• Journal of Information Processing Systems
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• v.16 no.3
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• pp.688-698
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• 2020

Point clouds have ability to express the spatial entities, however, the point clouds redundancy always involves some uncertainties in computer recognition and model construction. Therefore, point clouds thinning is an indispensable step in point clouds model reconstruction and other applications. To overcome the shortcomings of complex classification index and long time consuming in existing point clouds thinning algorithms, this paper proposes a point clouds fast thinning algorithm. Specifically, the two-dimensional index is established in plane linear array (x, y) for the scanned point clouds, and the thresholds of adjacent point distance difference and height difference are employed to further delete or retain the selected sample point. Sequentially, the index of sample point is traversed forwardly and backwardly until the process of point clouds thinning is completed. The results suggest that the proposed new algorithm can be applied to different targets when the thresholds are built in advance. Besides, the new method also performs superiority in time consuming, modelling accuracy and feature retention by comparing with octree thinning algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.343-345
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• 2024

얼굴 연령 분류 기법은 신원 확인 시스템 고도화, 유동 인구 통계 자동화 시스템 구축, 연령 제한 콘텐츠 관리 시스템 고도화 등 다양한 분야에 적용할 수 있는 확장 가능성을 가진다. 넓은 확장 가능성을 가지는 만큼 적용된 시스템의 안정성을 위해서는 얼굴 연령 분류 기법의 높은 정확도는 필수적이다. 따라서, 본 논문에서는 Vision Transformer(ViT) 기반 분류 알고리즘의 얼굴 연령 분류 성능을 비교 분석한다. ViT 기반분류 알고리즘으로는 최근 널리 사용되고 있는 ViT, Swin Transformer(ST), Neighborhood Attention Transformer(NAT) 세 가지로 선정하였으며, ViT의 얼굴 연령 분류 정확도 65.19%의 성능을 확인하였다.

• Asia pacific journal of information systems
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• v.29 no.2
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• pp.203-216
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• 2019

There is an accuracy-diversity dilemma with personalized recommendation services. Some researchers believe that accurate recommendations might reinforce customer satisfaction. However, others claim that highly accurate recommendations and customer satisfaction are not always correlated. Thus, this study attempts to establish the causal factors that determine customer satisfaction with personalized recommendation services to reconcile these incompatible views. This paper employs statistical analyses of simulation to investigate an accuracy-diversity dilemma with personalized recommendation services. To this end, we develop a personalized recommendation system and measured accuracy, diversity, and customer satisfaction using a simulation method. The results show that accurate recommendations positively affected customer satisfaction, whereas diverse recommendations negatively affected customer satisfaction. Also, customer satisfaction was associated with the recommendation product size when neighborhood size was optimal in accuracy. Thus, these results offer insights into personalizing recommendation service providers. The providers must identify customers' preferences correctly and suggest more accurate recommendations. Furthermore, accuracy is not always improved as the number of product recommendation increases. Accordingly, providers must propose adequate number of product recommendation.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.455-464
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• 2024

Seismic movements have varying effects on structures based on characteristics of local site. During an earthquake, weak soils are susceptible to damage due to amplified wave amplitudes. Soil-structure interaction issue has garnered increased attention in Türkiye, after devastating earthquakes in Kocaeli Gölcük (1999), Izmir (2020), Kahramanmaraş Pazarcık and Elbistan (2023). Consequently, liquefaction potential has been investigated in detail for different regions of Türkiye, mainly with available field test results. Çankırı, a city located close to North Anatolian Fault, is mainly built on alluvium, which is prone to liquefaction. However, no study on liquefaction hazard has been conducted thus far. In this study, groundwater level map, SPT map, and liquefaction risk map have been generated using Geographical Information System (GIS) for the Buğday Pazarı District of Çankırı province. Site investigations studies previously performed for 47 parcels (76 boreholes) were used within the scope of this study. The liquefaction assessment was conducted using Seed and Idriss's (1971) simplified method and the visualization of areas susceptible to liquefaction risk has been accomplished. The results of this study have been compared with the City Council's precautionary map which is currently in use. As a result of this study, it is recommended that minimum depth of boreholes in the region should be at least 30m and adequate number of laboratory tests particularly in liquefiable areas should be performed. Another important recommendation for the region is that detailed investigation should be performed by local authorities since findings of this study differ from currently used precautionary map.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.

• Journal of the Korean association of regional geographers
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• v.15 no.4
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• pp.510-523
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• 2009

Geodemographics(GD) can be defined as an analytical approach of socio-economic and behavioral data about people to investigate geographical patterns. GD is based on the assumptions that demographical and behavioral characteristics of people who live in the same neighborhood are similar and then the neighborhoods can be categorized with spatial classifications with the geographical classifications. Thus, this paper, in order to identify the applicability of the geographical classification of the GD, explores the concepts of the geodemographics into Seoul city areas with Korea census data sets that contain key characteristics of demographic profiles in the area. Then, this paper attempt to explain each area classification profile by using clustering techniques with Ward's and k-means statistical methods. For this as as as, this paper employs 2005 Census dataset released by Korea National Statistics Office and the neighborhood unit is based on Dong level, the smallest administrative boundary unit in Korea. After selecting and standardizing variables, several areas are categorized by the cluster techniques into 13, this paps as distinctive cluster profiles. These cluster profiles are used to cthite a short description and expand on the cluster names. Finally, the results of the classification propose a reasonable judgement for target area types which benefits for the people who make a spatial decision for their spatial problem-solving.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.3
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• pp.55-71
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• 2019

The purpose of this study is to propose an evaluation method to assess green environments of streetscapes to improve urban street green spaces in Jeonju City. Through a rapid assessment of urban street green spaces, we suggest an objective basis for expanding street green space as well as for adopting sustainable maintenance and improvement measures. We choose 12 sections of streetscapes (roads and sidewalks) to investigate existing street conditions which have more than four lanes and function as major road axes. Six large roads and six medium roads of Jeonju City center area are investigated as pilot assessment study sites. Site inventory checklists consist of environmental characteristics of streetscape, street tree status, and planting condition evaluation. Environmental characteristics of streetscapes are composed of physical and neighborhood factors. For instance, items for physical factors are types and width of road/sidewalks, paving materials, tree protection materials, and green strip. And surrounding landuse is a neighborhood factor. Assessment items for street tree status are street plant names (tree/shrubs/ground cover), size, and planting intervals. Planting condition evaluation items are tree shape, damage, canopy density, and planting types with existence of adjacent green space. Evaluation results are classified into three levels such as A(maintain or repair), B(greening enhancement), and C(structural improvement). In case of grade A, streetscapes have enough sidewalk width for maintaining green strip and a multi-layered planting(in large road only) with fairly good growing conditions of street trees. For grade B and C, streetscapes have a moderate level of sidewalk width with a single street tree planting. In addition, street tree growing conditions are appeared poor so that green enhancement or maintenance measures are needed. For median, only grade B and C are found as its planting growing foundations are very limited in space. As a result, acquiring enough sidewalk space is essential to enhance ecological quality of urban street green. Especially, it is necessary to have green strip with reasonable widths for plant growing conditions in sidewalks. In addition, we need to consider native species with multi-layer plant compositions while designing street green.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.184-199
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• 2024

This study was conducted in response to the global trend of emphasizing the importance of "everyday landscapes", focusing on the perspective of those who have returned to rural life. With a focus on the case of Gokseong-gun in Jeollanam-do, 460 diaries written by these individuals were collected and analyzed using text mining techniques such as "frequency analysis", "topic modeling", and "sentiment analysis". The analysis of noun morphemes was interpreted from a cognitive aspect, while adjective morphemes were interpreted from an emotional aspect. In particular, this study applied semantic network analysis to overcome the limitations of existing sentiment analysis, and extracted a word network list and examined the content of nouns connected to adjectives that express emotions to identify the targets and contents of sentiments. This method represents a differentiated approach that is not commonly found in existing research. One of the intriguing findings is that the urban-to-rural migrants identified everyday landscapes such as "flowers on neighborhood walking paths", "harvest of a garden", "neighborhood events", and "cozy cafe spaces" as important. These elements all contain visual and enjoyable aspects of everyday landscapes. Currently, many rural villages are attempting to add visual elements to their everyday landscapes by unifying roof colors or painting murals on walls. However, such artificial measures do not necessarily leave a lasting impression on people. A critical review of current policies and systems is necessary. This research is significant because it is the first to study everyday landscapes from the perspective of urban-to-rural migration using diaries and text mining. With a lack of domestic research on everyday landscapes, this study hopes to contribute to the activation of related research in Korea.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.