• Advances in nano research
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• v.16 no.6
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• pp.531-548
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• 2024

The study focuses on using remote sensing to gather data about the Earth's surface, particularly in urban environments, using satellites and aircraft-mounted sensors. It aims to develop a classification framework for road targets using multi-spectral imagery. By integrating Convolutional Neural Networks (CNNs) with XGBoost, the study seeks to enhance the accuracy and efficiency of road target identification, aiding urban infrastructure management and transportation planning. A novel aspect of the research is the incorporation of quantum sensors, which improve the resolution and sensitivity of the data. The model achieved high predictive accuracy with an MSE of 0.025, R-squared of 0.85, RMSE of 0.158, and MAE of 0.12. The CNN model showed excellent performance in road detection with 92% accuracy, 88% precision, 90% recall, and an f1-score of 89%. These results demonstrate the model's robustness and applicability in real-world urban planning scenarios, further enhanced by data augmentation and early stopping techniques.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.35-41
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• 2002

This research introduces the development of a project-level cash flow forecasting model in construction stage based on the planned earned value and the cost from a general contractors view on a jobsite. Most previous models have been developed to assist contractors in their pre-tendering or planning stage cash flow forecasts. The critical key to cash flow forecasting at the project level is how to build a cash-out model. The basic concept is to use moving weights of cost categories in a budget over project duration. The cost categories are classified to compile resources with almost the same time lags that are based on contracting payment conditions and credit times given by suppliers or venders. For cash-in, net planned monthly-earned values are simply transferred to the cash-in forecast, to be applied there with billing time and retention money. Validation of the model involves applying data from on-going 4 projects in progress for 12 months. Based on the results of the comparative analyses through the simulation of the proposed model and the existing models, the proposed model is more accurate, flexible and simpler than traditional models to the employee of construction jobsite who is not oriented financial knowledge.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.121-133
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• 2008

Conventional car following models are controlled when the velocity of following vehicle is equal to preceding vehicle without consideration of relative distance. Also, since the car following models are hardly consider the driver's behaviors and the environmental factors in driving, the models can't be adopted in reality. Hence, we developed the car following model applying Human Factors to consider driver's safety and comfortness. We simulated to compare the suggested model with the existing model, GGM(General GM). As results of simulations, the GGM model followed the preceding vehicle when the velocity of following vehicle was equal to preceding vehicle without relation of relative range. The other side, when the relative range was less or over than safety range, the suggested model made the relative range equal to safety range. Accordingly, we could be sure that the model would decrease the driver's discomfort and intensify the safety on driving without unnecessary waste of road. We identified that the suggested model is more realistic than the conventional GGM model.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Korean Journal of Construction Engineering and Management
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• v.6 no.6 s.28
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• pp.133-141
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• 2005

In the resent years, the importance of VE (value engineering) and LCC (life cycle cost) analysis for apartment building construction projects has been fully recognized. Accordingly theoretical models, guidelines, and supporting software systems were developed for the value engineering and life cycle cost analysis for construction management including large building systems. However, the level of consensus on VE and LCC analysis results is still low due to the lack of reliable data on maintenance. This paper presents time dependent LCC model based value analysis method for rational investment decision making and design alternative selection for construction of apartment building. The proposed method incorporates a time dependent LCC model and a performance evaluation technique by fuzzy logic theory to properly handle the uncertainties associated with statistics data and to analyze the value of alternatives more rationally. The presented time dependent VE and LCC analysis procedure were applied to a real world project, and this case study is discussed in the paper. The model and the procedure presented in this study can greatly contribute to design value engineering alternative selection, the estimation of the life cycle cost, and the allocation of budget for apartment building construction projects.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.1
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• pp.57-64
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• 2007

One of the cause of insulation failure in power cable is well known by electrical treeing discharge. This is occurred for imposed continuous stress at cable. And this event is related to safety, reliability and maintenance. In this paper, throughout analysis of partial discharge(PD) distribution when occurring the electrical tree, is studied for the purpose of knowing of electrical treeing discharge characteristics according to defects. Own characteristic of tree will be differently processed in each defect and this reason is the first purpose of this paper. To acquire PD data, three defective tree models were made. And their own data is shown by the phase-resolved partial discharge method (PRPD). As a result of PRPD, tree discharge sources have their own characteristics. And if other defects (void, metal particle) exist internal power cable then their characteristics are shown very different. This result Is related to the time of breakdown and this is importance of cable diagnosis. And classification method of PD sources was studied in this paper. It needs select the most useful method to apply PD data classification one of the proposed method. To meet the requirement, we select methods of different type. That is, neural network(NN-BP), adaptive neuro-fuzzy inference system and PCA-LDA were applied to result. As a result of, ANFIS shows the highest rate which value is 98 %. Generally, PCA-LDA and ANFIS are better than BP. Finally, we performed classification of tree progress using ANFIS and that result is 92 %.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.31-36
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• 2006

In this paper data of index is acquired by some experts. Then the ambipolar models of Fussy Synthesis Evaluation theory is adopted in this paper which helps to the synthesis analysis of the Aids to Navigation in DaLian port area. At the same time, in accordance with the suggestion of the experts and the real condition, the idiographic implement way is mentioned in the point of view of Quality. The two points of view make the scheme more scientific and feasible. After achieving the status, the project scheme of synthetical alteration to Aids to Navigation in DaLian port area is put forward. It arranges the improved ideas from the different point of view of content, efficiency expectation of Aids to Navigation which gives the reference of the alteration and development of the Aid to Navigation which gives the reference of the alteration and development of the Aids to Navigation in DaLian port area for the future.

• Journal of Navigation and Port Research
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• v.27 no.5
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• pp.493-498
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• 2003

Ship is being operated under a highly dynamic environments and many factors are related with ship's collision and those factors are interacting. So, An analysis on the ship's collision muses is very important to prepare countermeasures which will ensure the safe navigation. And the analysis confirmed that ship's collision is occurred most frequently and the muse is closely related with human factor. The main purpose of this study is to build a model of human factors in ship's collision muse using SD(System Dynamics} approach and to measure a effect which is risk control countermeasures of ship's collision. To achieve this aim, the structure analysis on the muses of ship's collision using FSM are performed, and the structure was changed by quantitative, qualitative factors and their feedback loops in casual map. This model was performed over 20 years(1993-2012) in a standard simulation model and 8 policy simulation models.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.8-17
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• 2002

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems with a less flexible result to the external behavior. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. In addition to that, tuning performance cannot be guaranteed with regards to a plant with non-linear characteristics or many kinds of disturbances. Along with these, this paper used immune algorithm in order that a PID controller can be more adaptable controlled against the external condition, including moise or disturbance of plant. Parameters P, I, D encoded in antibody randomly are allocated during selection processes to obtain an optimal gain required for plant. The result of study shows the artificial immune can effectively be used to tune, since it can more fit modes or parameters of the PID controller than that of the conventional tuning methods.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.323-330
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• 2011

Globalization of market and diversification of customers' needs make enterprises to collaboration of participants in supply chain. To establish collaboration, supply chain must have the flexibility and reconfigurability, which are supported by fractal based supply chain management (FrSCM). In this paper, base on the FrSCM, formulation of trust model among the enterprises in the supply chain, and development of profit sharing strategies in the supply chain based on the trust model are investigated. To evaluate trust model, generation of enterprise's goal and its description, extraction and systematic composition of trust factors and trust evaluation are investigated. Based on the developed model, we developed the fuzzy inference engine to evaluate the trust value in terms of numerical value. And then revenue sharing strategies are developed based on the fractal concept and trust model for the collaborative SCM. The fractal concept is used to obtain the optimal production and transportation plans. In addition, the trust model will be integrated into the RS model. In such an RS model, the supply chain will obtain the maximum total profit and profit of each participant depends on its trust value.