• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.7-14
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• 2019

With the development of information and communication technology, we have been able to access and manage documents containing corporate information anytime and anywhere using smart devices. As the work environment changes to smart work, the scope of information distribution is expanded, and more efforts are needed to manage security. This paper proposes a file sharing system that enables users who have smart devices to manage and share files through mutual cooperation. Proposed file sharing system, the user can add a partner to share files with each other when uploading files kept by spliting the part of the file and the other uses an algorithm to store on the server. After converting the file to be uploaded to base64, it splits it into encrypted files among users, and then transmits it to the server when it wants to share. It is easy to manage and control files using dedicated application to view files and has high security. Using the system developed with proposed algorithm, it is possible to build a system with high efficiency even for SMEs(small and medium-sized enterprises) that can not pay much money for security.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.53-60
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• 2018

With the development of information and communication technology, we have been able to access and manage documents containing corporate information anytime and anywhere using smart devices. As the work environment changes to smart work, the scope of information distribution is expanded, and more efforts are needed to manage security. This paper proposes a file sharing system that enables users who have smart devices to manage and share files through mutual cooperation. Proposed file sharing system, the user can add a partner to share files with each other when uploading files kept by spliting the part of the file and the other uses an algorithm to store on the server. After converting the file to be uploaded to base64, it splits it into encrypted files among users, and then transmits it to the server when it wants to share. It is easy to manage and control files using dedicated application to view files and has high security. Using the system developed with proposed algorithm, it is possible to build a system with high efficiency even for SMEs(small and medium-sized enterprises) that can not pay much money for security.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.67-72
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• 2019

This paper evaluates the performance of CCA (Compact Constellation Algorithm) adaptive equalization algorithm by varying the step size for minimization of the distortion effect in the communication channel. The CCA combines the conventional DDA and RCA algorithm, it uses the constant modulus of the transmission signal and the considering the output of decision device by the power of compact slice weighting value in order to improving the initial convergence characteristics and the equalization noise by misadjustment in the steady state. In this process, the compact slice weight values were fixed, and the performance of CCA adaptive equalization algorithm was evaluated by the varing the three values of step size for adaptation. As a result of computer simulation, it shows that the smaller step size gives slow convergence speed, but gives excellent performance after at steady state. Especially in SER performance, the small step size gives more robustness that large values.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.165-171
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• 2019

The study of the computer architecture has shown that major improvements in price-to-energy performance stems from domain-specific hardware development. This paper analyzes the tensor processing unit (TPU) ASIC which can accelerate the reasoning of the artificial neural network (NN). The core device of the TPU is a MAC matrix multiplier capable of high-speed operation and software-managed on-chip memory. The execution model of the TPU can meet the reaction time requirements of the artificial neural network better than the existing CPU and the GPU execution models, with the small area and the low power consumption even though it has many MAC and large memory. Utilizing the TPU for the tensor flow benchmark framework, it can achieve higher performance and better power efficiency than the CPU or CPU. In this paper, we analyze TPU, simulate the Python modeled OpenTPU, and synthesize the matrix multiplication unit, which is the key hardware.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1373-1378
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• 2018

In order to solve the problems such as reduction of agriculture population, aging and declining of grain self sufficiency rate, agriculture ICT convergence technology utilizing IoT technology is actively being developed. Agricultural ICT technology only concentrates on facility houses, and there is no automated control system in the field of cultivation. In this paper, we propose an irrigation control system that automatically controls the solenoid valves and water pumps in a large area with Lora wireless communication. The proposed system does not require a separate power source by using a small solar panel, and it is very convenient to install and operate supporting wireless auto setup by plug-and-play method. Therefore, it is expected that it will contribute to the reduction of labor force, quality of agricultural products, and productivity improvement.

• Journal of The Korean Association of Information Education
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• v.22 no.6
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• pp.681-687
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• 2018

Many papers are published in KCI-listed journals for objectively validating research results and evaluations. This study collected articles published from 2008 to October, 2018 (10 years) of KAIE, which is a representative KCI registration institute which publishes various research related to elementary information education, and classified and analyzed by 10 subject areas. As a result, 34.2% of the papers published in the topic related to software education were found in the whole papers, and 33% of the papers published in this society showed the highest number of citations of papers relating to software education. Many of the remaining articles analyzed by subject area were overlapped with software education in the field of small scale medicine. In the future, a considerable number of researchers are expected to focus on software education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1428-1434
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• 2018

Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. In this paper, we propose a method for extracting cataract suspicious areas automatically with FCM(Fuzzy C_Means) algorithm to overcome the weakness of previously attempted ART2 based method. The proposed method applies the fuzzy stretching technique and the Max-Min based average binarization technique to the dog eye images photographed by simple devices such as mobile phones. After applying the FCM algorithm in quantization, we apply the brightness average binarization method in the quantized region. The two binarization images - Max-Min basis and brightness average binarization - are ANDed, and small noises are removed to extract the final cataract suspicious areas. In the experiment with 45 dog eye images with canine cataract, the proposed method shows better performance in correct extraction rate than the ART2 based method.

• Journal of IKEEE
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• v.22 no.4
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• pp.1079-1087
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• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1049-1059
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• 2019

We propose a learning-based image segmentation algorithm. Starting from super-pixels, our method learns the probability of merging two regions based on the ground truth made by humans. The learned information is used in determining whether the two regions should be merged or not in a segmentation stage. Unlike exiting learning-based algorithms, we use both local and object information. The local information represents features computed from super-pixels and the object information represent high level information available only in the learning process. The object information is considered as privileged information, and we can use a framework that utilize the privileged information such as SVM+. In experiments on the Berkeley Segmentation Dataset and Benchmark (BSDS 500) and PASCAL Visual Object Classes Challenge (VOC 2012) data set, out model exhibited the best performance with a relatively small training data set and also showed competitive results with a sufficiently large training data set.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.495-507
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• 2019

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.