• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.75-78
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• 2002

SFFS(Solid Freeform Fabrication System) is commercializing to rapid prototyping concept in world-wide some corporations including the States, have much technological problems yet and need new mode for agile solid freeform fabrication as well as prototyping. In this paper, we design an automatic control algorithm that the cutting path of laser beam, on the SFFS, is controlled with constant speed. The designed algorithm for constant-speed path control is implemented and experimented in the $CAFL^{VM}$ (Computer Aided Fabrication of Lamination for Various Material) system, the new SFFS which is developed in this paper. Its process is an automated fabrication method in which a 3D object is constructed from STL(SToreoLithography) 2D data, derived from CAD 3D image, by sequentially laminating the part cross-sections. The constant-speed path control is started from the STL data. After STL file is modified in data format to be available for control. The fabrication of the 2D part is, with constant speed, conducted from the 23 position data by laser beam. we confirm its high-performance through experiment results from the application into $CAFL^{VM}$ system.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.773-782
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• 2014

The general path model (GPM) is one approach for performing degradation-based, or Type III, prognostics. The GPM fits a parametric function to the collected observations of a prognostic parameter and extrapolates the fit to a failure threshold. This approach has been successfully applied to a variety of systems when a sufficient number of prognostic parameter observations are available. However, the parametric fit can suffer significantly when few data are available or the data are very noisy. In these instances, it is beneficial to include additional information to influence the fit to conform to a prior belief about the evolution of system degradation. Bayesian statistical approaches have been proposed to include prior information in the form of distributions of expected model parameters. This requires a number of run-to-failure cases with tracked prognostic parameters; these data may not be readily available for many systems. Reliability information and stressor-based (Type I and Type II, respectively) prognostic estimates can provide the necessary prior belief for the GPM. This article presents the Bayesian updating framework to include prior information in the GPM and compares the efficacy of including different information sources on two data sets.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.237-246
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• 2022

Among the technologies of the 4th industrial revolution, drones that have grown rapidly and are being used in various industries can be operated by the pilot directly or can be operated automatically through programming. In order to be controlled by a pilot or to operate automatically, it is essential to predict and analyze the optimal path for the drone to move without obstacles. In this paper, after securing and analyzing the pilot training dataset through the unmanned aerial vehicle piloting training platform designed through prior research, the profile of the dataset that should be preceded to search and derive the optimal route of the unmanned aerial vehicle was designed. The drone pilot training data includes the speed, movement distance, and angle of the drone, and the data set is visualized to unify the properties showing the same pattern into one and preprocess the properties showing the outliers. It is expected that the proposed big data-based profile can be used to predict and analyze the optimal movement path of an unmanned aerial vehicle.

• Advances in aircraft and spacecraft science
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• v.11 no.1
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• pp.33-53
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• 2024

This paper presents a comprehensive investigation into the optimization of Flight Management Systems (FMS) with a particular emphasis on data processing efficiency by conducting a comparative study with conventional methods to edge-computing technology. The objective of this research is twofold. Firstly, it evaluates the performance of FMS navigation systems using conventional and edge computing methodologies. Secondly, it aims to extend the boundaries of knowledge in edge-computing technology by conducting a rigorous analysis of terrain data and its implications on flight path optimization along with communication with ground stations. The study employs a combination of simulation-based experimentation and algorithmic computations. Through strategic intervals along the flight path, critical parameters such as distance, altitude profiles, and flight path angles are dynamically assessed. Additionally, edge computing techniques enhance data processing speeds, ensuring adaptability to various scenarios. This paper challenges existing paradigms in flight management and opens avenues for further research in integrating edge computing within aviation technology. The findings presented herein carry significant implications for the aviation industry, ranging from improved operational efficiency to heightened safety measures.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.10-17
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• 2010

Sensor nodes used in Geosensor network are resource limited and power constrained. So it is necessary to research on routing protocols to gather data by using energy efficiently. Wireless sensor networks collect data gathered from sensor nodes by transfering it to the sink using multihop. However, it has two problems. First, the existing works require unnecessary data transmission for choosing a proper parent node to transfer data. Secondly, they have a large number of data transmission because each sensor node has a different path. To solves the problems, we, in this paper, propose a designated path based data aggregation scheme for efficient energy management in WSNs. The proposed scheme can reduce unnecessary data transmission by pre-determining a set of paths and can enable all the sensor nodes to participate in gathering data by running them in round-robin fashion. We show from performance analysis that the proposed scheme is more energy efficient than the existing directed diffusion(DD) and the hierarchical data aggregation(HDA).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.587-595
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• 2015

A by-pass path in wireless sensor networks is the alternative path which be able to forward data when a routing path is being broken. One reason of depleting energy is occurred by the path. The method for solving prior to addressed the problem is proposed. However, this method may deplete radio resource. The best path has advantage that network lifetime of sensor nodes is prolonged; on the contrary, in order to maintain the best path it have to share their information between the entire nodes. In this paper, we propose the best path searching algorithm in the distributed three dimensional sensor networks. Through the neighboring informations sharing in the proposed method, the proposed algorithm can decide the best k-path as well as the extension of network lifetime.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.558-566
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• 2005

The XML is the new standard fir data representation and exchange on the Internet. In this paper, we describe a new approach for evaluating a path-oriented query against XML document. In our approach, we propose the Parallel Match Indexing Fabric to speed up evaluation of path-oriented query using path signature and design the parallel match algorithm to perform a match process between a path signature of input query and path signatures of elements stored in the database. To construct a structure of the parallel match indexing, we first make the binary tie for all path signatures on an XML document and then which trie is transformed to the Parallel Match Indexing Fabric. Also we use the Parallel Match Indexing Fabric and a parallel match algorithm for executing a search operation of a path-oriented query. In our proposed approach, Time complexity of the algorithm is proportional to the logarithm of the number of path signatures in the XML document.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.45-54
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• 2013

Korea Expressway Corporation has been utilizing vehicles equipped with dedicated short range communication (DSRC) based on-board equipment (OBE) for collecting path travel times. A path based method (PBM) estimates the path travel time using probe vehicles traveling whole links on the path, so it is not always possible to obtain sufficient samples for calculating path travel time in the DSRC system. Having this problem in utilizing DSRC for travel time information, this study attempted to estimate path travel time with the help of a link based method (LBM) and examined whether the LBM can be used for obtaining reliable path travel times. Some comparisons were made and identified that the MAPE difference between the LBM and the PBM estimates are less than 3%, signaling that LBM can be used as a proxy for PBM in case of sparse sample conditions. Some limitations and a future research agenda have also been proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.50-55
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• 2021

Multi-path system is a system in which utilizes various networks simultaneously. It is expected that multi-path system can enhance communication speed, reliability, security of network. In this paper, we focus on path selection in multi-path system. To select optimal path, we propose deep reinforcement learning algorithm which is rewarded by the round-trip-time (RTT) of each networks. Unlike multi-armed bandit model, deep Q learning is applied to consider rapidly changing situations. Due to the delay of RTT data, we also suggest compensation algorithm of the delayed reward. Moreover, we implement testbed learning server to evaluate the performance of proposed algorithm. The learning server contains distributed database and tensorflow module to efficiently operate deep learning algorithm. By means of simulation, we showed that the proposed algorithm has better performance than lowest RTT about 20%.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.1-12
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• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.