• Journal of The Korea Institute of Healthcare Architecture
• /
• v.24 no.4
• /
• pp.17-25
• /
• 2018

Purpose: Korean society is undergoing the rapid increase and poverty of elderly population. Therefore, the appropriate supply and planning of public rental housing for the low-income elderly is more important. The purpose of this study is to present the basic data for the study and planning of the elderly housing complex by analyzing the cognitive characteristics of the elderly residing in the rental housing. Methods: A questionnaire survey was conducted to identify health characteristics of the elderly living in the 'Gongreung SH Apartment'. 100 random samples were collected and 82 valid samples were analysed. Statistical analysis was performed using Excel and R for the age, sex, health, outdoor walking frequency and characteristics of Cognitive map of the elderly. Results: The characteristics of cognitive map were classified into point shapes and linear shapes. The linear group was lower in average age than the point group and tended to draw the map wider. The wider the map was, the more the number of elements in cognitive map was. The number of elements on the cognitive map decreased as respondents' age increased. On the other hand it was not related to residence period and gender of the elderly. Implication: The cognitive extent of the residential environment tends to decrease with age. Men's cognitive range is wider than women's. There is no corelation between the number of cognitive elements and cognitive map type. Men tend to have a systematic image of city, whereas women focus on relational and social urban factors. For sustainable apartment complex design, various characteristics of the group including men and women, different age and different health status should be considered.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.100-103
• /
• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.

• ETRI Journal
• /
• v.44 no.5
• /
• pp.805-815
• /
• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1080-1099
• /
• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• Journal of Korean Society for Geospatial Information Science
• /
• v.22 no.4
• /
• pp.3-11
• /
• 2014

Recently, rapid urbanization has necessitated continuous updates in digital map to provide the latest and accurate information for users. However, conventional aerial photogrammetry has some restrictions on periodic updates of small areas due to high cost, and as-built drawing also brings some problems with maintaining quality. Alternatively, this paper proposes a scheme for efficient and accurate update of digital map using point cloud data acquired by Terrestrial Laser Scanner (TLS). Initially, from the whole point cloud data, the building sides are extracted and projected onto a 2D image to trace out the 2D building footprints. In order to register the footprint extractions on the digital map, 2D Affine model is used. For Affine parameter estimation, the centroids of each footprint groups are randomly chosen and matched by means of a modified RANSAC algorithm. Based on proposed algorithm, the experimental results showed that it is possible to renew digital map using building footprint extracted from TLS data.

• The KIPS Transactions:PartD
• /
• v.18D no.6
• /
• pp.501-508
• /
• 2011

A knowledge map, which has been recently applied in various fields, is discovering characteristics hidden in a large amount of information and showing a tangible output to understand the meaning of the discovery. In this paper, we suggested a knowledge map for research trend analysis based on keyword-relation networks which are constructed by using a database of the domestic journal articles in the computer engineering field from 2000 through 2010. From that knowledge map, we could infer influential changes of a research topic related a specific keyword through examining the change of sizes of the connected components to which the keyword belongs in the keyword-relation networks. In addition, we observed that the size of the largest connected component in the keyword-relation networks is relatively small and groups of high-similarity keyword pairs are clustered in them by comparison with the random networks. This implies that the research field corresponding to the largest connected component is not so huge and many small-scale topics included in it are highly clustered and loosely-connected to each other. our proposed knowledge map can be considered as a approach for the research trend analysis while it is impossible to obtain those results by conventional approaches such as analyzing the frequency of an individual keyword.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.12A
• /
• pp.1267-1275
• /
• 2007

An improved backoff algorithm for retransmission randomization for OFDMA/CDMA/slotted ALOHA used in the ranging subchannel of IEEE 802.16 network is proposed. Exploiting the fact that a base station coordinates channel access using UL-/DL-MAP in the IEEE 802.16 networks, we propose a minor modification of the existing IEEE 802.16 in order to increase throughput, decrease delay variation and achieve a graceful performance degradation in case of overload channel condition of the random access protocol. The algorithm basically estimates the number of backlogged users and arrival rate using which, the BS calculates retransmission probability for the subscriber stations involved in a collision. Computer simulation is performed to demonstrate the effectiveness of the proposed algorithm and to compare the performance with existing binary exponential backoff algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.1
• /
• pp.364-380
• /
• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.3C
• /
• pp.306-313
• /
• 2007

In this paper, we efficiently implemented turbo code algorithm in FPGA H/W(hardware) resource. The used turbo code algorithm has the characteristics; the size of constraint is 3, encoder type is 1/3, the size of random interleaver is 2048. The proposed H/W consists of MAP block for calculating alpha and delta using delta value, storing buffer for each value, multiplier for calculating lamda, and lamda buffer. The proposed algorithm and H/W architecture was verified by C++ language and was designed by VHDL. Finally the designed H/W was programmed into FPGA and tested in wireless communication environment for field availability. The target FPGA of the implemented H/W is VERTEX4 XC4VFX12-12-SF363 and it is stably operated in 131.533MHz clock frequency (7.603ns).

• Korean Journal of Remote Sensing
• /
• v.34 no.4
• /
• pp.671-680
• /
• 2018

Compared to the imagery produced by traditional satellites, PlanetScope satellite imagery has made it possible to easily capture remotely-sensed imagery every day through dozens or even hundreds of satellites on a relatively small budget. This study aimed to detect changed areas and update a land cover map using a PlanetScope image. To generate a classification map, pixel-based Random Forest (RF) classification was performed by using additional features, such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI). The classification result was converted to vector data and compared with the existing land cover map to estimate the changed area. To estimate the accuracy and trends of the changed area, the quantitative quality of the supervised classification result using the PlanetScope image was evaluated first. In addition, the patterns of the changed area that corresponded to the classification result were analyzed using the PlanetScope satellite image. Experimental results found that the PlanetScope image can be used to effectively to detect changed areas on large-scale land cover maps, and supervised classification results can update the changed areas.