Local Network Data Research Articles

Multi-source data fusion for power outage warning based on transformer model

Abstract In this study, we propose a method that uses the Transformer model to enhance power outage alerts by integrating data from multiple sources. By integrating data from various sources, including operational data from the local distribution network and meteorological data, we have constructed a comprehensive multi-source data framework for power outage warnings. The Transformer model, known for its ability to capture complex dependencies and patterns, has been employed to extract features and make accurate predictions. Results on actual power system data have shown that our approach significantly boosts the accuracy and stability of predictions. The fusion of multi-source data has enabled timely maintenance and protection measures, reducing the duration and impact of power outages. The findings from this study have provided valuable insights for power outage warnings and future research on multi-source data fusion.

APPROACHES TO INCREASE THE EFFICIENCY OF TRAINING AND TESTING BY CREATING A UNIFORM INFORMATION AND REFERENCE SYSTEM AND AUTOMATING THE ACTIVITIES OF THE INSTITUTE’S SCIENTISTS

The article is devoted to ways of solving certain problematic aspects of the preparation and conducting of tests, caused by the lack of a centralized approach to the creation, replenishment and access of scientists of the institute to the regulatory and legal framework and other materials necessary for the development of organizational and methodical documents for conducting tests. This causes long-term, not always high-quality preparation and testing of weapons, military, special equipment, ammunition, software and other types of experimental samples.
 The authors of the article outline approaches to increase the efficiency, automation, and quality of preparation and testing. For this purpose, it is proposed to create a single information and reference system at the institute, built on the basis of global (cloud environment) and local (local computer network) data exchange systems.
 The proposed approaches provide for the implementation of advanced information technologies that ensure the accumulation, search, access and use by scientists of the State Research Institute of Testing and Certification of Weapons and Military Equipment of the necessary regulatory and legal acts, organizational and methodological documents, reporting materials and other reference data at the stages preparation and testing of weapons, military, special equipment, ammunition, software and other types of prototypes.
 The introduction of the proposed approaches will reduce the cost of human resources for the preparation for the tests, will ensure the improvement of the quality of working out the organizational and methodical documents, which will ensure the proper conduct of the tests by the test team.

Creating a thematic geodatabase for monitoring the landslide processes of the landslide circus “Dalgia yar”

Impact on the process of landslide origin and activation is result of many factors both endogenous and exogenous. The purpose of this study is to provide possibility for analysis and assessment of the geo-processes in the “Dalgia yar” landslide located at Northern Black Sea coast of Bulgaria in order to prevent risks and disasters of natural and anthropogenic origin. An important stage was to seamlessly include data from different sources such as geodetic measurements, satellite SAR (Synthetic-aperture radar) data as well as geological and geophysical data. The established geodatabase structures the collected information on dangerous geo-processes in the mentioned area and introduces them into the GIS (Geographic information system) environment. Its purpose is to facilitate the analysis of the available geological data for this landslide and to integrate them with results of measurements from regular monitoring. Interferometric images (IFIs), data from permanent GNSS (Global Navigation Satellite Systems) stations and from local geodynamic GNSS network, geological, seismic and geophysical data, updated geological maps and maps of the risk of landslide processes are included in the database. The IFIs have been produced using well established procedure for processing large number of Sentinel-1 SAR data of the purposely created local archive. Other key element used to improve the final results of SAR data processing and important part of the geodatabase is the precise Digital Elevation Model (DEM), which is much better in terms of horizontal and vertical resolutions than the open accessed ones (SRTM). The coordinates and velocities of the GNSS points are obtained from adjustment and analysis of two epoch measurements of the geodynamic control network of landslides “Dalgia yar”. Since the area has complex geological structure, small scale maps reflecting the geological and geophysical hazards are integral part of the geodatabase. Having all this information the analysis concerning the ongoing geodynamical processes in the study area is significantly improved and more reliable information is produced for better regional planning by the local authorities and residents.

The Application of Computer Technology in Electronic Information Engineering

With the continuous progress and development of our country’s economy, computer network technology has also been continuously improved and upgraded, and the level of EI technology has matured day by day, and the requirements on enterprises have become more and more stringent. In order to more fully expand the scope of EI computing and ensure that the technology is in a normal state of development, it is necessary to conduct applied research and exploration of EI engineering based on computer technology in order to use it for people to provide better services and create More value. This article is based on the research on the actual application of computer technology applied in our country’s EI technology engineering. First, it uses the literature research method to summarize the characteristics of our country’s EI technology engineering and the actual application of computer technology in EI engineering. In this paper, the investigation and research method is used to investigate some of the problems and suggestions faced by the actual application of computer technology in our country’s EI technology engineering. Through the analysis of the results, the in-depth study of the development trend of our country’s EI engineering is predicted. Survey results: Among the problems faced by the application of computer technology in EI engineering, local area network instability problems occur more frequently, accounting for about 45%, and then data and information security problems, accounting for about 30%. In the proposal for the development of the application of computer technology in EI engineering, the introduction of advanced local area networks will account for about 40% of the equipment, and about 35% for the strengthening of data security technology. It can be seen from this that the main research direction of computer technology application in EI engineering is local area network stability and data security.

Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA

Study regionDelaware, USA and its surrounding watersheds. Study focusAn ensemble using multiple Kernel K-nearest neighbors (KKNN) models was trained to predict daily grids of SSM at 100-meter resolution based on SSM estimates from the European Space Agency’s Climate Change Initiative Soil Moisture Product, terrain data, soil maps, and local meteorological network data. Estimated SSM was evaluated against independent in situ SSM observations and were investigated for relationships with land cover class and vegetation phenology (i.e., NDVI). New hydrological insights for the regionDownscaled daily mean SSM estimates had lower error in space (27%) and greater predictive performance over time compared to the raw, coarse resolution remotely sensed SSM dataset when calibrated to field observed values. Downscaled SSM identified stronger and more widespread temporal relationships with NDVI than other estimation methods. However, both coarse and fine resolution datasets greatly underestimated SSM in wetland areas. The findings highlight the need for enhanced in situ SSM monitoring across diverse settings to improve landscape-level downscaled SSM. The downscaling methodology developed in this study was able to produce daily SSM estimates, providing a framework that can support future SSM modeling efforts, hydroecological investigations, and agricultural studies in this and other regions around the world when used in conjunction with ground-based monitoring networks.

Research on Distributed Data Sharing System based on Internet of Things and Blockchain

AbstractAiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

لرزه زمین ساخت پهنه های البرز خاوری و کپه داغ با استفاده از داده های شبکه لرزه نگاری محلی

The Kopeh Dagh and Binalud-Alla Dagh mountains are important structural elements located in the northeastern boundary of Arabia-Eurasia collision zone. Due to existence of large cities with a long history of civilization, there is a relatively rich body of data on historical seismicity in this area. Nonetheless, little adequate instrumental seismic data were available prior to this study. In this paper, we utilized the temporary China-Iran local seismic network data deployed in the area for 13 months. Based on this data, we determined 37 focal solutions through first motion polarity approach suggesting a combination of strike-slip, reverse and normal mechanisms. There is a significant concentration of epicenters and focal mechanisms around the eastern Alborz - Kopeh Dagh boundary along the Atrak River. The southern and western boundaries of the Binalud Mountains also show relatively high seismic activities. The Local magnitude (ML) of the events ranges from 3.5 to 4.9 with depths of up to 20 km, mostly concentrated at ~10 km. The principal stress axes and slip vectors obtained from the focal mechanisms agree well with kinematic state of main faults and tectonic regime of the area, confirming the seismogenic nature of these faults. In addition, they are consistent with right-lateral component of slip along thrust faults in eastern Kopeh Dagh and Binalud and both laft- and right-lateral motions in the central Kopeh Dagh.

Optimal Witnessing of Healthcare IoT Data Using Blockchain Logging Contract

Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. There is a need for a verification framework that various authorities can request a verification service for the local network data of a target IoT device. In this paper, we leverage blockchain as a distributed platform to realize an on-demand verification scheme. This allows authorities to automatically transact with connected devices for witnessing services. A public request is made for witness statements on the data of a target IoT that is transmitted on its local network, and subsequently, devices (in close vicinity of the target IoT) offer witnessing service. Our contributions are threefold: (1) We develop a system architecture based on blockchain and smart contract that enables authorities to dynamically avail a verification service for data of a subject device from a distributed set of witnesses which are willing to provide (in a privacy-preserving manner) their local wireless measurement in exchange of monetary return; (2) We then develop a method to optimally select witnesses in such a way that the verification error is minimized subject to monetary cost constraints; (3) Lastly, we evaluate the efficacy of our scheme using real Wi-Fi session traces collected from a five-storeyed building with more than thirty access points, representative of a hospital. According to the current pricing schedule of the Ethereum public blockchain, our scheme enables healthcare authorities to verify data transmitted from a typical wearable device with the verification error of the order 0.01% at cost of less than two dollars for one-hour witnessing service.

Can we reduce excision margins for head and neck melanoma? A 12-year retrospective study

Although wide local excision is the standard treatment for primary melanoma, the surgical margin remains controversial. Melanomas of the head and neck exhibit higher recurrence rates and worse prognosis than lesions in other body locations, and their close proximity to critical anatomical and functional structures means that wide excision margins are often not feasible. Surgeons must therefore achieve a balance of oncological safety and functional and aesthetic needs. The aim of this study was to retrospectively analyse melanoma data over a 12-year period at a large skin surgery unit to identify potential differences in outcomes in patients who had reduced wide local excision margins for primary head and neck melanoma. The study would provide further evidence for the need for large randomised prospective trials to reduce excision margins for head and neck melanoma. Local cancer network data were retrospectively analysed over a period of 12 years (2008-2019). Data included site, initial stage, multidisciplinary team (MDT) meeting recommendations for excision margins and the actual wide local excision margin taken, recurrence rate, and disease-specific and absolute survival. A total of 222/305 patients (73%) had the recommended excision margin, while in 27% margins were reduced due to anatomical or functional considerations. Recurrence rates were similar (recommended 11.7% vs narrow 13.3% excision margins) (p = 0.64). The mean follow-up time for all patients was 48.5 months. In aesthetically and functionally sensitive areas of the head and neck, wide local excision margins need to be carefully considered after MDT discussion and discussion with the patient. This study suggests the need for further multicentre trials to address the uniqueness of head and neck melanoma.

Characterization of sparse WLAN data traffic in opportunistic indoor environments as a prior for coexistence scenarios of modern wireless technologies

CR enabled radio environment provides the seamless operating framework to cope with the requirements of next generation wireless systems like higher data rates, massive machine time communication and interference free coexistence scenarios of wireless technologies. Traffic characterization studies assist to optimize coexistence framework by providing necessary information about the usage patterns of wireless services in observed radio bands. Prior knowledge about the observed sparse wireless local area network (WLAN) data traffic is a key for opportunistic radio resource allocation and utilization in the given coexistence scenarios of WLAN either with cellular systems (including 5G-NR/4G Long Term Evolution (LTE)) or with low power wireless systems (including Bluetooth and ZigBee). Parametric methodology is adapted to model the sparse WLAN data traffic observed in 2.4 GHz Industrial, Scientific and Medical (ISM) band as spectral and temporal activity. The multivariate Gaussian mixture model (MGMM) is proposed in both scenarios where either dependency is considered between the observed WLAN data traffic or assuming that the observed traffic is independent and identically distributed (i.i.d). It is to be validated that in order to have an efficient characterization of sparse WLAN data traffic, the dependency (correlation) between neighbored frequency subbands must be considered. By considering the dependency either between neighbored frequency subbands or neighbored time domain signals actually help to characterize the sparse WLAN data traffic in more realistic way which could not be possible by assuming them i.i.d. Such statistics to characterize sparse data traffic really help as user activity for efficient allocation and utilization of radio resources in CR enabled coexistence scenarios of wireless technologies where WLAN acitivity is considered nominal either as secondary user or classify it grey spectrum as primary user.

The Editor vs. the Algorithm: Economic Returns to Data and Externalities in Online News

We run a field experiment to quantify the economic returns to data and informational externalities associated with algorithmic recommendation relative to human curation in the context of online news. Our baseline results show that algorithmic recommendation can outperform human curation in terms of user engagement. We find significant heterogeneity with regards to the type of data (personal data is much more effective than aggregate data, but diminishing returns set in quickly), user types (users closer to average consumption patterns engage more), local data network effects (the size of the user base increases engagement only for users close to average consumption patterns), and data freshness (missing updating of personal data quickly deteriorates performance). We characterize circumstances where human curation leads to higher engagement than algorithmic recommendation to highlight complementarities between the human editor and the algorithm. Investigating informational externalities highlights that personalized recommendation reduces consumption diversity and that these are reinforced over time. Moreover, users associated with lower levels of digital literacy and more extreme political views engage more with algorithmic recommendations.

Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data

Evolutionary algorithms have been used extensively over the past 2 decades to provide solutions to the Transit Network Design Problem and the Transit Network and Frequencies Setting Problem. Genetic algorithms in particular have been used to solve the multi-objective problem of minimizing transit users’ and operational costs. By finding better routes geometry and frequencies, evolutionary algorithms proposed more efficient networks in a timely manner. However, to the knowledge of the authors, no experimentation included precise and complete pedestrian network data for access, egress and transfer routing. Moreover, the accuracy and representativeness of the transit demand data (Origin Destination matrices) are usually generated from fictitious data or survey data with very low coverage and/or representativity. In this paper, experiments conducted with three medium-sized cities in Quebec demonstrate that performing genetic algorithm optimizations using precise local road network data and representative public transit demand data can generate plausible scenarios that are between 10 and 20% more efficient than existing networks, using the same parameters and similar fleet sizes.

Understanding the Components, Calculation, and Impact of Monthly and Seasonal Variation of the Standardized Antimicrobial Utilization Ratio (SAAR).

This study sought to characterize the impact of 3 types of variation on the Standardized Antimicrobial Administration Ratio (SAAR) utilizing local National Healthcare Safety Network (NHSN) data. SAAR and antimicrobial days per 1,000 days present (AD/1000DP) were compiled monthly for Northwestern Memorial Hospital from 2014 to 2016. Antimicrobial consumption was aggregated into agent categories (via NHSN criteria). Month-to-month changes in SAAR and AD/1000DP were evaluated. Azithromycin and oseltamivir AD/1000DP from 2012 through 2017 were explored for seasonal variation. A sensitivity analysis was performed to explore the effect of seasonality and altered consumption at other hypothetical hospitals on the SAAR. Across agent categories for both the intensive care unit (n = 4) and general wards (n = 4), the average matched-month percent change in AD/1000DP was correlated with the corresponding change in SAAR (coefficient of determination of 0.99). The monthly mean ± standard deviation (SD) AD/1000DP was 235 (range, 47.2 to 661.5), and the mean ± SD SAAR was 1.09 ± 0.26 (range, 0.79 to 1.09) across the NHSN agent categories. Five seasons exhibited seasonal variation in AD/1000DP for azithromycin with a mean percent change of 26.76% (range, 22.27 to 30.69). Eight seasons exhibited seasonal variation in AD/1000DP for oseltamivir with a mean percent change of 129.1% (range, 32.01 to 352.74). The sensitivity analyses confirm that antimicrobial usage at comparator hospitals does not impact the local SAAR, and seasonal variation of antibiotics has the potential to impact SAAR. Month-to-month changes in the SAAR mirror monthly changes in an institution's AD/1000DP. Seasonal variation is an important variable for future SAAR consideration, and the variable antibiotic use at peer hospitals is not currently captured by the SAAR methodology.

Study of fault plane solutions and stress drop using local broadband network data: The 2011 Sikkim Himalaya earthquake Mw 6.9 and its aftershocks

Fault plane solutions of the September 18, 2011 Sikkim Himalaya earthquake Mw 6.9 and its four aftershocks (Mw>4.0) are studied by waveform inversion using the local broadband network data. The solutions show pure strike-slip mechanisms; one aftershock show thrust faulting with strike-slip component. Strike-slip mechanisms indicate predominant transverse tectonics in Sikkim, in the eastern Himalaya region, unlike predominant thrust tectonics in the western Himalaya. The 2011 main shock occurred at a much deeper depth (~47 km) compared to the shallower (<20 km) thrust events in the western Himalaya. Further, analysis of ground acceleration spectra reveals low stress drop (14-38 bars) in agreement with the relatively long source duration and small co-seismic slip of the main shock as well as the aftershocks. We interpret the low stress drop in terms of lower energy release due to reactivation of the Tista fault in opposite sense to that of the Himalayan thrust tectonics. The low stress drop also indicates a pre-existing brittle zone or fault zone at deeper depth or mantle depth.

Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

Anthropogenic heat flux (QF) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. QF estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of QF, and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and “hot spots” representing 30–40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total QF, but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple QF models should be applied with conservative spatial resolution in cities that, like London, exhibit time-varying energy use patterns.

Локализация коммуникаций как способ обеспечения информационной безопасности

The article is focused specially on the technological aspect of the safety of information. Insecurity of using the global network Internet as a main instrument for data transmission is also pointed. The idea of using the local network data transmission is taken into account as an alternative. The usage of locals networks for the transmission of non-public information excepts the possibility of an unauthorized access. The principles of construction of the data transmit local networks, its problems and the ways of its modernization for providing the fast and resilient transmission have been analyzed as well.

BIOMASS BURNING RELATED POLLUTIONS AND THEIR CONTRIBUTIONS TO THE LOCAL AIR QUALITY IN HONG KONG

Abstract. In this study, we present a quantitative estimation of the impacts of biomass burning emissions from different source regions to the local air quality in Hong Kong in 2014 using global chemistry transport model simulations, sun photometer measurements, satellite observations and local monitoring network data. This study focuses on two major biomass burning pollutants, black carbon aerosols and carbon monoxide (CO). The model simulations of atmospheric black carbon and CO show excellent agreement with sun photometer aerosol optical depth (AOD) measurements, satellite CO columns observations and local monitoring stations data. From the model simulation results, we estimated that biomass burning contributes 12 % of total black carbon and 16 % of atmospheric CO in Hong Kong on annual average. South East Asia shows the largest influence to the black carbon and CO levels in Hong Kong, accounts for 11 % of the total atmospheric black carbon and 8 % of CO. Biomass burning in North East Asia and Africa also show significant impacts to Hong Kong. Elevated levels of atmospheric black carbon aerosols and CO were observed during springtime (March and April) which is mainly due to the enhancement of biomass burning contributions. Black carbon and CO originating from biomass burning sources are estimated to contribute 40 % of atmospheric black carbon and 28 % of CO in Hong Kong during March 2014. An investigation focusing on the biomass burning pollution episode during springtime suggests the intensified biomass burning activities in the Indochinese Peninsula are the major sources of black carbon and CO in Hong Kong during the time.

A Data Encryption Transmission Scheme Based on WiFi

Aiming at the wireless local area network data transmission is not security, this article has carried on the further research. Because of WiFi itself security system imperfect, so the design is on the basis of the WLAN security system, using a combination of RSA and AES to double encryption of the message, at the same time combined with SHA algorithm to calculate the hash value as a digital signature, so as to ensure the confidentiality of transferring data, integrity, and non-repudiation. This design also uses Java programming for point-to-point socket communication to verify the safety and feasibility of the scheme.

Biomass burning sources and their contributions to the local air quality in Hong Kong

In this paper, we present a quantitative estimation of the impacts of biomass burning emissions from different source regions to the local air quality in Hong Kong in 2014 using global chemistry transport model simulations, sun photometer measurements, satellite observations and local monitoring network data. This study focuses on two major biomass burning pollutants, black carbon aerosols and carbon monoxide (CO). The model simulations of atmospheric black carbon and CO show excellent agreement with sun photometer aerosol optical depth (AOD) measurements, satellite CO columns observations and local monitoring stations data. From the model simulation results, we estimated that biomass burning contributes 12% of total black carbon and 16% of atmospheric CO in Hong Kong on annual average. South East Asia shows the largest influence to the black carbon and CO levels in Hong Kong, accounts for 11% of the total atmospheric black carbon and 8% of CO. Biomass burning in North East Asia and Africa also show significant impacts to Hong Kong. Elevated levels of atmospheric black carbon aerosols and CO were observed during springtime (March and April) which is mainly due to the enhancement of biomass burning contributions. Black carbon and CO originating from biomass burning sources are estimated to contribute 40% of atmospheric black carbon and 28% of CO in Hong Kong during March 2014. An investigation focusing on the biomass burning pollution episode during springtime suggests the intensified biomass burning activities in the Indochinese Peninsula are the major sources of black carbon and CO in Hong Kong during the time.

Big data analytics based fault prediction for shop floor scheduling

The current task scheduling mainly concerns the availability of machining resources, rather than the potential errors after scheduling. To minimise such errors in advance, this paper presents a big data analytics based fault prediction approach for shop floor scheduling. Within the context, machining tasks, machining resources, and machining processes are represented by data attributes. Based on the available data on the shop floor, the potential fault/error patterns, referring to machining errors, machine faults and maintenance states, are mined for unsuitable scheduling arrangements before machining as well as upcoming errors during machining. Comparing the data-represented tasks with the mined error patterns, their similarities or differences are calculated. Based on the calculated similarities, the fault probabilities of the scheduled tasks or the current machining tasks can be obtained, and they provide a reference of decision making for scheduling and rescheduling the tasks. By rescheduling high-risk tasks carefully, the potential errors can be avoided. In this paper, the architecture of the approach consisting of three steps in three levels is proposed. Furthermore, big data are considered in three levels, i.e. local data, local network data and cloud data. In order to implement this idea, several key techniques are illustrated in detail, e.g. data attribute, data cleansing, data integration of databases in different levels, and big data analytic algorithms. Finally, a simplified case study is described to show the prediction process of the proposed method.