Intelligent Warning Research Articles

Research on intelligent risk early warning of open-pit blasting site based on deep learning

ABSTRACT The safety of blasting site in open-pit mine can be greatly improved by risk early warning. Therefore, an intelligent real-time risk early warning method of blasting site in open-pit mine based on deep learning was proposed in this research. The mobile wireless webcams, H.264 video compression algorithm, and real-time transport protocol were applied to achieve real-time video acquisition and transmission of blasting site in open-pit mine. A single-stage deep neural network DG-YOLOv3 was proposed in this research. DG-YOLOv3 is an improvement of Gaussian YOLOv3, among which Darknet41 is used to improve the model’s detection speed and detection accuracy of small targets. To further improve the performance (i.e., speed and accuracy) of risk early warning, the surveillance videos were first split into pictures by frame. Then, the pictures were processed by weighted average grayscale and contrast limited adaptive histogram equalization. Experiments show that the mean average precision of DG-YOLOv3 proposed in this paper reaches 87.45 and the detection speed reaches 56.82 frames per second, which has better accuracy and speed compared with other algorithms. In addition, DG-YOLOv3 has good robustness in complex scenarios. Based on the detection results, the intelligent real-time risk early warning of the blasting site in open-pit mine is achieved finally.

Research on Intelligent Flight Test Monitoring Technology Based on Expert System

The current manual-based traditional flight test monitoring technology can no longer fulfill the requirements of high integration, system complexity and technological advancement of new aircraft. In addition, the existing flight monitoring systems are mostly single-point, single-system applications, including ground-assisted systems such as meteorology, radar, and response system. It is easy to cause problems such as data islands, which is not conducive to data sharing and comprehensive evaluation and analysis. This paper proposes a new method for analyzing and mining existing test data based on expert system technology to build a flight test expert system library. A new real-time monitoring system is designed that can meet the real-time processing and integration of multi-source data, comprehensive evaluation and analysis, intelligent warning and assistant decision-making. This system expands the function and intelligence of the existing system, and further improves the quality, safety and efficiency of the flight test monitoring system.

Product-harm crisis intelligent warning system design based on fine-grained sentiment analysis of automobile complaints

Due to the huge losses caused by product-harm crises and subsequent recalls in the automobile industry, companies must urgently design a product-harm crisis warning system. However, the designs of existing warning systems use the recurrent neural network algorithm, which suffers from gradient disappearance and gradient explosion issues. To compensate for these defects, this study uses a long and short-term memory algorithm to achieve a final prediction accuracy of 90%. This study contributes to the research and design of automatic crisis warning systems by considering sentiment and improving the accuracy of automobile product-harm crisis prediction.

Construction and application of comprehensive early warning system for coal and gas outburst in Xinjing Coal Mine

In order to further strengthen the prevention and control of coal and gas outburst in Xinjing Coal Mine, the online comprehensive analysis and early warning index system and rules of coal and gas outburst suitable for Xinjing Coal Mine were studied. Based on the corresponding early warning computer system and guarantee mechanism, a comprehensive early warning system for coal and gas outbursts in Xinjing Coal Mine was established, realizing real-time intelligent early warning of outburst dangers in working faces. The system realizes the standardization and dynamic management of outburst prevention information at the working face, as well as the real-time dynamic update and sharing of outburst prevention information, which improves the efficiency of mine outburst prevention management and the level of mine safety. The actual application of the system in Xinjing Coal Mine shows that the system can provide an effective reference for the comprehensive early warning of outbursts in other outburst mines of Yangquan Coal Industry Group.

Artificial Intelligence System for College Students’ Physical Fitness and Health Management Based on Physical Measurement Big Data

Most of the current health management products are used in medical institutions and generally do not pay enough attention to the student population. Based on this, this paper designs a student‐oriented and functional autonomous health management system. This paper proposes a personal health management system based on a multidimensional data model based on the main social characteristics of the population with chronic diseases and the actual needs of personal health management for chronic diseases. The value of various health data for health management is deeply analyzed and mined, and a multidimensional model data warehouse is constructed according to relevant national health data standards to create a standard data platform for intelligent health warning and disease risk assessment. This paper researches and designs a closed‐loop personal health management method based on the Plan‐Do‐Check‐Action (PDCA) cycle management model, with detailed functional design in four aspects: health data collection and recording, health assessment, health planning, and tracking and execution. This paper researches health data collection, processing, and storage technologies and adopts HDFS data storage technology, html, css, Java Script, java, and other software development technologies, combined with j Query, UEditor, Date Range Picker, and other plug‐ins, as well as SMS email generation interface, wireless Bluetooth transmission interface, etc. This system web and mobile application platforms are designed and developed. Relational database is used as the system database, and a snowflake‐type multidimensional data model is designed. Finally, the functions and performance of this system were tested, and the development and trial run of the basic version have been completed.

Intelligent Early Warning System for Construction Safety of Excavations Adjacent to Existing Metro Tunnels

With the increasing exploitation and utilization of underground spaces, the excavation of deep foundation pits adjacent to existing metro tunnels is becoming increasingly common. These excavations have the potential to cause safety problems for the operation of the nearby metro. Therefore, to prevent metro tunnel accidents from occurring during the construction process and to ensure the safety of lives and property, it is necessary to establish a risk‐based early warning system. During the excavation process, the main methods for preventing accidents in excavations adjacent to existing metro tunnels are manual analyses based on on‐site monitoring data. However, these methods make it difficult to enact effective control measures in a timely manner owing to the lag of information processing. However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. This study uses a backpropagation neural network to predict the real‐time deformation of the tunnel based on monitoring data from the adjacent construction site. A safety risk assessment model is then established based on the relevant specifications. Through the establishment of an intelligent warning system, the safety risk to the metro tunnel during the construction process can be displayed in a three‐dimensional (3D) form using the BIM. The operation results of the ANN–BIM system show that it can effectively present the safety risk to existing metro tunnels in a 3D manner, which can provide managers with rapid and convenient visual information to inform their decision‐making.

Pollution Reduction using Intelligent Warning Messages in VANET

Many conferences all over the world about environmental protection are situated. Air pollution resulted is an urgent issue for all people on the earth. Crowded cars in the intersections in traffic light intersections are one of the causes of air pollution. Also, rapid accelerations and deacceleration in the intersection cause air pollution. They also lead to packet transmission delay. This paper treats these issues using an intelligent warning message which reduces crowded cars, rapid accelerations, and deacceleration. Using vehicular ad hoc networks (VANETs), intelligent warning messages are used. Results show that our system outperforms previous studies such as traffic light control and pre-timed method in transmission delay, CO2 emission which causes air pollution.

가뭄 위험 수준 분석을 통한 지능형 재난 안전 경보 시스템 개발 연구

현재 위기경보는 재난유형별 상황 전개에 따른 메커니즘, 재난 유형별 위험 수준 특성 등에 대한 고려 없이 당장 획득 가능한 위험정보를 바탕으로 일괄적으로 위기경보를 발령, 격상, 해제하고 있으며, 위기경보가 행정적 절차로만 운영되다 보니 위기징후가 있음에도 인식하지 못하여 판단이 늦어져 사고가 발생하고 피해가 확산되는 경향이 크다.BR 본 논문에서는 국내외에서 진행하고 있는 위험수준 분석 방법을 사전 조사하고 가뭄 재난에 대하여 국내 실정에 적합한 위험 수준 평가 시 고려하여야 하는 8가지 요소에 대한 위험 수준 평가를 위한 기준을 제시하고 지수화 하였다. 자동화된 위험 수준 분석을 위하여 과거에 수집된 가뭄데이터를 수집하고 이를 반영하여 지도 학습 기반의 기계 학습 방법으로 판단트리 모델을 제안하였다.

Wearable Intelligent Warning System for Approaching High-Voltage Electrical Equipment

It is the inherent feature that high-voltage electrical equipment produces electric field around it. In this article, a wearable intelligent warning system based on electric field measurement is proposed to prevent the staff from being electrocuted or entering the area with high field intensity. In order to meet the requirements of lightweight, stable, and antijitter for wearable measurement, the electric field sensor with double-spherical-shell structure is chosen to be the core of the warning system, and the subsequent signal processing system has been developed. Furthermore, the influence of the human body on the electric field measurement has been studied by simulation, and the appropriate wearing position of measuring instrument has been determined considering factors, such as accuracy, convenience, and safety of measurement. Then, some experiments have been conducted to test the performance of the developed measuring instrument. In the accuracy verification experiments, the measurement results of the developed instrument are close to those of the commercial measuring instrument for comparison, and the maximum relative error is within 6%. In the experiments under human body influence, the measurement results of the developed instrument are stable, but those of instrument for comparison fluctuate greatly. As the arm swings, the coefficient of variation of the measurement result does not exceed 6%, which proves that it is suitable for mobile measurement. The simulation and experiment results verify that the developed wearable electric field measuring instrument has satisfactory performances to be used in the safety warning system for approaching high-voltage electrical equipment.

P1547THE EFFECT OF INTERNET-BASED CLOSED-LOOP MANAGEMENT ON QUALITY OF LIFE IN PATIENTS ON PERITONEAL DIALYSIS, A SELF-CONTROL STUDY

Abstract Background and Aims With the continuous advancement of information technology, mobile Internet has entered every family and shown its unique advantages. The development of hospital's information construction on the management of chronic disease, the continuous improvement of hospital APP and Wechat public number functions have provided intelligent services for patients throughout the process. Along with this process, the way of hospital patient follow-up has gradually expanded and improved, and the automation and intellectualization of follow-up work has become a reality. However, The establishment of peritoneal dialysis health management professional team and the implementation of closed-loop health management model, which is based on the Internet closed-loop management model has no clear impact on the self-management ability, quality of life and treatment effect of peritoneal dialysis patients. To explore the effectiveness of Internet based in-hospital and out of hospital closed loop management on the quality of life of patients with peritoneal dialysis, it is necessary to establish a set of closed loop management system for chronic diseases patients with promotion and practical significance. Method From July 2017 to July 2018, 214 patients with continuous peritoneal dialysis were followed up regularly in the Department of Nephrology of our hospital, and voluntarily participated in this study. The mean follow-up time was 12.6 ± 1.5 months. The differences of blood electrolytes, quality control indexes, self-management ability, quality of life of renal disease and hospital satisfaction were observed before and after closed-loop management. Results Ability of self-management behavior (p < 0.001), quality of life (p < 0.001), satisfaction (p < 0.001), the incidence of infection (p < 0.05), serum albumin (p < 0.05), hemoglobin (p < 0.05)were improved after participating in the closed-loop management. Conclusion The closed-loop management system based on Internet closed-loop management model is effective for the quality of life and treatment effect of peritoneal dialysis patients, which integrates the core functions of follow-up records, intelligent early warning, doctor-patient communication and health education. Through this closed-loop management, medical staff can digitally collect, store, transmit and process the information of in-hospital and out-of-hospital conditions through follow-up analysis, health guidance and remote assistance to achieve the purpose of patient health management. This study shows that this closed-loop management system can effectively manage the health of peritoneal dialysis patients, and provides a replicable model for the management of chronic diseases. Therefore, a comprehensive closed-loop management system based on mobile Internet and information technology, with clinical care as the leading factor and peritoneal dialysis patients as the terminal is a feasible and acceptable follow-up management method for peritoneal dialysis patients and hospitals.

Research and Application of Information Perception Technology for Mine Safety Helmet

Based on wireless sensor network technology in the Internet of Things, this paper studies and designs a mine environment perception system that is used in coal mines. The sense layer adopts the wireless sensor network based on WIFI technology, takes the miner safety helmet as the carrier, integrates the function module with STM32F107 controller as the core, and becomes the mobile node of the underground wireless sensing network, and realizes the monitoring of the miner’s work environment information and the underground personnel positioning. Through the test, the intelligent mobile terminal can actively perceive the surrounding environment of the underground personnel, realize the real-time monitoring and intelligent warning of the gas concentration, carbon monoxide concentration and temperature in the surrounding area of the miners, and can provide the positioning information for the ground-based biter, which is an important supplement to the safety monitoring of coal mine in China.

Development and Application of Portable Intelligent Warning System for Safe Tunnelling

With the emphasis on the construction of transportation infrastructure and the continuous advancement of county-level expressways’ “all-access and full-passage”, as well as the continuous improvement of water environmental protection during the construction process, especially in mountainous areas, the ratio of highway bridges to tunnels is getting higher and higher, tunnel engineering The construction is implemented as a control project in the construction of the highway. The combination of monitoring and measurement work has played an important role in all aspects of the tunnel construction, especially in terms of project quality, engineering cost, and progress. Monitoring and measurement should be implemented. Pay attention to the design of construction organization. As we all know, tunnel engineering is a kind of underground engineering, which itself has many characteristics such as complexity, concealment, and unmeasurability. There are many problems such as unpredictability in the development of the tunnel itself. During the construction process, the poor geological environment will not only cause problems to the technicians, but also cause the tunnel collapse, water inrush, etc., and even cause life to the construction personnel in severe cases. Hazards, thereby affecting the normal operation of the construction, causing delays in the construction period, and other issues, which will have a huge economic impact on the project. With the skillful application of monitoring and measurement, the problems existing in tunnel engineering can be predicted, so as to solve the problems and finally determine the design scheme. The application of monitoring and measurement in China’s highway tunnels is essential. Due to its concealment and the complexity of engineering geology, tunnel construction safety accidents occur frequently. Monitoring and measurement are important means of tunnel construction safety guarantee, and its informatization and intelligence are inevitable trends for future development. The article analyzes key technical issues. Proposed a reasonable solution, compiled a reasonable positioning early warning algorithm, developed an intelligent early warning system for tunnel construction safety and a portable wearable device, and successfully applied it in multiple tunnels, achieved good application results, and achieved intelligent monitoring of tunnel construction safety Visualization and visualization.

An infrared target intrusion detection method based on feature fusion and enhancement

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning. In view of the characteristics of intrusion targets as well as inspection difficulties, an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed. This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels. Among them, LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library, while motion frame difference method can detect the moving regions of the image, improve the integrity of target regions such as camouflage, sheltering and deformation. In order to integrate the advantages of the two methods, the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced. The enhancement module of the network strengthened and screened the targets, and realized the background suppression of infrared images. Based on the experiments, the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated, and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets, and it’s detection performance was far better than the comparison algorithm. The experiment results indicated that, compared with traditional infrared target detection methods, the proposed method could detect the infrared invasion target more accurately, and suppress the background noise more effectively.

An Intelligent Warning Method for Diagnosing Underwater Structural Damage

A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations.

An intelligent warning model for early prediction of cardiac arrest in sepsis patients

BackgroundSepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have been conducted to predict cardiac arrest using machine learning. However, no previous research has used machine learning for predicting cardiac arrest in adult sepsis patients. Moreover, the potential of some techniques, including ensemble algorithms, has not yet been addressed in improving the prediction outcomes. It is required to find methods for generating high-performance predictions with sufficient time lapse before the arrest. In this regard, various variables and parameters should also been examined. ObjectiveThe aim was to use machine learning in order to propose a cardiac arrest prediction model for adult patients with sepsis. It is required to predict the arrest several hours before the incidence with high efficiency. The other goal was to investigate the effect of the time series dynamics of vital signs on the prediction of cardiac arrest. Method30 h clinical data of every sepsis patients were extracted from Mimic III database (79 cases, 4532 controls). Three datasets (multivariate, time series and combined) were created. Various machine learning models for six time groups were trained on these datasets. The models included classical techniques (SVM, decision tree, logistic regression, KNN, GaussianNB) and ensemble methods (gradient Boosting, XGBoost, random forest, balanced bagging classifier and stacking). Proper solutions were proposed to address the challenges of missing values, imbalanced classes of data and irregularity of time series. ResultsThe best results were obtained using a stacking algorithm and multivariate dataset (accuracy = 0.76, precision = 0.19, sensitivity = 0.77, f1-score = 0.31, AUC= 0.82). The proposed model predicts the arrest incidence of up to six hours earlier with the accuracy and sensitivity over 70%. ConclusionWe illustrated that machine learning techniques, especially ensemble algorithms have high potentials to be used in prognostic systems for sepsis patients. The proposed model, in comparison with the exiting warning systems including APACHE II and MEWS, significantly improved the evaluation criteria. According to the results, the time series dynamics of vital signs are of great importance in the prediction of cardiac arrest incidence in sepsis patients.

Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

In view of rock burst accidents frequently occurring, a basic framework for an intelligent early warning system for rock bursts (IEWSRB) is constructed based on several big data technologies in the computer industry, including data mining, databases and data warehouses. Then, a data warehouse is modeled with regard to monitoring the data of rock bursts, and the effective application of data mining technology in this system is discussed in detail. Furthermore, we focus on the K-means clustering algorithm, and a data visualization interface based on the Browser/Server (B/S) mode is developed, which is mainly based on the Java language, supplemented by Cascading Style Sheets (CSS), JavaScript and HyperText Markup Language (HTML), with Tomcat, as the server and Mysql as the JavaWeb project of the rock burst monitoring data warehouse. The application of data mining technology in IEWSRB can improve the existing rock burst monitoring system and enhance the prediction. It can also realize real-time queries and the analysis of monitoring data through browsers, which is very convenient. Hence, it can make important contributions to the safe and efficient production of coal mines and the sustainable development of the coal economy.

A framework for Fog-assisted healthcare monitoring

In order to tackle some challenges in ubiquitous healthcare monitoring such as mobility, scalability, and network latency, a framework for Fog-assisted healthcare monitoring is proposed in this paper. This framework is composite of body-sensing layer, Fog layer (Fog-assisted gateway), Cloud layer (health Cloud). And then, this paper makes an intensive study in some key technologies of the proposed framework such as an IPv6-based network architecture, intelligent warning model based on subband energy feature, security framework of HL7 RIM-based data exchange, health risk assessment based on fusion of grey model and Markov model. Finally, results of experiment depict that the proposed intelligent warning model can make immediate distinction abnormal signals. Moreover, the proposed health assessment model confirms its effectiveness with respect to 245 patients in Xiamen District of Jimei.

Why Quantum Computing Will Not Destabilize International Security: The Political Logic of Cryptology

The implications of quantum information technology for cybersecurity and strategic stability seem worrisome. In theory, an adversary with a quantum computer could defeat the asymmetric encryption protocols that underwrite internet security, while an adversary using quantum communications guaranteed secure by the laws of physics could deny intelligence warning of surprise attack. To assess these claims, this article first develops a general political logic of cryptology grounded in the bargaining model of war, which understands uncertainty as an important cause of war and institutions as an important source of information. Cryptology of any technological vintage is shaped by both aspects of this logic, with ambiguous implications for strategic stability. In practice, strategic interaction between intelligence competitors using real quantum systems implemented in fallible human organizations will mitigate the impact of quantum computing. The upshot is that the revolutionary scientific innovation of quantum computing will probably have only marginal political impact, in part because the fields of cryptology and computing have already undergone important transformations in recent decades.

Application of an intelligent early-warning method based on DBSCAN clustering for drilling overflow accident

Oil and gas are still the necessities of production in today’s society. However, the exploration and mining of them are extremely complex and dangerous. Overflow accidents are undoubtedly one of the biggest threats to safe drilling operations during the oil and gas exploration. Due to the complexity of geological information or lack of adjacent well data in drilling process, the problem of overflow warning model based on sample information can not be established. Data mining is the process of revealing meaningful new patterns, relationships and trends by analyzing data, therefore, based on the correlation between the occurrence of overflow accidents and the change trend of casing pressure, a method of intelligent warning based on improved DBSCAN clustering method for drilling overflow accidents is proposed. The early warning method uses time-series scanning and stratification to rule the idea of clustering, not only improve the efficiency of clustering, but also enhance the clustering effect. According to the results of clustering fitting and the sensitivity of overflow accident, output the warning result of overflow accident. The data analysis is made by using the field data. The experimental results show that the flood warning method based on improved DBSCAN clustering can effectively predict the overflow accidents.

Design and implementation of supply security monitoring and analysis system for Chinese patent medicines supply in national essential medicines

The supply of Chinese patent medicine is influenced by the price of raw materials (Chinese herbal medicines) and the stock of resources. On the one hand, raw material prices show cyclical volatility or even irreversible soaring, making the price of Chinese patent medicine is not stable or even the highest cost of hanging upside down. On the other hand, due to lack of resources or disable some of the proprietary Chinese medicine was forced to stop production. Based on the micro-service architecture and Redis cluster deployment Based on the micro-service architecture and Redis cluster deployment, the supply security monitoring and analysis system for Chinese patent medicines in national essential medicines has realized the dynamic monitoring and intelligence warning of herbs and Chinese patent medicine by connecting and integrating the database of Chinese medicine resources, the dynamic monitoring system of traditional Chinese medicine resources and the basic medicine database of Chinese patent medicine.