• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10B
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• pp.947-956
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• 2003

The current IP Routing Protocolspacket networks also need to provide the network QoS based of DiffServ, RSVP, MPLStraffic model which is standardized as IETF reference model for NGN. The first topic of this paper is to propose Traffic-Balanced Routing Protocol(TBRP) to process existing best effort traffic. TBRP will process low priority interactive data and background data which is not sensitive to dealy. Secondly Hierarchical Traffic-Traffic-Scheduling Routing Protocol(HTSRP) is also proposed. HTSRP is the hierarchical routing algorithm for backbone and access networkin case of fixed-wireless convergence network. Finally, HTSRP_Q is proposed to meet the QoS requirement when user want interactive or streaming packet service. This protocol will maximize the usage of resources of access layer based on the QoS parameters and process delay-sensitive traffic. Service classes are categorized into 5 types by the user request, such as conversational, streaming, high priority interactive, low priority interactive, and background class. It could be processed efficiently by the routing protocolstraffic model proposed in this paper. The proposed routing protocolstraffic model provides the increase of efficiency and stability of the next generation network thanks to the routing according to the characteristic of the specialized service categories.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.25-34
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• 2013

In this paper, we propose an activity-performer bipartite matrix generation algorithm for analyzing workflow-supported human-resource affiliations in a workflow model. The workflow-supported human-resource means that all performers of the organization managed by a workflow management system have to be affiliated with a certain set of activities in enacting the corresponding workflow model. We define an activity-performer affiliation network model that is a special type of social networks representing affiliation relationships between a group of performers and a group of activities in workflow models. The algorithm proposed in this paper generates a bipartite matrix from the activity-performer affiliation network model(APANM). Eventually, the generated activity-performer bipartite matrix can be used to analyze social network properties such as, centrality, density, and correlation, and to enable the organization to obtain the workflow-supported human-resource affiliations knowledge.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.143-151
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• 2008

This paper presents the operation of Fuel Cell Distributed Generation(FCDG) systems in distribution systems. Hence, modeling, controller design, and simulation study of a Solid Oxide Fuel Cell(SOFC) distributed generation(DG) system are investigated. The physical model of the fuel cell stack and dynamic models of power conditioning units are described. Then, suitable control architecture based on fuzzy logic and the neural network for the overall system is presented in order to activate power control and power quality improvement. A MATLAB/Simulink simulation model is developed for the SOFC DG system by combining the individual component models and the controllers designed for the power conditioning units. Simulation results are given to show the overall system performance including active power control and voltage regulation capability of the distribution system.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.789-796
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• 2012

Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.323-324
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• 2006

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.

• Journal of Communications and Networks
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• v.10 no.3
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• pp.316-330
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• 2008

This paper proposes a novel method for transporting various types of user traffic effectively over the next generation network called integrated services digital network 3 (ISDN3) (or quantum network) using quantum packets. Basically, a quantum packet comprises one or more 53-byte quanta as generated by a "quantumization" process. While connection-oriented traffic is supported by fixed-size quantum packets each with one quantum to emulate circuit switching, connectionless traffic (e.g., IP packets and active packets) is carried by variable-size quantum packets with multiple quanta to support store-and-forward switching/routing. Our aim is to provide frame-like or datagram-like services while enabling cell-based multiplexing. The quantum packet method also establishes a flexible and extensible framework that caters for future packetization needs while maintaining backward compatibility with ATM. In this paper, we discuss the design of the quantum packet method, including its format, the "quantumization" process, and support for different types of user traffic. We also present an analytical model to evaluate the consumption of network resources (or network costs) when quantum packets are employed to transfer loss-sensitive data using three different approaches: cut-through, store-and-forward and ideal. Close form mathematical expressions are obtained for some situations. In particular, in terms of network cost, we discover two interesting equivalence phenomena for the cut-through and store-and-forward approaches under certain conditions and assumptions. Furthermore, analytical and simulation results are presented to study the system behavior. Our analysis provides valuable insights into the. design of the ISDN3/quantum network.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.461-470
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• 2014

This paper presents a probabilistic power flow (PPF) analysis method for distribution network incorporating the randomness and correlation of photovoltaic (PV) generation. Based on the multivariate kernel density estimation theory, the probabilistic model of PV generation is proposed without any assumption of theoretical parametric distribution, which can accurately capture not only the randomness but also the correlation of PV resources at adjacent locations. The PPF method is developed by combining the proposed PV model and Monte Carlo technique to evaluate the influence of the randomness and correlation of PV generation on the performance of distribution networks. The historical power output data of three neighboring PV generators in Oregon, USA, and 34-bus/69-bus radial distribution networks are used to demonstrate the correctness, effectiveness, and application of the proposed PV model and PPF method.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.217-223
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• 2023

In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.