scholarly journals Rancang Bangun Sensor Radar Sense And Avoid Uav Untuk Smart System Teletransport Alat Kesehatan

2021 ◽  
Vol 8 (4) ◽  
pp. 801
Author(s):  
Agus Hendra Wahyudi
Keyword(s):  
Neural Network ◽  
Unmanned Aircraft ◽  
Radar Sensor ◽  
Smart System ◽  
Range Resolution ◽  
Sense And Avoid ◽  
Neural Network Algorithm ◽  
The Right ◽  
Good Range ◽  
Ku Band

<p class="Abstrak">Perancangan sensor radar untuk <em> sense and avoid </em>(SAA) sistem pesawat tanpa awak (UAV) bertujuan agar operasi teletransport alat kesehatan dengan UAV VTOL berjalan dengan aman terhindar dari kecelakaan tabrakan di udara. Sensor radar ini didesain dengan bahan duroid 5880 dengan dielektrik konstant 2.2 dan ketebalan subtrate 1.57 mm. Bentuk antenna circular dan bekerja di pita ku-band 14 Ghz. Terdapat dua sensor untuk Tx dan Rx dalam satu substrate. Hasil simulasi sensor menunjukkan bandwitdh yang lebar 1.5 GHz  sehingga mampu menghasilkan resolusi range sangat baik yaitu 9.2 cm. Penguatan antenna dihasilkan 7.32 dB dan sudut beamwidth sensor 83<sup>O</sup> arah azimuth dan 78.2<sup>O </sup>arah elevasi. Sensor ini akan disematkan pada sistem SAA dengan algoritma neural network yang mendrive manuever UAV VTOL berbelok kesamping pada sudut dan jarak yang tepat sehingga terhindar dari tabrakan dengan objek penghalang.</p><p class="Abstrak"><strong><br /></strong></p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The design of the radar sensor for the sense and avoid (SAA) system of unmanned aircraft (UAV) aims to make teletransport operations of medical devices with UAV VTOL run safely avoiding collisions in the air. This radar sensor is designed with duroid 5880 material with a dielectric constant of 2.2 and a subtrate thickness of 1.57 mm. The antenna is circular and works on the 14 Ghz ku-band band. There are two sensors for Tx and Rx in one substrate. The sensor simulation results show a wide bandwidth of 1.5 GHz so that it is able to produce a very good range resolution of 9.2 cm. The antenna gain was 7.32 dB and the beamwidth angle of the sensor was 83<sup>O</sup> in the azimuth direction and 78.2<sup>O</sup> in the elevation direction. This sensor will be embedded in the SAA system with a neural network algorithm that drives the UAV VTOL maneuver to turn sideways at the right angle and distance so that it avoids collisions with obstructions.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK

2019 ◽  
Vol 15 (2) ◽  
pp. 163-170
Author(s):  
Nur Hadianto ◽  
Hafifah Bella Novitasari ◽  
Ami Rahmawati
Keyword(s):  
Neural Network ◽  
Roc Curve ◽  
Confusion Matrix ◽  
Total Income ◽  
Monthly Expenditure ◽  
The Neural Network ◽  
Average Education ◽  
Neural Network Algorithm ◽  
The Right ◽  
Very High

Payment of loans that experience difficulties in repayment or often called bad credit is a very detrimental thing for the bank, with the occurrence of bad credit the bank does not have the maximum ability to make money for investment. Choosing the right customer must go through the right analysis because the decision to approve or disagree with the loan is the main point that determines the possibility of bad credit. This study aims to classify eligible customers to obtain loans by taking into account existing parameters such as age, total income, number of families, monthly expenditure average, education level and others. This study uses a data mining classification method with a neural network model, to assess the accuracy of data processing using rapid miners then proceed with measurements using confusion matrix, ROC curve. The results of the neural network algorithm after going through confusion matrix testing, the ROC curve shows a very high accuracy value, and the dominant value of AUC and algorithm. The accuracy value is 98.24% with AUC of 0.979

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

Valid Signature Detection and Verification for Security of Individual Person

2016 ◽  
Vol 1 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Varun Sharma ◽  
Narpat Singh
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Recent Work ◽  
Research Work ◽  
Early Research ◽  
Individual Person ◽  
Signature Detection ◽  
Online Signature ◽  
The Right ◽  
Handwritten Signature

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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