Engineering Headway最新文献_第10页

Review of Deep Learning Using Convolutional Neural Network Model 使用卷积神经网络模型的深度学习回顾

Engineering Headway Pub Date : 2024-03-05 DOI: 10.4028/p-kzq3xe

Ari Kurniawan Saputra, E. Erlangga, Tia Tanjung, F. Ariani, Y. Aprilinda, R. Y. Endra

{"title":"Review of Deep Learning Using Convolutional Neural Network Model","authors":"Ari Kurniawan Saputra, E. Erlangga, Tia Tanjung, F. Ariani, Y. Aprilinda, R. Y. Endra","doi":"10.4028/p-kzq3xe","DOIUrl":"https://doi.org/10.4028/p-kzq3xe","url":null,"abstract":"Machine Learning can be used to process a lot of data and learn patterns from that data to predict the future. One of the most widely used parts of machine learning is Deep Learning. The Deep Learning method that currently provides the most significant results in image recognition is Convolutional Neural Network (CNN). Convolutional Neural Network (CNN) is one of the deep learning algorithms used for computer vision use cases such as image or video classification and detecting objects within images or even image areas. Some research related to the CNN model states that this model has a very good accuracy of 92% but with a fairly small amount of data and the use of epochs, namely 100, resulting in a higher validation error value than the error value in the training process, so that over fitting will occur. Based on several problems in the related research literature, this article aims to identify the weaknesses and shortcomings of Deep Learning algorithms using CNN models that refer to the state of the art, so that they can be used as a reference for further research. The state of the art related to research in the last five years, the Deep Learning algorithm using the CNN model found that (1) The number of epochs can affect the accuracy of the CNN model, (2) 2. The application of architecture can affect the accuracy of the CNN model, (3) the application of the type of layer can affect the accuracy of the CNN model. Based on several problems in the research literature related to the identification of weaknesses and shortcomings of Deep Learning using the CNN model which refers to Table 1. State of the Art summary of literature review research for the last five years, it can be concluded that to increase the accuracy of the CNN model, it is necessary to increase the number of epochs, apply the right architecture according to the problems in the research conducted, and use the type of layer. The hypothesis of this article can be used as a reference for further research related to Deep Learning using the CNN model.","PeriodicalId":512976,"journal":{"name":"Engineering Headway","volume":"19 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140264968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Implementation of Natural Language Processing in Manufacturing and Service Industry through Skateboard Monitoring Device 通过滑板监测设备在制造业和服务业中实现自然语言处理

Engineering Headway Pub Date : 2024-03-05 DOI: 10.4028/p-piuwy7

Tia Tanjung, R. Muhida, Muhammad Riza, Ari Kurniawan Saputra, E. Erlangga, Nofian Pratama, F. Ariani, Taqwan Thamrin, R. Y. Endra, A. K. Puspa, W. Susanty, A. Cucus

引用次数: 0

Identification of Endophytic Fungi on the Leaves of Jeruju (Acanthus ilicifolius) which are Potential as Antibacteria 鉴定具有抗菌潜能的刺五加叶内生真菌

Engineering Headway Pub Date : 2024-03-05 DOI: 10.4028/p-lzq74e

Gusti Ayu Widayanti, H. Widjajanti, S. Salni, A. Sutjipto

引用次数: 0

Development of the Obstacle Avoider of Fish Robot 鱼类机器人避障器的开发

Engineering Headway Pub Date : 2024-03-05 DOI: 10.4028/p-e5az8j

R. Muhida, Muhammad Riza, Bambang Pratowo, Zein Muhamad, A. Cucus, Taqwan Thamrin, A. Sutjipto, R. Muhida, Ari Legowo, Mochamad Safari, Handri Santoso

{"title":"Development of the Obstacle Avoider of Fish Robot","authors":"R. Muhida, Muhammad Riza, Bambang Pratowo, Zein Muhamad, A. Cucus, Taqwan Thamrin, A. Sutjipto, R. Muhida, Ari Legowo, Mochamad Safari, Handri Santoso","doi":"10.4028/p-e5az8j","DOIUrl":"https://doi.org/10.4028/p-e5az8j","url":null,"abstract":"The extraordinary swimming capacity of fish in nature makes them unique among Allah's creations. It is extremely difficult for a robotic system to achieve fish-like swimming behaviors, especially in terms of swimming performance. Many fish use their pectoral fins to provide thrust over a wide speed range and to carry out tricky maneuvers. In this paper, we report a robotic fish that can travel forward and backward using its propulsion system. In this report, the creation of a conceptual design for an interactive fish robot took into account a number of factors, including swimming ability, leakage testing, and motion controller. This needed considerable mechanical design work, and the result is a quick-return mechanism for the fish's body. We made the decision to divide the body into the head, body, and tail. In order to create the propulsion system, we employed five servo motors. Finally, controlling the robot's motion is absolutely essential, especially if there is an obstruction in its path. The servo controller, which is located at the fish's head, serves as the primary controller for all of the motors and sensors.","PeriodicalId":512976,"journal":{"name":"Engineering Headway","volume":"124 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140078964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Butterflies Diversity in Geothermal Powerplant Areas: Case Studies in Pt. Pertamina Geothermal Energy Lumut Balai, Muara Enim, South Sumatera 地热发电厂地区的蝴蝶多样性：南苏门答腊省穆拉埃尼姆市 Pt.

Engineering Headway Pub Date : 2024-03-05 DOI: 10.4028/p-6vijh1

Ina Aprillia, Muhammad Iqbal, Guntur Pragustiandi, A. Sutjipto, Arum Setiawan, Adhitya Wicaksono, I. Yustian

{"title":"Butterflies Diversity in Geothermal Powerplant Areas: Case Studies in Pt. Pertamina Geothermal Energy Lumut Balai, Muara Enim, South Sumatera","authors":"Ina Aprillia, Muhammad Iqbal, Guntur Pragustiandi, A. Sutjipto, Arum Setiawan, Adhitya Wicaksono, I. Yustian","doi":"10.4028/p-6vijh1","DOIUrl":"https://doi.org/10.4028/p-6vijh1","url":null,"abstract":"This study aims to assess the diversity of butterflies (Lepidoptera: Rhopalocera) at the location of the PT Pertamina Geothermal Energy (PGE) Lumut Balai Geothermal powerplant, Muara Enim, South Sumatra. This rapid survey was carried out on 4-15 June 2023, taking place in 7 locations namely Cluster 5, Cluster 6, Cluster 7, Cluster 9, Cluster 10, APL 17 and APL 18. The method used is the direct observation method by walking along a 1000 meter transect line (Yustian et al., 2017) in each type of habitat (secondary forest, mixed shrub secondary forest, coffee plantation , open areas and secondary forests near to rivers/water sources). The results obtained in this rapid survey are that there are 5 butterfly families consisting of Papilionidae, Pieridae, Nymphalidae, Hesperiidae and Lycaenidae with a total of 51 species and 254 individuals. The highest diversity index based on Shannon's diversity index and Margaleff's species richness index is in APL 17 with a secondary forest habitat type near to coffee plantations (H'=3.14, R=7.08) and the lowest is in Cluster 9 (H'=2.11, R=3.23) which is a secondary forest near to a geothermal power plant. Meanwhile, the highest evenness index was found in APL 17 (E=0.97) and the lowest in Cluster 7 (E=0.87) with a riparian habitat type. During the research, protected species were recorded, namely Troidesamphrysus and Troideshelena.","PeriodicalId":512976,"journal":{"name":"Engineering Headway","volume":"64 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140264308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0