Choosing the best machine tool in mechanical manufacturing

Machine tools are indispensable components and play an important role in mechanical manufacturing. The equipment of machine tools has a huge effect on the operational efficiency of businesses. Each machine tool type is described by many different criteria, such as cost, technological capabilities, accuracy, energy consumption, convenience in operation, safety for workers, working noise, etc. If the selection of machine is only based on one or several criteria, it will be really easy to make mistakes, which means it is not possible to choose the real best machine. A machine is considered to be the best only when it is chosen based on all of its criteria. This work is called multi-criteria decision-making (MCDM). In this study, the selection of machine tools has been done using two different multi-criteria decision-making methods, including the FUCA method (Faire Un Choix Adéquat) and the CURLI method (Collaborative Unbiased Rank List Intergration). These are two methods with very different characteristics. When using the FUCA method, it is necessary to normalize the data and determine the weights for the criteria. Meanwhile, if using the CURLI method, these two things are not necessary. The selection of these two distinct methods is intended to produce the most generalizable conclusions. Three types of machine tool, which are considered in this study, include grinding machine, drilling machine and milling machine. The number of grinders that were offered for selection was twelve, the number of drills that were surveyed in this study was thirteen, while nine were the number of milling machines that were given for selection. The objective of this study is to determine the best solution in each type of machine. The results of ranking the machines are very similar when using the two mentioned methods. Specially, in all the surveyed cases, the two methods FUCA and CURLI always find the same best alternative. Accordingly, it is possible to firmly come to a conclusion that the FUCA method and the CURLI method are equally effective in machine tool selection. In addition, this study has determined the best three machines corresponding to the three different machine type

Enabling Technologies for Web 3.0: A Comprehensive Survey

Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution can potentially democratize content access by utilizing the latest developments in enabling technologies. In this paper, we conduct an in-depth survey of enabling technologies in the context of Web 3.0, such as blockchain, semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality, Internet of Things technology, and their roles in shaping Web 3.0. We commence by providing a comprehensive background of Web 3.0, including its concept, basic architecture, potential applications, and industry adoption. Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain technologies that are pivotal to Web 3.0 development. Following that, other enabling technologies, including AI, semantic web, and 3D interactive web, are discussed. Utilizing these technologies can effectively address the critical challenges in realizing Web 3.0, such as ensuring decentralized identity, platform interoperability, data transparency, reducing latency, and enhancing the system's scalability. Finally, we highlight significant challenges associated with Web 3.0 implementation, emphasizing potential solutions and providing insights into future research directions in this field

Innovative development of a flying robot with a flexible manipulator for aerial manipulations

This paper presents an innovative development of a flying robot or an aerial robot, with a flexible manipulator, called the Dexterous Aerial Robotic System (DFTS), for aerial manipulations, especially for inspections and reparations of various structures such as wind turbines, power lines and open gas pipelines, decorations and painting of high industrial chimneys and walls of high buildings, as well as transport and delivery of courier shipments, relocation and manipulation of assemblies and units in inaccessible or dangerous environments. The proposed DFTS consists of two independent but interconnected systems or functional units, which have two main separate functions respectively, including a basic carrying function, and a precise positioning and stabilization function. The system with a basic carrying function is actually the main flying system, the un-manned aerial vehicle (UAV); it is remotely controlled and piloted. Meanwhile, the aerial manipulation platform, called the vertical take-off and landing platform VTOL, which is an active flying platform with 6 degrees of freedom (DOF) is used for positioning and stabilization; and it is attached to the UAV via the soft link. With the use of a long soft link, the problems which are caused by the air turbulent flows generated by the UAV are minimized, and the aerial manipulations of objects are safely controlled and operated. The VTOL which is equipped with a grasping mechanism was successfully developed, prototyped and tested. The experimental results showed that, the developed VTOL can self-stabilize with the inclination angle of being up to 8 degrees

Development of a smart system for early detection of forest fires based on unmanned aerial vehicles

The naturally occurring wildfires and the people-related forest fires are events, which in many cases have significant impact on the environment, the wildlife and the human population. The most devastating among these events usually start in unpopulated remote areas, which are difficult to inspect or are not constantly being monitored or observed. This gives the local small-sized fires enough time to evolve into full-scale wide-area disasters, which in turn makes their suppression and extinguishing very difficult. In this paper, we present an autonomous system for early detection of forest fires, named THEASIS-M. The presented system represents a solution that is based on a combination of innovative technologies, including computer vision algorithms, artificial intelligence and unmanned aerial vehicles. In the first part of the study, we provide an overview on the present applications of the UAVs in the forestry domain. The paper then introduces the general architecture of the THEASIS-M system and its components. The system itself is fully autonomous and is based on several different types of UAVs, including a fixed-wing drone, which provides the overall forest monitoring capabilities of the proposed solution, and a rotary-wing UAV that is used for confirmation and monitoring of the detected fire event. The widely used technologies for computer vision and image processing, which are used for the detection of fire and smoke in the real-time video streams sent from the UAVs to the ground control station, are highlighted in the next section of this study. Finally, the experimental tests and demonstrations of the proposed THEASIS-M system are presented and briefly discussed

Extension of physical activity recognition with 3D CNN using encrypted multiple sensory data to federated learning based on multi-key homomorphic encryption

Background and Objective: The internet of medical things is enhancing smart healthcare services using physical wearable sensor-based devices connected to the Internet. Machine learning techniques play an important role in the core of these services for remotely consulting patients thanks to the pattern recognition from on-device data, which is transferred to the central servers from local devices. However, transferring personally identifiable information data to servers could become a source for hackers to steal from, manipulate and perform illegal activities. Federated learning is a new branch of machine learning that creates directly training models from on-device data and aggregates these learned models on the servers without centralized data. Another way to protect data confidentiality on computer systems is data encryption. Data encryption transforms data into another form that only users with authority to a decryption key can read. In this work, we propose a novel method enabling preservation of client privacy and protection of client biomedical data from illegal hackers while transmitting through the internet.Methods: We propose a method applying 3-dimensional convolutional neural networks for human activity recognition using multiple sensory data. In order to protect the data, we apply the bitwise XOR operator encryption technique. Then, we extend our 3-dimensional convolutional neural network methods to both traditional federated learning and the federated learning based on multi-key homomorphic encryption using the proposed encrypting data.Results: Based on leave-one-out-cross-validation, the 3-dimensional method obtains an accuracy of 94.6% and of 94.9% (without data encrypting and without federated learning) tested on two different benchmarked datasets, Sport and DaLiAC respectively. Accuracy is decreased slightly to 89.5% (from 94.6% of the baseline) when we use the proposed encrypting data method. However, the encryption-data-based method still has a potential result compared to the state-of-the-art which only uses raw data. In addition, the proposed full federated learning scheme of this work shows that illegal persons who somehow can get the trained model transmitted via networks cannot infer the private result.Conclusions: This novel method for sensory data representation which translates temporal and frequency bio-signal values to voxel intensities that can encode 3-dimensionnal activity images. Secondly, the proposed 3-dimensional convolutional neural network methods outperform other deep-learning-based human activity recognition approaches. Finally, extensive experiments show the proposed data-encrypted federated learning approach can achieve feasibility in terms of efficiency in privacy preservation

An innovative and smart agriculture platform for improving the coffee value chain and supply chain

Vietnam is the world’s second biggest producer of coffee and has been an exporter for several decades. However, Vietnam has been facing many well-documented issues and challenges in the whole coffee supply chain such as climate change; low productivity, poor quality and high cost; excessive use of fertilizing products and irrigations; poor collection, processing and storage solution; low sustainability and limited value-added coffee products; and low applications of smart and sustainable agriculture solutions. This paper introduces an innovative and smart agriculture (INNSA) platform for the creation and operation of a sustainable coffee value chain, with the focus on enhanced quality and added values for key elements of the coffee supply chain in Vietnam. The platform is designed based on the foundations of the key enabling digital transformation and smart agriculture technologies: Smart devices and Internet of Things, big data, artificial intelligence, blockchain and source traceability technologies, and sustainable design and manufacturing. The INNSA platform has the following key features: a smart database with real-time data inputs and updates, cost-effectiveness, and open-architectures for effective integrations of the enabling digital transformation and smart agriculture technologies. The smart platform and ecosystem of INNSA provides the information portal and open-access database for all key factors of coffee supply chain, enabling them to join and interact with each other to bring the coffee industry of Vietnam to a higher level, well-recognized in terms of values, branding and sustainability

Adaptation strategies in the Mekong delta

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