Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net

Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC

The role of technological innovation and cleaner energy towards the environment in ASEAN countries: proposing a policy for sustainable development goals

The association between economic growth (EG) and environmental degradation (ED) has been highlighted extensively in prior studies. However, investigation regarding ‘technological innovation and clean energy role’ in dealing with environmental concerns has comprised limited context while considering the ASEAN economies under sustainable development goals. Therefore, the study attempts to investigate the phenomenon by using CS-ARDL analysis under short as well as long run. The findings through CSARDL in long- and short-run indicate that REN have impact carbon emission and ecological footprints negatively. Additionally, the EG in targeted economies is causing a higher level of CE and ecological footprints. Whereas, GDP2ofund to be significant in lowering the ED in the form of CE and ecological footprints. It is suggested that policies related to CE through EG should be developed in order to control the environmental issues in the future

Characterization of ZnO:Al deposited by co-sputtering for transparent conductive electrodes

Aluminum doped zinc oxide was prepared by magnetron sputtering methods at room temperature using a ZnO ceramic target doped 2%wt by Al2O3. The optical transmittance of the films is higher than 80% in the visible range. A direct bandgap type was reached by controlling deposition conditions; the bandgap value was in the range between 3.2 eV and 4.2 eV. Good electrical and optical properties were obtained for the films deposited by an appropriate co-sputtering of ZnO and Al targets. These films with a resistivity, about 1.3´10-2W.cm, and a transmittance, higher than 80%, can be applicable for transparent conducting electrodes

A Novel Blockchain Based Information Management Framework for Web 3.0

Web 3.0 is the third generation of the World Wide Web (WWW), concentrating on the critical concepts of decentralization, availability, and increasing client usability. Although Web 3.0 is undoubtedly an essential component of the future Internet, it currently faces critical challenges, including decentralized data collection and management. To overcome these challenges, blockchain has emerged as one of the core technologies for the future development of Web 3.0. In this paper, we propose a novel blockchain-based information management framework, namely Smart Blockchain-based Web, to manage information in Web 3.0 effectively, enhance the security and privacy of users data, bring additional profits, and incentivize users to contribute information to the websites. Particularly, SBW utilizes blockchain technology and smart contracts to manage the decentralized data collection process for Web 3.0 effectively. Moreover, in this framework, we develop an effective consensus mechanism based on Proof-of-Stake to reward the user's information contribution and conduct game theoretical analysis to analyze the users behavior in the considered system. Additionally, we conduct simulations to assess the performance of SBW and investigate the impact of critical parameters on information contribution. The findings confirm our theoretical analysis and demonstrate that our proposed consensus mechanism can incentivize the nodes and users to contribute more information to our systems

An in-situ thermoelectric measurement apparatus inside a thermal-evaporator

At the ultra-thin limit below 20 nm, a film's electrical conductivity, thermal conductivity, or thermoelectricity depends heavily on its thickness. In most studies, each sample is fabricated one at a time, potentially leading to considerable uncertainty in later characterizations. We design and build an in-situ apparatus to measure thermoelectricity during their deposition inside a thermal evaporator. A temperature difference of up to 2 K is generated by a current passing through an on-chip resistor patterned using photolithography. The Seebeck voltage is measured on a Hall bar structure of a film deposited through a shadow mask. The measurement system is calibrated carefully before loading into the thermal evaporator. This in-situ thermoelectricity measurement system has been thoroughly tested on various materials, including Bi, Te, and Bi$_2$Te$_3$, at high temperatures up to 500 K

Artificial intelligence-based solutions for coffee leaf disease classification

Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf

Synthesis and Optical Characterization of Building-Block Plasmonic Gold Nanostructures

Plasmonics, the field involves manipulating light at the nanoscale, has been being an emergent research field worldwide. Synthesizing the plasmonic gold nanostructures with controlled morphology and desired optical properties is of special importance towards specific applications in the field. Here, we report the chemical synthesis and the optical properties of various plasmonic Au nanostructures, namely Au nanoparticles (AuNPs), Au nanorods (AuNRs) and random Au nano-islands (AuNI) that are the building blocks for plasmonic research. The results show that the AuNPs exhibited a single plasmonic resonance, the AuNRs displayed two identical and separated modes of the resonance, and the random Au nano-islands presented a very broad resonance. Specifically, tailoring the anisotropy of the Au nanorods enabled extending their resonant frequencies from the visible to the near infrared ones, which is in accordance with the finite different time domain simulations

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

A framework for practically effective creation of postprocessors for 5-axis CNC machines with all possible configurations and working mechanisms

The 5-axis CNC (Computer Numerical Control) machining plays an important role in manufacturing, especially in making parts of complex shapes such as turbine or propeller blades. However, there are always challenges of generating 5-axis CNC machining programs when working with different 5-axis CNC machines of various structures; and 5-axis CNC programs must be automatically generated by CNC postprocessors which calculate the inverse kinematics and convert the Cutter Location data to the CNC programs that are used for operating 5-axis CNC machines. Since the family of 5-axis CNC machines has a wide spectrum of machine configurations, with hundreds of mechanisms of 5 degrees of freedom, it is therefore practically challenging for engineers and CNC machine operators to create CNC postprocessors for specific 5-axis CNC machines. It is more challenging to create CNC postprocessors if engineers or CNC operators do not have strong backgrounds and professional skills in mathematical modeling and kinematics of machines. This paper presents a universal and intuitive framework and practical guidance to create CNC postprocessors for all 5-axis CNC machines, with the focus on a novel mathematical formulation of inverse kinematics of three main groups of 5-axis CNC machines. The case studies of creating CNC postprocessors with the commercially available 5-axis CNC machines were successfully demonstrated, in which the simulation scenarios and experiments were implemented to verify the created CNC postprocessors. The proposed frameworks and guides for generating CNC postprocessors can be conveniently and effectively applied in industrial practices, without the required strong backgrounds and skills for engineers or CNC operators in mathematical modeling and kinematics of machines, especially mathematical modeling of multibody systems

Real time inverse kinematics of a general 5-axis CNC machine

In most of the previous investigations, the kinematics model of five-axis computer-numerical control (CNC) centers was formulated just at the position level, and the differential kinematic relationships (velocity, acceleration and jerk of the five joints of a five-axis center) that are necessary for several purposes, especially for investigating the relationship between the limits of a machine’s drives and the feed rate maximization (the productivity maximization), have been overlooked. Therefore, this paper addresses the differential kinematic modelling and analysis for the five-axis CNC centers. In particular, the differential kinematic equations are formulated in a parametric domain so that they are useful for investigating the kinematic behaviors of the five-axis centers in real time