2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)最新文献_第2页

Sensitivity of the structural behavior of SAC305 interconnects on the variations of creep parameters SAC305互连结构性能对蠕变参数变化的敏感性

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465551

R. Cruz, V. Gonda

{"title":"Sensitivity of the structural behavior of SAC305 interconnects on the variations of creep parameters","authors":"R. Cruz, V. Gonda","doi":"10.1109/SACI51354.2021.9465551","DOIUrl":"https://doi.org/10.1109/SACI51354.2021.9465551","url":null,"abstract":"Lead-free soldering materials have been of great interest in the field of advanced microelectronic packaging during the last decade. Compared to the eutectic tin-lead solder (Sn-Pb), the increased melting temperature influences mechanical properties that can compromise the integrity of the solder joint in a long period of working time. In the present work, a two-dimensional (2-D) Fan out Wafer Level Packaging structure was modelled with SAC305 for the soldering material. Anand creep model was utilized for the solder balls using different solder interconnect material properties determined for SAC305 taken from different literature sources. The 2-D model of the package was subjected to a cyclic thermo-mechanical load. The primary outcomes of creep behavior were studied using implicit visco-plastic analysis for the solder. Equivalent of Creep Strain (ECS), and Total Equivalent of Creep Strain (TECS) history showed different results for the various parameter sets of the same solder. Since SAC305 is one of the most widely used lead-free soldering materials, for simulation purposes, researchers must rely on the material properties obtained from experimental results. In this study, a more general view is presented regarding SAC305 visco-plastic properties.","PeriodicalId":321907,"journal":{"name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046702","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

Proof of Concept Clinical Trial of Bioimpedance-based NAFLD Diagnosis Technique 基于生物阻抗的NAFLD诊断技术的概念验证临床试验

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465553

Nina Gyorfi, Á. Odry, Z. Karádi, P. Odry, A. Vereczkei, B. Kuljic, Zoltán Vízvári, Attila Tóth

引用次数: 2

Can Bus Communication Demonstration Tool for Education 公共汽车能成为教育的沟通示范工具吗

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465593

Ahmed Al-Areqi, T. Szakács

{"title":"Can Bus Communication Demonstration Tool for Education","authors":"Ahmed Al-Areqi, T. Szakács","doi":"10.1109/SACI51354.2021.9465593","DOIUrl":"https://doi.org/10.1109/SACI51354.2021.9465593","url":null,"abstract":"Intelligent vehicles is a fast developing area. For the internal combustion engines are 150 years of dead-end road of vehicle technology – which 150 years are still counting – the greatest development in vehicle industry can now be seen in self driving technology. The demand on intelligent solutions is the fastest area in this industry. Intelligent solutions are requiring reliable communication channels. The most widely used vehicle communication is the Controller Area Network (CAN). The CAN (bus) is a communication protocol between the different control units in the car, where the controllers (computers) can exchange information directly (without a direct link unit between them) and it is a protocol based on the principle of messages. Each message has its own structure consisting of Message-ID, message start, message body, and end of the message. CAN messages do not contain the address of the sender or the addressee, but the content of the message itself contains an additional identifier for the information transmitted like RPM, temperature, etc. In this paper, we will propose an implementation of an education tool to demonstrate the principle of a CAN bus protocol. The demonstration tool comes with sensors, actuators, LCD, LED, input, and output functions. This tool will simplify the idea of how the CAN bus sends messages in the data bus with the priority of the order. Also, it will support the theoretical knowledge of the CAN bus by a practical demonstration of it.","PeriodicalId":321907,"journal":{"name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123820146","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

Automatic Mask Detecion using Convolutional Neural Networks and Variational Autoencoder 基于卷积神经网络和变分自编码器的自动掩码检测

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465587

Mxolisi Silabela, Bence Bogdandy, Zsolt Tóth

{"title":"Automatic Mask Detecion using Convolutional Neural Networks and Variational Autoencoder","authors":"Mxolisi Silabela, Bence Bogdandy, Zsolt Tóth","doi":"10.1109/SACI51354.2021.9465587","DOIUrl":"https://doi.org/10.1109/SACI51354.2021.9465587","url":null,"abstract":"The importance of proper hygienical behaivour is essential in today’s word especially during an ongoing pandemic. Wearing mask became mandatory in many countries during the COVID-19 Pandemic. Recognizing whether people are wearing masks is complicated image recognition task which could be facilitated and automated with machine learning techniques. Camera streams are widely available in indoor environments which can be used for object detection and image processing. Convolutional Neural Networks have been successfully applied in image classification and object recognition task in various application areas. There are already trained and openly available general purpose convolutional neural networks which can be used as an initial version for specific applications. A number of different image datasets are also available for research and industrial purposes. The InceptionV3 Neural Network architecture was used to tailored to determine whether a mask is being worn or not using transfer learning techniques, and convolutional neural networks. A variational autoencoder has also been trained to normalize the dataset with respect to skin colour, angle of the head and among other parameters. This paper describes the implementation of a mask recognition software using transfer learning, a convolutional neural network and a variational autoencoder.","PeriodicalId":321907,"journal":{"name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131520136","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: 1

Ensemble Algorithm using Transfer Learning for Sheep Breed Classification 基于迁移学习的绵羊品种分类集成算法

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465609

Divyansh Agrawal, S. Minocha, S. Namasudra, Sathish Kumar

{"title":"Ensemble Algorithm using Transfer Learning for Sheep Breed Classification","authors":"Divyansh Agrawal, S. Minocha, S. Namasudra, Sathish Kumar","doi":"10.1109/SACI51354.2021.9465609","DOIUrl":"https://doi.org/10.1109/SACI51354.2021.9465609","url":null,"abstract":"Sheep fostering is an increasing trend due to the huge demand for sheep wool, milk and mutton meat throughout the world. The export market value for sheep meat in Australia alone is approx. USD 5.23 billion. While the wool market worldwide was estimated USD 35 billion at the end of 2019 and it is predicted to reach USD 46.07 billion by 2025. Different sheep breeds have distinct characteristics like the wool of Merino sheep is costlier than most of the wool varieties available in the market. Therefore, it becomes important to identify the sheep breed to recognize the higher value characteristic of the corresponding sheep. This is indeed possible with human expertise, but this task is tedious and prone to human error. Thus, there is a need to identify sheep breeds with an accurate precision rate. This study aims to classify the sheep in a farm into four classes indigenous to Oceania. This paper proposes an ensemble model of the ResNet50 (Residual Network 50) and VGG16 (Visual Graphics Group 16) architectures that gives an improved sheep breed classification due to a boost in the learning. The ensemble model has been compared with five state-of-the-art transfer learning models, i.e. ResNet50, VGG16, VGG19, InceptionV3 (Inception Version 3) and Xception based on accuracy, log loss, recall score, F1 score and precision rate. The results show the efficiency of the proposed scheme.","PeriodicalId":321907,"journal":{"name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132604768","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: 14

Scalable Graph Convolutional Variational Autoencoders 可伸缩图卷积变分自编码器

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465579

Dániel Unyi, Bálint Gyires-Tóth

{"title":"Scalable Graph Convolutional Variational Autoencoders","authors":"Dániel Unyi, Bálint Gyires-Tóth","doi":"10.1109/SACI51354.2021.9465579","DOIUrl":"https://doi.org/10.1109/SACI51354.2021.9465579","url":null,"abstract":"Autoencoders are widely used for self-supervised representation learning. Variational autoencoders (VAEs), a special type of autoencoders, are proven to be effective in estimating the underlying probability distribution of the training data. Even though VAEs are well explored in many application domains, their utilization for graph-structured data is still under extensive research. Graph variational autoencoders achieved competitive results on various graph-related modeling tasks (e.g. link prediction and node clustering) by incorporating node features. However, current graph VAEs are unable to scale efficiently for larger graphs.In this paper, we propose a novel method that adapts the stochastic multiple partitions (SMP) algorithm to improve on scalability. We also introduce novel graph convolutional layers with general graph filters, which significantly improve the predictive performance of the neural network. The proposed method is evaluated on two popular large-graph datasets. According to the results, the proposed filters outperform the baseline filter in link prediction and node clustering for both datasets.","PeriodicalId":321907,"journal":{"name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117063265","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

ResNet neural network hyperparameter tuning for Rigid Pavement Failure Assessment 刚性路面破坏评估的ResNet神经网络超参数整定

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465547

Helarf Calsina Condori, Jose Emmanuel Cruz de la Cruz, Wilson Antony Mamani Machaca

{"title":"ResNet neural network hyperparameter tuning for Rigid Pavement Failure Assessment","authors":"Helarf Calsina Condori, Jose Emmanuel Cruz de la Cruz, Wilson Antony Mamani Machaca","doi":"10.1109/SACI51354.2021.9465547","DOIUrl":"https://doi.org/10.1109/SACI51354.2021.9465547","url":null,"abstract":"Rigid pavement roads do not have adequate maintenance, since the inspection stage is carried out “manually”, which is not reliable or efficient, as well as requiring a greater amount of labor, time, and high cost. To solve the problem, it is proposed to evaluate the rigid pavement condition using ResNet neural networks with images obtained through a conventional 2D camera. The objective of the work was to recognize three types of failures in the rigid pavement: joint peeling, corner peeling, and corner crack. For the preprocessing phase I use image normalization and resizing, the number of images was increased through geometric transformations by 12.21%. A convolutional neural network of ResNet-18 type architecture was used. As a learning transfer technique, model tuning was used, since we not only changed the output network, but also the hyperparameters of the convolutional layers. The contribution of the present work was the refinement of the hyperparameters for the modification of the ResNet-18 neural network taking into account the iteration in the learning rate that goes from 1e-4 to 1e-2. The results were: accuracy 88.73%, sensitivity 81.63%, a specificity of 92.47%, the precision of 85.10%, and finally an F1 score of 83.33%. Three of the model’s evaluation indices have values higher than 0.71 while the fourth has a value of 0.55, which indicates that there will be a good performance with the proposed model. This work can be improved by increasing the number of images or by making a hybrid model.","PeriodicalId":321907,"journal":{"name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115962844","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

Modular education framework supported by IT project management methods 由IT项目管理方法支持的模块化教育框架

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465559

J. Takács, Nikoletta Tolner, Gábor Orosz, Monika Pogátsnik

引用次数: 0

Interdisciplinary Optimization of Security Operations Centers with Digital Assistant 基于数字助理的安全运营中心跨学科优化

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465632

Bence Tureczki, K. Szenes

引用次数: 1

Two-Wheeled Self-Balancing Robot 两轮自平衡机器人

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2021-05-19 DOI: 10.1109/SACI51354.2021.9465568

Flavius-Catalin Paulescu, I. Szeidert, I. Filip, C. Vașar

引用次数: 0