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Synthesis of Na(Fe1-xAlx)PO4 cathode materials for sodium battery

Materials Today: Proceedings 2022年 65卷 2284-2289页

作者： Kaewluang, Napatsorn Pengpat, Kamonpan Tunkasiri, Tawee Intatha, Uraiwan Eitssayeam, Sukum Master's Degree Program in Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand Graduate School Chiang Mai University Chiang Mai Thailand Department of Physics and Materials Science Chiang Mai University Chiang Mai 50200 Thailand Center of Innovative Materials for Sustainability School of Science Mae Fah Luang University Chiang Rai 57100 Thailand Materials Science Research Center Faculty of Science Graduate School Chiang Mai University Thailand

The battery is one of the most important energy sources. The most popular types of battery for energy storage are lithium batteries. However, due to its high energy storage value and long service life, there are also disadvantages to the environment. Because lithium batteries are still toxic, making the disposal or recycling of lithium batteries difficult. This has led to the development of sodium-ion batteries (SIBs) in high-efficiency electrochemical energy storage devices and rechargeable electric vehicles to replace lithium batteries with limited lithium content. In comparison, sodium batteries use elemental sodium that is readily available, abundant, and has low production costs. For battery applications, Na+ sodium ion provides an efficient moving charge carrier, stable structure, and can make phosphate electrodes with a stable open structure. Furthermore, it gives a voltage value of 3.4 V. Therefore, a synthetic method study in this study. Na(Fe1-xAlx)PO4 through a solid-state process and calcination at 650, 675, 700, 725, and 750 °C to provide the substance with the desired phase. The samples were analyzed through the phase formation analysis process using X-ray diffraction (XRD), differential thermal analysis (DTA), and morphology characterized by scanning electron microscopy (SEM). This experiment and study guide the future development of sodium acid ion batteries. © 2022

关键词： Iron compounds

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression

Systematic Literature Review of Transformer Model Implementa...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Kenjovan Nanggala Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities.

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Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review

Exploring Recurrent Neural Network Models for Depression Det...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Brilyan Nathanael Rumahorbo Bens Pardamean Gregorius Natanael Elwirehardja Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD.

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review

Pap Smear Medical Image Classification Using Deep Learning: ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Elvan Selvano Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability.

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A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor

A Systematic Literature Review of CNN Approaches in Classify...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Aedentrisa Yasmanda Paulindino Bens Pardamean Gregorius Natanael Elwirehardja Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data.

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Enhancing Incremental Dataflow Analysis in an IDE 24

Enhancing Incremental Dataflow Analysis in an IDE

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Harianto, Koko Wang, Feng-Jian Abouzeid, Mahmoud M. National Yang Ming Chiao Tung University Eecs International Graduate Program Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350365658

Incremental dataflow analysis is a conventional technique adopted in syntax-directed editors, popularly used in Integrated Development Environments (IDEs). However, dataflow anomaly detection during program editing in IDEs remains a challenge due to the interaction with newer functionalities, such as AI-powered plugins like Copilot, which might insert and modify code lines directly. These interactions impact both the efficiency and effectiveness of dataflow anomaly detection processes. This paper introduces an enhanced incremental dataflow analysis approach in IDEs, focused on the analysis of dataflow within program structures. Our approach introduces a modified version of SP-tree, named SP-graph. This modification, involving the addition of supporting edges, facilitates data storage and enhances dataflow analysis incrementally. We also present a mechanism that streamlines dataflow anomaly detection within IDEs by identifying modifications that may impact dataflow, updating relevant data, and conducting immediate analysis. In addition, incremental concurrent anomaly detection in SP-graph shall be studied further. We demonstrate the effectiveness of our approach through a case study. © 2024 IEEE.

关键词： Data flow graphs

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Immersive Learning in Using the Metaverse of Satellite Communication Systems science 9

Immersive Learning in Using the Metaverse of Satellite Commu...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Akbar, Komputerio Meyliana Warnars, Harco Leslie Hendric Spits Jayadi, Riyanto Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798350383010

Education stands out as one of the most impactful applications of the metaverse, holding immense potential for the future. Within the realm of satellite communication system science education, the integration of immersive learning through the metaverse adds a layer of interest and interactivity to the teaching and learning process. Numerous design and evaluation techniques have been employed in a growing body of research, aiming to understand how immersive learning via the metaverse can effectively contribute to specific educational objectives. Hence, this research is dedicated to delving into the development of an immersive learning metaverse model for the science of satellite communication systems. The research contributes a model that advocates for a holistic learning approach by integrating metaverse technology, VR, the 6E Instructional Process Model, and the MSLQ evaluation instrument. The objective of this model is to enhance the efficiency, effectiveness, and interactivity of learning within the field of satellite communication systems science. © 2024 IEEE.

关键词： Satellite communication systems

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Passive Identification of Rogue Industrial Access Points Using a One-Class Machine Learning Model

Passive Identification of Rogue Industrial Access Points Usi...

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Annual Conference of Industrial Electronics Society

作者： André L. de S. Paula Eduardo K. Viegas Altair O. Santin Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Access point (AP) security has become increasingly important as wireless local area networks (WLANs) proliferate in industrial environments. Rogue APs are often used by attackers to conduct man-in-the-middle (MiTM) attacks. They can redirect users to malicious servers or do eavesdropping and manipulation of their *** this paper, we propose a novel one-class machine learning model to passively identify rogue APs in industrial environments. The implementation of the model is twofold. First, we passively extract the hardware and software characteristics of the evaluated AP according to its generated messages. This results in a comprehensive feature set that captures both low-level and high-level behaviors of the evaluated ***, we apply a one-class machine learning model to identify APs that significantly deviate from the previously known profile of legitimate APs. The combined evaluation of hardware and software behaviors integrated with an outlier detection scheme to effectively identify rogue APs is the insight of our proposal. We demonstrate the feasibility of our model, achieving an F1 score of 0.89 and a true positive rate of 0.9 in experiments conducted on our new publicly available dataset of 357 unique AP behaviors.

关键词： Wireless LAN Machine learning Network security Feature extraction Software Hardware Robustness Software reliability Servers Proposals

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3D Geometric Shape Assembly via Efficient Point Cloud Matching 41

3D Geometric Shape Assembly via Efficient Point Cloud Matchi...

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41st International Conference on Machine Learning, ICML 2024

作者： Lee, Nahyuk Min, Juhong Lee, Junha Kim, Seungwook Lee, Kanghee Park, Jaesik Cho, Minsu Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright 2024 by the author(s)

关键词： 3D modeling

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Towards a Feasible Palm Vein Verification Scheme Using Deep Autoencoder and Siamese Networks

Towards a Feasible Palm Vein Verification Scheme Using Deep ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Mateus Nunes Eduardo K. Viegas Altair O. Santin Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Palm vein pattern recognition offers a unique personal identification feature. Unfortunately, these techniques typically require a Near Infrared (NIR) camera sensor to extract the individual's venous pattern, challenging their wide deployment. This paper proposes a new feasible palm vein verification scheme using a Deep Autoencoder and a Siamese Network, implemented threefold. First, we capture the individual's palm using a traditional visible spectrum camera sensor and perform preprocessing tasks to correct imprecise positioning, easing palm support accessories requirements. Second, we eliminate NIR sensor requirement by fine-tuning a Deep Autoencoder model to convert images from the visible spectrum to their infrared counterparts. Third, generated images are processed by a lightweight Siamese network using a contrastive loss function for individual verification. Experiments conducted on a publicly available dataset with over a hundred individuals confirmed the feasibility of our proposal. Our scheme reaches up to 0.97 of true-negative rate, with only 0.01 decrease compared to traditional NIR-based approaches. In addition, individual identification can be conducted in less than 6 seconds in a resource-constrained environment thanks to our lightweight model's implementation.

关键词： Training Performance evaluation Image synthesis Databases Autoencoders Machine learning Cameras Feature extraction Pattern recognition Proposals

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