Artificial Intelligence Monitoring Research Articles

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction.

Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening.

Patient-based real-time quality control (PBRTQC) has gained attention because of its potential to continuously monitor the analytical quality insituations wherein internal quality control (IQC) is less effective. Therefore, we tried to investigate the application of PBRTQC method based on an artificial intelligence monitoring (AI-MA) platform in quality risk monitoring of Down syndrome (DS) serum screening. The DS serum screening item determination data and relative IQC data from January 4 to September 7 in 2021 were collected. Then, PBRTQC exponentially weighted moving average (EWMA) and moving average (MA) procedures were built and optimized in the AI-MA platform. The efficiency of the EWMA and MA procedures with intelligent and traditional control rules were compared. Next, the optimal EWMA procedures that contributed to the quality assurance of serum screening were run and generated early warning cases were investigated. Optimal EWMA and MA procedures on the AI-MA platform were built. Comparison results showed the EWMA procedure with intelligent QC rules but not traditional quality rules contained the best efficiency. Based on the AI-MA platform, two early warning cases were generated by using the optimal EWMA procedure, which finally found were caused by instrument failure. Moreover, the EWMA procedure could truly reflect the detection accuracy and quality insituations wherein traditional IQC products were unstable or concentrations were inappropriate. The EWMA procedure built by the AI-MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks.

Advancements in Skull Base Surgery: Navigating Complex Challenges with Artificial Intelligence.

This narrative review examines the evolving landscape of artificial intelligence (AI) integration in skull base surgery, exploring its multifaceted applications and impact on various aspects of patient care. Extensive literature review was conducted to gather insights into the role of AI in skull base surgery. Key aspects such as diagnosis, image analysis, surgical planning, navigation, predictive analytics, clinical decision-making, postoperative care, rehabilitation, and virtual simulations were explored. Studies were sourced from PubMed using keyword search strategy for relevant headings, sub-headings and cross-referencing. AI enhances early diagnosis through diagnostic algorithms that guide investigations based on clinical and radiological data. AI-driven image analysis enables accurate segmentation of intricate structures and extraction of radiomics data, optimizing preoperative planning and predicting treatment response. In surgical planning, AI aids in identifying critical structures, leading to precise interventions. Real-time AI-based navigation offers adaptive guidance, enhancing surgical accuracy and safety. Predictive analytics empower risk assessment, treatment planning, and outcome prediction. AI-driven clinical decision support systems optimize resource allocation and support shared decision-making. Postoperative care benefits from AI's monitoring capabilities and personalized rehabilitation protocols. Virtual simulations powered by AI expedite skill development and decision-making in complex procedures. AI contributes to accurate diagnosis, surgical planning, navigation, predictive analysis, and postoperative care. Ethical considerations and data quality assurance are essential, ensuring responsible AI implementation. While AI serves as a valuable complement to clinical expertise, its potential to enhance decision-making, precision, and efficiency in skull base surgery is evident.

Early monitoring-to-warning Internet of Things system for emerging infectious diseases via networking of light-triggered point-of-care testing devices.

Early monitoring and warning arrangements are effective ways to distinguish infectious agents and control the spread of epidemic diseases. Current testing technologies, which cannot achieve rapid detection in the field, have a risk of slowing down the response time to the disease. In addition, there is still no epidemic surveillance system, implementing prevention and control measures is slow and inefficient. Motivated by these clinical needs, a sample-to-answer genetic diagnosis platform based on light-controlled capillary modified with a photocleavable linker is first developed, which could perform nucleic acid separation and release by light irradiation in less than 30seconds. Then, on site polymerase chain reaction was performed in a handheld closed-loop convective system. Test reports are available within 20 min. Because this method is portable, rapid, and easy to operate, it has great potential for point-of-care testing. Additionally, through multiple device networking, a real-time artificial intelligence monitoring system for pathogens was developed on a cloud server. Through data reception, analysis, and visualization, the system can send early warning signals for disease control and prevention. Thus, anti-epidemic measures can be implemented effectively, and deploying and running this system can improve the capabilities for the prevention and control of infectious diseases.

The development of input-monitoring system on biofuel economics and social welfare analysis.

Biofuel production relies on stable supply of biomass which would be significantly influenced by climate-induced impacts. Since the actual agricultural outputs are relatively unpredictable in the face of uncertain environmental conditions and can only be realized in the harvest season, providing useful information regarding the stability of biomass supply to the downstream biofuel industry is crucial. This study firstly illustrates a theoretical framework to explore the resultant market equilibrium and optimal conditions of agricultural and bioenergy production in the face of highly uncertain environmental risks and then employs a two-stage stochastic programming model to investigate the optimal biofuel development and associated economic and environmental effects. The results show that total welfare may not always increase because the loss of other agricultural commodities induced by climate impacts may be greater than the gains received by biofuel production and emission reduction. This study provides insights into the area where artificial intelligence monitoring system can be implemented to analyze the input data associated with agricultural activities and help the biofuel industry to improve its production possibilities.

Artificial Intelligence Monitoring System Using ZigBee Wireless Network Technology in Warehousing and Logistics Innovation and Economic Cost Management

The study expects to reduce the development cost and energy consumption of the storage environment monitoring and improve the efficiency of the storage monitoring environment. Firstly, the storage environment monitoring system is designed in detail using ZigBee. It includes the design of terminal nodes, router nodes, coordinator nodes, and upper computer software. Here is a detailed description of the process of establishing a communication network for three different types of devices in the ZigBee network. The address allocation mechanism of the ZigBee network is introduced. Then, through the application of activity-based costing, the cost reduction is achieved as expected. Finally, the evaluation model of the storage logistics cost level is established, and the various reasons that affect the storage logistics cost are analyzed. The completed warehouse environment monitoring system is a tree topology network composed of a ZigBee coordinator, several ZigBee routing nodes, and terminal devices through the above design. The coordinator in the network is responsible for establishing the network, and it can communicate with the host computer through the serial port. The terminal node collects environmental parameter information and feeds the data back to the ZigBee coordinator through wireless multihop. The ZigBee routing node is mainly used to expand the range of the network to achieve the purpose of wireless monitoring of the entire storage environment. This provides a reference for the combination of wireless communication technology and sensor technology.

Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.

Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.

Activity classification and analysis during sports training session using fuzzy model

Sport training is a sporting performance preparation phase that consists of four parts: Training in conditioning, technical Training, Training in attitude, Training in psychology. The challenging factor of the sports training session is the realization of approximate and uncertainty. The valued idea involves the definition of fuzzy sets and rules and membership functions to overcome the challenges. A fuzzy logical explanation enables successful ambiguous situations, which are complex, continuous, and more practical, closer to the real world and human thought. The concept, which is highly valued, includes defining fluids and rules and membership functions to test training exercises for strength. This paper proposed a Fuzzy, assisted artificial intelligence monitoring framework (FAAIMF) that evaluates all athletes’ behaviors in an outdoor training setting using wearable inertial sensors. Data obtained from sensor fitted machines, feedback, and proper implementation requirements are considered in the design. The Random Forest Classifier uses a Discreet Transform Wavelet (DWT) to effectively and accurately effectively and accurately id. Second, the relative orientation of the wearable inertial sensors on a shield and thigh of a material from which the knee angle of flexion-extension is determined. The proposed method in various non-constrained settings for the exact classification of sports activities and accurate movement techniques assessment.

Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence

PurposeThe triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse prognosis. This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation. MethodsThe study included 1107 trauma patients, 16 years and older. The patients were trauma victims of Levels I and II triage and admitted to the Rajaee (Emtiaz) Trauma Hospital, Shiraz, in 2014–2015. The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients’ prognosis. Five modeling methods including the support vector machine, K-nearest neighbor algorithms, Bagging and Adaboost, and the neural network were compared by some evaluation criteria. ResultsLearning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99% accuracy. The most-fitted variables were Glasgow Coma Scale score, base deficit, and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions. ConclusionData mining could help in triage, initial treatment, and further decision-making for outcome measures in trauma patients. Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival. With artificial intelligence modeling system, diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation. Artificial intelligence monitoring may have a role in trauma care and should be further investigated.

ADOPTION OF INTELLIGENT COMPUTATIONAL TECHNIQUES FOR STEAM BOILERS TUBE LEAK TRIP

Frequent boiler tube trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. Several methodologies for the fault diagnosis in a plant have been developed. However these methodologies are difficult to be implemented. In this study, two artificial intelligent monitoring systems specialized in boiler trips have been proposed. The first intelligent monitoring system represents the use of pure artificial neural network system whereas the second intelligent monitoring system represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. In the first system using pure artificial neural network, the trip was predicted 5 minutes before the actual trip occurrence. The hybrid intelligent system was able to optimize the selection of the most influencing variables successfully and predict the trip 2 minutes before the actual trip. The first intelligent system performed better than the second one based on the prediction time. The proposed artificial intelligent system could be adopted on-line as a reliable controller of the thermal power plant boiler.

TECHNOLOGY AND AGING FUTURES: DISCOVERING THE CONSUMER ELECTRONICS SHOW (CES) 2019

The growing field of technology and aging or gerontechnology has largely been considered from a health perspective on technological intervention to ameliorate conditions of isolation, disconnection, inactivity, and loneliness, as well as provide efficient alert systems, transportation coordination, and emergency services. Contesting the image of a ‘digital divide’ separating younger from older generations, the recreational industry has also produced a seniors market of technological games, toys, apps, exercises, and social media. The four papers in this symposium, however, are individual critical reflections by a group of social scientists who visited the Consumer Electronics Show (CES) in January 2019 (Las Vegas) as part of an ethnographic project about the politics of the technical turn in gerontological studies. In particular, the authors gathered evidence from the CES to support their interests in four trends: a) The collecting, aggregating, and sharing of personal data by home surveillance, artificial intelligence monitoring, and self-tracking systems for commercial, insurance and work-place purposes, b) The popularization of healthy lifestyles based on technical and exclusionary models of ‘smart’, ‘fit’, and ‘optimal’ standards, c) The technical rhetoric that infuses designs for efficiency, speed, and convenience with anti-aging and ageist ideologies, d) The challenges to older people to manage their lives against the health risks, interventions, and expectations posed by technology-driven austerity programs. The papers have in common their creative interpretations of CES materials and shared concern about the many older groups whose insufficient access, skill, and resources will deny them participation in the technological imaginary of aging futures.

Artificial intelligence monitoring of active TB cost effective

Artificial intelligence monitoring of active TB cost effective

Condition Monitoring of Wind Turbine Gearbox Bearing Based on Deep Learning Model

Wind turbines condition monitoring and fault warning have important practical value for wind farms to reduce maintenance costs and improve operation levels. Due to the increase in the number of wind farms and turbines, the amount of data of wind turbines have increased dramatically. This problem has caused a need for efficiency and accuracy in monitoring the operating condition of the turbine. In this paper, the idea of deep learning is introduced into wind turbine condition monitoring. After selecting the variables by the method of the adaptive elastic network, the convolutional neural network (CNN) and the long and short term memory network (LSTM) are combined to establish the logical relationship between observed variables. Based on training data and hardware facilities, the method is used to process the temperature data of gearbox bearing. The purpose of artificial intelligence monitoring and over-temperature fault warning of the high-speed side of bearing is realized efficiently and conveniently. The example analysis experiments verify the high practicability and generalization of the proposed method.

Hybrid Intelligent Warning System for Boiler tube Leak Trips

Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.

The Application of LVQ Neural Network for Weld Strength Evaluation of RF-Welded Plastic Materials

Radio-frequency welding is a material joining process based on the dielectric loss principle, which is well suited to join plastic materials used in biomedical applications such as drainage and solution bags. For these kind of applications, it is very important that welds are of good quality due to the sterility and biological hazard concerns. This usually means that welds must have a certain predetermined strength. In production environment, destructive testing of sample products is a commonly used method of quality control. This is, however, time consuming and the results obtained on sample testing can by no means be fully relied upon for all the products within a batch. Therefore, a decision was made to design an artificial intelligence monitoring system that could determine the quality of each weld based on the measurements performed in real time. The main signal being measured is the displacement of the upper electrode. Various parameters of the displacement curve were found out to be in relation with the weld strength. Due to disturbances in air supply that result in variable welding force, a force sensor was added as well. The measurements obtained from both the sensors were used to form the input vector for linear vector quantization neural network. This type of network is suitable to put the input vectors in different classes. In our case, this means if a certain measurement corresponds to a good quality weld or a bad quality weld. The experiments have shown that the proposed neural network performs really well and could be of great value in a production environment.

Back Propagation Artificial Neural Network and Its Application in Fault Detection of Condenser Failure in Thermo Plant

Steam condenser is one of the most important equipment in steam power plants. If the steam condenser trips it may lead to whole unit shutdown, which is economically burdensome. Early condenser trips monitoring is crucial to maintain normal and safe operational conditions. In the present work, artificial intelligent monitoring systems specialized in condenser outages has been proposed and coded within the MATLAB environment. The training and validation of the system has been performed using real operational measurements captured from the control system of selected steam power plant. An integrated plant data preparation scheme for condenser outages with related operational variables has been proposed. Condenser outages under consideration have been detected by developed system before the plant control system"

Real-time on-line space research laboratory environment monitoring with off-line trend and prediction analysis

With the International Space Station currently operational, a significant amount of acceleration data is being down-linked, processed and analyzed daily on the ground on a continuous basis for the space station reduced gravity environment characterization, the vehicle design requirements verification and science data collection. To help understand the impact of the unique spacecraft environment on the science data, an artificial intelligence monitoring system was developed, which detects in near real time any change in the reduced gravity environment susceptible to affect the on-going experiments. Using a dynamic graphical display, the monitoring system allows science teams, at any time and any location, to see the active vibration disturbances, such as pumps, fans, compressor, crew exercise, re-boost and extra-vehicular activities that might impact the reduced gravity environment the experiments are exposed to. The monitoring system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many increments (an increment usually lasts 6 months) collected onboard the station for selected disturbances. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential systems failures. The monitoring system has two operating modes: online and offline. Both near real-time on-line vibratory disturbance detection and off-line detection and trend analysis are discussed in this paper.