• Proceedings of the KWS Conference
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• 1998.05a
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• pp.195-199
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• 1998

• Journal of IKEEE
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• v.26 no.2
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• pp.186-195
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• 2022

In this paper, we propose a new algorithm to improve the accuracy of indoor positioning techniques using Wi-Fi access points as beacon nodes. The proposed algorithm is based on the Weighted Centroid algorithm, a popular method widely used for indoor positioning, however, it improves some disadvantages of the Weighted Centroid method and also for other kinds of indoor positioning methods, by using the received signal strength correction method and genetic algorithm to prevent the signal strength fluctuation phenomenon, which is caused by the complex propagation environment. To validate the performance of the proposed algorithm, we conducted experiments in a complex indoor environment, and collect a list of Wi-Fi signal strength data from several access points around the standing user location. By utilizing this kind of algorithm, we can obtain a high accuracy positioning system, which can be used in any building environment with an available Wi-Fi access point setup as a beacon node.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.273-282
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• 2012

A new effective methodology for optimizing the enrichment of low-enriched zones as well as gadolinia fuel ($UO_2/Gd_2O_3$) rod designs in PLUS7 fuel assemblies was developed to minimize the maximum peak power in the core and to maximize the cycle lifetime. An automated link code was developed to integrate the genetic algorithm (GA) and the core design code package of ALPHA/PHOENIX-P/ANC and to generate and evaluate the candidates to be optimized efficiently through the integrated code package. This study introduces an optimization technique for the optimization of gadolinia fuel rod designs in order to effectively reduce the peak powers for a few hot assemblies simultaneously during the cycle. Coupled with the gadolinia optimization, the optimum enrichments were determined using the same automated code package. Applying this technique to the reference core of Ulchin Unit 4 Cycle 11, the gadolinia fuel rods in each hot assembly were optimized to different numbers and positions from their original designs, and the maximum peak power was decreased by 2.5%, while the independent optimization technique showed a decrease of 1.6% for the same fuel assembly. The lower enrichments at the fuel rods adjacent to the corner gap (CG), guide tube (GT), and instrumentation tube (IT) were optimized from the current 4.1, 4.1, 4.1 w/o to 4.65, 4.2, 4.2 w/o. The increase in the cycle lifetime achieved through this methodology was 5 effective full-power days (EFPD) on an ideal equilibrium cycle basis while keeping the peak power as low as 2.3% compared with the original design.

• Journal of Periodontal and Implant Science
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• v.48 no.4
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• pp.202-212
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• 2018

Purpose: Bone-to-implant contact (BIC) is difficult to measure on micro-computed tomography (CT) because of artifacts that hinder accurate differentiation of the bone and implant. This study presents an advanced algorithm for measuring BIC in micro-CT acquisitions using a spiral scanning technique, with improved differentiation of bone and implant materials. Methods: Five sandblasted, large-grit, acid-etched implants were used. Three implants were subjected to surface analysis, and 2 were inserted into a New Zealand white rabbit, with each tibia receiving 1 implant. The rabbit was sacrificed after 28 days. The en bloc specimens were subjected to spiral (SkyScan 1275, Bruker) and round (SkyScan 1172, SkyScan 1275) micro-CT scanning to evaluate differences in the images resulting from the different scanning techniques. The partial volume effect (PVE) was optimized as much as possible. BIC was measured with both round and spiral scanning on the SkyScan 1275, and the results were compared. Results: Compared with the round micro-CT scanning, the spiral scanning showed much clearer images. In addition, the PVE was optimized, which allowed accurate BIC measurements to be made. Round scanning on the SkyScan 1275 resulted in higher BIC measurements than spiral scanning on the same machine; however, the higher measurements on round scanning were confirmed to be false, and were found to be the result of artifacts in the void, rather than bone. Conclusions: The results of this study indicate that spiral scanning can reduce metal artifacts, thereby allowing clear differentiation of bone and implant. Moreover, the PVE, which is a factor that inevitably hinders accurate BIC measurements, was optimized through an advanced algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• International Journal of Highway Engineering
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• v.17 no.3
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• pp.97-105
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• 2015

PURPOSES : The Toll Collection System (TCS) operated by the Korea Expressway Corporation provides accurate traffic counts between tollgates within the expressway network under the closed-type toll collection system. However, although origin-destination (OD) matrices for a travel demand model can be constructed using these traffic counts, these matrices cannot be directly applied because it is technically difficult to determine appropriate passenger car equivalent (PCE) values for the vehicle types used in TCS. Therefore, this study was initiated to systematically determine the appropriate PCE values of TCS vehicle types for the travel demand model. METHODS : To search for the appropriate PCE values of TCS vehicle types, a traffic demand model based on TCS-based OD matrices and the expressway network was developed. Using the traffic demand model and a genetic algorithm, the appropriate PCE values were optimized through an approach that minimizes errors between actual link counts and estimated link volumes. RESULTS : As a result of the optimization, the optimal PCE values of TCS vehicle types 1 and 5 were determined to be 1 and 3.7, respectively. Those of TCS vehicle types 2 through 4 are found in the manual for the preliminary feasibility study. CONCLUSIONS : Based on the given vehicle delay functions and network properties (i.e., speeds and capacities), the travel demand model with the optimized PCE values produced a MAPE value of 37.7%, RMSE value of 17124.14, and correlation coefficient of 0.9506. Conclusively, the optimized PCE values were revealed to produce estimates of expressway link volumes sufficiently close to actual link counts.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.748-753
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• 2012

In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3544-3564
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• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.