• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.1
• /
• pp.60-65
• /
• 2004

Induction heating system is time varying system around curie point. So, it has many troubles which are system shut down and change the load impedance. In this paper has been designed the parallel resonant inverter which controlling the constant power and tracking the load resonant frequency with PLL is possible, in order to minimize switching losses and solve it's many troubles. The current full-bridge type parallel resonant inverter of an induction heating system was composed of IGBT in switching device. For regulating the output power of an induction heating system, the Fuzzy logic controller is used. The Fuzzy controller makes the control signal for a stable power regulating control and when reference is changed, it is superior to adaptability. It has been evaluated a stable behavior for a noise with switching and a load disturbance.

• Journal of Information Processing Systems
• /
• v.7 no.1
• /
• pp.53-62
• /
• 2011

With the steady growth of mobile technology and applications, demand for more storage in mobile devices has also increased. A lightweight block-level protocol, Internet Advanced Technology Attachment (iATA), has been developed to deliver a cost-effective storage network solution for mobile devices to obtain more storage. This paper seeks to contribute to designing and implementing Load Balancing (LB), Network Monitoring (NM) and Write Replication (WR) modules to improve the protocol's scalability and data availability. LB and NM modules are invoked to collect system resources states and current network status at each associate node (server machine). A dynamic weight factor is calculated based on the collected information and sent to a referral server. The referral server is responsible to analyze and allocate the most ideal node with the least weight to serve the client. With this approach, the client can avoid connecting to a heavily loaded node that may cause delays in subsequent in-band I/O operations. Write replication is applied to the remaining nodes through a WR module by utilizing the Unison file synchronization program. A client initially connected to node IP A for write operations will have no hindrances in executing the relevant read operations at node IP B in new connections. In the worst case scenario of a node crashing, data remain recoverable from other functioning nodes. We have conducted several benchmark tests and our results are evaluated and verified in a later section.

• Wind and Structures
• /
• v.21 no.5
• /
• pp.461-487
• /
• 2015

In recent years, the Graphics Processing Unit (GPU) has become a competitive computing technology in comparison with the standard Central Processing Unit (CPU) technology due to reduced unit cost, energy and computing time. This paper describes the derivation and implementation of GPU-based algorithms for the analysis of wind loading uncertainty on high-rise systems, in line with the research field of probability-based wind engineering. The study begins by presenting an application of the GPU technology to basic linear algebra problems to demonstrate advantages and limitations. Subsequently, Monte-Carlo integration and synthetic generation of wind turbulence are examined. Finally, the GPU architecture is used for the dynamic analysis of three high-rise structural systems under uncertain wind loads. In the first example the fragility analysis of a single degree-of-freedom structure is illustrated. Since fragility analysis employs sampling-based Monte Carlo simulation, it is feasible to distribute the evaluation of different random parameters among different GPU threads and to compute the results in parallel. In the second case the fragility analysis is carried out on a continuum structure, i.e., a tall building, in which double integration is required to evaluate the generalized turbulent wind load and the dynamic response in the frequency domain. The third example examines the computation of the generalized coupled wind load and response on a tall building in both along-wind and cross-wind directions. It is concluded that the GPU can perform computational tasks on average 10 times faster than the CPU.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.8
• /
• pp.89-96
• /
• 2009

In the future energy environment, a power information system will meet the real-time capability to process the emergency events, unexpected blackouts or over-load, and the high performance to provide the consumer service events such as remote meter reading. In addition to, it must have facility which is able to process a large information occurred on system effectively. In this paper, we developed a distributed power information system based on event with metadata processing technique which was both load balancing and decreased hot spot using XML that was efficient for information exchange. In order to experiment, we made a reduced future power system with controling power device using wireless communications and we could do experiments through it.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.4
• /
• pp.79-85
• /
• 2013

In SIP(Session Initiation Protocol) environment, the presence server should process SIP SUBSCRIBE request messages including multiple presence resources addresses from users, and also precess massive notification messages from the subscribed presence resources. The load of the presence server increases as number of users increase, and it limits system extendability. Therefore a new multiple presence servers architecture has been suggested in this research. In this architecture presence servers can be added dynamically and each server's load can be controlled effectively as number of users increase. Each presence server can monitor current load status of entire presence system by using presence event notification package which newly has been suggested in this paper. When a particular presence server's load increases over predefined limit, the presence service processing is distributed by selecting a server which has the smallest load, or by generating a new server dynamically. In this system the overall load of the entire system can be controlled optimally and extendability of the system can be increased. For this purpose a new presence event notification package and presence information data format have been suggested. The performance of the proposed system has been evaluated by experiments. They shows 44.3% increase in SUBSCRIBE message processing time, and 43.1% increase in Notification message processing time.

• The KIPS Transactions:PartD
• /
• v.15D no.1
• /
• pp.73-86
• /
• 2008

Because of its capability of automatic identification of objects, RFID(Radio Frequency Identification) technologies have extended their application areas to logistics, healthcare, and food management system. Load balancing is a basic technique for improving scalability of systems by moving loads of overloaded middlewares to under loaded ones. Adaptive load balancing has been known to be effective for distributed systems of a large load variance under unpredictable situations. There are needs for applying load balancing to RFID middlewares because they must efficiently treat vast numbers of RFID tags which are collected from multiple RFID readers. Because there can be a large amount of variance in loads of RFID middlewares which are difficult to predict, it is desirable to consider adaptive load balancing approach for RFID middlewares, which can dynamically choose a proper load balancing strategy depending on the current load. This paper proposes an adaptive load balancing approach for RFID middlewares and presents its design and implementation. First we decide a performance model by a experiment with a real RFID middleware. Then, a set of proper load balancing strategies for high/medium/low system loads is determined from a simulation of various load balancing strategies based on the performance model.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.6
• /
• pp.251-258
• /
• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• Transactions of Materials Processing
• /
• v.18 no.7
• /
• pp.513-518
• /
• 2009

Large load is required in forging of large-scale components which becomes a critical restriction in practice. In the present study, two methods of incremental forging were investigated for the purpose of reducing the load required for forging of large and thick plates. The forging was applied primarily to obtain fine grains by imposing large amount of plastic deformation to the plates. One was to use nine strokes with a flat die and the other was to use three strokes with a curved die. The die moves vertically in the former while it moves vertically as well as rolls horizontally in the latter. Deformation of the slab in each case was analyzed by rigid-plastic finite element method and as a result, variations of load and slab holding force, and distributions of effective strain and thickness were predicted.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.329-332
• /
• 1991

This paper presents an algorithm to reduce the time to solve Power Equations using a Neural Net. The Neural Net is trained with samples obtained through the conventional AC Load Flow. With these samples, the Neural Net is constructed and has the function of a linear interpolation network. Given arbitrary load level, this Neural Net generates voltage magnitudes and angles which are linear interpolation of real and reactive powers. Obtained voltage magnitudes and angles are substituted to Power Equations, Real and reactive powers are found. Thus, a new sample is generated. This new experience modifies weight matrix. Continuing to modify the weight matrix, the correct solution is achieved. comparing this method with AC Load flow, this method is faster. If we consider parallel processing, this method is far faster than conventional ones.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.244-245
• /
• 2011

In this paper, the algorithm for detecting load shedding based on Wavelet Singular Value Decomposition(WSVD) is proposed. WSVD is method of signal processing which combine Wavelet Transform(WT) and Singular Value Decomposition(SVD) to analyze transients in power system. 345kV Busan transmission system is modeled by EMTP-RV and simulations according to successive change of load capability are conducted. This paper analyzes characteristics of WSVD by using simulation results and proposes algorithm for detecting load shedding.