• Proceedings of the KSR Conference
• /
• 2008.11b
• /
• pp.2051-2057
• /
• 2008

This paper is a feasibility study on the calculation of operating cost in regards to the overall operation following the completion of a number of LRT lines currently in progress. Owing to the absence of operating experience in driveless LRT system at home, the difficulties lie in the assumption of the optimal operating budget applying domestic real situation. Nevertheless, with 34 years of accumulated operating experience in heavy rail transit system, Seoul Metro, the nation's biggest urban rail transit operator, performs O&M consultancy services for several on-going projects along with every effort to acquire know-how where the appropriateness of the cost estimation as a required deliverable is reviewed and a more efficient way is provided. The main focus of this study is to seek a method to calculate the optimal amount of operating expenses as well as a cost-effective alternative for possible weaknesses from the standpoint of the operator. The body of this paper discusses the five issues such as personnel cost, overhead, maintenance cost, additional purchase price, alternative investment fee from a more macroscopic point of view, and the conclusion deals with the adequacy of the reason for selection of institutions with various operating know-how.

• Journal of IKEEE
• /
• v.25 no.2
• /
• pp.337-343
• /
• 2021

Deep reinforcement learning is an artificial intelligence algorithm that enables learners to select optimal behavior based on raw and, high-dimensional input data. A lot of research using this is being conducted to create an optimal movement path of a mobile robot in an environment in which obstacles exist. In this paper, we selected the Dueling Double DQN (D3QN) algorithm that uses the prioritized experience replay to create the moving path of mobile robot from the image of the complex surrounding environment. The virtual environment is implemented using Webots, a robot simulator, and through simulation, it is confirmed that the mobile robot grasped the position of the obstacle in real time and avoided it to reach the destination.

• Physical Therapy Rehabilitation Science
• /
• v.9 no.3
• /
• pp.178-183
• /
• 2020

Objective: This study conducted in order to investigate the correlations between fall experience, balance, mobility, and confidence. We examined the difference between fall experience, and Berg Balance Scale (BBS), Timed-Up-and-Go test (TUG), Tinetti balance assessment (Tinetti balance [TiB], Tinetti gait [TiG]), and Activities-specific Balance Confidence (ABC) scale scores to see how fall experience, balance, mobility, and confidence of the persons with stroke affects their balance. Design: Cross-sectional study. Methods: Forty-one subjects participated in this study. The BBS includes 14 items, consisting of a 5-point scale from 0 to 4, totaling up to 56 points. The Timed Up and Go-Alone (TUGA) was used to measure the average time to take a 3 m round-trip by getting up and down from a 46-cm high chair with an armrest on a flat floor. The Timed-Up-and-Go-Cognitive (TUGC) was performed by counting backwards and the Timed Up and Go-Manual (TUGM) is performed by holding a cup full of water. The total score for the TiB is 16 points, and the TiG is 12 points, making a total of 28 points. There are 16 items total for the ABC scale. Results: According to the fall experience, BBS, the TUGA and TUGC values were significantly higher in the inexperienced group compared to the experienced group (p<0.05). The number of falls was significantly correlated with BBS, TUGA, TUGC, TUGM, TiB, TiG, TiB+TiG (p<0.05). Conclusions: This study supports that falls experience is strongly related to balance, mobility, and confidence. Optimal balance training programs for fall prevention is still insufficient and must be developed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.208-209
• /
• 2023

Intelligent maritime traffic information service for maritime traffic safety operates a service that provides safe and efficient optimal safety routes considering information such as water depth, maritime safety law, weather information, and fuel consumption. However, from a service user's point of view, they prefer a route that suits their personal navigation experience and style, such as unnecessary detours and conservative safety distances for maritime objects. In this study, the optimal safety route can be extracted based on the experience of service users without reflecting the separate maritime environment by adjusting the weight of the trunk line for the area where the ship frequently navigates with the ship's track data collected through LTE-M model was studied.

• Journal of Korean Society for Quality Management
• /
• v.40 no.4
• /
• pp.441-452
• /
• 2012

Purpose: In this paper, we have considered a problem of newsvendor model in an environment of random yields in quality and customer balking behavior, in which only the mean and the variance of the demand are known. In practice, the distributional information of the demand is very limited and only the mean and variance are guessed by experience. In addition, due to the customers balking behavior occurring when the available inventory level decreases, the product's demand becomes a function of inventory level so that the classical newsvendor's optimal order quantity is no longer optimal. Methods: We have developed an optimal order quantity model that enables us to incorporate the random yield of a product and the customer balking information such as a threshold inventory level of balking and the corresponding probability of a sale during the balking. Results: We illustrated the concepts developed here through simple numerical examples and showed the robustness of our model in a various setting of parameters. Conclusion: This paper provides a useful analysis showing that our distribution-specific and distribution-free approach to the optimal order quantity in the newsboy model can act as an effective tools to match supply with demand for these product lines.

• Journal of Ocean Engineering and Technology
• /
• v.20 no.1 s.68
• /
• pp.69-76
• /
• 2006

The design of efficient search path to maximize the Cumulative Detection Probability(CDP) is mainly dependent on experience and intuition when searcher detect the target using SONAR in the ocean. Recently with the advance of modeling and simulation method, it has been possible to access the optimization problems more systematically. In this paper, a method for the optimal search path calculation is developed based on the combination of the genetic algorithm and the calculation algorithm for detection range. We consider the discrete system for search path, space, and time, and use the movement direction of the SONAR for the gene of the genetic algorithm. The developed algorithm, OASPP(Optimal Acoustic Search Path Planning), is shown to be effective, via a simulation, finding the optimal search path for the case when the intuitive solution exists. Also, OASPP is compared with other algorithms for the measure of efficiency to maximize CDP.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.601-606
• /
• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.514-517
• /
• 1996

In solving multi-level knapsack problems, conventional heuristic approaches often assume a short-sighted plan within a static decision enviornment to find a near optimal solution. These conventional approaches are inflexible, and lack the ability to adapt to different problem structures. This research approaches the problem from a totally different viewpoint, and a new method is designed and implemented. This method performs intelligent actions based on memories of historic data and learning. These actions are developed not only by observing the attributes of the optimal solution, the solution space, and its corresponding path to the optimal solution, but also by applying human intelligence, experience, and intuition with respect to the search strategies. The method intensifies, or diversifies the search process appropriately in time and space. In order to create a good neighborhood structure, this method uses two powerful choice rules that emphasize the impact of candidate variables on the current solution with respect to their profit contribution. A side effect of so-called "pseudo moves", similar to "aspirations", supports these choice rules during the evaluation process. For the purpose of visiting as many relevant points as possible, strategic oscillation between feasible and infeasible solutions around the boundary is applied for intensification. To avoid redundant moves, short-term (tabu-lists), intermediate-term (cycle detection), and long-term (recording frequency and significant solutions for diversification) memories are used. Test results show that among the 45 generated problems (these problems pose significant or insurmountable challenges to exact methods) the approach produces the optimal solutions in 39 cases.lutions in 39 cases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.03a
• /
• pp.123-126
• /
• 1998

The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the Meaning of these foundational concepts, simple genetic algorithm(SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithms. In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. So we propose a co-evolutionary method finding optimal fuzzy rules. Our algorithm is that after constructing two population groups m de up of rule vase and its schema, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the proposed method to a path planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• Nuclear Engineering and Technology
• /
• v.54 no.8
• /
• pp.2941-2959
• /
• 2022

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (µ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of -1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.