• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.131-136
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• 2013

Multi-core processors can improve parallelism of application processes and thus can enhance the system throughput. Researchers have recently revealed that the processor affinity is an important factor to determine network I/O performance due to architectural characteristics of multi-core processors; thus, many researchers are trying to suggest a scheme to decide an optimal processor affinity. Existing schemes to dynamically decide the processor affinity are able to transparently adapt for system changes, such as modifications of application and upgrades of hardware, but these have limited access to characteristics of application behavior and run-time information that can be collected heuristically. Thus, these can provide only sub-optimal processor affinity. In this paper, we define meaningful system variables for determining optimal processor affinity and suggest a tool to gather such information. We show that the implemented tool can overcome limitations of existing schemes and can improve network bandwidth.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.4
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• pp.56-62
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• 2013

The system analysis and design program of the liquid rocket engine has been developed for preliminary conceptual design process. The program analyzes the engine system and obtains optimal design variables by optimization methods such as genetic algorithm for the higher specific impulse and thrust to weight ratio using given input parameters and requirements. For the users' convenience, the GUI has been offered. The 3-dimensional model for the visualization of results has been interconnected with the CATIA program. The results are expected to be applied to the design process of the space launch vehicle for the analysis and selection of the propulsion system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1371-1378
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• 2018

Bluetooth is the key technology in the wireless connection of many Internet of Things (IoT) devices, especially focused on smartphones today. In addition, Bluetooth communication between the IoT device and the user is mainly performed via Bluetooth Low Energy (BLE), but as the Bluetooth technology gradually develops, the security vulnerability of the existing BLE is more prominent. Research on Bluetooth accessibility has been conducted steadily so far, but there is lack of research for data protection in Bluetooth communication. Therefore, in this paper, when sending and receiving data in BLE communication between IoT and users, we propose effective methods for communicating with each other through the Format Preserving Encryption Algorithm (FEA), not the plain text, and measures performance of FEA which is optimized in Arduino and PC.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.73-84
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• 2020

As the Internet began to become popular, there have been hacking and attacks on networks including systems, and as the techniques evolved day by day, it put risks and burdens on companies and society. In order to alleviate that risk and burden, it is necessary to detect hacking and attacks early and respond appropriately. Prior to that, it is necessary to increase the reliability in detecting network intrusion. This study was conducted on applying weight initialization and weight optimization to the KDD'99 dataset to improve the accuracy of detecting network intrusion. As for the weight initialization, it was found through experiments that the initialization method related to the weight learning structure, like Xavier and He method, affects the accuracy. In addition, the weight optimization was confirmed through the experiment of the network intrusion detection dataset that the Adam algorithm, which combines the advantages of the Momentum reflecting the previous change and RMSProp, which allows the current weight to be reflected in the learning rate, stands out in terms of accuracy.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.2
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• pp.153-160
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• 2010

A magnetocardiogram (MCG) is a recording of the biomagnetic signals generated by cardiac electrical activity. Biomagnetic instruments are based on superconducting quantum interference devices (SQUIDs). A liquid cryogenic Dewar flask was used to maintain the superconductors in a superconducting state at a very low temperature (4 K). In this study, the temperature distribution characteristics of the liquid helium in the Dewar flask was investigated. The Dewar flask used in this study has a 30 L liquid helium capacity with a hold time of 5 d. The Dewar flask has two thermal shields rated at 150 and 40 K. The temperatures measured at the end of the thermal shield and calculated from the computer model were compared. This study attempted to minimize the heat transfer rate of the cryogenic Dewar flask using an optimization method about the geometric variable to find the characteristics for the design geometric variables in terms of the stress distribution of the Dewar flask. For thermal and optimization analysis of the structure, the finite element method code ANSYS 10 was used. The computer model used for the cryogenic Dewar flask was useful to predict the temperature distribution for the area less affected by the thermal radiation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.127-135
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• 2013

In order to acquire images in low-light environments, it is usually necessary to adopt long exposure times or resort to flash lights. However, flashes often induce color distortion, cause the red-eye effect and can be disturbing to subjects. On the other hand, long-exposure shots are susceptible to subject-motion, as well as motion-blur due to camera shake when performed hand-held. A recently introduced technique to overcome the limitations of traditional low-light photography is that of multi-spectral flash. Multi-spectral flash images are a combination of UV/IR and visible spectrum information. The general idea is that of retrieving details from the UV/IR spectrum and color from the visible spectrum. However, multi-spectral flash images themselves are subject to color distortion and noise. This works presents a method to compute multi-spectral flash images so that noise can be reduced and color accuracy improved. The proposed approach is a previously seen optimization method, improved by the introduction of a weight map used to discriminate uniform regions from detail regions. The weight map is generated by applying canny edge operator and it is applied to the optimization process for discriminating the weights in uniform region and edge. Accordingly, the weight of color information is increased in the uniform region and the detail region of weight is decreased in detail region. Therefore, the proposed method can be enhancing color reproduction and removing artifacts. The performance of the proposed method has been objectively evaluated using long-exposure shots as reference.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.331-337
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• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.221-228
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• 2017

With the recent development of IoT technology, medical services using IoT technology are increasing in many medical institutions providing health care services. However, as the number of IoT sensors attached to the user body increases, the healthcare information transmitted to the server becomes complicated, thereby increasing the time required for analyzing the user's healthcare information in the server. In this paper, we propose a deep learning based health care information management method to collect and process healthcare information in a server for a large amount of healthcare information delivered through a user - attached IoT device. The proposed scheme constructs a subnet according to the attribute value by assigning an attribute value to the healthcare information transmitted to the server, and extracts the association information between the subnets as a seed and groups them into a hierarchical structure. The server extracts optimized information that can improve the observation speed and accuracy of user's treatment and prescription by using deep running of grouped healthcare information. As a result of the performance evaluation, the proposed method shows that the processing speed of the medical service operated in the healthcare service model is improved by 14.1% on average and the server overhead is 6.7% lower than the conventional technique. The accuracy of healthcare information extraction was 10.1% higher than the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.555-565
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• 2021

The goal of this study is to improve flow and noise performances of existing backward-curved blade centrifugal fan system used for circulating cold air in a refrigerator freezer by optimally designing airfoil shape. The unique characteristics of the system is to drive cold airflow with two volute tongues in combination with duct system in a back side of a refrigerator without scroll housing generally used in a typical centrifugal fan system. First, flow and noise performances of existing fan system were evaluated experimentally. A P-Q curve was obtained using a fan performance tester in the flow experiment, and noise spectrum was measured in an anechoic chamber in the noise experiment. Then, flow characteristics were numerically analyzed by solving the three-dimensional unsteady Navier-Stokes equations and noise analysis was performed by solving the Ffowcs Williams and Hawkins equation with input from the flow simulation results. The validity of numerical results was confirmed by comparing them with the measured ones. Based on the verified numerical method, blade inlet and outlet angles were optimized for maximum flow rate using the two-factor central composite design of the response surface method. Finally, the flow and noise performances of a prototype manufactured with the optimum design were experimentally evaluated, which showed the improvement in flow and noise performance.