• Bulletin of the Korean Chemical Society
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• v.33 no.11
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• pp.3618-3624
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• 2012

Photodissociation dynamics of propiolic acid ($HC{\equiv}C-COOH$) at 212 nm in the gas phase was investigated by measuring rotationally resolved laser-induced fluorescence spectra of OH ($^2{\Pi}$) radicals exclusively produced in the ground electronic state. From the spectra, internal energies of OH and total translational energy of products were determined. The electronic transition at 212 nm responsible for OH dissociation was assigned as the ${\pi}_{C{\equiv}C}{\rightarrow}{\pi}^*{_{C=O}}$ transition by time-dependent density functional theory calculations. Potential energy surfaces of both the ground and electronically excited states were obtained employing quantum chemical calculations. It was suggested that the dissociation of OH from propiolic acid excited at 212 nm should take place along the $S_1/T_1$ potential energy surfaces after internal conversion and/or intersystem crossing from the initially populated $S_2$ state based upon the potential energy calculations and model calculations for energy partitioning of the available energy among products.

• International Journal of Contents
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• v.18 no.3
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• pp.11-20
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• 2022

Human Emotion Recognition is an exciting topic that has been attracting many researchers for a lengthy time. In recent years, there has been an increasing interest in exploiting contextual information on emotion recognition. Some previous explorations in psychology show that emotional perception is impacted by facial expressions, as well as contextual information from the scene, such as human activities, interactions, and body poses. Those explorations initialize a trend in computer vision in exploring the critical role of contexts, by considering them as modalities to infer predicted emotion along with facial expressions. However, the contextual information has not been fully exploited. The scene emotion created by the surrounding environment, can shape how people perceive emotion. Besides, additive fusion in multimodal training fashion is not practical, because the contributions of each modality are not equal to the final prediction. The purpose of this paper was to contribute to this growing area of research, by exploring the effectiveness of the emotional scene gist in the input image, to infer the emotional state of the primary target. The emotional scene gist includes emotion, emotional feelings, and actions or events that directly trigger emotional reactions in the input image. We also present an attention-based fusion network, to combine multimodal features based on their impacts on the target emotional state. We demonstrate the effectiveness of the method, through a significant improvement on the EMOTIC dataset.

• IMMUNE NETWORK
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• v.3 no.4
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• pp.302-309
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• 2003

Background: CTLA4 (CD152), which is expressed on the surface of T cells following activation, has a much higher affinity for B7 molecules comparing to CD28, and is a negative regulator of T cell activation. In contrast to stimulating and agonistic capabilities of monoclonal antibodies specific to CTLA-4, CTLA4Ig fusion protein appears to act as CD28 antagonist and inhibits in vitro and in vivo T cell priming in variety of immunological conditions. We've set out to confirm whether inhibition of the CD28-B7 costimulatory response using a soluble form of human CTLA4Ig fusion protein would lead to persistent inhibition of alloreactive T cell activation. Methods: We have used CHO-$dhfr^-$ cell-line to produce CTLA4Ig fusion protein. After serum free culture of transfected cell line we purified this recombinant molecule by using protein A column. To confirm characterization of fusion protein, we carried out a series of Western blot, SDS-PAGE and silver staining analyses. We have also investigated the efficacy of CTLA4Ig in vitro such as mixed lymphocyte reaction (MLR) & cytotoxic T lymphocyte (CTL) response and in vivo such as experimental autoimmune encephalomyelitis (EAE), graft versus host disease (GVHD) and skin-graft whether this fusion protein could inhibit alloreactive T cell activation and lead to immunosuppression of activated T cell. Results: In vitro assay, CTLA4Ig fusion protein inhibited immune response in T cell-specific manner: 1) Human CTLA4Ig inhibited allogeneic stimulation in murine MLR; 2) CTLA4Ig prevented the specific killing activity of CTL. In vivo assay, human CTLA4Ig revealed the capacities to induce alloantigen-specific hyporesponsiveness in mouse model: 1) GVHD was efficiently blocked by dose-dependent manner; 2) Clinical score of EAE was significantly decreased compared to nomal control; 3) The time of skin-graft rejection was not different between CTLA4Ig treated and control group. Conclusion: Human CTLA4Ig suppress the T cell-mediated immune response and efficiently inhibit the EAE, GVHD in mouse model. The mechanism of T cell suppression by human CTLA4Ig fusion protein may be originated from the suppression of activity of cytotoxic T cell. Human CTLA4Ig could not suppress the rejection in mouse skin-graft, this finding suggests that other mechanism except the suppression of cytotoxic T cell may exist on the suppression of graft rejection.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.183-200
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• 1994

Data-adaptive sequential suboptimal designs for very complex computer simulation codes are considered based on a spatial prediction model. These designs are constructed for two simulators of the computational nuclear fusion devices model. The difficulty of constructing the optimal designs due to the irregular design region, and its alternatives are also discussed with some computational algorithms for obtaining the designs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.60-79
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• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• ETRI Journal
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• v.45 no.5
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• pp.862-873
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• 2023

Determining whether an autonomous self-driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three-camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian-view intersection classification experiments show that our feature fusion model provides an area under the curve and F1-score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three- and one-camera models.

• Polymer(Korea)
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• v.32 no.1
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• pp.49-55
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• 2008

It is recognized that Schwann cells (SC) are essential for peripheral nerve development and regeneration. SIS (small intestinal submucosa) consists of some growth factors which can stimulate cell activity without immune rejection responges. SCs were harvested from the femurs and tibias of female Fischer rat and then suspended with $2{\times}10^6$ cell/sponge in SIS sponge. Fischer rat received an implant consisting of the SCs and the SIS sponge at the place of a 5 mm gap created by the sciatic nerve resection. Thin sections were stained with H &E staining and immunostaining of S-100, GFAP and NF after 1, 2, and 4 weeks. It was observed that the effects of the SIS sponge with SCs on neuroinduction(Group II, with scaffold & cell) are strong as much as uninjured model(Control I), and significantly stronger than SIS sponge model (Group 1, with scaffold only) and blank model (Control II). In conclusion, these results suggest that SIS sponge filled with SCs may have an important role for peripheral nerve regeneration of tissue engineering.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.345-353
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• 2015

In this paper, we proposed an information fusion method for enhancement of automatic dependent surveillance - broadcast (ADS-B) system which is one of the next generation navigation system. Although ADS-B provides better performance than traditional radar, ADS-B still has error due to dependence of global navigation satellite system (GNSS) information. In this paper, we improved the ADS-B performance using information fusion of multilateration (MLAT) and wide area multilateration (WAM). Information fusion provides accurate data compared to original data. Mostly, information fusion methods use Kalman filter or IMM(interacting multiple model) filter as a subfilter. However, we used Robust IMM filter as a subfilter to improve the aircraft tracking performance. Also, we use actual ADS-B data not virtual data to increase reliability of our information fusion method.

• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.647-655
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• 2008

Microsomal prostaglandin E2 synthase (mPGES-1) is a potent target for pain and inflammation. Various QSAR (quantitative structure activity relationship) analyses used to understand the factors affecting inhibitory potency for a series of MK886 analogues. We derived four QSAR models utilizing various quantum mechanical (QM) descriptors. These QM models indicate that steric, electrostatic and hydrophobic interaction can be important factors. Common pharmacophore hypotheses (CPHs) also have studied. The QSAR model derived by best-fitted CPHs considering hydrophobic, negative group and ring effect gave a reasonable result (q2 = 0.77, r2 = 0.97 and Rtestset = 0.90). The pharmacophore-derived molecular alignment subsequently used for 3D-QSAR. The CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) techniques employed on same series of mPGES-1 inhibitors which gives a statistically reasonable result (CoMFA; q2 = 0.90, r2 = 0.99. CoMSIA; q2 = 0.93, r2 = 1.00). All modeling results (QM-based QSAR, pharmacophore modeling and 3D-QSAR) imply steric, electrostatic and hydrophobic contribution to the inhibitory activity. CoMFA and CoMSIA models suggest the introduction of bulky group around ring B may enhance the inhibitory activity.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.580-591
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• 2019

This paper focuses on to improving object detection performance using the camera and LiDAR on autonomous vehicle platforms by fusing detected objects from individual sensors through a late fusion approach. In the case of object detection using camera sensor, YOLOv3 model was employed as a one-stage detection process. Furthermore, the distance estimation of the detected objects is based on the formulations of Perspective matrix. On the other hand, the object detection using LiDAR is based on K-means clustering method. The camera and LiDAR calibration was carried out by PnP-Ransac in order to calculate the rotation and translation matrix between two sensors. For Sensor fusion, intersection over union(IoU) on the image plane with respective to the distance and angle on world coordinate were estimated. Additionally, all the three attributes i.e; IoU, distance and angle were fused using logistic regression. The performance evaluation in the sensor fusion scenario has shown an effective 5% improvement in object detection performance compared to the usage of single sensor.