Development Of Intelligent Monitoring System Research Articles

Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

Personal Protective Equipment Detection: A Deep-Learning-Based Sustainable Approach

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time.

Iod-Nets – An IoT based intelligent health care monitoring system for ambulatory pregnant mothers and fetuses

Internet of things (IoT) and Artificial Intelligence (AI) have become the most predominant tools in healthcare applications for pervasive and smart systems for automated diagnosis. Based on the integration of IoT sensors and deep learning algorithms, this study proposes the development of intelligent monitoring systems for maternal and foetal signals in high-risk pregnancies. IoT sensors collect maternal clinical data, such as temperature, blood pressure, oxygen saturation level, heart rate, and fetal heart rate and store them on the cloud for monitoring and prediction purposes. Furthermore, a novel Optimized single-dimensional Convolutional Neural Network (1D-OCNN) is proposed for better classification and prediction of the different emergencies of both mother and fetus. The IoT systems have been designed based on the multiple sensors interfaced with the MICOT boards (Node MCU+MCP3008) and cloud mechanism. Nearly 9000 data were collected and used for the evaluation. The extensive experimentation is carried out using cloud-centric learning algorithms such as K-Nearest neighborhood (KNN), Random forest(RF), Support vector machines(SVM), Convolutional neural networks(CNN) and Extreme learning machines (ELM) and various parameters such as accuracy, precision, recall, sensitivity, and F1-score are calculated. The evaluation shows that the proposed classifier has outperformed the other learning algorithms regarding accuracy, precision, recall and F1-score. Based on the above results, the suggested system is a practical and efficient alternative for maternal and fetal monitoring using IoT and artificial intelligence.

META-MODEL OF DESIGN OF INFORMATION TECHNOLOGY PROTOTYPE OF CLASSIFICATION OF OBJECTS BY IMAGE SHAPE USING GMDH NEURAL NETWORKS IN THE NOTATION OF A UNIFIED MODELING LANGUAGE

The article is devoted to the development of intelligent monitoring systems of technological processes based on the use of machine vision systems. The principles of object-oriented formalization of the process of designing information technology of the classification of objects by the geometric shape of the image obtained from the machine vision system are proposed in the article. The proposed information technology is based on the use of machine learning technologies and provides for the selection of the best structure of the classifier model from a set of candidate models. Construction of candidate models is based on the use of the group method of data handling, based on the principles of self-organization of models. The outline of the image of the object obtained from the intelligent monitoring system is the input information. The set of input data contains a set of morphometric parameters that describe the geometric shape of the figure formed by the contour of the image of the object, as well as the label of the class to which the object belongs. The formation of the set of input data is implemented in the block «Image Processing». The decisive rule of classification is built in the block of synthesis of models of information technology of classification. GMDH neural networks were used as an algorithm for model synthesis. The choice of the best structure of the model is performed by a set of criteria. The information technology for constructing the classifier model is implemented by supplementing the block of algorithms for synthesis of MSUA models with the block «Class of models», which implements the process of selecting the class of functions for building models, and the block «Verification of models», which implements the best model structure. Construction of the meta-model of the design process was performed using a unified modeling language. The functional meta-model is represented by a use case diagram.

Energy balance and acoustic emission in titanium Grade 2 under fatigue

This work is devoted to the study of fatigue crack propagation features in titanium Grade 2 in order to develop effective methods for fatigue crack detection and prediction of its propagation rate. Two characteristic stages of heat dissipation are identified during fatigue crack growth. Also the stored energy during fatigue crack propagation is calculated by experimental data of mechanical work and heat dissipation. The stored energy rate is smoother than heat dissipation rate and it slows down when approaching the moment of damage. Cluster analysis of the signal allows us to identify two most probable clusters. The correlation between the acoustic emission energy and heat dissipation was found to be a harbinger of the approaching transition from stable to unstable crack growth. The relation between fatigue crack growth rate and the processes of energy dissipation and acoustic emission can be used in development of intelligent monitoring systems for assessment of current state of structure.

Privacy-preserving Secure Media Streaming for Multi-user Smart Environments

Over the last years, our lifestyle has been positively upset by the sudden advent of technology. The Internet of Things (IoT), offering universal and ubiquitous connectivity to both people and objects, revealed to be the silver bullet for enabling a vast number of previously unexpected applications. In particular, media streaming providers are growing in business and scope, and we can forecast that soon, video streaming will substitute TV broadcasting activities. With the increasing success of multi-user smart environments, empowered by new-generation smart devices and IoT architectures, multimedia contents (i.e., images and videos) need to be effectively accessed anytime and anywhere. Recent advances in computer vision technologies have made the development of intelligent monitoring systems for video surveillance and ambient-assisted living. Such a scenario permits better integration among technologies, multimedia content, and end-users. However, there are several challenges, and some are still open. More precisely, due to the sensitivity of some multimedia content (e.g., video-surveillance streams), it is paramount to preserve users’ privacy. Again, it is necessary to guarantee the integrity of usage rights during any multimedia transmission process, starting from the video encoding phase. In this way, the private content is disclosed only when the stream is decoded on the other endpoint, by the legitimate user. In this article, we present a secure video transmission strategy that can address the challenges mentioned above. The proposed strategy takes advantage of both watermarking and video scrambling techniques to make it possible for the secure and privacy-preserving transmission of multimedia streaming. Through our proposal, multimedia streaming is of low quality and thus unusable. However, it can be fully recovered and enjoyed only by authorized users. Finally, due to its low complexity and energy-efficiency, our proposal is particularly suitable for onboard implementations.

Pump Research and Development: Past, Present, and Future

As in all areas relevant to the development and production of pumps the tasks the hydraulic engineer has to deal with have undergone a remarkable change in recent decades: from a more or less unrestricted design of components to design roles dictated by manufacturing techniques. These general trends are accompanied by developments related to changes of the market requirements. This is demonstrated for three particular pump types. The consequences of the demand to save energy—with all the different aspects—are described for the example of heating circulation pumps. The still growing environmental awareness is a challenge for an enlargement of the presently valid operation limits of sealless pumps and for the development of intelligent monitoring systems. It is demonstrated that the developments in the field of boiler feed pumps are closely related to the growing unit sizes. Availability and reliability, and as far as very large pumps are concerned the efficiency, have always been and still are the dominant criteria.

Development of Intelligent Monitoring System for Grinding Process.

Development of Intelligent Monitoring System for Grinding Process.