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检索条件"机构=Aerospace Intelligent System and Data Engineering Research Group"

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intelligent aerospace Terminologies Recognition based on Spectrogram using Machine Learning

Intelligent Aerospace Terminologies Recognition based on Spe...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Sitorus, Zest Amborgang Abdie, Syafrudin Adisutjipto Institute of Aerospace Technology Aerospace Intelligent System and Data Engineering Research Group Yogyakarta Indonesia Politeknik Negeri Malang Cognitive Artificial Intelligence Research Group Malang Indonesia Adisutjipto Institute of Aerospace Technology Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Faculty of Industrial Technology Aerospace Avionics System Research Group Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327298

A speech recognition system is an application to recognize spoken words through audio inputs. On the other hand, spectrograms are visual representations of audio signals and effectively represent words numerically. In this research, we developed a spectrogram-based speech recognition system using machine learning's K-Nearest Neighbors (KNN) method to recognize aerospace terminologies, some uncommon for ordinary people. We used 300 audio signal data consisting of two categories, namely 15 spectrogram data for aerospace terminologies and 15 spectrogram data for non-aerospace ones, each taken ten times. By conducting a 70:30 training and testing scheme, strengthened by k-fold cross-validation, the optimum K for KNN is 13. With such value of K, our developed intelligent system for aerospace terminologies recognition based on spectrogram can achieve an accuracy of 75.56% with a precision of 75.56%, recall of 75.56%, and F-1 score of 75.56%. With that accuracy, our developed intelligent system is considered as a good system. © 2023 IEEE.

关键词： Terminology

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Application for Airport Tenant Selection Using Weighted Product-based Decision Support system

Application for Airport Tenant Selection Using Weighted Prod...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Saputra, Muhamad Andika Ayuningtyas, Astika Sudarmadi, Catur Adisutjipto Institute of Aerospace Technology Aerospace Intelligent System and Data Engineering Research Group Yogyakarta Indonesia Politeknik Negeri Malang Cognitive Artificial Intelligence Research Group Malang Indonesia Adisutjipto Institute of Aerospace Technology Informatics Study Program Faculty of Industrial Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Health Informatics Research Group Faculty of Industrial Technology Yogyakarta Indonesia

ISBN: (纸本)9798350327298

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A-1 to A-4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A-4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V-4 is 0.294. © 2023 IEEE.

关键词： Decision support systems

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Adisutjipto Institute of aerospace Technology Virtual Tour with Artificial Intelligence (AI) Agent 1

Adisutjipto Institute of Aerospace Technology Virtual Tour w...

引用

1st International Conference of Adisutjipto on aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Ayuningtyas, Astika Wahyudi Sumari, Arwin Datumaya Aryanto, Salam Nuryatno, Edi Triono Mulyani, Sri Kusumaningrum, Anggraini Adisutjipto Institute of Aerospace Technology Aerospace Intelligent Systems and Data Engineering Research Group Faculty of Industrial Technology Yogyakarta Indonesia Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Modeling and Simulation Research Group Faculty of Industrial Technology Yogyakarta Indonesia The University of Western Australia Uwa Centre for Medical Research Perth Australia Adisutjipto Institute of Aerospace Technology Faculty of Aerospace Engineering Technology Yogyakarta Indonesia

ISBN: (纸本)9798331507930

This study aims to develop an interactive virtual tour based on Android with a first-person shooter approach to introduce the Adisutjipto Institute of aerospace Technology (ITD Adisutjipto) virtually. This virtual tour utilizes an Artificial Intelligence (AI) agent as an intelligent guide who can answer questions and provide relevant information related to facilities and locations inside the ITD Adisutjipto building. The test results show that this virtual tour is effective and feasible to use, with a user satisfaction rate reaching 87.72%. This study contributes to the field of educational virtual technology by offering a more immersive and interactive experience. Users can easily explore the ITD Adisutjipto building virtually and obtain the information they need efficiently. This research is expected to be an innovative solution to introduce the ITD Adisutjipto to wider community, as well as support learning and research activities in the aerospace technology field. © 2024 IEEE.

关键词： Virtual environments

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Azimuth Ambiguity Suppression for Sparse SAR Imaging Based on Unfolded Deep Network 15

Azimuth Ambiguity Suppression for Sparse SAR Imaging Based o...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Wu, Yuwei Zhang, Zhe Song, Ruizhen Qiu, Xiaolan Yu, Weidong Department of Space Microwave Remote Sensing System Beijing100090 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783800762873

Sparse synthetic aperture radar (SAR) imaging allows for a reduction in the azimuth sampling rate based on compressive sensing theory. However, it relies on the sparsity of scenes, which can lead to significant azimuth ambiguity. To address this issue, we introduce a novel network designed to suppress azimuth ambiguity for sparse SAR imaging. Our proposed deep unfolded network is based on the Iterative Thresholding Algorithm Learned (LISTA). We replace the observation matrix with Matched Filter (MF) operators to reduce computational complexity. Experiments with real SAR scene data demonstrate the effectiveness and superiority of our approach in suppressing azimuth ambiguity. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Matched filters

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Adisutjipto Institute of aerospace Technology Virtual Tour with Artificial Intelligence (AI) Agent

Adisutjipto Institute of Aerospace Technology Virtual Tour w...

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aerospace Electrical engineering and Informatics (ICAAEEI), International Conference of Adisutjipto on

作者： Astika Ayuningtyas Arwin Datumaya Wahyudi Sumari Salam Aryanto Edi Triono Nuryatno Sri Mulyani Anggraini Kusumaningrum Aerospace Intelligent Systems and Data Engineering Research Group Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Department of Electrical Engineering Aerospace Intelligent Systems and Data Research Group (CAIRG) State Polytechnic of Malang Malang Indonesia Aerospace Modeling and Simulation Research Group Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia UWA Centre for Medical Research The University of Western Australia Perth Australia Faculty of Aerospace Engineering Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

关键词： Electrical engineering Electric potential Three-dimensional displays Buildings Media aerospace testing Artificial intelligence Informatics

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intelligent aerospace Terminologies Recognition based on Spectrogram using Machine Learning

Intelligent Aerospace Terminologies Recognition based on Spe...

引用

Electrical and Information Technology (IEIT), International Conference on

作者： Arwin Datumaya Wahyudi Sumari Zest Amborgang Sitorus Syafrudin Abdie Aerospace Intelligent System and Data Engineering Research Group Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Cognitive Artificial Intelligence Research Group Politeknik Negeri Malang Malang Indonesia Department of Electrical Engineering Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Avionics System Research Group Department of Electrical Engineering Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

A speech recognition system is an application to recognize spoken words through audio inputs. On the other hand, spectrograms are visual representations of audio signals and effectively represent words numerically. In this research, we developed a spectrogram-based speech recognition system using machine learning’s K-Nearest Neighbors (KNN) method to recognize aerospace terminologies, some uncommon for ordinary people. We used 300 audio signal data consisting of two categories, namely 15 spectrogram data for aerospace terminologies and 15 spectrogram data for non-aerospace ones, each taken ten times. By conducting a 70:30 training and testing scheme, strengthened by k-fold cross-validation, the optimum K for KNN is 13. With such value of K, our developed intelligent system for aerospace terminologies recognition based on spectrogram can achieve an accuracy of 75.56% with a precision of 75.56%, recall of 75.56%, and F-1 score of 75.56%. With that accuracy, our developed intelligent system is considered as a good system.

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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

引用

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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Application for Airport Tenant Selection Using Weighted Product-based Decision Support system

Application for Airport Tenant Selection Using Weighted Prod...

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Electrical and Information Technology (IEIT), International Conference on

作者： Arwin Datumaya Wahyudi Sumari Muhamad Andika Saputra Astika Ayuningtyas Catur Sudarmadi Aerospace Intelligent System and Data Engineering Research Group Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Cognitive Artificial Intelligence Research Group Politeknik Negeri Malang Malang Indonesia Informatics Study Program Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Health Informatics Research Group Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A_1 to A_4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A_4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V_4 is 0.294.

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APPLICATION TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of engineering & Technology 2023.

关键词： Radar imaging

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GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS ARRAY GEOMETRIES: THE NON-VANDERMONDE ATOMIC SOFT THRESHOLDING APPROACH

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS A...

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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of engineering & Technology 2023.

关键词： Automotive radar

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