• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9A
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• pp.874-881
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• 2010

Acoustic signal, which is a main carrier of underwater communication, attenuates along the traveled path heavily depending on the frequency as well as inter-node distance. In addition, since it has a long propagation delay, the conventional medium access control (MAC) schemes requiring complex signaling procedures and accordingly heavy overhead messages would not be appropriate in underwater communications. In this paper, we propose a code division multiple access (CDMA) scheme as a solution for MAC of underwater communication and evaluate the performance. A hierarchical data-gathering tree topology is considered and a staggered wake-up pattern is employed for the purpose of energy saving. As a performance measure, the data rate at each level of hierarchical topology is derived.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.524-526
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• 2022

Considering QoS only considering real-time multimedia service, it is possible to adjust the number of terminals and ensure them appropriately, but this study considers complex services considering real-time multimedia service and general data service. Since the amount of physical network resources is limited, the guarantee of the desired QoS can not be achieved unless the appropriate CAC is done. However, given the traffic profile and QoS spec of the entire network resource and the current service being provided, and the traffic profile and QoS spec of the newly requested service, it is quite difficult to determine exactly whether the new service request is acceptable from this. To do this, it is necessary to study in various directions from mathematical analysis to various simulations and statistical research based on data obtained from actual network operation.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.505-508
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• 2000

The success of target reconstruction in SAR(Synthetic Aperture Radar) imaging system is greatly dependent on the coherent detection. Primary causes of incoherent detection are uncompensated target or sensor motion, random turbulence in propagation media, wrong path in radar platform, and etc. And these appear as multiplicative phase error to the echoed signal, which consequently, causes fatal degradations such as fading or dislocation of target image. In this paper, we present iterative phase error estimation scheme which uses echoed data in all temporal frequencies. We started with analyzing wave equation for one point target and extend to overall echoed data from the target scene - The two wave equations governing the SAR signal at two temporal frequencies of the radar signal are combined to derive a method to reconstruct the complex phase error function. Eventually, this operation attains phase error correction algorithm from the total received SAR signal. We verify the success of the proposed algorithm by applying it to the simulated spotlight-mode SAR data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1398-1405
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• 2016

As wireless sensor networks become eligible as well as useful in several controled systems where surrounding environments are likely to be monitored, their stabilization become important research challenge. Generally, stabilization is mostly dependent on reliability of sensing value. To achieve such reliability in wireless sensor networks, the most of previous research work have tendency to deploy the same type of multiple sensor units on one node. However, these mechanisms lead to deployment problem by increasing cost of sensor node. Moreover, it may decrease reliability in the operation due to complex design. In order to solve this problem, in this paper, we propose a new validation scheme which is based on prediction and frequency value. In the proposed scheme, we take into exceptional cases account, for example, outbreak of fire. Finally, we demonstrate that the proposed scheme can detect abnormal sensing value more than 13 percent as compared to previous work through diverse simulation scenarios.

• Journal of IKEEE
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• v.24 no.3
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• pp.777-782
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• 2020

In this paper, we propose CNN-based HMI system using Doppler radar and voice sensor, and present hardware design and implementation results. To overcome the limitation of single sensor monitoring, the proposed HMI system combines data from two sensors to improve performance. The proposed system exhibits improved performance by 3.5% and 12% compared to a single radar and voice sensor-based classifier in noisy environment. In addition, hardware to accelerate the complex computational unit of CNN is implemented and verified on the FPGA test system. As a result of performance evaluation, the proposed HMI acceleration platform can be processed with 95% reduction in computation time compared to a single software-based design.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.75-80
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• 2020

Recently, the size of the IoT sensor has been decreased and the collecting direction of the IoT sensor for acquiring the data have been changed from 2D to 3D. It makes sensor structure complex. In the fabrication of the complex structure, 3D printing technology has more useful than traditional manufacturing technologies. Among 3D printing technologies, FDM (fused deposition modeling) is a candidate technology to fabricate a small IoT sensor because the price of the machine and the material is cheap. In the FDM process, a 3D shape is made by depositing the melted filament. Recently, the patent of FDM technology is expired and cheat machines are developed based on the open-source. In the FDM process, mechanical properties of a fabricated part is affected by a lots of factors such as the kind of material and process parameters. Among them, infill is affecting the mechanical properties and the production lead time as well. In this work, a new method to optimize the FDM process with the consideration of mechanical property and production lead time was proposed. To verify the method, the fabrications were performed with the different infill rates. The results of tensile tests were analyzed to verify the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.931-942
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• 2010

A home network provides users with a variety of information services. The kind and quality of the services can be substantially enhanced by utilizing a variety of data from sensors. However, home networks currently limit their potential by focusing on providing multimedia services rather than services utilizing sensor data. Outdoor electronics are frequently made in a form that emphasizes only certain limited functions in contrast to home appliances. Thus, sensors with one or two functions rather than many can be used in outdoor systems and their use will be more economical than using sensor nodes indoors with more complex home appliances. In this study, we chose to work with motion sensors as they have many potential uses, and we selected a parking lot control system with to use the motion sensors. This parking lot control system was implemented and applied as part of a home network. For this purpose, we defined and implemented a protocol to manage the network in a ubiquitous sensor network environment for the wireless home network in this study. Although a network management system in a Ubiquitous Sensor Network (USN) related to this study is being advanced for other projects, the protocol interface and message system have not yet been clearly defined for use in a general purpose network or in an extension into heterogeneous kinds of networks, communication support, etc. Therefore, USN network management should be conducted for management of faults, composition, power, and applications. To verify the performance of the protocol interface designed in this study, we designed and implemented the necessary units (sensor nodes, sensor gateway, and server) for each network section and, with them, proved the validity of this study.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1177-1179
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• 2003

Recently, the ground-based laser profiler is used for acquisition of 3D spatial information of a rchaeological objects. However, it is very difficult to measure complicated objects, because of a relatively low-resolution. On the other hand, texture mapping can be a solution to complement the low resolution, and to generate 3D model with higher fidelity. But, a huge cost is required for the construction of textured 3D model, because huge labor is demanded, and the work depends on editor's experiences and skills . Moreover, the accuracy of data would be lost during the editing works. In this research, using the laser profiler and a non-calibrated digital camera, a method is proposed for the automatic generation of 3D model by integrating these data. At first, region segmentation is applied to laser range data to extract geometric features of an object in the laser range data. Various information such as normal vectors of planes, distances from a sensor and a sun-direction are used in this processing. Next, an image segmentation is also applied to the digital camera images, which include the same object. Then, geometrical relations are determined by corresponding the features extracted in the laser range data and digital camera’ images. By projecting digital camera image onto the surface data reconstructed from laser range image, the 3D texture model was generated automatically.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.