Data Block Research Articles

Structural damage severity classification from time-frequency acceleration data using convolutional neural networks

Structural damage identification involves interpretation of vibration measurements to identify patterns that are indicative of changes in structural characteristics. Identification of such patterns using machine learning (ML) algorithms is a primary task in data-driven structural health monitoring. However, providing ML algorithms with features that are sensitive to damage has always been a challenge for accurate damage detection. In recent years, deep Convolutional Neural Networks (CNNs) proved to be versatile and powerful tools to identify the underlying patterns directly from low-level raw data. This study explores the applicability of CNNs to damage severity classification. A new format of input data for CNN damage identifiers is proposed based on time–frequency representations of acceleration data from multiple sensors. First, Continuous Wavelet Transformation (CWT) of acceleration response is employed to derive the time–frequency data, then, the results of transformations from multiple accelerometers are stored in separate channels creating a three-dimensional block of data. Such input data provide a CNN with structural information that expands into time, frequency, and spatial domains. To evaluate the accuracy of such a CNN-based classifier, a dataset comprised of free vibration response of a concrete beam under impact hammer tests is utilized. It is shown that a CNN trained with CWT data can successfully classify various severity of the damage, from minor to severe, with 100% accuracy. Also, this study examines the sensitivity of the CNN classifier to the number of training samples. It is demonstrated that the accuracy of such a network has a relatively lower sensitivity to the number of training samples and/or the number of measurement points to the degree that it maintains an F1-score above 0.813 when trained with data from one impact point and one sensor. On the other hand, for the case of a CNN classifier trained directly with time-series acceleration data the value of the F1-score drops to 0.590. Moreover, when compared with a multilayer perceptron trained with the extracted modal properties, it is illustrated that CNN classifiers have a better capability in capturing key features from the measurement, and thus produce more accurate predictions. Therefore, the combination of CNN classifiers with time–frequency data from multiple sensors results in damage identifiers that are less sensitive to sample size and more robust to changes in excitations and measurement points.

Selling through online marketplaces with consumer profiling

Retail platforms obtain consumers’ individual preferences by gathering vast amounts of data and can deliver such information to online retailers to support their pricing activities; this is called consumer-profiling services (CPS). We develop a game-theoretic model to study how a retail platform should provide CPS in light of retailers’ competition and consumers’ data-blocking activities. We show that exclusively providing data to high-quality retailers results in a net benefit for the platform and retailers. Low-quality retailers benefit from refusing the CPS provided by the platform to avoid head-to-head competition. In addition, we find that consumers’ data blocking can benefit both the platform and retailers when the data-blocking cost is moderate, which is counterintuitive. We also find that data blocking always hurts consumer surplus and social welfare. To test the robustness of the main model, three extensions are discussed: sequential pricing, asymmetric production costs, and positive service fees.

System-Information and Cognitive Technologies of Man-Made Infrastructure Cyber Security

The complication of technological processes due to the modernization of complex technological processes aggregates with a distributed spatial infrastructure requires the use of new control systems and computer and information network technologies. Accordingly, this poses the problem of revising the basic concepts of information-measuring systems building, developing software and hardware for the implementation of the target management process. This requires development of new approaches to presentation, processing, and display of data about the aggregates state of energy-active objects and the entire information structure. For management under threat conditions, it is necessary to take into account the features of the information infrastructure, data selection and processing methods, methods and algorithms for classifying the situation, which are formed from blocks of data obtained from each unit and the technological process as a whole. Information about the current state of system and infrastructure is necessary for the formation, adoption and implementation of management decisions which is the basis for coordination strategies synthesis. Appropriate target orientation, reasonable indicators of real process trajectories divergence from the target state determine the probability of object attack. Knowledge and decision-making procedures for the coordination of managerial actions is based on the strategic target orientation of the structure, their professionalism and the level of intellectual, cognitive and scientific training which is the basis for correct situation interpretation of countermeasures against threats formation. In the event of active threats complex on man-made systems in a certain region (resource, cognitive, system, information) and natural disasters or military operations, the threats lead to active destruction or failure of the production process. In order to functionally withstand related production structures, when loyal to the industrial relations concept, they need to integrate at the strategic management level on common goal basis to reduce risks. If necessary, to increase sustainability, they can be integrated at operational management level using interconnections at the production and resource levels. To do this, it is necessary to develop a strategic and goal-oriented management system behaviour line, which requires informational and intelligent data processing at the highest level using cognitive creative methods. For each level of the infrastructure hierarchy, oriented towards strategic goals in the global infrastructure dynamic environment, methods of assessing the situation to detect failures and the actions of attacks have been developed, based on which countermeasures are formed depending on the type of threats.

Comparison of different ways of handling consumer segments using L‐shape data

AbstractDifferent approaches for handling consumer segments in L‐shape data are compared in a study conducted in Norway. Consumers evaluated eight different yoghurt samples with profiles varying in three intrinsic attributes following a full factorial design. Three blocks of data were collected including sensory properties, liking ratings, and consumer attributes. Data were analyzed using two different approaches. In approach one, the one‐step simultaneous L‐Partial Least Square (L‐PLS) Regression with average consumer liking to represent the segments was used, while approach two was based on a two‐step procedure (TSP) based on Partial Least Square (PLS) Regression using dummy variables to represent the segments. The methods were compared in terms of interpretations, flexibility, and outcomes. Methodological implications, recommendations, and future research avenues are discussed.Practical ApplicationsThis manuscript has been devoted to two different ways of handling segmentation in L‐shape data of consumer liking, sensory properties, and consumer attributes. Overall, both L‐PLS and TSP approaches provide similar interpretation of results. The TSP approach, however, has the advantage of interpretating the horizontal and vertical direction in the L separately using standard regression methods. It is of interest of product development and marketing activities to identify which food product characteristics are important for consumer preferences and to better understand the characteristics of the consumers (e.g., socio‐demographics) that drive the consumer acceptance of the different products.

Research on Military Logistics Data Warehouse

According to military logistics data warehouse dealing with large-scale data effect is poor. This paper puts forward the construction scheme of dual computing engine military logistics data warehouse based on hive in order to improve the military logistics support capability. The scheme adopts Hadoop and Spark dual search architecture to support efficient storage of heterogeneous data. In addition, a data block placement strategy based on correlation analysis was used. This article use the intersection relationship matrix and the concurrency relationship matrix to calculate the correlation between the data block placed on the node and the data block to be placed. The access frequency of the target data block was integrated to select the appropriate node storage. The scheme can effectively improve hive’s query efficiency and provide more effective information aided decision support for commanders.

Detecting and testing multiple change points in distributions of damage-sensitive feature data for data-driven structural condition assessment: A distributional time series change-point analytic approach

Structural change detection is a core component of structural health monitoring (SHM). One appealing aspect of SHM is that structural changes can be detected by detecting the changes in extracted damage-sensitive features (DSFs). The feature changes of the data are usually attributed to the changes of the underlying distributions; thus, developing reliable change-point detectors for automatically detecting and testing various potential changes in the distributions of DSF data are particularly beneficial for automatic structural health diagnosis. However, methodological researches devoted to detecting change points for probability distributions are rare either in statistics or engineering literature, and state-of-the-art approaches mainly focus on single change-point detection and have restrictive assumptions on the distributions or the types of change points. This motivates us to develop a more flexible multiple change-point detection method for the distributions of DSF data employed for data-driven structural condition assessment. To accommodate a large dataset, the DSF data are summarized by a distributional time series that consists of probability density functions (PDFs) estimated from blocks of DSF data. Then, detecting the distributional changes for the massive DSF data can be phrased as detecting the changes in the resulting distributional time series with much less data objects. To overcome the obstacle that the PDF-valued data are special functional data residing in nonlinear abstract spaces, this study employs two transformations to convert the PDFs into more tractable vector spaces. Then, by using the linear structures possessed by the vector spaces, a nonparametric multiple change-point detection method is presented for the distributional time series based on the techniques of functional principal component analysis and E-Divisive data segmentation. The proposed method possesses various nice features, such as being capable for multiple change-point detection, having fewer restrictive assumptions and scalability to large datasets. Moreover, it also shows superiority in revealing the modes of distributional variations. An application to distributional change detection involved in cable condition assessment for a long-span cable-stayed bridge demonstrates the applicability and versatility of our proposal, and some interesting results are also discovered.

Multiblock spectral imaging for identification of pre-harvest sprouting in Hordeum vulgare

A novel data fusion method based on the use of visible/near-infrared (VNIR) and shortwave infrared (SWIR) imaging sensors, to distinguish between pregerminated and ungerminated barley grain is proposed. Spectral imaging was used to fingerprint germinated and ungerminated barley grain from a total of 5640 average spectra representing single barley kernels varying with respect to germination time. Chemometric approaches utilising partial least squares-discriminant analysis (PLS-DA) and multiblock sequential and orthogonalized partial least squares-linear discriminant analysis (SO-PLS-LDA) and sequential and orthogonalized covariance selection-linear discriminant analysis (SO-CovSel-LDA) were used to build classification models. SO-PLS-LDA achieved a total classification rate of 99.88%, while SO-CovSel-LDA resulted in a classification accuracy of 97.46% when a maximum of 8 variables were selected from each data block (VNIR and SWIR) – models were validated on an independent test set. The use of multiblock approaches led to increased prediction accuracy, compared to PLS-DA, and a viable solution to address the industry problem to detect pregerminated malting barley in a rapid, non-destructive manner. This represents a significant advance with respect to the current dated methods which are hindered by time-consuming wet chemistry techniques and human subjective bias. The potential of the proposed new technique also has the further advantage of moving toward multispectral systems which can be used to detect pre-harvest germinated barley using an even more computationally rapid and affordable online sorting machine incorporating the wavebands of importance selected by SO-CovSel-LDA. The study highlights how sequential and orthogonalised data fusion approaches, in the food and agricultural sector, are powerful solutions to real world problems.

Familywise error rate control for block response-adaptive randomization.

Response-adaptive randomization allows the probabilities of allocating patients to treatments in a clinical trial to change based on the previously observed response data, in order to achieve different experimental goals. One concern over the use of such designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. To address this, Robertson and Wason (Biometrics, 2019) proposed methodology that guarantees familywise error rate control for a large class of response-adaptive designs by re-weighting the usual -test statistic. In this article, we propose an improvement of their method that is conceptually simpler, in the context where patients are allocated to the experimental treatment arms in a trial in blocks (i.e. groups) using response-adaptive randomization. We show the modified method guarantees that there will never be negative weights for the contribution of each block of data to the adjusted test statistics, and can also provide a substantial power advantage in practice.

Data block decomposition and intelligent secure acquisition of microdata

P-sets (P stands for Packet) is a set model with dynamic characteristics, which is obtained by introducing dynamic characteristics into Cantor set and improving Cantor set. According to the fact that the characteristics of class I big data are completely consistent with the basic characteristics of P-sets, this paper gives research on theory and application on class I big data from the view of mathematics. Here we introduce Class I big data which need some new definitions of data block, microdata and data link. Based on these concepts, decomposition theorem of data block and microdata relation theorem are given, and then attribute reasoning theorem and microdata intelligent discovery and the intelligent secure acquisition algorithm of microdata are also proposed. By using these theoretical results, the applications of secure acquisition of microdata are presented. In summary, P-sets mathematical model provides a new theory and method for studying class I big data.

Stacked multi‐layer security for Hadoop distributed file system using HSCT steganography

SummaryBig data needs to have scalable storage platforms as well as parallel processing environments. Hadoop is preferable because it is an open‐source software which works in a distributed, parallel processing environment and no need of any special hardware. In recent developments of Hadoop, security was introduced as optional. A symmetric block cipher advanced encryption standard (AES) is used for encryption. A 128 MB block of data needs 8 million encryptions. A better way of hiding the data from unauthorized access is preferable. Steganography, a more contemporary security technology than cryptography, is the art and science of hiding subconscious information in hidden item while exposing opponents their existence. The cover is an image, and the secret information may be a photo, music, video, or text. A double layer hexcode substituted–color space transformed (HSCT) steganography with 3 band splitting of data ensures confidentiality of the data and one time pad key generated with client's ID provides authentication also. Considering the volume of data, steganography concept is much faster than AES or any other cipher. Hiding the data inside the random images will indulge in fast reconstruction of data when needed for processing using either MapReduce or tools like spark.

A Chaotic System and Count Tracking Mechanism-based Dynamic S-Box and Secret Key Generation

In cryptography, Block ciphers use S-Boxes to perform substitution and permutation operations on a data block. S-Boxes provide non-linearity and confusion of bits to the cryptographic algorithms. In addition, secret keys are critical security aspects for encrypting and decrypting messages. The uncertainty and randomness of the secret key and S-boxes used in the algorithm determine the extent of security against any cryptanalysis attack. This paper proposes a new mechanism to dynamically generate a secret key and S-Box each time while sending and receiving the message. These dynamically generated S-Boxes and keys depend on mutually decided security parameters and message transfer history. Furthermore, a new counter-based mechanism is introduced in this paper. These enhancement techniques are applied to the serpent cipher algorithm, and a data transfer simulation is performed to validate the efficacy of the proposed method. We observe that the dynamically generated S-box follows the strict avalanche criteria. We further validate that the encrypted message shows higher sensitivity to the S-box and the secret key in enhanced serpent cipher than the original version. However, to validate our proposed method, we test and analyze the improvements in the Serpent Cipher Algorithm.

A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.

Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge. We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis. The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.

ParallelChain: a scalable healthcare framework with low‐energy consumption using blockchain

AbstractThe blockchain‐enabled healthcare system assists in tackling the inherent issues of centralized healthcare systems, such as data stewardship, single point of failure, and data integrity. However, existing blockchain‐enabled healthcare systems suffer from high‐energy consumption, low transaction throughput, and poor scalability. This work proposes the healthcare framework ParallelChain to tackle the low scalability and high‐energy consumption issue. ParallelChain divides the network into clusters, enabling linear scalability of nodes. The parallel execution of transactions in each cluster improves the throughput. The leader selection process of ParallelChain does not require solving computer‐intensive puzzles, making it more energy efficient than proof‐of‐work. The performance of ParallelChain is validated using metrics such as data transfer, processing time, consensus delay, block congestion, and message exchange. It shows that the amount of data transmitted is 4.6 times more in Bitcoin than ParallelChain with varying nodes. The number of messages exchanged is reduced by 48%, and the processing time is reduced by 2.17 and 1.7 times for varying nodes and block sizes, respectively.

Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data.

Research questions in the human sciences often seek to answer if and when a process changes across time. In functional MRI studies, for instance, researchers may seek to assess the onset of a shift in brain state. For daily diary studies, the researcher may seek to identify when a person's psychological process shifts following treatment. The timing and presence of such a change may be meaningful in terms of understanding state changes. Currently, dynamic processes are typically quantified as static networks where edges indicate temporal relations among nodes, which may be variables reflecting emotions, behaviors, or brain activity. Here we describe three methods for detecting changes in such correlation networks from a data-driven perspective. Networks here are quantified using the lag-0 pair-wise correlation (or covariance) estimates as the representation of the dynamic relations among variables. We present three methods for change point detection: dynamic connectivity regression, max-type method, and a PCA-based method. The change point detection methods each include different ways to test if two given correlation network patterns from different segments in time are significantly different. These tests can also be used outside of the change point detection approaches to test any two given blocks of data. We compare the three methods for change point detection as well as the complementary significance testing approaches on simulated and empirical functional connectivity fMRI data examples.

Generalized extreme value analysis of criticality tallies in Monte Carlo calculation

Generalized extreme value (GEV) is the standard statistical model on the occurrences of extreme observations. The trinity theorem in GEV asserts that properly normalized maxima from blocks of data converge in distribution to one of the Weibull, Gumbel and Fréchet distributions as the data size increases. In this work, the methodology of GEV is applied to criticality tallies in Monte Carlo fission source cycles in order to evaluate the utility value of the distribution tail ends. Numerical results obtained under a sufficiently large number of particles per cycle show that the extreme value index (EVI) in GEV is not capable of being differentiated for the upper and lower distribution tail ends of criticality tallies. It is also shown that the EVI of criticality tallies falls within the range of Weibull distribution including the EVI of Gumbel distribution as the role of a boundary value layer. As a demonstration aimed at practicality, GEV is utilized for the population diagnosis under an insufficient number of particles per cycle. It turns out that the transition from one equilibrium to other equilibrium due to small population size makes the EVIs of upper and lower distribution tail ends depart from each other so that one of them falls in the range of Weibull distribution and the other in that of Fréchet distribution.

VLSI implementation of lightweight cryptography technique for FPGA-IOT application

The use of cryptography in algorithms is seen to be safe and effective. The secret key distribution is still regarded as a crucial problem despite the fact that it is similar to other symmetric encryption schemes. One single block of data (128 bits) must be encrypted or decrypted, and this requires more computer work that uses more power. The Internet is being extended to link almost everything on the world via the Internet of Things (IoT). In this article, a 256-bit key, lightweight cryptography-based data security technique is implemented for an IOT application. Instead of the traditional 2-D S-box and the prior 1-D S-box, a novel one-dimensional substitution Box (S-box) is suggested. The results of the simulations demonstrate that the suggested lightweight cryptography performs better than the traditional methods in terms of latency, throughput, transmission time, and efficiency rate.

IMPLEMENTATION FPGA BASED IMPLEMENTATION OF 128-BIT AES ALGORITHM USING VHDL

In current world of computations, data encryption is of prominent importance. Many algorithms were developed for data encryption and decryption to prevent hacking. The Advanced Encryption Standard (AES) is one of the data encryption techniques. Two famous kinds of hardware implementation techniques are pipelining and loop-unrolling techniques. In pipelining, registers are inserted between each combinational processing element so that each input data block can be processed simultaneously in each processing element. In this work, a pipelined implementation of AES encryption algorithm is developed. The number of rounds of AES-128 encryption is 10 and an architecture implementing this cipher is called fully pipelined, when all data blocks of 10 rounds can be processed simultaneously. In the loop-unrolling technique one or multiple rounds of the algorithm are processed in the same clock cycle. Here only one round of the algorithm is implemented as a combinational processing element and a data register is also used to store the result obtained in the previous clock cycle. In this work, a128-bit AES is implemented and for each round of AES encryption, a different sub-key is used as the round key, which is produced by the key schedule algorithm based on the loop-unrolled technique, to produce the required sub-key for each round is done. The no of I/O, Slices in the proposed work are 386, 229 respectively with Spartan6 fpga with a minimum period of 5.813ns,maximum Frequency of 172.031MHz, minimum input arrival time before clock of 4.823ns,maximum output required time after clock of 5.588ns, throughput of 2.2Gbps. The no of I/O, Slices are 387, 264 respectively with Artix7 fpga with a minimum period of 3.397ns, maximum Frequency of 294.366MHz, minimum input arrival time before clock of 1.649ns, maximum output required time after clock of 1.669ns, throughput of 3.77Gbps.

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis

A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.

A Robust Audio Steganography Method Using Partial Knight Tour for Concealing Messages of Text and Image

The rapid growth in transfer of data over the Internet realized the need to secured data transmission. Moreover, there is a need to send message that contains a text and image. The steganography is the most promising technique for facilitating the secure communication without affecting the data quality. In this paper, we propose a robust audio steganography approach that hides secret messages of text and image in an audio WAV file using partial knight tour positions in 4×8 square blocks. The cover audio is divided into 4×8 (or 32) audio sample blocks. The secret data are converted to bits and selected 4×8 audio bytes then can hide ASCII codes of 4 characters. The hiding of secret data block is based on a random selected starting audio byte, and on a random selected tour of 32 predefined tours. The algorithm changes the least significant bit (LSB) of the audio bytes based, as mentioned, on randomly selected tours and randomly selected starting audio byte. The resulted stego file can be, then, transmitted over the communication channel such as Internet to the receiver side. After receiving, the extraction process is performed to extract the secret message. The proposed algorithm is implemented us[ing C# programming languages. The PSNR and MSE parameters are used to evaluate the efficiency of the proposed algorithm and they will be checked after implementation. The proposed technique provides a high quality of Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR).

Distributed Semisupervised HMM for Dynamic Inferential Sensor Development

Hidden Markov model (HMM) has proven effective for inferential sensing of dynamical and non-Gaussian industrial processes. However, large-scale process data introduce tremendous computational costs to training the HMM due to the centralized serial learning mode. What is worse, industrial time-series data chains are frequently broken due to sensor or communication system failures, whereas the current learning paradigm for the HMM cannot handle this issue, resulting in poor generalization performance. To overcome these challenges, this article proposes a distributed semisupervised HMM (DisSsHMM). The DisSsHMM first divides the whole data into continuous data blocks (DBs), based on which the computations in both forward learning and backward learning are segmented. Then, based on the expectation–maximization algorithm, a fusion scheme is derived to integrate information extracted from each DB. This enables distributed training and making full utilization of available data. The performance of the DisSsHMM is evaluated using both numerical and real-world industrial cases, demonstrating that when compared with the traditional serial learning paradigm, the DisSsHMM can significantly improve the computational efficiency as well as the estimation accuracy for inferential sensing.