• Journal of Digital Contents Society
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• v.11 no.1
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• pp.91-97
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• 2010

Flash memory has been used by many digital devices for data storage, exploiting the advantages of non-volatility, low power, stability, and so on, with the help of high integrity, large capacity, and low price. As the fast growing popularity of flash memory, the density of it increases so significantly that its entire address mapping table becomes too big to be stored in SRAM. This paper proposes the associated page address cache with an efficient table management scheme for hybrid flash translation layer mapping. For this purpose, all tables are integrated into a map block containing entire physical page tables. Simulation results show that the proposed scheme can save the extra memory areas and decrease the searching time with less 2.5% of miss ratio on PC workload and can decrease the write overhead by performing write operation 33% out of total writes requested.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.239-250
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• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.138-142
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• 2014

This research presents an A* based algorithm which can be applied to Unmanned Ground Vehicle self-navigation in order to make the driving path smoother. Based on the grid map, A* algorithm generated the path by using straight lines. However, in this situation, the knee points, which are the connection points when vehicle changed orientation, are created. These points make Unmanned Ground Vehicle continuous navigation unsuitable. Therefore, in this paper, B-spline curve function is applied to transform the path transfer into curve type. And because the location of the control point has influenced the B-spline curve, the optimal control selection algorithm is proposed. Also, the optimal path tracking speed can be calculated through the curvature radius of the B-spline curve. Finally, based on this algorithm, a path created program is applied to the path results of the A* algorithm and this B-spline curve algorithm. After that, the final path results are compared through the simulation.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1457-1465
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• 2019

3D convolution is to stack multiple consecutive frames to form a cube, and then apply the 3D convolution kernel in the cube. In this structure, each feature map of the convolutional layer is connected to multiple adjacent sequential frames in the previous layer, thus capturing the motion information. However, due to the changes of pedestrian posture, motion and position, the convolution at the same place is inappropriate, and when the 3D convolution kernel is convoluted in the time domain, only time domain features of three consecutive frames can be extracted, which is not a good enough to get action information. This paper proposes an action recognition method based on feature fusion of 3D convolutional neural network. Based on the VGG16 network model, sending a pre-acquired optical flow image for learning, then get the time domain features, and then the feature of the time domain is extracted from the features extracted by the 3D convolutional neural network. Finally, the behavior classification is done by the SVM classifier.

• Journal of Distribution Science
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• v.17 no.7
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• pp.99-109
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• 2019

Purpose - The purpose of this development the business model of the healthcare sector in order to promote patents satisfaction towards medical sector services improvement for the medical business model innovation to possess a competitive advantage in the medical and pharmaceutical industry. Research design, data, and methodology - Safety standard protocol from existing multidisciplinary literature is a process of theorization, which uses grounded theory methodology rather than a description of the data and the targeted phenomenon by using Jabareen (2009). The first task is to map the spectrum of food safety literature regarding the phenomenon in safety management. This process includes developing the implementation factors and other sources such as existing business models and practices into the protocol design. Results - The study suggests the conceptual framework to improve the safety management for patients' environmental sustainability performance. Conclusion - The business model may support the beneficial aspect to healthcare government's policymakers, hospital employees, and medical specialist who can apply the practical perspective of its value regarding an educational protocol. Originality/value - This study contributes to and extends our understanding of environmental sustainability performance, identifying the rationale for safety standards performance in the healthcare industry with suggested hybrid safety standards market consumer interconnector.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.489-498
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• 2005

For better performance over a noisy channel, mobile wireless networks transmit packets with forward error correction (FEC) code to recover corrupt bits without retransmission. The static determination of the FEC code size, however, degrades their performance since the evaluation of the underlying channel state is hardly accurate and even widely varied. Our measurements over a wireless sensor network, for example, show that the average bit error rate (BER) per second or per minute continuously changes from 0 up to $10^{-3}$. Under this environment, wireless networks waste their bandwidth since they can't deterministically select the appropriate size of FEC code matching to the fluctuating channel BER. This paper proposes an adaptive FEC technique called adaptive FEC code control (AFECCC), which dynamically tunes the amount of FEC code per packet based on the arrival of acknowl­edgement packets without any specific information such as signal to noise ratio (SNR) or BER from receivers. Our simulation experiments indicate that AFECCC performs better than any static FEC algorithm and some conventional dynamic hybrid FEC/ARQ algorithms when wireless channels are modeled with two-state Markov chain, chaotic map, and traces collected from real sensor networks. Finally, AFECCC implemented in sensor motes achieves better performance than any static FEC algorithm.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.353-356
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• 2008

HEV(Hybrid Electrical Vehicle) is considered as one of the next generation vehicles. To develop the HEV, there must be a reliable simulator, by which the capacities of the power resources are tested, and the parameters of the HEV are optimized before developing the real model of the HEVs. This process can save the money for designing the HEV system and improve the system without experiments. Matlab Simulink is familiar to mechanical engineers and the program can simultaneously provide a system model and a controller in one program. Nowadays, the Simdriveline toolbox which is used for analysis a power-train system is applied to build a dynamic model for a HEV system. In this study, we make a HEV simulator with the Simdriveline toolbox and develop a controller. There are two simple strategies, applied to the controller. One strategy includes a power split ratio and a shift map which are created by user. Other strategy calculated an appropriate amount of resource's torque along specific results, and this is useful when users can't develop a fitting controller. The methodologies for configuring the simulator and its control system are presented in this paper.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.