Earticle

Trust and perceived risk have been identified as the two primary factors affecting engagement in online transactions. However, earlier studies have conceptualized the directionality of the causal link between these two factors differently. Some researchers have conceptualized trust as an antecedent to risk, while others see it as a consequence. To resolve this issue, we develop a mutual model in which trust and perceived risk bidirectionally influence each other. We then test the ability of the mutual model to provide a more realistic explanation of buyers’ decision-making than previously offered one-way models. We find a nearly equal effect of trust and perceived risk, which means that the bidirectional model should be more preferred by researchers than others. Based on these empirical findings, we discuss the implications of our research, including the appropriateness of our research question and several paradoxes identified in prior studies.

The problem of discrimination due to information alienation among Korean sign language users is continuously mentioned, and sign language translation research is actively being conducted to solve this problem. However, due to technological limits in translating text into Korean Sign Language, the need for specialist equipment causes annoyance and spatial constraints. Furthermore, it isn't easy to replicate the vocabulary and grammatical structure of the Korean language in Korean Sign Language. Furthermore, the service's commercialization is complicated by the need for more nonmanual signal (NMS) identification technology.

This study investigates the contribution of customer knowledge to customer intention to adopt mobile banking by evaluating the interaction among knowledge, perceived risk and trust, and behavioral intentions. Analysis is conducted through structural equation modelling using SPSS and AMOS and data from 783 customers representing the seven largest banks in Vietnam. Our study is the first one to find the existence of the bidirectional perspective between objective and subjective knowledge. The study further shows that the attenuation effect in the heuristic-systematic model could be used to explain the stronger influence of objective knowledge on intention compered to subjective knowledge. Our findings suggest that customer knowledge, perceived risk and trust impact the intention of mobile banking users in different manners and to different degrees. Particularly, objective customer knowledge is the most influential predictor of mobile banking adoption. Having a greater understanding of these relationships can help firms in deciding the kind of intervention that is most likely to convince customers to adopt a service.

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

현재 음성에서 감정 상태를 이해하기 위해 강조, 음높이 변화 및 맥락과 같은 감정적 특징을 인식하는 다양한 음성 감정인식(Speech Emotion Recognition)연구가 활성화 되고 있다. 본 논문은 음성 데이터에서 감정적 단서를 찾 기 위해 시간 영역과 주파수 영역의 다양한 특징을 추출하고 강조, 음높이 변화를 통해 감정을 인식하는 신경망 모 델을 개발하고자 한다. 부족한 데이터 셋으로 인한 과대적합을 예방하기 위해 컴퓨터 비전 분야에서 활용되는 데이 터 증강 기법을 적용하였다. 또한 음성 데이터의 전처리를 수행하고 시간 영역에서 ZCR(Zero Crossing Rate)과 RMS(Root Mean Square) 에너지 특징을 추출하였고 주파수 영역에서 MFCC(Mel-Frequency Cepstral Coefficient), 주파수 대역폭(Spectral Bandwidth), 주파수 중심(Spectral Centroid), 주파수 롤오프의 최대, 최소 값(Spectral Rolloff Max and Min) 같은 스펙트럴 특징을 추출하였다. 과거와 미래의 정보를 저장할 수 있 어 불연속적인 음성 데이터의 과거와 미래의 정보를 효과적으로 학습할 수 있는 양방향 LSTM 신경망 모델을 제안 하였으며, 8가지 감정표현(중립, 차분함, 행복, 슬픔, 분노, 두려움, 혐오, 놀람)이 포함되어 있는 RAVDESS와 7 가지 감정표현(중립, 행복, 슬픔, 분노, 두려움, 혐오, 놀람)이 포함되어 있는 TESS 데이터 셋에 대하여 각각 99.21%, 98.24%의 정확도를 확인하였다. 향후 연구에서는 음성 감정 인식 분야의 주요 어려움 중 하나인 적은 데이터 셋 문제를 메타러닝 기법을 활용하여 해결할 계획이다.

Currently, various studies are actively underway to understand emotional states in speech, recognizing emotional features such as emphasis, pitch changes, and context. This paper aims to develop a neural network model that extracts various features from the time and frequency domains in speech data and recognizes emotions through emphasis and pitch changes. To prevent underfitting and overfitting due to limited datasets, data augmentation techniques commonly used in computer vision were applied. Additionally, preprocessing of the speech data was performed and the time domain features such as Zero Crossing Rate(ZCR) and Root Mean Square(RMS) energy were extracted. Similarly, the frequency domain spectral features such as Mel-frequency cepstral coefficients(MFCCs), Spectral Bandwidth, Spectral Centroid, and Spectral Rolloff Max and Min were extracted. To effectively handle discontinuous speech data by storing past and future information, a bidirectional LSTM model was proposed. On the RAVDESS dataset, which includes eight types of emotional expressions(neutral, calm, happy, sad, angry, fearful, disgusted, surprised), and the TESS dataset, which includes seven types of emotional expressions(neutral, happy, sad, angry, fearful, disgusted, surprised), we confirmed accuracies of 99.21% and 98.24%, respectively. In future research, we plan to address one of the major challenges in the field of speech emotion recognition, the scarcity of datasets, by utilizing meta-learning techniques.

다양한 정보가 대량으로 유통되는 IT 환경에서 사용자의 요구를 빠르게 파악하여 의사결정을 도와줄 수 있는 추천 시스템이 각광을 받고 있다. 그러나 현재 추천 시스템은 사용자의 취향이나 관심사가 바뀌었을 때 선호도가 즉시 시스템에 반영이 되지 않을 수가 있으며, 광고 유도로 인하여 사용자의 선호도와 무관한 아이템이 추천될 수가 있다는 문제점이 있다. 본 연구에서는 이러한 문제점을 해결하기 위해 양방향 순환 신경망 언어 모델을 이용한 Fuzzy-AHP 기반 영화 추천 시스템을 제안하였다. 본 시스템은 사용자의 취향이나 관심사를 명확하고 객관적으로 반영하기 위해 Fuzzy-AHP를 적용하였다. 그리고 사용자가 선호하는 영화를 예측하기 위해 양방향 순환 신경망 언어 모델을 이용하여 실시간으로 수집되는 영화 관련 데이터를 분석하였다. 본 시스템의 성능을 평가하기 위해 그리드 서치를 이용하여 전체 단어 집합의 크기에 대한 학습 모델의 적합성을 확인하였다. 그 결과 본 시스템의 학습 모델은 전체 단어 집합의 크기에 따른 평균 교차 검증 지수가 97.9%로 적합하다는 것을 확인할 수 있었다. 그리고 본 모델은 네이버의 영화 평점 대비 평균 제곱근 오차가 0.66, LSTM 언어 모델은 평균 제곱근 오차가 0.805으로, 본 시스템의 영화 평점 예측성이 더 우수 함을 알 수 있었다.

In today’s IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users’ needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users’ preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users’ tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system’s superior performance in predicting movie ratings.

C2C e-commerce is more special in several e-commerce modes, both trade sides are retail customers, the credit problems depend on the management coming from the third party trading platform entirely, lacks of initiative in this management, and the one-way real-name certification leads to the problem of asymmetric credit in C2C transactions, credit asymmetry is the root of a lot of credit problems in C2C transactions. To this end, in this paper the dynamic weighted trust evaluation model based on bidirectional authentication mechanism is established, and in order to solve the inconvenience comes from buyer real-name authentication, this model put forward a unified platform for buyer's, the problem of asymmetric credit is solved well, at the same time to ensure the convenience of authentication and the confidentiality of information, unified authentication management is good for integrating various trading platform, makes certification more reliable. In this model all the factors affecting credit will be considered synthetically, including the transaction amount, transaction time, evaluation user types etc, effective to identify the type of evaluation users, it is well to reduce all the behaviors such as credit hype and fraudulent trading etc, the calculation results of credit finally will be reflected by dynamic graph, makes the evaluation results more intuitive and easy to understand credit trend of evaluated user.

Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink(R) and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3% during rectifier mode and 3.5% during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.

We proposed a method to improve moving object detection capability of Gaussian Mixture Model by suggesting shape adaptive bidirectional block matching algorithm. This method achieves more accurate detection and tracking performance at various motion types such as slow, fast, and bimodal motions than that of Gaussian Mixture Model. Experimental results showed that the proposed method outperformed the conventional methods.

An important challenge in Parkinson’s disease (PD) based neuroscience and neuroimaging is mapping the neuronal connectivity of the basal ganglia to understand how the disease affects brain circuitry. However, a majority of diffusion tractography studies have shown difficulties in revealing connections between distant anatomic brain regions and visualizing basal ganglia connectome. In this current study, we investigated the differences in basal ganglia connectivity between 6-OHDA induced ex-vivo PD mouse model and normal ex-vivo mouse model by using diffusion tensor imaging tractography from diffusion-weighted images obtained with a high resolution 9.4 T MR scanner. Connectivity pattern of the basal ganglia were compared between five 6-OHDA and five control ex-vivo mouse brains using results of probabilistic tractography generated with PROBTRACKX. When compared with control mouse, 6-OHDA mouse showed significant enhancements to motor territory-related subthalamopallidal and pallido-subthalamic connectivity. Multi-fiber tractography combined with diffusion MRI data has the potential to help recognize the abnormalities found in connectivity of psychiatric and neurologic disease models.

This paper proposes an open-switch fault tolerant strategy based on the model predictive control for a full bidirectional switches indirect matrix converter (FBS-IMC). Compared to the conventional Indirect Matrix Converter (IMC), the FBS-IMC can provide healthy current path when open-switch fault is occurred. To keep the continuous operation, the fault tolerant strategy is developed by means of reversing the DC-link voltage polarity regardless of the faulty switch location in the rectifier or inverter stage. Therefore, the proposed control strategy can maintain the same input and output performances during the faulty condition as the normal condition. The simulation results are given to verify the effectiveness of the proposed strategy.

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

본 연구는 질의 응답(QA) 시스템에서 사용하는 개체명 인식(NER)의 성능을 향상시키기 위하여 시퀀스 태깅 방법론을 적용한 새로운 방법론을 제안한다. 사용자의 질의를 입력 받아 데이터베이스에 저장된 정답을 추출하기 위해서는 사람의 언어를 컴퓨터가 알아들을 수 있도록 구조화 질의어(SQL)와 같은 데이터베이스의 언어로 전환하는 과정이 필요한데, 개체명 인식은 사용자의 질의에서 데이터베이스에 포함된 클래스나 데이터 명을 식별하는 과정이다. 기존의 데이터베이스에서 질의에 포함된 단어를 검색하여 개체명을 인식하는 방식은 동음이의어와 문장성분 구를 문맥을 고려하여 식별하지 못한다. 다수의 검색 결과가 존재하면 그들 모두를 결과로 반환하기 때문에 질의에 대한 해석이 여러 가지가 나올 수 있고, 계산을 위한 시간복잡도가 커진다. 본 연구에서는 이러한 단점을 극복하기 위해 신경망 기반의 방법론을 사용하여 질의가 가지는 문맥적 의미를 반영함으로써 이러한 문제를 해결하고자 했고 신경망 기반의 방법론의 문제점인 학습되지 않은 단어에 대해서도 문맥을 통해 식별을 하고자 하였다. Sequence Tagging 분야에서 최신 기술인 Bidirectional LSTM-CRF 모델을 도입함으로써 신경망 모델이 가진 단점을 해결하였고, 학습되지 않은 단어에 대해서는 온톨로지 기반 특성치를 활용하여 문맥을 반영한 추론을 사용하였다. 음악 도메인의 온톨로지(Ontology) 지식베이스를 대상으로 실험을 진행하고 그 성능을 평가하였다. 본 연구에서 제안한 방법론인 L-Bidirectional LSTM-CRF의 성능을 정확하게 평가하기 위하여 학습에 포함된 단어들뿐만 아니라 학습에 포함되지 않은 단어들도 포함한 질의를 평가에 사용하였다. 그 결과 L-Bidirectional LSTM-CRF 모형을 재학습 시키지 않아도 학습에 포함되지 않은 단어를 포함한 질의에 대한 개체명 인식이 가능함을 확인하였고, 전체적으로 개체명 인식의 성능이 향상됨을 확인할 수 있었다.

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

In this paper, we propose a new design method for bidirectional associative memories model with high error correction ratio. We extend the conventional GBAM model using bias terms and formulate a design procedure in the form of a constrained optimization problem. The constrained optimization problem is then transformed into a GEVP(generalized eigenvalue problem), which can be efficiently solved by recently developed interior point methods. The effectiveness of the proposed approach is illustrated by a example.

거리 기반의 이동성 관리 방식은 무선 네트워크 연구 분야에서 주요한 주제로 고려되었다. 적당한 이동성 모델로 수학적으로 분석하기 위한 많은 연구가 진행되었다. 특히 양방향의 랜덤 워크 모델은 단순성의 장점으로 인하여 많이 활용되었다. 그럼에도 불구하고 정확한 수식에 의한 분석은 지금까지 아직 이루어지지 못하였다. 본 고에서 우리는 양방향 랜덤 워크모델에 관한 정확한 수식을 제공한다. 이러한 수학적 모델은 거리 기반의 이동성 관리 방식에 관한 연구에 많은 도움이 될 것으로 기대된다.

Distance based mobility management schemes have been considered as a major issue in the wireless network research area. Accordingly, many efforts have been made to analyze them numerically with suitable mobility models. In particular, bidirectional random walk model has been employed frequently due to its simplicity. Nevertheless, the exact equations are not presented so far. In this paper, we provide the exact equations regarding the bidirectional random walk model, which is very useful for the analysis of the distance based mobility management schemes.

점점 더 많은 기업 또는 조직이 중요한 비즈니스 의사 결정 프로세스에 AI와 ML을 도입하기 시작하고 있다. 딥러닝 모델의 장점은 다양한 작업에 매우 높은 정확도를 보여주며, 노이즈(noise)와 데이터의 변화에도 매우 강한 장점과 대용량 데이터 처리를 위한 확장이 가능하다. 또한, 딥러닝의 단점으로는 데이터 종속성, 계산 비용, 그리고 편향성과 해석의 어려움 등이 존재한다. 본 연구에서는 딥러닝의 단점인 데이터 종속성, 편향성 문제와 해석의 어려움에 추가적인 정보를 제공하는 방법을 제안하고자 한다. 제안하는 XAI 방법론은 학습 데이터(training data) 측면과 ML 모델 측면을 고려하는 사전 예방 분석 단계(precautionary analysis step)와 Perturbation-based 방법과 Gradient-based 방법을 활용한 양방향의 설명 가능성을 제안한다.

More and more enterprises or organizations are starting to introduce AI and ML into their critical business decision-making processes. Despite the many advantages of deep learning models, drawbacks of deep learning include data dependency, computational costs, bias, and difficulty in interpretation. In this study, we propose a method to provide additional information on the shortcomings of deep learning, such as data dependency, bias problems, and difficulty of interpretation. The proposed XAI methodology proposes a precautionary analysis step that considers both the training data and ML model aspects, and bidirectional explainability utilizing Perturbation-based and Gradient-based methods.

본 연구에서는 음성감정인식의 적용 가능성과 실용성 향상을 위해 적은 수의 파라미터를 가지는 새로운 경량화 모델 RoutingConvNet(Routing Convolutional Neural Network)을 제안한다. 제안모델은 학습 가능한 매개변수를 줄이기 위해 양방향 MFCC(Mel-Frequency Cepstral Coefficient)를 채널 단위로 연결해 장기간의 감정 의존성을 학습하고 상황 특징을 추출한다. 저수준 특징 추출을 위해 경량심층 CNN을 구성하고, 음성신호에서의 채널 및 공간 신호에 대한 정보 확보를 위해 셀프어텐션(Self-attention)을 사용한다. 또한, 정확도 향상을 위해 동적 라우팅을 적용해 특징의 변형에 강인한 모델을 구성하였다. 제안모델은 음성감정 데이터셋(EMO-DB, RAVDESS, IEMOCAP)의 전반적인 실험에서 매개변수 감소와 정확도 향상을 보여주며 약 156,000개의 매개변수로 각각 87.86%, 83.44%, 66.06%의 정확도를 달성하였다. 본 연구에서는 경량화 대비 성능 평가를 위한 매개변수의 수, 정확도간 trade-off를 계산하는 지표를 제안하였다.

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.

본 연구의 목적은 중‧고령층의 사회적 관계와 우울의 종단적 상호인과관계를 검증하는 것이다. 이를 위해 한국복지패널(Korea Welfare Panel Study: KOWEPS)의 9차(2014년)~12차년도(2017년) 자료를 활용해, 만 55-74세의 중‧고령층 2,573명을 대상으로 자귀회귀교차지연모형(autoregressive cross-lagged modeling)을 실시하였다. 나아가 중년층(55-64세)과 고령층(65-74세) 연령대 집단별로 두 변인의 관계가 상이한지 살펴보았다. 연구결과, 우울과 사회적 관계의 종단적 상호인과관계는 연령집단별로 차이를 보이는 것으로 나타났다. 먼저 ‘사회적 관계(t)→우울(t+1)’은 두 집단에서 모두 유의하였는데, 이전시점의 사회적 관계 수준이 낮을수록 이후시점의 우울이 높았다. 그러나 ‘우울(t)→사회적 관계(t+1)’는 고령층 집단에서만 유의한 결과를 나타냈다. 이는 중년층과는 다르게 고령층의 경우, 우울이 사회적 관계의 저하로 이어질 수 있음을 시사한다. 본 연구는 주로 일방향 모형(unidirectional model)의 관점에서 논의되어 온 중･고령층의 사회적 관계와 우울에 대한 연구를 쌍방향 모형(reciprocal effects model)의 관점으로 확장시켜 살펴보았다는데 의의를 지닌다. 연구결과를 토대로 중‧고령층의 사회 및 정신적 건강을 위한 실천적, 정책적 방안에 대한 제언이 논의되었다.

Prior research has reported that the relationship between quality of social relationships and depression is reciprocal among middle-aged and older adults. However, little is known about a bidirectional relationship over time. To address this research gap, this study aims to investigate (1) a bidirectional relationship between social relationships and depression among middle-aged and older adults, and (2) whether there are significant differences between the age groups. Using 4-year data (2014-2017) from Korea Welfare Panel Study, a nationally representative longitudinal survey, a total of 2,573 Korean participants (1,147 middle-aged adults aged 55-64 and 1,426 the young-old aged 65-74) was selected. Autoregressive cross-lagged modeling was performed. The Center for Epidemiologic Studies Depression scale was used to measure depression. The quality of social relationships was assessed by a composite score of two Likert-scale items (satisfaction with family and friends relationship). The model showed adequate fit indices (RMSEA=.040, CFI=.957). The results revealed that there are significant differences between two age groups in the reciprocal association of social relationships with depression. The negative lagged effect of quality of social relationships on depression over the four years was significant in both groups. However, the negative lagged effect of depression on the quality of social relationships was only significant in the young-old, suggesting that higher levels of depression predicts lower levels of social relationships.

본 연구의 목적은 종단자료의 분석을 통해 노년기 우울과 생활만족도 간의 인과적 방향성을 검증함으로써 두 변수 간의 관계에 관한 이해도 증진에 도움이 될 수 있는 기초자료를 획득하는 것이다. 연구목적을 달성하기 위해 국민노후보장패널조사 제4차, 제5차, 제6차 자료를 사용하여 노년기 우울과 생활만족도를 잠재변수로 설정한 자기회귀 교차지연 모형을 검증하였다. 주요 분석결과에 의하면, 노년기 우울과 생활만족도의 종단적 추세 및 상호 인과성의 변화 양상은 다음과 같다. 첫째, 조사대상 기간 동안 이전 시점의 우울은 이후 시점의 우울을 안정적으로 예측하였다. 둘째, 조사대상 기간 동안 이전 시점의 생활만족도는 이후 시점의 생활만족도를 안정적으로 예측하였다. 셋째, 조사대상 기간 동안 이전 시점의 우울이 이후 시점의 생활만족도에 미치는 영향이 유의하지 않았으며, 그 반대의 인과관계도 유의하지 않았다. 끝으로, 본 연구는 노년기 우울과 생활만족도 간의 관계에 관한 이해도를 높이는데 기여할 수 있는 이론적 함의와 후속연구의 방향에 대하여 논의하였다.

The purpose of the present study was to investigate the causal relationship between depression and life satisfaction in old age through longitudinal data analysis. An autoregressive cross-lagged model was identified and tested. The present study analyzed the 4th, 5th and 6th wave data from Korean Retirement and Income Study (KReIS). The primary results were as follows. First, depression had a positive autoregressive effect. Second, life satisfaction had a positive autoregressive effect. Third, depression was not a causal predictor of life satisfaction and life satisfaction was not a causal predictor of depression. Finally, theoretical implications in terms of enhancing the understanding of the bidirectional relationship between depression and life satisfaction in old age, and a number of recommendations for the further research were also provided.