• 로봇학회논문지
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• 제19권2호
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• pp.139-148
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• 2024

In this paper, we propose a two-degree-of-freedom snake robot head system and an I-PID (Intelligent Proportional-Integral-Derivative)-based controller utilizing RBF (Radial Basis Function) neural network and adaptive robust terms as a control strategy to reduce rotation occurring in the snake robot head. This study proposes a two-degree-of-freedom snake robot head system to avoid complex snake robot dynamics. This system has a control system independent of the snake robot. Subsequently, it utilizes an I-PID controller to implement a control system that can effectively manage rotation at the snake robot head, the robot's nonlinearity, and disturbances. To compensate for the time delay estimation errors occurring in the I-PID control system, an RBF neural network is integrated. Additionally, an adaptive robust term is designed and integrated into the control system to enhance robustness and generate control inputs responsive to signal changes. The proposed controller satisfies stability according to Lyapunov's theory. The proposed control strategy was tested using a 9-degreeof-freedom snake robot. It demonstrates the capability to reduce rotation in Lateral undulation, Rectilinear, and Sidewinding locomotion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.896-915
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• 2023

The network function virtualization (NFV) uses virtualization technology to separate software from hardware. One of the most important challenges of NFV is the resource management of virtual network functions (VNFs). According to the dynamic nature of NFV, the resource allocation of VNFs must be changed to adapt to the variations of incoming network traffic. However, the significant delay may be happened because of the reallocation of resources. In order to balance the performance between delay and quality of service, this paper firstly made a compromise between VNF migration and energy consumption. Then, the long short-term memory (LSTM) was utilized to forecast network traffic. Also, the asymmetric loss function for LSTM (LO-LSTM) was proposed to increase the predicted value to a certain extent. Finally, an experiment was conducted to evaluate the performance of LO-LSTM. The results demonstrated that the proposed LO-LSTM can not only reduce migration times, but also make the energy consumption increment within an acceptable range.

• 한국항행학회논문지
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• 제25권5호
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• pp.409-414
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• 2021

미세먼지에 대한 심각성이 사회적으로 대두됨에 따라 대중들은 미세먼지 예보에 대한 정보의 높은 신뢰성을 요구하고 있다. 이에 따라 다양한 신경망 알고리즘을 이용하여 미세먼지 예측을 위한 연구가 활발히 진행되고 있다. 본 논문에서는 미세먼지 예측을 위해 다양한 알고리즘으로 연구되고 있는 신경망 알고리즘들 중 대표적인 알고리즘들의 예측 성능 비교를 진행하였다. 신경망 알고리즘 중 DNN(deep neural network), RNN(recurrent neural network), LSTM(long short-term memory)을 이용하였으며, 하이퍼 파라미터 탐색을 이용하여 최적의 예측 모델을 설계하였다. 각 모델의 예측 성능 비교 분석 결과, 실제 값과 예측 값의 변화 추이는 전반적으로 좋은 성능을 보였다. RMSE와 정확도를 기준으로 한 분석에서는 DNN 예측 모델이 다른 예측 모델에 비해 예측 오차에 대한 안정성을 갖는 것을 확인하였다.

• 한국항공운항학회지
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• 제25권4호
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• pp.178-186
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• 2017

Air travel has become an essential part of the global society and its sustainable development is expected. Airlines profit structure and network operation will be influenced by internalization of external costs like emission charge. This additional cost of the airlines will be directly pose air ticket fare increase and demand of air passenger will be decreased. EU-ETS is a part of environmental binding to airlines fly to EU territory airports. This study analyzes the impact of emission charges by application of EU-ETS on airlines network change. For long-term forecast, a reliable estimation of the future price of carbon dioxide (CO2) will be used.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.203-215
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• 2006

The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.

• Smart Structures and Systems
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• 제10권3호
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• pp.271-298
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• 2012

Researchers have made significant progress in recent years towards realizing effective structural health monitoring (SHM) utilizing wireless smart sensor networks (WSSNs). These efforts have focused on improving the performance and robustness of such networks to achieve high quality data acquisition and distributed, in-network processing. One of the primary challenges still facing the use of smart sensors for long-term monitoring deployments is their limited power resources. Periodically accessing the sensor nodes to change batteries is not feasible or economical in many deployment cases. While energy harvesting techniques show promise for prolonging unattended network life, low power design and operation are still critically important. This research presents the WiSeMote: a new, fully integrated ultra-low power wireless smart sensor node and a flexible base station, both designed for long-term SHM deployments. The power consumption of the sensor nodes and base station has been minimized through careful hardware selection and the implementation of power-aware network software, without sacrificing flexibility and functionality.

• 전기학회논문지
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• 제66권7호
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• pp.1001-1006
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• 2017

In this study the NARX was proposed as a novel approach to forecast electric load more accurately. The NARX model is a recurrent dynamic network. ISO-NewEngland dataset was employed to evaluate and validate the proposed approach. Obtained results were compared with NAR network and some other popular statistical methods. This study showed that the proposed approach can be applied to forecast electric load and NARX has high potential to be utilized in modeling dynamic systems effectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.447-449
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• 1996

This paper describes an algorithm for short term load forecasting using the Kohonen neural network. Single layer Kohonen neural network presents a lot of advantageous features for practical application. It takes less training time compared to other networks such as BP network, and moreover, its self organized feature can amend the distorted data. The originality of proposed approach is to use a Kohonen map toclassify data representing load patterns and to use directly the information stored in the weight vectors of the Kohonen map to pridict the load. Proposed method was tested with KEPCO hourly record(1993-1995) show better forecasting results compared with conventional exponential smoothing method.

• Journal of Positioning, Navigation, and Timing
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• 제13권2호
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• pp.137-147
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• 2024

This paper presents a novel Long Short-Term Memory (LSTM) network architecture for the integration of an Inertial Measurement Unit (IMU) and Global Navigation Satellite Systems (GNSS). The proposed algorithm consists of two independent LSTM networks and the LSTM networks are trained to predict attitudes and velocities from the sequence of IMU measurements and mechanization solutions. In this paper, three GNSS receivers are used to provide Real Time Kinematic (RTK) GNSS attitude and position information of a vehicle, and the information is used as a target output while training the network. The performance of the proposed method was evaluated with both experimental and simulation data using a lowcost IMU and three RTK-GNSS receivers. The test results showed that the proposed LSTM network could improve positioning accuracy by more than 90% compared to the position solutions obtained using a conventional Kalman filter based IMU/GNSS integration for more than 30 seconds of GNSS outages.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.