• Journal of the Korean Wood Science and Technology
• /
• 제45권6호
• /
• pp.797-808
• /
• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

• Journal of Advanced Marine Engineering and Technology
• /
• 제32권5호
• /
• pp.775-784
• /
• 2008

It is not easy to access very high speed Internet services at sea due to some technical and economical problems. In order to realize the very high speed Internet services at sea like on land, new communication network models based on MANET should be adopted. In this paper, a new MANET model at sea is provided, which considered the ocean environments, and the characteristics and movement of vessels. On the basis of the fact that most vessels navigate on the predetermined courses, which are the shortest paths between source and destination ports in most cases, a type of location oriented routing protocol is proposed in this paper. The Hybrid Course-Based Routing Protocol(HCBR) makes use of the static information such as courses and positions of ports to proactively find the shortest paths not only among ports but also the cross points of courses. HCBR also makes use of the locational information of vessels obtained via GPS and AIS systems to reactively discover the shortest route by which data packets are delivered between them. We have simulated the comparison of the performance of HCBR with those of LAR scheme 1 and scheme2, the most typical protocols using geographical information. The simulation results show that HCBR guarantees the route discovery even without using any control packet. They also show that HCBR is more reliable(40%) and is able to obtain more optimal routes(10%) than LAR scheme1 and scheme2 protocols.

• 한국GIS학회:학술대회논문집
• /
• 한국GIS학회 1999년도 추계학술대회 발표요약문
• /
• pp.66-71
• /
• 1999

For given 3D geographic data which is usually of DEM(Data Elevation Model) format, we have to represent and manipulate the data in various ways. For example, we have to draw a part of them in drawing canvas. To do this we give users a way of selecting area they want to visualize. And we have to give a base tool for users to select the local area which can be chosen for some geographic operation. In this paper, we propose a 3D data processing method for representation and manipulation. The method utilizes the major properties of DEM and TIN(Triangular Irregular Network), respectively. Furthermore, by approximating DEM with a TIN of an appropriate resolution, we can support a fast and realistic surface modeling. We implement the structure with the following 4 level stages. The first is an optimal resolution of DEM which represent all of wide range of geographic data. The second is the full resolution DEM which is a subarea of original data generated by user's selection in our implemeatation. The third is the TIN approximation of this data with a proper resolution determined by the relative position with the camera. And the last step is multi-resolution TIN data whose resolution is dynamically decided by considering which direction user take notice currently. Specialty, the TIN of the last step is designed for realtime camera navigation. By using the structure we implemented realtime surface clipping, efficient approximation of height field and the locally detailed surface LOD(Level of Detail). We used the initial 10-meter sampling DEM data of Seoul, KOREA and implement the structure to the 3D Virtual GIS based on the Internet.

• Spatial Information Research
• /
• 제5권1호
• /
• pp.11-26
• /
• 1997

본 논문은 제2의 금융중심지로 부상하고 있는 강남구를 사례로 GIS 기법을 활용하여 은행점포의 입지를 분석하였다. 금융자율화 이후 급격한 증가추세를 보이고 있는 은행점포의 수는 상당한 지역적인 편재현상을 나타내고 있다. 통계기법을 활용하여 은행 입지에 영향을 주는 입지요인을 추출하였고, 은행이용 고객들에 대한 설문조사를 통하여 은행점포의 상권을 조사하였다. 이러한 기초자료를 바탕으로 하여 GIS기법을 활용하여 입지분석을 실시하였다. 먼저 은행이용고객들에게 최적의 서비스를 공급할 수 있는 목적함수를 토대로한 입지-배분 모델을 적용하여 추출된 입지와 현재의 은행점포의 입지를 비교하였다. 또한 그리드 연산 기법을 활용하여 잠재 수요력과 잠재 공급력의 차이를 통해 앞으로 은행점포의 신규 설립이 가능한 지구를 추출하였다. 본 연구를 통해 GIS기법을 활용하여 입지분석을 실시하는 경우 제한점과 앞으로 해결되어야 할 문제점에 대해 논하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권3호
• /
• pp.1204-1227
• /
• 2020

The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

• Geomechanics and Engineering
• /
• 제12권3호
• /
• pp.523-539
• /
• 2017

Clay soils are typical for their swelling properties upon absorption of water during rains and development of cracks during summer time owing to the profile desorption of water through the inter-connected soil pores by water vapour diffusion leading to evaporation. This type of unstable soil phenomenon by and large poses a serious threat to the strength and stability of structures when rest on such type of soils. Even as lime and cement are extensively used for stabilization of clay soils it has become imperative to find relatively cheaper alternative materials to bring out the desired properties within the clay soil domain. In the present era of catastrophic environmental degradation as a side effect to modernized manufacturing processes, industrialization and urbanization the creative idea would be treating the waste products in a beneficial way for reuse and recycling. Bottom ash and ecosand are construed as a waste product from cement industry. An optimal combination of bottom ash-eco sand can be thought of as a viable alternative to stabilize the clay soils by means of an effective dispersion dynamics associated with the inter connected network of pore spaces. A CATIA model was created and imported to ANSYS Fluent to study the dispersion dynamics. Ion migration from the bottom ash-ecosand pile was facilitated through natural formation of cracks in clay soil subjected to atmospheric conditions. Treated samples collected at different curing days from inner and outer zones at different depths were tested for, plasticity index, Unconfined Compressive Strength (UCS), free swell index, water content, Cation Exchange Capacity (CEC), pH and ion concentration to show the effectiveness of the method in improving the clay soil.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제9권1호
• /
• pp.9-16
• /
• 2020

빅 데이터를 연구 목적으로 제3자에게 배포할 때 프라이버시 정보를 보호하기 위해서 k-익명화 기법이 널리 사용되어 왔다. k-익명화 기법을 적용할 때, 해결 해야할 어려운 문제 중의 하나는 최적의 k값을 결정하는 것이다. 현재는 대부분 전문가의 직관에 근거하여 수동으로 결정되고 있다. 이러한 방식은 익명화의 성능을 떨어뜨리고 시간과 비용을 많이 낭비하게 만든다. 이러한 문제점을 해결하기 위해서 기계학습 기반의 k값 결정방식을 제안한다. 본 논문에서는 제안된 아이디어를 실제로 적용한 구현 및 실험 내용에 대해서 서술 한다. 실험에서는 심층 신경망을 구현하여 훈련하고 테스트를 수행 하였다. 실험결과 훈련 에러는 전형적인 신경망에서 보여지는 패턴을 나타냈으며, 테스트 실험에서는 훈련에러에서 나타나는 패턴과는 다른 패턴을 보여주고 있다. 제안된 방식의 장점은 k값 결정시 시간과 비용을 줄일 수 있다는 장점이 있다.

• Journal of Preventive Medicine and Public Health
• /
• 제36권2호
• /
• pp.147-152
• /
• 2003

Objectives : To develop a Diagnosis-Related Group (DRG) fraud candidate detection method, using data mining techniques, and to examine the efficiency of the developed method. Methods ; The Study included 79,790 DRGs and their related claims of 8 disease groups (Lens procedures, with or without, vitrectomy, tonsillectomy and/or adenoidectomy only, appendectomy, Cesarean section, vaginal delivery, anal and/or perianal procedures, inguinal and/or femoral hernia procedures, uterine and/or adnexa procedures for nonmalignancy), which were examined manually during a 32 months period. To construct an optimal prediction model, 38 variables were applied, and the correction rate and lift value of 3 models (decision tree, logistic regression, neural network) compared. The analyses were peformed separately by disease group. Results : The correction rates of the developed method, using data mining techniques, were 15.4 to 81.9%, according to disease groups, with an overall correction rate of 60.7%. The lift values were 1.9 to 7.3 according to disease groups, with an overall lift value of 4.1. Conclusions : The above findings suggested that the applying of data mining techniques is necessary to improve the efficiency of DRG fraud candidate detection.

• 한국정밀공학회지
• /
• 제28권4호
• /
• pp.482-489
• /
• 2011

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can't be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

• 상하수도학회지
• /
• 제25권4호
• /
• pp.581-589
• /
• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.