Hard Disk Research Articles

Cost-effective data classification storage through text seasonal features

Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices and orchestrating a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. As research on emerging storage media (e.g. Non-Volatile Memory (NVM)) delves deeper, in scenarios characterized by dynamically evolving storage demands, conventional paradigms of rigid classification strategy and Solid-State Drive (SSD) and Hard Disk Drive (HDD) storage architectures fall short of addressing such complex situations. In this paper, we propose an effective data classification storage method using text seasonal features based on the traditional access frequency analysis strategy. First, to procure richer semantic information, we employ external knowledge actualizing the short-text feature expansion. Then, we leverage the ensemble learning stacking method optimized models to improve the accuracy of data classification based on seasonal features. Additionally, following the seasonal feature, we further classify the data into hot, warm, and cold and place them separately on the NVM, SSD, and HDD, which conserves storage energy consumption and operational costs while ensuring the quality of user access. The experimental results demonstrate that data classification accuracy can reach more than 95.10%, and the energy consumption and operating cost can be reduced by more than 30.22% and 8.73%, respectively.

Simple and efficient methods for local structural analysis in polydisperse hard disk systems

Simple and efficient methods for local structural analysis in polydisperse hard disk systems

Towards robotic disassembly: A comparison of coarse-to-fine and multimodal fusion screw detection methods

To accelerate the transition towards a more circular economy it is believed essential to increase the economic viability and hence the adoption of de- and remanufacturing processes. For this, industry 4.0 technologies, including robotization, computer vision and artificial intelligence, are commonly considered as the main enablers to reduce costs while boosting performances of de- and remanufacturing processes. However, the essential step in de- and remanufacturing of screw detection for robotic disassembly is still confronted with various challenges, such as false negatives due to deep screw holes and false positives caused by similar patterns and colors, e.g. letters in labels. Previous studies improved the detection accuracy by capturing images of only a fine part of the product and neglected the negative impact of this imaging approach on the overall time efficiency of the disassembly process. Therefore, two of the most distinct approaches are evaluated in the presented research: 1. A Coarse-to-Fine detection method, where potential screws across the entire product are detected by an area camera in the coarse stage to generate an optimal unscrewing path and screw types and locations are further refined in a second instance, the fine stage. 2. A multimodal fusion detection method, where a combination of a high-resolution color line and 3D triangulation line camera system captures full-product images allowing a single run screw detection in the RGB, Intensity and Depth (RGBID) images. Furthermore, a pooling-based aligned fusion module (PAlign) is introduced to improve the feature representation and mitigate the encountered spatial misalignment of different modality branches. Two industrially representative datasets comprising images of laptops and hard disk drives are collected for the training and evaluation of the screw detection and unscrewing task. Experimental results show that the RGBID fusion method with PAlign module achieves an unscrewing success rate of 80.4 % with an average detection and unscrewing speed of 2.27 s per screw, which is almost double the speed when compared to the Coarse-to-Fine approach. Through a detailed analysis comparing these most distinct screw detection methods in terms of imaging approach, lighting and camera hardware (1/2, area/line, low/high resolution, RGB/RGBID, 600/25.000€), this study provides concrete insights and recommendations aimed at enhancing the efficiency of disassembly systems for sustainable demanufacturing.

Data recovery methods for DNA storage based on fountain codes

Today's digital data storage systems typically offer advanced data recovery solutions to address the problem of catastrophic data loss, such as software-based disk sector analysis or physical-level data retrieval methods for conventional hard disk drives. However, DNA-based data storage currently relies solely on the inherent error correction properties of the methods used to encode digital data into strands of DNA. Any error that cannot be corrected utilizing the redundancy added by DNA encoding methods results in permanent data loss. To provide data recovery for DNA storage systems, we present a method to automatically reconstruct corrupted or missing data stored in DNA using fountain codes. Our method exploits the relationships between packets encoded with fountain codes to identify and rectify corrupted or lost data. Furthermore, we present file type-specific and content-based data recovery methods for three file types, illustrating how a fusion of fountain encoding-specific redundancy and knowledge about the data can effectively recover information in a corrupted DNA storage system, both in an automatic and in a guided manual manner. To demonstrate our approach, we introduce DR4DNA, a software toolkit that contains all methods presented. We evaluate DR4DNA using both in-silico and in-vitro experiments.

A Novel Approach for Surveillance Compression using Neural Network Technique

The integration of closed-circuit television (CCTV) monitoring is crucial in the field of video processing, which provides an efficient method for comprehensive surveillance. However, a key challenge associated with this practice is its substantial demand for storage space. Typically, surveillance footage is stored in hard disk drives, and due to limited storage spaces, it is deleted after some time. To address this issue, an innovative method for compressing CCTV video, named object detection-based surveillance compression (ODSC), is introduced. Our ODSC model is divided into two steps: -i) depending upon the objects in the video, determine the significant and non-significant frames of surveillance video using the neural network approach YOLOv5s & YOLOv7-tiny and Yolov8s ii) construct the video of significant frames. Following a comprehensive analysis of the experimental outcomes, it is noted that YOLOv8s stands out with a remarkable detection accuracy of 99.7% on the COCO dataset. Our ODSC approach is reducing the storage space greatly and achieving an average compression ratio of up to 96.31% using YOLOv8s, which surpasses the existing state-of-the-art methods.

Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process

Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process

New Weibull Log-Logistic grey forecasting model for a hard disk drive failures

Forecasting the failure of hard disk drives is important in server operation and has attracted increasing attention. However, current disk drive warning systems suffer from high false positive rates and high resource consumption when dealing with hard disk drive overall failure. Therefore, to accurately and stably predict hard disk drive overall failure, this paper develops a new Weibull Log-Logistic grey forecasting model with multiple interaction effects. Firstly, to capture and fit the trend of hard disk failure data flexibly and reduce the volatility, a new accumulation generation operator is established by introducing the Weibull Log-Logistic mixture distribution. Secondly, the proposed model constructs a multiple interaction term to describe the nonlinear relationship of the dependent variables on the system behavior series and the multiple interaction effects between the independent variables. Then, the parameter estimation method is designed to improve the fitting accuracy of the new model, in which linear estimation method, nonlinear estimation method, and meta-heuristic optimization algorithm are used to calculate the parameter values according to their categories. Finally, four varieties of hard disk drive data sets from BackBlaze are selected as study cases to validate the effectiveness of the proposed model. The results show that the mean MAPE of the proposed method is 2.1181%, 2.2306%, 8.1712%, and 4.3417%, respectively, corresponding to (hard disk drives ST8000NM0055, ST12000NM0007, ST10000NM0086, and ST14000NM0138), and the average value of the evaluation metrics are optimal in all competing models. Furthermore, it is observed that the number of reallocated sectors has the greatest influence in causing the failure of hard disk drives.

Remaining lifespan prediction on multiple types of hard disks under conditions of data imbalance

Most of the previous regression models for predicting the remaining useful life (RUL) of hard disks primarily focused on failure data obtained through SMART monitoring. However, models that only considered failure data were not accurate enough. In order to address the issue of imbalanced sample quantities in different categories within the dataset, this paper proposes a method for predicting the RUL of hard disks under imbalanced conditions. It takes into account the imbalance issues in both SMART failure data and healthy data, as well as the imbalance in monitoring data attributes between hard disk drives and solid-state drives. The paper improves upon the traditional Transformer time series model and constructs a comprehensive and unified model for the RUL of hard disks across multiple categories. The proposed model achieves an RMSE score of 3.325, and has 66 % in MAE improved to the conventional models. The proposed model has promising prospects for future applications in predicting the remaining life of hard disks.

Time Series Storage Method of Multi-Valued Attribute Data in Energy Big Data Center

Herein, the time series storage method of multi-valued attribute data, aimed at improving the efficiency of writing and querying data in “energy” big data centers is reported. Through rule construction and rule iteration of feature sequence, the time series features of multi-valued attribute data are extracted, and the “component attribute nearest neighbor propagation method” is used for clustering; the data are divided into cold, warm, and hot data. A storage engine with a solid state disk layer, mechanical hard disk layer, and memory layer has been designed, and the efficiency of data writing and query through migration operation is improved. The experimental results demonstrate that this method can effectively extract the time series features of multi-valued attribute data, and the Pre value of the clustering time series is higher than 0.93, which effectively improves the data writing and query efficiency of the energy big data center.

X Meta : SSD-HDD-Hybrid Optimization for Metadata Maintenance of Cloud-Scale Object Storage

Object storage has been widely used in the cloud. Traditionally, the size of object metadata is much smaller than that of object data, and thus existing object storage systems (like Ceph and Oasis) can place object data and metadata respectively on hard disk drives (HDDs) and solid-state drives (SSDs) to achieve high I/O performance at a low monetary cost. Currently, however, a wide range of cloud applications organize their data as large numbers of small objects of which the data size is close to (or even smaller than) the metadata size, thus greatly increasing the cost if placing all metadata on expensive SSDs. This paper presents x Meta , an SSD-HDD-hybrid optimization for metadata maintenance of cloud-scale object storage. We observed that a substantial portion of the metadata of small objects is rarely accessed and thus can be stored on HDDs with little performance penalty. Therefore, x Meta first classifies the hot and cold metadata based on the frequency of metadata accesses of upper-layer applications, and then adaptively stores the hot metadata on SSDs and the cold metadata on HDDs. We also propose a merging mechanism for hot metadata to further improve the efficiency of SSD storage, and optimize range key query and insertion for hot metadata by designing composite keys. We have integrated the x Meta metadata service with Ceph to realize a high-performance, low-cost object store (called xCeph). The extensive evaluation shows that xCeph outperforms the original Ceph by an order of magnitude in the space requirement of SSD storage, while improving the throughput by up to 2.7 ×.

Dual-layer FePt-C granular media for multi-level heat-assisted magnetic recording

Multi-level magnetic recording is a new concept for increasing the data storage capacity of hard disk drives. However, its implementation has been limited by a lack of suitable media capable of storing information at multiple levels. Herein, we overcome this problem by developing dual FePt-C nanogranular films separated by a Ru-C breaking layer with a cubic crystal structure. The FePt grains in the bottom and top layers of the developed media exhibited different effective magnetocrystalline anisotropies and Curie temperatures. The former is realized by different degrees of ordering in the L10-FePt grains, whereas the latter was attributed to the diffusion of Ru, thereby enabling separate magnetic recording on each layer under different magnetic fields and temperatures. Furthermore, magnetic measurements and heat-assisted magnetic recording simulations showed that these media enabled 3-level recording and could potentially be extended to 4-level recording, as the ↑↓ and ↓↑ states exhibited non-zero magnetization.

Multiuser Based Inventory Information System In Kopel Dolog Semarang

Inventory of goods or inventory is goods or work equipment used in processing the business being carried out or operational equipment of a company. Without inventory, business activities will not be carried out well, for this reason the existence of inventory is very important. The Goods Inventory Information System in a cooperative has quite an important role. One of the problems that often arises in an inventory system is not being able to know the exact number and condition of goods available. In terms of presenting information, the inventory system is implemented on a multiuser basis, where a multiuser system is a computer system that can execute several applications owned by two or more users concurrently and independently. Concurrent means applications can be active simultaneously and compete with each other to use resources such as CPU, memory, hard disk, and so on. Independent means that each application can carry out its tasks without having to care about what other users are running This information system makes it easy to provide inventory information. Apart from that, this system also makes it easier and faster to access information, data and its processing. The new system aims to make it easier for a company to carry out its operational activities, where this system will always ensure that data or information is provided to users quickly, precisely, accurately and at any time it is needed. From the system that has been implemented previously with matters relating to system weaknesses in the company, information or data about the inventory system which was initially very slow to access has now become easier. So that the company's operational activities run smoothly and all weaknesses in the old system can be replaced automatically.
  

Exploring data storage innovations: From DNA to holography

This paper delves into the evolution of data storage technologies, focusing on the solid-state drive (SSD) and hard disk drive (HDD) and their working principles. It explores the advancements in DNA as a storage medium, highlighting its exceptional data density and potential as a long-term storage solution. The paper discusses the challenges and limitations of DNA data storage, emphasizing the need for further research to overcome these hurdles. Additionally, the paper introduces holographic data storage as a novel optical storage technology and discusses its potential applications. It also touches on the decline of the optical disc industry due to the emergence of alternative storage technologies. The use of metal nanoparticles (MNPs) in optical disks is explored, emphasizing their role in high-contrast imaging and data storage. The paper concludes by highlighting the diverse methods of data storage, each with its unique advantages and drawbacks, and anticipates future improvements in electronic information processing technology, leading to faster data transmission and increased data storage capacity within the same volume of storage media.

A 3D nanoscale optical disk memory with petabit capacity.

High-capacity storage technologies are needed to meet our ever-growing data demands1,2. However, data centres based on major storage technologies such as semiconductor flash devices and hard disk drives have high energy burdens, high operation costs and short lifespans2,3. Optical data storage (ODS) presents a promising solution for cost-effective long-term archival data storage. Nonetheless, ODS has been limited by its low capacity and the challenge of increasing its areal density4,5. Here, to address these issues, we increase the capacity of ODS to the petabit level by extending the planar recording architecture to three dimensions with hundreds of layers, meanwhile breaking the optical diffraction limit barrier of the recorded spots. We develop an optical recording medium based on a photoresist film doped with aggregation-induced emission dye, which can be optically stimulated by femtosecond laser beams. This film is highly transparent and uniform, and the aggregation-induced emission phenomenon provides the storage mechanism. It can also be inhibited by another deactivating beam, resulting in a recording spot with a super-resolution scale. This technology makes it possible to achieve exabit-level storage by stacking nanoscale disks into arrays, which is essential in big data centres with limited space.

Perspective on nanoscale magnetic sensors using giant anomalous Hall effect in topological magnetic materials for read head application in magnetic recording

Recent advances in the study of materials with topological electronic band structures have revealed magnetic materials exhibiting giant anomalous Hall effects (AHEs). The giant AHE has not only attracted the research interest in its mechanism but also opened up the possibility of practical application in magnetic sensors. In this article, we describe simulation-based investigations of AHE magnetic sensors for applications to read head sensors (readers) of hard disk drives. With the shrinking of magnetic recording patterns, the reader technology, which currently uses multilayer-based tunnel magnetoresistance (TMR) devices, is associated with fundamental challenges, such as insufficient spatial resolution and signal-to-noise ratio (SNR) in sensors with dimensions below 20 nm. The structure of an AHE-based device composed of a single ferromagnetic material is advantageous for magnetic sensors with nanoscale dimensions. We found that AHE readers using topological ferromagnets with giant AHE, such as Co2MnGa, can achieve a higher SNR than current TMR readers. The higher SNR originates from the large output signal of the giant AHE as well as from the reduced thermal magnetic noise, which is the dominant noise in TMR readers. We highlight a major challenge in the development of AHE readers: the reduction in the output signal due to the shunting of the bias current and the leakage of the Hall voltage through the soft magnetic shields surrounding the AHE reader. We propose reader structures that overcome this challenge. Finally, we discuss the scope for future research to realize AHE readers.

A study on acceleration of SP decoder using reliability of recording sequence by neural network based on parity check result in SMR system

We study signal processing methods to realize ultra-high-density hard disk drives (HDDs). Among them, we have applied the neural network to improve decoding performance in low-density parity-check (LDPC) coding and iterative decoding in the shingled magnetic recording (SMR) system. In this study, we realize acceleration of the sum-product (SP) decoding by updating the decoding reliability of the SP decoding using the reliability of the recording sequence calculated by the neural network based on the parity check results. As a result, the proposed system achieved error-free with the fewest number of iterations in the SP decoder compared to our previous studies.

Bit-island arrangement and signal processing in double-layer magnetic recording technology

The escalating demand for high-density storage of hard disk drives has prompted the innovation of advanced technologies to significantly boost an areal density (AD). Among these, three-dimensional magnetic recording systems incorporating double recording layers and bit-patterned magnetic recording (BPMR) have currently emerged as one promising solution. Therefore, this article investigates the integration of double-layer magnetic recording and BPMR technologies which is called double-layer BPMR, aiming for a substantial increase in AD. The primary focus lies on minimizing inter-layer interference and mitigating two-dimensional interferences by optimizing bit-island sizes for both upper and lower layers under the incorporation of staggered-like recording media arrangement and an array of magneto-resistive readers. Moreover, we also employ a rate-5/6 modulation code for compensating these effects. Detailed examinations reveal that our proposed system has a superior bit-error rate performance compared with conventional single-layer BPMR system.

Data Wiping Journey Through Redkey

Red Key is a powerful and trustworthy digital security system that grants users several advantages. Red Key guarantees the protection of sensitive data and reduces the risk of unwanted access with its increased security features. Red Key's user-friendly interface makes it simple for people to manage their security settings and offers a seamless user experience. Additionally, users can adjust their security preferences to their unique demands. The ability of Red Key to integrate with other security systems allows for smooth communication and collaboration, offering a complete security solution. Red Key receives regular upgrades to keep up with the most recent security protocols, protecting it from new attacks. Red Key is a trustworthy and effective digital security system overall. File Hex viewer which indicates the content on hard disk before and after the data wiping process, is a highlight of RedKey. It emphasizes on complete data-wiping process. The algorithm approach of RedKey makes the data-wiping process so unique that no other competitive device is capable of the same. This article emphasizes the working structural view of the device with its pros and cons making the end-user usage process simple.

The LINUX Operating System: An Introduction

Linux is a Unix-like operating system for Intel 386/486/ Pentium based IBM-PCs and compatibles. The kernel of this operating system was written from scratch by Linus Torvalds and, although copyright by the author, may be freely distributed. A world-wide group of enthusiastic volunteers has collaborated in developing many aspects of Linux on the Internet. Linux can run the powerful set of compilers and programming tools (the 'GNU' corpus) of the Free Software Foundation, and XFree86, a port of the X Window System from MIT. Most capabilities associated with high performance workstations, such as networking, shared file systems, electronic mail, TEX, I*TE.X, etc. are freely available for Linux. It can thus transform cheap IBM-PC compatible machines into Unix workstations with considerable capabilities. The author explains how Linux may be obtained, installed and networked. He also describes some interesting applications for Linux that are freely available. One useful feature of Linux is its ability to coexist with other operating systems. Thus a user who has made an investment in DOS/ MS Windows software, may continue running these applications on his machine and install Linux on a separate partition on his existing hard disk. If needed, files from DOS/ MS Windows partitions can be accessed by Linux. The enormous consumer market for IBM-PC compatible machines continually drives down prices of CPU chips, memory, hard disks, CDROMs etc. Linux can convert such machines into powerful workstations that can be used for teaching, research and software development. For professionals who use Unix based workstations at work, Linux permits virtually identical working environments on their personal home machines. For cost conscious educational institutions (especially in developing nations), Linux can create world-class computing environments from cheap, easily maintained, PC clones. Finally, for university students, especially in science and engineering, Linux provides an essentially cost-free path away from DOS into the world of Unix and X Windows

Electrically suppressed hysteresis at nanoscale sliding interface

Contact adhesion hysteresis is widely observed between two quasi-static interfaces of micro-nano devices. Representatively, the hysteresis behavior has been found at the nanoscale sliding interface, which poses a significant obstacle to enhancing the tribological properties of the interfaces. In this paper, the hysteresis suppression mechanism by employing interfacial potential is investigated with a hard disk drive. Applying an external voltage to the head-disk interface results in the suppression of sliding contact hysteresis, which is also theoretically studied using a lumped parameter model. The motion state map indicates that inhibition of transformation from freedom flying state to bouncing state leads to the suppression of contact hysteresis. Subsequent wear experiments confirm that applying an external voltage is an effective method to enhance wear durability due to the reduction of hysteresis.