Operational Monitoring Research Articles

ВЫЧИСЛИТЕЛЬНЫЕ МОДЕЛИ И ТЕХНОЛОГИИ ДЛЯ ПОЛУЧЕНИЯ ИНФОРМАЦИОННЫХ ОЦЕНОК ИЗМЕНЕНИЙ СОСТОЯНИЙ НЕОПРЕДЕЛЕННОСТИ ПАРАМЕТРА

The paper considers the issues of further development of computational models for obtaining information estimates of changes in the uncertainty states of various parameters. The proposed models are based on the analysis of changes in the values of entropy potentials and the corresponding basic values used to characterize the states of uncertainty of parameters at various stages. Such models are in demand, for example, for the organization of operational monitoring and management of uncertainty states of parameters in software and parametric control systems, where the basic values can change very significantly. The estimates obtained are objective and unified based on the information generated by the change in these states. The proposed models are compact and convenient for finding information estimates in various situations. The use of the proposed model variants will contribute to improving the efficiency and efficiency of monitoring and managing changes in the uncertainty states of parameters of objects and systems of various nature.

Quantitative approach to electricity market monitoring: development of an effective system

This paper presents a comprehensive quantitative approach to electricity market monitoring through the development of an effective system. The proposed system consists of three stages: operational monitoring of market participants’ behavior, analysis of collective behavior and economic parameters, and midterm assessment of market competition indices. By leveraging quantitative analysis techniques, such as statistical methods, the system aims to identify patterns signaling significant changes in participants’ behavior. Additionally, the system reduces the number of labor-intensive studies leading to false results, automates monitoring partially, and provides long-term analysis capabilities. Ultimately, this quantitative-based system enhances transparency, reliability, and responsiveness to unforeseen shocks in the electricity market, ensuring effective policy assessment and decision-making.

ИССЛЕДОВАНИЕ ПРИНЦИПОВ ФОРМИРОВАНИЯ СОСТАВА СОЦИАЛЬНО ЗНАЧИМЫХ ТОВАРОВ В РОССИИ

The purpose of this article was to determine the principles taken into account for the formation of the composition of socially significant goods in Russia and to substantiate proposals for their improvement. To achieve this goal, existing definitions of socially significant goods were studied in accordance with the current state regulatory documents of the Russian Federation and scientific research by modern authors. The composition of socially significant goods was also established and the legal grounds for the formation of a set of such goods in Russia were identified. The study showed that when determining the list of socially significant goods by state economic regulation bodies, the set of principles of state regulation taken into account is limited. The formation of the composition of some socially significant goods is based on the principle of limiting price increases, and in relation to other socially significant goods, the principle of mandatory availability for sale is applied. The paper proposes to clarify the totality of socially significant goods in Russia. Socially significant goods must satisfy the basic needs of people, ensure healthy, complete consumption, be available for sale and be established in regulatory documents of Russia. The article also substantiated the need to expand the composition of the used principles for forming the composition of socially significant goods. It is necessary to simultaneously apply to all socially significant goods the principles of mandatory availability of these goods for sale, limiting price increases, minimizing the tax burden, and ensuring operational monitoring of their performance.

Video surveillance-based multi-task learning with swin transformer for earthwork activity classification

Bulldozers, pivotal in earthworks, traditionally undergo supervision through labor-intensive and potentially unreliable manual methods. This research proposes a vision-based method for automating the monitoring of bulldozer operations. First, this research develops a specialized dataset for deep learning, the bulldozer earthmoving activity dataset. Following this, a novel multi-task video classification network (MTVTNet), the multi-task video transformer network, utilizing a video swin transformer architecture, is proposed. This network is adept at concurrently detecting the shoveling action, state, and soil classification of a bulldozer. The effectiveness of this model is demonstrated through its application in a real-world construction setting, achieving a remarkable 99.68% mean average precision. This method not only facilitates comprehensive automated supervision of bulldozer earthmoving activities but also serves as a valuable data source for assessing the operational efficiency of these machines.

스마트시티 시민참여 리빙랩 구축을 위한 중소도시 사례 연구

Recently smart City has become a world-widely hot issue. Many cities have been trying to be smarter in solving urban problems.In this study, a living lab for creating a small and medium-sized city based on civic participation was established through living lab cases and literature studies. The basic direction of the living lab model for creating a smart city based on civic participation was set. The process consisted of six steps: ① Composition of the participation group-② Problem Discovery-③Service Derivation-④Priority Selection-Implementation- ⑥Verification. To review this, it was compared and reviewed with Living Lab, a small and medium-sized city located in Chungcheongnam-do. As a result, there was a common fact that the public-led smart city living lab did not have a support system for continuous monitoring to realize the spread of services. Accordingly, it was concluded that attention should be paid to operation management and monitoring by verifying the living lab model for creating a smart city based on civic participation.

Research on Edge-Computing-Based High Concurrency and Availability “Cloud, Edge, and End Collaboration” Substation Operation Support System and Applications

With the continuous promotion of digital transformation in the field of power transformation, the diversification of application scenarios, and the scale of pilot construction, the real-time, concurrency, and security requirements for data fusion and application support of the power monitoring system, management information system, and other business platforms are getting higher and higher, and this paper puts forward a high concurrency and availability “cloud-side-end collaboration” based on edge computing. This paper proposes a high concurrency and availability “cloud, edge and end collaboration” architecture based on edge computing for substation operation support systems. First, this paper summarizes the development status of domestic substation operation support systems and analyzes the advantages and disadvantages of various technical architectures. Then, a “cloud-side-end cooperative” substation operation support system architecture with “high real-time, high concurrency, high security and high stability” is proposed, which focuses on remote inspection, remote operation, and remote safety control of substation businesses from the perspective of engineering applications. It realizes transparent monitoring of equipment operation, unified management of operation data, and integration of production command and decision-making; solves the problems of dispersed coexistence of multiple systems for dispatching, monitoring, analysis, management, and other businesses, switching between multiple systems, and insufficient real-time and stability of the system; and controls the risks of the grid, reduces the potential safety hazards, and solves the contradiction between the continuous growth of the grid equipment and the shortage of production personnel. The results of engineering application examples show that the proposed architecture compared with the existing system architecture has greater advantages and can meet the requirements of large-scale access to the substation, with feasible popularization and application.

Psychophysical State Aspect during UAV Operations

The development of unmanned aerial vehicles (UAVs) and the increasing air traffic of these devices make it necessary to pay attention to the issue of the human factor in UAV operations. In this article, tests were conducted in real conditions on the unmanned aerial vehicle operator’s (UAVO) psychophysical state during training. The parameters of the human cardiovascular system, and more specifically the heart rate variability (HRV), were used to conduct research and analysis. The purpose of this research is to elaborate the typical HRV parameters for student operators during UAVO training. These reference values could be used during UAVO training to assess candidates’ psychophysical state objectively and could allow for the monitoring of operators’ state and management of their cognitive load. Monitoring operators’ state may have a positive impact on increasing training effectiveness. Research confirmed the thesis that HRV parameters are significantly different during performed tasks with cognitive load and can be used to assess candidates’ psychophysical state objectively. This can help flight instructors perform student assessment, meaning that they would not have to rely only on their subjective feelings.

Evaluating Coastal Douglas Fir Growth Responses to Nitrogen Application Using Tree Ring Chronologies

Dendrochronology is a technique that can be applied as a retrospective monitoring (RM) approach to evaluate the performance of nutrient application in forest ecosystems. Applying the RM approach across operations lacks experimental controls, which may adversely affect accuracy and precision of estimates due to greater mismatches in stand conditions between treated and untreated plots. To test the rigor of the RM approach, we collected increment cores of coastal Douglas fir (Pseudotsuga menziesii var. menziesii) at eight sites of an experiment where stands were fertilized in 1971. First, we tested the approach under ideal conditions by sampling from treated and untreated plots of the experiment. Second, we tested the approach using newly established surrogate control (SC) plots, which differed in ecological site classification from those of the treated plots to understand how robust the approach was to mismatches in conditions between treated and untreated samples. We hypothesized that detrending ring width would mitigate error in responses to nutrient application resulting from mismatches in site classification. Within the experiment, the approach indicated an average increase of 15% (5% to 26%, p < 0.05) growth response to operational doses of urea. Different responses were found when the analysis relied on SC plots. Detrending low-frequency variation in ring widths eliminated differences in results arising, at least in part, to mismatches in site class. However, it also reduced the growth response using the experimental control plots to 10%. Dendrochronology with detrending shows promise in the ability to mitigate variation introduced by mismatches in ecological site classification that may occur in operational monitoring. Based on these results, we see potential to implement RM with operations to evaluate and optimize stand selection criteria.

Optimization of Real-Time Operational Data Monitoring Through Installing The Device on the Unit

Coal is the largest natural resource in Indonesia and has a vital role in global energy in various industries. PT Putra Perkasa Abadi (PPA), a mining contractor in South Kalimantan, faces problems with manual reporting being late and prone to errors. This project aims to speed up the digitization of the reporting process by providing PPA Team devices in the Heavy Equipment (A2B) and Support units. This is in line with the commitment of the PPA Mining Work Safety, Environment and Quality (K3PLM) Policy. This implementation starts from observation and data collection by selecting SS6 devices and creating designs for each unit. Continue with device installation, analyze problems, and repair installation. Following, carry out trials and socialize the use of the device to operators. With the implementation of the PPA Team application on the SS6 Monitor, Operators at PPA can independently input data on operational activities, including check-in / check-out units. It will reduce paper in operational reporting by providing an SS6 Monitor device. In addition, cost calculations can be adjusted to actual data, and operators can monitor unit Hour Meter (HM) data and operational activities directly on the SS6 Monitor. This reduces the duration of data input time, but also reduces the intensity of crowding on communication radios, and minimizes data input errors. Therefore, optimizing operational monitoring at PPA by installing devices on units can increase efficiency, including HM input for cost calculations, efficient unit HM reporting, fast check-in / check-out processes, digital unit breakdown reporting for more efficient communication, rapid digital validation of P2H, and moving from paper forms to digital tools for more sustainable work practices.

Application Of Performance Management On D'sayur Tanjungpinang

D’Sayur as an e-commerce platform that focuses mainly on selling fresh vegetables, faces rapid market dynamics and fierce competition. Operational complexity and optimal customer service needs lead to the need to implement performance management. The purpose of this study is to examine the impact of the implementation of performance management on Dsayur with a focus on operational changes, customer feedback and its contribution to D’Sayur's business growth located Jl. R.H.Fisabilillah No.7 KM 8 Atas, Tanjungpinang. Research methods include implementation management analysis, operational monitoring and customer response evaluation. A qualitative approach is used to refine the results. The implementation of Sayur activities is able to improve operational efficiency, achieve better customer satisfaction and have a positive impact on the overall growth of the company. The results of this study provide an in-depth understanding of the importance of performance management to support the smooth operation and growth of the vegetable business. The impact includes the development of performance management strategies that are targeted and responsive.

Methodology of operational monitoring of crop status based on the internet of things technologies

Digital technologies are being actively introduced into Russian agriculture at different levels of information analysis (from the plot to the field, farm, region and country as a whole). In crop production at the field level, one of the most important values is the introduction of systems for accurate, rapid and automated monitoring of crop condition, the success of which largely predetermines the effectiveness of precision farming systems. The aim of the research is to develop a methodology for using Internet of Things technologies for non-contact monitoring of crops and related meteorological and soil-hydrological parameters. A wireless network is used as the basis for monitoring, which includes sensor nodes equipped with sensors for meteorological parameters, soil moisture and cameras equipped with a fish-eye lens. Sensor nodes equipped with sensors and cameras are placed in the field according to a specially designed scheme, individualized for each field. Development of the scheme of sensor placement on the field is based on the analysis of long-term archives of satellite data of high spatial resolution and refined soil maps of large scale. Information from sensors is wirelessly transmitted to the network coordinator (or base station) and then to the remote server in the database, and there it is automatically analyzed and interpolated for the whole field. Based on the analysis, recommendations for correction of agrotechnology of crop cultivation are formed. Elements of the methodology were tested on a number of test fields and showed high efficiency. Implementation of the proposed approaches can serve as an alternative to the use of remote sensing data for crop monitoring in offline precision farming systems.

Analysis of the Slanted-Edge Measurement Method for the Modulation Transfer Function of Remote Sensing Cameras

The modulation transfer function (MTF) serves as a crucial technical index for assessing the imaging quality of remote sensing cameras, which is integral throughout their entire operational cycle. Currently, the MTF evaluation of remote sensing cameras primarily relies on the slanted-edge method. The factors influencing the slanted-edge method’s effectiveness are broadly classified into two categories: algorithmic factors and image factors. This paper innovatively comprehensively analyzes the influencing factors of the slanted-edge method and proposes an improved slanted-edge method to calculate the MTF testing method of remote sensing cameras, which is applied to the MTF testing of remote sensing cameras. Since the traditional algorithm can only be applied in the small angle situation, this paper proposes a new method of slanted-edge method test calculation based on the optimal oversampling rate (OSR) adaptive model of the slanted edge and uses simulation experiments to verify the reliability of the algorithm model through the deviation of the slanted-edge angle calculation and MTF measurement, and the results show that the algorithm improves the accuracy of the MTF measurement compared with the ISO-cos and OMINI-sine methods. Then, the effects of the slanted-edge angle, image region of interest (ROI), as well as image contrast and signal-to-noise ratio (SNR) on the accuracy of the MTF calculation by the slanted-edge method were quantitatively analyzed as the constraints of the slanted-edge method test. Based on the laboratory target experiment, the algorithm flow and various influencing factors obtained in the simulation stage are verified, and the experimental results are more consistent with the various test results obtained in the simulation stage. Consequently, the slanted-edge method introduced in this paper is applicable for future remote sensing camera MTF testing. This approach offers a valuable reference for on-orbit focusing, satellite operational condition monitoring, lifespan estimation, and image restoration.

Tar pollution event (2021) at the Southeastern Levantine oligotrophic basin, short-term impacts and operational oceanography perspectives

The Levantine basin (LB) in the Southeastern Mediterranean Sea is a high-risk oil pollution hot spot owing to its dense maritime traffic and intense oil and gas exploration and exploitation activities. In February 2021 the Israeli LB shorelines were impacted by an exceptional tar pollution event (~550 tons; average distribution: ~3 kg tar m−1 front beach) of an unknown oil spill source. Here we report on the immediate numerical modelling assessment of the oil spill propagation and tar distribution; operational use of underwater gliders for tracking water column anomalies of dissolved polycyclic aromatic hydrocarbons (PAHs) and turbidity signals; the beached tar composition and amounts and the short-term response of the microbial population along the ~180 km shoreline. This pollution event emphasizes the need for improving the early warning systems for oil spills and implementing continuous operational monitoring at high-risk, ecologically sensitive and valuable resource areas like the Israeli LB waters.

Non-Intrusive Continuous Monitoring of Smart City Platforms

Smart city platforms provide several services to facilitate the development of applications. Such platforms typically manage several applications, deal with a large volume of data, and serve many devices and users that generate a high volume of requests. The large number of requests to handle and the complex operations to perform often cause overloads on the platform, degrading the quality of service provided to users and applications. In this context, monitoring the underlying computational infrastructure in which smart city platforms and applications are deployed and the platform operations is essential. The monitoring process can allow for examining fluctuations in the behavior of the platform's components to detect performance degradation and overloads (including unforeseen ones), contribute to avoiding interruptions in the platform's services, and increase its scalability to assimilate significant amounts of requests, devices, and users. This paper presents a strategy and architecture to enable the non-intrusive monitoring of operations on smart city platforms and their underlying infrastructure. The proposal covers monitoring at multiple levels and is based on the aspect-oriented programming (AOP) paradigm so that it is possible to monitor the platform's operations without intervening in the platform's implementation or generating coupling regarding monitoring. This paper presents the implementation of the monitoring architecture and its instantiation in the context of Smart Geo Layers (SGeoL), a platform that has been used in several real-world smart city applications. This paper also reports the results of computational experiments to evaluate the performance of the proposed monitoring architecture for response time to requests, CPU usage, and RAM utilization. The obtained results show an evident increase in response time with the number of simultaneous requests and a significant correlation between the response time and the CPU utilization in the deployment of the monitoring architecture.

A New Methodology-Based Sensorial System with Which to Determine the Volume of Liquid Contained in a Cylindrical Tank Subjected to Full Variations in Its Orientation

It is necessary to determine the volume of water contained in a tank for a wide range of applications, such as the automation and monitoring of industrial operations. In the context of the marine industry, the aforementioned information plays a vital role in the effective management of submerged devices, specifically in relation to their depths and/or inclinations. In these cases, it is not feasible to quantify the volume of liquid in a tank by means of direct measurements, owing to the fact that devices can be subjected to changes in their orientation. This variation in inclination could have a variety of causes, such as the implementation of automated emersion–immersion maneuvers in a TEC or variations in depth in an AUV. Nevertheless, it can be deduced by considering the level of the tank and its geometric properties. This paper presents a new methodology-based sensorial system (composed of three capacitive sensors and an inclinometer) for accurate determination of the volume of a liquid contained within a cylindrical tank subjected to full variations in its orientation. The effectiveness of the proposed methodology-based sensorial system has been verified by the results obtained from experiments conducted on a laboratory platform, thus demonstrating the high reliability of the model experiment and the relative errors study carried out.

AI and Machine Learning for Predictive Monitoring in IT Operations

AI and Machine Learning for Predictive Monitoring in IT Operations

Analysis and identification of transformer acoustic signal based on compressed sensing

The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed by considering the energy distribution characteristics using wavelet information entropy. The acoustic signal characteristics of transformer core fault simulation data are extracted by this method, and the fault diagnosis simulation is completed by using a particle swarm -SVM classifier. The results show that this method can obtain higher fault identification accuracy under the condition of a high compression ratio.

Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset

While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forage in rangeland environments and associated ecological implications. The free provision of remote sensing data from modern broadband multispectral sensors with improved spatial and spectral properties offers prospects for reliable and wall-to-wall monitoring of grassland senescence in rangeland ecosystems. The current study tested the potential of the modern multispectral remote sensing dataset (i.e., Sentinel 2 and Landsat) in mapping the senescent grass, and to identify the optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses. Locational information for both senescent and non-senescent grasses was acquired on the field and was used to train the classification process. A Random Forest classification approach was employed using the Landsat 8 and Sentinel 2 multispectral datasets to spectrally discern between senescent and non-senescent grasses. Our analysis yielded overall classification accuracies of 0.82 and 0.78 and kappa coefficients of 0.64 and 0.56 for Sentinel 2 and Landsat 8, respectively. Using the stepwise selection approach, the study further identified that the Red Edge Position (REP), and the visible green and red bands of the electromagnetic spectrum were the optimal waveband positions for separating between senescent and non-senescent grasses based on the broadband multispectral remote sensing. This study has demonstrated the value of the broadband multispectral remote sensing data in mapping autumn grassland senescence, and this lays a foundation for effective operational scale monitoring of foraging resources at the landscape scale, particularly during dry periods.

Power Consumption Minimization of a Low-Cost IoT Data Logger for Photovoltaic System

This paper introduces an innovative IoT-based data logger for photovoltaic (PV) system monitoring, emphasizing low power consumption and affordability. The system comprises a PV panel, a charging controller, and a backup battery, focusing on monitoring their voltages and currents through a network of voltage and current sensors. Data is stored on an SD card and displayed in real-time on a web server. The FireBeetle 2 ESP32-E microcontroller, chosen for its efficient deep-sleep mode power management, is central to the data logger's design. This study employs several low-power strategies, notably reducing supply voltage and CPU frequency to decrease power consumption significantly. A data buffering mechanism stores sensor readings in the microcontroller's flash memory, transferring them to the SD card hourly to balance power efficiency and data security. Wi-Fi connection intervals are optimized to 45 seconds, balancing power use and system monitoring frequency. The data logger averages a power consumption of 122.78 mW and demonstrates its efficacy against the commercial DI-145 model. Priced at C$ 55.05, the system is both cost-effective and scalable, capable of monitoring multiple PV panels and batteries, reducing per-unit costs. The study underscores the successful integration of affordability, low-power operation, and efficient monitoring in a PV system data logger, showcasing its potential in future renewable energy research.

The intelligent monitoring method of main distribution microgrid operation panorama considering the high percentage of new energy sources

In the context of new power system construction, the proportion of new energy generation connected to the power system is increasing year by year. However, due to the strong randomness and volatility of new energy generation, a large number of accesses may threaten the safety of power system operations. Therefore, it is one of the important aspects in the construction of new power systems to improve the grid operation monitoring technology. This paper proposes a panoramic intelligent monitoring method for the operation of the main distribution microgrid considering the high percentage of new energy sources, in order to protect the safe operation of the new power system.