• 디지털융복합연구
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• 제11권11호
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• pp.389-395
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• 2013

IT 기술과 의료기술이 융합되면서 환자의 체내에 의료장비를 부착한 환자의 수가 증가하고 있다. 그러나 환자의 생체정보를 제 3자가 악의적으로 도청 및 변경하는 문제점이 발생하고 있다. 본 논문에서는 환자와 병원관계자 사이에서 환자의 생체정보를 제3자가 도청하거나 변조없이 키를 공유하도록 양자 암호 기반의 키 분배 모델을 제안한다. 제안 모델의 양자 정보는 메시지 직접전달보다는 임의의 비트들을 전달하여 키를 공유하는 one-time pad 키를 사용한다. 또한, 제안 모델은 체내삽입장치의 생체정보가 제3자에게 불필요하게 노출되지 않아 환자의 익명성을 보장받는다.

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2001년도 추계학술발표대회
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• pp.103-106
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• 2001

Bioluminescent bacteria fusing the stress promoter and lux gene have been developed as a toxicity biosensor. The light emitting bioluminescent bacteria have been used to measure the toxicity of many different chemicals. In this study, specially, DPD2540 (fabA::luxCDABE) was used to detect and classify phenolic toxicity to the cells membrane fatty acids, and then the relationship between phenolic toxicity and the distribution of various phenols in the cell was determined, with a model and equations provided. In addition, to show the possibility of detecting and classifying the toxicity of a chemical mixture, which may be present in wastewater, various bioluminescent bacteria having different stress promoters were used and their distinct response to the sample mixture was measured. To extend the applicable area of these bioluminescent bacteria to field, the portable biosensor using freeze-drying methods was developed and confirmed successfully.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(B)
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• pp.231-234
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• 2011

Audiovisual-based human emotion recognition can be considered a good approach for multimodal humancomputer interaction. However, the optimal multimodal information fusion remains challenges. In order to overcome the limitations and bring robustness to the interface, we propose a framework of automatic human emotion recognition system from speech and face display. In this paper, we develop a new approach for fusing information in model-level based on the relationship between speech and face expression to detect automatic temporal segments and perform multimodal information fusion.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.902-904
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• 2003

The main objective of this paper is to investigate the potential utility of multisensor remotely sensed data for improved coastal wetland mapping. Five data fusion models, three algebraic models (Multiplicative (MT), Brovey (BT) and Wavelet transform (WT)) and two spectral domain models (Principals component transform (PCT) and Intensity-Hue-Saturation (IHS)) were implemented and tested over the multisensor data. The fused images were then compared based on visual and statistical approaches. The results show that the wavelet transform provides greater flexibility for combining optical data sets and has good potential for preserving the spatial and spectral content of the original images . However, this model yields poor information when combining optical and microwave data. Brovey transform is more reliable for fusing optical and microwave image data and yields improved information about different wetland features of the coastal zone.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1992년도 학술논문발표회 논문집 제11권 1호
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• pp.3-8
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• 1992

The theory of subjective preference with temporal and spatial factors which include sound signals arriving at both ears is described. Then, auditory evoked potentials which may relate to a primitive subjective response namely subjective preference are discussed. According to such fundamental phenomena, a workable model of human auditory-brain system is proposed. For eample, important subjective attributes, such as loudness, coloration, threshold of preception of a reflection and echo distrubance as well as subjective preference in relation to the initial time delay gap between the direct sound and the first reflection, and the subsequent reverberation time are well described by the autocorrelation function of source signals. Speech clarity, subjective diffuseness as well as subjective preference are related to the magnitude of inter-aural crosscorrelation function (IACC). Even the caktail party effects may be eplained by spatialization of human brain, i.e., independence of temporal and spatial factors.

• International Journal of Aeronautical and Space Sciences
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• 제11권1호
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• pp.31-40
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• 2010

For weapon cueing and Head-Mounted Display (HMD), it is essential to continuously estimate the motion of the helmet. The problem of estimating and predicting the position and orientation of the helmet is approached by fusing measurements from inertial sensors and stereo vision system. The sensor fusion approach in this paper is based on nonlinear filtering, especially expended Kalman filter(EKF). To reduce the computation time and improve the performance in vision processing, we separate the structure estimation and motion estimation. The structure estimation tracks the features which are the part of helmet model structure in the scene and the motion estimation filter estimates the position and orientation of the helmet. This algorithm is tested with using synthetic and real data. And the results show that the result of sensor fusion is successful.

• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.207-214
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• 2010

This paper proposes about decentralized control approach based Leader-Follower formation control using LOS (Line of Sight) algorithm and unknown input observer. The position of robots which is a basic information in multi-robot or single robot motion control is determined by localization algorithm fusing UPS (Ultrasonic Position System) and kinematics model. For formation control, a decentralized control approach individually installing a local controller in leader and follower robot is adopted. Leader robot is controlled to track a specified trajectory by LOS algorithm, and the other robots follow the leader by local controller based on tracking platoon level function, self-sensing data and estimated information from unknown input observer. The performance of proposed method is proven through the formation experiment of two vehicle models.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1998년도 춘계학술대회 논문집
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• pp.367-370
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• 1998

In this paper, based on the research of high temperature YBCO superconductor, using the Yb instead of Y, with the YbBCO superconductor powder which was combined by means of conventional solid reaction, textured directional crystal was prepared by MCP method and the character was analyzed. Mixing the starting elements and calcining at 890$^{\circ}C$, 900$^{\circ}C$, 910$^{\circ}C$, single phase YbBCO, Yb$_2$BaCuO$\sub$5/ and BaCuO$\sub$2/ were certified. And from the powder which was calcined at 900$^{\circ}C$ the, sample which became texture-growth by MCP method was well oriented. The result of DTA measurement, the fusing point of YbBCO superconductor and it\`s critical current was measured to be 979$^{\circ}C$, 87K respectively. The critical current density was obtained at the value of 700A/$\textrm{cm}^2$(77K. 0H) calculated by Bean's Model using the measured hysteresis curve of VSM.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.