Data Processing Load Research Articles

Supply Chain System Optimization Model for Logistics Enterprises in the Internet of Things Era Based on Improved ECC Algorithm

In this article, a new method to optimize the supply chain system (SCS) of logistics companies in the era of Internet of Things (IoT) using an improved elliptic curve cryptography (ECC) algorithm was put forward.The focus of this research was to address the challenges faced by logistics organizations in adapting to the complexity of modern supply chains and meeting the growing demands of customers. The research aimed to improve the efficiency, security and reliability of supply chain management systems by utilizing advances in ECC algorithms.By conducting comprehensive performance tests of the proposed system, including interface testing, joggle testing, and system response time analysis, the study demonstrated the superiority of the system over traditional systems. The research findings indicated that the system based on the improved ECC algorithm outperformed traditional systems in terms of interface effectiveness, usability, and system response time. The improved ECC algorithm enhanced the security of the encryption process, making it more resistant to unauthorized access, and improved the system’s overall data processing load capacity. Additionally, the system demonstrated superior performance in managing logistics data and information, facilitating better decision-making and efficiency in logistics operations. Overall, this research contributes to the advancement of supply chain management in the IoT era by providing a practical and efficient solution for logistics enterprises to optimize their supply chain systems. The innovative application of the improved ECC algorithm offers a secure, reliable, and efficient approach to managing logistics operations, addressing the challenges posed by the complexities of modern supply chains.

Distributed fiber optic sensing based on multipath data compression fusion

In conventional distributed sensing systems, the sampling rate exceeds the requisite sampling rate for system spatial resolution due to direct sampling of electrical signals. Furthermore, unrelated sensing paths exhibit distinct coherent fading points. Through the application of sampling point compression and multipath fusion, this study effectively resolves issues such as coherent fading and low signal-to-noise ratio (SNR) commonly encountered in single-path distributed sensing systems. In the initial stage, within two independent single-mode optical fiber structures, a consistent rotation of data along different fiber paths is performed. Subsequently, a segmentation compression and fusion method is applied. Experimental results demonstrate effective suppression of coherent fading along the sensing paths, an improvement of 9 dB in SNR, unaffected spatial resolution, and a significant reduction in data processing load for subsequent processing modules. This method can be widely applied in distributed sensing systems that allow for multipath division.

Efficient data-driven event-triggered predictive control for cyber-physical systems

As a multi-dimensional complex system that integrates computing, network, and physical environments, cyber-physical system (CPS) involves a large amount of system data in its real-time control process. The efficiency of data processing and transmission in CPS plays an important role in its control performance, however, few results have addressed such problems. This paper proposes a novel data-driven event-trigger mechanism to alleviate the data processing and transmission load for the data-driven predictive control method in CPS. The design of the event-triggered law can be transformed into solving a linear matrix inequality (LMI) based on the measured system data, while ensuring the stability of the integrated system. The improved performances are illustrated by the explicit simulation results, which indicates its potential application in CPS.

Energy-Aware Framework for Underwater Mine Detection System Using Underwater Acoustic Wireless Sensor Network

Underwater mines are considered a major threat to aquatic life, submarines, and naval activities. Detecting and locating these mines is a challenging task, due to the nature of the underwater environment. The deployment of underwater acoustic sensor networks (UWASN) can provide an efficient solution to this problem. However, the use of these self-powered sensors for intensive data sensing and wireless communication is often energy-scaring and might call into question the viability of their application. One attractive solution to extend the underwater wireless sensor network will be the adoption of cluster-based communication, since data processing and communication loads are distributed in a timely manner over the members of the cluster. In this context, this study proposes an energy-efficient solution for high-accuracy underwater mine detection based on the adequate clustering approach. The proposed scheme uses a processing approach based on wavelet transformation to extract relevant features to efficiently distinguish mines from other objects using the Naïve Bayes algorithm for classification. The main novelty of this approach is the design of a new low-complexity scheme for efficient sensor-based acoustic object detection that outperforms most of the existing solutions. It consumes a low amount of energy, while ensuring 95.12% target detection accuracy.

Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are proposed to implement lightweight Machine Learning (ML) models that estimate indoor environmental quality (IEQ) parameters, such as Artificial Neural Networks of Multilayer Perceptron type. Their implementation is based on centralised and distributed parallel IoT architectures, connected via wireless, which share commercial off-the-self modules for data acquisition and sensing, such as sensors for temperature, humidity, illuminance, CO2, and other gases. The centralised method uses a Graphics Processing Unit and the Message Queuing Telemetry Transport protocol, but the distributed method utilises low-performance ARM-based devices and the Message Passing Interface protocol. Although multiple IEQ parameters are measured, the training and testing of ML models is accomplished with experiments focused on small temperature and illuminance datasets to reduce data processing load, obtained from sudden spikes, square profiles and sawteeth test cases. The results show a high estimation performance with F-score and Accuracy values close to 0.95, and an almost theorical Speedup with a reduction in power consumption close to 37% in the distributed parallel approach. In addition, similar or slightly better performance is achieved compared to equivalent IoT architectures from related research, but error reduction of 35–76% is accomplished with an adequate balance between performance and energy efficiency.

Framework Design of an Edge Gateway System Supporting Multi-Protocol Standardized Access Detection

Recently, intelligent city construction has been promoted with the development of the Internet of things (IoT). The edge IoT gateway plays a critical role as the data aggregation core and processing center. Most existing gateways mainly solve heavy data storage and processing loads in cloud computing centers. There is less attention paid to multi-protocol data transmission and fusion. However, multiple products with different protocols in an IoT system require a flexible gateway compatible with multiple protocols. This paper proposes a multi-protocol edge gateway. The frame design was based on the actual demand for edge data acquisition. The gateway hardware platform used an RK3399 chip transplanted from the embedded operating system. It could support simultaneous multi-protocol access to ZigBee, LoRa, Bluetooth, and Wi-Fi. We combined the plug-and-play (PnP) hardware device access detection scheme with the system onboard interface driver to realize dynamic access detection and unified device management. In addition, the gateway also integrated data storage and access functions and partial edge computing functions. Finally, the experiment results verified that the multi-protocol edge gateway could meet the demand for data access and device control.

Pair Selection Optimization for InSAR Time Series Processing

AbstractThe ever‐increasing amount of Synthetic Aperture Radar (SAR) data motivates the development of automatic processing chains to fully exploit the opportunities offered by these large databases. The Synthetic Aperture Radar Interferometry (InSAR) Mass processing Toolbox for Multidimensional time series is an optimized tool to automatically download SAR data, select the interferometric pairs, perform the interferometric mass processing, compute the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is available. New challenges relate to data management and processing load. We address them through methodological improvements dedicated to optimizing the InSAR pair selection. The proposed algorithm narrows the classical selection based on the shortest temporal and spatial baselines thanks to a coherence proxy and balances the use of each image as Primary and Secondary images thanks to graph theory methods. We apply the processing to three volcanic areas characterized with different climate, vegetation, and deformation characteristics: the Virunga Volcanic Province (DR Congo), the Reunion Island (France), and the Domuyo and Laguna del Maule area (Chile‐Argentina border). Compared to pair selection based solely on baseline criteria, this new tool produces similar velocity maps while reducing the total number of computed differential InSAR interferograms by up to 75%, which drastically reduces the computation time. The optimization also allows to reduce the influence of DEM errors and atmospheric phase screen, which increase the signal‐to‐noise ratio of the inverted displacement time series.

A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data

<abstract> <p>The evolution of smart meters has led to the generation of high-resolution time-series data - a stream of data capable of unveiling valuable knowledge from consumption behaviours for different applications. The ability to extract hidden knowledge from such massive amounts of data requires that it be analysed intelligently. Hence, for a clear representation of the various consumption behaviours of consumers, a good number of data mining technologies are usually employed. This paper presents a systematic review of the various data mining techniques and methodologies employed while profiling energy data streams. The review identifies the strengths and shortcomings of existing data mining methods as applied in research, focusing more on data processing techniques and load clustering. Also discussed are data mining methods used to profile consumption data, their pros and cons. It was inferred during the research that the choice of data mining technique employed is highly dependent on the application it is intended for and the intrinsic nature of the dataset.</p> </abstract>

Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions

A smart city requires an intelligent infrastructure to improve the quality of life with sustainable environment for its citizens. There is an exponential demand for efficient, secure, reliable, and uninterrupted electricity supply, so there is a need for an intelligent grid, which uses Information and Communications Technology (ICT) to optimize the generation, circulation, and ingestion of electricity. Thus, Smart Grid (SG) acts as an intelligent grid, which plays an important role in the overall growth of any smart city. Further, the Big Data (BD) generated from SG, provides noteworthy information that could significantly benefit different applications of SG, such as demand response and load profiling. However, an insecure technique for decision-making may lead to the breach of SG data where hackers gained full access to consumer data. On the contrary, a secure technique for decision-making can provide satisfaction to all the stakeholders, including consumers and utility providers. Motivated from these facts, this paper presents a comprehensive literature survey and analysis of state-of-the-art proposals for Secure Data Analytics (SDA) in the SG system. However, to achieve SDA for the SG systems is one of the critical tasks. The existing research and development endeavors not fully exploited the SDA in the SG system. In this paper, we discuss the distinctive nature of SDA and its complexity over the SG data. A detailed taxonomy abstracted into a novel process model, which highlights various research challenges such as secure data collection and preprocessing, secure load data processing and storage, load prediction, load management and analysis, data security and privacy issues, and data communication. Finally, a case study is presented to demonstrate the process model.

Robust Automated Photometry Pipeline for Blurred Images

The primary task of the 1.26 m telescope jointly operated by the National Astronomical Observatory and Guangzhou University is photometric observations of the g, r, and i bands. A data processing pipeline system was set up with mature software packages, such as IRAF, SExtractor, and SCAMP, to process approximately 5 GB of observational data automatically every day. However, the success ratio was significantly reduced when processing blurred images owing to telescope tracking error; this, in turn, significantly constrained the output of the telescope. We propose a robust automated photometric pipeline (RAPP) software that can correctly process blurred images. Two key techniques are presented in detail: blurred star enhancement and robust image matching. A series of tests proved that RAPP not only achieves a photometric success ratio and precision comparable to those of IRAF but also significantly reduces the data processing load and improves the efficiency.

Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network.

The contribution of this paper is to show the opportunities for using the compressive sensing (CS) technique for detecting harmonics in a frequency sparse signal. The signal in a ship’s electrical network, polluted by harmonic distortions, can be modeled as a superposition of a small number of sinusoids and the discrete Fourier transform (DFT) basis forms its sparse domain. According to the theory of CS, a signal may be reconstructed from under-sampled incoherent linear measurements. This paper highlights the use of the discrete Radon transform (DRT) techniques in the CS scheme. In the reconstruction algorithm section, a fast algorithm based on the inverse DRT is presented, in which a few randomly sampled projections of the input signal are used to correctly reconstruct the original signal. However, DRT requires a very large set of measurements that can defeat the purpose of compressive data acquisition. To acquire the wideband data below the Nyquist frequency, the K-rank-order filter is applied in the sparse transform domain to extract the most significant components and accelerate the convergence of the solution. While most CS research efforts focus on random Gaussian measurements, the Bernoulli matrix with different values of the probability of ones is applied in the presented algorithm. Preliminary results of numerical simulation confirm the effectiveness of the algorithm used, but also indicate its limitations. A significant advantage of the proposed approach is the speed of analysis, which uses fast Fourier transform (FFT) and inverse FFT (IFFT) algorithms widely available in programming environments. Moreover, the data processing algorithm is quite simple, and therefore memory usage and burden of the data processing load are relatively low.

Live reconstruction of global loads on a powerboat using local strain FBG measurements

Ships often operate under challenging conditions, considering that marine environment can cause failures of the structure related to overloads, fatigue, corrosion and erosion. As a consequence, advanced methods and procedures are under development for the evaluation of the on-site structural performance for both traditionally and newly designed ships. One of the main challenges in this field is the live monitoring of the loads acting on the ship hull; the load data processing can lead, through suited algorithms, to a real-time control of ship trim and, as a consequence, to the development of automatic or semi-automatic trim control systems. The presented procedure for load reconstruction requires a well-suited sensing network. In this kind of application, a high resolution, large sampling frequencies and low sensibility to possible noise factors such as moisture, electromagnetic fields or vibrations are required. These requirements lead to the choice of Fiber Bragg Grating sensors. In this paper, an experimental methodology is proposed to reconstruct the characteristics of loads acting on a fast ship, starting from a finite number of local strain data obtained with FBG sensors. Sensors positions have been defined considering the ship hull as a beam subjected to a set of standard loads acting on a fast ship and taking into account the maximum strain positions. The development of the FE model of the ship hull, obtained from a three-dimensional CAD of a real powerboat obtained with a 3D scan, allows the calculation of the strain field related to a set of standard loads applied on the ship. The above-mentioned data are used as input for a fast-computational algorithm, in which standard and actual strain fields - provided by a network of FBG sensors - are compared to reach the reconstruction of global loads acting on the structure. The algorithm has been effectively applied in sailing condition to the powerboat, detecting the acting loads on the hull in real time.

Peak Load Prediction Using Fuzzy Logic For The 150 kV Sulselrabar System

Prediction of electrical load on 150 kV Sulselrabar electrical system, analyzed using approach at night peak load using Fuzzy Logic based intelligent method. The peak load characteristics are certainly different from the load in normal time, therefore a special approach is needed to predict the peak night load. As input data will be used data of night peak load in 2010 until 2015, on the same day and date, each 4 days before day-H or day date which will be predicted load. For the data processing stage is divided into several stages, namely pre-processing, processing, and post-processing. The load data processing follows several procedures, ie computing WDmax, LDmax, TLDmax and VLDmax each year. Data processing is processed using excel software and then using Matlab software to run Fuzzy Logic. From the analysis results obtained, Error Prediction The peak evening load is very small that is equal to -0.070033687%. As comparison data used actual day-H data is April 2016. The graph of analysis result also shown in this paper.Keywords– Fuzzy Logic Control, Load Forecasting, Error, VLDmax

The Analysis of Harmonics on LED Lamps

The development of the era affects the technological advances that currently appear two types of loads, those are linear and non-linear loads. LED lamp included non-linear loads so causes harmonics. For that reason, the comparison of power consumption between LED lamp load and the combination of TL-D and XL LED lamps to find which one is better to use. Due to the occurrence of harmonics by the LED lamp load, it is necessary to improve the harmonics by designing a Single Tuned Filter passive filter with the help of ETAP Power Station 12.6 applications. This research method with literature study, measurement, load data processing due to harmonics to the comparison of TL LED and TL-D lamps in New FPEB Building, and the design of passive filter design so that can get the desired result. The current and voltage harmonics on the SDP are not compliant with IEEE 519-2014 standard, that is current harmonics on each floor of it ranges above 45.95% to 94.73%, in contrast to voltage harmonics ranging from 2.2% to 2.8% which still within the standards allowed that is under 5%. As a result of harmonics causing total power loss before exposed to harmonics of 7715.11 Watt and total harmonized power of 9639.92 Watt. So the total loss of power due to harmonics of LED lamp loads of 1924.81 Watt. At investment cost, lamp replacement cost based on lifetime, and lower power consumption cost of LED type lamp compared to combination of TL-D and XL LED lamps. In Single Tuned Filter type filter simulation designed to reduce 3rd, 5th, and 7th harmonics can decrease THDi respectively R, S, and T phases of 7.19%, 7.01%, and 10.95%.

Analysis for Industrial Grid Containing Nonlinear Loads

Harmonics is the most important issue among all issues related to the power quality. Harmonics comes from different non-linear loads like uninterruptible power supplies systems (UPS), variable speed motor drives, rectifiers, personal computers, adjustable speed drives, fluorescent lighting, rectifier banks, data processing loads, arc furnaces, and so on. Simulation of a real power supply system that consists of linear loads and nonlinear loads as variable speed drives has been carried out in the work. Variable speed drives generate harmonics into supply system that contains different values of compensated capacitors. Calculations of equivalent system reactance for generated harmonics, voltage harmonics, voltage harmonics in percentage, THD (Total Harmonic Distortion) of voltage, frequency characteristics, current flows in the system capacitor units have been worked out using Mathcad software. The obtained results have been compared with the European Standard (EN 50160) for supply quality requirements of the European Union.

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

The Research on Cloud Platform Considered Privacy Household Load Data Processing

To solve the problems such as randomness user behavior, effective privacy protection and massive data processing shortage, this paper has proposed a household load data processing method with privacy protection, combined data mining and cloud computing. First, it gave the platform architecture and individual protection model. Then, it proposed Mask-k_means database encryption method, algorithm parallelization was implemented by MapReduce. Finally, this paper varied the method was and implemented household load data processing based on statistics and real-measured data respectively. One conclusion is that feasibility of cluster analysis for multi-user was low, while single-user load analysis is high. The other is that this method is simple and practical. This can provide a new way to household load big data under smart electricity consuming.

A Road Load Data Processing Technique for Durability Optimization of Automotive Products

A Road Load Data Processing Technique for Durability Optimization of Automotive Products

Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals

Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naïve Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination.

Selection Methods of Rational Parameters of Core-Drill Working Tools

The paper presents the results of experiments in a circle drilling. Namely, the results of data processing load on the cutter (blade) moving in a certain radius, with step-checkboarded and sequential circuits of arrangement on the crown of ring bore. The paper presents the stresses of this incisor, with the two constellations, their relationship to each other. As a result, the conclusions brought under any scheme of arrangement of cutting elements are obtained by the least energy-intensive process.