• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.52-58
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• 2013

This paper proposes an adaptive algorithm based on a method of dynamic error signal generation suitable for signal state by examining the equalizer output signal in blind equalization. In the proposed method, it estimates the error signals using single modulus and multiple modulus each effective to the early stage of equalization or steady-state, and it generates a new error signal from the two error estimates. Two equalizer structures are implemented and their performances are compared: 1-equalizer structure that generates a new error signal by combining the two error estimates weightedly and updates the equalizer using this, and 2-equalizer structure that updates two equalizers respectively depending on the weights of the two error signals. In the proposed method, as the error signals were generated optimally before and after the initial convergence respectively, it was confirmed by computer simulations that the equalizer was updated effectively.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• Nutrition Research and Practice
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• v.4 no.5
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• pp.414-420
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• 2010

This study examines the combined effects of caloric restriction on body composition, blood lipid, and satiety in slightly overweight women by varying food density and aerobic exercise. Twenty-three women were randomly assigned to one of two groups for a four-week weight management program: the high-energy density diet plus exercise (HDE: n = 12, $22{\pm}2$ yrs, $65{\pm}7$ kg, $164{\pm}5$ cm, $35{\pm}4%$ fat) and low-energy density diet plus exercise (LDE: n = 11, $22{\pm}1$ yrs, $67{\pm}7$ kg, $161{\pm}2$ cm, $35{\pm}4%$ fat) groups. Subjects maintained a low-calorie diet (1,500 kcal/day) during the program. Isocaloric ($483{\pm}26$ for HDE, $487{\pm}27$ kcal for LDE) but different weight ($365{\pm}68$ for HDE, $814{\pm}202$ g for LDE) of lunch was provided. After lunch, they biked at 60% of maximum capacity for 40 minutes, five times per week. The hunger level was scaled (1: extremely hungry; 9: extremely full) at 17:30 each day. Before and after the program, the subjects' physical characteristics were measured, and fasting blood samples were drawn. The daily energy intake was $1,551{\pm}259$ for HDE and $1,404{\pm}150$ kcal for LDE (P > 0.05). After four weeks, the subjects' weights and % fat decreased for both LDE (-1.9 kg and -1.5%, P < 0.05) and HDE (-1.6 kg and -1.4%, respectively, P < 0.05). The hunger level was significantly higher for HDE ($2.46{\pm}0.28$) than for LDE ($3.10{\pm}0.26$) (P < 0.05). The results suggest that a low-energy density diet is more likely to be tolerated than a high-energy density diet for a weight management program combining a low-calorie diet and exercise, mainly because of a reduced hunger sensation.

• Journal of Power Electronics
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• v.18 no.3
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• pp.841-852
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• 2018

This paper presents a dual-algorithm search method (GWO-GSO) combining grey wolf optimization (GWO) and golden-section optimization (GSO) to realize maximum power point tracking (MPPT) for photovoltaic (PV) systems. First, a modified grey wolf optimization (MGWO) is activated for the global search. In conventional GWO, wolf leaders possess the same impact on decision-making. In this paper, the decision weights of wolf leaders are automatically adjusted with hunting progression, which is conducive to accelerating hunting. At the later stage, the algorithm is switched to GSO for the local search, which play a critical role in avoiding unnecessary search and reducing the tracking time. Additionally, a novel restart judgment based on the quasi-slope of the power-voltage curve is introduced to enhance the reliability of MPPT systems. Simulation and experiment results demonstrate that the proposed algorithm can track the global maximum power point (MPP) swiftly and reliably with higher accuracy under various conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2797-2820
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• 2015

In order to accommodate huge number of antennas in a limited antenna size, a large scale antenna array is expected to have a three dimensional (3D) array structure. By using the Active Antenna Systems (AAS), the weights of the antenna elements arranged vertically could be configured adaptively. Then, a degree of freedom (DOF) in the vertical plane is provided for system design. So the three-dimension MIMO (3D MIMO) could be realized to solve the actual implementation problem of the massive MIMO. However, in 3D massive MIMO systems, the pilot contamination problem studied in 2D massive MIMO systems and the inter-cell interference as well as inter-vertical sector interference in 3D MIMO systems with vertical sectorization exist simultaneously, when the number of antenna is not large enough. This paper investigates the interference management towards the above challenges in 3D massive MIMO systems. Here, vertical sectorization based on vertical beamforming is included in the concerned systems. Firstly, a cooperative joint vertical beams adjustment and pilot assignment scheme is developed to improve the channel estimation precision of the uplink with pilots being reused across the vertical sectors. Secondly, a downlink interference coordination scheme by jointly controlling weight vectors and power of vertical beams is proposed, where the estimated channel state information is used in the optimization modelling, and the performance loss induced by pilot contamination could be compensated in some degree. Simulation results show that the proposed joint optimization algorithm with controllable vertical beams' weight vectors outperforms the method combining downtilts adjustment and power allocation.

• Korean Journal of Acupuncture
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• v.36 no.4
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• pp.274-280
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• 2019

Objectives : This preliminary study investigated the effect of acupuncture treatment on short-term ketogenic diet-induced weight loss and glucose intolerance in mice. Methods : Six-week old male C57BL/6J mice were randomly assigned to 3 groups: normal, KD, and KD+ACU. All the mice except normal group were fed with ketogenic diet formula for 7 weeks and mice in KD+ACU group received acupuncture treatment three times a week. Body weights were measured three times a week, and glucose level was measured on week 1,3,5, and 7. Ketone level was measured on week 3,5, and 7. Results : Ketogenic diet showed short-term weight loss effect, however, acupuncture treatment did not affect on the weight loss. Ketone level was increased in KD fed mice compared to normal diet fed mice and the level was decreased in KD+ACU group on week 3. However, the change was not significantly different compared to KD group on week 7. Glucose intolerance was improved in KD+ACU group compared to KD group. Conclusions : Acupuncture treatment was effective in relieving glucose intolerance, and the results suggest that combining acupuncture treatment with ketogenic diet may complement each therapeutic intervention by improving glucose intolerance but not effecting on weight loss. This study provides meaningful evidence as a preliminary study of acupuncture treatment on ketogenic diet.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.72-79
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• 2017

In this paper, we present a new saliency detection algorithm using the mutual information of wavelet subbands. Our method constructs an intermediate saliency map using the power operation and Gaussian blurring for high-frequency wavelet coefficients. After combining three intermediate saliency maps according to the direction of wavelet subband, we find the main directional components using entropy measure. The amount of mutual information of each subband is obtained centering on the subband having the minimum entropy The final saliency map is detected using Minkowski sum based on weights calculated by the mutual information. As a result of the experiment on CAT2000 and ECSSD databases, our method showed good detection results in terms of ROC and AUC with few computation times compared with the conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.163-170
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• 2023

The user-based collaborative filtering technique, one of the implementation methods of the recommendation system, recommends the preferred items of neighboring users based on the calculations of neighboring users with similar rating histories. However, it fundamentally has a data scarcity problem in which the quality of recommendations is significantly reduced when there is little common rating history. To solve this problem, many existing studies have proposed various methods of combining Jaccard index with a similarity measure. In this study, we introduce a time-aware concept to Jaccard index and propose a method of weighting common items with different weights depending on the rating time. As a result of conducting experiments using various performance metrics and time intervals, it is confirmed that the proposed method showed the best performance compared to the original Jaccard index at most metrics, and that the optimal time interval differs depending on the type of performance metric.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7324-7330
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• 2014

Color distortion of the dark images acquired under a low-light-level environment with a weak light source can be cause of the performance decreation of various vision systems. Therefore, recovering the original color of the images is an important process for enhancing the performance of the system. For this, this study proposes a color restoration method using a dichromatic reflection model. This paper assumes that the dark images can be classified into two parts affected by specular or diffuse reflection. Two different color constancy methods were then applied to the images to remove the effects of each reflection and two images were created as a result. The resulting images produced a one color-corrected image by combining with different weights according to the position in the images. For the performance evaluation, this paper used a synthesized image, and considered the Euclidean distance and angular error as an evaluation factor. In addition, a performance comparison was performed with the existing various color constancy method to achieve the objectivity of evaluation. The experimental results showed that the proposed method can be a more suitable solution for color restoration than the existing method.