NOMF) for image denoising. An inmemory computing framework enables hardware implementation of NOMF leveraging the inherent read disturb phenomenon of 6T-SRAM. To demonstrate the energy-saving and effectiveness of the algorithm, we fabricated the proposed architecture in a 65nm CMOS process. As compared to fully digital implementation, IMF enables > 70x energy savings and a > 3x improvement of processing time when tested with the video recordings from a DAVIS sensor and achieves a peak throughput of 134.4 GOPS. Furthermore, the peak energy efficiencies of the NOMF is 51.3 TOPS/W, comparable with state of the art inmemory processors. We also show that the accuracy of the images obtained by NOMF provide comparable accuracy in tracking and classification applications when compared with images obtained by conventional median filtering., 13 pages

In seismic facies analysis, waveform clustering of self-organizing map (SOM) usually classifies waveforms truncated by horizon with a fixed time window. However, the fixed time window is not an appropriate method for the situation in which the thickness of the target layer varies in the horizontal direction. In order to adapt SOM to this situation and simultaneously preserve the advantages of SOM, we replace the vanilla SOM measure with dynamic time warp (DTW) distance. Compared with Euclidean distance, DTW distance requires more computational amount about path searching and weight parameter updating in SOM. Therefore, considering the redundancy of seismic data, we introduce a hierarchical clustering strategy, which uses cluster-based stratified sampling and hierarchical mapping to reduce the computational cost of training and prediction. In the visualization section, by combining with hue, saturation, value (HSV) coloring and hierarchical mapping, the method can quickly display the lateral distribution of stratigraphic. The proposed method is validated by a mound shape model in a simulation test. Finally, the method was successfully applied to the field data from a reef-bank reservoir. The experimental and application results show that, when the top and bottom interfaces of the destination layer can be clearly identified, the proposed method can effectively cluster the waveforms with variable length, and the computational cost is acceptable.

Bubble size distribution (BSD) is a crucial indicator of operating conditions in froth flotation. However, current BSD-based recognition methods have some limitations in the BSD estimation and don't achieve satisfactory results. Therefore, an alternative operating condition recognition method is proposed. Firstly, the temporal cumulative distribution function (TCDF) is designed to represent the BSD on the froth video. Then, the piecewise basis function is established to fit the TCDF. Secondly, the TCDF-Minimum Redundancy Maximum Relevancy (MRMR) feature is generated by MRMR to increase the sensitivity of TCDF features to the operating conditions. Lastly, the proposed TCDF-MRMR feature together with froth velocity and texture features are fed into an AdaBoost-extreme learning machine (ELM) model to recognize the operating conditions. Experiments in a zinc flotation process demonstrate that the TCDF-MRMR feature is more effective than other BSD-based features, and the AdaBoost-ELM shows better performance than traditional single recognition models.

Total variation (TV) and non-local self-similarity (NSS) are powerful tools for successfully enhancing compressive sensing performance. However, standard TV approaches often over-smooth detailed edges in the image, due to the uniform regularization of gradient magnitude. In this paper, a novel compressed sensing method for the reconstruction of medical images is proposed, the image edges are well preserved with the proposed reweighted TV. The redundancy of the NSS patch also is leveraged through the sparse regression model. The proposed model was solved with an efficient strategy of the Alternating Direction Method of Multipliers (ADMM) algorithm. Experimental results on thesimulated phantom, brain Magnetic resonance imaging (MRI) show that the proposed method outperforms the state-of-the-art compressed sensing approaches.

Non-intrusive load monitoring (NILM) has become a key technology in the power Internet of Things as well as an important information source for load characteristics analysis. Whether the selected features are appropriate determines the effectiveness of the load identification in NILM. In order to reduce the redundancy and improve the relevancy of the selected features, a feature selection method that balances redundancy and relevancy is proposed. Based on the approximate Markov blanket decision, this paper puts forward the basis of feature set redundancy elimination. This is used to determine the priority of feature selection by taking the number of features pre-selection as a measure. The mutual information method is combined with the CRITIC weight to implement redundancy elimination for the initial feature set of the load, and the feature correlation ranking algorithm is established based on the Relief-F method. Based on the analysis idea of balancing redundancy and relevancy, a feature selection strategy is established for the initial feature set, in order to obtain the optimal feature subset with minimum redundancy and maximum relevancy. Finally, the K-nearest neighbor identification algorithm after k-value optimization is used to simulate the proposed method. The results show that comparing with the other feature selection methods, the proposed method is promising in terms of robustness and generalization, and shows an active effect on improving identification accuracy.

With continuously decreasing circuit scale, more and more test patterns (test contents) are added to test set to achieve acceptable defect levels, which seriously affect test time and, consequently, test cost. Theoretically, dropping invalid (pattern that can make the test pass) test patterns can reduce test cost. In fact, When the valid (pattern that can make the test fail) and invalid patterns overlap seriously, identifying effective patterns performs poorly, which will not achieve the expected results. This paper proposes a kind of eliminate redundancy method, so that this LDA-MRMR algorithm which can provide test cost reduction without increasing the defect level obviously. Experimental results demonstrate that the proposed method sacrifices 3% of the predictive accuracy in exchange for 3.7 times time saving compared with traditional methods. In addition, the algorithm is completely software-based and does not require any additional hardware overhead and is directly compatible with traditional integrated circuit testing process.

Remote sensing (RS) crossmodal text-image retrieval has become a research hotspot in recent years for its application in semantic localization. However, since multiple inferences on slices are demanded in semantic localization, designing a crossmodal retrieval model with less computation but well performance becomes an emergent and challenging task. In this paper, considering the characteristics of multi-scale and target redundancy in RS, a concise but effective crossmodal retrieval model (LW-MCR) is designed. The proposed model incorporates multi-scale information and dynamically filters out redundant features when encoding RS image while text features are obtained via lightweight group convolution. To improve the retrieval performance of LW-MCR, we come up with a novel hidden supervised optimization method based on knowledge distillation. This method enables the proposed model to acquire dark knowledge of the multi-level layers and representation layers in the teacher network, which significantly improves the accuracy of our lightweight model. Finally, on the basis of contrast learning, we present a method employing unlabeled data to boost the performance of RS retrieval model further. The experiment results on four RS image-text datasets demonstrate the efficiency of LW-MCR in RS crossmodal retrieval tasks.

Background We aimed to verify whether the taste and chemical composition influence the selection of plants in each medicinal category, whether within a socio-ecological system or between different socio-ecological systems. To this end, we use the theoretical bases of the Utilitarian Redundancy Model and the Utilitarian Equivalence Model. We studied the local medical systems of four rural communities in northeastern Brazil, used as models to test our assumptions. Methods The data on medicinal plants and local therapeutic function were obtained from semi-structured interviews associated with the free-listing method, allowing to generate indexes of similarity of therapeutic use between the plants cited in each region. During the interviews, each informer was also asked to report the tastes of the plants cited. Subsequently, we classified each plant in each region according to the most cited taste. The data about the chemical composition of each plant were obtained from a systematic review, using Web of Knowledge and Scopus databases. Results Pairs of plants with similar tastes are 1.46 times more likely to have the same therapeutic function within a local medical system (redundancy), but not between medical systems (equivalence). We also find that chemical compounds are not primarily responsible for utilitarian redundancy and equivalence. However, there was a tendency for alkaloids to be doubly present with greater expressiveness in pairs of equivalent plants. Conclusions The results indicate that each social group can create its means of using the organoleptic characteristics as clues to select new species as medicinal. Furthermore, this study corroborates the main prediction of the Utilitarian Equivalence Model, that people in different environments choose plants with traits in common for the same functions.

Land cover multiresolution mapping of remote sensing images contributes greatly to land-use management, environmental protection, and city planning. In traditional mapping of this type, the representation of different land-use types depends on the image resolution, and the geometric, topologic, and semantic characteristics are not considered. This approach can cause a loss of useful information and the redundancy of useless information. In this study, we propose a superpixel-based land cover (multiresolution representation SULR) method for remote sensing images that employs multifeature fusion. In this process, we first define three basic superpixel operations, collapse, connection, and cutting, as the basic operators of multiresolution land cover mapping. Then, the topological adjacent land parcels are combined through the amalgamation of polygons with heterogeneous properties and aggregation of polygons with homogeneous properties based on the three proposed superpixel operators. Finally, the geometric boundaries of parcels are simplified by combining the superpixel collapse operator and image thinning technologies. Compared with traditional image scale transformation methods, the proposed method can more effectively achieve multiresolution mapping of land cover from remote sensing images by considering the geometric, topologic, and semantic characteristics of land parcels.

Alarm systems are critical for process safety and efficiency of complex industrial facilities. However, the presence of alarm floods severely compromises the performance of alarm systems. To cope with alarm floods, data mining has been applied to discover interesting patterns from historical alarm data, and such patterns can be used for alarm suppression, root cause analysis, and decision supports. However, most existing methods ignored the timestamps in pattern extraction or obtained complete patterns with significant redundancy. In this article, a new method is proposed to extract alarm flood patterns using a modified CloFAST algorithm. The contributions are twofold: first, a closed alarm sequence mining approach is proposed based on the CloFAST algorithm with improvements to incorporate timestamps and tolerate alarm order switchings; second, a pattern distillation strategy is designed to merge similar alarm sequences and export compact alarm sequential patterns. The proposed method is capable of avoiding influences of order ambiguities and also minimizing the redundancy of extracted patterns. The effectiveness of the proposed method is demonstrated by an industrial case study involving alarm data from a large-scale industrial facility.

Many pioneering approaches have verified the effectiveness of utilizing the global temporal and local object information for video understanding tasks and have achieved significant progress. However, existing methods utilize object detectors to extract all objects overall video frames. This may bring performance degradation due to the information redundancy both spatially and temporally. To address this problem, we propose an adaptive spatial location module for the video captioning task which dynamically predicts an important position of each video frame in the procedure of generating the description sentence. The proposed adaptive spatial location method not only makes our model focus on local object information, but also reduces time and memory consumption brought by the temporal redundancy in extensive video frames and improves the accuracy of generated description. Besides, we propose a balanced loss function to address the class imbalance problem existing in training data. The proposed balanced loss assigns different weight to each word of ground-truth sentence in the training process which can generate more diversified description sentences. Extensive experimental results on the MSVD and MSR-VTT dataset show that the proposed method achieves competitive performance compared to state-of-the-art methods.

Hyperspectral image (HSI) registers hundreds of spectral bands, whose intraclass variability and interclass similarity are resourceful information to be mined. Intraclass variability reflects the nonuniform and redundancy of the spatial and semantic features extracted from HSI. Interclass similarity represents the inherent relationship between adjacent features and snapshots. Existing models extract the superficial correlation representation for HSI to tackle the classification task but fail to embed the interclass and intraclass correlations due to these models' intrinsic bottlenecks. Confronting the challenges of capturing interrelation for complex data in practice, we propose a Context-Aware Attentional Graph U-Net (CAGU) to improve these two modes of representation, which is more flexible in feature enhancement. In this method, attentional Graph U-Net is capable of extracting the intraclass embeddings within a non-Euclidean space by combining similar distributing feature vertices. The gated recurrent unit (GRU) is another critical component of our model to capture the context-aware dynamic interclass embeddings. Extensive experiments demonstrate that our model can efficiently outperform state-of-the-art methods across-the-board on five wide-adopted public data sets, namely, Pavia University, Indian Pines, Salinas Scene-show, Houston 2013, and Houston 2018, on par with the same scale of model parameters.