• Wind and Structures
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• v.1 no.2
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• pp.183-199
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• 1998

This paper discusses some of the findings arising from long-term monitoring of the wind effects on a transmission tower located on an exposed site in South-West England. Site wind speeds have been measured, together with the foundation loads at the base of each of the four legs. The results show good correlation between the wind speeds and leg strains (loads) for a given wind direction, as expected, for wind speeds in excess of 10 m/s. Comparisons between the measured strains and those determined from the UK Code of Practice for lattice towers (BS8100), for the same wind speed and direction, show that the Code over-estimates most of the measured foundation loads by a moderate amount of about 14% at the higher wind speeds. This tends to confirm the validity of the Code for assessing design foundation loads. A finite element analysis model has been used to examine the dynamic behaviour of the tower and conductor system. This shows that, in the absence of the conductor, the tower alone has similar natural frequencies of approximately 2.2 Hz in the both the first (transversal) and second (longitudinal) modes, whilst for the complete system and conductor oscillations dominate, giving similar frequencies of approximately 0.1 Hz for both the first and second modes.

• Earthquakes and Structures
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• v.8 no.4
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.147-154
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• 2024

The continuous increase in urban population due to migration of mases from rural areas to big cities has set urban water supply under serious stress. Urban water resources face scarcity of available water quantity, which ultimately effects the water supply. It is high time to address this challenging problem by taking appropriate measures for the improvement of water utility services linked with better understanding of demand side management (DSM), which leads to an effective state of water supply governance. We propose a dynamic framework for preventive DSM that results in optimization of water resource management. This paper uses Agent Based Modeling (ABM) with Digital Twin (DT) to model water consumption behavior of a population and consequently forecast water demand. DT creates a digital clone of the system using physical model, sensors, and data analytics to integrate multi-physical quantities. By doing so, the proposed model replicates the physical settings to perform the remote monitoring and controlling jobs on the digital format, whilst offering support in decision making to the relevant authorities.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.23-47
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• 2003

Traditional studies for customer relationship management (CRM) generally focus on static CRM in a specific time frame. The static CRM and customer behavior knowledge derived could help marketers to redirect marketing resources fur profit gain at that given point in time. However, as time goes, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Customer-based analysis should observe the past purchase behavior of customers to understand their current and likely future purchase patterns in consumer markets, and to divide a market into distinct subsets of customers, any of which may conceivably be selected as a market target to be reached with a distinct marketing mix. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date. In this paper, we propose a dynamic CRM model utilizing data mining and a Monitoring Agent System (MAS) to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the Internet retailer. The proposed model includes an extensive analysis about a customer career path that observes behaviors of segment shifts of each customer: prediction of customer careers, identification of dominant career paths that most customers show and their managerial implications, and about the evolution of customer segments over time. furthermore, we show that dynamic CRM could be useful for solving several managerial problems which any retailers may face.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.53-65
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• 2021

C-ITS(Cooperative Intelligent Transport System) that pursues traffic safety and convenience uses various sensors to generate traffic information. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of CCTV in collecting video information has become more important due to advances in AI(Artificial Intelligence) technology. In this study, we propose to identify and track dynamic objects(vehicles, people, etc.) in CCTV images, and to analyze and provide information about them in various environments. To this end, we conducted identification and tracking of dynamic objects using the Yolov4 and Deepsort algorithms, establishment of real-time multi-user support servers based on Kafka, defining transformation matrices between images and spatial coordinate systems, and map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that CCTVs can serve as important sensors to provide relevant information by analyzing road conditions in real time in terms of road infrastructure beyond a simple monitoring role.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.1 s.53
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• pp.88-96
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• 2009

This study proposes a wireless measurement system that is a new safety management system by using an FBG sensor and a PDA. The sensor part has many advantages of implementing a wireless measurement system, and the study emploies an FBG-LVDT sensor, FBG-STRAIN sensor, FBG-TEMP sensor, and FBG-ACC sensor, using FBG sensors. Also, the study show a configuration of a signal process system for operating a wireless transmission system of FBG sensors applied to the signal process system, and engrafted the cutting edge information technology industry in order to display from a remote distance using a PDA. In order to verify the applicability of the developed FBG sensors and wireless measurement monitoring system to the field, their accuracy, and usability, the study has conducted a static and dynamic test to a bridge in the field. The study made an assessment of service for the vibration of the bridge by applying dynamic data measured by an FBG-LVDT sensor and FBG-ACC sensor to Meister's curve and prepared methods for assessing the vibration of the bridge by proposing a standard of vibration limitation given the service of vibration of the bridge. As a follow up for this study, it would be necessary to set up an overall model for the standard of service assessment established in this study.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.1-8
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• 2006

This paper describes the procedures developing the algorithm for analyzing signals acquired from the Bridge Weigh-in-Motion (BWIM) system installed in Seohae Bridge as a part of the bridge monitoring system. Through the analysis procedure, information about heavy traffics such as weight, speed, and number of axles are attempted to be extracted from time domain strain data of the BWIM system. One of numerous pattern recognition techniques, artificial neural network (ANN) is employed since it can effectively include dynamic effects, bridge-vehicle interaction, etc. A number of vehicle running experiments with sufficient load cases are executed to acquire training and/or test set of ANN. Extracted traffic information can be utilized for developing quantitative database of loading effect. Also, it can contribute to estimate fatigue lift or current health condition, and design truck can be revised based on the database reflecting recent trend of traffic.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.3
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• pp.23-30
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• 2010

Seismic qualification (SQ) is required prior to the installation of safety related electrical cabinets in nuclear power plants (NPPs). Modal identification of the electrical equipment is one of the most significant steps to perform SQ, and is an essential process to construct a realistic analytical model. In this study, shaking table tests were conducted to identify a variation of the dynamic characteristics of a seismic monitoring system cabinet installed in NPPs according to the excitation level. Modal identification of the cabinet has been performed by a frequency domain decomposition method. The results of this study show that the dynamic properties of the cabinet are nonlinearly varied according to the excitation level and the specimen behaves significantly in a nonlinear manner under safe shutdown earthquake motion in Korea. The main sources of the nonlinear behavior of the specimen have been judged by friction forces and geometrical nonlinearity rather than material nonlinearity. The nonlinear variation of the dynamic characteristics of the electrical cabinet might be accepted as an important fact that should be considered during the SQ of safety related equipment.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.7
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• pp.369-374
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• 2015

This study analyzed analyzes the energy performance of six houses in Daejeon completed which were built in 2011. Observed The observed houses, which were all designed and constructed inof the same size and structure, are were highly insulated with triple Low-E coating windows; the insulation level of the walls is was $0.13W/m^2K$ and that of the roof is was $0.10W/m^2K$. As electric houses, all of the energy supplied to the houses, including for cooking, is was supplied by electricity. A and 3~4 kWp of photovoltaic system and a 3~5 kW of ground source heat pump (GSHP) were installed in each house tofor providing provide space heating/and cooling and hot water are installed. We constructed a Web-based remote monitoring system in order to understand energy consumption and the dynamic behavior of the energy system. T, and the results of our metering data analysis of 2013 are as follows. First, the annual residential energy consumption is was 4,400 kWh (${\sigma}=1,209$) and GSHP energy consumption is was 5,182 kWh (${\sigma}=1,164$). Second, residential energy consumption ranked highest in average energy usage, with at 45% of the total, followed by heating with at 30%, hot water supply with at 17% and cooling with at 6%. Third, the average energy independence rate is was 51.8%, the GFA (Gross gross floor area) criteria average energy consumption unit is was $48.7kWh/m^2yr$ (${\sigma}=10.1$), and the net energy consumption unit (except the energy yield of the PV systems) is was $24.7kWh/m^2yr$ (${\sigma}=8.8$).