Integrated robotic systems combining manipulators with mobile robots provide outstanding improvem... more Integrated robotic systems combining manipulators with mobile robots provide outstanding improvement opportunities for semi-automatic assembly processes leveraged by Industry 4.0. Factory operations are released from the rigid layout constraints imposed by conventional fixed robots. Thus, they introduce new challenges in managing the recharge cycles as the energy consumption of mobile manipulators is not simply related to the travelled distance but to the overall tasks executed. Its estimation requires a systemic approach. In the proposed solution, an intelligent monitoring system is implemented on board. Data gathered online, and Key Performance Indicators (KPIs) calculated during the working tasks are exploited by Machine Learning (ML) to optimize energy recharging cycles. Although the development of an intelligent monitoring framework for a mobile manipulator was the original objective of the research, the monitoring system is exploited here for energy management only, leaving sp...
Depression and several other behavioral health disorders are serious public health concerns world... more Depression and several other behavioral health disorders are serious public health concerns worldwide. Persistent behavioral health issues have a wide range of consequences that affect people personally, culturally and socially. Major depressive disorder (MDD) is a psychiatric ailment that affects people of all ages worldwide. It has grown into a major global health issue as well as an economic burden. Clinicians are using several medications to limit the growth of this disease at an early stage in young people. The goal of this research is to improve the depression diagnosis by altering Electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99.7%, while Support vector machine (S...
Within the context of Industry 4.0 and of the new emerging Industry 5.0, human factors are becomi... more Within the context of Industry 4.0 and of the new emerging Industry 5.0, human factors are becoming increasingly important, especially in Human-Robot Collaboration (HRC). This paper provides a novel study focused on the human aspects involved in industrial HRC by exploring the effects of various HRC setting factors. In particular, this paper aims at investigating the impact of industrial HRC on user experience, affective state, and stress, assessed through both subjective measures (i.e., questionnaires) and objective ones (i.e., physiological signals). A collaborative assembly task was implemented with different configurations, in which the robot movement speed, the distance between the operator and the robot workspace, and the control of the task execution time were varied. Forty-two participants were involved in the study and provided feedbacks on interaction quality and their affective state. Participants’ physiological responses (i.e., electrodermal activity and heart rate) were...
The advent of new technologies and their implementation in manufacturing is accelerating the prog... more The advent of new technologies and their implementation in manufacturing is accelerating the progress of Industry 4.0 (I4.0). Among the enabling technologies of I4.0, collaborative robots (cobots) push factory reconfiguration and prompt for worker empowerment by exploiting the respective assets of both humans and robots. Indeed, human has superior dexterity, flexibility, problem-solving ability. Robot excels in strength, endurance, accuracy and is expendable for risky activities. Therefore, task assignment problem in a production line with coexisting humans and robots cannot limit to the workload balancing among workers but should make the most of everyone respective abilities. The outcomes should not be only an increased productivity, but also improved production quality, human safety and well-being. Task assignment strategy should rely on a comprehensive assessment of the tasks from the viewpoint of suitability to humans or robots. As there are several conflicting evaluation crite...
Collaborative Networks and Digital Transformation, 2019
Measuring performances of collaborative robots in Industry 4.0 applications is an open research a... more Measuring performances of collaborative robots in Industry 4.0 applications is an open research area since the emergence of collaborative and mobile robots as a support for semi-automatic manufacturing processes. A compelling management problem is the definition of convenient performance measures on which to assess the new generation of robots, to improve process performances both at the robotic cell design stage and at the production stage. A consequent problem is to gather the required data to measure performances. Data must be obtained automatically and in real time. Different levels of communication protocols have to be harmonized in order to transfer data from robots and other factory machines to the cloud on the internet and eventually to the production control system. A case study allows to demonstrate the operation of data acquisition system for collaborative and mobile robots and the real–time monitoring dashboard. The outcome of the study is the gathering of data at field level, the evaluation of robot performances at machine level in order to execute the real time production control at factory level.
The importance of sustainable building maintenance is growing as part of the Sustainable Building... more The importance of sustainable building maintenance is growing as part of the Sustainable Building concept. The integration and implementation of new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generate a large amount of data that will be utilized to better manage the sustainable building maintenance and staff. Anomaly prediction models assist facility managers in informing operators to perform scheduled maintenance and visualizing predicted facility anomalies on building information models (BIM). This study proposes a Machine Learning (ML) anomaly prediction model for sustainable building facility maintenance using an IoT sensor network and a BIM model. The suggested framework shows the data management technique of the anomaly prediction model in the 3D building model. The case study demonstrated the framework’s competence to predict anomalies in the heating ventilation air conditioning...
This paper presents analyses and test results of engine management system’s operational architect... more 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 w...
Bluetooth Low Energy is an interesting short-range radio technology that could be used for connec... more Bluetooth Low Energy is an interesting short-range radio technology that could be used for connecting tiny devices into the Internet of Things (IoT) through gateways or cellular networks. For example, they are widely used in various contexts, from building and home automation to wearables. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data, was designed and calibration methods were performed. A detailed deviation is calculated between reference sensor and sensor node. The data obtained from laboratory experiments were used to evaluate battery life of the node. An Android application for devices such as Smartphones and Tablets can be used to collect data from a smart sensor, which becomes more accurate.
The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robot... more The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots’ physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and...
Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirement... more 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 reliabi...
The emergence of mobile robots as a flexible upgrade of industrial AGVs and the simultaneous diff... more 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.
With the advancements in industry technology and applications, many concepts have emerged in manu... more 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...
Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers... more Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of skills and physical limitations. To enable the full exploitation of collaborative robots traditional robot programming must be overcome. Reduction of robot programming time and worker cognitive effort during the job become compelling requirements to be satisfied. Reinforcement learning (RL) plays a core role to allow robot to adapt to a changing and unstructured environment and to human undependable execution of repetitive tasks. The paper focuses on the utilization of RL to allow a robust industrial assembly process in a HRC workcell. The result of the study is a method for the online generation of robot assembly task sequence that adapts to the unpredictable and inconstant behavior of the...
Measuring performances of collaborative robots in Industry 4.0 applications is an open research a... more Measuring performances of collaborative robots in Industry 4.0 applications is an open research area since the emergence of collaborative and mobile robots as a support for semi-automatic manufacturing processes. A compelling management problem is the definition of convenient performance measures on which to assess the new generation of robots, to improve process performances both at the robotic cell design stage and at the production stage. A consequent problem is to gather the required data to measure performances. Data must be obtained automatically and in real time. Different levels of communication protocols have to be harmonized in order to transfer data from robots and other factory machines to the cloud on the internet and eventually to the production control system. A case study allows to demonstrate the operation of data acquisition system for collaborative and mobile robots and the real–time monitoring dashboard. The outcome of the study is the gathering of data at field ...
Mobile devices, such as smartphones and tablets have already become an integral part of our lives... more Mobile devices, such as smartphones and tablets have already become an integral part of our lives. For example, they are widely used throughout society with several applications in various business sectors which include smart houses, irrigation systems, healthcare and many more. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data was designed. This can be used with normal android devices (Smartphones, Tablets) to collect information from a smart sensor. Moreover, a BLE acquisition algorithm was successfully implemented on the firmware of the device.
The introduction of the Internet of Things (IoT) in the construction industry is evolving facilit... more The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digit...
Integrated robotic systems combining manipulators with mobile robots provide outstanding improvem... more Integrated robotic systems combining manipulators with mobile robots provide outstanding improvement opportunities for semi-automatic assembly processes leveraged by Industry 4.0. Factory operations are released from the rigid layout constraints imposed by conventional fixed robots. Thus, they introduce new challenges in managing the recharge cycles as the energy consumption of mobile manipulators is not simply related to the travelled distance but to the overall tasks executed. Its estimation requires a systemic approach. In the proposed solution, an intelligent monitoring system is implemented on board. Data gathered online, and Key Performance Indicators (KPIs) calculated during the working tasks are exploited by Machine Learning (ML) to optimize energy recharging cycles. Although the development of an intelligent monitoring framework for a mobile manipulator was the original objective of the research, the monitoring system is exploited here for energy management only, leaving sp...
Depression and several other behavioral health disorders are serious public health concerns world... more Depression and several other behavioral health disorders are serious public health concerns worldwide. Persistent behavioral health issues have a wide range of consequences that affect people personally, culturally and socially. Major depressive disorder (MDD) is a psychiatric ailment that affects people of all ages worldwide. It has grown into a major global health issue as well as an economic burden. Clinicians are using several medications to limit the growth of this disease at an early stage in young people. The goal of this research is to improve the depression diagnosis by altering Electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99.7%, while Support vector machine (S...
Within the context of Industry 4.0 and of the new emerging Industry 5.0, human factors are becomi... more Within the context of Industry 4.0 and of the new emerging Industry 5.0, human factors are becoming increasingly important, especially in Human-Robot Collaboration (HRC). This paper provides a novel study focused on the human aspects involved in industrial HRC by exploring the effects of various HRC setting factors. In particular, this paper aims at investigating the impact of industrial HRC on user experience, affective state, and stress, assessed through both subjective measures (i.e., questionnaires) and objective ones (i.e., physiological signals). A collaborative assembly task was implemented with different configurations, in which the robot movement speed, the distance between the operator and the robot workspace, and the control of the task execution time were varied. Forty-two participants were involved in the study and provided feedbacks on interaction quality and their affective state. Participants’ physiological responses (i.e., electrodermal activity and heart rate) were...
The advent of new technologies and their implementation in manufacturing is accelerating the prog... more The advent of new technologies and their implementation in manufacturing is accelerating the progress of Industry 4.0 (I4.0). Among the enabling technologies of I4.0, collaborative robots (cobots) push factory reconfiguration and prompt for worker empowerment by exploiting the respective assets of both humans and robots. Indeed, human has superior dexterity, flexibility, problem-solving ability. Robot excels in strength, endurance, accuracy and is expendable for risky activities. Therefore, task assignment problem in a production line with coexisting humans and robots cannot limit to the workload balancing among workers but should make the most of everyone respective abilities. The outcomes should not be only an increased productivity, but also improved production quality, human safety and well-being. Task assignment strategy should rely on a comprehensive assessment of the tasks from the viewpoint of suitability to humans or robots. As there are several conflicting evaluation crite...
Collaborative Networks and Digital Transformation, 2019
Measuring performances of collaborative robots in Industry 4.0 applications is an open research a... more Measuring performances of collaborative robots in Industry 4.0 applications is an open research area since the emergence of collaborative and mobile robots as a support for semi-automatic manufacturing processes. A compelling management problem is the definition of convenient performance measures on which to assess the new generation of robots, to improve process performances both at the robotic cell design stage and at the production stage. A consequent problem is to gather the required data to measure performances. Data must be obtained automatically and in real time. Different levels of communication protocols have to be harmonized in order to transfer data from robots and other factory machines to the cloud on the internet and eventually to the production control system. A case study allows to demonstrate the operation of data acquisition system for collaborative and mobile robots and the real–time monitoring dashboard. The outcome of the study is the gathering of data at field level, the evaluation of robot performances at machine level in order to execute the real time production control at factory level.
The importance of sustainable building maintenance is growing as part of the Sustainable Building... more The importance of sustainable building maintenance is growing as part of the Sustainable Building concept. The integration and implementation of new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generate a large amount of data that will be utilized to better manage the sustainable building maintenance and staff. Anomaly prediction models assist facility managers in informing operators to perform scheduled maintenance and visualizing predicted facility anomalies on building information models (BIM). This study proposes a Machine Learning (ML) anomaly prediction model for sustainable building facility maintenance using an IoT sensor network and a BIM model. The suggested framework shows the data management technique of the anomaly prediction model in the 3D building model. The case study demonstrated the framework’s competence to predict anomalies in the heating ventilation air conditioning...
This paper presents analyses and test results of engine management system’s operational architect... more 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 w...
Bluetooth Low Energy is an interesting short-range radio technology that could be used for connec... more Bluetooth Low Energy is an interesting short-range radio technology that could be used for connecting tiny devices into the Internet of Things (IoT) through gateways or cellular networks. For example, they are widely used in various contexts, from building and home automation to wearables. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data, was designed and calibration methods were performed. A detailed deviation is calculated between reference sensor and sensor node. The data obtained from laboratory experiments were used to evaluate battery life of the node. An Android application for devices such as Smartphones and Tablets can be used to collect data from a smart sensor, which becomes more accurate.
The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robot... more The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots’ physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and...
Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirement... more 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 reliabi...
The emergence of mobile robots as a flexible upgrade of industrial AGVs and the simultaneous diff... more 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.
With the advancements in industry technology and applications, many concepts have emerged in manu... more 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...
Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers... more Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of skills and physical limitations. To enable the full exploitation of collaborative robots traditional robot programming must be overcome. Reduction of robot programming time and worker cognitive effort during the job become compelling requirements to be satisfied. Reinforcement learning (RL) plays a core role to allow robot to adapt to a changing and unstructured environment and to human undependable execution of repetitive tasks. The paper focuses on the utilization of RL to allow a robust industrial assembly process in a HRC workcell. The result of the study is a method for the online generation of robot assembly task sequence that adapts to the unpredictable and inconstant behavior of the...
Measuring performances of collaborative robots in Industry 4.0 applications is an open research a... more Measuring performances of collaborative robots in Industry 4.0 applications is an open research area since the emergence of collaborative and mobile robots as a support for semi-automatic manufacturing processes. A compelling management problem is the definition of convenient performance measures on which to assess the new generation of robots, to improve process performances both at the robotic cell design stage and at the production stage. A consequent problem is to gather the required data to measure performances. Data must be obtained automatically and in real time. Different levels of communication protocols have to be harmonized in order to transfer data from robots and other factory machines to the cloud on the internet and eventually to the production control system. A case study allows to demonstrate the operation of data acquisition system for collaborative and mobile robots and the real–time monitoring dashboard. The outcome of the study is the gathering of data at field ...
Mobile devices, such as smartphones and tablets have already become an integral part of our lives... more Mobile devices, such as smartphones and tablets have already become an integral part of our lives. For example, they are widely used throughout society with several applications in various business sectors which include smart houses, irrigation systems, healthcare and many more. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data was designed. This can be used with normal android devices (Smartphones, Tablets) to collect information from a smart sensor. Moreover, a BLE acquisition algorithm was successfully implemented on the firmware of the device.
The introduction of the Internet of Things (IoT) in the construction industry is evolving facilit... more The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digit...
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Papers by Khurshid Aliev