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The Impact of Government Development Policy on Land Investment and Land Price: Evidences from Linyi
Shengyang Zhong(종성양),Ziyang Zheng(정지양),Zhao Liu(유초) 한국콘텐츠학회 2021 한국콘텐츠학회논문지 Vol.21 No.12
토지는 중국 정부의 중요한 자원이다. 부동산 토지 개발은 도시경제 발전의 중요한 부분이다. 이 논문은 중국 임기시를 중심으로 정부 개발 정책이 토지개발 및 토지 가격에 어떤 영향을 주는지 연구하였다. 정부는 도시계획, 신도시 개발, 경제 개발 특별구 설립하는 여러 가지 개발 정책을 통해서 토지개발 및 토지가격에 큰 영향을 준다. 이 논문은 린이 시에 있는 196개 토지개발 프로젝트과 관련 있는 데이터를 수집하였다. 수집한 프로젝트들은 세 가지 지역에 위치한다(린이시의 옛 구역, 신도시 지역, 경제 개발 특별구). 논문에 활용한 데이터는 개발토지의 투자액, 토지가격, 토지면적, 건설면적, 건설밀도, 토지위치 등이다. 부동산 개발의 투자액 및 토지가격은 종속변수로서 회귀 모형을 만든다. 토지개발 및 토지가격에 대한 영향 변수를 분석한다. 정부 개발 정책은 토지개발 및 토지가격에 미치는 영향의 합리성을 찾아서 향후의 개발 정책에 반영할 수 있는 의견을 제공할 수 있다. Land is key natural resource that Chinese government actually owns. Real estate and land development have played an important part in China’s urban development and economic development. The Chinese local governments’ land development policies can mainly be characterized as the establishment of economic development zones and the development of new towns. Given the great importance of these measures, we can expect that these policies can generate noticeable impacts on land development and land price. However, little research has explored these impacts empirically. Using the data collected from land development projects of three districts in Linyi city—old town, new town, economic development zone, this paper attempts to investigate the impact of government development policy on land development and land price. This research chooses investment amount and land price as dependent variables. The multiple regression results demonstrate that the local government’s land Development policies can affect land investment size and land price significantly. As we have noticed, the target of government development policy is to make use of urban land resources more scientifically and efficiently. Based on my empirical analysis, some useful insights can be provided for improving our understanding concerning the effects of these government land development policies.
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Ship Number Recognition Method Based on An improved CRNN Model
Wenqi Xu,Yuesheng Liu,Ziyang Zhong,Yang Chen,Jinfeng Xia,Yunjie Chen 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.3
Text recognition in natural scene images is a challenging problem in computer vision. The accurate identification of ship number characters can effectively improve the level of ship traffic management. However, due to the blurring caused by motion and text occlusion, the accuracy of ship number recognition is difficult to meet the actual requirements. To solve these problems, this paper proposes a dual-branch network based on the CRNN identification network. The network couples image restoration and character recognition. The CycleGAN module is used for blur restoration branch, and the Pix2pix module is used for character occlusion branch. The two are coupled to reduce the impact of image blur and occlusion. Input the recovered image into the text recognition branch to improve the recognition accuracy. After a lot of experiments, the model is robust and easy to train. Experiments on CTW datasets and real ship maps illustrate that our method can get more accurate results.
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Depth tracking of occluded ships based on SIFT feature matching
Yadong Liu,Yuesheng Liu,Ziyang Zhong,Yang Chen,Jinfeng Xia,Yunjie Chen 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.4
Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.
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