• Smart Structures and Systems
• /
• v.12 no.2
• /
• pp.209-233
• /
• 2013

With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for self-diagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

• Smart Structures and Systems
• /
• v.4 no.6
• /
• pp.779-794
• /
• 2008

Observer-based fault detection and isolation (FDI) filter design method is a model-based method. By carefully choosing the observer gain, the residual outputs can be projected onto different independent subspaces. Each subspace corresponds to the monitored structural element so that the projected residual will be nonzero when the associated structural element is damaged and zero when there is no damage. The key point of detection filter design is how to find an appropriate observer gain. This problem can be interpreted in a geometric framework and is found to be equivalent to the problem of finding a decentralized static output feedback gain. But, it is still a challenging task to find the decentralized controller by either analytical or numerical methods because its solution set is, generally, non-convex. In this paper, the concept of detection filter and iterative LMI technique for decentralized controller design are combined to develop an algorithm to compute the observer gain. It can be used to monitor structural element state: healthy or damaged. The simulation results show that the developed method can successfully identify structural damages.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.3
• /
• pp.1125-1131
• /
• 2014

Application of residential demand response (DR) programs are currently realized up to a limited extent due to customers' difficulty in manually responding to the time-differentiated prices. As a solution, this paper proposes an automatic home load management (HLM) framework to achieve the household minimum payment as well as meet the operational constraints to provide customer's comfort. The projected HLM method controls on/off statuses of responsive appliances and the charging/discharging periods of plug-in hybrid electric vehicle (PHEV) and battery storage at home. This paper also studies the impacts of different time-varying tariffs, i.e., time of use (TOU), real time pricing (RTP), and inclining block rate (IBR), on the home load management (HLM). The study is effectuated in a smart home with electrical appliances, a PHEV, and a storage system. The simulation results are presented to demonstrate the effectiveness of the proposed HLM program. Peak of household load demand along with the customer payment costs are reported as the consequence of applying different pricings models in HLM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.992-995
• /
• 2010

We propose an Power line communication, also known as Power line Digital Subscriber Line (PDSL), mains communication, power line telecom (PLT), or power line networking (PLN), or Broadband over Power Lines (BPL) are systems for carrying data on a conductor also used for electric power transmission. Power line communications can also be used to interconnect home computers, peripherals or other networked consumer peripherals. Specifications for power line home networking have been developed by a number of different companies within the framework of the HomePlug Powerline Alliance, the Universal Powerline Association and the HD-PLC Alliance.

• Journal of Internet Computing and Services
• /
• v.13 no.4
• /
• pp.33-43
• /
• 2012

Smart phones can be used in parking management to receive important notifications. An infrastructure based from such approach should consider message persistence which is currently supported through push notification systems. However, the broadcast method produces high overhead when sending notifications throughout the clients. This paper uses a filtering method to prevent unnecessary message overheads. The heuristic approach selects the mobile clients with corresponding preference values based on the notification tag values. The simulation result showed that the proposed filtering method was faster in re-establishing connections and had lower message overhead compared to conventional messaging frameworks.

• Journal of Korea Multimedia Society
• /
• v.17 no.10
• /
• pp.1189-1197
• /
• 2014

Recently, general purpose graphic processing units(GPUs) are being widely used in mobile embedded systems such as smart phone and tablet PCs. Because of architectural limitations of mobile GPGPUs, only a single program is allowed to occupy a GPU at a time in a non-preemptive way. As a result, it is difficult to meet performance requirements of applications such as frame rate or response time if applications running on a GPU are not scheduled properly. To tackle this difficulty, we propose to specify applications using synchronous data flow model of computation such that applications are formed with edges and nodes. Then nodes of applications are scheduled onto a GPU unlike conventional scheduling an application as a whole. This approach allows applications to share a GPU at a finer granularity, node (or task)-level, providing several benefits such as eliminating need for manually partitioning applications and better GPU utilization. Furthermore, any scheduling policy can be applied in response to the characteristics of applications.

• Smart Structures and Systems
• /
• v.16 no.1
• /
• pp.47-65
• /
• 2015

A novel finite element (FE) model updating method based on multi-resolution analysis (MRA) is proposed. The true stiffness of the FE model is considered as the superposition of two pieces of stiffness information of different resolutions: the pre-defined stiffness information and updating stiffness information. While the resolution of former is solely decided by the meshing density of the FE model, the resolution of latter is decided by the limited information obtained from the experiment. The latter resolution is considerably lower than the former. Second generation wavelet is adopted to describe the updating stiffness information in the framework of MRA. This updating stiffness in MRA is realized at low level of resolution, therefore, needs less number of updating parameters. The efficiency of the optimization process is thus enhanced. The proposed method is suitable for the identification of multiple irregular cracks and performs well in capturing the global features of the structural damage. After the global features are identified, a refinement process proposed in the paper can be carried out to improve the performance of the MRA of the updating information. The effectiveness of the method is verified by numerical simulations of a box girder and the experiment of a three-span continues pre-stressed concrete bridge. It is shown that the proposed method corresponds well to the global features of the structural damage and is stable against the perturbation of modal parameters and small variations of the damage.

• International journal of advanced smart convergence
• /
• v.10 no.3
• /
• pp.1-9
• /
• 2021

In the Internet of Thing (IoT) framework, massive machine-type communications (MMTC) have required large spectral efficiency. For this, non-orthogonal multiple access (NOMA) has emerged as an efficient solution. Recently, a non-successive interference cancellation (SIC) NOMA scheme has been implemented without loss. This lossless NOMA without SIC is achieved via correlated superposition coding (SC), in contrast to conventional independent SC. However, conventional minimum high-correlated SC for only 2-user NOMA schemes was investigated in the lossless 2-user non-SIC NOMA implementation. Thus, this paper investigates a 3-user low-correlated SC scheme, especially for an inflated achievable sum rate, with a design of 3-user low-correlated SC. First, we design the 3-user low-correlated SC scheme by taking the minimum sum rate between 3-user SIC NOMA and 3-user non-SIC NOMA, both with correlated SC. Then, simulations demonstrate that the low correlation in the direction of the first user's power allocation inflates the sum rate in the same direction, compared to that of conventional minimum high-correlated SC NOMA, and such inflation due to low correlation is also observed similarly, in the direction of the second user's power allocation. Moreover, we also show that the two low correlations of the first and second users inflates doubly in the both directions of the first and second users' power allocations. As a result, the proposed 3-user low-correlated SC could be considered as a promising scheme, with the inflated sum rate in the future fifth-generation (5G) NOMA networks.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.29 no.3
• /
• pp.100-110
• /
• 2021

Building Information Modeling(BIM) technology continues to develop and is now being applied to infrastructure facilities. As smart technology based on BIM is applied to the construction industry, the construction of the same data in a common environment is considered to be a very important step in various applications. This study was conducted on methods to establish an open BIM-based airport facility information common data environment. Therefore, this study develops the element technology to establish a common data environment for the integrated management of airport facility information, and aims to standardize data in the common data environment among various technologies and applications. In order to achieve the aim, in this study, the required technologies were derived by examining the common data environment and examining the civil engineering facility field and the case of integrated airport facility management. Through this, standards and development for information standard framework, information requirement level, BIM guidebook, and BIM library were developed in connection with national and higher standards. Through these standards, it is expected that the integrated management of various airport facilities will be efficiently carried out through a standard data platform in the future.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.132-139
• /
• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.