• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.469-480
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• 2016

The present study aims to develop a real-time surface image velocimeter (SIV) using an Android smartphone. It can measure river surface velocity by using its built-in sensors and processors. At first the SIV system figures out the location of the site using the GPS of the phone. It also measures the angles (pitch and roll) of the device by using its orientation sensors to determine the coordinate transform from the real world coordinates to image coordinates. The only parameter to be entered is the height of the phone from the water surface. After setting, the camera of the phone takes a series of images. With the help of OpenCV, and open source computer vision library, we split the frames of the video and analyzed the image frames to get the water surface velocity field. The image processing algorithm, similar to the traditional STIV (Spatio-Temporal Image Velocimeter), was based on a correlation analysis of spatio-temporal images. The SIV system can measure instantaneous velocity field (1 second averaged velocity field) once every 11 seconds. Averaging this instantaneous velocity measurement for sufficient amount of time, we can get an average velocity field. A series of tests performed in an experimental flume showed that the measurement system developed was greatly effective and convenient. The measured results by the system showed a maximum error of 13.9 % and average error less than 10 %, when we compared with the measurements by a traditional propeller velocimeter.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.327-336
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• 2015

Mobile game servers usually execute frequent dynamic memory allocation for generating the buffers that deal with clients requests. It causes to deteriorate the performance of game servers since it increases system workload and memory fragmentation. In this paper, we propose fixed-sized memory pool management method. Memory pool for the proposed method has a sequential memory structure based on circular linked list data structure. It solves memory fragmentation problem and saves time for searching the memory blocks which are required for memory allocation and deallocation. We showed the efficiency of the proposed method by evaluating the performance of dynamic memory allocation, through the proposed method and the memory pool management method based on boost open source library.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.440-445
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• 2017

Due to the advance of e-commerce systems, the number of people using online shopping and products has significantly increased. Therefore, the need for an accurate recommendation system is becoming increasingly more important. Recurrent neural network is a deep-learning algorithm that utilizes sequential information in training. In this paper, an evaluation is performed on the application of recurrent neural networks to recommendation systems. We evaluated three recurrent algorithms (RNN, LSTM and GRU) and three optimal algorithms(Adagrad, RMSProp and Adam) which are commonly used. In the experiments, we used the TensorFlow open source library produced by Google and e-commerce session data from RecSys Challenge 2015. The results using the optimal hyperparameters found in this study are compared with those of RecSys Challenge 2015 participants.

• Journal of Microbiology and Biotechnology
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• v.15 no.6
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• pp.1189-1196
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• 2005

The cyclohexanol dehydrogenase (ChnA), produced by Rhodococcus sp. TK6, which is capable of growth on cyclohexanol as the sole carbon source, has been previously purified and characterized. However, the current study cloned the complete gene (chnA) for ChnA and its flanking regions using a combination of a polymerase chain reaction (PCR) based on the N-terminal amino acid sequence of the purified ChnA and plaque hybridization from a phage library of Rhodococcus sp. TK6. A sequence analysis of the 5,965-bp DNA fragment revealed five potential open reading frames (ORFs) designated as partial pte (phosphotriesterase), acs (acyl-CoA synthetase), scd (short chain dehydrogenase), stp (sugar transporter), and chnA (cyclohexanol dehydrogenase), respectively. The deduced amino acid sequence of the chnA gene exhibited a similarity of up to $53\%$ with members of the short-chain dehydrogenase/reductase (SDR) family. The chnA gene was expressed using the pET21 a(+) system in Escherichia coli. The activity of the expressed ChnA was then confirmed (13.6 U/mg of protein) and its properties investigated.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7847-7852
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• 2015

Purpose: Research carried out with gynecologic cancer patients using CAM was reviewed to provide a source for discussing which CAM method is used for which purpose, patients' perceptions on the effects/side effects occurred during/after using CAM and their sources of information regarding CAM. Materials and Methods: This literature review was carried out for the period between January 2000 and March 2015 using Scopus, Dynamed, Med-Line, Science Dırect, Ulakbim, Research Starters, Ebscohost, Cinahl Complete, Academic Onefile, Directory of Open Access Journals, BMJ Online Journals (2007-2009), Ovid, Oxford Journal, Proquest Hospital Collection, Springer-Kluwer Link, Taylor & Francis, Up To Date, Web Of Science (Citation Index), Wiley Cochrane-Evidence Base, Wiley Online Library, and Pub-Med search databases with "complementary and alternative medicine, gynecologic cancer" as keywords. After searching through these results, a total of 12 full length papers in English were included. Results: CAM use in gynecologic cancer patients was discussed in 8 studies and CAM use in breast and gynecologic cancer patients in 4. It was determined that the frequency of CAM use varies between 40.3% and 94.7%. As the CAM method, herbal medicines, vitamins/minerals were used most frequently in 8 of the studies. When the reasons why gynecologic cancer patients use CAM are examined, it is determined that they generally use to strengthen the immune system, reduce the side effects of cancer treatment and for physical and psychological relaxation. In this review, most of the gynecologic cancer patients perceived use of CAM as beneficial. Conclusions: In order that the patients obtain adequate reliable information about CAM and avoid practices which may harm the efficiency of medical treatment, it is recommended that "Healthcare Professionals" develop a common language.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1160-1172
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• 2019

Federated authentication is a user authentication and authorization infrastructure that spans multiple security domains. Many overseas Web applications have been adopting SAML-based federated authentication. However, in Korea, it is difficult to apply the authentication because of the high market share of a specific Web (application) server, which is hard to use open-source SAML software and the high adoption of Java-based standard framework which is not easy to integrate with SAML library. This paper proposes the SAML4J, which is developed in order to have Web applications easily and safely integrated with the Java-based framework. SAML4J has a developer-friendly advantage of using a session storage independent of the framework and processing Web SSO flows through simple API. We evaluate the functionality, performance, and security of the SAML4J to demonstrate the high feasibility of it.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.1-8
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• 2009

In this paper, we deal an implementation of the software that produces sub-optimal solution of cutting-ironbar planning problem using dynamic programming. Generally, it is required to design an optimization algorithm to accept the practical requirements of cutting ironbar manufacturing. But, this problem is a multiple-sized 1-dimensional cutting stock problem and Linear Programming approaches to get the optimal solution is difficult to be applied due to the problem of explosive computation and memory limitation. In order to overcome this problem, we reform the problem for applying Dynamic Programming and propose a cutting-ironbar planning algorithm searching the sub-optimal solution in the space of fixed amount of combinated columns by using heuristics. Then, we design a graphic user interfaces and screen displays to be operated conveniently in the industry workplace and implement the software using open-source GUI library toolkit, GTK+.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.