• 반도체디스플레이기술학회지
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• 제20권4호
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• pp.151-156
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• 2021

Low-end IoT devices do not have enough computation and memory resources for DNN learning and inference. Integer quantization of real-type neural network models can reduce model size, hardware computational burden, and power consumption. This paper describes the design and implementation of a web-based quantization platform for CNN deep learning accelerator chips. In the web service platform, we implemented visualization of the model through a convenient UI, analysis of each step of inference, and detailed editing of the model. Additionally, a data augmentation function and a management function of files that store models and inference intermediate results are provided. The implemented functions were verified using three YOLO models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

• 가정과삶의질연구
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• 제28권2호
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• pp.193-202
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• 2010

The purpose of this study is to investigate the prevalence of children's somatic symptoms and the relationships among parent and child variables such as parenting attitude, children's anxiety, emotional expressiveness and somatic symptoms. The subjects were 523 6th grade students in four elementary schools located in Seoul, Busan and Daejeon. All subjects were tested with the Korean version of Children's Somatization Inventory (K-CSI), Parental Rearing Attitude Inventory, State-Trait Anxiety for Children (STAIC), and The Emotional Expressiveness Questionnaire. The collected data were analyzed by the frequency and percentage of somatic symptoms the children had experienced during the last 2 weeks; Pearson's correlation and multiple regression were used for the selected data of 10 somatic symptoms which were experienced by over 10% of the children. The results are summarized as follows: First, abdominal pain, headache, dizziness, and memory failure, in that order, were experienced in over 20% of the children. Second, children's somatic symptoms had significant relations with mothers' and fathers' loving-denial attitudes and their anxiety and emotional expressiveness. Third, children's somatic symptoms were influenced by their anxiety and emotional expressiveness, but not by parenting attitude.

• 스마트미디어저널
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• 제2권3호
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• pp.34-38
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• 2013

상품권을 이용한 상거래에는 인쇄 및 관리를 포함한 여러 비용이 추가된다. 상품권을 디지털화하여 모바일 폰을 통해 유통할 경우, 이러한 비용이 많이 감소된다. 또한, 스마트폰 활성화에 따라 모바일 거래가 증가하고 있기에 근거리 통신기능을 갖춘 NFC 기술이 주목받고 있다. 그리하여, 모바일 전자결제를 위해 NFC 기반의 전자상품권을 이용한 방법이 연구되고 있다. 마케팅 전략에 도움이 될 수 있는 전자상품권의 유통경로를 분석하기 위해, 본 논문에서는 전자상품권에 유용한 데이터를 저장하기 위한 방법을 제안한다. 제안한 방법은 객체메모리모델을 활용한다. 제안된 전자상품권은 상품권 자체 정보뿐만 아니라, 유통이력정보 블록을 포함한다. 향후에는 제안한 전자상품권을 구현하고 테스트한다.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• 대한산업공학회지
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• 제36권3호
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• pp.193-202
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• 2010

The recommender system is a typical software solution for personalized services which are now popular in e-commerce sites. Most of the existing recommender systems are based on customers' explicit rating data on items (e.g., ratings on movies), and it is only recently that recommender systems based on implicit ratings have been proposed as a better alternative. Implicit ratings of a customer on those items that are clicked but not purchased can be inferred from the customer's navigational and behavioral patterns. In this article, a dimensionality reduction (DR) technique is newly applied to the implicit rating-based recommender system, and its effectiveness is assessed using an experimental e-commerce site. The experimental results indicate that the performance of the proposed approach is superior or at least similar to the conventional collaborative filtering (CF)-based approach unless the number of recommended products is 'large.' In addition, the proposed approach requires less memory space and is computationally more efficient.

• 해양환경안전학회지
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• 제29권6호
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• pp.543-551
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• 2023

기후변화 영향으로 이상고수온, 태풍, 홍수, 가뭄 등 재난 및 안전 관리기술은 지속적으로 고도화를 요구받고 있으며, 특히 해수면 온도는 한반도 주변에서 발생되는 여름철 적조 발생과 동해안 냉수대 출현, 소멸 등에 영향을 신속하게 분석할 수 있는 중요한 인자이다. 따라서, 본 연구에서는 해수면 온도 자료를 해양 이상현상 및 연구에 적극 활용되기 위해 통계적 방법과 딥러닝 알고리즘을 적용하여 예측성능을 평가하였다. 예측에 사용된 해수면 수온자료는 흑산도 조위관측소의 2018년부터 2022년까지 자료이며, 기존 통계적 ARIMA 방법과 Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU)을 사용하였고, LSTM의 성능을 더욱 향상할 수 있는 Sequence-to-Sequence(s2s) 구조에 Attention 기법을 추가한 Attention Long Short-Term Memory (LSTM)기법을 사용하여 예측 성능 평가를 진행하였다. 평가 결과 Attention LSTM 모델이 타 모델과 비교하여 더 좋은 성능을 보였으며, Hyper parameter 튜닝을 통해 해수면 수온 성능을 개선할 수 있었다.

• 정보처리학회논문지D
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• 제15D권5호
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• pp.609-620
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• 2008

모바일 단말에서의 맵 데이터 활용 서비스는 내비게이션, 위치기반서비스 등 그 활용 범위가 다양해지고 있다. 이러한 모바일 단말에서 저장/관리되는 맵 데이터의 크기는 갈수록 커져 수 GB에 이른다. 기존 내비게이션 시스템의 경우, 리소스 제약으로 인해 모바일 단말의 성능을 최대로 활용하기 위하여 맵 데이터를 읽기 최적화된 물리적인 저장형식(PSF: Physical Storage Format)으로 저장 관리하고 있다. 즉, 맵 데이터가 변경된 경우 변경된 부분만의 업데이트가 불가능하여 전체 데이터를 업데이트해야 하는 단점이 있다. 일반적으로 2 GB 데이터를 모바일 단말의 플래시메모리에 기록하는 시간은 수십 분이 소요된다. 따라서 본 연구에서는 이러한 기존의 내비게이션용 맵 데이터의 부분 업데이트가 불가능한 점을 해결하고자 모바일 공간 DBMS을 개발하였다. 그리고, 모바일 공간 DBMS에서의 내비게이션 서비스의 성능을 보장하기 위한 기법을 제시하고 실험을 통하여 이를 검증하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제11권1호
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• pp.75-84
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• 2019

This study was a cross-sectional descriptive research that investigate the level of addiction and analyzed the impact of computer game addiction from 20 to 30 years old on communication capabilities. The purpose of this study was to investigate the level of game addiction according to general characteristics and to confirm the effect of computer game addiction on communication ability. There were a total of 220 participants in the study. This survey for this study was a self-reporting questionnaire, research tools for this study were developed by Lee (2000) using game addiction and Communication ability developed by Frits et al., (1999). Data analysis was analyzed with frequency analysis, t-test, ANOVA, and Regression using SPSS 18.0. As a result of analysis, the difference between game addiction and communication ability by gender was appeared to be different under statistical significance levels: game addiction (t=-.9.2, p<0.01), subcomponents of communication ability, concentration (t=7.20, p<0.01), understanding (t=6.13, p<0.01), response (t=6.22, p<0.01), memory (t=2.88, p<0.05). The highest level of game addiction was appeared between 28 and 30 years old under statistical significance (p<0.01). Finally computer game addiction affects understanding, a subcomponent of communication capabilities.

• 한국정보기술학회논문지
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• 제17권11호
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• pp.17-26
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• 2019

플래시 스토리지 장치의 신뢰성을 향상시키기 위해서 사용되는 기술 중의 하나가 RAID-5 기술이 있다. RAID-5에는 고유한 패리티 업데이트 오버헤드가 있는데, 특히 부분 스트라이프 쓰기에 대한 패리티 오버헤드는 플래시 기반 RAID-5 기술의 중요한 문제 중 하나이다. 본 논문에서는 RAID-5에서 발생하는 런타임 부분 패리티 오버헤드를 제거하기 위해 효율적인 패리티 로그 아키텍처를 설계하였다. 런타임 동안, 전체 스트라이프 쓰기가 완료될 때까지 부분 패리티가 버퍼 메모리에 유지되며, 스트라이프 쓰기가 완료될 때 패리티는 전체 스트라이프 쓰기로 기록된다. 페리티 로그는 전체 스트라이프 그룹이 데이터 쓰기에 사용될 때까지 메모리에서 유지된다. 이 패리티 로그를 사용하면 갑작스러운 전력 손실로부터 부분 패리티를 복구할 수 있으므로 데이터 손실에도 문제가 발생하지 않는다. 패리티 로그 방법은 작은 패리티 로그 양으로 부분 패리티 쓰기 오버헤드를 제거할 수 있으므로, 같은 신뢰성 수준에서 쓰기 오버헤드를 줄일 수 있다.