Earticle

도로상에서 발생하는 교통 혼잡비용은 지역 간 도로 보다는 도심부 내에서 비중 있게 발생하며, 이는 전체 혼 잡비용의 약 63.39%를 차지하고 있다. 따라서, 교통혼잡비용의 절감을 위해서는 도심부의 교통 혼잡을 해소 하는 것이 중요하다. 도심부의 교통 혼잡은 반복정체와 비반복정체로 구분되며, 비반복 정체를 신속하고 정확 하게 검지하는 것이 교통 혼잡의 해소에 있어 무엇보다 중요하다. 그러나 돌발상황 검지에 관한 연구는 대부 분 연속류를 대상으로 수행되어 왔다. 도심부 단속류 도로의 경우, 신호 교차로·주정차 차량 등 다양한 변수가 존재하기 때문에 연속류에 적용되는 돌발상황 검지 알고리즘을 수정없이 적용하기에 무리가 있다. 따라서 본 연구에서는 도심부 단속류 도로를 대상으로 수집된 GPS 기반의 차량궤적 데이터에 인공신경망을 적용하여 돌발상황검지 모형을 구축하였다. 제안된 모형의 정확도 검증 결과, 돌발상황 검지율 46.15%, 오보율 25.00%가 도출되었다. 이러한 결과는 단속 류를 대상으로 하는 초기 연구 결과로서 의미가 있다. 또한 내비게이션 장치와 같은 차량 궤적 데이터만을 활 용하여 비반복정체를 검지 할 수 있는 가능성을 제시 했다는 것에 의미를 찾을 수 있을 것이다.

Traffic congestion cost is more likely to occur in the inner city than interregional road, and it accounts for about 63.39% of the whole. Therefore, it is important to mitigate traffic congestion of the inner city. Traffic congestion in the urban could be divided into Recurrent congestion and Non-recurrent congestion. Quick and accurate detection of Non-recurrent congestion is also important in order to relieve traffic congestion. The existing studies about incident detection have been variously conducted, however it was limited to Uninterrupted Traffic Flow Facilities such as freeway. Moreover study of incident detection on the interrupted Traffic Flow Facilities is still inadequate due to complex geometric structure such as traffic signals and intersections. Therefore, in this study, incident detection model was constructed using by Artificial Neural Network to aim at urban arterial road that is interrupted traffic flow facility. In the result of the reliability assessment, the detection rate were 46.15% and false alarm rate were 25.00%. These results have a meaning as a result of the initial study aimed at interrupted traffic flow. Furthermore, it demonstrates the possibility that Non-recurrent congestion can be detected by using car navigation data such as car navigator system device.

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.

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.

This paper designs a disease prediction algorithm to diagnose migraine among the types of diseases in advance by learning algorithms using machine learning-based time series analysis. This study utilizes patient data statistics, such as electroencephalogram activity, to design a prediction algorithm to determine the onset signals of migraine symptoms, so that patients can efficiently predict and manage their disease. The results of the study evaluate how accurate the proposed prediction algorithm is in predicting migraine and how quickly it can predict the onset of migraine for disease prevention purposes. In this paper, a machine learning algorithm is used to analyze time series of data indicators used for migraine identification. We designed an algorithm that can efficiently predict and manage patients’ diseases by quickly determining the onset signaling symptoms of disease development using existing patient data as input. The experimental results show that the proposed prediction algorithm can accurately predict the occurrence of migraine using machine learning algorithms.

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.

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.

머신러닝은 학습 데이터로부터 목적함수를 구성하고, 테스트 데이터를 통해 목적함수의 확인함으로써 발생하는 데이터에 대한 예측을 수행한다. 머신러닝에서 입력데이터는 전처리 과정을 통해 정규화 과정을 거친다. 이런 정규화는 입력데이터의 평균과 표준편차를 이용하여 표준화하거나, 수치 데이터가 아닌 nominal value는 one-hot 코드 형태로 변환하는 방식을 이용한다. 그러나 이 전처리 과정만으로 문제를 해결할 수 없다. 이러한 이유로 본 논문에서 입력데이터의 정규화를 위해 온톨로지를 이용하는 방법을 제안한다. 이를 위한 테스트 데이터는 모바일 기기로부터 수집된 와이파이 장치의 RSSI값을 이용하고, 수집된 데이터의 노이즈와 이질적 문제는 온톨로지를 이용하여 정제하는 방법을 제시한다.

Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.

Galaxies can be "pre-processed" in the low-density outskirts by ambient medium in the filaments or tidal interactions with other galaxies while falling into the cluster. In order to probe how early on and by which mechanisms galaxies can be affected before they enter high-density cluster environments, we are carrying out an atomic hydrogen ($H\small{I}$) imaging study of a sample of galaxies selected from three filamentary structures around the Virgo cluster. Our sample consists of 14 late-type galaxies, which are potentially interacting with their surroundings. The $H\small{I}$ observations have been done using the Westerbork Synthesis Radio Telescope, the Giant Metrewave Radio Telescope, and the Jansky Very Large Array with column density sensitivity of ${\approx}3-5{\times}10^{19}cm^{-2}$ in $3{\sigma}$ per channel, which is low enough to detect faint $H\small{I}$ features in the outer disks of galaxies. In this work, we present the Hi data of two galaxies that were observed with GMRT. We examine the $H\small{I}$ morphology and kinematics to find the evidence for gas-gas and/or tidal interactions, and discuss which mechanism(s) could be responsible for pre-processing in these cases.

Development of highly efficient and energy-saving tractors has been one of the issues in agricultural machinery. For design of such tractors, measurement and analysis of load on major power transmission parts of the tractors are the most important pre-requisite tasks. Objective of this study was to perform pre-processing procedures before effective analysis of load data of agricultural tractors (30, 75, and 82 kW) during major field operations such as plow tillage, rotary tillage, baling, bale wrapping, and to select the suitable pre-processing method for the analysis. A load measurement systems, equipped in the tractors, were consisted of strain-gauge, encoder, hydraulic pressure, and radar speed sensors to measure torque and rotational speed levels of transmission input shaft, PTO shaft, and driving axle shafts, pressure of the hydraulic inlet line, and travel speed, respectively. The entire sensor data were collected at a 200-Hz rate. Plow tillage, rotary tillage, baling, wrapping, and loader operations were selected as major field operations of agricultural tractors. Same or different farm works and driving levels were set differently for each of the load measuring experiment. Before load data analysis, pre-processing procedures such as outlier removal, low-pass filtering, and data division were performed. Data beyond the scope of the measuring range of the sensors and the operating range of the power transmission parts were removed. Considering engine and PTO rotational speeds, frequency components greater than 90, 60, and 60 Hz cut off frequencies were low-pass filtered for plow tillage, rotary tillage, and baler operations, respectively. Measured load data were divided into five parts: driving, working, implement up, implement down, and turning. Results of the study would provide useful information for load characteristics of tractors on major field operations.