• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.750-751
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• 2012

스마트폰의 급속한 확산과 함께 스마트폰 애플리케이션 시장이 빠르게 성장하고 있다. 이러한 성장세에 따라 많은 애플리케이션 개발자들이 생겨났으며, 다양한 콘텐츠와 수많은 애플리케이션이 개발되어지고 있다. 여기서 우리는 모바일 기기들의 제한적인 요소를 간과해서는 안 된다. 제한적인 모바일기기에서 유저가 만족할 만할 애플리케이션을 개발하기 위해서는 효율적인 자원 활용과 함께 효율적인 프로그래밍을 해야 할 필요가 있다. 본 논문은 안드로이드 NDK 및 SDK를 기반으로 Native C와 Java를 이용해 애플리케이션을 설계하고, 각 애플리케이션간의 알고리즘 수행속도, 프로세서 점유율측면에서 성능측정 실험을 수행했다. 실험 결과를 통해 보다 우수한 성능의 안드로이드 애플리케이션 개발 방법에 관해 연구했다. 성능측정 항목으로는 JNI delay, Integer, Floating point, Memory access algorithm, String이며, 실험은 삼성 갤럭시 S1에서 수행하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.340-343
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• 2012

현재 상용화되고 전자칠판에 쓰이는 모든 소프트웨어는 2D 판서를 지원하는 소프트웨어였으나 최근 3D TV등의 지속적인 보급과 3D 전자칠판의 점차적인 보급으로 인하여 최근의 추세에 맞추어 3D 화면에 동작하는 3D 판서를 요구하고 있다. 업계에서는 점차 3D 판서가 대두됨에 따라 3D판서를 할 수 있는 새로운 기법이 등장해야 하는 시기이다. 본 논문은 3D 전자칠판의 하드웨어에 이용될 수 있는 알고리즘의 연구이다. 3D 전용 패널을 활용한 하드웨어에 최적화된 3D 판서 소프트웨어에 포함 될 방법론을 제시한다. 이를 위해 기존의 판서 소프트웨어의 방법론과 함께 기본적인 3D의 원리를 이용하여 구현된 알고리즘을 제안한다.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.677-687
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• 2021

The tendency to renewables is one of the consequences of changing attitudes towards energy issues. As a result, solar energy, which is the leader among renewable energies based on availability and potential, plays a crucial role in full filing global needs. Significant problems with the solar thermal power plants (STPP) are the operation time, which is limited by daylight and is approximately half of the power plants with fossil fuels, and the capital cost. Exergy analysis survey of STPP hybrid with PCM storage carried out using Engineering Equation Solver (EES) program with genetic algorithm (GA) for three different scenarios, based on eight decision variables, which led us to decrease final product cost (electricity) in optimized scenario up to 30% compare to base case scenario from 28.99 $/kWh to 20.27 $/kWh for the case study. Also, in the optimal third scenario of this plant, the inner carbon dioxide gas cycle produces 1200 kW power with a thermal efficiency of 59% and also 1000 m3/h water with an exergy efficiency of 23.4% and 79.70 kg/h with an overall exergy efficiency of 34% is produced in the tetrageneration plant.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2341-2347
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• 2021

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• Progress in Superconductivity and Cryogenics
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• v.23 no.4
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• pp.56-60
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• 2021

The microwave Radar used for special purposes in the past is being applied in various areas due to the technological advancement and cost reduction, and is particularly applied to autonomous driving in the automobile field. The FMCW (Frequency Modulated Continuous Wave) Radar can acquire level information of liquid in vessel based on the beat frequency obtained by continuously transmitting and receiving signals by modulating the frequency over time. However, for cryogenic fluids with small impedance differences between liquid medium and gas medium, such as liquid nitrogen and liquid hydrogen, it is difficult to apply a typical Radar-based level meter. In this study, we develop an 80 GHz FMCW Radar for level measurement of cryogenic fluids with small impedance differences between media and analyze its characteristics. Here, because of the low intrinsic impedance difference, most of the transmitted signal passes through the liquid nitrogen interface and is reflected at the bottom of the vessel. To solve this problem, a radar measurement algorithm was designed to detect multiple targets and separate the distance signal to the bottom of the vessel in order to estimate the precise position on the liquid nitrogen interface. Thereafter, performance verification experiments were performed according to the liquid nitrogen level using the developed radar level meter.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1795-1811
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• 2019

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.251-256
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• 2019

This paper presents an algorithm for recognizing and tracking a vehicle at a distance using a monocular camera installed at the center of the windshield of a vehicle to operate an autonomous vehicle in a racing. The vehicle is detected using the Haar feature, and the size and position of the vehicle are determined by detecting the shadows at the bottom of the vehicle. The region around the recognized vehicle is determined as ROI (Region Of Interest) and the vehicle shadow within the ROI is found and tracked in the next frame. Then the position, relative speed and direction of the vehicle are predicted. Experimental results show that the vehicle is recognized with a recognition rate of over 90% at a distance of more than 100 meters.