• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.8B
• /
• pp.637-646
• /
• 2012

Recently, Wireless Sensor Networks (WSNs) are used in many applications. When sensor nodes are deployed on special areas, where humans have any difficulties to get in, the nodes form network topology themselves. By using the sensor nodes, users are able to obtain environmental information. Due to the lack of the battery capability, sensor nodes should be efficiently managed with energy consumption in WSNs. In specific applications (e.g. in intrusion detections), intruders tend to occur unexpectedly. For the energy efficiency in the applications, an appropriate algorithm is strongly required. In this paper, we propose tree-based routing algorithm for the specific applications, which based on the intrusion detection. In addition, In order to decrease traffic density, the proposed algorithm provides enhanced method considering link cost and load balance, and it establishes efficient links amongst the sensor nodes. Simultaneously, by using the proposed scheme, parent and child nodes are (re-)defined. Furthermore, efficient routing table management facilitates to improve energy efficiency especially in the limited power source. In order to apply a realistic military environment, in this paper, we design three scenarios according to an intruder's moving direction; (1) the intruder is passing along a path where sensor nodes have been already deployed. (2) the intruders are crossing the path. (3) the intruders, who are moving as (1)'s scenario, are certainly deviating from the middle of the path. In conclusion, through the simulation results, we obtain the performance results in terms of latency and energy consumption, and analyze them. Finally, we validate our algorithm is highly able to adapt on such the application environments.

• Journal of Korea Port Economic Association
• /
• v.26 no.4
• /
• pp.329-353
• /
• 2010

This paper deal with government logistics policy in related low carbon in China. The government policy of promoting low-carbon way is more dependent on the top-down enforcement rather than voluntary market principles. It will succeeded in transforming the environment-friendly image, to focus on creating a mindset the company can go on voluntary carbon-reduction. The three factors of low-carbon economy and the new energy and industrial development policy is technology and funding, and that most of the government's policy has a crucial role. Due to the nature of the Chinese economy, government policies impact on the development of the industry is very important, and even for China's industrial restructuring of the logistics industry in the areas of government policy support for green economic growth, its role is expected to be very large. In Future, Chinese government will promote low carbon policies through the optimization of the logistics network to reduce energy waste, pursue the low carbon-reduction of logistics machinery and equipment, and develop an mode to appropriate demand for green low-carbon economic growth.

• Korean Journal of Environment and Ecology
• /
• v.35 no.2
• /
• pp.193-203
• /
• 2021

As endangered species are gradually increasing due to land development by humans, it is essential to secure sufficient protected areas (PAs) proactively. Therefore, this study checked priority conservation areas to select candidate PAs when considering the impact of land development. We determined the conservation priorities by analyzing four scenarios based on existing conservation areas and reflecting the development impact using MARXAN, the decision-making support software for the conservation plan. The development impact was derived using the developed area ratio, population density, road network system, and traffic volume. The conservation areas of endangered species were derived using the data of the appearance points of birds, mammals, and herptiles from the 3rd National Ecosystem Survey. These two factors were used as input data to map conservation priority areas with the machine learning-based optimization methodology. The result identified many non-PAs areas that were expected to play an important role conserving endangered species. When considering the land development impact, it was found that the areas with priority for conservation were fragmented. Even when both the development impact and existing PAs were considered, the priority was higher in areas from the current PAs because many road developments had already been completed around the current PAs. Therefore, it is necessary to consider areas other than the current PAs to protect endangered species and seek alternative measures to fragmented conservation priority areas.

• Korean Chemical Engineering Research
• /
• v.59 no.2
• /
• pp.209-218
• /
• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.95-104
• /
• 2020

In this paper, we propose the real time indoor air quality monitoring and controlling platform on cloud using IoT sensor data such as PM10, PM2.5, CO2, VOCs, temperature, and humidity which has direct or indirect impact to indoor air quality. The system is connected to air ventilator to manage and optimize the indoor air quality. The proposed system has three main parts; First, IoT data collection service to measure, and collect indoor air quality in real time from IoT sensor network, Second, Big data processing pipeline to process and store the collected data on cloud platform and Finally, Big data analysis and visualization service to give real time insight of indoor air quality on mobile and web application. For the implication of the proposed system, IoT sensor kits are installed on three different public day care center where the indoor pollution can cause serious impact to the health and education of growing kids. Analyzed results are visualized on mobile and web application. The impact of ventilation system to indoor air quality is tested statistically and the result shows the proper optimization of indoor air quality.

• Journal of the Korean Geotechnical Society
• /
• v.37 no.5
• /
• pp.47-63
• /
• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• The Journal of the Korea Contents Association
• /
• v.21 no.5
• /
• pp.561-580
• /
• 2021

This study analyses the historical and cultural streets at Pinggang Road in the city of Suzhou, by understanding the development and conservation of the area, and uses the following ways to investigate its development, re-organization, and current state. This paper comprehensively compares, collates and investigates 4 different historical and cultural areas in Insadong and Samcheong-dong in South Korea, and South Luogu Lane in China. From initial research and analysis, this paper gathers the cultural, economic, and societal perspectives as non-physical measures, and spatial structure, road structure, and building maintenance as physical factor framework. It is significant in that it can provide an evaluation model for the preservation and regeneration of historical and cultural streets by presenting the viewpoint of complex development of non-physical and physical elements in Pyeonggang-ro. In addition, it is necessary to conduct optimization and specific research on insufficient areas, such as maintenance and development of programs and signature systems for visitors, and continuous development of historical and cultural network platforms by combining on-site surveys. Basic data should be provided for reference on the street.

• Journal of Navigation and Port Research
• /
• v.46 no.3
• /
• pp.280-288
• /
• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Smart Media Journal
• /
• v.11 no.10
• /
• pp.65-75
• /
• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.59-76
• /
• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.