This page has only limited features, please log in for full access.
Over time, the fabrication of lattice, porous structures has always been a controversial field for researchers and practitioners. Such structures could be fabricated in a stochastic way, thus, with limited control over the actual porosity percentage. The emerging technology of 3D printing, offered an automated process that did not require the presence of molds and operated on a layer-by-layer deposition basis, provided the ability to fabricate almost any shape through a variety of materials and methods under the umbrella of the ASTM terminology “additive manufacturing”. In the field of biomedical engineering, the technology was embraced and adopted for relevant applications, offering an elevated degree of design freedom. Applications range in the cases where custom-shaped, patient-specific items have to be produced. Scaffold structures were already a field under research when 3D printing was introduced. These structures had to act as biocompatible, bioresorbable and biodegradable substrates, where the human cells could attach and proliferate. In this way, tissue could be regenerated inside the human body. One of the most important criteria for such a structure to fulfil is the case-specific internal geometry design with a controlled porosity percentage. 3D printing technology offered the ability to tune the internal porosity percentage with great accuracy, along with the ability to fabricate any internal design pattern. In this article, lattice scaffold structures for tissue regeneration are overviewed, and their evolution upon the introduction of 3D printing technology and its employment in their fabrication is described.
Antreas Kantaros; Dimitrios Piromalis. Fabricating Lattice Structures via 3D Printing: The Case of Porous Bio-Engineered Scaffolds. Applied Mechanics 2021, 2, 289 -302.
AMA StyleAntreas Kantaros, Dimitrios Piromalis. Fabricating Lattice Structures via 3D Printing: The Case of Porous Bio-Engineered Scaffolds. Applied Mechanics. 2021; 2 (2):289-302.
Chicago/Turabian StyleAntreas Kantaros; Dimitrios Piromalis. 2021. "Fabricating Lattice Structures via 3D Printing: The Case of Porous Bio-Engineered Scaffolds." Applied Mechanics 2, no. 2: 289-302.
Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.
Izzat Al-Darraji; Dimitrios Piromalis; Ayad Kakei; Fazal Khan; Milos Stojmenovic; Georgios Tsaramirsis; Panagiotis Papageorgas. Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model. Electronics 2021, 10, 831 .
AMA StyleIzzat Al-Darraji, Dimitrios Piromalis, Ayad Kakei, Fazal Khan, Milos Stojmenovic, Georgios Tsaramirsis, Panagiotis Papageorgas. Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model. Electronics. 2021; 10 (7):831.
Chicago/Turabian StyleIzzat Al-Darraji; Dimitrios Piromalis; Ayad Kakei; Fazal Khan; Milos Stojmenovic; Georgios Tsaramirsis; Panagiotis Papageorgas. 2021. "Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model." Electronics 10, no. 7: 831.
The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.
Eleni Symeonaki; Konstantinos Arvanitis; Dimitrios Piromalis. A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0. Applied Sciences 2020, 10, 813 .
AMA StyleEleni Symeonaki, Konstantinos Arvanitis, Dimitrios Piromalis. A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0. Applied Sciences. 2020; 10 (3):813.
Chicago/Turabian StyleEleni Symeonaki; Konstantinos Arvanitis; Dimitrios Piromalis. 2020. "A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0." Applied Sciences 10, no. 3: 813.
Wireless Sensor and Actuators Networks (WSANs) constitute one of the most challenging technologies with tremendous socio-economic impact for the next decade. Functionally and energy optimized hardware systems and development tools maybe is the most critical facet of this technology for the achievement of such prospects. Especially, in the area of agriculture, where the hostile operating environment comes to add to the general technological and technical issues, reliable and robust WSAN systems are mandatory. This paper focuses on the hardware design architectures of the WSANs for real-world agricultural applications. It presents the available alternatives in hardware design and identifies their difficulties and problems for real-life implementations. The paper introduces SensoTube, a new WSAN hardware architecture, which is proposed as a solution to the various existing design constraints of WSANs. The establishment of the proposed architecture is based, firstly on an abstraction approach in the functional requirements context, and secondly, on the standardization of the subsystems connectivity, in order to allow for an open, expandable, flexible, reconfigurable, energy optimized, reliable and robust hardware system. The SensoTube implementation reference model together with its encapsulation design and installation are analyzed and presented in details. Furthermore, as a proof of concept, certain use cases have been studied in order to demonstrate the benefits of migrating existing designs based on the available open-source hardware platforms to SensoTube architecture.
Dimitrios Piromalis; Konstantinos Arvanitis. SensoTube: A Scalable Hardware Design Architecture for Wireless Sensors and Actuators Networks Nodes in the Agricultural Domain. Sensors 2016, 16, 1227 .
AMA StyleDimitrios Piromalis, Konstantinos Arvanitis. SensoTube: A Scalable Hardware Design Architecture for Wireless Sensors and Actuators Networks Nodes in the Agricultural Domain. Sensors. 2016; 16 (8):1227.
Chicago/Turabian StyleDimitrios Piromalis; Konstantinos Arvanitis. 2016. "SensoTube: A Scalable Hardware Design Architecture for Wireless Sensors and Actuators Networks Nodes in the Agricultural Domain." Sensors 16, no. 8: 1227.