Earticle

본 연구는 청년을 대상으로 자아존중감과 사건 정서에 따라 자기존중기억의 회상 양상에 어떤 차이가 나타나는지 알아보기 위해 수행되었다. 연구 참가자는 대학생 84명이었으며 긍정적 및 부정적 자기존중기억을 각 세 가지씩 회상하도록 하여 자전적 인터뷰(AI)의 평가법으로 기억 특성을 분석하였다. 그 결과, 자아존중감이 높은 청년은 낮은 청년보다 자신의 경험에 대한 기억이 유의하게 선명하였고 더 많은 단어로 더 긴 시간 동안 자기 경험을 말하면서 경험에 관련된 자신 및 타인에 대한 정보를 더 많이 회상하였다. 또한 자아존중감이 낮은 청년은 높은 청년보다 부정적 사건에 대한 정서 강도가 더 큰 것으로 나타났다. 그리고 자아존중감의 고저에 상관없이 청년은 긍정경험보다 부정경험에서 나의 정보를 더 많이 회상하였고 긍정적 경험보다 부정적 경험 후에 정서 및 사고 변화가 더 컸으며, 부정적 경험은 성취보다 사회적 관계라는 주제에 초점이 더 맞추어져 있었다. 이러한 결과는 청년에게 자전적 기억의 자기향상기능이 작동한다기보다 부정적 경험의 영향이 크며, 자아존중감이 낮은 청년은 부정적 사건에 대한 강한 부적 정서로 인해 자기개념 손상이 클 수 있음을 시사한다.

This study investigated the self-enhancement function of autobiographical memory based on self-esteem in younger adults. A total of 84 university students (42 males and 42 females) completed the Rosenberg Self-esteem Scale. They recalled three positive and three negative self-esteem memories and rated the quality of each memory. Based on their self-esteem scores, they were divided into high and low self-esteem level groups. The analysis of variance showed that memories of high self-esteem younger adults were more vivid and they recalled more information about themselves and others for a longer time. Regardless of self-esteem level, younger adults recalled more information about themselves in negative events and experienced more changes in emotions and thoughts after negative events. Negative emotional valence of negative events was higher in younger adults with low self-esteem. The content analysis showed that self-esteem memories of negative experiences focused on social relations rather than achievement. These findings suggest that the self-enhancement function of autobiographical memory does not work in younger adults, and the effects of negative experiences are strong. Self-concepts can be damaged more in younger adults with low self-esteem.

Due to the openness of the Android-based open market, the distribution of malicious applications developed by attackers is increasing rapidly. In order to reduce the damage caused by the malicious applications, the mechanism that allows more accurate way to determine normal apps and malicious apps for common mobile devices should be developed. In this paper, the normal system call event patterns were analyzed from the most highly used game app in the Android open market, and the malicious system call event patterns were also analyzed from the malicious game apps extracted from 1260 malware samples distributed by Android MalGenome Project. Using the Strace tool, system call events are aggregated from normal and malicious apps. And analysis of relevance to each event set was performed. Through this process of analyzing the system call events, we can extract a similarity to determine whether any given app is malicious or not.

5G와 인공지능, 자율 주행 차량 시스템에 관한 기술의 지속적인 발전에 힘입어 V2X를 비롯한 C-ITS 환경에 대한 표준과 서비스가 연구되고 있다. V2V 환경에서 차량 시스템에서 수집 및 생성되는 데이터를 기반 차량간 데이터를 교환하기 위한 표준으로 BSM(basic safety message) 이 채택되었다. 본 논문에서는 BSM 메시지를 안전하게 저장하고, Event-Sourcing과 CQRS 패턴을 사용해서 저장된 메시지를 효과적으로 확인할 수 있는 프레임워크를 제안한다. 제안된 프레임워크는 해시 함수를 사용해서 BSM 메시지를 안전하게 저장 및 관리할 수 있으며, 시계열을 기반으로 저장된 BSM 데이터를 실시간으로 확인할 수 있으며 상태 재현이 가능한 장점이 있다.

With the continuous development of technologies related to 5G, artificial intelligence, and autonomous vehicle systems, standards and services for V2X and C-ITS environments are being studied a lot. BSM (basic safety message) was adopted as a standard for exchanging data between vehicles based on data collected and generated by vehicle systems in a V2V environment. In this paper, we propose a framework that can safely store BSM messages and effectively check the stored messages using Event-Sourcing and CQRS patterns. The proposed framework can securely store and manage BSM messages using hash functions. And it has the advantage of being able to check the stored BSM data in real time based on the time series and to reproduce the state.

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

생명과학 관련 문서에서 자동으로 이벤트를 추출하는 것은 관련 연구자들의 연구에 많은 도움을 줄 수 있다. 본 논문에서는 생명과학 관련 문서 중 특히 효모와 관련된 문서를 대상으로 간단한 자연언어 처리 기술을 적용하여 유의미한 정보를 추출한 결과를 제시하고자 한다. 실험은 효모 관련 문서에서 고빈도의 이벤트 표현 동사에 대한 패턴 정보를 조사한 후, 패턴 정보에 의거하여 이벤트를 추출하였다. 평가 결과, 비교적 간단한 자연언어 처리 기술 안으로도 유의미한 정보들을 추출할 수 있었다.

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

<효의정충예행록>을 분석하여 총 237건의 전고를 표제어, 활용 양상, 위치의 항목대로 정리하였다. 그 결과 첫째, 윤리적․도덕적 전범이 되는 인물 관련 전고의 출현 빈도수가 가장 많았음을 확인하였다. 둘째, 효자․충신․부덕(婦德) 관련 인물전고가 가장 많이 등장하는데, 이 전고들이 효자 혹은 효의 실현으로 수렴되는 것을 확인하였다. 셋째, 한 인물이 다양한 양상의 전고로 활용되었는데 맹자․유방이 다섯 가지 양상으로 활용되었고, 공자가 여섯 가지 활용양상을 보여 가장 다양한 활용도를 보였다. 역사적 실체인 만귀비 관련 전고들이 여성 악인 형상화에 활용되었고, 당태종과 울지공 관련 전고는 등장인물간 갈등 해결의 양상과 의미를 보여주기도 하였다. 특히 <열녀전>에 실려 있는 두 편의 자료에 등장하는 목강의 전고를 결합하여 가장 전범이 되는 여성의 캐릭터의 이미지를 만들었고, <홍불기>라는 문학작품으로 만들어진 전고를 활용하여 등장인물들의 관계와 관계의 정리 양상을 그려내기도 하였다. 이러한 인물전고 활용 양상을 통하여 ‘효의(孝義)․정충(貞忠)․예행(禮行)을 내세우면서도 결국은 모든 요소들이 효의 실행으로 귀결되는 주제의식을 다시 한 번 더 확인할 수 있었다. 그리고 가장 전범이 되는 인물들 조차도 다양하고 입체적으로 활용하고 있음을 알 수 있었다. 특히 <열녀전>과 <홍불기>의 내용과 전고를 그대로 활용하는 것이 아니라 창의적인 결합을 통해 <효의정충예행록> 만의 특징을 만들어내고 있음을 알 수 있었다.

A total of 237 idioms originated in an ancient event were selected through the analysis of <Hyoeuijungchoungyaehaengrok>. And the idioms originated in an ancient event are organized into heading title, utilization patterns, location. As a result, First, the frequency of appearance of figures related to ethical and moral examples was the highest. Second, there are many stories related to filial piety, loyalty, and passionate women about the authentic precedents. by the way, The idioms originated in an ancient event were gathered as filial piety or filial piety. Third, a character was used as the authentic precedent in various aspects. Mengz? and Liu B?ng was used in five ways. k?ngz? was used in six ways. k?ngz? showed the most diverse utilization. Relating to Man Guifei as a historical entity the idioms originated in an ancient was used as the image of a female villain. Dang the idioms originated in an ancient related to Tae-jong and Uljigong showed the aspect and meaning of conflict resolution among the characters. Especially, There are two stories related to Mokkang in <Yeolnyeojeon>. Combining the authentic precedent of Mokkang together, it created the image of the female character that is the most exemplary. The idioms originated in an ancient Made from a literary work called <Hong Bul-gi> expresses the aspect of the relationship of the character. Through this aspect of utilization, Confucian ethics were put forward. In the end, everything was put together as a practice of filial piety. And even the most exemplary characters embraced the idioms originated in an ancient in various and three dimensions. Especially, did not accept the <Yeolnyeojeon> and <Hong Bul-gi> as it was. Through creative combination, the <Hyoeuijungchoungyaehaengrok>'s unique features were created.

본 연구는 개인정보를 수집하지 않는 환경에서 생성되는 비식별 행동 이벤트를 활용하여 시공간 기반 행동패턴을 분류하는 SPAB-Net 모델을 제안한다. 제안된 모델은 행동 이벤트를 시공간 시퀀스로 구조화하고, 공간 인코더와 시간 인코더를 결합한 하이브리드 딥러닝 구조를 적용한다. 또한 Attention 기반 특징 융합을 통해 시공간상호작용을 효과적으로 반영한다. 실험 결과, SPAB-Net은 기존 CNN-LSTM, 3D CNN, Transformer 모델 대비높은 분류 성능을 보였다. 특히 개인정보를 사용하지 않으면서도 높은 정확도를 달성하였다. 이는 프라이버시 보호와 AI 성능을 동시에 만족할 수 있음을 의미한다. 본 연구는 스마트시티 및 공공서비스 환경에서의 활용 가능성을 제시한다.

This study proposes SPAB-Net, a spatio-temporal behavior pattern classification model based on de-identified behavioral events in privacy-preserving environments. The model structures behavioral events into spatio-temporal sequences and employs a hybrid deep learning architecture combining spatial and temporal encoders. An attention-based fusion mechanism is introduced to effectively capture interactions between spatial and temporal features. Experimental results show that SPAB-Net outperforms baseline models, including CNN-LSTM, 3D CNN, and Transformer. Notably, the model achieves high accuracy without relying on personally identifiable information. These findings demonstrate that privacy-preserving AI can maintain strong predictive performance. The proposed approach is applicable to smart city and public service domains.

This paper analyzes the ‘V+gei(給)+IO+DO’ and the ‘V+DO+gei(給)+IO’ construction in which representative three-place verbs ‘song(送)1/2’ and ‘ji(寄)’ appear. This paper observes the aspectual characteristics of ‘song(送)1’ and ‘song(送)2’/‘ji(寄)’, and based on this, the semantic structure of the ‘V+gei(給)+IO+DO’ and the ‘V+DO+gei(給)+IO’ construction and the cognitive mechanism of each form are clearly distinguished. In addition, statistical analysis of CCL are used to support the argument of this paper. The ①‘song(送)1/song(送)2/ji(寄)+gei(給)+IO+DO‘ represents a single continuous giving event of ‘motion+reach’ meaning. The ②‘song(送)1+DO+gei(給)+IO’ represents a complex continuous event through a semantic structure in which the verb event and the ‘gei(給)’ event are separated. Although the result meaning of the ‘gei(給)’ event can have a relatively more separated effect compared to the ①, the appearance of ② has not relatively high preference because it does not correspond to the meaning of a single event of ‘motion+reach’ indicated by ‘song(送)1’. On the other hand, the ③‘song(送)2/ji(寄)+DO+gei(給)+IO’ represents a complex continuous event in which the verb event and the ‘gei(給)’ event are meaningfully clearly distinguished, and the appearance of ③ has relatively high preference because the meaning of ‘song(送)2’/‘ji(寄)’ matches the separated characteristics of the form.

본 연구는 대기 프로세스의 비정상성의 효과를 반영할 수 있는 물리적 기반의 지역 기후 모델을 이용하는 새로운 방법을 제시하기 위한 기반연구이다. 이를 위해 WRF를 이용하여 과거 호우사상을 재현하였고 모의능력을 평가하였다. 모형의 초기조건 및 경계조건을 설정하기 위해서 NCEP의 재분석 자료와 해수면 온도 자료, 지상관측 자료가 사용되었다. 미세물리 과정과 적운 모수화 과정을 변경해 가며 총 9개의 수치 실험이 실시되었다. 수치모의 결과는 강수량이 초기조건 및 경계조건과 적운 모수화에 영향을 받는 것으로 나타났다. 물리 옵션 중에서 WRF single-Moment 6-class 방안과 New Kain-Fritsch 방안은 강우의 공간적인 분포는 합리적으로 재현하였지만, 총 발생강수량이 과대 산정되었으며, 시간적인 분포는 재현하지 못했다. 이는 방재분야에 WRF를 적용하기 위해서는 새로운 방법론이 필요하다는 것을 나타낸다.

This paper presented a base study for new methodology that uses a physical based regional climate model that can also incorporate the effect of the nonstationarity of atmospheric process. To this end, we examined the capability of the WRF in reproducing the selected historical heavy rainfall events over Korea. WRF used the NCEP FNL reanalysis data, RTG Sea Surface Temperature data and ADP Global Surface Observational Weather data for its initial and boundary conditions. Nine experiments are carried out by changing the physics option (microphysics processes and cumulus parameterization process). Numerical simulation results showed a big variation in rainfall according to initial and boundary condition and cumulus parameterization. In physics option, WRF single-Moment 6-class scheme and New Kain-Fritsch scheme were reasonably reproduced in spatial distribution of rainfall, but the amount was overestimated and dose not reproduce temporal distribution of rainfall. This indicates that a new methodology necessary to WRF for hazard mitigation.