2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)最新文献_第4页

Towards Standard Approaches for the Evaluation of Autonomous Surgical Subtask Execution 自主手术子任务执行评估的标准方法探讨

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2021-07-07 DOI: 10.1109/INES52918.2021.9512901

T. D. Nagy, T. Haidegger

引用次数: 5

Moving Obstacle Segmentation with an Optical Flow-based DNN: an Implementation Case Study 基于光流的深度神经网络移动障碍物分割:一个实现案例研究

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2021-07-07 DOI: 10.1109/INES52918.2021.9512898

A. I. Károly, Renáta Nagyné Elek, T. Haidegger, P. Galambos

{"title":"Moving Obstacle Segmentation with an Optical Flow-based DNN: an Implementation Case Study","authors":"A. I. Károly, Renáta Nagyné Elek, T. Haidegger, P. Galambos","doi":"10.1109/INES52918.2021.9512898","DOIUrl":"https://doi.org/10.1109/INES52918.2021.9512898","url":null,"abstract":"Moving object detection is a crucial component of automotive systems’ safety functions. AI-based approaches for object detection are the most common solutions in the case of self-driving vehicles. For autonomous navigation in an industrial setting, a deep learning model that relies on stronger assumptions regarding the environment, can be implemented. This is due to the fact that an industrial environment is more strictly controlled than an urban area. However, the detection of moving obstacles is still a challenging task and its solution can serve as the basis for more advanced models. In this paper, we introduce an optical flow-based deep neural network approach for moving object segmentation and state of motion estimation in industrial environment as an implementation case study. The algorithm is based on our earlier optical flow egomotion filtering method and optical flow-based Deep Neural Network, called OFSNet. The aim of this paper is to introduce the main hardware and software modules of the moving object segmentation system, the integration process, the communication considerations and the logistics management system. The proposed system was installed and tested on a mobile robot platform in a mock warehouse environment. All of the program codes, documentations and installation steps are publicly available at GitHub.","PeriodicalId":427652,"journal":{"name":"2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125156272","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

Optimization-based Multi-actuator Control for Autonomous Vehicles 基于优化的自动驾驶汽车多致动器控制

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2021-07-07 DOI: 10.1109/INES52918.2021.9512906

A. Kovács, I. Vajk

引用次数: 0

Perspective Algorithms in Control of Turbojet Engines 涡轮喷气发动机控制中的透视算法

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2021-07-07 DOI: 10.1109/INES52918.2021.9512922

R. Andoga

{"title":"Perspective Algorithms in Control of Turbojet Engines","authors":"R. Andoga","doi":"10.1109/INES52918.2021.9512922","DOIUrl":"https://doi.org/10.1109/INES52918.2021.9512922","url":null,"abstract":"Turbojet engines are nowadays mostly controlled by full authority digital engine control (FADEC) systems using electronic engine control units. Microprocessor based control systems allow application of sophisticated control algorithms. The aim of the presentation is to show the state-of-the-art control systems and perspective algorithms based on integrated situational control methodology, which are aimed to increase the efficiency, reliability, and safety of operation of turbojet engines. Situational control is applied in this case in control and management of turbojet engines under all operational conditions including atypical ones using intelligent elements and strong integration with diagnostic systems. The concept of highly integrated control and diagnostic system is leading to a novel modular control framework with morphing structure and a degree of intelligence. It will be presented for application on general turbojet engines with specific implementation tested on a small turbojet engine in laboratory conditions as a proof of concept with positive results. These algorithms are designed as modular and robust with perspective applications in control of other large scale systems.","PeriodicalId":427652,"journal":{"name":"2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115809835","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

Load Frequency Control Analysis of PV System Using PID and ANFC Controller 基于PID和ANFC控制器的光伏系统负荷频率控制分析

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2021-07-07 DOI: 10.1109/INES52918.2021.9512913

R. Rituraj, A. Várkonyi-Kóczy

{"title":"Load Frequency Control Analysis of PV System Using PID and ANFC Controller","authors":"R. Rituraj, A. Várkonyi-Kóczy","doi":"10.1109/INES52918.2021.9512913","DOIUrl":"https://doi.org/10.1109/INES52918.2021.9512913","url":null,"abstract":"This paper deals with the Adaptive neuro-fuzzy inference system (ANFIS)–based load frequency controller (LFC). These controllers are projected for load frequency control of thermal-Photovoltaic (PV) power generation entity as a hybrid power system. In this study, random solar isolation is applied to the proposed hybrid power system. The proposed hybrid power system consists of a PV power unit with a maximum power point tracking control, a PV inverter, and an AC load. Simulations are performed with structural change in the load setting. The solar isolation results are compared with conventional proportional-integral-derivative (PID) and fuzzy logic controller (FLC). The results are then projected with an ANFIS based LFC. The simulation results observed that ANFIS attains a relatively better response for the frequency deviation profile. It typically controls the frequency deviation of a given hybrid power system and thereby advances the dynamic performances. The results also show that the performance of the hybrid power system with the use of ANFIS based neuro-fuzzy controllers attains relatively better than those which attains by the PID and FLC.","PeriodicalId":427652,"journal":{"name":"2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122516523","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

Connection of IT Systems Under Level One 一级以下IT系统的连接

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2021-07-07 DOI: 10.1109/INES52918.2021.9512923

Livia Roka-Madarasz

引用次数: 1

Net Photosynthesis Prediction by Deep Learning for Commercial Greenhouse Production 基于深度学习的温室生产净光合作用预测

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2021-07-07 DOI: 10.1109/INES52918.2021.9512919

Y. Qu, A. Clausen, B. Jørgensen

{"title":"Net Photosynthesis Prediction by Deep Learning for Commercial Greenhouse Production","authors":"Y. Qu, A. Clausen, B. Jørgensen","doi":"10.1109/INES52918.2021.9512919","DOIUrl":"https://doi.org/10.1109/INES52918.2021.9512919","url":null,"abstract":"The amount of net photosynthesis of leaves is a significate factor for the growth of plants. Therefore, monitoring the real-time net photosynthesis plays an essential role in improving the quality of productions in commercial greenhouses. Net photosynthesis mainly depends on three environmental parameters, that are light level, temperature and CO2 concentration. However, it is challenging to calculate accurate net photosynthesis due to the highly nonlinear relation. In this paper, Deep Learning (DL) is utilized to model this relationship in order to predict the net photosynthesis based on the three inputs. Firstly, the architecture of a Deep Neural Network (DNN) model is designed according to the features of this problem, and three activation functions are concerned for the DNN model design. Secondly, a training dataset is established, and two schedules of Learning Rate (LR), fixed LR and exponential decay LR, are elaborated. Then, to select the optimal hyperparameters for the DNN model, experiments of hyperparameters tuning related to activation functions and LR schedules are implemented, respectively. Finally, through a comprehensive evaluation of the training speed and the prediction accuracy, a DNN model that is with ReLU activation function and decay LR is determined. This DNN model can perform a dramatically high prediction accuracy in a fast training convergence speed for solving the proposed net photosynthesis prediction problem.","PeriodicalId":427652,"journal":{"name":"2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115575355","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: 3