Earticle

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

과거 양식장에서 어류 질병은 세균성이었던 반면 최근은 바이러스성 및 혼합된 형태가 되면서 어류 질병의 빈도가 높아졌다. 양식장이라는 밀폐된 공간에서 바이러성 질병은 확산속도가 높으므로 집단 폐사로 이어질 확률이 매우 높다. 집단 폐사를 방지하기 위해서는 어류 질병의 빠른 식별이 중요하다. 그러나 어류의 질병 진단은 고도의 전문지식이 필요하고 매번 어류의 상태를 눈으로 확인하기 어렵다. 질병의 확산을 막기 위해서는 병이든 어류의 자동식별 시스템이 필요하다. 본 논문에서는 딥러닝 기반의 넙치의 질병 식별 시스템의 성능을 높이기 위해서 기존 전처리 방법을 비교 실험한다. 대상 질병은 넙치에서 가장 빈번히 발생하는 3가지 질병 스쿠티카병, 비브리오증, 림포시스티스를 선정하였고 이미지 전처리 방법으로 RGB, HLS, HSV, LAB, LUV, XYZ, YCRCV를 사용하였다. 실험결과 일반적인 RGB를 사용하는 것보다 HLS가 가장 좋은 결과를 얻을 수 있었다. 간단한 방법으로 질병의 인식률을 향상해 어류 질병 식별 시스템을 고도화 할 수 있을 것으로 예상한다.

In the past, fish diseases were bacterial in aqua farms, but in recent years, the frequency of fish diseases has increased as they have become viral and mixed. Viral diseases in an enclosed space called a aqua farm have a high spread rate, so it is very likely to lead to mass death. Fast identification of fish diseases is important to prevent group death. However, diagnosis of fish diseases requires a high level of expertise and it is difficult to visually check the condition of fish every time. In order to prevent the spread of the disease, an automatic identification system of diseases or fish is needed. In this paper, in order to improve the performance of the disease identification system of Paralichthys olivaceus based on deep learning, the existing pre-processing method is compared and tested. Target diseases were selected from three most frequent diseases such as Scutica, Vibrio, and Lymphocystis in Paralichthys olivaceus. The RGB, HLS, HSV, LAB, LUV, XYZ, and YCRCV were used as image pre-processing methods. As a result of the experiment, HLS was able to get the best results than using general RGB. It is expected that the fish disease identification system can be advanced by improving the recognition rate of diseases in a simple way.

주요한 수질지표 중의 하나인 생물화학적 산소요구량(BOD) 농도는 호소와 하천에서 생태학적 측면에서 관측항목으로 취급하고 있다. 본 연구에서는 대한민국의 도산 및 황지지점에서 BOD 농도예측을 위하여 새로운 이단계 하이브리드 패러다임(웨이블릿 기반 게이트 순환 유닛, 웨이블릿 기반 일반화된 회귀신경망, 그리고 웨이블릿 기반 랜덤 포레스트) 을 활용하였다. 이러한 모형들은 각 대응하는 독립모형들(게이트 순환 유닛, 일반화된 회귀신경망, 그리고 랜덤 포레스트) 과 함께 평가되었다. 다양한 수질 및 수량지표들이 여러 개의 입력조합(분류1-5) 을 기본으로 하여 독립 및 이단계 하이브리드 모형을 개발하기 위하여 구현되었다. 언급한 모형들은 root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), 그리고 correlation coefficient (CC) 를 포함한 세 개의 통계지표로서 평가되었으며, 통계결과치를 분석하면 이단계 하이브리드 모형들이 항상 대응하는 독립모형들의 예측 정도를 개선하지 않은 것으로 나타났다. 대한민국의 도산관측소에서는 DWT-RF5 (RMSE = 0.108 mg/L) 모형이 다른 최적모형과 비교하여 BOD 농도의 더 정확한 예측을 나타내었으며, 황지관측소에서는 DWT-GRNN4 (RMSE = 0.132 mg/L) 모형이 BOD 농도를 예측하는 최고의 모형이다.

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

국제기아와 같이 멀리 떨어진 사람들을 돕기 위한 메시지는 상황적인 요인의 영향을 많이 받는다. 이러한 관점에서 본 연구는 메시지의 특성(이익/손실 프레이밍)과 개인의 특성(돕기 활동에 대한 사전 태도)이 상호작용하여 메시지 태도와 행위 의도에 영향을 미치는지 살펴보았다. 연구는 220명이 참여한 실험으로 진행하였다. 메시지 태도와 행위 의도에 대한 분석 결과에서 이익/손실 프레이밍과 사전 태도의 상호작용이 유의미하게 발현되었다. 그러한 상호작용은 사전 태도가 상대적으로 우호적인 집단에 대하여 이익 프레이밍이 더 효과적으로 작용하여 발생하였다. 손실 프레이밍에서도 같은 방향성이 나타났지만 그 크기는 상대적으로 작았다. 이러한 상호작용의 방향성은 기대 만족감이 매개하는 것으로 분석되었다. 본 연구의 결과를 토대로 타인을 돕기 위한 메시지의 적절한 기획을 위한 실용적, 이론적 의미를 논하였다.

Messages to help people far away, such as International relief campaign, are heavily influenced by situational factors. In this light, the study examined whether the message characteristics (gain/loss framing) and the individual characteristics (previous attitude to helping activity) interact to affect the message attitude and behavioral intention. The study was conducted by an experiment involving 220 subjects. The analysis resulted in the significant interactions between gain/loss framing and pre-attitude by the same direction for message attitudes and behavioral intention. It was shown that gain framing worked more effectively for the relatively more friendly group towards helping activities than for the less friendly one. The same direction was shown in the loss framing, but its difference was smaller. The process of the interactions appeared as mediated by anticipated satisfaction. The practical and theoretical implications were discussed for proper planning of messages to help others.

본 논문에서는 실내 임펄시브 노이즈 채널 환경에서 비콘 기반의 측위 시스템의 측위 정밀도를 향상시킬 수 있는 전처리 기법을 제안하였다. 임펄시브 노이즈는 복잡한 실내 구조 환경이나 간섭 환경에서 발생하며 이는 무선 통신에서 신호 복조 오류 확률을 증가시켜 정확한 데이터 복조를 어렵게 한다. 제안한 전처리 기법은 사용자의 위치 좌표를 산출하기 위한 비콘 기반의 삼각측량법을 수행하기 이전에 적용 및 수행되며, 제안 기법을 데이터 복소의 오류 확률을 감소시켜 정확한 데이터를 삼각측량법의 입력값으로 제공한다. 신뢰성 있는 데이터 입력을 통해 위치 좌표 결과값의 신뢰도를 향상시키는 매커니즘이다. 따라서 임펄시브 노이즈 완화를 위해 신호의 시간-주파수 분해능이 우수한 웨이블릿 잡음 제어 방법을 기반으로 임펄시브 노이즈에 특성에 따라 노이즈를 제거하는 적응적 임계 함수를 제안하였다. 컴퓨터 시뮬레이션을 통해 제안한 적응적 임계 함수가 기존의 기법과 대비로 비교적 Bit Error Rate 성능 및 Signal-to-Noise Ratio 성능을 향상시키는 결과를 확인하였다.

In this paper, we propose a pre-processing scheme for improving indoor positioning accuracy in impulsive noise channel environments. The impulsive noise can be generated by multi-path fading effects by complicated indoor structures or interference environments, which causes an increase in demodulation error probability. The proposed pre-processing scheme is performed before a triangulation method to calculate user’s position, and providing reliable input data demodulated from a received signal to the triangulation method. Therefore, we studied and proposed an adaptive threshold function for mitigation of the impulsive noise based on wavelet denoising. Through results of computer simulations for the proposed scheme, we confirmed that Bit Error Rate and Signal-to-Noise Ratio performance is improved compared to conventional schemes.

사회재난의 위험성이 증가함에 따라 리스크 평가에 대한 중요성이 증가하면서 이에 대한 연구가 활발하게 수행되고 있다. 리스크 평가를 바탕으로 위험지도를 제작하여 이를 바탕으로 정책 결정과 시민들에게 공표하는 등 재난안전 측면에 있어 위험 지도는 매우 중요한 연구이다. 그러나 위험지도를 제작하는 과정에서 데이터의 속성마다 적절한 표준화가 이루어지지 않을 경 우 최종 위험지도의 결과를 신뢰하기 어렵다. 사회재난의 경우 지역 특성에 따라서 일부 지역에만 발생하거나 계절적 요인으로 인해 특정 기간에 집중되어있다. 따라서 특정 재난은 이상치 속성을 보이는 데이터 분포를 가지고 있어 위험지도를 작성하기 전 전처리 과정이 필요하다. 본 논문에서는 이상치 속성을 갖는 재난을 대상으로 새로운 표준화 기법을 제안하였으며 이상치 탐색을 위해 통계적 기법을 적용하였다. 또한 이상치 탐색 후 제안한 방법을 통해 표준화를 위한 범위설정을 재검토하였다. 이 상치를 고려하기 전과 후의 결과를 비교하였으며 이를 바탕으로 데이터 전처리의 필요성을 제시하고 가축질병을 중심으로 적 용한 결과를 나타내었다.

본 연구는 개인 특성에 따른 건강검진과 민간보험 이용 행태를 파악하기 위한 것이다. 한국보 건사회연구원과 건강보험관리공단에서 제공하는 한국의료패널 데이터를 전 처리 과정을 거쳐 변환한 후, 개인의 사회적, 경제적, 건강요인에 따른 이용 행태를 STATA 10.0으로 분석하였다. 연구결과, 만 성질환자와 경제적으로 여유있는 계층이 건강검진을 많이 이용하고, 민간보험 가입에는 가구소득 및 경제활동 등 경제적 요인이 주요 영향요인으로 분석되었다. 그리고 건강을 우려하는 그룹이 건강검진 을 적극적으로 이용할 것이라는 예측과 달리 건강하다고 인식하는 그룹에서 건강검진을 많이 이용하 는 것으로 파악되었다.

Purpose: This study is to examine the personal characteristics, such as economic, health, social prospects and helath-scan or private insurance usage. Methods: The data used in this study were collected from Korea Health Panel, and testified using regression in STATA 10.0. Results: According to the results of the study, chronic illness and wealth group more use healthscan service than the opposite, and economic factors are related to taking of private health insurance. Also, it show that the healthy group more use the health-scan service than unhealthy group.

In this article, to predict the wear amount of nano particles in a worn nano composite, computational analysis pre/post-processor were developed using ABAQUS and visual basic programs. The abrasion, which is one of nano particles release scenarios, was applied in the computational analysis. Moreover, reciprocation, which is the abrasion type, was selected and incarnated in abrasion computational analysis. Also, to predict wear amount of nano composite in computational analysis, archard equation was applied and the predicted wear amount was evaluated compare with experimental value. The predicted wear amount of nano composite was increased in accordance with increasing force and was similar to result of experimental value.

Painting pretreatment is an important task in determining the life of painting as it removes rust or foreign substances from the painting surface and gives adhesion between the painting surface and the painting surface. Since painting pretreatment is an important task, IMO strictly requires that the painting pretreatment surface be maintained at a Sa 2.5 grade and the surface roughness is 30μm~75μm. Painting pre-processing is an important task that determines the lifespan of a painting, but it is done through visual inspection by the inspector, and the quality varies depending on the inspector. In this study, in order to develop a quality measurement system for the painting pretreatment surface, Matlab2023b was used to determine the range of appropriate quality brightness by comparing the brightness of the painting pretreatment surface and surface roughness.

본 연구는 엿듣기 패러다임(overhearing paradigm)과 선다형 과제를 사용하여, 선행 발화(pre-sequence)가 전달하는 가능성(높음, 중간, 낮음)과 중국 체류 기간이 제2언어로서 중국어를 학습하는 한국인 학습자의 제안 전략 선택 및 온라인 처리에 미치는 영향을 조사한다. 전략 선택 측면에서, 학습자들은 높은 가능성의 맥락에서는 원어민과 유사한 수행을 보였으나, 더 큰 모호성과 사회적 위험을 수반하는 중간 및 낮은 가능성의 맥락에서는 어려움을 겪는 것으로 나타났다. 또한, 장기 체류 집단은 단기 체류 집단보다 더 나은 수행을 보였다. 온라인 처리 측면에서, 반응 시간 데이터는 기저의 인지 메커니즘에 더 깊은 차이가 있음을 보여주었다. 즉, 단기 체류 집단은 원어민과 장기 체류 집단 모두에게서 관찰되는 '망설임' 현상이 결여되어 있었으며, 이는 미성숙한 암시적 화용 모니터링 시스템을 시사한다. 이러한 결과는 장기적인 몰입을 통한 화용 발달이 단지 외현적 행동의 적절성 향상뿐만 아니라, 목표어의 화용 모니터링 시스템을 구축하고 내면화하는 과정을 포함함을 시사한다. 이 연구 결과를 바탕으로, 향후 중국어 교육에서는 맥락 비교와 같은 방법을 사용하여 선행 발화와 같은 화용 현상에 대한 명시적 교수를 강화하고, 이를 통해 학습자의 맥락 단서에 대한 민감성과 온라인 화용 모니터링 능력을 향상시킬 것을 제안한다.

Employing an overhearing paradigm and a multiple-choice task, this study investigates the influence of possibility (high, medium, low) conveyed by pre-sequences and length of stay in China on the strategy selection and online processing of Korean learners of Chinese as a second language. In terms of strategy selection, the results show that while learners performed similarly to native speakers in high-possibility contexts, their difficulties emerged in medium- and low-possibility contexts, which involve greater ambiguity and social risk; furthermore, the longer-stay group outperformed the shorter-stay group. In terms of online processing, reaction time data revealed deeper differences in underlying cognitive mechanisms: the shorter-stay group lacked the "hesitation" phenomenon observed in both native speakers and the longer-stay group, indicating an immature implicit pragmatic monitoring system. These findings suggest that pragmatic development through long-term immersion involves not only an improvement in the appropriateness of explicit behavior but the establishment and internalization of a target language pragmatic monitoring system. Based on these findings, it is suggested that future Chinese language pedagogy should enhance explicit instruction on pragmatic phenomena such as pre-sequences, using methods like contextual comparison to improve learners' sensitivity to contextual cues and their online pragmatic monitoring abilities.

Over the period of time, automated classification of gender has gained enormous significance and has become an active area of research. Many researchers have put a lot of effort and have produced quality research in this area. Still, there is an immense potential in this field because of its utility in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have utmost importance in this area. Gender classification can be used as part of a face recognition process. This paper presents a comprehensive comparison of state-of-the-art research techniques. We have divided the classification process into three stages and have presented a categorical review of existing literatures. Their analysis has been presented along-with their strengths and weaknesses. We have also discussed standard data sets. This can help the novel researcher a comprehensive review. Future dimensions are presented considering the limitations found in the literature.

Knowledge Discovery in Databases (KDD) covers various processes of exploring useful information from voluminous data. These data may contain several inconsistencies, missing records or irrelevant features, which make the knowledge extraction, a difficult process. So, it is essential to apply pre-processing techniques to these data in order to enhance its quality. Detailed description of data cleaning, imbalanced data handling and dimensionality reduction pre-processing techniques are depicted in this paper. Another important aspect of Knowledge Discovery is to filter, integrate, visualize and evaluate the extracted knowledge. In this paper, several visualization techniques such as scatter plots, parallel co-ordinates and pixel oriented technique are explained. The paper also includes detail descriptions of three visualization tools which are DBMiner, Spotfire and WinViz along with their comparative evaluation on the basis of certain criteria. It also highlights the research opportunities and challenges of Knowledge Discovery process.

Delta modulation plays a key role in data communication; the problem encountered in delta modulation is the slope over load error, which is inherent in the system. In order for the signal to have good fidelity, the slope-overload error needs to be as small as possible. Adaptive delta modulation reduces the slope over load error to a greater extent. ADM attempts to increase the dynamic range and the tracking capabilities of fixed step-size delta modulation. The adaptive algorithms adjust the step size (from a range of step sizes) to the power level of the signal and thus enhance the dynamic range of the coding system appreciably. This paper suggests a novel 1-bit Adaptive Delta Modulation technique for improving the signal-to-noise ratio (SNR) of Adaptive Delta Modulators (ADM). Various step-size algorithms are discussed and also their performance comparison is made. A new technique has also been discussed with a suitable pre-filer used for improving the SNR.

Ontology matching is one of the most important challenges in the semantic web. It is a technique which is used to find the semantic correspondences between entities which are modeled in different ontologies. Despite many research efforts in ontology matching, the matching process still suffers from severe problems with respect to the quality of the matching results. Furthermore, finding the correspondences, for the most part, is very time-consuming. In this paper, we examine how a pre-processing phase can improve the results of the matching process. A pre-processing phase is added to the matchers for the analysis of input ontologies so as to detect some inappropriate patterns which are modeled by various developers. Then, refactoring operations are utilized on detected patterns for achieving assimilated ontologies. Finally, our proposed approach is tested by ASMOV (Automated Semantic Matching of Ontologies with Verification), which is one of the top matchers in OAEI (Ontology Alignment Evaluation Initiative). Experimental results show that ontologies achieved from this proposed approach are more efficient than the original unrepaired ones, with respect to standard evaluation measures.

In this paper, two adaptive image enhancement and de-noising chains are produced. Our aim is to enhance the face image quality that stored in a large database for face recognition applications. Each processing chain consists of three steps, the first chain is proposed to enhance Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) recognition rate, in the first step of this chain, the face images are de-noised with Haar wavelet de-noising filter at level ten of decomposition, in the second step, the de-noised image is adjusted to enhance the image contrast, and in the third step the high pass filter ‘Laplacian of Gaussian filter’ is used for detecting edges in face images. The second chain is proposed to enhance Linear Discriminat Analysis LDA and Kernel Fisher Analysis (KFA) recognition rate, in the first step of this chain the image contrast is adjusted and entered to histogram equalization as second step and in the third step the image is de-noised with Haar wavelet de-noising filter at level ten of decomposition. Our approaches produced good result and contributed in raising the recognition rate in PCA, KPCA, LDA and KFA up to 10%, 20%, 5% and 4% respectively when 400 face images are used.

Fluid simulation is an important application of High Performance Computing. The hybrid atomistic-continuum coupling fluid simulation can effectively handle the contradictory of the reduction of the simulation scale and the increase of the computation load when trying to improve the accuracy. The pre-processing procedure of the coupling simulation is quite important in the whole simulation process and requires the support of an efficient, user-friendly interface. Under the demand of efficient coupling fluid simulation, in this paper we design and implement a visualized pre-processing framework HACP2 based on SALOME, for the unified molecular dynamic-computational fluid dynamics modeling and apply it into the coupling simulation process. The experimental verification indicates that our HACP2 framework can offer efficient, easy-to-use pre-processing for the hybrid atomistic-continuum coupling simulation, and effectively improve the efficiency of the coupling simulation.

Needs for the collection of data for text mining of the case with many protolanguages or emotional words distributed in SNS and following trends, and the treatment process method of purification of improved data in a previous step are raised. In data collection for mining, the online trend dictionary based on tag was referred and semi-structured data was effectively parsing processed based on tags of dictionaries according to domains of treating languages, and data for analysis was collected. Additionally, there were the cases to show inefficiency in the text processing of the general genre or the limitation of noun extraction, however, it can be suggested as an alternative on searching trend vocabularies which requires the timeliness or the class processing for corpus work of sentiment dictionary.

Yong, Namseok & Lee, Miseon. 2012. Semantic Effects of a Pre-verbal Argument on the Online Processing of Korean Sentences: An Eye-tracking Study. Korean Journal of Linguistics, 37-3, 639-657. The goal of this study was to examine the role of the lexical semantic information of pre-verbal arguments in the online processing of sentences. More specifically, it was examined whether the semantic information of the subject can predictively restrict the semantic domain of the upcoming argument even before the verb is introduced into the string. Given that the speakers of SOV languages can produce and process verb-final sentences without any difficulties, other information than that from a verb should be involved in the sentence processing so that the speakers do not have to wait until the end of a clause. In our eye-tracking experiment, 38 participants showed anticipatory eye-movements to the picture of the direct object (e.g., food) as soon as hearing a semantically related subject (e.g., cook), but did not when the subject (e.g., Yenghi) was semantically neutral to the object. These results confirm the predictive mechanism of the parsing system and the semantic effect of a pre-verbal argument: that is, using the semantic information of a pre-verbal argument which is available early in the input, the parser can rapidly limit the semantic type of the following argument. In this way, a verb-final language can be processed immediately and incrementally. (Hanyang University)

Blood collection devices and pre-analytical processing can affect accuracy of laboratory test results. Antiseptics can cause hemolysis and interfere with blood analytes level. Needles and syringes may release coating substances and exert shear forces that influence cells. Blood collection tube wall, stopper, stopper lubricants, surfactants and separating gels may contain materials, interact with cellular components, proteins and drugs. Clinical laboratory personnel have to understand potential sources of error and interactions during clinical laboratory testing. To minimize negative effects on clinical laboratory testing, concepts of quality control, proficiency testing and standard operating procedures may be applied and continuously managed.