{"title":"Retracted: Research on the Application of Environmental Art Design Based on the Combination of VR and Panoramic Video Technology","authors":"Scientific Programming","doi":"10.1155/2023/9831013","DOIUrl":"https://doi.org/10.1155/2023/9831013","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42389661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: SLAM 3D Digital Terrain Mapping with SqueezeNet Driven by Road Traffic Data","authors":"Scientific Programming","doi":"10.1155/2023/9789583","DOIUrl":"https://doi.org/10.1155/2023/9789583","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42893307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Analysis and Optimization of Online Music Teaching System Based on Dynamic Model","authors":"Scientific Programming","doi":"10.1155/2023/9846576","DOIUrl":"https://doi.org/10.1155/2023/9846576","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43451197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Mind Map Construction for English Grammar Teaching Based on Knowledge Map","authors":"Scientific Programming","doi":"10.1155/2023/9758137","DOIUrl":"https://doi.org/10.1155/2023/9758137","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46415858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter Ardhianto, Y. P. Santosa, Christian Moniaga, Maya Putri Utami, Christine Dewi, Henoch Juli Christanto, Abbott Po Shun Chen
The biggest challenge for architecture designers is the time required for the design process. Especially landscape architects who have different work limits from architects in general. In contrast to architects in general, who are assisted in producing design plans by building standards, building requirements, and space programs that adapt to the type of project being undertaken. At the same time, some design jobs demand high-productivity landscape animation presentation in a short time. The long process involved in designing animation often makes it difficult for designers to produce optimal work. This study proposes generative zooming animation with artificial intelligence support to shorten the designer’s work process and energy optimization. Deep learning with Vector Quantized Generative Adversarial Network and Contrastive Language-Image Pre-Training was used to generate alternative landscape designs from text prompt-based and compile them in animation. Our experiment shows that one frame can be generated roughly in 3.636 ± 0.089 s, which is significantly faster than the conventional method to create animation. Moreover, our method is able to achieve a good-quality image, which scored 3.2904 using inception score evaluation. The effectiveness of deep learning in visual landscape and animation creation can help designers speed up the design process. Furthermore, working time efficiency without compromising design quality will increase designer productivity and economic growth.
{"title":"Generative Deep Learning for Visual Animation in Landscapes Design","authors":"Peter Ardhianto, Y. P. Santosa, Christian Moniaga, Maya Putri Utami, Christine Dewi, Henoch Juli Christanto, Abbott Po Shun Chen","doi":"10.1155/2023/9443704","DOIUrl":"https://doi.org/10.1155/2023/9443704","url":null,"abstract":"The biggest challenge for architecture designers is the time required for the design process. Especially landscape architects who have different work limits from architects in general. In contrast to architects in general, who are assisted in producing design plans by building standards, building requirements, and space programs that adapt to the type of project being undertaken. At the same time, some design jobs demand high-productivity landscape animation presentation in a short time. The long process involved in designing animation often makes it difficult for designers to produce optimal work. This study proposes generative zooming animation with artificial intelligence support to shorten the designer’s work process and energy optimization. Deep learning with Vector Quantized Generative Adversarial Network and Contrastive Language-Image Pre-Training was used to generate alternative landscape designs from text prompt-based and compile them in animation. Our experiment shows that one frame can be generated roughly in 3.636 ± 0.089 s, which is significantly faster than the conventional method to create animation. Moreover, our method is able to achieve a good-quality image, which scored 3.2904 using inception score evaluation. The effectiveness of deep learning in visual landscape and animation creation can help designers speed up the design process. Furthermore, working time efficiency without compromising design quality will increase designer productivity and economic growth.","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47302632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In deep neural networks, the activation function is an important component. The most popular activation functions at the moment are Sigmoid, Sin, rectified linear unit (ReLU), and some variants of ReLU. However, each of them has its own weakness. To improve the network fitting and generalization ability, a new activation function, TSin, is designed. The basic design idea for TSin function is to rotate the Sin function 45° counterclockwise and then finetune it to give it multiple better properties needed as an activation function, such as nonlinearity, global differentiability, unsaturated property, zero-centered property, monotonicity, quasi identity transformation property, and so on. The first is a theoretical derivation of TSin function by formulas. Then three experiments are designed for performance test. The results show that compared with some popular activation functions, TSin has advantages in terms of training stability, convergence speed, and convergence precision. The study of TSin not only provides a new choice of activation function in deep learning but also provides a new idea for activation function design in the future.
{"title":"A Novel Activation Function of Deep Neural Network","authors":"Xiangyang Lin, Qinghua Xing, Zhang Han, Chen Feng","doi":"10.1155/2023/3873561","DOIUrl":"https://doi.org/10.1155/2023/3873561","url":null,"abstract":"In deep neural networks, the activation function is an important component. The most popular activation functions at the moment are Sigmoid, Sin, rectified linear unit (ReLU), and some variants of ReLU. However, each of them has its own weakness. To improve the network fitting and generalization ability, a new activation function, TSin, is designed. The basic design idea for TSin function is to rotate the Sin function 45° counterclockwise and then finetune it to give it multiple better properties needed as an activation function, such as nonlinearity, global differentiability, unsaturated property, zero-centered property, monotonicity, quasi identity transformation property, and so on. The first is a theoretical derivation of TSin function by formulas. Then three experiments are designed for performance test. The results show that compared with some popular activation functions, TSin has advantages in terms of training stability, convergence speed, and convergence precision. The study of TSin not only provides a new choice of activation function in deep learning but also provides a new idea for activation function design in the future.","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45357332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Collocation Features in Translated Texts Based on English Analogy Corpus","authors":"Scientific Programming","doi":"10.1155/2023/9763290","DOIUrl":"https://doi.org/10.1155/2023/9763290","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43825838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Processing of the Conceptual Semantics of Verbs and Clauses in English Learners under the Background of Wireless Communication and Artificial Intelligence","authors":"Scientific Programming","doi":"10.1155/2023/9864635","DOIUrl":"https://doi.org/10.1155/2023/9864635","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41751642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Remote English Teaching Resource Sharing Based on Internet O2O Model","authors":"Scientific Programming","doi":"10.1155/2023/9782074","DOIUrl":"https://doi.org/10.1155/2023/9782074","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44034161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Design of Packaging Design Evaluation Architecture Based on Deep Learning","authors":"Scientific Programming","doi":"10.1155/2023/9791531","DOIUrl":"https://doi.org/10.1155/2023/9791531","url":null,"abstract":"<jats:p />","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45361635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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