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Deep Learning Attention-Ranked Media Space Generation for Virtual Reality Equirectangular Scene Augmentation 面向虚拟现实等矩形场景增强的深度学习关注度分级媒体空间生成
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223646
Joshua Bercich, Vera Chung, Xiaoming Chen
{"title":"Deep Learning Attention-Ranked Media Space Generation for Virtual Reality Equirectangular Scene Augmentation","authors":"Joshua Bercich, Vera Chung, Xiaoming Chen","doi":"10.1109/TENSYMP55890.2023.10223646","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223646","url":null,"abstract":"Virtual Reality has fastened its growth radicalising industries such as tertiary education, marketing, and entertainment. Developments in virtual world-building like the Metaverse yields challenges such as the prohibitive technical skill requirement. This work constructed a method of generating attention-ranked media spaces through deep learning as a solution to this issue mitigating unskilled demand for scene augmentation. Two segmentation tasks were addressed: true-perspective view-port media space inferencing, and gaze attention predictions for equirectangular 360-degree projections. Combining results produced ranked spaces providing multimedia implantation locations. Ablation studies assessed TranSalNet, a leading attention Transformer, for attention-saliency accounting for model pre-encoders. This was compared against U-Net for media space generation. Weak attention supervision and architecture overparameterisation limitations were addressed with modified Salient Object Subitizing and DT-Fixup algorithms respectively. These contributions yielded an overall improvement from second-best models demonstrating experimental success.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124435070","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
Design and Implementation of Side Channel Attack Based on Deep Learning LSTM 基于深度学习LSTM的侧信道攻击设计与实现
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223652
A. A. Ahmed, M Zahid Hasan
{"title":"Design and Implementation of Side Channel Attack Based on Deep Learning LSTM","authors":"A. A. Ahmed, M Zahid Hasan","doi":"10.1109/TENSYMP55890.2023.10223652","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223652","url":null,"abstract":"Encryption algorithms and encryption devices both play a key role in ensuring the safety of data that has been encrypted. Various types of attacks, such as energy analysis, can be used to assess the reliability of the encryption devices. Since it was originally introduced, side channel attacks' deep learning-based methodology has drawn plenty of attention. This is one of several different attack strategies. In this paper, a side channel attack method based on the LSTM deep learning network is suggested. The method use Correlation Power Analysis (CPA) to find the relevant information in the side channel power consumption data. The choice of a suitable interest interval to utilize as the feature vector in the creation of the neural network model is then guided by the position of the interest points. The trials' findings show that the LSTM model outperforms both MLP and CNN in terms of how well it executes side channel attacks.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130756759","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
Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study 基于滤波培养数据的风力发电机叶片振动信号故障诊断比较研究
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223618
M. R. Sethi, Subhransu Sekhar Parhi, S. Sahoo, J. Dhanraj, V. Sugumaran, Smruti Ranjan Mohanty
{"title":"Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study","authors":"M. R. Sethi, Subhransu Sekhar Parhi, S. Sahoo, J. Dhanraj, V. Sugumaran, Smruti Ranjan Mohanty","doi":"10.1109/TENSYMP55890.2023.10223618","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223618","url":null,"abstract":"Due to the excellent wind resource and benefits of reducing land usage and visual impact concerns, wind turbines are installed more frequently in isolated onshore and offshore places. A wind turbine's rotor blades are vital in converting wind energy into electricity. The damage to the blades influences the power generation and turbine shutdown. In addition to the ongoing push to reduce the cost of wind energy, condition monitoring is currently generating a lot of attention since it is one of the most excellent solutions for maintenance problems. A pattern recognition system in machine learning approaches can detect and diagnose the faults in wind turbine blades. This proposed study demonstrates the effectiveness of machine learning models in identifying blade faults using filtered and unfiltered vibration signals. The logistic regression model using the resample filter-based vibration signal shows the best classification accuracy of 99.75 percent in 0.69 seconds.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131449989","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
Enhancing Facial Emotion Detection with CNN: Exploring the Impact of Hyperparameters 用CNN增强面部情绪检测:探索超参数的影响
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223480
Baljap Singh, Jaspreet Singh, Gaurav Soni
{"title":"Enhancing Facial Emotion Detection with CNN: Exploring the Impact of Hyperparameters","authors":"Baljap Singh, Jaspreet Singh, Gaurav Soni","doi":"10.1109/TENSYMP55890.2023.10223480","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223480","url":null,"abstract":"In recent years, computer vision and machine learning have seen a surge in research on Facial Emotion Recognition (FER). The significance of accurately recognizing and interpreting facial expressions for effective communication and social interaction cannot be overstated, making FER a topic of interest in diverse several disciplines, such as psychology, human-computer interaction, and security. Our research paper focuses on deep learning techniques for facial emotion recognition (FER). We specifically investigate the usefulness of Convolutional Neural Networks (CNN) in FER, due to their excellent performance in image classification challenges and their capacity to automatically identify key characteristics from images. Many datasets are researched and analyzed in this study for training expression recognition algorithms. Many datasets are researched and analyzed in this study for training expression recognition algorithms. Our study enhances the state-of-the-art model's accuracy to 98.85%, outperforming other existing models. This study will provide further information about face emotion detection and recognition. It will also highlight the aspects that influence its efficiency.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131507462","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
Fast Simulation Method with Reinforcement Learning for Automated Optimization of Electronic Systems 基于强化学习的电子系统自动优化快速仿真方法
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223650
{"title":"Fast Simulation Method with Reinforcement Learning for Automated Optimization of Electronic Systems","authors":"","doi":"10.1109/TENSYMP55890.2023.10223650","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223650","url":null,"abstract":"Design automation of electronic systems is challenging due to the growing design space, high performance tradeoffs, and rapid technological advances. To solve this problem, this paper presents an automated optimization framework that combines Fast Simulation with deep reinforcement learning for automatic circuit design. Fast Simulation can quickly and accurately evaluate circuit performance by neural networks. Deep reinforcement learning is used to find optimal parameters in the design space. Compared with existing reinforcement learning methods, the proposed method can automatically generate labels for the optimization results of the reinforcement learning agent by the simulator to retrain the neural network. To this end, the proposed optimization method performs better designs and reduces the required number of simulations.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131563718","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
Image Coding Based on the Automatic Generation of Structural Shapes Using Evolutionary Methods 基于进化方法的结构形状自动生成图像编码
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223644
Shota Fujiwara, Seishi Takamura
{"title":"Image Coding Based on the Automatic Generation of Structural Shapes Using Evolutionary Methods","authors":"Shota Fujiwara, Seishi Takamura","doi":"10.1109/TENSYMP55890.2023.10223644","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223644","url":null,"abstract":"This study examines the efficient representation of natural objects with structural shapes, such as ferns and snow crystals. Based on the L-System, a formal grammar suitable for representing such structural shapes, evolutionary methods generate rules and parameters to produce images similar to a given image. MSE and SSIM have been the standard measures for quantifying similarity, but they were ineffective for this purpose because they were sensitive to differences in shape. Therefore, in this study, we used the LPIPS rating scale based on deep learning to quantify similarity robust to shape differences. Experimental results confirmed that the codes of the L-System rule that produce images similar to the input image could be obtained.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128972035","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
Low-Complexity DFT-Based Multibeamforming Arrays with Configurable Beam Profiles 具有可配置波束轮廓的低复杂度dft多波束形成阵列
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223668
Pathmapirian Nanthakumar, C. Wijenayake, C. Edussooriya, A. Madanayake
{"title":"Low-Complexity DFT-Based Multibeamforming Arrays with Configurable Beam Profiles","authors":"Pathmapirian Nanthakumar, C. Wijenayake, C. Edussooriya, A. Madanayake","doi":"10.1109/TENSYMP55890.2023.10223668","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223668","url":null,"abstract":"A low-complexity multibeamforming scheme for uniform linear arrays is proposed based on warped and approximated discrete Fourier transform (DFT), albeit in the spatial domain. Conventional DFT- and approximated-DFT-based multibeamformers provide a low-complexity alternatives to produce multiple simultaneous beams with fixed beam profiles. The proposed approach aims to produce configurable (i.e., steerable) multibeam profiles using the warped DFT. Design examples are presented for an 8-element uniform linear array verifying, 81% and 18 % reduction in multiplier and adder complexities, respectively, when compared to a conventional narrowband multibeamformer having dedicated steering weights for each beam. Three system configurations are proposed using approximations for warped transform and the DFT. Hardware designs implemented on Xilinx Ultrascale KCU105 FPGA device verify 5.2 ns best case critical path delay.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132327258","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
Use of Deep Neural Networks to Predict Lithium-Ion Cell Voltages During Charging and Discharging 利用深度神经网络预测锂离子电池充放电电压
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223633
See Fung Lee, Jeevan Kanesalingam, Hock Guan Ho, S. Jayaprakasam
{"title":"Use of Deep Neural Networks to Predict Lithium-Ion Cell Voltages During Charging and Discharging","authors":"See Fung Lee, Jeevan Kanesalingam, Hock Guan Ho, S. Jayaprakasam","doi":"10.1109/TENSYMP55890.2023.10223633","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223633","url":null,"abstract":"This paper uses machine learning to predict either the voltage, current, or state of charge (SOC) during a discharging of a Li-Ion cell. Due to the non-linear characteristics of a Li-Ion cells, machine learning is used to create a model for prediction. Predicting the Li-Ion cell characteristics is useful for products in determining the end of discharge (EOD) levels under different loading conditions. The model used is a Deep Neural Network (DNN) with 2 hidden layers with 128 nodes each and is implemented using Python. To train the model, approximately 6000 data points of charging and discharging data of a LCO prismatic CS2 1100mAh Li-Ion cell is used. It was found that the accuracy of the model is approximately 5% and worsens at lower SOC.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622060","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
Disaster-Responsive Fetal Movement Monitoring System for Flood Affected Rural Areas 受灾农村胎儿运动监测系统
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223629
Mubah Mustafa, Ali Nawaz Khan, Muhammad Jawad
{"title":"Disaster-Responsive Fetal Movement Monitoring System for Flood Affected Rural Areas","authors":"Mubah Mustafa, Ali Nawaz Khan, Muhammad Jawad","doi":"10.1109/TENSYMP55890.2023.10223629","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223629","url":null,"abstract":"Climate-related weather disasters not only cause billions of dollars in damages but also have long-lasting effects on human health. In Year 2022, approximately 600,000 pregnant women were affected by devastating floods in Pakistan. The majority of these pregnant women belonged to rural areas and had limited access to healthcare facilities. Fetal movement serves as a reliable indicator of a healthy fetus. The proposed approach involves using an accelerometer measurement of fetal movement, along with a mobile application that allows for easy usage outside clinical environments, enabling remote fetal movement monitoring. By preprocessing a pre-recorded dataset of the 3D accelerometer measurements, four state-of-the-art machine learning algorithms are implemented to classify fetal movement with a relative degree of accuracy. The Extreme Gradient Boost algorithm demonstrates superior performance in classifying fetal movement, achieving an accuracy of 94.58% and an average accuracy of 87.03% through k-fold cross-validation.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123399601","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
Anomaly Detection in Streaming Environment by Evolving Neural Network with Interim Decision 基于演化神经网络的流环境异常检测
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223647
Subhadip Boral, Sayan Poddar, Ashish Ghosh
{"title":"Anomaly Detection in Streaming Environment by Evolving Neural Network with Interim Decision","authors":"Subhadip Boral, Sayan Poddar, Ashish Ghosh","doi":"10.1109/TENSYMP55890.2023.10223647","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223647","url":null,"abstract":"Algorithms in a streaming environment recognise a concept in real time since patterns are presented continuously; yet, in the presence of anomalies, algorithms fail to recognise the underlying concept, making anomaly identification a critical task. Neural networks detect anomalies efficiently; however, to use neural networks in a streaming environment, the architecture must be adaptive to learn ideas that vary over time. The proposed architecture places each node of the neural network in individual sites, and each node is made up of a perceptron. These perceptrons have two activation functions: one is used for architecture training, while the other is utilised for classification. Each layer has a decision-making node that takes decisions from the last layer nodes and decides the anomalous nature by majority voting. These layer-wise decisions help to select the training phase and appropriate architecture based on the desired performance. To demonstrate its usefulness, the proposed structure is tested on real-world data sets and compared to conventional and alternative neural networks.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"12 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129257432","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
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