AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

Potential of the Arkhangelsk seismic network for European Arctic monitoring

The Arkhangelsk Seismic Network(ASN)of the N.Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences,founded in 2003,includes 10 permanent seismic stations located on the coasts of the White,Barents,and Kara Seas and on the Arctic archipelagos of Novaya Zemlya,Franz Josef Land,and Severnaya Zemlya.The network is registered with the International Federation of Digital Seismograph Networks and the International Seismological Center.We used not only ASN data to process earthquakes but also the waveforms of various international seismic stations.The 13,000 seismic events were registered using ASN data for 2012-2022,and for 5,500 of them,we determined the parameters of the earthquake epicenters from the European Arctic.The spatial distribution of epicenters shows that the ASN monitors not only the main seismically active zones but also weak seismicity on the shelf of the Barents and Kara Seas.The representative magnitude of ASN was ML,rep=3.5.The level of microseismic noise has seasonal variations that affect the registration capabilities of each station included in the ASN and the overall sensitivity of the network as a whole.In summer,the sensitivity of the ASN decreased owing to the increasing microseismic and ambient noises,whereas in winter,the sensitivity of the ASN increased significantly because of the decrease.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.

Applying Neural Networks for Tire Pressure Monitoring Systems

A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire.A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data.Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks(RNN-LSTM)and a convolutional neural network(CNN)developed in Python with Tensorflow.Bayesian Optimization via SigOpt was used to optimize training and model parameters.The predictive accuracy and training speed of the two models with various parameters are compared.Finally,future work and improvements are discussed.

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network

The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining device reliability.Existing temperature monitoring methods based on the electro-thermal coupling model have limitations,such as ignoring device interactions and high computational complexity.To address these issues,an analysis of the parameters influencing IGBT failure is conducted,and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory(MMALSTM)recursive neural network is proposed,which takes the forward voltage drop and collector current as features.Compared with the traditional electricalthermal coupling model method,it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process.The simulation model of a highspeed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature.The simulation outcomes,which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model,confirm the reliability of this approach for predicting the temperature of IGBT power modules.

Real-Time Monitoring System for Rotor Temperature of a Large Turbogenerator Based on SmartMesh IP Wireless Network Communication Technology

In this study,a real-time rotor temperature monitoring system for large turbogenerators using SmartMesh IP wireless network communication technology was designed and tested.The system is capable of providing comprehensive,accurate,continuous,and reliable real-time temperature monitoring for turbogenerators.Additionally,it has demonstrated satisfactory results in a real-time monitoring test of the rotor temperature of various famous large-scale turbogenerators and giant nuclear power half-speed turbogenerators designed and manufactured in China.The development and application of this wireless temperature measurement system would aid in improving the intelligent operation quality,safety,and stability of China’s large turbine generators and even the entire power system.

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

Wildfire Monitoring and Detection System Using Wireless Sensor Network: A Case Study of Tanzania

This paper proposes a wildfire monitoring and detection system based on wireless sensor network. This system detects fire by monitoring surrounding temperature, humidity and smoke. Once fire is detected, a warning message containing probable location of that fire is immediately sent to the responsible authority over cellular network. In order for the system to be more effective, communities living near forests or national parks can send warning messages through the same system to the responsible authority using their mobile handsets once they witness wildfire or illegal activities. For the system to be fully functional, the only requirement is the availability of cellular network coverage in forests or national parks to enable short message services to take place. The system prototype is developed using Arduino microcontroller, several sensors to detect temperature, relative humidity and smoke as well as wireless network connection modules. At the control center Telerivet messaging platform is used to design the messaging service. The experimental results justify the capability of the proposed system in detecting wildfire in real time.

Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks

A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.

Sea Route Monitoring System Using Wireless Sensor Network Based on the Data Compression Algorithm

The wireless sensor network （WSN） plays an important role in monitoring the environment near the harbor in order to make the ships nearby out of dangers and to optimize the utilization of limited sea routes. Based on the historical data collected by the buoys with sensing capacities, a novel data compression algorithm called adaptive time piecewise constant vector quantization （ATPCVQ） is proposed to utilize the principal components. The proposed system is capable of lowering the budget of wireless communication and enhancing the lifetime of sensor nodes subject to the constrain of data precision. Furthermore, the proposed algorithm is verified by using the practical data in Qinhuangdao Port of China.

Remote Monitoring System of Ship Running State under Wireless Network

In order to improve the stability and safety of ship operation, real-time monitoring to running state of the ship, a ship remote monitoring system, the running state of the design of wireless network system, including ship running state feature acquisition module, AD conversion module, bus transmission module, wireless network communication module, integrated control module and human-computer interaction module, ship communication network uses remote satellite communications and wireless sensor networking technology, SIP session initiation protocol H. 23 protocol based on IETF and wireless network communication for ship design. To focus on the implementation of the multi thread control method for ship running state monitoring instruction, monitoring system application development and integration in cross compiler GCC compiler environment, design software platform monitoring system using heterogeneous and hierarchical middleware technology, network access services and real-time monitoring service ship monitoring system, the system test results show that the designed remote monitoring system of ship running state can monitor the running state characteristics of ship in real time, and the system has good adaptability and reliability.

Proactive Spectrum Monitoring for Suspicious Wireless Powered Communications in Dynamic Spectrum Sharing Networks

This paper studies the proactive spec-trum monitoring with one half-duplex spectrum moni-tor(SM)to cope with the potential suspicious wireless powered communications(SWPC)in dynamic spec-trum sharing networks.The jamming-assisted spec-trum monitoring scheme via spectrum monitoring data(SMD)transmission is proposed to maximize the sum ergodic monitoring rate at SM.In SWPC,the suspi-cious communications of each data block occupy mul-tiple independent blocks,with a block dedicated to the wireless energy transfer by the energy-constrained suspicious nodes with locations in a same cluster(symmetric scene)or randomly distributed(asymmet-ric scene)and the remaining blocks used for the in-formation transmission from suspicious transmitters(STs)to suspicious destination(SD).For the sym-metric scene,with a given number of blocks for SMD transmission,namely the jamming operation,we first reveal that SM should transmit SMD signal(jam the SD)with tolerable maximum power in the given blocks.The perceived suspicious signal power at SM could be maximized,and thus so does the correspond-ing sum ergodic monitoring rate.Then,we further reveal one fundamental trade-off in deciding the op-timal number of given blocks for SMD transmission.For the asymmetric scene,a low-complexity greedy block selection scheme is proposed to guarantee the optimal performance.Simulation results show that the jamming-assisted spectrum monitoring schemes via SMD transmission achieve much better perfor-mance than conventional passive spectrum monitor-ing,since the proposed schemes can obtain more accu-rate and effective spectrum characteristic parameters,which provide basic support for fine-grained spectrum management and a solution for spectrum security in dynamic spectrum sharing network.

Is tumor necrosis factor-α monoclonal therapy with proactive therapeutic drug monitoring optimized for inflammatory bowel disease? Network meta-analysis

BACKGROUND The efficacy and safety of anti-tumor necrosis factor-α(TNF-α)monoclonal antibody therapy[adalimumab(ADA)and infliximab(IFX)]with therapeutic drug monitoring(TDM),which has been proposed for inflammatory bowel disease(IBD)patients,are still controversial.AIM To determine the efficacy and safety of anti-TNF-αmonoclonal antibody therapy with proactive TDM in patients with IBD and to determine which subtype of IBD patients is most suitable for proactive TDM interventions.METHODS As of July 2023,we searched for randomized controlled trials(RCTs)and observa-tional studies in PubMed,Embase,and the Cochrane Library to compare anti-TNF-αmonoclonal antibody therapy with proactive TDM with therapy with reactive TDM or empiric therapy.Pairwise and network meta-analyses were used to determine the IBD patient subtype that achieved clinical remission and to determine the need for surgery.RESULTS This systematic review and meta-analysis yielded 13 studies after exclusion,and the baseline indicators were balanced.We found a significant increase in the number of patients who achieved clinical remission in the ADA[odds ratio(OR)=1.416,95%confidence interval(CI):1.196-1.676]and RCT(OR=1.393,95%CI:1.182-1.641)subgroups and a significant decrease in the number of patients who needed surgery in the proactive vs reactive(OR=0.237,95%CI:0.101-0.558)and IFX+ADA(OR=0.137,95%CI:0.032-0.588)subgroups,and the overall risk of adverse events was reduced(OR=0.579,95%CI:0.391-0.858)according to the pairwise meta-analysis.Moreover,the network meta-analysis results suggested that patients with IBD treated with ADA(OR=1.39,95%CI:1.19-1.63)were more likely to undergo TDM,especially in comparison with patients with reactive TDM(OR=1.38,95%CI:1.07-1.77).CONCLUSION Proactive TDM is more suitable for IBD patients treated with ADA and has obvious advantages over reactive TDM.We recommend proactive TDM in IBD patients who are treated with ADA.

Quality of Service and Security on Cisco Network Devices, Coupled with the Development of a Mobile Application Prototype Software for Server Room Temperature Monitoring

In an era where digital technology is paramount, higher education institutions like the University of Zambia (UNZA) are employing advanced computer networks to enhance their operational capacity and offer cutting-edge services to their academic fraternity. Spanning across the Great East Road campus, UNZA has established one of the most extensive computer networks in Zambia, serving a burgeoning community of over 20,000 active users through a Metropolitan Area Network (MAN). However, as the digital landscape continues to evolve, it is besieged with burgeoning challenges that threaten the very fabric of network integrity—cyber security threats and the imperatives of maintaining high Quality of Service (QoS). In an effort to mitigate these threats and ensure network efficiency, the development of a mobile application to monitor temperatures in the server room was imperative. According to L. Wei, X. Zeng, and T. Shen, the use of wireless sensory networks to monitor the temperature of train switchgear contact points represents a cost-effective solution. The system is based on wireless communication technology and is detailed in their paper, “A wireless solution for train switchgear contact temperature monitoring and alarming system based on wireless communication technology”, published in the International Journal of Communications, Network and System Sciences, vol. 8, no. 4, pp. 79-87, 2015 [1]. Therefore, in this study, a mobile application technology was explored for monitoring of temperatures in the server room in order to aid Cisco device performance. Additionally, this paper also explores the hardening of Cisco device security and QoS which are the cornerstones of this study.

On-line Condition Monitoring and Diagnostic Network System for Rotating Machine Set

Several industrial computers and a server are combined to set up the on-line monitoring and diagnostic system of turbo-generator sets. The main function of the system is to monitor machine sets' running condition. Through analyzing running data, technicians can detect whether there exist faults and where they occur. To share and transmit the dynamic information of the turbo-generator sets, a distributed network system is introduced. NetWare network operating system is used in the LAN (Local Area Network) system. The LAN is extended to realize the sharing of data and remote transmission of information. Furthermore, functions of monitoring and diagnostic clients are listed.

Study on an Audio and Video Network Monitoring System for Weather Modification Operation

An audio and video network monitoring system for weather modification operation transmitting information by 3G, ADSL and Internet has been developed and applied in weather modification operation of Tai＇an City. The all-in-one machine of 3G audio and video network highly integrates all front-end devices used for audio and video collection, communication, power supply and information storage, and has advantages of wireless video transmission, clear two-way voice intercom with the command center, waterproof and dustproof function, simple operation, good portability, and long working hours. Compression code of the system is transmitted by dynamic bandwidth, and compression rate varies from 32 kbps to 4 Mbps under different network conditions. This system has forwarding mode, that is, monitoring information from each front-end monitoring point is trans- mitted to the server of the command center by 3G/ADSL, and the server codes＇and decodes again, then beck-end users call images from the serv- er, which can address 3G network stoppage caused by many users calling front-end video at the same time. In addition, the system has been ap- plied in surface weather modification operation of Tai＇an City, and has made a great contribution to transmitting operation orders in real time, monitoring, standardizing and recording operating process, and improving operating safety.

Remote Multimedia Monitoring System Based on Embedded Web Server for Networked Manufacturing

A new-style remote monitoring system is proposed, which is based on enterprises' embedded web servers and can be widely used in enterprises' networked manufacturing systems. The principle and characteristics of remote monitoring system based on embedded web server are analyzed. Such a kind of system for networked manufacturing is designed, and it proves efficient and feasible in promoting communication among enterprises, improving designing and scheduling, decreasing facility failure and reducing product cost.

Comparison of Using an Electronic System and Conventional Monitoring Method for Monitoring the Quality of Drinking Water and Defects Discovery in Rural Area Water Distribution Network of Abarkouh, Iran

The used water for human consumption must be free of microorganisms and chemicals that cause risk in the human health. In this study, water quality of 18 rural area of Abarkouh was determined and compared the conventional monitoring method (According to ISIR (Institute of Standards and Industrial Research of Iran), 1053 and 4208) and use of electronic system method (Patent in industrial property general office of Iran, 77815). Free chlorine monitoring and pH test done by health workers in the conventional method and the results will be sent to the Health Network monthly. Sampling for microbiological testing is done monthly based on population (According to ISIR, 4208). On the electronic system, the procedure is also done by health workers, but the result will be sent to the receiver device by using a cell phone. According to the chlorine test results if the free chlorine residual reported zero, microbiological sampling was done by a health expert. Finally, the number of chlorine test and microbiological sampling and the results of these experiments collected in the both methods and recorded in SPSS 22 then were analyzed by using chi-square test and Fisher exact test. The result of microbiological experiments shows that the sampling rate decreased 29% in using of electronic system method in comparison to the conventional monitoring method while the number of microbial defect detection increased 19% in drinking water networks monitoring by electronic system. Using of electronic system monitoring can reduce the rate and cost of microbiological sampling and its experiments and increase accuracy of these tests, in this way it will increase the quality and safety of drinking water in distribution network in small and dispersed rural communities.

Fire monitoring in coal mines using wireless underground sensor network and interval type-2 fuzzy logic controller

From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.