2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)最新文献

Digital Twin: Network Provisioning of Mission Critical Communication in Cyber Physical Production Systems 数字孪生:网络物理生产系统中关键任务通信的网络配置

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784852

G. Szabó, Sándor Rácz, Norbert Reider, Hubertus A. Munz, József Peto

{"title":"Digital Twin: Network Provisioning of Mission Critical Communication in Cyber Physical Production Systems","authors":"G. Szabó, Sándor Rácz, Norbert Reider, Hubertus A. Munz, József Peto","doi":"10.1109/ICIAICT.2019.8784852","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784852","url":null,"abstract":"Digital Twin (DT) is widely used in various industrial sectors to optimize the operations and maintenance of physical assets, system and manufacturing processes. This paper applies the DT concept on analyzing the effects of network on the control of a real robot where its DT runs in a complex robot cell executing agile tasks. We present the design steps applied to reach a realizable DT in the Gazebo environment. We evaluate our proposed method with real network configurations against a fully simulated scenario solving the Agile Robotics for Industrial Automation Competition (ARIAC) [1]. The evaluation results show that for low delay setups the simulation provides accurate results. For high delay setups the DT should be used for network assessment. It is also shown that 5G Ultra-Reliable Low-Latency Communication (URLLC) is required for the best productivity performance of the robot cell, while the evaluated public LTE+ network can also be used with limited overall accuracy. The DT in action can be seen on [2].","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121005638","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: 13

River Water Pollution Pattern Prediction using a Simple Neural Network 基于简单神经网络的河流水污染模式预测

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784854

Kennedy, P. Kusuma, C. Setianingsih

引用次数: 1

A Particle Swarm Optimization Based Path Planning Method for Autonomous Systems in Unknown Terrain 基于粒子群优化的未知地形自治系统路径规划方法

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784851

Sumana Biswas, S. Anavatti, M. Garratt

{"title":"A Particle Swarm Optimization Based Path Planning Method for Autonomous Systems in Unknown Terrain","authors":"Sumana Biswas, S. Anavatti, M. Garratt","doi":"10.1109/ICIAICT.2019.8784851","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784851","url":null,"abstract":"Path planning of an autonomous system in unknown terrain is a challenging task. For a risk free and robust navigation, autonomous systems must utilize intelligence to determine the types of terrain and the traversability when optimizing its total cost (function). This paper presents a Particle Swarm Optimization based path planning for autonomous systems in unknown terrain environments. In this work, a new method is proposed toward terrain traversability analysis and estimation. Environmental data is gathered from sensors. Using this information, the proposed method identifies the terrain ahead and classifies them based on their traversability. Different weights are assigned against different types of terrain and these weights measure the characteristics of traversability on this terrain. The methodology autonomously plans a most traversable optimal path. Furthermore, this algorithm is capable to work in dynamic environments by avoiding collisions with obstacles. All simulations are carried out in MATLAB. Simulation results show the effectiveness and robustness of the proposed methodology.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128030982","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: 5

Transfer Learning for Alzheimer's Disease Detection on MRI Images 基于MRI图像的阿尔茨海默病检测迁移学习

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784845

Amir Ebrahimi-Ghahnavieh, S. Luo, R. Chiong

{"title":"Transfer Learning for Alzheimer's Disease Detection on MRI Images","authors":"Amir Ebrahimi-Ghahnavieh, S. Luo, R. Chiong","doi":"10.1109/ICIAICT.2019.8784845","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784845","url":null,"abstract":"In this paper, we focus on Alzheimer's disease detection on Magnetic Resonance Imaging (MRI) scans using deep learning techniques. The lack of sufficient data for training a deep model is a major challenge along this line of research. From our literature review, we realised that one of the current trends is using transfer learning for 2D convolutional neural networks to classify subjects with Alzheimer's disease. In this way, each 3D MRI volume is divided into 2D image slices and a pre-trained 2D convolutional neural network can be re-trained to classify image slices independently. One issue here, however, is that the 2D convolutional neural network would not be able to consider the relationship between 2D image slices in an MRI volume and make decisions on them independently. To address this issue, we propose to use a recurrent neural network after a convolutional neural network to understand the relationship between sequences of images for each subject and make a decision based on all input slices instead of each of the slices. Our results show that training the recurrent neural network on features extracted from a convolutional neural network can improve the accuracy of the whole system.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127123301","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: 54

IoT Enabled Indoor Autonomous Mobile Robot using CNN and Q-Learning 使用CNN和Q-Learning的物联网室内自主移动机器人

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784847

M. Saravanan, P. S. Kumar, Amit Sharma

{"title":"IoT Enabled Indoor Autonomous Mobile Robot using CNN and Q-Learning","authors":"M. Saravanan, P. S. Kumar, Amit Sharma","doi":"10.1109/ICIAICT.2019.8784847","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784847","url":null,"abstract":"This paper focuses on the construction of IoT enabled mobile robot with an arm which can reach the destination autonomously and perform suitable actions in an indoor environment. Object detection and optimal navigation are the required features of a mobile robot that will be achieved through the combined architecture of building necessary Deep Learning and Reinforcement Learning models workable in a lesser memory space. To facilitate navigation in an indoor environment, initially, the environment has been mapped into a minimum number of grids for the experimental purpose. For handling huge memory requirement to run the models for processing, we occasionally transfer required intelligence from cloud setup to RPi, where RPi act as a Fog node in Industry 4.0 environment. The practicality of the robot has been gauged in three different cases (i) where the destination of the robot is known with 100% probability, (ii) where the destination of the robot is uncertain i.e. with lower probability and (iii) the destination is not known. In the first two cases, the objects are assumed to be stationary. Whereas in the third case, the objects can also be dynamic i.e. moving objects. As an application we have chosen Indoor Plant Monitoring System, where the objective is to measure the readings like Soil Moisture, Temperature, etc., of the indoor plant and forward the readings to Ericsson's IOT Accelerator platform. After analyzing the sensor values, a robot arm can initiate specific actions on its own. Here, the application of AI algorithms will not only help the robot to reach the destination, but it also triggers the robot to perform the functions optimally. As an experiment, we have studied the effect of learning rate on the total number of actions and introduces optimal reward from start to end of a journey in $4text{X}4$ grid world environment and finally tested for tangible performance towards navigation and object detection.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115575012","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: 13

Blockchain for Reliable and Secure Distributed Communication Channel 区块链是可靠和安全的分布式通信通道

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784853

Jovan Karamacoski, Natasa Paunkoska, N. Marina, Magdalena Punceva

{"title":"Blockchain for Reliable and Secure Distributed Communication Channel","authors":"Jovan Karamacoski, Natasa Paunkoska, N. Marina, Magdalena Punceva","doi":"10.1109/ICIAICT.2019.8784853","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784853","url":null,"abstract":"Distributed storage systems (DSS) have a potential of storing big amounts of data. Specialized DSS codes were defined with the main idea to enhance the reliability, availability, maintenance and data security in DSS systems. In this paper, we establish a new way of communication, named Distributed Communication Channel (DCC), that uses the coding mechanism that is similar to the one applied in the DSS. DCC represents a communication channel between two or more parties, that exchange information through multiple physical channels. It adapts the DSS concept using the benefits established with respect to the communication reliability and security against intruders and attacks. Moreover, with this concept we offer an increased security using the recent developments used in the Blockchain technology. The blockchain is used as a tool to securely bypass the transformation matrix or the exchange of encryption keys, between the sender and the receiver in the communication channel. The whole system uses the so-called spatial spreading mechanism to transfer the data through independent channels. Therefore, it is more reliable and secure, than the basic systems using the DSS algorithm.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115630667","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: 5

IoT-NDN: An IoT Architecture via Named Data Netwoking (NDN) IoT-NDN:基于命名数据网络(NDN)的物联网架构

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784859

M. A. Hail

{"title":"IoT-NDN: An IoT Architecture via Named Data Netwoking (NDN)","authors":"M. A. Hail","doi":"10.1109/ICIAICT.2019.8784859","DOIUrl":"https://doi.org/10.1109/ICIAICT.2019.8784859","url":null,"abstract":"Internet of Things (IoT) systems have become the central part of future internet research. In the IoT, heterogeneous devices are connected to sense the environment or to observe individual tasks. Many research fields use the seamless IoT infrastructure to interact with the integrated devices and diverse services. Furthermore, IoT is a promising technology to increase the comfort and quality of life and opens new ways of interaction between people and things. Real life applications in healthcare sectors, home automation, industry, smart cities, monitoring scenarios, etc. benefit from the low-cost wireless technology in IoT on one hand. On the contrary, IoT system has many challenging features: many devices are resource-constrained with energy and memory, are highly heterogeneous and their applications continuously transmit transient information. Furthermore, Requesting, delivering and updating the information in IoT are challenging because of the resource limitation. Named Data Networking (NDN) is one of the latest and the most important Information-Centric Networking (ICN) approaches which uses named data to deliver data in the network. Based on hierarchically structured names, NDN matches the application pattern of IoT systems and uses its communication concept to optimize the power supply and distribute the data efficiently in the network. This paper discusses the main concepts of NDN including naming, routing, forwarding and caching in IoT infrastructure. To achieve an efficient system in different applications scenario in future IoT, an IoT architecture is proposed via NDN called IoT-NDN. The objective of this research work is the design and development of IoT-NDN for different fields in IoT systems. The deployment of IoT-NDN is challenging and requires proper design choices. The proposed solution and challenges of all mentioned issues are discussed in this paper.","PeriodicalId":277919,"journal":{"name":"2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124328629","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: 17

Overcoming Glossophobia Based on Virtual Reality and Heart Rate Sensors 基于虚拟现实和心率传感器克服演讲恐惧症

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784846

D. Herumurti, A. Yuniarti, Panji Rimawan, A. Yunanto

引用次数: 16

Ultra Wideband (UWB) Microstrip Patch Antenna with Adjustable Notch Frequencies 带可调陷波频率的超宽带微带贴片天线

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784734

In-Kyu Kim, Jiwan Ghimire, Janam Maharjan, Iram Nadeem, Sun-Woong Kim, Dong-You Choi

引用次数: 2

A Deep Learning Approach to Predict Malnutrition Status of 0-59 Month's Older Children in Bangladesh 预测孟加拉国0-59个月大儿童营养不良状况的深度学习方法

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2019-07-01 DOI: 10.1109/ICIAICT.2019.8784823

Mehrab Shahriar, Mirza Shaheen Iqubal, S. Mitra, A. Das

引用次数: 10