Vector Quantization Research Articles

Quantized kernel recursive minimum error entropy algorithm

In this paper, we propose a online vector quantization (VQ) method based on the kernel recursive minimum error entropy (KRMEE) algorithm. According to information theoretic learning (ITL), the minimum error entropy criterion (MEE) is robust and can effective resistance to non-Gaussian noise. By combining the kernel recursive least squares (KRLS) algorithm with MEE criterion, KRMEE algorithm has been generated, which has excellent performance in non-Gaussian environments. However, with the size of data increases, the computational complexity will raise. We propose a quantized to solve this problem, the input space of the algorithm is quantized to suppress the linear growth radial basis function (RBF) network in kernel adaptive filtering (KAF). The VQ method is different from novelty criterion (NC), approximate linear dependency (ALD) criterion, and other sparsity methods, the online VQ method need to construct the dictionary, and calculate the distance by Euclidean norm. We propose a novel quantized kernel recursive minimum error entropy (QKRMEE) algorithm by combining VQ method with KRMEE algorithm, and update the solution with a recursive algorithm. In Mackey-Glass time series and a real-world datasets, Monte Carlo simulation experiments show that the proposed algorithm achieves better predictive performance in non-Gaussian noise environment. Meanwhile, the algorithm can restrain the growth of RBF network well, thus reducing the computational complexity and memory consumption effectively.

Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination

The estimation of power curve is the central task for efficient operation and prediction of wind power generation. It is often the case, however, that the actual data exhibit a great deal of variations in power output with respect to wind speed, and thus the power curve estimation necessitates the detection and proper treatment of outliers. This study proposes a novel procedure for outlier detection and elimination for estimating power curves of wind farms by employing clustering algorithms of vector quantization and density-based spatial clustering of applications with noise. Testing different parametric models of power output curve, the proposed methodology is demonstrated for obtaining power curves of individual wind turbines in a Korean wind farm. It is asserted that the outlier elimination procedure for power curve modeling outlined in this study can be highly efficient at the presence of noises.

Detection of the Use of Masks as an Effort to Prevent Covid-19 Using Gray Level Co-Occurrence Matrix (GLCM) Based on Learning Vector Quantization (LVQ)

Covid-19 is a disease caused by the SARS-CoV-2 virus. Transmission of Covid-19 can be through the flow of air (aerosol), splashes of liquid (droplets). One of the prevention efforts to break the chain of transmission is to use a mask when interacting with other people. Monitoring and controlling the use of masks will be safer and more efficient when implementing a mask detection system. This study will analyze GLCM for extraction method and LVQ for classification method. The results of GLCM successfully provide statistical features that represent image characteristics well. While the LVQ can provide classification results with a good percentage of accuracy. The results of the best percentage accuracy for the first rank are 83.15% in the composition ratio of 90: 10. Furthermore, the percentage of accuracy for the second rank is 76.03% at the composition ratio of 70: 30 and the third rank is 72.47% at the composition ratio of 80: 20. This indicates that the composition more training data does not guarantee the level of achievement of a higher percentage of accuracy. There is an optimal maximum number of epochs where the number of epochs that exceeds the optimal number of epochs will not experience a change in the percentage of accuracy. For each value the learning rate (alpha) can give the results of the percentage of accuracy with different graphic patterns and will stop at the optimal maximum number of epochs.

A secure pixel level self-recovery scheme for digital images

This paper presents a secure image authentication scheme for tamper localization and recovery at pixel level. The proposed scheme encrypts the watermark comprising tamper localization code and self-recovery code using chaotic sequence to ensure security. This scheme uses pixel to block conversion technique for ensuring lossless recovery of the original image from an untampered watermarked image. For enhancing the localization accuracy, a multilevel tamper localization strategy is used. The experimental results show that the proposed scheme generates watermarked images with minimal information loss and can withstand copy-move, image splicing, content removal, vector quantization, collage and content only attacks. This scheme has better security, better tamper localization accuracy and better recovered image quality under extensive tampering and takes less computation time in comparison to the state-of-the-art schemes.

Data sampling approach using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction

On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist.

Resizing codebook of vector quantization without retraining

Resizing codebook of vector quantization without retraining

Non-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images Compression

This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

A Novel Fault Detection Model Based on Vector Quantization Sparse Autoencoder for Nonlinear Complex Systems

To solve the problem of nonlinear factors in the fault detection process of complex systems, this article proposes a fault detection model based on vector quantization sparse autoencoder. First, a feature extraction model, which consists of a self-normalizing convolutional autoencoder module, a vector quantization module, a gradient module, and a loss module, is developed. The first module employs self-normalizing convolutional layers with good stability and generalization ability to extract the nonlinear structural features of complex systems. A nearest neighbor search strategy is implemented in the vector quantization module to further mine the nonlinear information. The gradient module adopts a straight-through estimation technique to improve the training efficiency. Sparse constraints are introduced into the loss module to obtain the essential features and enhance interpretability. Thereafter, a construction rule based on local Mahalanobis distance and K nearest neighbors is designed to calculate K Mahalanobis neighbor metrics that depend on the sparse features obtained by the feature extraction model. A comprehensive statistic for fault detection is constructed to accurately track the operating status of complex systems by combining the loss metric and the K Mahalanobis neighbor metric. Finally, the threshold of the fault detection statistics is determined by modeling the generalized extreme value distribution. Three case studies, a numerical simulation, the Tennessee Eastman benchmark process, and a typical circuit system, are adopted to demonstrate the effectiveness and merits of the proposed fault detection model.

Digital Twin Evolution for Hard-to-Follow Aeronautical Ad-Hoc Networks in Beyond 5G

The aircrafts were top of the places that disrupted the seamless connectivity requirement of 5G and beyond. The Aeronautical Ad-hoc Networks (AANETs) take the attention of both industry and academia to satisfy this connectivity requirement with the low cost, easy deployment, and continuous coverage features. On the other hand, the ultra-dynamic characteristics of AANET with unstructured topology make its environment hard-to-follow. Here, Artificial Intelligence (AI)-based methodologies have an essential role in handling the management complexity of this hard-to-follow environment. However, these methodologies increase the computational complexity of aircraft due to the continuous update, convergence time, and scalability issues. At that point, we propose the utilization of the Digital Twin (DT) technology to handle the management complexity of AANET while solving the main issues of AI-based methodologies on it. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time. Therefore, this work introduces the utilization of DT technology for the AANET orchestration and, accordingly, proposes a DT-enabled AANET (DT-AANET) topology management framework. This framework consists of the Physical AANET Twin and Controller, including Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment while the operational module executes the AI-based computations on them through unsupervised learning-based training or supervised learning-based prediction. Finally, we present a case study based on Learning Vector Quantization (LVQ) to show the usability of the proposed framework and support this through evaluation results.

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

This paper aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Fusion of sensors’ data combined with novelty detection algorithm and learning vector quantisation (LVQ) neural networks is used to detect tool wear and present diagnostic and prognostic information. To reduce the number of sensors required in the monitoring system and support sensor fusion, the ASPS approach (Automated Sensor and Signal Processing Selection System) is used to select the most appropriate sensors and signal processing methods for the design of the condition monitoring system. The experimental results show that the proposed approach has demonstrated its efficacy in the implementation of an effective solution for the monitoring tool wear in turning. The results prove that the fusion of sensitive sensory characteristic features and the use of AI methods have been successful for the detection and prediction of the tool wear in turning processes and show the capability of the proposed approach to reduce the complexity of the design of condition monitoring systems and the development of a sensor fusion system using a self-learning method.

Leakage detection in water distribution networks using machine-learning strategies

Abstract This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%.

Transfer Deep Reinforcement Learning-Based Energy Management Strategy for Plug-In Hybrid Electric Heavy-Duty Trucks under Segmented Usage Scenarios

Energy management strategy (EMS) is a way to reduce the energy consumption of hybrid power systems. This article proposes a unique deep reinforcement learning- (DRL-) based EMS for plug-in hybrid electric heavy-duty trucks (PHETs), combining driving cycle pattern recognition (DPR) and deep transfer learning (DTL). The proposed EMS can cope well with the complex usage scenarios of PHETs and the difficulty of generating EMS. While ensuring the minimum overall driving cost, the strategy can improve the convergence speed of the DRL method and the generalizability under segmented usage scenarios. Firstly, representative driving cycles that reflect different usage scenarios are constructed based on a naturalistic data-driven method. Secondly, a plug-in hybrid electric heavy-duty truck (PHET) driving pattern recognizer based on a learning vector quantization neural network (LVQ) is built. Thirdly, the deep deterministic policy gradient (DDPG) algorithm is innovatively combined with the DTL algorithm. The pretrained neural network in the corresponding usage scenarios is transferred to the natural driving cycles based on DTL. Moreover, the proposed EMS gives an emphasized consciousness on the battery degradation cost. Finally, the strategy is tested under natural driving cycles in different usage scenarios and proven through comparison with the current state-of-the-art techniques, deep reinforcement learning-based strategy, and dynamic programming (DP). The results show that the proposed strategy outperforms existing cutting-edge deep reinforcement learning techniques in terms of convergence speed, battery life extension, fuel consumption, and overall driving cost reduction. The proposed control strategy can improve the convergence speed by nearly 50%, while effectively extending the battery life and reducing the overall driving cost compared to the existing state-of-the-art strategies. The battery degradation rate is reduced by 48.46%, 57.95%, and 36.99%, and the driving cost is reduced by 17.76%, 8.51%, and 7.12%, respectively, under each usage scenario.

On triangle inequalities of correlation-based distances for gene expression profiles

BackgroundDistance functions are fundamental for evaluating the differences between gene expression profiles. Such a function would output a low value if the profiles are strongly correlated—either negatively or positively—and vice versa. One popular distance function is the absolute correlation distance, d_a=1-|rho |, where rho is similarity measure, such as Pearson or Spearman correlation. However, the absolute correlation distance fails to fulfill the triangle inequality, which would have guaranteed better performance at vector quantization, allowed fast data localization, as well as accelerated data clustering.ResultsIn this work, we propose d_r=sqrt{1-|rho |} as an alternative. We prove that d_r satisfies the triangle inequality when rho represents Pearson correlation, Spearman correlation, or Cosine similarity. We show d_r to be better than d_s=sqrt{1-rho ^2}, another variant of d_a that satisfies the triangle inequality, both analytically as well as experimentally. We empirically compared d_r with d_a in gene clustering and sample clustering experiment by real-world biological data. The two distances performed similarly in both gene clustering and sample clustering in hierarchical clustering and PAM (partitioning around medoids) clustering. However, d_r demonstrated more robust clustering. According to the bootstrap experiment, d_r generated more robust sample pair partition more frequently (P-value <0.05). The statistics on the time a class “dissolved” also support the advantage of d_r in robustness.Conclusiond_r, as a variant of absolute correlation distance, satisfies the triangle inequality and is capable for more robust clustering.

A Novel Energy Management Strategy Integrating Deep Reinforcement Learning and Rule Based on Condition Identification

In order to reduce the driving cost of plug-in hybrid electric vehicles, a novel energy management strategy integrating deep reinforcement learning and rule based on condition identification is proposed. Firstly, principal component analysis is used to reduce the dimension of kinematic sequences features, and fuzzy clustering is used to classify the kinematics sequences, and then the working conditions are reorganized according to the classification results to obtain the three kinds of training conditions, and learning vector quantization neural network is used to train and identify the type of working conditions. Then, the rule taking engine torque, state of charge, and motor torque as control variables and driving mode as output is designed, which is integrated into the agent of deep reinforcement learning. Combined with the designed energy management strategy trained under three working conditions, the numerical simulation is carried out under the verification working condition based on condition identification, the simulation results are systematically analyzed to show the effectiveness of the designed energy management strategy.

CUDA and OpenMp Implementation of Boolean Matrix Product with Applications in Visual SLAM

In this paper, the concept of ultrametric structure is intertwined with the SLAM procedure. A set of pre-existing transformations has been used to create a new simultaneous localization and mapping (SLAM) algorithm. We have developed two new parallel algorithms that implement the time-consuming Boolean transformations of the space dissimilarity matrix. The resulting matrix is an important input to the vector quantization (VQ) step in SLAM processes. These algorithms, written in Compute Unified Device Architecture (CUDA) and Open Multi-Processing (OpenMP) pseudo-codes, make the Boolean transformation computationally feasible on a real-world-size dataset. We expect our newly introduced SLAM algorithm, ultrametric Fast Appearance Based Mapping (FABMAP), to outperform regular FABMAP2 since ultrametric spaces are more clusterable than regular Euclidean spaces. Another scope of the presented research is the development of a novel measure of ultrametricity, along with creation of Ultrametric-PAM clustering algorithm. Since current measures have computational time complexity order, O(n3) a new measure with lower time complexity, O(n2), has a potential significance.

Cryptanalysis of Reversible Data Hiding in Encrypted Images Based on the VQ Attack

Reversible data hiding in encrypted images (RDHEI) is commonly used for privacy protection in images stored on cloud storage. Currently, block permutation and co-modulation (BPCM) encryption is commonly utilized in most existing RDHEI schemes to generate encrypted images. In this paper, we analyze the vulnerabilities of RDHEI based on BPCM encryption and then propose a cryptanalysis method based on the vector quantization (VQ) attack. Unlike the existing cryptanalysis method, our method does not require the help of a plaintext image instead of adopting the symmetric property between the original cover image and the encrypted cover image. To obtain the pixel-changing pattern of a block before and after co-modulation, the concept of a pixel difference block (PDB) is first defined. Then, the VQ technique is used to estimate the content of the ciphertext block. Finally, we propose a sequence recovery method to help obtain the final recovered image based on the premise that the generator is compromised. The experimental results demonstrate that when the block size is 4 × 4, our proposed cryptanalysis method can decrypt the contents of the ciphertext image well. The average similarity can exceed 75% when comparing the edge information of the estimated image and the original image. It is concluded from our study that the BPCM encryption algorithm is not robust enough.

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde–Buzo–Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

Lossy Compression Approaches Based on Vector Quantization

Lossy Compression Approaches Based on Vector Quantization

Secret Key Generation With Multiantenna Relays

One effective way of generating and distributing secret keys for secure communication is to exploit the randomness of wireless channels. Since a potential eavesdropper has a different propagation channel than two legitimate users, it can be used to generate unique and secure keys. If the two devices are far apart, a relay can aid the process. This paper presents a novel precoding scheme for the relay which delivers the keys directly and with very little processing required at the end devices. We also compare scalar and vector quantisation and show that a tree-based vector quantisation scheme has better performance than the former but much lower complexity than the latter.

DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

The exponential growth of data necessitates an effective data storage scheme, which helps to effectively manage the large quantity of data. To accomplish this, Deoxyribonucleic Acid (DNA) digital data storage process can be employed, which encodes and decodes binary data to and from synthesized strands of DNA. Vector quantization (VQ) is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency. This article introduces a new DNA Computing with Water Strider Algorithm based Vector Quantization (DNAC-WSAVQ) technique for Data Storage Systems. The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage. Besides, the DNAC-WSAVQ model initially performs DNA encoding on the input images to generate a binary encoded form. In addition, a Water Strider algorithm with Linde-Buzo-Gray (WSA-LBG) model is applied for the compression process and thereby storage area can be considerably minimized. In order to generate optimal codebook for LBG, the WSA is applied to it. The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures. The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.