• ETRI Journal
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• v.33 no.5
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• pp.661-669
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• 2011

In this paper, we propose an efficient soft-output signal detection method for spatially multiplexed multiple-input multiple-output (MIMO) systems. The proposed method is based on the ordered successive interference cancellation (OSIC) algorithm, but it significantly improves the performance of the original OSIC algorithm by solving the error propagation problem. The proposed method combines this enhanced OSIC algorithm with a multiple-channel-ordering technique in a very efficient way. As a result, the log likelihood ratio values can be computed by using a very small set of candidate symbol vectors. The proposed method has been synthesized with a 0.13-${\mu}m$ CMOS technology for a $4{\times}4$ 16-QAM MIMO system. The simulation and implementation results show that the proposed detector provides a very good solution in terms of performance and hardware complexity.

• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1554-1560
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• 2019

Development of digital main control rooms (MCRs) has greatly changed operating environments by altering operator tasks, and thus the unique characteristics of digital MCRs should be considered in terms of human reliability analysis. Digital MCR tasks can be divided into primary tasks that directly supply control input to the plant equipment, and secondary tasks that include interface management conducted via soft controls (SCs). Operator performance regarding these secondary tasks must be evaluated since such tasks did not exist in previous analog systems. In this paper, we analyzed SC-related tasks based on simulation data, and classified the error modes of the SCs following analysis of all operational tasks. Then, we defined the factors to be considered in human reliability analysis methods regarding the SCs; such factors are mainly related to interface management and computerized operator support systems. As these support systems function to reduce the number of secondary tasks required for SC, we conducted an assessment to evaluate the efficiency of one such support system. The results of this study may facilitate the development of training programs as well as help to optimize interface design to better reflect the interface management task characteristics of digitalized MCRs.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3277-3290
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• 2023

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.

• Journal of Wind Energy
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• v.13 no.4
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• pp.70-79
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• 2022

It is difficult to control differential settlement and long-term settlement on soft ground with the template used in the pre-filing method of offshore wind power. In this study, the template adopted a suction foundation with high utility on soft ground. To analyze the penetration performance of the doughnut-type suction foundation, step-by-step numerical analysis was applied by calculating the minimum suction pressure needed for ground penetration at that depth. Scale model tests were performed and compared with the numerical analysis results. The ratio of the inside diameter compared to the outside diameter is higher, and penetration by suction was more advantageous than push-in load penetration. The step-by-step numerical analysis method showed an error within 2 % compared to the model tests, so the numerical analysis method confirmed results that the penetration performance of the doughnut-type suction foundation is valid.

• Journal of the Korean Geotechnical Society
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• v.35 no.8
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• pp.43-56
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• 2019

This research has been conducted to suggest an estimation equation of compression index to be practically applied to the southeastern areas by finding out physical and mechanical characteristics of 229 points on the southeastern coast areas (Busan area: 78 points, Gimhae area: 103 points, Geoje area: 18 points, Changwon area: 30 points) through indoor tests and analyzing its relation to the compression index. From the results, we could not only find out correlation between natural water content, liquid limit and initial void ratio versus compression index for soft ground on each of the southeastern coast areas, but also an integrated correlation equation of the southeastern coast areas. In addition, we have performed a comparative analysis of the existing estimated equation of compression index and that found in this paper. As a result, the existing estimated equation suggested by foreign researchers has shown considerable error to be applied to the soft ground on the southeastern coast areas in Korea. The estimated equation of compression index with the water content out of the existing estimated equations has shown minimum 10.8% to maximum 48.1% of error rate, minimum 13.4% to maximum 288.5% of error rate with liquid limit or minimum 9.4% to maximum 211.4% of error rate with initial void ratio. On the other hand, error rates calculated with the estimated equations of compression index from this research have shown minimum 10.5% to maximum 13.4% with water content, minimum 11.6% to maximum 21.3 with liquid limit or minimum 7.1% to maximum 11.7% with initial void ratio, for better results than those with existing estimated equations. In addition, relation between compression index and expansion index has shown Cs = (1/5 ~ 1/12)Cc similar to the existing relation of Cs = (1/5 ~ 1/10)Cc.

• Journal of the korean academy of Pediatric Dentistry
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• v.27 no.4
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• pp.517-523
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• 2000

Nasopharyngeal closure is a sphincter mechanism between the activities of the soft palate, lateral pharyngeal wall and the posterior pharyngeal wall, which divides the oral cavity and the nasal cavity. It participates in physiological activities such as swallowing, breathing and pronunciation. In case of an error in this mechanism, it is called a nasopharyngeal incompetence. The causes of this error are defects in (1) length, function, posture of the soft palate (2) depth and width of the nasopharynx, (3) activity of the posterior and lateral pharyngeal wall. The purpose of this study is to analyze the nasopharynx of cleft palate patients using lateral cephalograms and at the same time, evaluate the degree of hypernasality of each vowels to find its relationship with nasopharyngeal incompetence. The following results were obtained: 1. The length of the soft palate was markedly short than normal. 2. The adequate ratio was smaller than the normal value. 3. As the adequate ratio decreased, when articulating vowels, anatomic mVPI increased. 4. When articulating each vowels, anatomic VPI was in proportion with the degree of hypernasality. 5. The degree of hypernasality was greater in high vowels(/i/, /u/) than low vowel(/a/). From the above results, it can be concluded that in cleft palate patients, lateral cephalograms can be used effectively in diagnosing and evaluating nasopharyngeal incompetence. The anatomic structure of the nasopharynx has close relation to the degree of hypernasality.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.3-11
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• 2015

An advanced receiver which can manage inter-cell interference is required to cope with the explosively increasing mobile data traffic. 3rd Generation Partnership Project (3GPP) has discussed network assisted interference cancellation and suppression (NAICS) to improve signal-to-noise-plus-interference ratio (SINR) and receiver performance by suppression or cancellation of interference signal from inter-cells. In this paper, we propose the advanced receiver based on soft decision to reduce the interference from neighbor cell in LTE-Advanced downlink system. The proposed receiver can suppress and cancel the interference by calculating the unbiased estimation value of interference signal using minimum mean square error (MMSE) or interference rejection combing (IRC) receiver. The interference signal is updated using soft information expressed by log-likelihood ratio (LLR). We perform the system level simulation based on 20MHz bandwidth of 3GPP LTE-Advanced downlink system. Simulation results show that the proposed receiver can improve SINR, throughput, and spectral efficiency of conventional system.

• Journal of the Korean Geotechnical Society
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• v.15 no.5
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• pp.29-41
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• 1999

This Paper applied a simple strength parameter averaging method to double layered systems consisting of the strong sand layer overlying the soft clay deposit. This study derived a formula which defines a critical depth as the strength parameters, and used the correction parameter, $\alpha$ to reduce an error of the strength parameter averaging method. The results of the method were presented in the form of dimensionless charts and were compared with the results of several solutions proposed by Satyanarayana & Grag, Sreenivasulu, and Meyerhof & Hanna. The results of the proposed method coincided with the method of Meyerhof & Hanna and the results obtained from FLAC. But the Satyanarayana & Grag method and the Sreenivasulu method overestimated the bearing capacity. Consequently, the bearing capacity of foundation on sand layer overlying soft clay layer can be approximately estimated by using the proposed dimensionless charts.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.149-154
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• 2017

The MCT-Adaboost algorithm chooses an optimal set of features in each rounds. On each round, it chooses the best feature by calculate minimizing error rate using feature index and MCT kernel distribution. The involved process of weak classification executed by a hard decision. This decision occurs some problems when it chooses ambiguous kernel feature. In this paper, we propose the modified MCT-Adaboost classification using soft decision. The typical MCT-Adaboost assigns a same initial weights to each datum. This is because, they assume that all information of database is blind. We assign different initial weights with our propose new algorithm using some statistical properties of involved features. In experimental results, we confirm that our method shows better performance than the traditional one.