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Dr. Khurshid Aliev
Politecnico di Torino

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Research Keywords & Expertise

0 Electronics and Communication Engineering
0 Machine Learning & Artificial Intelligence
0 Collaborative Robotics
0 Intenet of Things
0 Wireless and sensor networks

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Journal article
Published: 10 February 2021 in Applied Sciences
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Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system , it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors.

ACS Style

Khurshid Aliev; Dario Antonelli. Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning. Applied Sciences 2021, 11, 1621 .

AMA Style

Khurshid Aliev, Dario Antonelli. Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning. Applied Sciences. 2021; 11 (4):1621.

Chicago/Turabian Style

Khurshid Aliev; Dario Antonelli. 2021. "Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning." Applied Sciences 11, no. 4: 1621.

Conference paper
Published: 10 November 2020 in Collaboration in a Hyperconnected World
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Attraction towards Industry 4.0 is evolving in the academic and industrial communities providing new solutions to reduce the workload of human operators by integrating new technologies into the manufacturing processes. To reduce human operators’ time and/or reduce performance of boring tasks, collaborative robots (cobots) can be integrated into workplaces. The term cobots (collaborative robots) designed for cage-free work or that which contains robots that can directly work with human workers without safety barriers on the manufacturing floor. Recent cobots consist of human like arms which can be a supporting tool for the human worker or it can assist him as a co-worker in the same workplace. This paper provides results of study of human operator’s workplaces in small and medium enterprises (SME) to integrate enabling technologies of industry 4.0 and to find new solutions to reduce work load and to increase the productivity of production. In SMEs there are many cases where human operators perform monotonous tasks, implementation of cobots and mobile robots to the workplaces can provide good support for monotonous tasks of human workers, handling the tasks that require high precision or repeatability. The paper describes the integration of cobots into the workplace of a manufacturing company where monotonous, cumbersome and stressing activities affect the wellness of the workers. The paper analyzes the current workflow and the ergonomic load of the worker, further developing the appropriate task distribution between human and robotic operators and demonstrates open source technologies to accomplish human robot collaborative applications.

ACS Style

Paolo Chiabert; Khurshid Aliev. Analyses and Study of Human Operator Monotonous Tasks in Small Enterprises in the Era of Industry 4.0. Collaboration in a Hyperconnected World 2020, 83 -97.

AMA Style

Paolo Chiabert, Khurshid Aliev. Analyses and Study of Human Operator Monotonous Tasks in Small Enterprises in the Era of Industry 4.0. Collaboration in a Hyperconnected World. 2020; ():83-97.

Chicago/Turabian Style

Paolo Chiabert; Khurshid Aliev. 2020. "Analyses and Study of Human Operator Monotonous Tasks in Small Enterprises in the Era of Industry 4.0." Collaboration in a Hyperconnected World , no. : 83-97.

Conference paper
Published: 26 April 2019 in Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)
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The emergence of mobile robots as a flexible upgrade of industrial AGVs and the simultaneous diffusion of collaborative manipulators pose new problems for the organization of work in industrial plants. The new robots address work environments characterized by limited automation and unstructured layouts. Present study is aimed at demonstrating that, using commercially available technologies, it is possible to assure a fruitful collaborative interaction among three main actors of the factory of tomorrow: the human operator, the mobile robot and the manipulator.

ACS Style

Khurshid Aliev; Dario Antonelli. Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2019, 248 -260.

AMA Style

Khurshid Aliev, Dario Antonelli. Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2019; ():248-260.

Chicago/Turabian Style

Khurshid Aliev; Dario Antonelli. 2019. "Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 248-260.

Journal article
Published: 01 January 2019 in IFAC-PapersOnLine
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ACS Style

Khurshid Aliev; Dario Antonelli; Giulia Bruno. Task-based Programming and Sequence Planning for Human-Robot Collaborative Assembly. IFAC-PapersOnLine 2019, 52, 1638 -1643.

AMA Style

Khurshid Aliev, Dario Antonelli, Giulia Bruno. Task-based Programming and Sequence Planning for Human-Robot Collaborative Assembly. IFAC-PapersOnLine. 2019; 52 (13):1638-1643.

Chicago/Turabian Style

Khurshid Aliev; Dario Antonelli; Giulia Bruno. 2019. "Task-based Programming and Sequence Planning for Human-Robot Collaborative Assembly." IFAC-PapersOnLine 52, no. 13: 1638-1643.

Conference paper
Published: 08 December 2018 in Lecture Notes in Control and Information Sciences
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With the advancements in industry technology and applications, many concepts have emerged in manufacturing. Since the term Industry 4.0 was published to highlight a new industrial revolution, many manufacturing organizations and companies in Europe, North and South America are researching on this topic. Even the Industry 4.0 concept is included on government duty, sponsored by national initiatives and research funding. However, developing country like Uzbekistan, with high industrial potential are experiencing a different position and the technology roadmap of accomplishing Industry 4.0 is not clear yet. In the last 20 years, Uzbekistan managed to join the group of lower-middle income countries; the ultimate development goal of the country in the next stage is to reach the development benchmark comparable to the higher-middle income group by 2030. Therefore, this paper aims to depict the current state of manufacturing systems in Uzbekistan and identify the gaps with the Industry 4.0 requirements. The findings of this paper can serve for researches from emerging countries as technological roadmap towards Industry 4.0 paradigm and can assist industrial people in understanding and achieving the requirements of Industry 4.0.

ACS Style

Ikrom Kambarov; Gianluca D’Antonio; Khurshid Aliev; Paolo Chiabert; Jamshid Inoyatkhodjaev. Uzbekistan Towards Industry 4.0. Defining the Gaps Between Current Manufacturing Systems and Industry 4.0. Lecture Notes in Control and Information Sciences 2018, 250 -260.

AMA Style

Ikrom Kambarov, Gianluca D’Antonio, Khurshid Aliev, Paolo Chiabert, Jamshid Inoyatkhodjaev. Uzbekistan Towards Industry 4.0. Defining the Gaps Between Current Manufacturing Systems and Industry 4.0. Lecture Notes in Control and Information Sciences. 2018; ():250-260.

Chicago/Turabian Style

Ikrom Kambarov; Gianluca D’Antonio; Khurshid Aliev; Paolo Chiabert; Jamshid Inoyatkhodjaev. 2018. "Uzbekistan Towards Industry 4.0. Defining the Gaps Between Current Manufacturing Systems and Industry 4.0." Lecture Notes in Control and Information Sciences , no. : 250-260.

Journal article
Published: 01 January 2018 in International Journal of Advanced Computer Science and Applications
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Nowadays, Internet of Things (IoT) is receiving a great attention due to its potential strength and ability to be integrated into any complex system. The IoT provides the acquired data from the environment to the Internet through the service providers. This further helps users to view the numerical or plotted data. In addition, it also allows objects which are located in long distances to be sensed and controlled remotely through embedded devices which are important in agriculture domain. Developing such a system for the IoT is a very complex task due to the diverse variety of devices, link layer technologies, and services. This paper proposes a practical approach to acquiring data of temperature, humidity and soil moisture of plants. In order to accomplish this, we developed a prototype device and an android application which acquires physical data and sends it to cloud. Moreover, in the subsequent part of current research work, we have focused towards a temperature forecasting application. Forecasting metrological parameters have a profound influence on crop growth, development and yields of agriculture. In response to this fact, an application is developed for 10 days ahead maximum and minimum temperatures forecasting using a type of recurrent neural network.

ACS Style

Khurshid Aliev; Mohammad Moazzam; Sanam Narejo; Eros Pasero; Alim Pulatov. Internet of Plants Application for Smart Agriculture. International Journal of Advanced Computer Science and Applications 2018, 9, 1 .

AMA Style

Khurshid Aliev, Mohammad Moazzam, Sanam Narejo, Eros Pasero, Alim Pulatov. Internet of Plants Application for Smart Agriculture. International Journal of Advanced Computer Science and Applications. 2018; 9 (4):1.

Chicago/Turabian Style

Khurshid Aliev; Mohammad Moazzam; Sanam Narejo; Eros Pasero; Alim Pulatov. 2018. "Internet of Plants Application for Smart Agriculture." International Journal of Advanced Computer Science and Applications 9, no. 4: 1.

Chapter
Published: 30 August 2017 in Blockchain Technology and Innovations in Business Processes
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This paper presents analyses and test results of engine management system’s operational architecture with an artificial neural network (ANN). The research involved several steps of investigation: theory, a stand test of the engine, training of ANN with test data, generated from the proposed engine control system to predict the future values of fuel consumption before calculating the engine speed. In our paper, we study a small size 1.5 L gasoline engine without direct fuel injection (injection in intake manifold). The purpose of this study is to simplify engine and vehicle integration processes, decrease exhaust gas volume, decrease fuel consumption by optimizing cam timing and spark timing, and improve engine mechatronic functioning. The method followed in this work is applicable to small/medium size gasoline/diesel engines. The results show that the developed model achieved good accuracy on predicting the future demand of fuel consumption for engine control unit (ECU). It yields with the error rate of 1.12e-6 measured as Mean Square Error (MSE) on unseen samples.

ACS Style

Khurshid Aliev; Sanam Narejo; Eros Pasero; Jamshid Inoyatkhodjaev. A Predictive Model of Artificial Neural Network for Fuel Consumption in Engine Control System. Blockchain Technology and Innovations in Business Processes 2017, 213 -222.

AMA Style

Khurshid Aliev, Sanam Narejo, Eros Pasero, Jamshid Inoyatkhodjaev. A Predictive Model of Artificial Neural Network for Fuel Consumption in Engine Control System. Blockchain Technology and Innovations in Business Processes. 2017; ():213-222.

Chicago/Turabian Style

Khurshid Aliev; Sanam Narejo; Eros Pasero; Jamshid Inoyatkhodjaev. 2017. "A Predictive Model of Artificial Neural Network for Fuel Consumption in Engine Control System." Blockchain Technology and Innovations in Business Processes , no. : 213-222.