• 한국콘텐츠학회논문지
• /
• 제7권6호
• /
• pp.107-118
• /
• 2007

본 논문은 국내 영상저작물의 저작권 침해 판단을 사례로 들어 저작권 침해에 대한 법원의 판단기준을 고찰하였다. 국내 영상저작물 관련한 기존의 판례를 검토해보면, 의거관계의 추정에 의심이 가는 경우에는 양 저작물간에 상당한 유사성이 인정된다 하더라도, 실질적 유사성 부분에 대한 심사를 약하게 적용하여 저작권 침해를 구성하지 않는 경우가 대부분이다. 의거관계가 인정되더라도 실질적 유사성에 대해 심사하여, 비록 아이디어를 차용하였다고 하더라도, 독창적인 표현의 부분에 대해서는 실질적 유사성을 부인하고 있다. 영상저작물의 저작권 침해와 관련한 판례에서 법원은 '까레이스키' 사건에서는 지적재산권의 침해에 관하여 주관적 요건으로서 "의거관계"와 객관적 요건으로서 "실질적 유사성"에 기초하여 사실관계를 정리하고 판단함으로써 판단기준을 제시하였고, '여우와 솜사탕'사건에서는 종래의 판례에서와 같이 "의거관계"와 "실질적 유사성" 판단을 하였을 뿐만 아니라,더 나아가 "실질적 유사성" 판단에 있어 요건을 세분화하여 사실관계를 적용하였다. 이러한 판례는 향후 유사한 사건의 해결에 유력한 참고자료가 될 것이 분명하다.

• Interdisciplinary Bio Central
• /
• 제3권4호
• /
• pp.14.1-14.9
• /
• 2011

Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of 'Word' to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

• 한국지진공학회논문집
• /
• 제24권5호
• /
• pp.219-232
• /
• 2020

The slip-weakening model developed by Ohnaka and Yamashita is extended over the breakdown zone by equating the scaling relationships for the breakdown zone and the whole rupture area. For the extension, the study uses the relationship between rupture velocity and radiation efficiency, which was derived in the theory of linear elastic fracture mechanics, and the definition of f_max given in the specific barrier model proposed by Papageorgiou and Aki. The results clearly show that the extended scaling relationship is governed by the ratio of rupture velocity to S wave velocity, and the velocity ratio can be determined by the ratio of characteristic frequencies of a Fourier amplitude spectrum, which are corner frequency, f_c, and source-controlled cut-off frequency, f_max, or vice versa. The derived relationship is tested by using the characteristic frequencies extracted from previous studies of more than 130 shallow crustal events (focal depth less than 25 km, M_W 3.0~7.5) that occurred in Japan. Under the assumption of a dynamic similarity, the rupture velocity estimated from f_max/f_c and the modified integral timescale give quite similar scale-dependence of the rupture area to that given by Kanamori and Anderson. Also, the results for large earthquakes show good agreement to the values from a kinematic inversion in previous studies. The test results also indicate the unavailability of the spectral self-similarity proposed by Aki because of the scale-dependent rupture velocity and the rupture velocity-dependent f_max/f_c; however, the results do support the local similarity asserted by Ohnaka. It is also remarkable that the relationship between the rupture velocity and f_max/f_c is quite similar to Kolmogorov's hypothesis on a similarity in the theory of isotropic turbulence.

• 한국멀티미디어학회논문지
• /
• 제17권12호
• /
• pp.1418-1429
• /
• 2014

In this paper we suggest a new two-wheelers detection algorithm using local cell features. The first, we propose new feature vector matrix extraction algorithm using the correlation two cells based on local cell histogram and shifting from the result of histogram of oriented gradients(HOG). The second, we applied new weighting values which are calculated by the modified histogram intersection showing the similarity of two cells. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권8호
• /
• pp.3950-3964
• /
• 2017

With the increasing popularity of 3D technology such as 3D printing, 3D modeling, etc., there is a growing need to search for similar models on the internet. Matching non-rigid shapes has become an active research field in computer graphics. In this paper, we present an efficient and effective non-rigid model retrieval method based on topological structure and local volume. The integral geodesic distances are first calculated for each vertex on a mesh to construct the topological structure. Next, each node on the topological structure is assigned a local volume that is calculated using the shape diameter function (SDF). Finally, we utilize the Hungarian algorithm to measure similarity between two non-rigid models. Experimental results on the latest benchmark (SHREC' 15 Non-rigid 3D Shape Retrieval) demonstrate that our method works well compared to the state-of-the-art.

• Journal of Information Processing Systems
• /
• 제13권4호
• /
• pp.914-927
• /
• 2017

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

• 문화기술의 융합
• /
• 제5권3호
• /
• pp.339-344
• /
• 2019

서열 정렬에 있어서 전체를 비교하여 두 서열 사이의 최대의 유사성 또는 상동성을 찾는 전역 정렬은 넓은 범위를 선호하게 되는 편향성을 갖게 된다. 비일치 부분을 과감히 제거하고 높은 일치도를 갖는 부분 영역을 정렬하게 되면 정렬점수를 높이는 효과를 갖게 된다. 여러 개의 부분 지역 정렬을 탐색하게 하는 다중 지역정렬 방법을 적용하여 다수의 지역정렬을 수행하는 알고리즘을 구현하고 결과를 분석해 본다. 지역 정렬에 일반적으로 사용되는 Smith-Waterman 알고리즘의 제한점 중 하나인 서열이 길어지는 것을 방지하고, sub-optimal sequence를 찾기 위한 방법을 응용하여 다중지역 정렬을 수행한다.

• 인터넷정보학회논문지
• /
• 제7권4호
• /
• pp.33-43
• /
• 2006

본 논문에서는 조영 영상을 받아들여 관상동맥을 효과적으로 추출하는 방법을 소개한다. 혈관 추출에 일반적으로 사용되는 디지털 혈관조영술(DSA : Digital Subtraction Angiography)은 조영제 투입 전에 촬영된 마스크 영상과 조영제 투입 후의 혈관 대비가 나타나는 라이브 영상과의 차이를 이용하여 빠르게 혈관 영역만을 검출하는 방법이다. 그러나 이 방법은 배경의 움직임에 민감하고 두 영상간의 지역적인 배경 명암 분포의 변화에 따라 오 검출이 발생할 수 있다는 단점을 가진다. 따라서 본 논문에서는 배경 텍스쳐의 유사도를 분석하여 움직임의 차이가 가장 작은 영상을 선택함으로써 배경의 움직임에 기인하는 구조적인 문제를 해결하고, 선택된 영상의 지역적 명암 보정을 통해 혈관 영역만을 효과적으로 추출하는 방법을 제안한다. 실험 결과는 제안된 방법이 기존의 방법보다 오 인식률은 감소하고 정확도는 증가함을 보여준다.

• Journal of Crop Science and Biotechnology
• /
• 제10권1호
• /
• pp.27-32
• /
• 2007

RAPD, Inter-SSR, and AFLP markers were used to assess the genetic diversity of lettuce cultivars and the phylogenetic relationships in Lactuca spp. A total of 216 polymorphic bands from seven RAPD primers, four Inter-SSR primers, and five AFLP primer combinations were used to elucidate the genetic similarity among lettuce cultivars. Forty-four lettuce accessions were subdivided into discrete branches according to plant type: crisphead, butterhead, and stem type, with some exceptions. The leafy- and cos-type accessions were intermingled in other groups with no discrete branch indicating that these are more diverse than others. Three accessions, including the Korean cultivar 'Cheongchima', the Korean local landrace 'Jinjam', and the German cultivar 'Lolla Rossa' were classified as the most diverse accessions. Twenty bands were unique in specific cultivars. Among these, three were specific in a plant type; one in Korean leafy type, one in crisphead type, and one in cos type lettuce. In the phylogenetic analysis among Lactuca species, L. saligna, L. serriola, and L. georgica clustered in a sister branch of the L. sativa complex. Two L. virosa accessions show the highest intra-specific relationships. L. perennis outlied from all the other Lactuca species at a genetic similarity of 0.53 and clustered with two Cichorium species, C. intybus and C. endivia, with genetic similarity of 0.67. The phylogenetic tree was supported by data from polymorphism of chloroplast genome which was revealed by PCR-RFLP.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권5호
• /
• pp.1223-1237
• /
• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.