• Journal of Dental Anesthesia and Pain Medicine
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• v.19 no.1
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• pp.1-10
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• 2019

Periodontal procedures require adequate anesthesia not only to ensure the patient's comfort but also to enhance the operator's performance and minimize chair time. In the maxilla, anesthesia is often achieved using highly traumatic nerve blocks, apart from multiple local infiltrations through the buccal vestibule. In recent years, anterior middle superior alveolar (AMSA) field block has been claimed to be a less traumatic alternative to several of these conventional injections, and it has many other advantages. This critical review of the existing literature aimed to discuss the rationale, mechanism, effectiveness, extent, and duration of AMSA injections for periodontal surgical and non-surgical procedures in the maxilla. It also focused on future prospects, particularly in relation to computer-controlled local anesthetic delivery systems, which aim to achieve the goal of pain-free anesthesia. A literature search of different databases was performed to retrieve relevant articles related to AMSA injections. After analyzing the existing data, it can be concluded that this anesthetic technique may be used as a predictable method of effective palatal anesthesia with adequate duration for different periodontal procedures. It has additional advantages of being less traumatic, requiring lesser amounts of local anesthetics and vasoconstrictors, as well as achieving good hemostasis. However, its effect on the buccal periodontium appears highly unpredictable.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.979-986
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• 2002

Constraint Programming is an appealing technology for modeling and solving various real-world problems. and metaheuristic is the most successful technique available for solving large real-world vehicle routing problems. Constraint Programming and metaheuristic are complementary to each other. This paper describes how iterative improvement techniques can be used in a Constraint Programming framework(LOG Solver and ILOG Dispatcher) for Vehicle Routing Problem. As local search gets trapped in local solution, the improvement techniques are used in conjunction with metaheuristic method.

• Journal of Korea Multimedia Society
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• v.10 no.3
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• pp.391-400
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• 2007

According to the recent advances in multimedia computing, storage and searching technology have made large volume of music contents become prevalent. Also there has been increasing needs for the study on efficient categorization and searching technique for music contents management. In this paper, a new classifying method using the local information of music content and music tone feature is proposed. While the conventional classifying algorithms are based on entire information of music content, the algorithm proposed in this paper focuses on only the specific local information, which can drastically reduce the computing time without losing classifying accuracy. In order to improve the classifying accuracy, it uses a new classification feature based on music tone. The proposed method has been implemented as a part of MuSE (Music Search/Classification Engine) which was installed on various systems including commercial PDAs and PCs.

• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.211-224
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• 2008

As a lot of information and media are given to users in Internet space nowadays, users feel disoriented or "lost in space" intensively. So it is suggested that we have the system to reduce information overload and to propose effective and efficient information. In this study we present a visualizing technique which uses fisheye views on data adjacency to combine global context and local details for presentation of many results in limited space. Data Adjacency on graph theory is applied to set up degree of interest which is main focus in fisheye views. Graph theory is useful to solve the problem resulted from various combinational optimization, especially it has advantages to analyze issues in information space like Internet. To test the usability of the proposed visualization technique, we compared the effectiveness of different visualization techniques. Results show that our method is evaluated with respect to less time and high satisfaction for a task accomplishment.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.9
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• pp.889-897
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• 2010

Recently, a cutting-edge large-scale image database system has demanded these attributes: search with alarming speed, performs with high accuracy, archives efficiently and much more. An image identifier (descriptor) is for measuring the similarity of two images which plays an important role in this system. The extraction method of an image identifier can be roughly classified into two methods: a local and global method. In this paper, the proposed image identifier, LFH(Local Feature's Histogram), is obtained by a histogram of robust and distinctive local descriptors (features) constrained by a district sub-division of a local region. Furthermore, LFH has not only the properties of a local and global descriptor, but also can perform calculations at a magnificent clip to determine distance with pinpoint accuracy. Additionally, we suggested a way to extract LFH via GPU (OpenGL and GLSL). In this experiment, we have compared the LFH with SIFT (local method) and EHD (global method) via storage capacity, extraction and retrieval time along with accuracy.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.359-370
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• 2017

This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of the Korean Geographical Society
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• v.44 no.2
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• pp.176-186
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• 2009

Transportation policy to facilitate the public mode use is of the foremost importance to the local governments of Metropolitan Seoul, regarding the economic and environmental consequences of the increasing use of car. Understanding the travel behaviour is essential to the establishment of proper policy to guide more people to the use of public modes instead of private. The paper reports a result of sequential analysis of individual travel behaviour in Metropolitan Seoul, using a multi-dimensional motif search technique applied to Smart Card data that integrates individuals' different public mode uses. Groups of travel patterns with similar sequential information identified distinctive travel behaviour between Seoul north and south and between metro and bus uses. Travel patterns are more bounded within north Seoul and south Seoul respectively than crossing Han River between north and south. Within north and south, travel patterns visiting northern CBD and southern CBD, respectively, as well as their local neighbour in north and south, often use metro and metro-local bus combination, while travel patterns visiting only the north and south locals without CBDs more use only the local bus line and even only the areal bus line.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.1
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• pp.51-59
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• 1999

Today there are many problems of facility management, water supply and sewerage, because expansion of urban and growing of population, limitation of old facility management. The nearest, it is needed to use GIS technique to reduce the pollution of underground water from sewage leaking and to make quick search of an accident area for water service pipe damage. Therefore, this paper shows that GIS is efficient and scientific technique for management of Urban facilities, water supply and sewerage. Finally, I think that this study help managers of facilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.