This article introduces China’s first Center for Music Performance Science and Musicians’ Medicine. In the center, motion capture (MoCap) technology is used to study piano performance and musicians’ health. An idea and methodology to assess the performance of piano performance are developed in the center. The center uses high-precision MoCap system to analyze movement efficiency, posture, joint angles, and coordination of pianists. By addressing physical challenges, the center promotes healthier, more efficient practice ways, especially for adolescent piano learners. The pioneering research results bridge the gap between music performance (art) and science, positioning China as a leader in music performance science and musicians’ health.
Water pollution poses serious threats to public health and the environment, therefore requiring efficient and scalable monitoring solutions. This paper presents a cyber–physical system (CPS) that integrates paper-based biosensors with an edge IoT architecture and long-range wide area network (LoRaWAN) for real-time assessment of water quality. The biosensors detect pollutants such as arsenic, lead, and nitrates with a detection limit of 0.5 ppb. The system proposed was compared with existing LSTM systems based on two performance metrics: detection accuracy and latency. Paper-based biosensors were fabricated using silver nanoparticle-functionalized substrates to show high sensitivity and low-cost pollutant detection. TCN algorithm deployment at the edge allows for real-time processing for time-series data analysis due to its high accuracy and low latency properties compared with LSTM models, which were mainly chosen due to their usage in most applications dealing with time-series-based analysis. Experimentation was carried out by deploying the developed CPS in controlled environments, simulating pollutants at different levels, and executing the models to test their accuracy in detecting pollutants and the latency of data processing. The TCN framework achieved a detection accuracy of 98.7%, which surpassed LSTM by 92.4%. In addition, TCN reduced latency in processing by 38% to enable fast data analysis and decision making. LoRaWAN allowed for perfect packet transmission of up to 15 km, while the loss rate stayed as low as 2.1%. These results establish the proposed CPS as reliable, efficient, and scalable for real-time water contamination monitoring. Thus, this research introduces the integration of paper-based biosensors with advanced computational frameworks.
Margherita Evangelisti, Vittorio Di Federico, Marco Maglionico
A recent evaluation of the UWWTD confirmed that overflows from combined systems and surface water runoff are a significant pressure of the aquatic environment in terms of pollution. Increasing urbanization, climate change, and the evolution of pollutants suggest that CSOs may worsen in the future, impacting on the ecological status of rivers. In the Italian case study, an urban drainage model of the Bologna sewer network is applied to quantify the pollution load discharged from CSOs, which represents the main parameter for the design of treatment technology.
Autism spectrum disorder (ASD) is a global concern, with a prevalence rate of approximately 1 in 36 children according to estimates from the Centers for Disease Control and Prevention (CDC). Diagnosing ASD poses challenges due to the absence of a definitive medical test. Instead, doctors rely on a comprehensive evaluation of a child’s developmental background and behavior to reach a diagnosis. Although ASD can occasionally be identified in children aged 18 months or younger, a reliable diagnosis by an experienced professional is typically made by the age of two. Early detection of ASD is crucial for timely interventions and improved outcomes. In recent years, the field of early diagnosis of ASD has been greatly impacted by the emergence of deep learning models, which have brought about a revolution by greatly improving the accuracy and efficiency of ASD detection. The objective of this review paper is to examine the recent progress in early ASD detection through the utilization of multimodal deep learning techniques. The analysis revealed that integrating multiple modalities, including neuroimaging, genetics, and behavioral data, is key to achieving higher accuracy in early ASD detection. It is also evident that, while neuroimaging data holds promise and has the potential to contribute to higher accuracy in ASD detection, it is most effective when combined with other modalities. Deep learning models, with their ability to analyze complex patterns and extract meaningful features from large datasets, offer great promise in addressing the challenge of early ASD detection. Among various models used, CNN, DNN, GCN, and hybrid models have exhibited encouraging outcomes in the early detection of ASD. The review highlights the significance of developing accurate and easily accessible tools that utilize artificial intelligence (AI) to aid healthcare professionals, parents, and caregivers in early ASD symptom recognition. These tools would enable timely interventions, ensuring that necessary actions are taken during the initial stages.
Neelamadhab Padhy, Sridev Suman, T Sanam Priyadarshini
et al.
The objective of this article is to recommend products using association rule mining from an E-commerce site. This helps us to recommend products through utilizing the filtering concept. In this article, we use the Apriori and FP-Growth algorithms. Our model not only suggests products but also gives tips on how to make strong suggestion systems that can deal with a lot of data and give quick responses. Our objective is to predict ratings so that the users could be recommended and buy products. There are 1,048,100 records in the dataset. This dataset consists of four features, and these are are follows: {user-id, productid, Ratings, and timing}. Here, we consider the rating as our dependent attribute, and others factors are independent features. In this article, we use collaborative filtering algorithms (SVD, SVD+, and ALS) and also item-based filtering techniques (KNNBasic) to recommend products. Apart from these, sssociation rule mining, hybridization of Apriori, and FP-Growth are used. K-means clustering is used to identify anomalies as well as to create a dashboard, using Power BI for data visualization. Apart from these, we have also developed a hybridization algorithm using Apriori and FP-Growth. Among all the recommendation algorithms, SVD outperforms in recommending the product, and the average RMSE and MAE values are 1.31, and 1.04, respectively.
Takashi MASUTANI, Sunao TOMITA, Haruki SATO
et al.
Particle dampers (PD) can be embedded in structures fabricated via selective laser melting (SLM) because the raw powder (particle diameter : several tens of micrometers) remains in closed spaces during SLM. However, most PD studies have focused on coarse particle sizes larger than 1 mm and low frequencies below 100 Hz; there is insufficient evaluation of PD attenuation characteristics and particle behavior for particle sizes from tens of micrometers to sub-millimeters and in the 100 to 1000 Hz band. In this study, the equivalent viscous damping coefficients of spherical zirconia balls with nominal sizes of 0.05, 0.10, 0.20, and 0.40 mm are measured under different vibration accelerations (from 9 to 148 m/s2(rms)) and frequencies (from 100 to 800 Hz) to experimentally investigate the effect of fine particle diameters on the damping properties. It is deduced that in the frequency range above 200 Hz, the equivalent viscous damping coefficients of the coarse particle diameter are greater than those of fine particles at accelerations below the range of 15.7–22.4 m/s2(rms). In contrast, the equivalent viscous damping coefficients of the fine particles are greater than those of the coarse particles at accelerations above the 15.7–22.4 m/s2(rms) range. In addition, to determine the reason for these tendencies, the behavior of particles with a nominal size of 0.05 mm, which is close to the raw powder, is observed through a window using a high-speed microscope camera. The results reveal that in the low acceleration range, the inertial force does not overcome the static friction force owing to poor flowability caused by adhesion force, and the particles are always stationary with respect to the sealed container.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
Amirabbas Mottahedin, Carlo Giudicianni, Maria C. Cunha
et al.
This paper presents a novel methodology for the coupled optimization of pipe sizing and isolation valve placement in water distribution networks (WDNs). By employing a bi-objective genetic algorithm, the methodology searches for the best solutions in the trade-off between minimizing the average demand shortfalls caused by segment isolations and minimizing the total installation costs of pipes and valves. The optimization also incorporates a constraint that ensures the telescopic distribution property of pipe diameters, guaranteeing that the pipe diameters narrow down from source(s) to external areas. The outcomes are compared with the traditional design approach, which entails, in separate steps, the least-cost optimization for pipe sizing and the placement of isolation valves at all (N-rule) or all but one (N-1 rule) pipes connected to the generic junction.
Abdullah All Mamun Anik, Naeem Mian, Olaide Felix Olabode
et al.
In order to identify thermal problems in automated machinery, measurement solutions need to continuously monitor variations in temperature. The present push for the usage of IoT devices has led to the introduction of smart invisible temperature measuring systems into many businesses. These systems guarantee that temperature fluctuation trends are continuously logged and fed into the thermal reimbursement mechanism. The sensor infrastructure of a machine may consist of thousands of temperature sensors, according to its size. To repeatedly receive synchronized data from these sensors electronically a reliable system that can switch within protocols for wireless communication in the event that the current wireless techniques fails—for example, due to a lost connection—is required. With cognitive transitioning, this project seeks to construct an interconnected network with many integrated communication protocols.
Tool wear leads to a reduction in dimensional accuracy and surface quality, as well as unexpected sudden tool failure. A broken tool can cause irreparable damage to an expensive workpiece, resulting in increased operating costs and production delays. Since the mechanical strength of small-diameter drills is inadequate for the load and prone to breakage, tool condition monitoring and diagnosis is important to prevent sudden tool breakage, increase productivity, and promote automation in machining process. The present work is aimed to investigate a tool condition monitoring method based on the analysis of acoustic emission (AE) signals emitted during small-hole drilling. We propose DDM (Deep feature Distribution Modeling), a method for image-level anomaly detection and anomaly segmentation in time-series signal analysis. The peck drilling experiments on SKD61 steels were performed with high-speed steel (HSS) drills. The continuous wavelet transform (CWT) was applied to generate time-frequency (TF) image of the AE signals during the drilling process. The TF images were quantified as anomaly scores using the DDM, which establishes normality by fitting a multivariate Gaussian (MVG) to pre-trained deep features. The anomaly detection capability of the DDM and the convolutional autoencoder (CAE) was compared using dummy data for validation. The digital microscope was employed to measure tool wear. Chip morphology was also observed by the laser microscopy. As the tool wear progressed, the anomaly score increased or decreased, with several sharp increases observed between holes 3805 and 3869 just prior to tool failure. An increase in the width of the shear layer spacing of the chips was also observed just prior to failure. Changes in the anomaly score associated with tool wear were more clearly identified by creating anomaly maps. The present investigation shows that waveform processing of AE signals using the CWT and anomaly detection based on the DDM are efficient methods for tool condition monitoring. Our proposed approach makes it possible to visualize the differences in anomaly states using a more subdivided layer context by generating multiple anomaly maps with deep feature vectors obtained from multiple layers.
Engineering machinery, tools, and implements, Mechanical engineering and machinery
Simona Cariello, Dario Sanalitro, Alessandro Micali
et al.
In this work, we propose a brain–computer-interface (BCI)-based smart-home interface which leverages motor imagery (MI) signals to operate home devices in real-time. The idea behind MI-BCI is that different types of MI activities will activate various brain regions. Therefore, after recording the user’s electroencephalogram (EEG) data, two approaches, i.e., Regularized Common Spatial Pattern (RCSP) and Linear Discriminant Analysis (LDA), analyze these data to classify users’ imagined tasks. In such a way, the user can perform the intended action. In the proposed framework, EEG signals were recorded by using the EMOTIV helmet and OpenVibe, a free and open-source platform that has been utilized for EEG signal feature extraction and classification. After being classified, such signals are then converted into control commands, and the open communication protocol for building automation KNX (“Konnex”) is proposed for the tasks’ execution, i.e., the regulation of two switching devices. The experimental results from the training and testing stages provide evidence of the effectiveness of the users’ intentions classification, which has subsequently been used to operate the proposed home automation system, allowing users to operate two light bulbs.
Engineering machinery, tools, and implements, Technological innovations. Automation
A photovoltaic (PV) cell is generally used as renewable energy source. For an accurate study of various PV applications, modeling this basic device in a PV generator is essential. However, the manufacturers do not usually provide the model parameters and their values vary over time due to PV degradation and the change in weather conditions. Thus, finding an optimal technique for estimating the appropriate parameters is crucial. This problem can be solved by metaheuristic optimization algorithms, namely particle swarm optimization (PSO). However, early convergence is the main defect of PSO. This work presents an enhancement in the optimization method (PSO) for identifying the optimal parameters of a PV generating unit. In this method, the identification of parameters of the single diode model is based on an opposition-based initialization particle swarm optimization technique. The optimization algorithm is implemented in MATLAB which gives good results.
The smart intersection (SI) systems, as they are named in the Republic of Korea, are part of the ITS services implemented under local government projects with financial support from the central government. They collect real-time traffic data available at signalized intersections with advanced detection systems for surveillance purposes only. A traffic signal method utilizing such valuable data has been desirable but unavailable as yet in practice. This paper proposes a new approach to designing traffic signal timings, reflecting the demand changing in real time, by utilizing SI surveillance data. The proposed artificial neural network model generates suitable traffic signal timings trained to be near optimum based on surveillance data for each directional movement.
Chronic hepatitis C is an important threat to the world’s public health. In Taiwan, 2~4% of the population is infected with hepatitis C, and 10~15% of those cases will lead to liver cirrhosis. This study examined the effect of a comprehensive screening test project conducted by the Addiction Treatment Center in southern Taiwan. In collaboration with the Drug Addiction Treatment Center, 154 screening tests were completed. It is demonstrated that through active reach-out screening service with innovative process design, the vulnerable groups of people with a potentially high prevalence of HCV could be targeted and cured. Nevertheless, close surveillance and follow-up would be necessary to prevent the reoccurrence.
By analyzing the possible faults such as wear, tooth breakage and deformation caused by couplings used in coal mine machinery and equipment, combined with practical application experience, the paper analyzes that the basic reason of causing coupling damage is the transient impact caused by overloading and design defects. In order to protect mechanical equipment from damage or shutdown caused by coupling failure such as overload and deformation, the advantages and disadvantages of rigid coupling, flexible coupling and safety coupling are compared, the author analyzes several kinds of couplings commonly used in coal mine machinery equipment, as well as the practical application occasions of various couplings, including the factors to be considered in the process of design and selection, etc., this paper provides a reference for the selection and design of couplings of coal mine machinery, and also provides ideas for the selection and design of couplings of non-coal mining machinery and other practical applications.
Natalija Sadretdinova, Sergij Bereznenko, Larysa Bilotska
et al.
Functionality and comfort are important requirements for adaptive clothing. To ensure the compliance of clothing with these requirements, it is necessary to take into account, on the one hand, consumer conditions, on the other hand specific needs driven from social and psychophysiological adaptation to the living conditions. Thus, for people who are restricted to the sitting position for their entire life due to their disabilities, it is important to avoid skin diseases that occur in conditions of constant contact of the skin with hard surfaces under pressure. Therefore, the aim of our work was to improve functional clothing for disabled people based on the analysis of ergonomics and consumer requirements through the application of new technologies.
Textile bleaching, dyeing, printing, etc., Engineering machinery, tools, and implements
The accurate prediction of the load curve is not only vital to the establishment of the generation scheduling, unit start-stop, and maintenance plan, but also has an important influence on ensuring the smooth operation of the generation side and consumption side, reducing the cost of power generation and improving the economic benefits. Based on this, a short-term power load curve forecasting method based on Savitzky-Golay time series filtering is constructed. Firstly, the relevant factors that affect the load are used as input components of the forecasting model, such as holidays, weather, etc. Secondly, the Savitzky-Golay filter is used to smooth the load sequence to weaken the adverse effects of local load fluctuations on the forecast. Finally, combining with the training and testing process of the Deep Neural Network (DNN) model, the training samples were constructed by using the load time series after filtering, holidays, weather and other data, so as to realize the prediction of the short-time power load curve in the next seven days. The prediction effect of the model is verified by the daily load data influencing factor data collected from a certain power plant. The simulation results show that the accuracy of the short-time power load curve prediction model based on the cooperation of Savitzky-Golay and DNN can reach 95%.
The high-frequency (HF) box frame is an important structure component of the phased array radar, which is used to provide sufficient stiffness for the antenna array to achieve various precisions index. Truss skin type and plate beam type frames are the two most widely used HF box structures. This paper mainly studies the application scope, mechanical properties and optimization strategies of two mainstream HF box structures. The different mechanical properties of the truss skin type and the plate beam type HF box frame are analyzed and compared by finite element method (FEM). What’s more, the optimization strategies and application scenarios of two types of HF box frame are summarized in this paper, which provides the design principle of HF box frame during the development of phased array radar.
In the actual production measurement process, the vision of machine vision is to broaden the application scene of surface structured light and realize the surface measurement and detection of large parts. This paper introduces a 3D image mosaic system based on fringe projection. The sensor system is composed of a DLP projector and a 3D camera, and is fixed at the end of the robot arm. The three-dimensional point cloud data of the workpiece surface to be measured is obtained by moving the manipulator and using the raster projection method. An algorithm of rough positioning using manipulator is proposed, and the automobile splicing experiment is carried out. Experimental results show that this method can effectively obtain 3D point cloud data and has good stitching accuracy.
The paper proposes a design method of periodic acoustic structures composed of acoustic filters. Based on acoustic-electric analogy, the second-order Butterworth acoustic filter is first designed, which comprises a main pipe, a Helmholtz resonator, a short tube and an expansion cavity. Second, periodic structures are constructed by taking the acoustic filter as an original unit cell or modifying the acoustic filter to obtain a modified unit cell, and periodically arraying the unit cells along the axial direction of the main pipe. Then, influences of axial and circumferential periodic arrangements of the acoustic structures on the filtering performances are investigated, respectively. Next, multi-period acoustic structure models are established and their transmission losses are simulated and compared. Finally, low-frequency broadband periodic acoustic structures are constructed. The research shows that, the axial period of the acoustic structure composed of the original unit cell has no influence on the resonance frequency of acoustic structure, but contributes to transmission losses and filtering frequency ranges; the increase in the circumferential period can result in a shift in the resonance frequency towards high frequencies and a great increment in the filtering frequency bandwidth; combining unit cells with different resonance frequencies can significantly widen the filtering frequency range; low-frequency broadband performances of the acoustic structure can be implemented by combining unit cells with different resonance frequencies and increasing axial and circumferential periods of the acoustic structures.
In metal machining, regenerative chatter seriously affects tool life, part surface quality and cutting efficiency. To suppress chatter generated during milling, researchers have proposed a variable spindle speed technique. But the previous VSS milling model is based on two degrees of freedom system (two-DOF). In this paper, a three-DOF system suitable for VSS milling of thin-walled parts is proposed by introducing the axial contact angle. The improved semi-discretization method is used to solve the dynamical equation and obtain the stability lobe diagram (SLD). Comparing the SLDs under variable speed and constant speed, it shows that the VSS can obtain more stable regions than that of constant spindle speed (CSS). The experimental results show that VSS milling can suppress chatter.