가상 현실은 몰입감과 존재감을 극대화시킬 수 있다는 장점이 있어 비디오 게임, 교육, 치료 목적을 가진 다양한 콘텐츠에 널리 활용되고 있다. 또한 GearVR, DayDream, Occulus Quest 2 등의 HMD 등이 개발됨에 따라 가상 현실은 널리 보급되었다. 그러나 이러한 장비들을 사용하면 사용자가 실제 물리적인 세계를 볼 수 없다는 단점이 있어 실제 물체와 충돌하거나 넘어지는 위험한 상황에 빠질 수 있다. 만약 사용자가 시스 루 HMD를 사용하면 실제 물리적인 세계를 볼 수 있으므로 물체를 피할 수 있고 이러한 위험을 피할 수 있 는데 이러한 시스루 HMD는 광학 기반 방식이나 비디오 기반 방식으로 분류된다. 이러한 광학 기반 HMD는 렌즈를 통과하는 물리적 물체들을 볼 수 있지만 비용 문제가 있으며 비디오 기반 HMD는 비용과 카메라의 원본 이미지에 다양한 가상 효과를 추가할 수 있다는 장점이 있다. 본 논문은 이러한 장점들을 활용하여 저 렴한 비용으로 게임에 활용될 수 있도록 개발된 시스루 HMD를 설명하였으며 해당 HMD를 활용하는 게임 을 설명하였다. 본 논문을 통하여 개발된 비디오 시스루 HMD를 통해 더 많은 사용자가 저렴한 비용으로 혼합 현실 기반 게임을 즐길 수 있을 것으로 예상된다.

In marine ecosystems, the biosynthesis and catabolism of dimethylsulfoniopropionate (DMSP) by marine bacteria is critical to microbial survival and the ocean food chain. Furthermore, these processes also influence sulfur recycling and climate change. Recent studies using emerging genome sequencing data and extensive bioinformatics analysis have enabled us to identify new DMSP-related genes. Currently, seven bacterial DMSP lyases (DddD, DddP, DddY, DddK, DddL, DddQ and DddW), two acrylate degrading enzymes (DddA and DddC), and four demethylases (DmdA, DmdB, DmdC, and DmdD) have been identified and characterized in diverse marine bacteria. In this review, we focus on the biochemical properties of DMSP cleavage enzymes with special attention to DddD, DddA, and DddC pathways. These three enzymes function in the production of acetyl coenzyme A (CoA) and CO2 from DMSP. DddD is a DMSP lyase that converts DMSP to 3-hydroxypropionate with the release of dimethylsulfide. 3-Hydroxypropionate is then converted to malonate semialdehyde by DddA, an alcohol dehydrogenase. Then, DddC transforms malonate semialdehyde to acetyl-CoA and CO2 gas. DddC is a putative methylmalonate semialdehyde dehydrogenase that requires nicotinamide adenine dinucleotide and CoA cofactors. Here we review recent insights into the structural characteristics of these enzymes and the molecular events of DMSP degradation.

본 연구에서는 가래나무를 흡즙하는 Stomaphis matsumotoi Sorin, 1995를 국내 최초로 보고한다. 무시충의 생체 사진과 표본을 통한 형태 정보를 수록하였다.

본 연구를 통해 라오스지역에서 분포하는 미소 나방인 Promalactis vittapenna종을 처음으로 기록하였다. 미기록 종의 분류학적 진단과 분포 정보, 성충과 생식기 도해사진을 제시하였다. 또한, 라오스 전체 종에 대한 분류학적 키를 함께 제공하였다.

Environmental damage caused by marine plastic debris occurs and has become a major contributor to marine pollution. This study analyzed the current state of marine plastic debris pollution and proposed essential strategies to reduce damage. To assess the current state of pollution arising from marine plastic debris, this study investigated the properties of plastic debris, reviewed case studies of ecological impacts, and examined the inflow and distribution of marine plastic debris. The results of this study indicate that the major deleterious effects of marine plastics are entanglement and ingestion. In addition, the amount of plastic waste entering the sea was estimated to be 230 Mt in 2015 and may increase to 554 Mt in 2050. In this study, three key strategies were proposed to reduce damage and preserve the ecosystem, including: 1) removing plastic debris in the marine environment, 2) limiting the release of plastic debris to the marine environment, and 3) preventing damage to humans and marine life from plastic debris. To minimize the environmental damage caused by marine plastic debris, the proposed response strategies should be implemented in parallel.

The decrease in under keel clearance (UKC) due to the increase of draft that occurs during advancing and turning of very large vessels of different types was analyzed based on computational fluid dynamics (CFD). The trim change in the Duisburg test case (DTC) container ship was much smaller than that of the KRISO very large crude oil carrier 2 (KVLCC2). The sinkage of both ships increased gradually as the water depth became shallower. The amount of sinkage change in DTC was greater than that in KVLCC2. The maximum heel angle was much larger for DTC than for KVLCC2. Both ships showed outward heel angles up to medium-deep water. However, when the water depth became shallow, an inward heel was generated by the shallow water effect. The inward heel increased rapidly in very shallow water. For DTC, the reduction ratio was very large at very shallow water. DTC appeared to be larger than KVLCC2 in terms of the decreased UKC because of shallow water in advancing and turning. In this study, a new result was derived showing that a ship turning in a steady state due to the influence of shallow water can incline inward, which is the turning direction.

Deep learning, which has recently shown excellent performance, has a problem that the amount of computation and required memory are large. Model compression is very useful because it saves memory and reduces storage size while maintaining model performance. Model compression methods reduce the number of edges by pruning weights that are deemed unnecessary in the calculation. Existing weight pruning methods using ADMM construct an optimization problem by a layer-by-layer addition of pre-defined removal-ratio constraints. Decomposing into two subproblems through the ADMM process, one can solve them through gradient descent and projection. However, the layer-by-layer removal ratios must be structurally specified, causing a sharp increase in training time due to a large number of parameters, and hardly feasible to use for large models that actually require weight pruning. Our proposed method performs weight pruning, producing similar performance, by setting a global removal ratio for the entire model without prior knowledge of structural characteristics in order to solve the shortcomings of the existing ADMM weight-pruning methods. To effectively avoid performance degradation, the method removes a relatively small number of previous layers in charge of feature extraction. Experiments show high-quality performance, not necessarily setting layer-by-layer removal ratios. Additionally, experiments increasing layers yield an insight for feature extraction in pruned layers. The experiment of the proposed method to the LeNet-5 model using MNIST data results in a higher compression ratio of 99.3% outperforming those of other existing algorithms. We also demonstrate the effectiveness of the proposed method in YOLOv4, an object detection model requiring substantial computation.

In defense acquisition system, testing and evaluation is a very important procedure that can ensure the completeness of capability while deciding whether to mass-produce or purchase weapons systems. But it always includes realistic restrictions that involve a variety of stakeholders, but lack of time, resources, and budget. Therefore, in the process of planning a test and evaluation, proper number of prototypes and reliability of test results, along with test items and evaluation criteria, are frequently discussed as sensitive agendas. In reality, however, rather than statistical judgments, the number of prototypes and tests are determined by business logic such as duration and budget. Otherwise, most theoretical studies do not adequately reflect the business logic of test assessment. In this study, we propose a number of prototype and tests method that can statistically reasonably verify the performance of the inorganic system considering the characteristics of each test and evaluation project. To this end, we consider the theory related to determining the number of prototypes and tests, and present examples by separating whether to secure the magnitude of effects that have a significant impact on statistical judgment. This study could contribute to the development of empirical methodologies that can adequately coordinate reality and theory in the field of defense test evaluation while ensuring statistical reliability of test evaluation results.