• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.165-172
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• 2023

Heterogeneous computing is a technology that utilizes different types of processors to perform parallel processing. It maximizes task processing and energy efficiency by leveraging various computing resources such as CPUs, GPUs, and FPGAs. On the other hand, edge computing has developed with IoT and 5G technologies. It is a distributed computing that utilizes computing resources close to clients, thereby offloading the central server. It has evolved to intelligent edge computing combined with artificial intelligence. Intelligent edge computing enables total data processing, such as context awareness, prediction, control, and simple processing for the data collected on the edge. If heterogeneous computing can be successfully applied in the edge, it is expected to maximize job processing efficiency while minimizing dependence on the central server. In this paper, experiments were conducted to verify the feasibility of various parallel programming models on high-end and low-end edge devices by using benchmark applications. We analyzed the performance of five parallel programming models on the Raspberry Pi 4 and Jetson Orin Nano as low-end and high-end devices, respectively. In the experiment, OpenACC showed the best performance on the low-end edge device and OpenSYCL on the high-end device due to the stability and optimization of system libraries.

• ETRI Journal
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• v.44 no.5
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• Bulletin of the Korean Chemical Society
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• v.35 no.1
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• pp.253-256
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• 2014

Functionalization of zigzag graphene nanoribbon (ZGNR) segment containing 120 C atoms with pyridine (3NV-ZGNR) defects was investigated on the basis of density-functional theory (DFT) calculations, results show that edge-modified ZGNRs by Sc can adsorb multiple hydrogen molecules in a quasi-molecular fashion, thereby can be a potential candidate for hydrogen storage. The stability of Sc functionalization is dictated by a strong binding energy, suggesting a reduction of clustering of metal atoms over the metal-decorated ZGNR.

• Journal of the Semiconductor & Display Technology
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• v.9 no.2
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• pp.123-126
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• 2010

An implementation of modified stereo matching using efficient belief propagation (BP) algorithm is presented in this paper. We do recommend the use of the simple sobel, prewitt edge operator. The application of B band sobel edge operator over image demonstrates result with somewhat noisy (distinct border). When we adopt the only MRF + BP algorithm, however, borders cannot be distinguished due to that the message functions in the BP algorithm is just the mechanism which passes energy data to the only large gap of each Message functions In order to address the abovementioned disadvantageous phenomenon, we use the sobel edge operator + MRF + BP algorithm to distinguish the border that is located between the similar message data. Using edge information, the result shows that our proposed process diminishes the propagation of wrong probabilistic information. The enhanced result is due to that our proposed method effectively reduced errors incurred by ambiguous scene properties.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.123-131
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• 2018

This study compared PENELOPE with measured values from low energy peak to high energy peak to reduce peak to compton ratio and continuum background spectrum using $^{60}Co$, $^{137}Cs$ and mixed volume source. In addition, the change in backscattering and compton edge efficiency was compared with that of PENELOPE through changes in the vicinity of low energy. The results from the mixed volume source are applied to the soil samples to determine how much the minimum detection limits of the soil samples are reduced in the suppression and unsuppressed mode. The compton suppression of the low energy region of $^{60}CO$ (1,173 keV) was considerable, and the Compton edge RF for the $^{137}Cs$ (661 keV) peak was 2.8. In particular, the $^{60}Co$ source emits coincidence gamma rays of 1,173.2 keV and 1,332.5 keV, so compton inhibition was reduced by approximately 21%. RF of compton edges of 1,173 keV and 1,332 keV emitted from a $^{60}Co$ source was 3.2 and 3.4, and the peak to compton edge ratio was improved to 8: 1. And Compared with Penelope, the uncertainty was well within 2%. In compton unsuppressed mode, MDA values of 661 keV, 1,173 keV and 1,332 keV were 0.535, 0.173 and 0.136 Bq/kg, respectively, but decreased in compton suppressed mode to 0.121, 0.00826 and 0.00728 Bq/kg. Thus, Compton suppressed could reduce the background radioactivity and the radioactivity contained in the detector itself.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.269-274
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• 2008

In this paper, we propose a new stereo correspondence method for generating excellent external energy for snake-based object segmentation methods in stereo images. Our method first generates an edge-based disparity map by performing stereo correspondence between multi-level edge maps of the stereo image pair. Only edges of similar strength are considered for matching. To filter the disparity map for edges of the object of interest, the method estimates the object's disparity value by matching the pattern of edges of the region of interest in the left image against candidate patterns in the right image. The filtered edge map is then used to generate external energy for the snake. The proposed method has been tested on two snake models and results show a noticeable enhancement on performance of the snake when compared with other methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.75-81
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• 2014

In a distribution of the digital image, there is a serious problem that is distributed an illegal forgery image by pirates. For the problem solution, this paper proposes an image forensic decision algorithm using an edge energy information of forgery image. The algorithm uses SA (Streaking Artifacts) and SPAM (Subtractive Pixel Adjacency Matrix) to extract the edge energy informations of original image according to JPEG compression rate(QF=90, 70, 50 and 30) and the query image. And then it decides the forge whether or not by comparing the edge informations between the original and query image each other. According to each threshold in TCJCR (Threshold by Combination of JPEG Compression Ratios), the matching of the edge informations of original and query image is excused. Through the matching experiments, TP (True Positive) and FN (False Negative) is 87.2% and 13.8% respectively. Thus, the minimum average decision error is 0.1349. Also, it is confirmed that the performed class evaluation of the proposed algorithm is 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic) curve is 0.9388 by sensitivity and 1-specificity.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.803-809
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• 2023

This paper analyzes and evaluates to optimally build a data collection system essential for factory energy management systems on an edge-based lightweight platform. A "Sensor/OPC-UA simulator" was developed based on sensors in an actual food factory and used to evaluate the storage engine of edge devices. The performance of storage engines in edge devices was evaluated to suggest the optimal storage engine. The experimental results show that when using the RocksDB storage engine, it has less than half the memory and database size compared to using InnoDB, and has a 3.01 times faster processing time. This study enables the selection of advantageous storage engines for managing time-series data on devices with limited resources and contributes to further research in this field through the sensor/OPC simulator.