Data Loss Research Articles

Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system

DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

A new 2D cross hyperchaotic Sine-modulation-Logistic map and its application in bit-level image encryption

Chaotic theory is being increasingly researched for application in image encryption scheme (IES). This paper presents a novel two-dimensional cross hyperchaotic Sine-modulation-Logistic map (2D-CHSLM) based on the famous Sine and Logistic maps. The hyperchaotic behaviors of 2D-CHSLM are demonstrated through bifurcation diagrams, trajectory plots, Lyapunov exponents, histogram, correlation dimension, sensitivity to initial conditions, sample entropy, 0–1 test, and NIST 800–22 test. A new bit-level IES with traditional an encryption structure is proposed utilizing 2D-CHSLM. The 2D-CHSLM-IES performs 2D-CHSLM-based bit-level confusion using a zigzag transform and 2D-CHSLM-based cross coupled diffusion operations to generate a highly secure cipher-image. Simulations and security analyses, including key space, key sensitivity, histogram, correlation, information entropy, encryption quality, known-plain and chosen-plain attacks, floating Frequency, differential attack, noise and data loss attacks, demonstrate the robust security of the proposed 2D-CHSLM-IES.

The condition monitoring scheme for industrial IoT scenario: A distributed modeling for high-dimensional nonstationary data

Based on large-scale data collection and high-speed transmission, Industrial Internet of Things (IIoT) promotes the rapid development of intelligent manufacturing. IIoT systems are usually disturbed by complex external factors, which lead to high-dimensional nonstationary operating data. Besides, unexpected data transmission interruptions, sensor failures, and network delays lead to data loss. This paper proposes a distribution & communication strategy for monitoring high-dimensional nonstationary processes with missing values in IIoT scenarios. First, a deep learning-based imputation network is proposed to impute the missing values. Then a decomposition strategy based on degree of cointegration is proposed, which decomposes a high-dimensional nonstationary process into multiple blocks. And a communication strategy is proposed to mine the internal relationship between different blocks. Finally, faulty information is detected by a distributed framework. Two real cases from IIoT are applied to illustrate the monitoring performance of the proposed method. The results show that the proposed method outperforms existing benchmarks in data imputation and monitoring performance.

USE OF IIoT TECHNOLOGIES IN ENGINE MONITORING AND CONTROL SYSTEMS

Determining the operating parameters of engine systems is very important to ensure its long-term reliability. Modern power plants use a large number of modern electronic components (sensors, controllers, etc.). All systems generate a huge volume of data that must be tracked, processed and analyzed, which in turn creates calls to gateways and servers. However, the current state of development of microcontrollers and their power allow the use of fog computing, which can lead to more efficient use of both the power plant and data transmission systems. Also, the use of modern microcontrollers and fog computing allows modernizing old power plants with minimal effort and creating new information nodes for monitoring the state of nodes and engine systems and easily combining them with systems already existing on the engine. The selection of a microcontroller and justification of its type for the modernization of the fuel system of a diesel engine are considered. Rapid processes occur in the high-pressure fuel system of diesel engines, which can be determined by changes in fuel pressure. For detailed measurement of processes in the fuel system, measurements should be made with a frequency of at least 24 kHz, i.e.: if the crankshaft rotation frequency is 4000 min-1, then 24,000 measurements should be made every second (when registering data after 1 degree of crankshaft rotation). Another indicator that imposes its requirements is the bit rate of the analog-to-digital converter (ADC), which depends on the range of recorded values. For example, an ADC with a resolution of 8 bits is capable of outputting 256 discrete values (0…255). That is, the higher the bit rate, the more sensitive the converter is to signal changes, and 8 bits may not be enough. Thus, even a small number of sensors can generate a huge amount of data. Transmitting large amounts of data is irrational and can lead to data loss and "clogging" of transmission channels, which challenges the computational performance of the microprocessor.

Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss

Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by using the adaptive learning capabilities of IS. Hybrid Automatic Repeat Request (HARQ), an error recovery method, is utilized to detect network errors in order to correct and retransmit data to the destination during error-prone network conditions. Queue learning action sets are used to discover the finest path for efficient data transmission. The OMNeT++ simulator is used to assess the performance of the proposed BIP-GANs protocol. The performance results are compared with the prevailing data transmission protocols, such as EAP-IFBA, DNN-CSO, and ALO-DHT. Findings: The suggested BIP-GANs data transmission protocol outperforms with promising results, with an energy consumption rate of 6%, 8 seconds data transmission speed, 6 second less delay rate, 98% robustness (security and confidentiality), 98.5% alive nodes, and 99% network life span, which is higher than the prevailing methods. Novelty: This research study provides a comprehensive solution to secure and efficient data transmission by integrating the AI-based bio-inspired BIP-GANs method to address the security and data transmission challenges of existing methods such as EAP-IFBA, DNN-CSO, and ALO-DHT in terms of energy consumption rate, transmission speed, delay rate, life span of the network, robustness, and number of alive nodes. Keywords: Artificial Intelligence, Generative Adversarial Networks, Error Recovery, Secured Data Transmission, Deep Learning, Bio­Inspired Algorithm

Iterative UKF under generalized maximum correntropy criterion for intermittent observation systems with complex non-Gaussian noise

The traditional unscented Kalman filters (UKFs) under the maximum correntropy criterion provide a powerful tool for nonlinear state estimation with heavy-tailed non-Gaussian noise. Nevertheless, the above-mentioned filters may yield biased estimates because the Gaussian kernel function can only handle certain types of non-Gaussian noise. Additionally, the use of statistical linearization methods can result in approximation errors when solving linear observation equations, while the system may also experience observation data loss. Therefore, a new iterative UKF with intermittent observations under the generalized maximum correntropy criterion is proposed for systems with complex non-Gaussian noise, called GMCC-IO-IUKF. Firstly, the connection between the UKF with and without intermittent observations is established by designing a coefficient matrix including intermittent observation variables, so as to derive the UKF with intermittent observations under the maximum correntropy criterion. Secondly, for the measurement update of GMCC-IO-IUKF, a nonlinear regression augmented model that can deal with both prediction and observation errors is established using the coefficient matrix and the nonlinear function. To better adapt to different types of non-Gaussian noise, the generalized Gaussian kernel function is substituted for the traditional Gaussian kernel function. Theoretically, GMCC-IO-IUKF can achieve better estimation performance by directly employing the nonlinear function and the latest iteration value. Finally, a classical target tracking model is used to evaluate the estimation performance and feasibility of our proposed GMCC-IO-IUKF algorithm. It appears from the experiment results that our proposed GMCC-IO-IUKF can not only promote estimation precision but also handle complex non-Gaussian noise flexibly.

A New Diffusion Strategy Using an Epidemic Spreading Model for Encryption.

The diffusion phenomenon that exhibits intrinsic similarities is pervasive in cryptography and natural systems, evident in liquid diffusion, epidemic spread, animal migration, and encryption techniques. In cryptography, bytes are systematically diffused in a sequential manner to encrypt the value of each byte in the plaintext in a linear fashion. In contrast, within an epidemic spreading model, the diffusion process can be represented within a complex, multilayered network, encompassing layers such as familial and social transmission dynamics. Transmission links establish connections both within and between individual layers. It has had a more rapid spread than linear approaches due to the particularization of non-linear transmission. In this study, the novelty of a cryptography diffusion strategy based on an epidemic model is first proposed, in which pixels and their dynamic adjacency are considered as vertices and edges, respectively, within a complex network framework. Subsequently, the encryption process is governed by the Susceptible-Vaccinated-Infected-Recovered (SVIR) model integrated with chaotic dynamics. Simulation results demonstrate that the proposed algorithm exhibits faster encryption speed while effectively resisting brute force, statistical, and differential attacks. Furthermore, it demonstrates strong robustness against noise interference and data loss.

Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm

This paper offers a fresh perspective to memory allocation in edge-PLCs within industrial IoT environments, leveraging a hybrid a structure that incorporates Deep Q-Networks (DQN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), complemented by Quantum Genetic Algorithm Optimization. In addressing the dynamic challenges of memory management, our method combines the reinforcement learning capabilities of DQN with the temporal contextualization provided by BiLSTM networks. The DQN component learns to optimize memory allocation policies based on immediate rewards and feedback, while the BiLSTM network captures long-term dependencies in data, enhancing predictive modeling for future data arrival rates. Moreover, we introduce Quantum Genetic Algorithm Optimization, a cutting-edge approach that infuses quantum-inspired principles into the traditional genetic algorithm framework, to further refine the memory allocation process. By leveraging principles of quantum computing, this optimization algorithm explores the solution space more efficiently, allowing for faster convergence and improved performance. Through simulation experiments, we exhibit the potency of our hybrid approach in reducing data loss probability and enhancing system performance in edge-PLCs. Our findings underscore the significance of integrating advanced machine learning techniques with quantum-inspired optimization algorithms to address complex challenges in industrial IoT environments, offering a promising avenue for enhancing memory allocation efficiency in edge computing systems.

Semi-supervised segmentation of medical images focused on the pixels with unreliable predictions

Pseudo-labeling is a well-studied approach in semi-supervised learning. However, unreliable or potentially incorrect pseudo-labels can accumulate training errors during iterative self-training steps, leading to unstable performance. Addressing this challenge typically involves either discarding unreliable pseudo-labels, resulting in the loss of important data, or attempting to refine them, risking the possibility of worsening the pseudo-labels in some cases/pixels. In this paper, we propose a novel method based on pseudo-labeling for semi-supervised segmentation of medical images. Unlike existing approaches, our method neither discards any data nor worsens reliable pseudo-labels. Our approach generates uncertainty masks for the predictions, utilizing reliable pixels without any modification as ground truths and modifying the unreliable ones rather than discarding them. Furthermore, we introduce a novel loss function that incorporates both mentioned parts by multiplying each term by its corresponding uncertainty mask, encompassing reliable and unreliable pixels. The reliable pixels are addressed using a masked cross-entropy loss function, while the modification of the unreliable pixels is performed through a deep-learning-based adaptation of active contours. The entire process is solved within a single loss function without the need to solve traditional active contour equations. We evaluated our approach on three publicly available datasets, including MRI and CT images from cardiac structures and lung tissue. Our proposed method outperforms the state-of-the-art semi-supervised learning methods on all three datasets. Implementation of our work is available at https://github.com/behnam-rahmati/Semi-supervised-medical.

Sistem Manajemen Kepegawaian dan Pencatatan Pekerja Migran Indonesia di BP3MI Riau dengan Metode Prototipe

Balai Pelayanan Pelindungan Pekerja Migran Indonesia (BP3MI) Riau, a government agency dedicated to protecting Indonesian migrant workers. The agency faces challenges with its unstructured and inefficient data management system. The use of Microsoft Excel for documenting the data of Indonesian Migrant Workers (PMI) and employees at BP3MI Riau is prone to data entry errors, time inefficiencies, unstructured data, potential damage, limited access, restricted analysis, difficulties in tracking changes, and a high risk of data loss. To address these issues, research was conducted to develop a more effective employee management and PMI data recording system using the prototype method. This method emphasizes user collaboration in understanding system requirements, resulting in practical solutions. The prototype method stages include user requirements gathering, designing input, process, and output needs, followed by rapid UML model design, program code implementation, and finally, testing. The system development results show that users have a very positive impression from the usability testing, achieving a score of 97.76%. This indicates the effectiveness of the prototype. The system significantly improves manual methods, enhancing the efficiency, accuracy, and accessibility of data at BP3MI Riau, and lays the foundation for future advancements with greater integration and automation prospects in BP3MI Riau's operations.

Understanding Silent Data Corruption in Processors for Mitigating its Effects

Silent Data Corruption (SDC) in processors can lead to various application-level issues, such as incorrect calculations and even data loss. Since traditional techniques are not effective in detecting these errors, it is very hard to address problems caused by SDCs in processors. For the same reason, knowledge about these SDCs in the wild is limited. In this paper, we conduct an extensive study on CPU SDCs in a large production CPU population, encompassing over one million processors. In addition to collecting overall statistics, we perform a detailed study to understand 1) whether certain processor features are particularly vulnerable and their potential impacts on applications; 2) the reproducibility of CPU SDCs and the triggering conditions (e.g., temperature) of those less reproducible SDCs; and 3) the challenges to mitigate and handle CPU SDCs. We further investigate the implications which our observations obtained from the above researches have, on the SDC fault models, SDC mitigation strategies and the future research fields. In addition, we design an efficient SDC mitigation approach called Farron, which uses prioritized testing to detect highly reproducible SDCs and temperature control to mitigate less reproducible SDCs. Our experimental results indicate that Farron can achieve better coverage of CPU SDCs with lower overall overhead, compared to the baseline used in Alibaba Cloud. This demonstrates that our observations are able to assist in SDC mitigation.

Data losses and synchronization according to delay in PLC-based industrial automation systems

PLC (Programmable Logic Controllers) based data collection is integral to industrial automation and data acquisition processes. The pipeline – between the device and the database – is a complex system with many components and often causes some time delay, due to the synchronization and the applied hardware and software components. This delay can also lead to data loss on the database site. In this study, we aimed to examine this problem connected to the synchronized behavior of four counter networks within the TIA software for PLCs, particularly focusing on the implications of a 1 Hz (Hz) clock frequency on counter synchronization. Meticulous experiments were conducted and the integration of Node-RED, as an instrumental tool in industrial automation, facilitated counter-behavior monitoring. Recorded values of counter and timestamps were meticulously stored in four separate databases (MSSQL, MySQL, MongoDB, and Apache Cassandra) for comprehensive data analysis. Throughout the experiments, inconsistencies in the counter values were encountered, leading to the discovery of missing values that varied across the ten tests. To detect the reason, a unique delay calculation method was developed. According to our results, we were able to do post-synchronization with millisecond-level accuracy. It can help reveal the missing values that depend on the Node-RED and the PLC cycle time differences.

Optimizing BHF/PVDF Composites via Compression Molding for High-Frequency Applications and Electromagnetic Shielding

Electromagnetic interference (EMI) poses significant challenges to the reliable operation of communication systems, medical devices, and electronic equipment, often resulting in signal degradation, data loss, and equipment failure. To mitigate these issues, this study investigates the development of barium hexaferrite (BHF)/polyvinylidene fluoride (PVDF) composites, synthesized via compression molding, to optimize their electrical and magnetic properties for high-frequency EMI shielding applications. Through comprehensive characterization, including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), vibrating sample magnetometry (VSM), and vector network analysis (VNA), we demonstrate that increasing the BHF content within the composites enhances their complex permittivity, impedance matching, and attenuation coefficients, making them highly effective for EMI shielding. Notably, the composite containing 20 wt.% PVDF achieved a reflection loss (RL) of -42 dB at a thickness of 2 mm, indicating superior shielding effectiveness. These results underscore the potential of BHF/PVDF composites as a viable solution for advanced EMI suppression in high-frequency electronic and military applications, combining economic feasibility with robust performance.

GU-Net: Causal relationship-based generative medical image segmentation model

Due to significant anatomical variations in medical images across different cases, medical image segmentation is a highly challenging task. Convolutional neural networks have shown faster and more accurate performance in medical image segmentation. However, existing networks for medical image segmentation mostly rely on independent training of the model using data samples and loss functions, lacking interactive training and feedback mechanisms. This leads to a relatively singular training approach for the models, and furthermore, some networks can only perform segmentation for specific diseases. In this paper, we propose a causal relationship-based generative medical image segmentation model named GU-Net. We integrate a counterfactual attention mechanism combined with CBAM into the decoder of U-Net as a generative network, and then combine it with a GAN network where the discriminator is used for backpropagation. This enables alternate optimization and training between the generative network and discriminator, enhancing the expressive and learning capabilities of the network model to output prediction segmentation results closer to the ground truth. Additionally, the interaction and transmission of information help the network model capture richer feature representations, extract more accurate features, reduce overfitting, and improve model stability and robustness through feedback mechanisms. Experimental results demonstrate that our proposed GU-Net network achieves better segmentation performance not only in cases with abundant data samples and relatively simple segmentation targets or high contrast between the target and background regions but also in scenarios with limited data samples and challenging segmentation tasks. Comparing with existing U-Net networks with attention mechanisms, GU-Net consistently improves Dice scores by 1.19%, 2.93%, 5.01%, and 5.50% on ISIC 2016, ISIC 2017, ISIC 2018, and Gland Segmentation datasets, respectively.

Research and Implementation of Aurora Protocol NFC System

Abstract Aurora protocol is a high-speed serial communication protocol with characteristics such as high speed, low latency, open source and free. It is widely used in application fields that require a large amount of data transmission. However, Aurora communication systems also has the problem of data loss, making flow control a challenge; This paper analyzes the problem of data loss, finds out the root cause, and designs an NFC(Native Flow Control) system for the Aurora protocol. The system uses AXI4-Stream bus for communication and solves the problem by controlling the Aurora module to send NFC PAUSE code at the sending end. At the same time, by setting a reasonable upper limit value in the flow control program, the problem of data loss caused by NFC Latency is solved, and effective flow control is achieved.

Dynamic state estimation of distribution network under Markov DOS attack

AbstractEnergy information is vulnerable to malicious denial of service (DoS) attacks due to the diversity and openness of the smart grid environment. In order to cope with the above challenges, this paper first proposes to adopt Markov hopping model to describe the random packet loss of measurement due to DoS attacks. Then, based on Holt two‐parameter exponential smoothing and untraced Kalman filtering techniques, a one‐step predictive value compensation method for measurement data loss is proposed, and an improved dynamic untraced particle filtering algorithm based on data fusion compensation strategy is designed. Finally, an IEEE‐30 bus system is used to simulate the proposed dynamic state estimation method, which proves that the proposed method can effectively resist DoS attack.

РАЗРАБОТКА АЛГОРИТМА ОБРАБОТКИ РЕЗУЛЬТАТОВ МУЛЬТИСПИРАЛЬНОЙ КОМПЬЮТЕРНОЙ ТОМОГРАФИИ ГЛАЗНИЦЫ В АВТОМАТИЧЕСКОМ РЕЖИМЕ ДЛЯ ПОСТРОЕНИЯ ТРЕХМЕРНОЙ МОДЕЛИ ГЛАЗНИЦЫ И ОРБИТАЛЬНОГО ИМПЛАНТАТА

The aim of the work is to develop a technology for constructing a 3D model of the lower and medial bones of the facial skull while preserving complete information about their geometry based on the results of multispiral computed tomography (DICOM files). Material and methods: During the study, DICOM images of patients were analyzed. Results. An algorithm for preparing DICOM images for constructing a three-dimensional model of the facial bones has been developed. The DICOM image processing algorithm reduced the level of data loss on the thin bones of the orbit when constructing a three-dimensional model of the facial bones from 22–31% to 3–5%. The developed software automatically changes the color of the pixels of the thin orbital bones from gray to white. The thin orbital bones were expanded by one pixel using the DICOM image processing algorithm. Conclusions. The analysis of the image processing results by the developed software was carried out using the 3D Slicer software. As a result of the development of this software, it was possible to obtain a full-size 3D model of the orbit with a bone defect and a 3D model of an individual orbital implant.

Handling Encryption and Data Loss prevention in the Cloud-Based Systems

The current study focused on assessing the handling encryption and data loss prevention in the cloud. Cloud-based systems also known as cloud computing, increase organisational efficiency for proper data management processes. Cloud-based data loss or leakage prevention (DLP) improve data visualisation; hence, the companies can understand the risks and issues through proper data management. This study highlighted the way handling encryption and data loss prevention in the cloud can be useful for the development of business processes and for managing all operations through proper data management. IoT-based and AI-based cloud computing assess root causes and provide a clear insight into business problems. Hence, they can improve their data loss risk management services and promote continuous improvement.

Design and deployment of zero-trust SMPC algorithm to enhance financial cybersecurity in small and medium scale supply chains in the United States

This study designs and deploys zero-trust secure multiparty computation (SMPC) algorithms to enhance financial cybersecurity in small and medium-sized enterprises (SMEs) within the U.S. supply chain. Utilizing TensorFlow for machine learning, Apache Kafka for real-time data processing, and SMPC protocols, the proposed solution aims to provide robust, scalable, and economically viable cybersecurity measures. The research involved developing advanced machine learning-based zero-trust algorithms using TensorFlow, integrating SMPC protocols for secure data computation, and utilizing Apache Kafka for real-time data processing. The algorithms were tested and validated in both simulated and real-world SME environments to evaluate their effectiveness. The implementation of zero-trust SMPC algorithms led to significant improvements in various cybersecurity metrics. The true positive rate (TPR) increased from 85% to 98%, and the false positive rate (FPR) decreased from 5% to 1%. Average incident response time was reduced from 4 hours to 1 hour, and the average cost per incident decreased by 80%, with data loss per incident reduced by 90%. Compliance with GDPR and CCPA standards improved by 35.71% and 38.46%, respectively. User satisfaction increased by 41.67%, and system availability improved from 95% to 99%, with network latency decreasing by 60%. The results demonstrate that zero-trust SMPC algorithms significantly enhance financial cybersecurity for SMEs, reducing security incidents and financial impacts, improving regulatory compliance, and increasing user satisfaction and system performance. These advancements are crucial for strengthening the resilience and stability of the U.S. supply chain, supporting economic growth.

Agent-SwinPyramidNet: an enhanced deep learning model with AMTCF-VMD for anomaly detection in oil and gas pipelines

PurposeThe anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approachFirst, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.FindingsIn the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.Originality/valueThis paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.