Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

In the fault diagnosis of the shaft orbit of rotating machinery, there are few prejudgments about the severity of the faults, which is very important for fault repair. Therefore, a fine-grained recognition method is proposed to detect different severity faults by shaft orbit. Since different shaft orbits represent different type and different severity of faults, the convolutional neural network (CNN) is applied for identifying the shaft orbits to recognize the type and severity of the fault. The recognition rate of proposed fine-grained fault identification method is 97.96 % on the simulated shaft orbit database, and it takes only 0.31 milliseconds for the recognition of single sample. Experimental result indicated that the classification performance of the proposed method are better than the traditional machine learning models. Moreover, the method is applied for the identification of the measured shaft orbits of rotor with different degree of imbalance faults, and the testing accuracy of the experiments in measured shaft orbits is 97.14 %, which has verified the effectiveness of the proposed fine-grained fault recognition method

Development of artificial intelligence model for supporting implant drilling protocol decision making

Purpose: This study aimed to develop an artificial intelligence (AI) model to support the determination of an appropriate implant drilling protocol using cone-beam computed tomography (CBCT) images. Methods: Anonymized CBCT images were obtained from 60 patients. For each case, after implant placement, images of the bone regions at the implant site were extracted from 20 slices of CBCT images. Based on the actual drilling protocol, the images were classified into three categories: protocols A, B, and C. A total of 1,200 images were divided into training and validation datasets (n = 960, 80%) and a test dataset (n = 240, 20%). Another 240 images (80 images for each type) were extracted from the 60 cases as test data. An AI model based on LeNet-5 was developed using these data sets. The accuracy, sensitivity, precision, F-value, area under the curve (AUC) value, and receiver operating curve were calculated. Results: The accuracy of the trained model is 93.8%. The sensitivity results for drilling protocols A, B, and C were 97.5%, 95.0%, and 85.0%, respectively, while those for protocols A, B, and C were 86.7%, 92.7%, and 100%, respectively, and the F values for protocols A, B, and C were 91.8%, 93.8%, and 91.9%, respectively. The AUC values for protocols A, B, and C are 98.6%, 98.6%, and 99.4%, respectively. Conclusions: The AI model established in this study was effective in predicting drilling protocols from CBCT images before surgery, suggesting the possibility of developing a decision-making support system to promote primary stability.Sakai T., Li H., Shimada T., et al. Development of artificial intelligence model for supporting implant drilling protocol decision making. Journal of Prosthodontic Research 67, 360 (2023); https://doi.org/10.2186/jpr.JPR_D_22_00053

Optimization of AI models as the Main Component in Prospective Edge Intelligence Applications

Artificial Intelligence (AI) is a successful paradigm with application in many fields; however, there can be some challenging scenarios like the deployment of AI models in remote locations or with limited connectivity, possibly needing to perform inference closer to where data is collected. A potential solution is the study of ways to optimize AI models, for deployment of intelligent algorithms closer to the edge. This thesis focuses on applications of AI that need to have characteristics that make them suitable for implementation on portable devices (e.g., aeroponics container, drone, mobile robot); thus, the development and implementation of custom models, and their optimization (i.e., reduction in size and inference time) is of upmost importance and the main goal of this dissertation. For this task, a number of options have been explored, including developing techniques to select relevant features from the samples that the model analyzes, and pruning and quantization. Therefore, this thesis proposes a scheme for the development, implementation, and optimization of custom AI models used mainly in agriculture, so that they have the desired characteristics that are needed for their deployment in edge devices. This main goal is fulfilled by implementing a number of sequential steps that include the validation of the hypothesis that there is at least an AI model capable of generating useful predictions for the applications being studied, the exploration and implementation of an approach for their optimization, and their final implementation in hardware of limited resources. The main contributions of this thesis are on the general workflow for optimization of custom models, as well as in the proposed scheme for feature selection based on model interpretability approaches. This carries most of the novelty of the thesis, since we have not found previous implementations of these ideas, at least in the field under study. This optimization is mainly based on a feature selection approach, but finally complemented with pruning and quantization. The implementation of some of these models on an edge-like device, demonstrates the feasibility of this approach. Finally, although this thesis tries to be a self-contained work, encompassing all the aspects required to go from AI model design to implementation on an edge device, there are some aspects that could be further studied, analyzed, and improved. Furthermore, it is almost impossible to keep the pace with all the new developments in the fields of AI, edge computing, hardware and software tools, etc. which opens the field for new discussions and proposals. This work tries to fill some gaps and to propose some ideas that hopefully will be useful for future researchers in the development of new technologies and solutions

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43 with 39 fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49 with 31.8 fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5 with 0.38 fewer FLOPs

Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network

COVID-19 is a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed to detect COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset, consisting of 121 cough audio recordings with a sample rate of 48,000 and a duration of 1 second for all audio data. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. This study used accuracy, area under curve (AUC), precision, recall, and F1 score as evaluation metrics. The AlexNet model, utilizing an input size of 227×227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. This research contributes to identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. By exploring the effectiveness of CNN models with different mel-spectrogram image sizes, this study offers novel insights into the optimal and fast audio-based method for early detection of COVID-19. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19

Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis

Gas sensors are critical components when adhering to health safety and environmental policies in various manufacturing industries, such as the petroleum and oil industry; scent and makeup production; food and beverage manufacturing; chemical engineering; pollution monitoring. In recent times, gas sensors have been introduced to medical diagnostics, bioprocesses, and plant disease diagnosis processes. There could be an adverse impact on human health due to the mixture of various gases (e.g., acetone (A), ethanol (E), propane (P)) that vent out from industrial areas. Therefore, it is important to accurately detect and differentiate such gases. Towards this goal, this paper presents a novel electronic nose (e-nose) detection method to classify various explosive gases. To detect explosive gases, metal oxide semiconductor (MOS) sensors are used as reliable tools to detect such volatile gases. The data received from MOS sensors are processed through a multivariate analysis technique to classify different categories of gases. Multivariate analysis was done using three variants—differential, relative, and fractional analyses—in principal components analysis (PCA). The MOS sensors also have three different designs: loading design, notch design, and Bi design. The proposed MOS sensor-based e-nose accurately detects and classifies three different gases, which indicates the reliability and practicality of the developed system. The developed system enables discrimination of these gases from the mixture. Based on the results from the proposed system, authorities can take preventive measures to deal with these gases to avoid their potential adverse impacts on employee health

Micro-hotplate based CMOS sensor for smart gas and odour detection

Low cost, highly sensitive, miniature CMOS micro-hotplate based gas sensors have received great attention recently. The global sensor market is expanding rapidly with an expected increase of 5 ~ 8% grow thin the next five years. The application areas for a gas sensor include but are not limited to, air quality monitoring, industrial and laboratory conditions, military, and biomedical sectors. It is the key hardware component of an electronic nose, as well as the signal processing on the software side. In this thesis, both aspects of such a system were studied with new sensor technologies and improved signal processing algorithms. In addition, this thesis also described different applications and research projects using these sensor technologies and algorithms. A novel plasmonic structure was employed as an infrared source for anon- dispersive infrared gas sensor. This structure was based on a CMOS micro hot plate with three metal layers and periodic cylindrical dots to induce plasmon resonance, that allowed a tunable narrow band infrared radiation with high sensitivity and selectivity. Five gases were studied as target gases, namely, carbon monoxide, carbon dioxide, acetone, ammonia and hydrogen sulfide. These emitter sources were fabricated and characterised with a gascell, optical filters and commercial detectors under different gas concentrations and humidity levels. The results were promising with the lowest detection limit for ammonia at 10 ppm with 5 ppm resolution. On the data processing side, various signal processing methods were explored both on-board and on-board. Temperature modulation was the on-board method by switching the operating temperatures of a micro hotplate. This technique was proven to over come and reduce some typical sensor issues, such as drift, slow re-sponse/recovery speed (from tens of seconds to a few seconds) and even cross sensitivities. Off-board post processing methods were also studied, including principal component analysis, k-nearest neighbours, self-organising maps and shallow/deep neural networks. The results from these algorithms were compared and overall an 85% or higher classification accuracy could be achieved. This work showed the potential to discriminate gases/odours, which could lead to the development of a real-time discrimination algorithm for low cost wearable devices

Electronics for Sensors

The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces